manual_feature_engineering_part_1.ipynb

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    "# Introduction: Manual Feature Engineering\n",
    "\n",
    "If you are new to this competition, I highly suggest checking out [this notebook](https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction/) to get started.\n",
    "\n",
    "In this notebook, we will explore making features by hand for the Home Credit Default Risk competition. In an earlier notebook, we used only the `application` data in order to build a model. The best model we made from this data achieved a score on the leaderboard around 0.74. In order to better this score, we will have to include more information from the other dataframes. Here, we will look at using information from the `bureau` and `bureau_balance` data. The definitions of these data files are:\n",
    "\n",
    "* bureau: information about client's previous loans with other financial institutions reported to Home Credit. Each previous loan has its own row.\n",
    "* bureau_balance: monthly information about the previous loans. Each month has its own row.\n",
    "\n",
    "Manual feature engineering can be a tedious process (which is why we use automated feature engineering with featuretools!) and often relies on domain expertise. Since I have limited domain knowledge of loans and what makes a person likely to default, I will instead concentrate of getting as much info as possible into the final training dataframe. The idea is that the model will then pick up on which features are important rather than us having to decide that. Basically, our approach is to make as many features as possible and then give them all to the model to use! Later, we can perform feature reduction using the feature importances from the model or other techniques such as PCA. \n",
    "\n",
    "The process of manual feature engineering will involve plenty of Pandas code, a little patience, and a lot of great practice manipulating data. Even though automated feature engineering tools are starting to mature, feature engineering still involves lots of data wrangling for now. "
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    "# pandas and numpy for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# matplotlib and seaborn for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Suppress warnings from pandas\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('fivethirtyeight')"
   ]
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    "## Example: Counts of a client's previous loans\n",
    "\n",
    "To illustrate the general process of manual feature engineering, we will first simply get the count of a client's previous loans at other financial institutions. This requires a number of Pandas operations we will make heavy use of throughout the notebook:\n",
    "\n",
    "* `groupby`: group a dataframe by a column. In this case we will group by the unique client, the `SK_ID_CURR` column\n",
    "* `agg`: perform a calculation on the grouped data such as taking the mean of columns. We can either call the function directly (`grouped_df.mean()`) or use the `agg` function together with a list of transforms (`grouped_df.agg([mean, max, min, sum])`)\n",
    "* `merge`: match the aggregated statistics to the appropriate client. We need to merge the original training data with the calculated stats on the `SK_ID_CURR` column which will insert `NaN` in any cell for which the client does not have the corresponding statistic\n",
    "\n",
    "We also use the (`rename`) function quite a bit specifying the columns to be renamed as a dictionary. This is useful in order to keep track of the new variables we create.\n",
    "\n",
    "This might seem like a lot, which is why we'll eventually write a function to do this process for us. Let's take a look at implementing this by hand first. "
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <th>CREDIT_CURRENCY</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>CREDIT_DAY_OVERDUE</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
       "      <th>DAYS_ENDDATE_FACT</th>\n",
       "      <th>AMT_CREDIT_MAX_OVERDUE</th>\n",
       "      <th>CNT_CREDIT_PROLONG</th>\n",
       "      <th>AMT_CREDIT_SUM</th>\n",
       "      <th>AMT_CREDIT_SUM_DEBT</th>\n",
       "      <th>AMT_CREDIT_SUM_LIMIT</th>\n",
       "      <th>AMT_CREDIT_SUM_OVERDUE</th>\n",
       "      <th>CREDIT_TYPE</th>\n",
       "      <th>DAYS_CREDIT_UPDATE</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "    </tr>\n",
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       "      <td>-153.0</td>\n",
       "      <td>-153.0</td>\n",
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       "      <td>91323.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-131</td>\n",
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       "      <td>Credit card</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-16</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>-16</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1197.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>-21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   SK_ID_CURR  SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY  DAYS_CREDIT  \\\n",
       "0      215354       5714462        Closed      currency 1         -497   \n",
       "1      215354       5714463        Active      currency 1         -208   \n",
       "2      215354       5714464        Active      currency 1         -203   \n",
       "3      215354       5714465        Active      currency 1         -203   \n",
       "4      215354       5714466        Active      currency 1         -629   \n",
       "\n",
       "   CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  \\\n",
       "0                   0               -153.0             -153.0   \n",
       "1                   0               1075.0                NaN   \n",
       "2                   0                528.0                NaN   \n",
       "3                   0                  NaN                NaN   \n",
       "4                   0               1197.0                NaN   \n",
       "\n",
       "   AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  \\\n",
       "0                     NaN                   0         91323.0   \n",
       "1                     NaN                   0        225000.0   \n",
       "2                     NaN                   0        464323.5   \n",
       "3                     NaN                   0         90000.0   \n",
       "4                 77674.5                   0       2700000.0   \n",
       "\n",
       "   AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT  AMT_CREDIT_SUM_OVERDUE  \\\n",
       "0                  0.0                   NaN                     0.0   \n",
       "1             171342.0                   NaN                     0.0   \n",
       "2                  NaN                   NaN                     0.0   \n",
       "3                  NaN                   NaN                     0.0   \n",
       "4                  NaN                   NaN                     0.0   \n",
       "\n",
       "       CREDIT_TYPE  DAYS_CREDIT_UPDATE  AMT_ANNUITY  \n",
       "0  Consumer credit                -131          NaN  \n",
       "1      Credit card                 -20          NaN  \n",
       "2  Consumer credit                 -16          NaN  \n",
       "3      Credit card                 -16          NaN  \n",
       "4  Consumer credit                 -21          NaN  "
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   "source": [
    "# Read in bureau\n",
    "bureau = pd.read_csv('../input/bureau.csv')\n",
    "bureau.head()"
   ]
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       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
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       "   SK_ID_CURR  previous_loan_counts\n",
       "0      100001                     7\n",
       "1      100002                     8\n",
       "2      100003                     4\n",
       "3      100004                     2\n",
       "4      100005                     3"
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   "source": [
    "# Groupby the client id (SK_ID_CURR), count the number of previous loans, and rename the column\n",
    "previous_loan_counts = bureau.groupby('SK_ID_CURR', as_index=False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})\n",
    "previous_loan_counts.head()"
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       "      <td>2.0</td>\n",
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      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "           ...           FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0          ...                          0                0                0   \n",
       "1          ...                          0                0                0   \n",
       "2          ...                          0                0                0   \n",
       "3          ...                          0                0                0   \n",
       "4          ...                          0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  previous_loan_counts  \n",
       "0                        0.0                         1.0                   8.0  \n",
       "1                        0.0                         0.0                   4.0  \n",
       "2                        0.0                         0.0                   2.0  \n",
       "3                        NaN                         NaN                   0.0  \n",
       "4                        0.0                         0.0                   1.0  \n",
       "\n",
       "[5 rows x 123 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join to the training dataframe\n",
    "train = pd.read_csv('../input/application_train.csv')\n",
    "train = train.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Fill the missing values with 0 \n",
    "train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "53d83da3d8a28541c2dd49be8047e110034c10e6"
   },
   "source": [
    "Scroll all the way to the right to see the new column. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "173b8548125c8a344f67d397d7c24af223af7254"
   },
   "source": [
    "## Assessing Usefulness of New Variable with R-Value\n",
    "\n",
    "To determine if the new variable is useful, we can calculate the Pearson Correlation Coefficient (r-value) between this variable and the target. This measures the strength of a linear relationship between two variables and ranges from -1 (perfectly negatively linear) to +1 (perfectly positively linear). The r-value is not best measure of the \"usefulness\" of a new variable, but it can give a first approximation of whether a variable will be helpful to a machine learning model. The larger the r-value of a variable with respect to the target, the more a change in this variable is likely to affect the value of the target. Therefore, we look for the variables with the greatest absolute value r-value relative to the target.\n",
    "\n",
    "We can also visually inspect a relationship with the target using the Kernel Density Estimate (KDE) plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f5bc72f178a44d45399f620ee43483df2482c02f"
   },
   "source": [
    "### Kernel Density Estimate Plots\n",
    "\n",
    "The kernel density estimate plot shows the distribution of a single variable (think of it as a smoothed histogram). To see the different in distributions dependent on the value of a categorical variable, we can color the distributions differently according to the category. For example, we can show the kernel density estimate of the `previous_loan_count` colored by whether the `TARGET` = 1 or 0. The resulting KDE will show any significant differences in the distribution of the variable between people who did not repay their loan (`TARGET == 1`) and the people who did (`TARGET == 0`). This can serve as an indicator of whether a variable will be 'relevant' to a machine learning model. \n",
    "\n",
    "We will put this plotting functionality in a function to re-use for any variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "4a5fab58ab8327c7f53361b584603d78df96c7a4",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plots the disribution of a variable colored by value of the target\n",
    "def kde_target(var_name, df):\n",
    "    \n",
    "    # Calculate the correlation coefficient between the new variable and the target\n",
    "    corr = df['TARGET'].corr(df[var_name])\n",
    "    \n",
    "    # Calculate medians for repaid vs not repaid\n",
    "    avg_repaid = df.ix[df['TARGET'] == 0, var_name].median()\n",
    "    avg_not_repaid = df.ix[df['TARGET'] == 1, var_name].median()\n",
    "    \n",
    "    plt.figure(figsize = (12, 6))\n",
    "    \n",
    "    # Plot the distribution for target == 0 and target == 1\n",
    "    sns.kdeplot(df.ix[df['TARGET'] == 0, var_name], label = 'TARGET == 0')\n",
    "    sns.kdeplot(df.ix[df['TARGET'] == 1, var_name], label = 'TARGET == 1')\n",
    "    \n",
    "    # label the plot\n",
    "    plt.xlabel(var_name); plt.ylabel('Density'); plt.title('%s Distribution' % var_name)\n",
    "    plt.legend();\n",
    "    \n",
    "    # print out the correlation\n",
    "    print('The correlation between %s and the TARGET is %0.4f' % (var_name, corr))\n",
    "    # Print out average values\n",
    "    print('Median value for loan that was not repaid = %0.4f' % avg_not_repaid)\n",
    "    print('Median value for loan that was repaid =     %0.4f' % avg_repaid)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "783e2b23b473f5f61d17ba5b5b64349d3a1d60cf"
   },
   "source": [
    "We can test this function using the `EXT_SOURCE_3` variable which we [found to be one of the most important variables ](https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction) according to a Random Forest and Gradient Boosting Machine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "f9e51eb7f02c5612edef3c950ae89da3b8ececf4",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between EXT_SOURCE_3 and the TARGET is -0.1789\n",
      "Median value for loan that was not repaid = 0.3791\n",
      "Median value for loan that was repaid =     0.5460\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target('EXT_SOURCE_3', train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c4598cb54ad22b04646683832e2e4c4bc98ff8e9"
   },
   "source": [
    "Now for the new variable we just made, the number of previous loans at other institutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "bbae3d4e85f106c1e7b457ad71e1549c0e7b1267",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between previous_loan_counts and the TARGET is -0.0100\n",
      "Median value for loan that was not repaid = 3.0000\n",
      "Median value for loan that was repaid =     4.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target('previous_loan_counts', train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a85c4e3f8ccb54bb4dbbd85a414744d301916fd8"
   },
   "source": [
    "From this it's difficult to tell if this variable will be important. The correlation coefficient is extremely weak and there is almost no noticeable difference in the distributions. \n",
    "\n",
    "Let's move on to make a few more variables from the bureau dataframe. We will take the mean, min, and max of every numeric column in the bureau dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "11f1b0ab2146ec95a50055d2788b5f05c43e0afb"
   },
   "source": [
    "## Aggregating Numeric Columns\n",
    "\n",
    "To account for all the numeric information in the `bureau` dataframe, we can compute statistics for all the numeric columns. To do so, we simply `groupby` the client id, `agg` the grouped dataframe, and merge the result back into the training data. The `agg` function will only calculate the values for the numeric columns where the operation is considered valid. We will stick to using `'mean', 'max', 'min', 'sum'` but any function can be passed in here. We can even write our own function and use it in an `agg` call. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "582885d850fa1316ce869b856b9d886ad542b04b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th colspan=\"4\" halign=\"left\">DAYS_CREDIT</th>\n",
       "      <th colspan=\"4\" halign=\"left\">CREDIT_DAY_OVERDUE</th>\n",
       "      <th>DAYS_CREDIT_ENDDATE</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AMT_CREDIT_SUM_OVERDUE</th>\n",
       "      <th colspan=\"4\" halign=\"left\">DAYS_CREDIT_UPDATE</th>\n",
       "      <th colspan=\"4\" halign=\"left\">AMT_ANNUITY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>-735.000000</td>\n",
       "      <td>-49</td>\n",
       "      <td>-1572</td>\n",
       "      <td>-5145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93.142857</td>\n",
       "      <td>-6</td>\n",
       "      <td>-155</td>\n",
       "      <td>-652</td>\n",
       "      <td>3545.357143</td>\n",
       "      <td>10822.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24817.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>-103</td>\n",
       "      <td>-1437</td>\n",
       "      <td>-6992</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>-349.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>-7</td>\n",
       "      <td>-1185</td>\n",
       "      <td>-3999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>-606</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-544.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>-43</td>\n",
       "      <td>-2131</td>\n",
       "      <td>-3264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-1734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-488.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-532.000000</td>\n",
       "      <td>-382</td>\n",
       "      <td>-682</td>\n",
       "      <td>-1064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>-62</td>\n",
       "      <td>-373</td>\n",
       "      <td>-572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-54.333333</td>\n",
       "      <td>-11</td>\n",
       "      <td>-121</td>\n",
       "      <td>-163</td>\n",
       "      <td>1420.500000</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4261.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  SK_ID_CURR  DAYS_CREDIT                  CREDIT_DAY_OVERDUE              \\\n",
       "                     mean  max   min   sum               mean max min sum   \n",
       "0     100001  -735.000000  -49 -1572 -5145                0.0   0   0   0   \n",
       "1     100002  -874.000000 -103 -1437 -6992                0.0   0   0   0   \n",
       "2     100003 -1400.750000 -606 -2586 -5603                0.0   0   0   0   \n",
       "3     100004  -867.000000 -408 -1326 -1734                0.0   0   0   0   \n",
       "4     100005  -190.666667  -62  -373  -572                0.0   0   0   0   \n",
       "\n",
       "  DAYS_CREDIT_ENDDATE   ...    AMT_CREDIT_SUM_OVERDUE      DAYS_CREDIT_UPDATE  \\\n",
       "                 mean   ...                       min  sum               mean   \n",
       "0           82.428571   ...                       0.0  0.0         -93.142857   \n",
       "1         -349.000000   ...                       0.0  0.0        -499.875000   \n",
       "2         -544.500000   ...                       0.0  0.0        -816.000000   \n",
       "3         -488.500000   ...                       0.0  0.0        -532.000000   \n",
       "4          439.333333   ...                       0.0  0.0         -54.333333   \n",
       "\n",
       "                    AMT_ANNUITY                         \n",
       "   max   min   sum         mean      max  min      sum  \n",
       "0   -6  -155  -652  3545.357143  10822.5  0.0  24817.5  \n",
       "1   -7 -1185 -3999     0.000000      0.0  0.0      0.0  \n",
       "2  -43 -2131 -3264          NaN      NaN  NaN      0.0  \n",
       "3 -382  -682 -1064          NaN      NaN  NaN      0.0  \n",
       "4  -11  -121  -163  1420.500000   4261.5  0.0   4261.5  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by the client id, calculate aggregation statistics\n",
    "bureau_agg = bureau.drop(columns = ['SK_ID_BUREAU']).groupby('SK_ID_CURR', as_index = False).agg(['mean', 'max', 'min', 'sum']).reset_index()\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "812f7b1e0d6016768d9276da790b0986181f9fdd"
   },
   "source": [
    "We need to create new names for each of these columns. The following code makes new names by appending the stat to the name. Here we make use of the fact that the dataframe has a multi-level index. I find these confusing and hard to work with, so I try to reduce to a single level index as quickly as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "01acafad68d285c875018ae916c002e9dad1a2fa",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# List of column names\n",
    "columns = ['SK_ID_CURR']\n",
    "\n",
    "# Iterate through the variables names\n",
    "for var in bureau_agg.columns.levels[0]:\n",
    "    # Skip the id name\n",
    "    if var != 'SK_ID_CURR':\n",
    "        \n",
    "        # Iterate through the stat names\n",
    "        for stat in bureau_agg.columns.levels[1][:-1]:\n",
    "            # Make a new column name for the variable and stat\n",
    "            columns.append('bureau_%s_%s' % (var, stat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "4f34abd311ffcb34e9feb0786998e478ffd90766",
    "collapsed": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_sum</th>\n",
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       "      <th>bureau_CREDIT_DAY_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_ENDDATE_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_OVERDUE_min</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
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       "      <th>bureau_AMT_ANNUITY_min</th>\n",
       "      <th>bureau_AMT_ANNUITY_sum</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>24817.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>-874.000000</td>\n",
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       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408</td>\n",
       "      <td>-1326</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-382</td>\n",
       "      <td>-682</td>\n",
       "      <td>-1064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>-62</td>\n",
       "      <td>-373</td>\n",
       "      <td>-572</td>\n",
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       "      <td>4261.5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\n",
       "0      100001              -735.000000                     -49   \n",
       "1      100002              -874.000000                    -103   \n",
       "2      100003             -1400.750000                    -606   \n",
       "3      100004              -867.000000                    -408   \n",
       "4      100005              -190.666667                     -62   \n",
       "\n",
       "   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                   -1572                   -5145   \n",
       "1                   -1437                   -6992   \n",
       "2                   -2586                   -5603   \n",
       "3                   -1326                   -1734   \n",
       "4                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_mean  bureau_CREDIT_DAY_OVERDUE_max  \\\n",
       "0                             0.0                              0   \n",
       "1                             0.0                              0   \n",
       "2                             0.0                              0   \n",
       "3                             0.0                              0   \n",
       "4                             0.0                              0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_min  bureau_CREDIT_DAY_OVERDUE_sum  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              0   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   bureau_DAYS_CREDIT_ENDDATE_mean           ...            \\\n",
       "0                        82.428571           ...             \n",
       "1                      -349.000000           ...             \n",
       "2                      -544.500000           ...             \n",
       "3                      -488.500000           ...             \n",
       "4                       439.333333           ...             \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_OVERDUE_min  bureau_AMT_CREDIT_SUM_OVERDUE_sum  \\\n",
       "0                                0.0                                0.0   \n",
       "1                                0.0                                0.0   \n",
       "2                                0.0                                0.0   \n",
       "3                                0.0                                0.0   \n",
       "4                                0.0                                0.0   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_mean  bureau_DAYS_CREDIT_UPDATE_max  \\\n",
       "0                      -93.142857                             -6   \n",
       "1                     -499.875000                             -7   \n",
       "2                     -816.000000                            -43   \n",
       "3                     -532.000000                           -382   \n",
       "4                      -54.333333                            -11   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_min  bureau_DAYS_CREDIT_UPDATE_sum  \\\n",
       "0                           -155                           -652   \n",
       "1                          -1185                          -3999   \n",
       "2                          -2131                          -3264   \n",
       "3                           -682                          -1064   \n",
       "4                           -121                           -163   \n",
       "\n",
       "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
       "0              3545.357143                 10822.5                     0.0   \n",
       "1                 0.000000                     0.0                     0.0   \n",
       "2                      NaN                     NaN                     NaN   \n",
       "3                      NaN                     NaN                     NaN   \n",
       "4              1420.500000                  4261.5                     0.0   \n",
       "\n",
       "   bureau_AMT_ANNUITY_sum  \n",
       "0                 24817.5  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                  4261.5  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the list of columns names as the dataframe column names\n",
    "bureau_agg.columns = columns\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "13c6a8b69a3bdbd1bf2d420d984f78400fc969d3"
   },
   "source": [
    "Now we simply merge with the training data as we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "2da1e6aa5651f4567813896d79d2e087fd5e7749",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
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       "      <td>100002</td>\n",
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       "      <td>Cash loans</td>\n",
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       "      <td>202500.0</td>\n",
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       "      <td>Revolving loans</td>\n",
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       "      <td>67500.0</td>\n",
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       "      <td>-682.0</td>\n",
       "      <td>-1064.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-783.000</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>-783.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "            ...            bureau_AMT_CREDIT_SUM_OVERDUE_min  \\\n",
       "0           ...                                          0.0   \n",
       "1           ...                                          0.0   \n",
       "2           ...                                          0.0   \n",
       "3           ...                                          NaN   \n",
       "4           ...                                          0.0   \n",
       "\n",
       "  bureau_AMT_CREDIT_SUM_OVERDUE_sum bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                               0.0                       -499.875   \n",
       "1                               0.0                       -816.000   \n",
       "2                               0.0                       -532.000   \n",
       "3                               NaN                            NaN   \n",
       "4                               0.0                       -783.000   \n",
       "\n",
       "  bureau_DAYS_CREDIT_UPDATE_max bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                          -7.0                       -1185.0   \n",
       "1                         -43.0                       -2131.0   \n",
       "2                        -382.0                        -682.0   \n",
       "3                           NaN                           NaN   \n",
       "4                        -783.0                        -783.0   \n",
       "\n",
       "  bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_mean  \\\n",
       "0                       -3999.0                      0.0   \n",
       "1                       -3264.0                      NaN   \n",
       "2                       -1064.0                      NaN   \n",
       "3                           NaN                      NaN   \n",
       "4                        -783.0                      NaN   \n",
       "\n",
       "   bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \n",
       "0                     0.0                     0.0                     0.0  \n",
       "1                     NaN                     NaN                     0.0  \n",
       "2                     NaN                     NaN                     0.0  \n",
       "3                     NaN                     NaN                     NaN  \n",
       "4                     NaN                     NaN                     0.0  \n",
       "\n",
       "[5 rows x 171 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge with the training data\n",
    "train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "290cf9aaa44d462f0f987ef35bb22b0010bd01a0"
   },
   "source": [
    "### Correlations of Aggregated Values with Target\n",
    "\n",
    "We can calculate the correlation of all new values with the target. Again, we can use these as an approximation of the variables which may be important for modeling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "8cf94533bb8df06623f70d0cb01c24156f8af689",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# List of new correlations\n",
    "new_corrs = []\n",
    "\n",
    "# Iterate through the columns \n",
    "for col in columns:\n",
    "    # Calculate correlation with the target\n",
    "    corr = train['TARGET'].corr(train[col])\n",
    "    \n",
    "    # Append the list as a tuple\n",
    "\n",
    "    new_corrs.append((col, corr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "774fe7e4c3e2708de823d3f94f65137b56bc2c2a"
   },
   "source": [
    "In the code below, we sort the correlations by the magnitude (absolute value) using the `sorted` Python function. We also make use of an anonymous `lambda` function, another important Python operation that is good to know. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "2802a2568214396e919837353683c63867d38a17",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bureau_DAYS_CREDIT_mean', 0.08972896721998122),\n",
       " ('bureau_DAYS_CREDIT_min', 0.0752482510301036),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_mean', 0.06892735266968668),\n",
       " ('bureau_DAYS_ENDDATE_FACT_min', 0.05588737984392113),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_sum', 0.05373489560102051),\n",
       " ('bureau_DAYS_ENDDATE_FACT_mean', 0.05319962585758661),\n",
       " ('bureau_DAYS_CREDIT_max', 0.049782054639973095),\n",
       " ('bureau_DAYS_ENDDATE_FACT_sum', 0.04885350261111589),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_mean', 0.04698275433483543),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_min', 0.042863922470730176),\n",
       " ('bureau_DAYS_CREDIT_sum', 0.04199982481484684),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_sum', 0.04140363535306004),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_max', 0.036589634696329046),\n",
       " ('bureau_DAYS_CREDIT_ENDDATE_min', 0.03428110992161599),\n",
       " ('bureau_DAYS_CREDIT_UPDATE_max', 0.028233546896535826)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the correlations by the absolute value\n",
    "# Make sure to reverse to put the largest values at the front of list\n",
    "new_corrs = sorted(new_corrs, key = lambda x: abs(x[1]), reverse = True)\n",
    "new_corrs[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "581f41a665ed3b23d8b28a6e3050a11bc8a68dcf"
   },
   "source": [
    "None of the new variables have a significant correlation with the TARGET. We can look at the KDE plot of the highest correlated variable, `bureau_DAYS_CREDIT_mean`, with the target in  in terms of absolute magnitude correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "be155ee8fd05290d11a205b95f4b553b8ade06e3",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between bureau_DAYS_CREDIT_mean and the TARGET is 0.0897\n",
      "Median value for loan that was not repaid = -835.3333\n",
      "Median value for loan that was repaid =     -1067.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target('bureau_DAYS_CREDIT_mean', train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "58ca4b2852b486fa10e1bf31c2a7e61e9de52dd7"
   },
   "source": [
    "The definition of this column is: \"How many days before current application did client apply for Credit Bureau credit\". My interpretation is this is the number of days that the previous loan was applied for before the application for a loan at Home Credit. Therefore, a larger negative number indicates the loan was further before the current loan application. We see an extremely weak positive relationship between the average of this variable and the target meaning that clients who applied for loans further in the past potentially are more likely to repay loans at Home Credit. With a correlation this weak though, it is just as likely to be noise as a signal. \n",
    "\n",
    "#### The Multiple Comparisons Problem\n",
    "\n",
    "When we have lots of variables, we expect some of them to be correlated just by pure chance, a [problem known as multiple comparisons](https://towardsdatascience.com/the-multiple-comparisons-problem-e5573e8b9578). We can make hundreds of features, and some will turn out to be corelated with the target simply because of random noise in the data. Then, when our model trains, it may overfit to these variables because it thinks they have a relationship with the target in the training set, but this does not necessarily generalize to the test set. There are many considerations that we have to take into account when making features! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "645ab51cb64cd20f122d61e17ef8ad1ed78443cc"
   },
   "source": [
    "## Function for Numeric Aggregations\n",
    "\n",
    "Let's encapsulate all of the previous work into a function. This will allow us to compute aggregate stats for numeric columns across any dataframe. We will re-use this function when we want to apply the same operations for other dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "8203852b47164d7800f69e572dda2cbe83c50976",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def agg_numeric(df, group_var, df_name):\n",
    "    \"\"\"Aggregates the numeric values in a dataframe. This can\n",
    "    be used to create features for each instance of the grouping variable.\n",
    "    \n",
    "    Args:\n",
    "        df (dataframe): the dataframe to calculate the statistics on\n",
    "        group_var (string): the variable by which to group df\n",
    "        df_name (string): the variable used to rename the columns\n",
    "        \n",
    "    Returns:\n",
    "        agg (dataframe): a dataframe with the statistics aggregated for \n",
    "        all numeric columns. Each instance of the grouping variable will have \n",
    "        the statistics (mean, min, max, sum; currently supported) calculated. \n",
    "        The columns are also renamed to keep track of features created.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # First calculate counts\n",
    "    counts = pd.DataFrame(df.groupby(group_var, as_index = False)[df.columns[1]].count()).rename(columns = {df.columns[1]: '%s_counts' % df_name})\n",
    "    \n",
    "    # Group by the specified variable and calculate the statistics\n",
    "    agg = df.groupby(group_var).agg(['mean', 'max', 'min', 'sum']).reset_index()\n",
    "    \n",
    "    # Need to create new column names\n",
    "    columns = [group_var]\n",
    "    \n",
    "    # Iterate through the variables names\n",
    "    for var in agg.columns.levels[0]:\n",
    "        # Skip the grouping variable\n",
    "        if var != group_var:\n",
    "            # Iterate through the stat names\n",
    "            for stat in agg.columns.levels[1][:-1]:\n",
    "                # Make a new column name for the variable and stat\n",
    "                columns.append('%s_%s_%s' % (df_name, var, stat))\n",
    "              \n",
    "    #  Rename the columns\n",
    "    agg.columns = columns\n",
    "    \n",
    "    # Merge with the counts\n",
    "    agg = agg.merge(counts, on = group_var, how = 'left')\n",
    "    \n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "2784423d6e900b66f108cb2e6a425d63eb28743e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_sum</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_mean</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_ENDDATE_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
       "      <th>bureau_AMT_ANNUITY_sum</th>\n",
       "      <th>bureau_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>-735.000000</td>\n",
       "      <td>-49</td>\n",
       "      <td>-1572</td>\n",
       "      <td>-5145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93.142857</td>\n",
       "      <td>-6</td>\n",
       "      <td>-155</td>\n",
       "      <td>-652</td>\n",
       "      <td>3545.357143</td>\n",
       "      <td>10822.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24817.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>-103</td>\n",
       "      <td>-1437</td>\n",
       "      <td>-6992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>-7</td>\n",
       "      <td>-1185</td>\n",
       "      <td>-3999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>-606</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-544.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>-43</td>\n",
       "      <td>-2131</td>\n",
       "      <td>-3264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-1734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-488.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-532.000000</td>\n",
       "      <td>-382</td>\n",
       "      <td>-682</td>\n",
       "      <td>-1064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>-62</td>\n",
       "      <td>-373</td>\n",
       "      <td>-572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-54.333333</td>\n",
       "      <td>-11</td>\n",
       "      <td>-121</td>\n",
       "      <td>-163</td>\n",
       "      <td>1420.500000</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\n",
       "0      100001              -735.000000                     -49   \n",
       "1      100002              -874.000000                    -103   \n",
       "2      100003             -1400.750000                    -606   \n",
       "3      100004              -867.000000                    -408   \n",
       "4      100005              -190.666667                     -62   \n",
       "\n",
       "   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                   -1572                   -5145   \n",
       "1                   -1437                   -6992   \n",
       "2                   -2586                   -5603   \n",
       "3                   -1326                   -1734   \n",
       "4                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_mean  bureau_CREDIT_DAY_OVERDUE_max  \\\n",
       "0                             0.0                              0   \n",
       "1                             0.0                              0   \n",
       "2                             0.0                              0   \n",
       "3                             0.0                              0   \n",
       "4                             0.0                              0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_min  bureau_CREDIT_DAY_OVERDUE_sum  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              0   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   bureau_DAYS_CREDIT_ENDDATE_mean      ...        \\\n",
       "0                        82.428571      ...         \n",
       "1                      -349.000000      ...         \n",
       "2                      -544.500000      ...         \n",
       "3                      -488.500000      ...         \n",
       "4                       439.333333      ...         \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_OVERDUE_sum  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                0.0                      -93.142857   \n",
       "1                                0.0                     -499.875000   \n",
       "2                                0.0                     -816.000000   \n",
       "3                                0.0                     -532.000000   \n",
       "4                                0.0                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_mean  \\\n",
       "0                           -652              3545.357143   \n",
       "1                          -3999                 0.000000   \n",
       "2                          -3264                      NaN   \n",
       "3                          -1064                      NaN   \n",
       "4                           -163              1420.500000   \n",
       "\n",
       "   bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\\n",
       "0                 10822.5                     0.0                 24817.5   \n",
       "1                     0.0                     0.0                     0.0   \n",
       "2                     NaN                     NaN                     0.0   \n",
       "3                     NaN                     NaN                     0.0   \n",
       "4                  4261.5                     0.0                  4261.5   \n",
       "\n",
       "   bureau_counts  \n",
       "0              7  \n",
       "1              8  \n",
       "2              4  \n",
       "3              2  \n",
       "4              3  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg_new = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_agg_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "171660155dd332d9df85f61fdd7004a9f2593e59"
   },
   "source": [
    "To make sure the function worked as intended, we should compare with the aggregated dataframe we constructed by hand. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "57dab1b1b23d0bd0167c9367688b56b43066a721",
    "collapsed": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
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       "      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_ENDDATE_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
       "      <th>bureau_AMT_ANNUITY_sum</th>\n",
       "      <th>bureau_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
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       "      <td>-6</td>\n",
       "      <td>-155</td>\n",
       "      <td>-652</td>\n",
       "      <td>3545.357143</td>\n",
       "      <td>10822.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24817.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>-874.000000</td>\n",
       "      <td>-103</td>\n",
       "      <td>-1437</td>\n",
       "      <td>-6992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>-7</td>\n",
       "      <td>-1185</td>\n",
       "      <td>-3999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>-606</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-544.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>-43</td>\n",
       "      <td>-2131</td>\n",
       "      <td>-3264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-1734</td>\n",
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       "      <td>-532.000000</td>\n",
       "      <td>-382</td>\n",
       "      <td>-682</td>\n",
       "      <td>-1064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>-62</td>\n",
       "      <td>-373</td>\n",
       "      <td>-572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-11</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\n",
       "0      100001              -735.000000                     -49   \n",
       "1      100002              -874.000000                    -103   \n",
       "2      100003             -1400.750000                    -606   \n",
       "3      100004              -867.000000                    -408   \n",
       "4      100005              -190.666667                     -62   \n",
       "\n",
       "   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                   -1572                   -5145   \n",
       "1                   -1437                   -6992   \n",
       "2                   -2586                   -5603   \n",
       "3                   -1326                   -1734   \n",
       "4                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_mean  bureau_CREDIT_DAY_OVERDUE_max  \\\n",
       "0                             0.0                              0   \n",
       "1                             0.0                              0   \n",
       "2                             0.0                              0   \n",
       "3                             0.0                              0   \n",
       "4                             0.0                              0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_min  bureau_CREDIT_DAY_OVERDUE_sum  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              0   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   bureau_DAYS_CREDIT_ENDDATE_mean      ...        \\\n",
       "0                        82.428571      ...         \n",
       "1                      -349.000000      ...         \n",
       "2                      -544.500000      ...         \n",
       "3                      -488.500000      ...         \n",
       "4                       439.333333      ...         \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_OVERDUE_sum  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                0.0                      -93.142857   \n",
       "1                                0.0                     -499.875000   \n",
       "2                                0.0                     -816.000000   \n",
       "3                                0.0                     -532.000000   \n",
       "4                                0.0                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_mean  \\\n",
       "0                           -652              3545.357143   \n",
       "1                          -3999                 0.000000   \n",
       "2                          -3264                      NaN   \n",
       "3                          -1064                      NaN   \n",
       "4                           -163              1420.500000   \n",
       "\n",
       "   bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\\n",
       "0                 10822.5                     0.0                 24817.5   \n",
       "1                     0.0                     0.0                     0.0   \n",
       "2                     NaN                     NaN                     0.0   \n",
       "3                     NaN                     NaN                     0.0   \n",
       "4                  4261.5                     0.0                  4261.5   \n",
       "\n",
       "   bureau_counts  \n",
       "0              7  \n",
       "1              8  \n",
       "2              4  \n",
       "3              2  \n",
       "4              3  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg['bureau_counts'] = bureau_agg_new['bureau_counts']\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "af5a3d2ebc6850707c0bb021bc08df50b98d008b"
   },
   "source": [
    "If we go through and inspect the values, we do find that they are equivalent. We will be able to reuse this function for calculating numeric stats for other dataframes. Using functions allows for consistent results and decreases the amount of work we have to do in the future! \n",
    "\n",
    "### Correlation Function\n",
    "\n",
    "Before we move on, we can also make the code to calculate correlations with the target into a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "f55cdb9e28c9fcdb16b7922ce90c99ddda590e67",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to calculate correlations with the target for a dataframe\n",
    "def target_corrs(df):\n",
    "\n",
    "    # List of new correlations\n",
    "    new_corrs = []\n",
    "\n",
    "    # Iterate through the columns \n",
    "    for col in df.columns:\n",
    "        print(col)\n",
    "        # Skip the target column\n",
    "        if col != 'TARGET':\n",
    "            # Calculate correlation with the target\n",
    "            corr = df['TARGET'].corr(df[col])\n",
    "\n",
    "            # Append the list as a tuple\n",
    "            new_corrs.append((col, corr))\n",
    "            \n",
    "    # Sort by absolute magnitude of correlations\n",
    "    new_corrs = sorted(new_corrs, key = lambda x: abs(x[1]), reverse = True)\n",
    "    \n",
    "    return new_corrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a86ad170924ebf66122d24083679d2a061a080ff"
   },
   "source": [
    "## Categorical Variables\n",
    "\n",
    "Now we move from the numeric columns to the categorical columns. These are discrete string variables, so we cannot just calculate statistics such as mean \n",
    "and max which only work with numeric variables. Instead, we will rely on calculating value counts of each category within each categorical variable. As an example, if we have the following dataframe:\n",
    "\n",
    "| SK_ID_CURR | Loan type |\n",
    "|------------|-----------|\n",
    "| 1          | home      |\n",
    "| 1          | home      |\n",
    "| 1          | home      |\n",
    "| 1          | credit    |\n",
    "| 2          | credit    |\n",
    "| 3          | credit    |\n",
    "| 3          | cash      |\n",
    "| 3          | cash      |\n",
    "| 4          | credit    |\n",
    "| 4          | home      |\n",
    "| 4          | home      |\n",
    "\n",
    "we will use this information counting the number of loans in each category for each client. \n",
    "\n",
    "| SK_ID_CURR | credit count | cash count | home count | total count |\n",
    "|------------|--------------|------------|------------|-------------|\n",
    "| 1          | 1            | 0          | 3          | 4           |\n",
    "| 2          | 1            | 0          | 0          | 1           |\n",
    "| 3          | 1            | 2          | 0          | 3           |\n",
    "| 4          | 1            | 0          | 2          | 3           |\n",
    "\n",
    "\n",
    "Then we can normalize these value counts by the total number of occurences of that categorical variable for that observation (meaning that the normalized counts must sum to 1.0 for each observation).\n",
    "\n",
    "| SK_ID_CURR | credit count | cash count | home count | total count | credit count norm | cash count norm | home count norm |\n",
    "|------------|--------------|------------|------------|-------------|-------------------|-----------------|-----------------|\n",
    "| 1          | 1            | 0          | 3          | 4           | 0.25              | 0               | 0.75            |\n",
    "| 2          | 1            | 0          | 0          | 1           | 1.00              | 0               | 0               |\n",
    "| 3          | 1            | 2          | 0          | 3           | 0.33              | 0.66            | 0               |\n",
    "| 4          | 1            | 0          | 2          | 3           | 0.33              | 0               | 0.66            |\n",
    "\n",
    "Hopefully, encoding the categorical variables this way will allow us to capture the information they contain. If anyone has a better idea for this process, please let me know in the comments!\n",
    "We will now go through this process step-by-step. At the end, we will wrap up all the code into one function to be re-used for many dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we select only the categorical columns (`dtype == 'object'`), then we group the dataframe by the client id and calculate value counts for a single categorical column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "d6fef73daa791c4a5998dd80bf2a2af5519c521f",
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>CREDIT_ACTIVE</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Active</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <th>Closed</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          CREDIT_ACTIVE\n",
       "SK_ID_CURR CREDIT_ACTIVE               \n",
       "100001     Closed                     4\n",
       "           Active                     3\n",
       "100002     Closed                     6\n",
       "           Active                     2\n",
       "100003     Closed                     3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_name = 'SK_ID_CURR'\n",
    "\n",
    "# Select the categorical columns\n",
    "cats = bureau.select_dtypes('object')\n",
    "\n",
    "# Make sure to put the identifying id on the column\n",
    "cats[id_name] = bureau[id_name]\n",
    "\n",
    "col = 'CREDIT_ACTIVE'\n",
    "\n",
    "# Group by the id, select the column, and calculate value counts\n",
    "tg = pd.DataFrame(cats.groupby(id_name)[col].value_counts())\n",
    "\n",
    "tg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <th>CREDIT_CURRENCY</th>\n",
       "      <th>CREDIT_TYPE</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closed</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>215354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>215354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>215354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>215354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Active</td>\n",
       "      <td>currency 1</td>\n",
       "      <td>Consumer credit</td>\n",
       "      <td>215354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CREDIT_ACTIVE CREDIT_CURRENCY      CREDIT_TYPE  SK_ID_CURR\n",
       "0        Closed      currency 1  Consumer credit      215354\n",
       "1        Active      currency 1      Credit card      215354\n",
       "2        Active      currency 1  Consumer credit      215354\n",
       "3        Active      currency 1      Credit card      215354\n",
       "4        Active      currency 1  Consumer credit      215354"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <th>CREDIT_ACTIVE_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Closed</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001</td>\n",
       "      <td>Active</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100002</td>\n",
       "      <td>Closed</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100002</td>\n",
       "      <td>Active</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100003</td>\n",
       "      <td>Closed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR CREDIT_ACTIVE  CREDIT_ACTIVE_counts\n",
       "0      100001        Closed                     4\n",
       "1      100001        Active                     3\n",
       "2      100002        Closed                     6\n",
       "3      100002        Active                     2\n",
       "4      100003        Closed                     3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the column and reset the index\n",
    "tg = tg.rename(columns = {col: '%s_counts' % col}).reset_index()\n",
    "tg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step involves making use of a powerful pandas function, `pivot_table`. We can use this function to create new columns that counts the number of each type of `CREDIT_ACTIVE` for each `SK_ID_CURR`. To do so, we specify that the values to aggregate are the `CREDIT_ACTIVE_counts`, the index is the `SK_ID_CURR`, the columns to group by are the `CREDIT_ACTIVE`, and the `aggfunc` is `mean` (we could also use `sum` here with the same result). We will also specify that `fill_value = None` which put in NaN in any cells where the client has not had that type of `CREDIT_ACTIVE`. What this function does is create a new column for each value of `CREDIT_ACTIVE` with the count of that type of `CREDIT_ACTIVE` for each client. \n",
    "\n",
    "This might be a little confusing, so let's see the result. This is a function that is best learned by practicing! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CREDIT_ACTIVE</th>\n",
       "      <th>Active</th>\n",
       "      <th>Bad debt</th>\n",
       "      <th>Closed</th>\n",
       "      <th>Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CREDIT_ACTIVE  Active  Bad debt  Closed  Sold\n",
       "SK_ID_CURR                                   \n",
       "100001              3         0       4     0\n",
       "100002              2         0       6     0\n",
       "100003              1         0       3     0\n",
       "100004              0         0       2     0\n",
       "100005              2         0       1     0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(tg, values = '%s_counts' % col, index = [id_name], \n",
    "                       columns = col, aggfunc = 'mean', fill_value = 0)\n",
    "\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to rename the columns. We will use a list comprehension for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CREDIT_ACTIVE_Active_counts  CREDIT_ACTIVE_Bad debt_counts  \\\n",
       "SK_ID_CURR                                                               \n",
       "100001                                3                              0   \n",
       "100002                                2                              0   \n",
       "100003                                1                              0   \n",
       "100004                                0                              0   \n",
       "100005                                2                              0   \n",
       "\n",
       "            CREDIT_ACTIVE_Closed_counts  CREDIT_ACTIVE_Sold_counts  \n",
       "SK_ID_CURR                                                          \n",
       "100001                                4                          0  \n",
       "100002                                6                          0  \n",
       "100003                                3                          0  \n",
       "100004                                2                          0  \n",
       "100005                                1                          0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First remove the `name` of the columns\n",
    "table.columns.name = None\n",
    "\n",
    "# New column names\n",
    "table_cols = ['%s_%s_counts' % (col, x) for x in table.columns]\n",
    "\n",
    "# Assign the new column names to the dataframe\n",
    "table.columns = table_cols\n",
    "\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, to normalize the counts by the total, we need to calculate the total, merge it into the dataframe, and divide the columns of counts by this total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_counts</th>\n",
       "      <th>previous_loan_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CREDIT_ACTIVE_Active_counts  CREDIT_ACTIVE_Bad debt_counts  \\\n",
       "SK_ID_CURR                                                               \n",
       "100001                                3                              0   \n",
       "100002                                2                              0   \n",
       "100003                                1                              0   \n",
       "100004                                0                              0   \n",
       "100005                                2                              0   \n",
       "\n",
       "            CREDIT_ACTIVE_Closed_counts  CREDIT_ACTIVE_Sold_counts  \\\n",
       "SK_ID_CURR                                                           \n",
       "100001                                4                          0   \n",
       "100002                                6                          0   \n",
       "100003                                3                          0   \n",
       "100004                                2                          0   \n",
       "100005                                1                          0   \n",
       "\n",
       "            previous_loan_counts  \n",
       "SK_ID_CURR                        \n",
       "100001                         7  \n",
       "100002                         8  \n",
       "100003                         4  \n",
       "100004                         2  \n",
       "100005                         3  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = table.merge(previous_loan_counts, left_index=True, right_on = id_name,\n",
    "                    how = 'left').set_index(id_name)\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_counts</th>\n",
       "      <th>previous_loan_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts_norm</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts_norm</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts_norm</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_counts_norm</th>\n",
       "      <th>previous_loan_counts_norm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100005</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CREDIT_ACTIVE_Active_counts  CREDIT_ACTIVE_Bad debt_counts  \\\n",
       "SK_ID_CURR                                                               \n",
       "100001                                3                              0   \n",
       "100002                                2                              0   \n",
       "100003                                1                              0   \n",
       "100004                                0                              0   \n",
       "100005                                2                              0   \n",
       "\n",
       "            CREDIT_ACTIVE_Closed_counts  CREDIT_ACTIVE_Sold_counts  \\\n",
       "SK_ID_CURR                                                           \n",
       "100001                                4                          0   \n",
       "100002                                6                          0   \n",
       "100003                                3                          0   \n",
       "100004                                2                          0   \n",
       "100005                                1                          0   \n",
       "\n",
       "            previous_loan_counts  CREDIT_ACTIVE_Active_counts_norm  \\\n",
       "SK_ID_CURR                                                           \n",
       "100001                         7                          0.428571   \n",
       "100002                         8                          0.250000   \n",
       "100003                         4                          0.250000   \n",
       "100004                         2                          0.000000   \n",
       "100005                         3                          0.666667   \n",
       "\n",
       "            CREDIT_ACTIVE_Bad debt_counts_norm  \\\n",
       "SK_ID_CURR                                       \n",
       "100001                                     0.0   \n",
       "100002                                     0.0   \n",
       "100003                                     0.0   \n",
       "100004                                     0.0   \n",
       "100005                                     0.0   \n",
       "\n",
       "            CREDIT_ACTIVE_Closed_counts_norm  CREDIT_ACTIVE_Sold_counts_norm  \\\n",
       "SK_ID_CURR                                                                     \n",
       "100001                              0.571429                             0.0   \n",
       "100002                              0.750000                             0.0   \n",
       "100003                              0.750000                             0.0   \n",
       "100004                              1.000000                             0.0   \n",
       "100005                              0.333333                             0.0   \n",
       "\n",
       "            previous_loan_counts_norm  \n",
       "SK_ID_CURR                             \n",
       "100001                            1.0  \n",
       "100002                            1.0  \n",
       "100003                            1.0  \n",
       "100004                            1.0  \n",
       "100005                            1.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new columns with normalized counts\n",
    "# Iterate through each column of counts\n",
    "for col in table.columns:\n",
    "    # For each column, divide by the counts\n",
    "    table['%s_norm' % col] = table[col] / table['previous_loan_counts']\n",
    "    \n",
    "table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we scroll to the right, we can see that there are normalized counts for each of the columns. As a sanity check, the `previous_loan_count_norm == 1.0` for every client as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do the same thing but for every categorical column in the `bureau` dataframe. We'll do it one time by itself, and then put this method into a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "1cf070929dee41c371a536710d4398eab32d5a20",
    "collapsed": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts</th>\n",
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       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SK_ID_CURR  CREDIT_ACTIVE_Active_counts  CREDIT_ACTIVE_Bad debt_counts  \\\n",
       "0       215354                            6                              0   \n",
       "7       162297                            3                              0   \n",
       "13      402440                            1                              0   \n",
       "14      238881                            3                              0   \n",
       "21      222183                            5                              0   \n",
       "\n",
       "    CREDIT_ACTIVE_Closed_counts  CREDIT_ACTIVE_Sold_counts  \\\n",
       "0                             5                          0   \n",
       "7                             3                          0   \n",
       "13                            0                          0   \n",
       "14                            5                          0   \n",
       "21                            3                          0   \n",
       "\n",
       "    CREDIT_CURRENCY_currency 1_counts  CREDIT_CURRENCY_currency 2_counts  \\\n",
       "0                                  11                                  0   \n",
       "7                                   6                                  0   \n",
       "13                                  1                                  0   \n",
       "14                                  8                                  0   \n",
       "21                                  8                                  0   \n",
       "\n",
       "    CREDIT_CURRENCY_currency 3_counts  CREDIT_CURRENCY_currency 4_counts  \\\n",
       "0                                   0                                  0   \n",
       "7                                   0                                  0   \n",
       "13                                  0                                  0   \n",
       "14                                  0                                  0   \n",
       "21                                  0                                  0   \n",
       "\n",
       "    CREDIT_TYPE_Another type of loan_counts  \\\n",
       "0                                         0   \n",
       "7                                         0   \n",
       "13                                        0   \n",
       "14                                        0   \n",
       "21                                        0   \n",
       "\n",
       "                     ...                     \\\n",
       "0                    ...                      \n",
       "7                    ...                      \n",
       "13                   ...                      \n",
       "14                   ...                      \n",
       "21                   ...                      \n",
       "\n",
       "    CREDIT_TYPE_Interbank credit_counts  \\\n",
       "0                                     0   \n",
       "7                                     0   \n",
       "13                                    0   \n",
       "14                                    0   \n",
       "21                                    0   \n",
       "\n",
       "    CREDIT_TYPE_Loan for business development_counts  \\\n",
       "0                                                  0   \n",
       "7                                                  0   \n",
       "13                                                 0   \n",
       "14                                                 0   \n",
       "21                                                 0   \n",
       "\n",
       "    CREDIT_TYPE_Loan for purchase of shares (margin lending)_counts  \\\n",
       "0                                                   0                 \n",
       "7                                                   0                 \n",
       "13                                                  0                 \n",
       "14                                                  0                 \n",
       "21                                                  0                 \n",
       "\n",
       "    CREDIT_TYPE_Loan for the purchase of equipment_counts  \\\n",
       "0                                                   0       \n",
       "7                                                   0       \n",
       "13                                                  0       \n",
       "14                                                  0       \n",
       "21                                                  0       \n",
       "\n",
       "    CREDIT_TYPE_Loan for working capital replenishment_counts  \\\n",
       "0                                                   0           \n",
       "7                                                   0           \n",
       "13                                                  0           \n",
       "14                                                  0           \n",
       "21                                                  0           \n",
       "\n",
       "    CREDIT_TYPE_Microloan_counts  CREDIT_TYPE_Mobile operator loan_counts  \\\n",
       "0                              0                                        0   \n",
       "7                              0                                        0   \n",
       "13                             0                                        0   \n",
       "14                             0                                        0   \n",
       "21                             0                                        0   \n",
       "\n",
       "    CREDIT_TYPE_Mortgage_counts  CREDIT_TYPE_Real estate loan_counts  \\\n",
       "0                             0                                    0   \n",
       "7                             1                                    0   \n",
       "13                            0                                    0   \n",
       "14                            0                                    0   \n",
       "21                            0                                    0   \n",
       "\n",
       "    CREDIT_TYPE_Unknown type of loan_counts  \n",
       "0                                         0  \n",
       "7                                         0  \n",
       "13                                        0  \n",
       "14                                        0  \n",
       "21                                        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_name = 'SK_ID_CURR'\n",
    "\n",
    "# Select the categorical columns\n",
    "cats = bureau.select_dtypes('object')\n",
    "\n",
    "# Make sure to put the identifying id on the column\n",
    "cats[id_name] = bureau[id_name]\n",
    "\n",
    "cats_cols = list(cats.columns)\n",
    "\n",
    "# Iterate through each categorical column\n",
    "for col in cats_cols:\n",
    "    if col == id_name:\n",
    "        next\n",
    "    else:\n",
    "        # Group by the id, select the column, calculate value counts\n",
    "        tg = cats.groupby(id_name)[col].value_counts()\n",
    "        \n",
    "        # Rename the column\n",
    "        tg = pd.DataFrame(tg).rename(columns = {col: '%s_counts' % col}).reset_index()\n",
    "\n",
    "        # Pivot table with counts the value to aggregate, index is the id, \n",
    "        # columns are the categorical name, the aggfunc is the mean and fill\n",
    "        # in missing values with 0\n",
    "        table = pd.pivot_table(tg, values = '%s_counts' % col, index = id_name, \n",
    "                               columns = col, aggfunc = 'mean', fill_value = 0)\n",
    "            \n",
    "        # Remove the table name\n",
    "        table.columns.name = None\n",
    "    \n",
    "        # New names of columns\n",
    "        table_columns = ['%s_%s_counts' % (col, x) for x in table.columns]\n",
    "        table.columns = table_columns\n",
    "        \n",
    "        # Merge the created table with the categorical dataframe\n",
    "        cats = cats.merge(table, left_on = 'SK_ID_CURR', right_index = True, how = 'left', copy = False)\n",
    "        cats = cats.drop(columns = [col])\n",
    "        \n",
    "cats = cats.drop_duplicates()\n",
    "cats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to get the normalized count for all the columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the counts for each client\n",
    "counts = pd.DataFrame(bureau.groupby(id_name)[id_name].count()).rename(columns = {id_name: 'all_counts'})\n",
    "\n",
    "# Merge the counts into the categorical data\n",
    "cats = cats.merge(counts, left_on = id_name, right_index = True, how = 'left')\n",
    "cats = cats.set_index(id_name)\n",
    "\n",
    "# Normalize all the columns by the total_counts\n",
    "for col in list(cats.columns):\n",
    "    cats['%s_normalized' % col] = cats[col] / cats['all_counts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "74e8aa2917ee0753b9b6b6a7e0b2e863c11e7d6d",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 219)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "           ...            \\\n",
       "0          ...             \n",
       "1          ...             \n",
       "2          ...             \n",
       "3          ...             \n",
       "4          ...             \n",
       "\n",
       "   CREDIT_TYPE_Loan for business development_counts_normalized  \\\n",
       "0                                                0.0             \n",
       "1                                                0.0             \n",
       "2                                                0.0             \n",
       "3                                                NaN             \n",
       "4                                                0.0             \n",
       "\n",
       "  CREDIT_TYPE_Loan for purchase of shares (margin lending)_counts_normalized  \\\n",
       "0                                                0.0                           \n",
       "1                                                0.0                           \n",
       "2                                                0.0                           \n",
       "3                                                NaN                           \n",
       "4                                                0.0                           \n",
       "\n",
       "  CREDIT_TYPE_Loan for the purchase of equipment_counts_normalized  \\\n",
       "0                                                0.0                 \n",
       "1                                                0.0                 \n",
       "2                                                0.0                 \n",
       "3                                                NaN                 \n",
       "4                                                0.0                 \n",
       "\n",
       "  CREDIT_TYPE_Loan for working capital replenishment_counts_normalized  \\\n",
       "0                                                0.0                     \n",
       "1                                                0.0                     \n",
       "2                                                0.0                     \n",
       "3                                                NaN                     \n",
       "4                                                0.0                     \n",
       "\n",
       "  CREDIT_TYPE_Microloan_counts_normalized  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     NaN   \n",
       "4                                     0.0   \n",
       "\n",
       "  CREDIT_TYPE_Mobile operator loan_counts_normalized  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                NaN   \n",
       "4                                                0.0   \n",
       "\n",
       "   CREDIT_TYPE_Mortgage_counts_normalized  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     NaN   \n",
       "4                                     0.0   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan_counts_normalized  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             NaN   \n",
       "4                                             0.0   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_counts_normalized  all_counts_normalized  \n",
       "0                                                0.0                     1.0  \n",
       "1                                                0.0                     1.0  \n",
       "2                                                0.0                     1.0  \n",
       "3                                                NaN                     NaN  \n",
       "4                                                0.0                     1.0  \n",
       "\n",
       "[5 rows x 219 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats = cats.reset_index()\n",
    "train = train.merge(cats, on = 'SK_ID_CURR', how = 'left')\n",
    "print('Training Data Shape: ', train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a30770ca960821a6c915f6acd54161a37bffb432"
   },
   "source": [
    "### Function to Handle Categorical Variables\n",
    "\n",
    "To make the code more efficient, we can now write a function to handle the categorical variables for us. This will take the same form as the `agg_numeric` function in that it accepts a dataframe and a grouping variable. Then it will calculate the counts of each category for all categorical variables in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "ddc7decf9497eaa536255d601f76c3a6259fca6b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_cats(df, group_var, df_name):\n",
    "    \"\"\"Computes value counts of categories in all categorical variables for a dataframe.\n",
    "    The value counts are calculated for every instance of the group_var. The dataframe that is\n",
    "    returned includes the overall counts of each occurence of the grouping variable \n",
    "    as well as the value counts of categories for each categorical variable for each \n",
    "    occurence of the grouping variable\n",
    "    \n",
    "    Args:\n",
    "        df (dataframe): The dataframe to calculate the value counts for.\n",
    "        group_var (string): the variable by which to group df\n",
    "        df_name (string): the variable used to rename the columns\n",
    "    \n",
    "    Returns:\n",
    "        tg (dataframe): a dataframe with value counts of each category in every categorical variable\n",
    "        of the original df. The overall counts are also merged into the dataframe.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Select the categorical columns\n",
    "    cats = df.select_dtypes('object')\n",
    "\n",
    "    # Make sure to put the identifying id on the column\n",
    "    cats[group_var] = df[group_var]\n",
    "\n",
    "    cats_cols = list(cats.columns)\n",
    "\n",
    "    # Iterate through each categorical column\n",
    "    for col in cats_cols:\n",
    "        if col == group_var:\n",
    "            next\n",
    "        else:\n",
    "            # Group by the grouping variable, select the column, calculate value counts\n",
    "            tg = cats.groupby(group_var)[col].value_counts()\n",
    "\n",
    "            # Rename the column\n",
    "            tg = pd.DataFrame(tg).rename(columns = {col: '%s_counts' % col}).reset_index()\n",
    "\n",
    "            # Pivot table with counts the value to aggregate, index is the grouping variable, \n",
    "            # columns are the categorical name, the aggfunc is the mean and fill\n",
    "            # in missing values with 0\n",
    "            table = pd.pivot_table(tg, values = '%s_counts' % col, index = group_var, \n",
    "                                   columns = col, aggfunc = 'mean', fill_value = 0)\n",
    "\n",
    "            # Remove the table name\n",
    "            table.columns.name = None\n",
    "\n",
    "            # New names of columns\n",
    "            table_columns = ['%s_%s_counts' % (col, x) for x in table.columns]\n",
    "            table.columns = table_columns\n",
    "\n",
    "            # Merge the created table with the categorical dataframe\n",
    "            cats = cats.merge(table, left_on = group_var, right_index = True, how = 'left')\n",
    "            cats = cats.drop_duplicates()\n",
    "            cats = cats.drop(columns = [col])\n",
    "\n",
    "    # Get the counts for each client\n",
    "    counts = pd.DataFrame(df.groupby(group_var)[group_var].count()).rename(columns = {group_var: '%s_counts' % df_name})\n",
    "\n",
    "    # Merge the counts into the categorical data\n",
    "    cats = cats.merge(counts, left_on = group_var, right_index = True, how = 'left')\n",
    "    cats = cats.set_index(group_var)\n",
    "\n",
    "    # Normalize all the columns by the total_counts\n",
    "    for col in list(cats.columns):\n",
    "        cats['%s_normalized' % col] = cats[col] / cats['%s_counts' % df_name]\n",
    "    \n",
    "    # Reset the index to make group var a column\n",
    "    cats = cats.reset_index()\n",
    "    \n",
    "    # Drop the duplicate rows\n",
    "    cats = cats.drop_duplicates()\n",
    "    \n",
    "    return cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "d0f1629b07ec6e75e98da459f22611d275fcb282",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_counts</th>\n",
       "      <th>CREDIT_CURRENCY_currency 1_counts</th>\n",
       "      <th>CREDIT_CURRENCY_currency 2_counts</th>\n",
       "      <th>CREDIT_CURRENCY_currency 3_counts</th>\n",
       "      <th>CREDIT_CURRENCY_currency 4_counts</th>\n",
       "      <th>CREDIT_TYPE_Another type of loan_counts</th>\n",
       "      <th>...</th>\n",
       "      <th>CREDIT_TYPE_Loan for business development_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for purchase of shares (margin lending)_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for the purchase of equipment_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for working capital replenishment_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Microloan_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_counts_normalized</th>\n",
       "      <th>bureau_counts_normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215354</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162297</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>402440</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>238881</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>222183</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  CREDIT_ACTIVE_Active_counts  CREDIT_ACTIVE_Bad debt_counts  \\\n",
       "0      215354                            6                              0   \n",
       "2      162297                            3                              0   \n",
       "5      402440                            1                              0   \n",
       "6      238881                            3                              0   \n",
       "8      222183                            5                              0   \n",
       "\n",
       "   CREDIT_ACTIVE_Closed_counts  CREDIT_ACTIVE_Sold_counts  \\\n",
       "0                            5                          0   \n",
       "2                            3                          0   \n",
       "5                            0                          0   \n",
       "6                            5                          0   \n",
       "8                            3                          0   \n",
       "\n",
       "   CREDIT_CURRENCY_currency 1_counts  CREDIT_CURRENCY_currency 2_counts  \\\n",
       "0                                  4                                  0   \n",
       "2                                  3                                  0   \n",
       "5                                  1                                  0   \n",
       "6                                  4                                  0   \n",
       "8                                  4                                  0   \n",
       "\n",
       "   CREDIT_CURRENCY_currency 3_counts  CREDIT_CURRENCY_currency 4_counts  \\\n",
       "0                                  0                                  0   \n",
       "2                                  0                                  0   \n",
       "5                                  0                                  0   \n",
       "6                                  0                                  0   \n",
       "8                                  0                                  0   \n",
       "\n",
       "   CREDIT_TYPE_Another type of loan_counts            ...             \\\n",
       "0                                        0            ...              \n",
       "2                                        0            ...              \n",
       "5                                        0            ...              \n",
       "6                                        0            ...              \n",
       "8                                        0            ...              \n",
       "\n",
       "   CREDIT_TYPE_Loan for business development_counts_normalized  \\\n",
       "0                                                0.0             \n",
       "2                                                0.0             \n",
       "5                                                0.0             \n",
       "6                                                0.0             \n",
       "8                                                0.0             \n",
       "\n",
       "   CREDIT_TYPE_Loan for purchase of shares (margin lending)_counts_normalized  \\\n",
       "0                                                0.0                            \n",
       "2                                                0.0                            \n",
       "5                                                0.0                            \n",
       "6                                                0.0                            \n",
       "8                                                0.0                            \n",
       "\n",
       "   CREDIT_TYPE_Loan for the purchase of equipment_counts_normalized  \\\n",
       "0                                                0.0                  \n",
       "2                                                0.0                  \n",
       "5                                                0.0                  \n",
       "6                                                0.0                  \n",
       "8                                                0.0                  \n",
       "\n",
       "   CREDIT_TYPE_Loan for working capital replenishment_counts_normalized  \\\n",
       "0                                                0.0                      \n",
       "2                                                0.0                      \n",
       "5                                                0.0                      \n",
       "6                                                0.0                      \n",
       "8                                                0.0                      \n",
       "\n",
       "   CREDIT_TYPE_Microloan_counts_normalized  \\\n",
       "0                                      0.0   \n",
       "2                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "8                                      0.0   \n",
       "\n",
       "   CREDIT_TYPE_Mobile operator loan_counts_normalized  \\\n",
       "0                                                0.0    \n",
       "2                                                0.0    \n",
       "5                                                0.0    \n",
       "6                                                0.0    \n",
       "8                                                0.0    \n",
       "\n",
       "   CREDIT_TYPE_Mortgage_counts_normalized  \\\n",
       "0                                0.000000   \n",
       "2                                0.166667   \n",
       "5                                0.000000   \n",
       "6                                0.000000   \n",
       "8                                0.000000   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan_counts_normalized  \\\n",
       "0                                             0.0   \n",
       "2                                             0.0   \n",
       "5                                             0.0   \n",
       "6                                             0.0   \n",
       "8                                             0.0   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_counts_normalized  \\\n",
       "0                                                0.0    \n",
       "2                                                0.0    \n",
       "5                                                0.0    \n",
       "6                                                0.0    \n",
       "8                                                0.0    \n",
       "\n",
       "   bureau_counts_normalized  \n",
       "0                       1.0  \n",
       "2                       1.0  \n",
       "5                       1.0  \n",
       "6                       1.0  \n",
       "8                       1.0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_counts = count_cats(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "cecd442561b3a8405721875878bcbb90c94f2517"
   },
   "source": [
    "### Applying Operations to another dataframe\n",
    "\n",
    "We will now turn to the bureau balance dataframe. This dataframe has monthly information about each client's previous loan(s) with other financial institutions. Instead of grouping this dataframe by the `SK_ID_CURR` which is the client id, we will first group the dataframe by the `SK_ID_BUREAU` which is the id of the previous loan. This will give us one row of the dataframe for each loan. Then, we can group by the `SK_ID_CURR` which will perform the aggregations for the loans of each client. The final result will be a dataframe with one row for each client, with stats calculated across all of their loans. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "b49d1c1553eb3c0a8887d967fe9c981b015a7e40",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>STATUS</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "   SK_ID_BUREAU  MONTHS_BALANCE STATUS\n",
       "0       5715448               0      C\n",
       "1       5715448              -1      C\n",
       "2       5715448              -2      C\n",
       "3       5715448              -3      C\n",
       "4       5715448              -4      C"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in bureau balance\n",
    "bureau_balance = pd.read_csv('../input/bureau_balance.csv')\n",
    "bureau_balance.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "815ea915661653e3322e5cbf0f60614754411526"
   },
   "source": [
    "First, we can calculate the value counts of each status for each loan. Fortunately, we already have a function that does this for us! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "891d69c6cf3634d2fab71b0c8cc1eb212f433083",
    "collapsed": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "    SK_ID_BUREAU  STATUS_0_counts  STATUS_1_counts  STATUS_2_counts  \\\n",
       "0        5715448                8                0                0   \n",
       "3        5715449                5                0                0   \n",
       "6        5715451               17                0                0   \n",
       "9        5715452                8                0                0   \n",
       "12       5715453                8                0                0   \n",
       "\n",
       "    STATUS_3_counts  STATUS_4_counts  STATUS_5_counts  STATUS_C_counts  \\\n",
       "0                 0                0                0                9   \n",
       "3                 0                0                0                6   \n",
       "6                 0                0                0                5   \n",
       "9                 0                0                0               15   \n",
       "12                0                0                0               20   \n",
       "\n",
       "    STATUS_X_counts  bureau_balance_counts  STATUS_0_counts_normalized  \\\n",
       "0                10                     27                    0.296296   \n",
       "3                 1                     12                    0.416667   \n",
       "6                 4                     26                    0.653846   \n",
       "9                10                     33                    0.242424   \n",
       "12               10                     38                    0.210526   \n",
       "\n",
       "    STATUS_1_counts_normalized  STATUS_2_counts_normalized  \\\n",
       "0                          0.0                         0.0   \n",
       "3                          0.0                         0.0   \n",
       "6                          0.0                         0.0   \n",
       "9                          0.0                         0.0   \n",
       "12                         0.0                         0.0   \n",
       "\n",
       "    STATUS_3_counts_normalized  STATUS_4_counts_normalized  \\\n",
       "0                          0.0                         0.0   \n",
       "3                          0.0                         0.0   \n",
       "6                          0.0                         0.0   \n",
       "9                          0.0                         0.0   \n",
       "12                         0.0                         0.0   \n",
       "\n",
       "    STATUS_5_counts_normalized  STATUS_C_counts_normalized  \\\n",
       "0                          0.0                    0.333333   \n",
       "3                          0.0                    0.500000   \n",
       "6                          0.0                    0.192308   \n",
       "9                          0.0                    0.454545   \n",
       "12                         0.0                    0.526316   \n",
       "\n",
       "    STATUS_X_counts_normalized  bureau_balance_counts_normalized  \n",
       "0                     0.370370                               1.0  \n",
       "3                     0.083333                               1.0  \n",
       "6                     0.153846                               1.0  \n",
       "9                     0.303030                               1.0  \n",
       "12                    0.263158                               1.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of each type of status for each previous loan\n",
    "bureau_balance_cats = count_cats(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_cats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4b1b1623a426efd45f7488636ffffc6b54c68d8e"
   },
   "source": [
    "Now we can handle the one numeric column. The `MONTHS_BALANCE` column has the \"months of balance relative to application date.\" This might not necessarily be that important as a numeric variable, and in future work we might want to consider this as a time variable. For now, we can just calculate the same aggregation statistics as previously. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "da531864649dc2b8dab18c31019fc36d6ec6469d",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_mean</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_max</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_min</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_sum</th>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_mean  \\\n",
       "0       5001709                               -48.0   \n",
       "1       5001710                               -41.0   \n",
       "2       5001711                                -1.5   \n",
       "3       5001712                                -9.0   \n",
       "4       5001713                               -10.5   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_max  bureau_balance_MONTHS_BALANCE_min  \\\n",
       "0                                  0                                -96   \n",
       "1                                  0                                -82   \n",
       "2                                  0                                 -3   \n",
       "3                                  0                                -18   \n",
       "4                                  0                                -21   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_sum  bureau_balance_counts  \n",
       "0                              -4656                     97  \n",
       "1                              -3403                     83  \n",
       "2                                 -6                      4  \n",
       "3                               -171                     19  \n",
       "4                               -231                     22  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate value count statistics for each `SK_ID_CURR` \n",
    "bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataframes have the calculations done on each _loan_. Now we need to aggregate these for each _client_. We can do this by merging the dataframes together first and then since all the variables are numeric, we just need to aggregate the statistics again, this time grouping by the `SK_ID_CURR`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-26</td>\n",
       "      <td>-351</td>\n",
       "      <td>27</td>\n",
       "      <td>380361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5715449</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-11</td>\n",
       "      <td>-66</td>\n",
       "      <td>12</td>\n",
       "      <td>380361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5715451</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>-17.5</td>\n",
       "      <td>-5</td>\n",
       "      <td>-30</td>\n",
       "      <td>-455</td>\n",
       "      <td>26</td>\n",
       "      <td>380361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5715452</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0.242424</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-32</td>\n",
       "      <td>-528</td>\n",
       "      <td>33</td>\n",
       "      <td>380361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5715453</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>-18.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-37</td>\n",
       "      <td>-703</td>\n",
       "      <td>38</td>\n",
       "      <td>380361.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  STATUS_0_counts  STATUS_1_counts  STATUS_2_counts  \\\n",
       "0       5715448                8                0                0   \n",
       "1       5715449                5                0                0   \n",
       "2       5715451               17                0                0   \n",
       "3       5715452                8                0                0   \n",
       "4       5715453                8                0                0   \n",
       "\n",
       "   STATUS_3_counts  STATUS_4_counts  STATUS_5_counts  STATUS_C_counts  \\\n",
       "0                0                0                0                9   \n",
       "1                0                0                0                6   \n",
       "2                0                0                0                5   \n",
       "3                0                0                0               15   \n",
       "4                0                0                0               20   \n",
       "\n",
       "   STATUS_X_counts  STATUS_0_counts_normalized     ...      \\\n",
       "0               10                    0.296296     ...       \n",
       "1                1                    0.416667     ...       \n",
       "2                4                    0.653846     ...       \n",
       "3               10                    0.242424     ...       \n",
       "4               10                    0.210526     ...       \n",
       "\n",
       "   STATUS_4_counts_normalized  STATUS_5_counts_normalized  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "\n",
       "   STATUS_C_counts_normalized  STATUS_X_counts_normalized  \\\n",
       "0                    0.333333                    0.370370   \n",
       "1                    0.500000                    0.083333   \n",
       "2                    0.192308                    0.153846   \n",
       "3                    0.454545                    0.303030   \n",
       "4                    0.526316                    0.263158   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\n",
       "0                               -13.0                                  0   \n",
       "1                                -5.5                                  0   \n",
       "2                               -17.5                                 -5   \n",
       "3                               -16.0                                  0   \n",
       "4                               -18.5                                  0   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \\\n",
       "0                                -26                               -351   \n",
       "1                                -11                                -66   \n",
       "2                                -30                               -455   \n",
       "3                                -32                               -528   \n",
       "4                                -37                               -703   \n",
       "\n",
       "   bureau_balance_counts  SK_ID_CURR  \n",
       "0                     27    380361.0  \n",
       "1                     12    380361.0  \n",
       "2                     26    380361.0  \n",
       "3                     33    380361.0  \n",
       "4                     38    380361.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_cats = bureau_balance_cats.drop(columns = ['bureau_balance_counts', 'bureau_balance_counts_normalized'])\n",
    "\n",
    "# Dataframe grouped by the loan\n",
    "bureau_by_loan = bureau_balance_cats.merge(bureau_balance_agg, on = 'SK_ID_BUREAU', how = 'outer')\n",
    "\n",
    "# Merge to include the SK_ID_CURR\n",
    "bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how = 'left')\n",
    "\n",
    "bureau_by_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>client_STATUS_0_counts_mean</th>\n",
       "      <th>client_STATUS_0_counts_max</th>\n",
       "      <th>client_STATUS_0_counts_min</th>\n",
       "      <th>client_STATUS_0_counts_sum</th>\n",
       "      <th>client_STATUS_1_counts_mean</th>\n",
       "      <th>client_STATUS_1_counts_max</th>\n",
       "      <th>client_STATUS_1_counts_min</th>\n",
       "      <th>client_STATUS_1_counts_sum</th>\n",
       "      <th>client_STATUS_2_counts_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_min_sum</th>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_sum_mean</th>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_sum_max</th>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_sum_min</th>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_sum_sum</th>\n",
       "      <th>client_bureau_balance_counts_mean</th>\n",
       "      <th>client_bureau_balance_counts_max</th>\n",
       "      <th>client_bureau_balance_counts_min</th>\n",
       "      <th>client_bureau_balance_counts_sum</th>\n",
       "      <th>client_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001.0</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-165</td>\n",
       "      <td>-400.000000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-2800</td>\n",
       "      <td>24.571429</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002.0</td>\n",
       "      <td>5.625000</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-226</td>\n",
       "      <td>-337.625000</td>\n",
       "      <td>-6</td>\n",
       "      <td>-632</td>\n",
       "      <td>-2701</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100005.0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-18</td>\n",
       "      <td>-30.333333</td>\n",
       "      <td>-3</td>\n",
       "      <td>-78</td>\n",
       "      <td>-91</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100010.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-127</td>\n",
       "      <td>-1656.000000</td>\n",
       "      <td>-702</td>\n",
       "      <td>-2610</td>\n",
       "      <td>-3312</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100013.0</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-226</td>\n",
       "      <td>-1689.000000</td>\n",
       "      <td>-780</td>\n",
       "      <td>-2346</td>\n",
       "      <td>-6756</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>230</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  client_STATUS_0_counts_mean  client_STATUS_0_counts_max  \\\n",
       "0    100001.0                     4.428571                          12   \n",
       "1    100002.0                     5.625000                          18   \n",
       "2    100005.0                     4.666667                           7   \n",
       "3    100010.0                    10.000000                          10   \n",
       "4    100013.0                    19.750000                          34   \n",
       "\n",
       "   client_STATUS_0_counts_min  client_STATUS_0_counts_sum  \\\n",
       "0                           1                          31   \n",
       "1                           2                          45   \n",
       "2                           2                          14   \n",
       "3                          10                          20   \n",
       "4                           0                          79   \n",
       "\n",
       "   client_STATUS_1_counts_mean  client_STATUS_1_counts_max  \\\n",
       "0                     0.142857                           1   \n",
       "1                     3.375000                           6   \n",
       "2                     0.000000                           0   \n",
       "3                     0.000000                           0   \n",
       "4                     1.750000                           3   \n",
       "\n",
       "   client_STATUS_1_counts_min  client_STATUS_1_counts_sum  \\\n",
       "0                           0                           1   \n",
       "1                           0                          27   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           7   \n",
       "\n",
       "   client_STATUS_2_counts_mean      ...        \\\n",
       "0                          0.0      ...         \n",
       "1                          0.0      ...         \n",
       "2                          0.0      ...         \n",
       "3                          0.0      ...         \n",
       "4                          0.0      ...         \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_min_sum  \\\n",
       "0                                          -165   \n",
       "1                                          -226   \n",
       "2                                           -18   \n",
       "3                                          -127   \n",
       "4                                          -226   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_sum_mean  \\\n",
       "0                                    -400.000000   \n",
       "1                                    -337.625000   \n",
       "2                                     -30.333333   \n",
       "3                                   -1656.000000   \n",
       "4                                   -1689.000000   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_sum_max  \\\n",
       "0                                            -1   \n",
       "1                                            -6   \n",
       "2                                            -3   \n",
       "3                                          -702   \n",
       "4                                          -780   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_sum_min  \\\n",
       "0                                         -1326   \n",
       "1                                          -632   \n",
       "2                                           -78   \n",
       "3                                         -2610   \n",
       "4                                         -2346   \n",
       "\n",
       "   client_bureau_balance_MONTHS_BALANCE_sum_sum  \\\n",
       "0                                         -2800   \n",
       "1                                         -2701   \n",
       "2                                           -91   \n",
       "3                                         -3312   \n",
       "4                                         -6756   \n",
       "\n",
       "   client_bureau_balance_counts_mean  client_bureau_balance_counts_max  \\\n",
       "0                          24.571429                                52   \n",
       "1                          13.750000                                22   \n",
       "2                           7.000000                                13   \n",
       "3                          36.000000                                36   \n",
       "4                          57.500000                                69   \n",
       "\n",
       "   client_bureau_balance_counts_min  client_bureau_balance_counts_sum  \\\n",
       "0                                 2                               172   \n",
       "1                                 4                               110   \n",
       "2                                 3                                21   \n",
       "3                                36                                72   \n",
       "4                                40                               230   \n",
       "\n",
       "   client_counts  \n",
       "0              7  \n",
       "1              8  \n",
       "2              3  \n",
       "3              2  \n",
       "4              4  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')\n",
    "bureau_balance_by_client.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To recap, for the `bureau_balance` dataframe we:\n",
    "\n",
    "1. Calculated numeric stats grouping by each loan\n",
    "2. Made value counts of each categorical variable grouping by loan\n",
    "3. Merged the stats and the value counts on the loans\n",
    "4. Calculated numeric stats for the resulting dataframe grouping by the client id\n",
    "\n",
    "The final resulting dataframe has one row for each client, with statistics calculated for all of their loans with monthly balance information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will hold off on calculating the correlations until we have all the variables together in one dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "90aa8e57fbff00777a1e62b060c2c06cf7845381"
   },
   "source": [
    "# Putting the Functions Together\n",
    "\n",
    "We now have all the pieces in place to take the information from the previous loans at other institutions and the monthly payments information about these loans and put them into the main training dataframe. Let's do a reset of all the variables and then use the functions we built to do this from the ground up. This demonstrate the benefit of using functions for repeatable workflows! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "26848bd0252a31559e254c454b905305701ee522",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Free up memory by deleting old objects\n",
    "import gc\n",
    "gc.enable()\n",
    "del train, bureau, bureau_balance, bureau_agg, bureau_agg_new, bureau_balance_agg, bureau_balance_cats, bureau_by_loan, bureau_balance_by_client, bureau_counts, cats, tg\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "dc574b9486fb40f72146e6481b341931dd180e3d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in new copies of all the dataframes\n",
    "train = pd.read_csv('../input/application_train.csv')\n",
    "bureau = pd.read_csv('../input/bureau.csv')\n",
    "bureau_balance = pd.read_csv('../input/bureau_balance.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "feba956fa449260cc71505cb9156887a62ca8337"
   },
   "source": [
    "### Value Counts of Bureau Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "491f45986682beb4ee2bd62c32c622830b3b0e76",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CREDIT_ACTIVE_Active_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Bad debt_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts</th>\n",
       "      <th>CREDIT_ACTIVE_Sold_counts</th>\n",
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       "      <th>CREDIT_CURRENCY_currency 4_counts</th>\n",
       "      <th>CREDIT_TYPE_Another type of loan_counts</th>\n",
       "      <th>...</th>\n",
       "      <th>CREDIT_TYPE_Interbank credit_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for business development_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for purchase of shares (margin lending)_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for the purchase of equipment_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Loan for working capital replenishment_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Microloan_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Mobile operator loan_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Mortgage_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Real estate loan_counts_normalized</th>\n",
       "      <th>CREDIT_TYPE_Unknown type of loan_counts_normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>215354</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>402440</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>222183</td>\n",
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       "      <td>3</td>\n",
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       "<p>5 rows × 47 columns</p>\n",
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      ],
      "text/plain": [
       "   SK_ID_CURR  CREDIT_ACTIVE_Active_counts  CREDIT_ACTIVE_Bad debt_counts  \\\n",
       "0      215354                            6                              0   \n",
       "2      162297                            3                              0   \n",
       "5      402440                            1                              0   \n",
       "6      238881                            3                              0   \n",
       "8      222183                            5                              0   \n",
       "\n",
       "   CREDIT_ACTIVE_Closed_counts  CREDIT_ACTIVE_Sold_counts  \\\n",
       "0                            5                          0   \n",
       "2                            3                          0   \n",
       "5                            0                          0   \n",
       "6                            5                          0   \n",
       "8                            3                          0   \n",
       "\n",
       "   CREDIT_CURRENCY_currency 1_counts  CREDIT_CURRENCY_currency 2_counts  \\\n",
       "0                                  4                                  0   \n",
       "2                                  3                                  0   \n",
       "5                                  1                                  0   \n",
       "6                                  4                                  0   \n",
       "8                                  4                                  0   \n",
       "\n",
       "   CREDIT_CURRENCY_currency 3_counts  CREDIT_CURRENCY_currency 4_counts  \\\n",
       "0                                  0                                  0   \n",
       "2                                  0                                  0   \n",
       "5                                  0                                  0   \n",
       "6                                  0                                  0   \n",
       "8                                  0                                  0   \n",
       "\n",
       "   CREDIT_TYPE_Another type of loan_counts  \\\n",
       "0                                        0   \n",
       "2                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "\n",
       "                          ...                          \\\n",
       "0                         ...                           \n",
       "2                         ...                           \n",
       "5                         ...                           \n",
       "6                         ...                           \n",
       "8                         ...                           \n",
       "\n",
       "   CREDIT_TYPE_Interbank credit_counts_normalized  \\\n",
       "0                                             0.0   \n",
       "2                                             0.0   \n",
       "5                                             0.0   \n",
       "6                                             0.0   \n",
       "8                                             0.0   \n",
       "\n",
       "   CREDIT_TYPE_Loan for business development_counts_normalized  \\\n",
       "0                                                0.0             \n",
       "2                                                0.0             \n",
       "5                                                0.0             \n",
       "6                                                0.0             \n",
       "8                                                0.0             \n",
       "\n",
       "   CREDIT_TYPE_Loan for purchase of shares (margin lending)_counts_normalized  \\\n",
       "0                                                0.0                            \n",
       "2                                                0.0                            \n",
       "5                                                0.0                            \n",
       "6                                                0.0                            \n",
       "8                                                0.0                            \n",
       "\n",
       "   CREDIT_TYPE_Loan for the purchase of equipment_counts_normalized  \\\n",
       "0                                                0.0                  \n",
       "2                                                0.0                  \n",
       "5                                                0.0                  \n",
       "6                                                0.0                  \n",
       "8                                                0.0                  \n",
       "\n",
       "   CREDIT_TYPE_Loan for working capital replenishment_counts_normalized  \\\n",
       "0                                                0.0                      \n",
       "2                                                0.0                      \n",
       "5                                                0.0                      \n",
       "6                                                0.0                      \n",
       "8                                                0.0                      \n",
       "\n",
       "   CREDIT_TYPE_Microloan_counts_normalized  \\\n",
       "0                                      0.0   \n",
       "2                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "8                                      0.0   \n",
       "\n",
       "   CREDIT_TYPE_Mobile operator loan_counts_normalized  \\\n",
       "0                                                0.0    \n",
       "2                                                0.0    \n",
       "5                                                0.0    \n",
       "6                                                0.0    \n",
       "8                                                0.0    \n",
       "\n",
       "   CREDIT_TYPE_Mortgage_counts_normalized  \\\n",
       "0                                0.000000   \n",
       "2                                0.166667   \n",
       "5                                0.000000   \n",
       "6                                0.000000   \n",
       "8                                0.000000   \n",
       "\n",
       "   CREDIT_TYPE_Real estate loan_counts_normalized  \\\n",
       "0                                             0.0   \n",
       "2                                             0.0   \n",
       "5                                             0.0   \n",
       "6                                             0.0   \n",
       "8                                             0.0   \n",
       "\n",
       "   CREDIT_TYPE_Unknown type of loan_counts_normalized  \n",
       "0                                                0.0   \n",
       "2                                                0.0   \n",
       "5                                                0.0   \n",
       "6                                                0.0   \n",
       "8                                                0.0   \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_cats = count_cats(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "# Drop the counts because these will be in the aggregated dataframe\n",
    "bureau_cats = bureau_cats.drop(columns = ['bureau_counts', 'bureau_counts_normalized'])\n",
    "bureau_cats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c4c80230ee098fa39f4446b7161f105cd18965bc"
   },
   "source": [
    "### Aggregated Stats of Bureau Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "5f9d2491e244da4811f78f0340fd9d42127e9076",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
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       "      <th>bureau_CREDIT_DAY_OVERDUE_max</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_min</th>\n",
       "      <th>bureau_CREDIT_DAY_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_ENDDATE_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_AMT_CREDIT_SUM_OVERDUE_sum</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
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       "      <th>bureau_AMT_ANNUITY_sum</th>\n",
       "      <th>bureau_counts</th>\n",
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       "      <td>10822.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24817.5</td>\n",
       "      <td>7</td>\n",
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       "      <td>-1437</td>\n",
       "      <td>-6992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>-349.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-499.875000</td>\n",
       "      <td>-7</td>\n",
       "      <td>-1185</td>\n",
       "      <td>-3999</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
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       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>-1400.750000</td>\n",
       "      <td>-606</td>\n",
       "      <td>-2586</td>\n",
       "      <td>-5603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-544.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-816.000000</td>\n",
       "      <td>-43</td>\n",
       "      <td>-2131</td>\n",
       "      <td>-3264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>-867.000000</td>\n",
       "      <td>-408</td>\n",
       "      <td>-1326</td>\n",
       "      <td>-1734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-488.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-532.000000</td>\n",
       "      <td>-382</td>\n",
       "      <td>-682</td>\n",
       "      <td>-1064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>-62</td>\n",
       "      <td>-373</td>\n",
       "      <td>-572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-54.333333</td>\n",
       "      <td>-11</td>\n",
       "      <td>-121</td>\n",
       "      <td>-163</td>\n",
       "      <td>1420.500000</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4261.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\n",
       "0      100001              -735.000000                     -49   \n",
       "1      100002              -874.000000                    -103   \n",
       "2      100003             -1400.750000                    -606   \n",
       "3      100004              -867.000000                    -408   \n",
       "4      100005              -190.666667                     -62   \n",
       "\n",
       "   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
       "0                   -1572                   -5145   \n",
       "1                   -1437                   -6992   \n",
       "2                   -2586                   -5603   \n",
       "3                   -1326                   -1734   \n",
       "4                    -373                    -572   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_mean  bureau_CREDIT_DAY_OVERDUE_max  \\\n",
       "0                             0.0                              0   \n",
       "1                             0.0                              0   \n",
       "2                             0.0                              0   \n",
       "3                             0.0                              0   \n",
       "4                             0.0                              0   \n",
       "\n",
       "   bureau_CREDIT_DAY_OVERDUE_min  bureau_CREDIT_DAY_OVERDUE_sum  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              0   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   bureau_DAYS_CREDIT_ENDDATE_mean      ...        \\\n",
       "0                        82.428571      ...         \n",
       "1                      -349.000000      ...         \n",
       "2                      -544.500000      ...         \n",
       "3                      -488.500000      ...         \n",
       "4                       439.333333      ...         \n",
       "\n",
       "   bureau_AMT_CREDIT_SUM_OVERDUE_sum  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
       "0                                0.0                      -93.142857   \n",
       "1                                0.0                     -499.875000   \n",
       "2                                0.0                     -816.000000   \n",
       "3                                0.0                     -532.000000   \n",
       "4                                0.0                      -54.333333   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
       "0                             -6                           -155   \n",
       "1                             -7                          -1185   \n",
       "2                            -43                          -2131   \n",
       "3                           -382                           -682   \n",
       "4                            -11                           -121   \n",
       "\n",
       "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_mean  \\\n",
       "0                           -652              3545.357143   \n",
       "1                          -3999                 0.000000   \n",
       "2                          -3264                      NaN   \n",
       "3                          -1064                      NaN   \n",
       "4                           -163              1420.500000   \n",
       "\n",
       "   bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  bureau_AMT_ANNUITY_sum  \\\n",
       "0                 10822.5                     0.0                 24817.5   \n",
       "1                     0.0                     0.0                     0.0   \n",
       "2                     NaN                     NaN                     0.0   \n",
       "3                     NaN                     NaN                     0.0   \n",
       "4                  4261.5                     0.0                  4261.5   \n",
       "\n",
       "   bureau_counts  \n",
       "0              7  \n",
       "1              8  \n",
       "2              4  \n",
       "3              2  \n",
       "4              3  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_agg = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
    "bureau_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3ce2211679505e646121cc0b2f8b217ab9718ba6"
   },
   "source": [
    "### Value counts of Bureau Balance dataframe by loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "0e55cc0b41e66e77dfd19ac1b808edc4581a1206",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>STATUS_0_counts</th>\n",
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       "      <td>5715452</td>\n",
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       "      <td>10</td>\n",
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      ],
      "text/plain": [
       "    SK_ID_BUREAU  STATUS_0_counts  STATUS_1_counts  STATUS_2_counts  \\\n",
       "0        5715448                8                0                0   \n",
       "3        5715449                5                0                0   \n",
       "6        5715451               17                0                0   \n",
       "9        5715452                8                0                0   \n",
       "12       5715453                8                0                0   \n",
       "\n",
       "    STATUS_3_counts  STATUS_4_counts  STATUS_5_counts  STATUS_C_counts  \\\n",
       "0                 0                0                0                9   \n",
       "3                 0                0                0                6   \n",
       "6                 0                0                0                5   \n",
       "9                 0                0                0               15   \n",
       "12                0                0                0               20   \n",
       "\n",
       "    STATUS_X_counts  STATUS_0_counts_normalized  STATUS_1_counts_normalized  \\\n",
       "0                10                    0.296296                         0.0   \n",
       "3                 1                    0.416667                         0.0   \n",
       "6                 4                    0.653846                         0.0   \n",
       "9                10                    0.242424                         0.0   \n",
       "12               10                    0.210526                         0.0   \n",
       "\n",
       "    STATUS_2_counts_normalized  STATUS_3_counts_normalized  \\\n",
       "0                          0.0                         0.0   \n",
       "3                          0.0                         0.0   \n",
       "6                          0.0                         0.0   \n",
       "9                          0.0                         0.0   \n",
       "12                         0.0                         0.0   \n",
       "\n",
       "    STATUS_4_counts_normalized  STATUS_5_counts_normalized  \\\n",
       "0                          0.0                         0.0   \n",
       "3                          0.0                         0.0   \n",
       "6                          0.0                         0.0   \n",
       "9                          0.0                         0.0   \n",
       "12                         0.0                         0.0   \n",
       "\n",
       "    STATUS_C_counts_normalized  STATUS_X_counts_normalized  \n",
       "0                     0.333333                    0.370370  \n",
       "3                     0.500000                    0.083333  \n",
       "6                     0.192308                    0.153846  \n",
       "9                     0.454545                    0.303030  \n",
       "12                    0.526316                    0.263158  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_cats = count_cats(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "# Drop the counts because these will be in the aggregated dataframe\n",
    "bureau_balance_cats = bureau_balance_cats.drop(columns = ['bureau_balance_counts', 'bureau_balance_counts_normalized'])\n",
    "bureau_balance_cats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7c2378a4fc130bc7de954c454d50e26adda6f9f6"
   },
   "source": [
    "### Aggregated stats of Bureau Balance dataframe by loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "eb25294df7f998058e15e6ae24438c772d4ae86c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_mean</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_max</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_min</th>\n",
       "      <th>bureau_balance_MONTHS_BALANCE_sum</th>\n",
       "      <th>bureau_balance_counts</th>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_mean  \\\n",
       "0       5001709                               -48.0   \n",
       "1       5001710                               -41.0   \n",
       "2       5001711                                -1.5   \n",
       "3       5001712                                -9.0   \n",
       "4       5001713                               -10.5   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_max  bureau_balance_MONTHS_BALANCE_min  \\\n",
       "0                                  0                                -96   \n",
       "1                                  0                                -82   \n",
       "2                                  0                                 -3   \n",
       "3                                  0                                -18   \n",
       "4                                  0                                -21   \n",
       "\n",
       "   bureau_balance_MONTHS_BALANCE_sum  bureau_balance_counts  \n",
       "0                              -4656                     97  \n",
       "1                              -3403                     83  \n",
       "2                                 -6                      4  \n",
       "3                               -171                     19  \n",
       "4                               -231                     22  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
    "bureau_balance_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregated Stats of Bureau Balance by Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe grouped by the loan\n",
    "bureau_by_loan = bureau_balance_cats.merge(bureau_balance_agg, on = 'SK_ID_BUREAU', how = 'outer')\n",
    "\n",
    "# Merge to include the SK_ID_CURR\n",
    "bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how = 'left')\n",
    "\n",
    "# Aggregate by the client\n",
    "bureau_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1f8f616b645ba974517be24adb30b4af4cf1afe3"
   },
   "source": [
    "## Merge the training data with all the computed dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "f61f183707d7ca8d363a344b073dd9122acfdcc5",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of Features:  122\n"
     ]
    }
   ],
   "source": [
    "original_features = list(train.columns)\n",
    "print('Original Number of Features: ', len(original_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "02f4310f7485d299c1588805ce24f3360afa6f42",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Merge with the value counts of bureau\n",
    "train = train.merge(bureau_cats, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Merge with the stats of bureau\n",
    "train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Merge with the monthly information grouped by client\n",
    "train = train.merge(bureau_by_client, on = 'SK_ID_CURR', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing counts with 0\n",
    "train['bureau_counts'] = train['bureau_counts'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "f672226eee3059311d476c0eb68946988f9f5712",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features using previous loans from other institutions data:  302\n"
     ]
    }
   ],
   "source": [
    "new_features = list(train.columns)\n",
    "print('Number of features using previous loans from other institutions data: ', len(new_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e16d0c10cf2a0cc778db576a4ad233960c70a028"
   },
   "source": [
    "# Feature Engineering Outcomes\n",
    "\n",
    "After all that work, now we want to take a look at the variables we have created. We can look at the percentage of missing values, the correlations of variables with the target, and also the correlation of variables with the other variables. The correlations between variables can show if we have collinear varibles, that is, variables that are highly correlated with one another. Often, we want to remove one in a pair of collinear variables because having both variables would be redundant. We can also use the percentage of missing values to remove features with a substantial majority of values that are not present. __Feature selection__ will be an important focus going forward, because reducing the number of features can help the model learn during training and also generalize better to the testing data. The \"curse of dimensionality\" is the name given to the issues caused by having too many features (too high of a dimension). As the number of variables increases, the number of datapoints needed to learn the relationship between these variables and the target value increases exponentially. \n",
    "\n",
    "Feature selection is the process of removing variables to help our model to learn and generalize better to the testing set. The objective is to remove useless/redundant variables while preserving those that are useful. There are a number of tools we can use for this process, but in this notebook we will stick to removing columns with a high percentage of missing values and variables that have a high correlation with one another. Later we can look at using the feature importances returned from models such as the `Gradient Boosting Machine` or `Random Forest` to perform feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ab576d2c245270ca843f825afab02583c091897d"
   },
   "source": [
    "## Missing Values\n",
    "\n",
    "An important consideration is the missing values in the dataframe. Columns with too many missing values might have to be dropped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "eef874f5edd28e7449e198264987b93566a5227a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to calculate missing values by column# Funct \n",
    "def missing_values_table(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 302 columns.\n",
      "There are 246 columns that have missing values.\n"
     ]
    },
    {
     "data": {
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       "      <th>% of Total Values</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>bureau_AMT_ANNUITY_max</th>\n",
       "      <td>227502</td>\n",
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       "    <tr>\n",
       "      <th>bureau_AMT_ANNUITY_min</th>\n",
       "      <td>227502</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_AMT_ANNUITY_mean</th>\n",
       "      <td>227502</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_2_counts_normalized_mean</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_3_counts_normalized_min</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_3_counts_normalized_max</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_3_counts_normalized_mean</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_2_counts_normalized_sum</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_2_counts_normalized_min</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_2_counts_normalized_max</th>\n",
       "      <td>215280</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Missing Values  % of Total Values\n",
       "bureau_AMT_ANNUITY_max                          227502               74.0\n",
       "bureau_AMT_ANNUITY_min                          227502               74.0\n",
       "bureau_AMT_ANNUITY_mean                         227502               74.0\n",
       "client_STATUS_2_counts_normalized_mean          215280               70.0\n",
       "client_STATUS_3_counts_normalized_min           215280               70.0\n",
       "client_STATUS_3_counts_normalized_max           215280               70.0\n",
       "client_STATUS_3_counts_normalized_mean          215280               70.0\n",
       "client_STATUS_2_counts_normalized_sum           215280               70.0\n",
       "client_STATUS_2_counts_normalized_min           215280               70.0\n",
       "client_STATUS_2_counts_normalized_max           215280               70.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_train = missing_values_table(train)\n",
    "missing_train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see there are not any columns with an overwhelming percentage of missing values. There is no well-established threshold for removing missing values, and the best course of action depends on the problem. \n",
    "\n",
    "Let's now read in the testing data, perform the same operations, and look at the missing values in the testing data. We already have calculated all the counts and aggregation statistics, so we only need to merge the testing data with the appropriate data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Record Information for Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "48be68ba3403d8e3f208b436219003b1f6097fad",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in the test dataframe\n",
    "test = pd.read_csv('../input/application_test.csv')\n",
    "\n",
    "# Merge with the value counts of bureau\n",
    "test = test.merge(bureau_cats, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Merge with the stats of bureau\n",
    "test = test.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Merge with the value counts of bureau balance\n",
    "test = test.merge(bureau_by_client, on = 'SK_ID_CURR', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing counts with 0\n",
    "test['bureau_counts'] = test['bureau_counts'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Testing Data:  (48744, 301)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Testing Data: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "518da486b84f28f7ca9c1b366fdb2719f0e19f38"
   },
   "source": [
    "We need to align the testing and training dataframes, which means matching up the columns so they have the exact same columns. This shouldn't be an issue here, but when we one-hot encode variables, we need to align the dataframes to make sure they have the same columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "39d6e6a2b8af1bb15df0f98ff5eb601c2e828099",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_labels = train['TARGET']\n",
    "\n",
    "# Align the dataframes, this will remove the 'TARGET' column\n",
    "train, test = train.align(test, join = 'inner', axis = 1)\n",
    "\n",
    "train['TARGET'] = train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "f3d3fcc7cf9a68208a6d6303565883cd58e7c531",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 302)\n",
      "Testing Data Shape:  (48744, 301)\n"
     ]
    }
   ],
   "source": [
    "print('Training Data Shape: ', train.shape)\n",
    "print('Testing Data Shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframes now have the same columns (with the exception of the `TARGET` column in the training data). This means we can use them in a machine learning model which needs to see the same columns in both the training and testing dataframes.\n",
    "\n",
    "Let's save a copy of the data before we make any modifications (such as removing columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "0cee6aab9a65fa1d12ad97aad018907564b477dd",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.to_csv('train_bureau_raw.csv', index = False)\n",
    "test.to_csv('test_bureau_raw.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "c1f8d15be36a58cc8fb59a80e6cf3e06325f22d1",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_train_vars = list(missing_train.index[missing_train['% of Total Values'] > 90])\n",
    "len(missing_train_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the training data there are no columns with more than 90% missing values. Let's examine the testing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "ff8e3932b44097ae679ea7bb9b0464a077e07727",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 301 columns.\n",
      "There are 243 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>33495</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>33495</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>33495</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>33347</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>33347</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>33347</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>32797</td>\n",
       "      <td>67.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>32780</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>32780</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>32780</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Missing Values  % of Total Values\n",
       "COMMONAREA_MEDI                    33495               68.7\n",
       "COMMONAREA_MODE                    33495               68.7\n",
       "COMMONAREA_AVG                     33495               68.7\n",
       "NONLIVINGAPARTMENTS_MODE           33347               68.4\n",
       "NONLIVINGAPARTMENTS_MEDI           33347               68.4\n",
       "NONLIVINGAPARTMENTS_AVG            33347               68.4\n",
       "FONDKAPREMONT_MODE                 32797               67.3\n",
       "LIVINGAPARTMENTS_MEDI              32780               67.2\n",
       "LIVINGAPARTMENTS_MODE              32780               67.2\n",
       "LIVINGAPARTMENTS_AVG               32780               67.2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_test = missing_values_table(test)\n",
    "missing_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "1ecf51b6455fdef32ea7b50cdaf05eed211b15e7",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_test_vars = list(missing_test.index[missing_test['% of Total Values'] > 90])\n",
    "len(missing_test_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are no variables (columns) with more than 90% missing values (an arbitrary threshold I was going to use to remove columns), we will keep all the variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9425c558df0c882cffe4fc07da41394a6fb3b422"
   },
   "source": [
    "## Correlations\n",
    "\n",
    "First let's look at the correlations of the variables with the target. We can see in any of the variables we created have a greater correlation than those already present in the training data (from `application`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "87c47e087eaacb846572ffdd58c22ca1f0ce0010",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate all correlations in dataframe\n",
    "corrs = train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_DAYS_CREDIT_mean</th>\n",
       "      <td>0.089729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_min_mean</th>\n",
       "      <td>0.089038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <td>0.078239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_ACTIVE_Active_counts_normalized</th>\n",
       "      <td>0.077356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_mean_mean</th>\n",
       "      <td>0.076424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_DAYS_CREDIT_min</th>\n",
       "      <td>0.075248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_min_min</th>\n",
       "      <td>0.073225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_MONTHS_BALANCE_sum_mean</th>\n",
       "      <td>0.072606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
       "      <td>0.068927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  TARGET\n",
       "TARGET                                          1.000000\n",
       "bureau_DAYS_CREDIT_mean                         0.089729\n",
       "client_bureau_balance_MONTHS_BALANCE_min_mean   0.089038\n",
       "DAYS_BIRTH                                      0.078239\n",
       "CREDIT_ACTIVE_Active_counts_normalized          0.077356\n",
       "client_bureau_balance_MONTHS_BALANCE_mean_mean  0.076424\n",
       "bureau_DAYS_CREDIT_min                          0.075248\n",
       "client_bureau_balance_MONTHS_BALANCE_min_min    0.073225\n",
       "client_bureau_balance_MONTHS_BALANCE_sum_mean   0.072606\n",
       "bureau_DAYS_CREDIT_UPDATE_mean                  0.068927"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs = corrs.sort_values('TARGET', ascending = False)\n",
    "\n",
    "# Ten most positive correlations\n",
    "pd.DataFrame(corrs['TARGET'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>client_STATUS_C_counts_sum</th>\n",
       "      <td>-0.048912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_C_counts_normalized_mean</th>\n",
       "      <td>-0.055936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_C_counts_max</th>\n",
       "      <td>-0.061083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_STATUS_C_counts_mean</th>\n",
       "      <td>-0.062954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_counts_max</th>\n",
       "      <td>-0.068792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREDIT_ACTIVE_Closed_counts_normalized</th>\n",
       "      <td>-0.079369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_bureau_balance_counts_mean</th>\n",
       "      <td>-0.080193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>-0.155317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>-0.160472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>-0.178919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          TARGET\n",
       "client_STATUS_C_counts_sum             -0.048912\n",
       "client_STATUS_C_counts_normalized_mean -0.055936\n",
       "client_STATUS_C_counts_max             -0.061083\n",
       "client_STATUS_C_counts_mean            -0.062954\n",
       "client_bureau_balance_counts_max       -0.068792\n",
       "CREDIT_ACTIVE_Closed_counts_normalized -0.079369\n",
       "client_bureau_balance_counts_mean      -0.080193\n",
       "EXT_SOURCE_1                           -0.155317\n",
       "EXT_SOURCE_2                           -0.160472\n",
       "EXT_SOURCE_3                           -0.178919"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ten most negative correlations\n",
    "pd.DataFrame(corrs['TARGET'].dropna().tail(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest correlated variable with the target (other than the `TARGET` which of course has a correlation of 1), is a variable we created. However, just because the variable is correlated does not mean that it will be useful, and we have to remember that if we generate hundreds of new variables, some are going to be correlated with the target simply because of random noise. \n",
    "\n",
    "Viewing the correlations skeptically, it does appear that several of the newly created variables may be useful. To assess the \"usefulness\" of variables, we will look at the feature importances returned by the model shortly! For curiousity's sake (and because we already wrote the function) we can make a kde plot of two of the newly created variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between client_bureau_balance_counts_mean and the TARGET is -0.0802\n",
      "Median value for loan that was not repaid = 19.3333\n",
      "Median value for loan that was repaid =     25.1429\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target(var_name='client_bureau_balance_counts_mean', df=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This variable represents the average number of monthly records per loan for each client. For example, if a client had three previous loans with 3, 4, and 5 records in the monthly data, the value of this variable for them would be 4. Based on the distribution, clients with a greater number of average monthly records per loan were more likely to repay their loans with Home Credit. Let's not read too much into this value, but it could indicate that clients who have had more previous credit history are generally more likely to repay a loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between CREDIT_ACTIVE_Active_counts_normalized and the TARGET is 0.0774\n",
      "Median value for loan that was not repaid = 0.5000\n",
      "Median value for loan that was repaid =     0.3636\n"
     ]
    },
    {
     "data": {
      "image/png": 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Yz/v6OleLIsekgcwMQew9b54mMry2HnoM14exNpv5qSIiIiIiIulRToqIiIiIiAwpClJEJCNihzFN8EhnArohr9D21Rizspf96c+EjEOGMeZrvR2vwa6fiIgkpu5eIpIRxpjpvbzcYHvm7Mh7hbavxpgpJB9idb+1dlcu65NJkSGZa5O9bq1dl8PqiIhIihSkiIiIiIjIkJJXQxA3NTUpohIRERERyTPV1dVpTRCrnBQRERERERlSFKSIiIiIiMiQoiBFsmrt2rV9LyRDgo5V/tCxyh86VvlDxyp/6FgNDwpSRERERERkSFGQIiIiIiIiQ0peje4lIiIiIsOTtZaWlhZKS0tpamoa7OqIh8/no6KiAmPSGsQrKQUpIiIiIjLktbS0UFJSwpgxYygtLR3s6ohHZ2cnLS0tVFZWZmR96u4lIiIiIkNeOBymuLh4sKshSRQXFxMOhzO2PgUpIiIiIiIypChIERERERHpQ0NDA3PnzmXu3LkcdthhHHHEEd3POzs7efjhh6mpqWHNmjXd76mrq2Ps2LHMnTuXk08+mauuuoqurq7u15ctW8Z5553H8ccfz+mnn84ll1zCypUrAbj11ltd25g7dy6LFy/u/v+ECRM48cQTmTt3LldddVXW9//OO+/kuOOO48QTT+SZZ57J+vaUkyIiIiIi0ofa2lpeeuklwAkgKioquPbaa7tfX7RoEXPmzGHRokXccMMN3eXTpk3jpZdeIhQKccEFF/DXv/6VSy65hF27dnH55Zdz7733cvLJJwOwdOlSNm7cyKxZswC45pprXNsAuOiiiwA477zz+O53v8txxx2X1f0GeO+991i0aBGvvvoq9fX1XHDBBSxbtgy/35+1baolRURERERkAFpaWnjttdf48Y9/zOLFixMu4/f7OeGEE6ivrwfgnnvu4bLLLusOUADmzJnDggULclLndDz22GNcfPHFlJSUMHXqVA455BCWLVuW1W2qJUVERERE8k7Nb7ZldH2NV0zo93sfffRR5s2bx/Tp0xk5ciRvv/02xx57rGuZ9vZ23njjDW677TbAaZ247LLLel3v3XffzQMPPABATU0NjzzySEr1aW5u5txzz0342r333svMmTO54YYbePHFF+Nev/jii7n++utdZfX19Zx44ondz8ePH98dbGWLghTJqFDY8o/dnYwIGGbXFg12dURERESybtGiRVx99dWA0x1r0aJF3UHKxo0bmTt3Lhs2bGDhwoXMnj074TrmzZtHc3MzZ555JrfffjuQuLtXKiorK7u7piVz6623prw+a23adRgoBSmSMdZaPvb0Xp7e1gHAQWU+Tqos5qNFbZwxvoTqYvUuFBERkcLS0NDACy+8wKpVqzDGEA6HMcZw8803Az05KTt27GDBggU89thjzJ8/n5kzZ7J8+XLOO+88AJ555hmWLFnCE088MeA6ZbolZfz48Wzb1tNytX37dsaNGzfgevZGQYpkzLv7gt0BCsCutjCPtAV4ZFcDAQMXHVLGHafUUKVgRURERArEkiVLuPTSS7nrrru6y+bPn8/SpUuZMKGnC9nYsWO56aab+MEPfsD8+fO58sormTdvHvPmzevOS2ltbc1InTLdknLuuefyH//xH3z2s5+lvr6e9evXc8IJJwy0mr1SkCIZ88/GrqSvBS38eX0bM6oCfOXYqhzWSkRERArRQHJIMunBBx+Ma3lYuHAhDz74INddd52rfMGCBdx222288sornHrqqfzmN7/hpptuor6+njFjxlBbW8tXv/rV7uVjc1IA/vCHPzBlypTs7lACRxxxBBdeeCEnn3wygUCAO+64I6sjewGYwehj1l9NTU35U9lh6H/e3s8tbzX3usxZ40tY/KHROaqRpGPt2rXMmDFjsKshKdCxyh86VvlDx2roa2pqorq6mvb2dkpLSwe7OpJA9BglUl1dbdJZl/rdSMZs2B90Pf/3meVcNNbdurKp2b2MiIiIiIiXghTJmI3NIdfzBZNL+fxUd5CyuSVEKKwGMRERERFJTkGKZIy3JWVaVYDyAIwu7TnNgha2tYa8bxURERER6aYgRTJif2eY3e3h7udFPphY7iRUTa10J1ZtalaQIiIiIiLJKUiRjNjoyTWZUhEg4HPyo6ZWugeRU16KiIiIiPRGQYpkxMb97taRQ6p6Wk+mVriDlDoFKSIiIiLSCwUpkhEbPIHHtJjWkynq7iUiIiJ5rqGhgblz5zJ37lwOO+wwjjjiiO7nnZ2dPPzww9TU1LBmzZru99TV1TF27Fjmzp3LySefzFVXXUVXV8+gQsuWLeO8887j+OOP5/TTT+eSSy5h5cqVgDPZYuw25s6dy+LFi7v/P2HCBE488UTmzp3LVVddlfV9X7BgARMmTOArX/lKVrcVpckcJSPWe5LmD63qObXU3UtERETyXW1tbfcs7rfeeisVFRVce+213a8vWrSIOXPmsGjRIm644Ybu8mnTpvHSSy8RCoW44IIL+Otf/8oll1zCrl27uPzyy7n33nu7Z5xfunQpGzduZNasWQBcc801rm0AXHTRRQCcd955fPe73+W4447L6n4DlJSU8PWvf53Vq1ezevXqrG8P1JIiGeId2esQV5CilhQREREpXC0tLbz22mv8+Mc/ZvHixQmX8fv9nHDCCdTX1wNwzz33cNlll3UHKABz5sxhwYIFOalzOsrLy5kzZw4lJSU526ZaUiQjNvYSpIwf4afIB12Rwb/2doTZ3xmmqlgxsoiIiPRPxb+ekdH1tfz2uX6/99FHH2XevHlMnz6dkSNH8vbbb3Pssce6lmlvb+eNN97gtttuA+C9997jsssu63W9d999Nw888AAANTU1PPLIIynVp7m5mXPPPTfha/feey8zZ87khhtu4MUXX4x7/eKLL+b6669PaTvZpCBFBuxAV5gdbT3DD/sNTKroaT3x+wyTK/ysj0mur2sJcVStghQRERHJf4sWLeLqq68GnO5YixYt6g5SNm7cyNy5c9mwYQMLFy5k9uzZCdcxb948mpubOfPMM7n99tuBxN29UlFZWdndNS2ZW2+9Ne315pKCFBkw70zzkyv8FEWGH46aWhlwBSmbmoMcVVuUk/qJiIiIZEtDQwMvvPACq1atwhhDOBzGGMPNN98M9OSk7NixgwULFvDYY48xf/58Zs6cyfLlyznvvPMAeOaZZ1iyZAlPPPHEgOuklhQRes9HiXKS5zu6nyt5XkRERArBkiVLuPTSS7nrrru6y+bPn8/SpUuZMGFCd9nYsWO56aab+MEPfsD8+fO58sormTdvHvPmzevOS2ltbc1IndSSIkL8RI6HVCYIUircyfN1Sp4XERGRARhIDkkmPfjgg3EtDwsXLuTBBx/kuuuuc5UvWLCA2267jVdeeYVTTz2V3/zmN9x0003U19czZswYamtr+epXv9q9fGxOCsAf/vAHpkyZkt0dSuKoo46iubmZrq4uHn30URYvXszMmTOztj1jrc3ayjOtqakpfyo7jFz38j5+u6Yn8r/lpGqumVUBwNq1a5kxYwYPbWrj0882dC9z9oQSHvzg6JzXVZKLHisZ+nSs8oeOVf7QsRr6mpqaqK6upr29ndLS0sGujiQQPUaJVFdXm4QvJKHMZRmw+O5e/rhlNAyxiIiIiKRKQYoM2Ib97oAjUXevKZ6yzS1BQmE1jImIiIhIPAUpMiBtQcu21p4gxWfiAxKA6mIftSU9p1tnGOpb1ZoiIiIiIvEUpMiAeEfpmljup8SfuMthXJevFgUpIiIiIhJPQYoMSCrDD0dN9bSwaBhiERERSZXP56Ozs3OwqyFJdHZ24vNlLrTIyRDExphJwP3AWCAM3GOt/aFnmTOAJcDGSNFia+3Nuaif9N+GFIYfjlLyvIiIiPRXRUUFLS0tNDY2UlVVNdjVEQ+fz0dFRUXG1pereVKCwJestW8aYyqBZcaYp6y1qzzLvWitXZCjOkkGbPQkzU9LMLJXlLclpU4tKSIiIpIiYwyVlZXs2LGDSZMmDXZ1JMty0t3LWltvrX0z8v9mYDUwofd3ST5IpyVlSoW6e4mIiIhI33Kek2KMmQocB7yW4OU5xpjlxpjHjTGzclox6Zf0clLU3UtERERE+pbTGeeNMRXA88D3rLWLPa9VAWFrbYsxZj7wQ2uta+rX2Bnn165dm4sqSy86w/D+V8oI0zOa14tzWilN0uMraGHuK2WEbM/yz89pZUTyHmIiIiIikqdmzOi5lE93xvlc5aRgjCkCFgF/8AYoANba/TH/f8wYc7cxZrS1dk+i9cXutAyONY1dhNnV/XzCCD9HzXQfl7Vr17qO1eQVO9gY04ISOGgqM2qLsl9Z6ZP3WMnQpWOVP3Ss8oeOVf7QsRoectLdyxhjgF8Bq621dyZZZmxkOYwxJ0XqtjcX9ZP+ictH6SVpPkrDEIuIiIhIX3LVknIa8CngHWPM25GyrwGTAay1Pwc+AlxtjAkCbcClNpd90SRtGzwje/WWjxKlCR1FREREpC85CVKstS8BvfZDs9b+BPhJLuojmbExjaT5KLWkiIiIiEhfNOO89Ju3u9e0XoYfjtJcKSIiIiLSFwUp0m/pDD8cNaVCwxCLiIiISO8UpEi/dIUtmz35JNMq00+cr2sJElbqkYiIiIjEUJAi/bKrLUwoJrYYXeqjvKjv06mmxEdNcU96UkcIdrSGs1FFEREREclTClKkX/a0u1tRDipN/VRS8ryIiIiI9EZBivTL3nZ368coBSkiIiIikiEKUqRf9niClNGlfeejRHnnSqnTXCkiIiIiEkNBivRLfJCS+qk0doQ7SPGuS0RERESGNwUp0i97PTkp6XT38gY03vwWERERERneFKRIvwykJSU+SFFLioiIiIj0UJAi/TKQnJTaEvdp503CFxEREZHhTUGK9Is3SEmvu5dyUkREREQkOQUp0i/ePJJ0unt5A5qGjrBmnRcRERGRbgpSpF+8rR9jylI/lUr8hqqinlnnwxb2dag1RUREREQcClIkbV1hS1NnT8uHAUYWp3cqeVtT1OVLRERERKIUpEjavInutSU+/D6TZOnENMKXiIiIiCSjIEXSNpDhh6NGKXleRERERJJQkCJpG8hEjlHewEbDEIuIiIhIlIIUSVsmWlI067yIiIiIJKMgRdK2ewATOUYpcV5EREREklGQImkbyESOUd7ARt29RERERCRKQYqkzZuTkpnuXgpSRERERMShIEXSFjeRo3JSRERERCSDFKRI2uK7ew08J0XdvUREREQkSkGKpM0bUGSiu9fejjDW2iRLi4iIiMhwoiBF0paJIYhHBHyMCPTMUt8VhqZOBSkiIiIioiBF0hQKW/Z1uIOU2n4EKaAuXyIiIiKSmIIUSUtDR5jY9o6aYkORzyRdvjdKnhcRERGRRBSkSFoyMZFj93tLNAyxiIiIiMRTkCJpyUQ+SlRcd68OBSkiIiIioiBF0pSJiRx73utuhVFLioiIiIiAghRJUyZbUpSTIiIiIiKJKEiRtMQHKf3PSfF291JLioiIiIiAghRJk3eYYG+gkY64CR0VpIiIiIgIClIkTZnt7qWcFBERERGJpyBF0uLNG8lkTopaUkREREQEFKRImrytHQPp7hWfkxLCWptkaREpNF1hS1h/8yIikkBgsCsg+SWTifOVRYZiH3RGVtkeggNBS0VR/2awF5H88fz2Dv7j+Qb2doT5wLgSLjl0BOdPKaWiSPfORERELSmShrC1NHRkriXFGJNgGGJ1+RIZDm54vZHd7WHCFp7d3sHVL+7jsD/t4MrnG3hue/tgV09ERAaZghRJ2b4O54IiqqrYUOIfWKvHKE9LjPJSRArfnvYQq/YF48pbg5a/bGjjgr/t5Za39g9CzUREZKhQkCIpi+vqVTLw00ctKSLDz+u7Ovtc5qfvthAMK19FRGS4UpAiKctkPkrPOjTrvMhw89pOd5Ayu7aIieXu75MDQcuapvjWFhERGR4UpEjKMjmyV7J1NKglRaTgvb7bHaR88agKVnz0YD4wrsRVvmJvVy6rJSIiQ4iCFEmZN19kIHOk9KxDEzqKDCedIcube9xBykkHFeMzhhPHFLnKl+/tu1uYiIgUJgUpkrLdGZzIMdk69nQoSBEpZCt4BXeCAAAgAElEQVQauuiI+SqZMMLPxApnNPyjRxXHLSsiIsOTghRJWS66e6klRaSwvbYrvhUl6phR7paUd/Z2abJHEZFhSkGKpCy+u1fmE+f3KnFepKC9vqvD9fzkg3uClCkVfqqKe4Y1399lqWvWd4KIyHCkIEVSFj+6l4YgFpHUWWvjRvY6OaYlxRjD0bXevBR1+RIRGY5yEqQYYyYZY541xqw2xqw0xlyXYBljjPmRMWadMWaFMeb4XNRNUucdHjgbifOazFGkcG1uCbGjredvvMxvmO0JSo6Jy0tR8ryIyHCUq5aUIPAla+0RwCnAZ40xR3qWOReYEXn8J/CzHNVNUpSN0b2qiw2xk9Y3d1k6QuqDLlKIvJM4Hj+miCKfcZUdPUotKSIikqMgxVpbb619M/L/ZmA1MMGz2IeB+63jVaDGGDMuF/WTvllrs5KT4jNGyfMiw4Q3SInt6hXlTZ5fvrcLq+R5EZFhJ+c5KcaYqcBxwGuelyYAW2KebyU+kJFB0tRpCcZcJ1QEDKUBk/wNaRhdolnnRYaD3kb2ippRFaAspnl1T3uY+lbduBARGW4CudyYMaYCWAR8wVq73/tygrckvX22du3aTFZN+lDXZoCy7udV/lDKx6Cv5UbYEqCnVWbF+i2MaNBFyWDQ31X+yLdj1RqCdxvKiP2qH9W8lUS7MX1ECe8093wnPP5OHaePyt+bF/l2rIYzHav8oWOVH2bMmNHv9+YsSDHGFOEEKH+w1i5OsMhWYFLM84nA9mTrG8hOS/r27OwA9nQ/H1dZwowZk5K/IWLt2rV9HqtJWxt4o6mt+3nJqHHMOHREv+sq/ZPKsZKhIR+P1fPb2wmzt/v5YdUB3ndk4n04aU8j77x3oPv5ntLRzJhRlfU6ZkM+HqvhSscqf+hYDQ+5Gt3LAL8CVltr70yy2EPApyOjfJ0CNFlr63NRP+lbNoYfTrYu5aSIFJ5UunpFJcpLERGR4SVXLSmnAZ8C3jHGvB0p+xowGcBa+3PgMWA+sA5oBa7IUd0kBd6k+VEZSJrvWZcmdBQpdN6k+d6CFO9cKSsUpIiIDDs5CVKstS+ROOckdhkLfDYX9ZH0qSVFRPorbC2v7+57ZK+oI0YWETB0D9ax9UCIhvYQtRm8OSIiIkObZpyXlHhH3BqTwSBFQxCLFLZ/NgbZ39kzDkpNsWFGdfJ7ZCV+w8yRntaUBrWmiIgMJwpSJCXx3b0yGaRo1nmRQpZofhSf6X0Ic+WliIgMbwpSJCXx3b0y1+1C3b1ECturcfkoJX2+R3kpIiLDm4IUSUluc1KUOC9SSFbtcwcYJ45Jno8SpZYUEZHhTUGKpMQbOGSyu1etZ8b5xk5LVzjpPJ4ikkestWxsDrrKDqvpe8yW2bVFrtFW1u8P0tylVlYRkeFCQYr0yVpLQ0f2clICPsPIEnf/9AZ1+RIpCPs6wq6k+TK/YWxZ398fFUU+psck11vgXSXPi4gMGwpSpE+tQUtHTENKsQ/KA70nvabLm+OivBSRwrCp2d0KO7XSj+kjaT5KeSkiIsOXghTpk7cVpbbEl/JFRqqUPC9SmLxdvaZWpj49l/JSRESGLwUp0qdEQUqmjSrRrPMihWhjgpaUVB3tCVLU3UtEZPhQkCJ92ucJUkZmMB8lytuSsrdDLSkihcDbkjItjZaUw2vcQUpdSzDJkiIiUmgUpEifvEFKNlpSaj1Birf1RkTy08b9niClKvUg5eAyH8UxXw1NnZb9nfpuEBEZDhSkSJ+8AcPIbAQpnnVqdC+RwrApriUl9e5ePmOYUO5efkuLuoKKiAwHClKkT96AISstKd4gRS0pInmvLWjZ3trzt+wzMLki9ZYUgEme5bccUJcvkeGuNQTPb2/nkbo2Dmj+pIKV3q+FDEu5SJxXdy+RwuPNIZlQ7qfYn97IgJMq1JIiMty1dIV5emsHS3d28OquTt7dW0aIvQDMrAnw4ocPosiX2VFHZfApSJE+5aK716gS94WIghSR/BeXj5JG0nzUJHX3EhnWNrcE+eAju9nRFntd0BOQvNcY5Omt7Zw7uSz3lZOsUncv6VNjLlpS4oYgVpAiku8STeSYLrWkiAxv96w64AlQ4i3breHJC5GCFOlTXHevLAxB7F2nd0QxEck/Axl+OEo5KSLD27I9nRlZRvKPghTpUy66e1UXG2K7kzZ3WTpDNuPbEZHciR/ZK/0gZbJaUkSGrbC1rNjrbiX5xekjuffodlfZsj2dhK2uGQqNghTpUy4S533GMLJYyfMihWQgs81HjR/hJzYddmdbmPagLkZEhoP1+4MciPl7ry3xcckhZRxdGaa6uOebYX+nZf1+tbIWGgUp0qtQ2NLY4b4gyEZLCmiEL5FCEgpb6rwtKWlM5BhV7DeMG+H+bth2QK0pIsPBck8ryjGjijDGYAycMLrY9ZryUgqPghTpVVNnmNgQparIZG2Yv1GaK0WkYGxvDRE7OXxtiY/q4v795CgvRWR4ShSkRB0/xhOkKC+l4ChIkV7ty1ErSqJ1a4Qvkfzl7eqVzkzzXt4RvjYrL0VkWHjbE3jEBiknjC5yvfbmbgUphUZBivQqFyN7JVu3RvgSyV9xSfP96OoVpblSRIYfay3LG7wtKT2tJ8d7unu909BFhwbcKSgKUqRXcSN79bO7RirU3UuGkmDY8pVXG5n8++18+Ik9bGlRF6N0eIOUqRUDCFK83b10LEQKXl1LiP2dPUFHVZFxDb5x8Ag/E2NuYHSG4d0G5aUUEgUp0quctqSou5cMIXcsb+aXqw+wv8vyfH0H8x/fE3fhLclt3O8Z2auq/9294oYhVuK8SMHz5qMcPaoIn3HnxJ7ozUtRl6+CoiBFepWLOVKiNLqXDBWv7uzgf5Y3u8q2tISY/9hu1jXpTl0qMjGRY5RmnRcZfpbv9eajFMct481LUfJ8YVGQIr3a1579OVKSrVtBigyGps4wV76wj3CCrs3bW8PMf3wPq/cpUOlLJoOUiZ6clO0HQgQTHSARKRi9jewV5R3h6809+m4uJApSpFf7OgcvSPEGSCLZZq3lS0sbe71Tv6stzILH97Bir+7YJbOvI0xTTF/yUj+MHdH/747yIp8rZy1oob5VrSkihcpam1KQcsyoImJnRVjbFKRRNzgLhoIU6VVDLltSPN299nboIkRy64H1bTy4oc1VdtUR5Xz6sBGusr0dYT78tz26UE5io2fm56mVgbi+5OlSly+R4WN7a5g9MdcfIwKG6QlGCKwo8jGzxl3+lrp8FQwFKdKrXOakaHQvGUwb9wf58tJGV9ns2iJufl81d51aw5Uzy12v7euw/GxlSy6rmDe8Xb2mDqCrV1RckKLkeZGC5Z0f5ajaIvxJJpKOm3leXb4KhoIU6VXc6F45nMyxscMSUr9zyYGwtVz1wj5agu4uSvd+YCQlfoPPGP7nlGo+c6Q7UFm8sQ1rdY56ZXIixyi1pIgMH975UY5O0NUrSiN8FS4FKdIr74SK2RyCOOAzVBX33CmxQGOnWlMk+16s7+R1zw/bLSfVMLOm54fRGMONx1dR5u85R7ceCPEP/SDGyUpLSrnmShEZLlLJR4nyJs8v29Opm0cFQkGK9CqX3b1AXb5kcDxS585DOWdCCVccPiJuuYoiHx+aVOoqW+TJYZEEs81no7uXWlJECpZ3YJJjEww/HHVETcB182hXW5it6g5aEBSkSFLtQUtrTPcXv3FmfM2muGGINcKXZFnYWh7Z7A40rphZjkmS6H3RtDLX8//b1KZuiR6bPBM5ThvARI5RykkRGR52toaob+357S/xw+E1yW90BHyGYz3zpWgo4sKgIEWS8g4/PLLEl/TCLVPiZp1XS4pk2bLdXa4fxPKA4czxpUmXP2diKZUxwfrOtjAv71SXr6j2oGV7zKhnBphcMfCWFO86traE1KVDpACt8OSjzBpZRFGSpPmo473J8+qGWxAUpEhSuRx+uHsbmnVecuxhb1eviaWUBZL/IJYFDPMnu4OYxRtas1K3fFTXEiQ2dJhQ7qfEP/CbGzXFhoqY49IWsq4hSkWkMKSTjxKlmecLk4IUSSqXI3sl24YmdJRsstbGBSnnT0neihJ18TR3vsqSuja61OULSDTT/MC7eoEzcIHyUkQK33JPPsoxveSjRHmT59/e06VuuAVAQYokleukeVB3L8mtlfuCruFyi31OS0pfzhhfwsiSnrv6+zosz23vyEod883GuHyUgXf1ilJeikjhe7sfLSlTKvyMjumJcSBo2dCsEQDznYIUSaoxh8MPR40qdV+EqLuXZJO3FeXM8SVUFfd9nhf7DQunuBPoF6nLFxA/slcmhh+OmuTJS9msYYhFCkpDe8jVQhowcERN30GKMYYZ1d5hynUTI9+lfNVpjFlojMncr40MeUOhu5dG95JsiuvqNbUsyZLxLvJ0+Xp0czvtQXUv2NSS+YkcoyaVq7uXSCHzJs3PHFlEaS85grEme1paN+v7Ie+lc9X5HaDeGPMTY8zJ2aqQDB2D0d3Luw1195JsWd8UZNW+njvxfgPnTuq7q1fU3LHFHFTWc742d1me2tae0TrmozpPS8qUDIzsFaWcFJHCtnKf+/vj6Nq+W1Gi1NJaeFK+6rTWHgOcDbQBi4wx/zTG3GiMmZqluskgG4yWlFGeLmXeGe9FMsXbinLa2JK47oa98fsMF3haXhYP84kdrbVxdy+nZrIlRTkpIgVtiyew8Hbh6o23JUU3MfJfWled1trl1tqvAJOAzwIfBdYbY14wxnzCGKMclwLi7Wo1GInzykmRbOnPqF5e3okdn9jSTkvX8D1nd7eHXRPAVhaZjH5veO+Uei9oRCS/eQML742J3kxRd6+Ck/avhzHmUOCbwM+A0sj/fwl8Dngwo7WTQeVtxRisnBRN2CaZtrUlyDLPjMTnTU49HyXqpIOKmRiTJ9EWsjyzbfiO8lXX7L4omFzhz+gEsAeX+Ygd16Cp07K/c/gGhSKFJi5IKU89SNFNjMKTTuL8Z40xrwKvAQcDn7LWHm6t/Z619nfAPOCDWaqnDILBCFJKA4bymCS5oIX9XQpSJLMe3ezOHXnfmCLGp/FjGOUzhoVT3S0wfx/GeSnekb2mZHBkL3A+7wlKnhcpWFsOuL9DvIFHbyaU+4m9JVLfGqYzpOuHfJbOL8i5wP8Dllhr46bytNa2GmMuyljNZNDF5aTkYAhicLqVHQj2XHjs6whTncKwsCKpiu/qlX4rStTZE0q5e+WB7ud/396BtTajLQj5oi6FfBTTsBv/yjfwv/sG/ro12PIqgqfMo+u0D8KIij63Maki4JrbZsuBILPSSK4VkaGppSvMvo6eoKLIB2NHpP7bX+I3jB3ho77VuXaxwLYDoYzO1SS5lc6Re85a+xdvoTHmi9baOwGstU9mrGYyqKy1cS0pI3MUKNSW+NgakxC7tz3M1MqcbFqGgYb2EK/sdN9nWTCAIGXOwSWU+qE9cspuaQmxbn+QGdXD78I52chevs3rCbz4uBOYbN8U9z7/upUU/+UegqeeQ9e8CwlPOiTpNpQcK1KYvH/LE8r9+NK82TO5IkB9a8/3++aWoIKUPJbOVec3k5TfmImKyNCyv8sSO+XDiIBJeazygfKO8KXkecmkZ7d3EI45t4+sCXDIAH7EygKGUw8ucZX9fZjmpcR39/JT9PRfKfvmlRQ/+WDCACXKdLRT9OzDjLjx3yj73rX41q1MuJyGIRYpTH3moxxopujxBwg8+xD+1uaE6/B+P3hbdyW/9PnLbIw5K7qsMeZMcHX5OwRIfKa41/FrYAGwy1o7O8HrZwBLgI2RosXW2pv7Wq9kz2DkoyTbloIUyaSntrpzRs6ZmP6oXl5nTijh79t7ApO/b+/gqiP77rpUaGIvCHw2zPufuoeS5/+a9nr8a96h7LYv0Pbl7xOeeazrNU3oKFKYestHMXt2UHbLdfj27gRgdqCI0Knn0PXBj7haXtXSWlhSuX34q8i/JcCvY8otsAO4NoV13Af8BLi/l2VetNYuSGFdkgODMfxwlDdI2atZ5yVDwta6ggmAszMQpJw1vpRvsL/7+Uv1HXSGLMX+4ZOXEgxbtkW6aZYH2/n96p9y8N4345azxkf40CMIzT6R0OHH4FvzDkXPPoyvcY9rOdPVRdldX6ftaz8kPHl6d/lETyLtNs2VIlIQvAHFxEjAYRp2UXbbF7sDFABfsAvfC49R9MJjBI84jq4PfoTQsXOYrAkdC0qfQYq1dhqAMeZ+a+2n+7MRa+0LmvQxv+zrHMSWFHX3kixZsbeLXW0951NlkeHkg4oHvN4jRwYYW+ZjR2TdB4KW13Z18v5xJX28s3BsPRAiZGFcxz6WvHMHx7dscr1uS0rp+NR1BI+fC+U9SWahI4+na8En8L/1MkV/X0JgVU9gY9oOUHrHf9H2jZ9ix4wDcA35DApSRApFou5epnEvZbd/Cd/u7UnfF1j9FoHVb9F18llMXvClXtcp+SWdGef7FaCkYY4xZrkx5nFjzKwsb0v6MJRaUjTrvGTK055ckdPHlWSktcMYw5kT3C0yz24fXkMR1zUHOfLAVl5585txAUp45Gjavv5jgu8/1xWgdAsECL3vA7R/9U46Pnql6yVfUwNl3/8yZv8+AMaPcAcp9W0hgmENMyqS77Z6bjgcaloo/Z8v4duxxVVuSxMPdFL02t85ZtUzrjJN6Jjfem1JMcasttYeEfn/FpwuXnGstZMHWI83gSnW2hZjzHzg/4AZvb1h7dq1A9yk9Oaf2wNAzx1mf/t+1q7d2691pXusOvf5cXoXOur2NLF27e5+bVvSU+h/V4+sKwF6LnKPLm5i7dqGjKz7SJ/7vH18QxOXVe3KyLoTGWrH6t2NB3h4xf8wqcP9ebYePIkNH7uWrk4glTof9j4mnLSBg17vudjw7dyGueULrPvklwmXlDKyqIx9XU5wGbawdOV6xpYO3UBlqB0rSU7HavBsbCwleu+8pusAJ//iO/h3uwOUxsOPY9OFV1K17h3GvP4MlZvXuF4f9/CvKTtuNm1+57u4/kCQ1WvWkqNxfySBGTN6vZzvVV/dvWJvaX2y31vpg7V2f8z/HzPG3G2MGW2t3ZPsPQPZaembv2U/sWMiTDuolhkzqtJez9q1a9M+VltGtMOanoCos6icGTOmpL1tSU9/jlU+aewI887L9a6yjx83Oa3JwnpTMzHEN9bs6H7+XoufkZMOYXRp+pNE9mXIHatQkMr7rmdKh/tGRvDYOYSv/gZTS0ekt77pX6fLB0Wv9gQqI+rrmPXYfbR/8TamrN7Hvr1d3a8VHTSJGQcPza51Q+5YSVI6VoOnM2TZ/ZLTpask1MljK26nutkdoASPOYXA57/D9EARawNFHHT+x2hdv5qyWz+P6XK+D4pbGrlh55N8c/z5AIQwjBg3LeMTy0pu9NqHx1r7Usz/n0/2GGgljDFjTWTmM2PMSZF69e+2vWSENw9kZI4mcgSN7iXZ8Xx9B7GTD8+sCWQsQAEYU+bnmFE9c6NY4Lntw2Mo4uI/38P0re+4ylYd80Har/supBugAPh8dFz53wRnv89VHFi5jOIl98fNOq+8FJH8tr011N1V59ptf+Ok5vWu14OzTqD9c9+GgHv+qfChR9B1zsWusms3PkRtV89NVnX5yl8pX3kaY75ojDk28v9TjDGbjTEbjDFzUnjvH4GlwOHGmK3GmH83xnzGGPOZyCIfAd41xiwHfgRcaq0dum33w0DjIA5B7M1/aWjXF4wMnHfo4XkTBj6ql9dZ44fffCmBpU9T/MSfXWXPVx9B3SXXgW8ArUiBItqv/TahaTNdxUWP/Yljg+5udApSRPJbNJDwh0Ncs+0p12uhw4+h/brvQXHi1tLOBZ/AjugZ8r2yq5Ub6pZ0P9+iEb7yVjpXntfTM4/JrcCdwPeAu/p6o7X2MmvtOGttkbV2orX2V9ban1trfx55/SfW2lnW2mOstadYa19Jd0cks7ytF7kMUjSZo2SatZZntnnnR8l896BEyfOFfL/FV7eWkl9/31W2paSWS2ddy+TqDHy+pSNo++JthKtru4tMsItPLb0XYj7XLQpSRPJaNJBYsPdNJsd0G7VFxbRd+20o6eWmUnklnee7MxKu2fYUU9qcXFa1pOSvdK48q621TcaYSuAY4MfW2l8Bh2enajKY4rp7leQu66w8YCiOOTPbQ9AaVKAi/bdyX5D61p5zaETAMCcLOQwnH1RMeUyGZn1rmPcaC/QuXksTpT/6Bqazp7Wo3RTx0VlfoKGkOq5LVr9V1dD5sc+4iqZvXMb5e5d1P1dLikh+iw4V/LltT7rKg3POhsqaPt/fdfaFhGsP6n5eYoN8a9ODzrr1/ZC30glSthhjTgUuBV6w1oaMMVWAjn4B8g5BnMuWFGOMJnSUjPK2orx/XAklWZhoscRvmDvWPe+Kd9sFIRyi9O7v4Nuzw1V8zWFX8EbVoUyq8BPwZe7zDZ56DqHDjnaV3bnu95SGOgEFKSL5bsuBELNatnBm4ypXedc5F6W2guISOi+6wlX0iZ0vc3RLHZubC/RG0TCQzpXnV4AHga8D34mULQBez3SlZPB55ybJZZACmtBRMsubj3LOhOyNBBXf5avw8lKKnvgLgZVvuMp+Ov4c7h/3AQCmZHBAAgCMoePTX8D6er4XprXv5qubHwIUpIjkuy0tIa7xtKKEDjua8OTpKa8jeNoHCU2c1v3ch+WWDX9Sd688ls5kjo9Za8dba6daa6Pt7H8BFmanajJYusKW/V09/b0NUF2c4yBFEzpKhuzvDPPqzk5X2dkTM580HzXPEwC9vKOD9mDh5KX46tZS/OC9rrKtE4/ky9N7+oRPqcz8sMvhSYfQdfaFrrKvbH6EQ9p2sqc9XFCfschw07iviU/ufNlV5v1775PPT+dH/9NV9C8NK5ixdQUhTfial9K68jTGVBtjTjLGnGWMOQt4f+QhBcQ7sldNicGfwa4bqfAmz6u7l/TXC/UdxF6/Tq8KMDWLY+ZPrwowMSYfoz3kDH9cEDo7KPnF9zChnu4TtryKn837Ml2+ns804y0p0c1feAXh6pHdz0ttF3eu+x3gDGEqIvknbC1nr3uW8nDP92SoZjTBE9K/vAwdcwqhmce4yq7a+iT1+n7IS+kMQXw5sB14GPhVzOPeXt4meWgwR/ZKtk1195L+ejpu6OHsTvpnjOFDk9wtNQ/XtWV1m7lS/Jd78G/b5Cprv+KLrAhXu8qmZqElBYARFXRe4k6iX7D3LRbsebM78VZE8suuA0H+c6t72OHgWQsh0I+bHcbQcfG/u4r+pWE52xtaBlJFGSTpXH1+D/iItfZga+20mMch2aqcDI74kb0UpEh+ClvLU1vdrRjnZLGrV9T5U9zbeGxzO8E8727gf/cNip9c5CrrmvshQu87gzrPPATZnN05eNoHCc2Y7Sr7/vrfs725QFqrRIaZ1mWvMr19Z/fzTl+A4Jnn93t94RlHsat8TPfzsnAXvL10QHWUwZHO1WcAeLLPpSTvDebIXt3bLHXfiVV3r/zXFrTctaKZExft5MyHd3H/mgN0hrJ74f5ifQfbYpr5y/yG08ZmtyUF4LSxJa5huxs6wry8o7OXdwxxLfsp+eVtrqLw6LF0fPLzWGupa3a3YkypyFJLCnQn0YdNz/fSjLad1L75bPa2KSJZM+qF/3M9f2nqadiqkUmWToExvDvjNFfR2BUv9n99MmjSufq8HbjRGJP7K1bJqaHYkqLE+fwVtpY/rWvlfYt38q1l+1m3P8hbe7r4/MuNHPfgTu5e2cKBruwc3z+sa3U9P39qKWWB7OdXFfkM504qc5XlbZcvaym57058jXt6ioyP9qu+BmXl7O0IcyAm6ac8YBhdmt3vjPDk6bx71NmuslNf/TOE1eVLJJ+YHVuYsG6Zq+wfx5434PXWH+XOZzl0wxvQkaffwcNYujPO3wg0G2M2xz6yVDcZJIM9/HCibaq7V356bns7H3hoN595cR9bEwwTu601xNdeb+Kov+zkh+80Z3QElqbOMA9vcuejfGL6iIytvy8Lp7q7fD1S10Y4D2efD7z8JEX/eM5V1nXeZYQj85YkakUxJvuBYN1ZHyMY8xM2rnEbgX88n/XtikjmFD3tbkV5vfJQwoceMfD1zjiCupJR3c9LQp0E3n51wOuV3Ern6vOTwNnAfOBTnocUkKEQpGh0r/x361v7ueBve3mnoavPZRs6wtz0xn6+8EojNkMX8ks2tdEW051sYrmf94/LflevqDPGlVIR02qzoy3MP3blV5cvs2MrJb+7y1UWmnIYnRde3v28zjNR2uQs5qPEqpk0if892N2lo+ih30FY3xUieSEcIvDqM66in0z4IJMzMDrg5MoiFo85yVUW8NxskaEvnXlSnk/2yGYFJffiRvfKcteNRNSSkt8erWvj9reb48qLfXDNrHKuOqKcsgQzvv9ubSv3vncgI3X437Xurl6XTR+BLwd3+KNKA4lG+cqj2eeDXZT+7GZMe08XCVtUTPtnvg6Bou6yTS05zEeJMaHcz21TFhKm55j6t27E/+bLvbxLRIYK34b38DU3dj9v9I/gwYNOZlL5wL9DJlX4eXDMya4y//JX1eUrz6QzBHGJMeZ7xpgNxpimSNkHjTGfy171ZDDE5aTkeCJHUE5KPqtrDnLNS/viyi+eVsbrFx3MLSfVcPspNbxzycF8+ehKqordgcMNrzXx0o6BjdS0rqmLVz2tFh/PYVevqPOnuPNSHqpry1hLUbYVL/oV/k1rXGUdn/gcdvwUV5m3JSWbc9DEGl3qY1PFeB446BRXefFD90OefMYiw1nAM+LW32qPptNXxKQM3OioLvbx3ugZbI7p8mU6O/Avf23A65bcSefq8wfAbOATQPQXYCVwdaYrJYNrKLSkVBUbYm+0N3fZrI8EJQPXGbL823MNNHX2HKuAgSUfGspLAH8AACAASURBVMWvzqh1XcCOLvVz4wlVPHneGFe3qKCFy59tYItnWNt0/NGTMD/n4GKmVeXm4jnW2RNLiB2obnNLiBUpdH8bbP53/kHxY39ylQVPPJ3gGfHDgtZ5W1KyNUeKh88YJpT7uXXKBa5yf91a/Ms13KjIUOf3BCmPjjqOUj8ZG3hjcmURfx3zPldZ4PXnMrJuyY10zoQLgY9ba5cCYQBr7TZgQjYqJoPHOwTxYIzu5TMmbrvq8jX0fWtZE8v2uC/Cbzqxig+MTz43ycyaIn5xunu4yT3tYT7xTAOtwfSPeShs+dM6d5P+J2bkvhUFoKLIx1kTPF2+Ng3tLl9m/z5KfnmLqyxcO4b2K74MCbrLbfK0pGRrtvlEJpT7WVU+kUWj3RcixUt+p9YUkSHM7N2Jf8v67uchDE+MOoaJ5YGMDbyRqMtXYPmr0DG0v4OlRzpXn504c6V0M8aMAfZmtEYy6PZ4gpQxpbm5M+qlvJT88mhdG3evdOeT/MukUj43q6LP9543pYz/PrbSVbaiwRmmON3uUS945kYZETB8eGpZL+/IrkRdvoascJiSX96Gr6mnu54z3PCNUFEVt3gobNk6SC0p4AQpALd4W1M2rMb/7hs5q4eIpMfbirK0+jAaiioz0tUranKFn1erprO1uOcmmOlsx79CXb7yRTpByl+A3xpjpgEYY8YBPwH+1Ou7JK+ErWWvJxjwjrSVK97teoMnGToS5aFMLPfzs/ePTPmu2H8dW8l5k92tDg9uaOPH77akVRfv3CgLp5RSWTR40zudO6mU2KlZ1jQF+Wfj0OzyVfTkIgKeH/CuhZ8iPPOYhMtvaw0RM0UKo0t9VOTws54YCVKWV07loVHHu14rXvJbtaaIDFHefJRHRx0HkOEgJYA1PhZ5W1M0ylfeSOfX5GvARuAdoAZYC9QD385CvWSQ7OsIEztVRVWxoSTBKEy54O2XuqdNE7UNRdZarnlpX1weyq/PGJlWV0GfMfz89JHMrHF3F/rWsv08sy215vnGjjCP1Hm7epWnXIdsqCnxcbpn6OOHNg291hT/itcofuBnrrLQjNl0fjj5KPM5nWk+gQnlPefK96Zc6HrNv/Zd/O+9ndP6iEgKOtrwr37TVfRINEjJwMheUdGAZ9FBnqGI314KnQMbnEVyI50gZTrwHnALcBMwx1r7BWttfg38L73a7WmtGD0I+ShRB5W5v6x2qSVlSPr92lZe3uH+GrjpxCpOOij9OUkqi3z877xRVMeM+BW28G/PNbBhf9+J9H/d2EZ7zHXz5Ao/p40tTrsembZwqrfL19DqE+3bvJ7Sn34bEzPHiB1RQftnbgR/8hyTuHyUHI3sFTUh5oJmWdUhvDbuONfrRY8/kNP6iEjf/CuXYbp6WpM3lI5h9QgnvXlSBnPaJkeClKVVM9gW2+WrQ12+8kWfV6DG8WucFpSvAecDVwJvGWN+Y3IxtbDkTFw+Stng5KMAjPG0pOxWS8qQs7stxDf+0eQqO2t8SUp5KMkcUhXg12fU4ov5ZmnqtHz8mb00dyUPVN9r7OLby9x1yfXcKMnMn1xKbC3eaejiue1DI1Ax+/ZQ+oP/xrT3dJOzxtD+n1/Djh7b63vX7/cOP5zb74uJnruuPzzkw67ngeWv4tu6IZdVEpE+JOzqFfmeznR3L3Dy6hZrlK+8lMpt8v8EzgBOsdZOsdbOsdZOBuYA7weuymL9JMe8M7sPVj4KwJgy97Z3taklZaj5+j+aaIzp5lXmN9x5as2AR2eZN6GUm05wJ2q/1xjkMy/sI5wgz2BrS5CL/7bXVRe/cYKUoeCgMj9nT3C3LN28bP/gz5nS0UbpD76Gr2G3q7jz458ldNypfb59daM7SDm8pijJktkxwROkPFQyndChR7rK1JoiMoSEw86kijEejckny2SQUlNsuoe3j8tLWb4UgkMzN1B6pHIF+ing89baf8QWRp5/IfK6FAhva0WmxivvD++oYt6uaDK4ntvezp/Xu3Mr/vu4yoxN5vf52RV85BB3N6lHN7dz87L9dMTMmdPQHuLiJ/e6RvQC+OqxmatLJtxwnDvoenNPF49sHsTWlHCI0p99F3+de8LGzrMvpOuci1NaxXv73D/y3nyibKuOuQgBaA8b9p7zMdcygaVPYxp25bReIpKYr24NvsaeQWGb/aW8UDMTcG4sjR+RuSDFGMP4yI2Ml6sPY0dRdc9r7W341q/O2LYkO1L5RTkSeD7Ja88Dv8tcdWSwxQ8/PJg5KeruNVS1BS3Xv9LoKps1MsA1A+jm5WWM4Uen1bCmMeiaAPGud1r43ZpWLp0+gksOLePLSxv5Z5P7jv4Vh4/gK8dUelfZfwea8e3Yim/Xdsyubfh2bce3ux462rCVNdiqkdiqyL/VtYSnzCA8YaprXpHjxxRz/pRSHo7JR/nem/uZP6kUvy/HXdKspfiPdxN462VXcfCYU+j8+GcTzofi1dIVdk3k6DNwWHVuW1JMZELH2OO/dvpJjD54Ir6dW51lQiGKnlxE56Wad1hksHm7ej018ig6fc73xrgRfgIZ/i4cX+5nTVMQa3w8M3I2n9jV850XWLWMzsOPzuj2JLNSCVL81trmRC9Ya5uNMYN3FSsZF9/da/ByUpQ4P3TdsXw/G2NGdjLAD08bSVGGf2BGBHz8fl4tZz602zU09t6OMD9d2cJPV8YPT3z+lFLuOGXgXc5obSHw+nMUvfwk/jUr0n57uLqW0KwTnMeRx2NrD+Lrx1fx6Ob27hH03msM8sD6Vj6eyxHIgl2U/PYHFL3wmKs4NHk67dd8s9dE+VhrPF29plX6KQvkPv/HG6RsbYXOcy+h9L47u8uKnn2YzoWfghGZC6JFJH3e+VGio3pBZrt6RcW2zPx95CxXkOJf9SZceEXGtymZk8qvUZEx5kwg2a/P0OlPIQPm7VI1mC0p3q5mu9tCWGszNhut9M+qfV388B13cPAfM8s5cUx2RtGaXBHgt2fV8rGn9nIg2HsOx9yxxfzy9Nr+t0yEgvhXLiPw0t8IvPkSpqv/gxf6mhrwvfIURa885ax6/FSOOvUcPjP2ZO6u7+nGduvbzVx8yIjcDPXdsp/SH3+TgGdo3vDI0bRffyuUpp7Ds6rR29Urt60oUd68lG0HQgRP+xDhxb/Bt9+Zu8e0t1L07MN0nXfZYFRRRHAG6fBv6uleajE8MerY7ufegTAyIfb74e8jZ7le861fBe2taX3vSW6lEmDsAn7dx+tSIPa0D52clMoiQ6mf7iFl20PQErRUFilIGSydIcvVL+5zTeA3boSPb5wQPxt5Js0dW8JLHz6In61q4YH1ra45WaJm1xbxh3mjKO3P3Xxr8f/jeUoe+Dm+PTsyUON4/u2b8D/4S35ofsWCkbO57+DTeWj0CWxpKea+fx7gqiOze5ff7NhC2Z03dHeDirIjymm//v+zd97hUVXpH/+ee+/UVFJJCKGGKh0EBMGGKNjRXXfdVdey6rquq2vDrqhrB8va2/qzrNgVbFgAQXrvhJYQEkJCejLt3nt+fySZmXPvTDKTTEvmfJ6H5+GcW+ZNZnLnvOd93+/7b9C0zKDut7uajaQM7REdJyUvUe+kwJgI1xkXwvSZ56vL8MOncJ05BzBEX5Kaw4lHtAXzJT0LcMzoqRPRbjiEAu97HjZn4GhyDnrWlQFoTgUV92yDMmqiv8s5UaZdJ4VS2jcCdnBiBG1NSjTVvQghyLSIOOyV915hU6PaPTze+femOmw5zu6gPzExFcnG8L8n/ZIlPDkpFQ+NT8FXRTb8d08jfitvjnQMS5XwyYx0pHTADqGoEKb3X4S4Z0ub56m5faBm50HNygXNzIGa1Qs0IRGkvhaktgqkvgakrhrC0RKIe7aAOHwXxROq4syqrTizaitqRCs+ypqEr+yn4LKBU5BoDE96pbhzI8wv3A/SxEbA1Mxc2G79N2hun6DvuVsTSRka4aL5VnSRlBYBBdfpF8C46AMQZ/P7INRUQlr1E+RpZ0fcRg6Ho69HWZc3nhmHsmje3z3XZo3AeS1OCgCIOzdwJyWG4alaHIZY6pMCAFlmgXFSjtkU9E/mH9tosPKoAws0aV4X9rXg3D7miNphkQh+P8CK3w+woqheRmmTgvGZxqDrYUhdNYyfvAlp+WIQP1LAakZPyFPOhGvKmaDZeYHfXHZBOLAL0o4NEHdsgLB/J9MosZVUpQnXlf2M68p+xtE9eTCcMQvySTOCjmr4g1SUwbjoA0i/fgOisFFSZdBI2P7xMJCU2qF779JEUqKV7qVNETnS2PJzJibDNX02jEs+dR8zfPsR5KkzAYFvdHA4EcXpgLhjPTP1Y9ZYwNOeya3EFUq09/ypx3Cchx/cY3HnhpC/Jid08NUex42iUlQ5NJGUKHacB4AMiwjAs2PLe6VEh1qniuuWV8N7Kd/LKmJ+CHqidIY+SVLwXc4phfTrtzB9+B+Qpkb9YVGEPHkGXCefBXXQyI4taCUD1EEj4Rw0srkws64GhtU/QVrxHcSiQp+X9KwuAT5+DcZP3oAydDSU0ZMhj54cnHPUAikvgfHr9yH99oPOOQEA19SZcFz5rw6nPtU6VUbyWSJAQUpsRFJKGj12uWZeDMNPn7sdRLH0EMStq6GMbr8HDIfDCR3inq0gTod7rKZlYYUhD4Dn7zXc6V4A8KVlCBYQ4t6YEov3A3U1QHLHNms44YU7KRw31U7VrToENPcgMEaimLcNsjTpZtpIDycy3L6qhln8AcBLJ/dAapSd2KBpaoDpnWdgWPOLz8PyyIlw/OFvHUp/apPkVLjOnAPXmXMgFO+DtOJ7iL8tgVhfozuVUBXSzo2Qdm6E6YP/QM3pDXnUZCjDxkHN7AmanuXz5xLKiiGUFjUX/q/+GYT6/ltxXHItXLP/GJDMsD/2aFK9BiRLUXtWaBchpY0KFJVCFAhoZg7kE0+FYfVP7uPGRR/ANmpyp35+DocTHOKujcxYGXEijmg2HcPhpKQaCSwiga2lt1aJkAhX3gAYD+9znyPt2gR54qkhf21O5+FOCseN1gGIZtF8K9peKcd4r5SI88mBJiw8wDZt/PvwREzPNfm5IjYR9u+E+eV5zf1NNKg5veH4w41QRk0Kux1q/kA4/zgQ+N112Lt8BUq/XYyZFRthoL4/20LZYRjLDgPfLXTPjTBbQTJ6AtYEkGOlTHM0v6+bmQPHZTcF1Em+PbSpXtEqmgeaZap7mAiqHc2LEIUC5TbVnebhmnUp46SIhdsh7NkCdchon/fjcDihR9zFKgo2Dh6N2r2eXVGDEJ41R2svpX11nmdW5cAxyPVyUsSdG7mTEqNwJ4XjpsKmdVKiW48C6GtieNf5yFJUL+PWVexu/7AeUtjVvEKKqsLw7f9g/PRNXeoTNZrhnHMVXGdcCEgRXmhLEgaddgqEcVMwbfEhTNq/HJcfXY4xDUXtX2pvAkoOBPQyanYenOf9CfKkMwApNI/8XTr54eh+leQlSKh2eGw60qi4nRS1TwHkESdC2rbWfdz49fuwcyeFw4kMtkYIh/YwU4fzRwB7PY5DjlWEEKboZq7GSTmYPwq5+Ng95nUpsQt3UjhutI0cYyGSou3TwiMpkaPaoeKSJcdR5yX3axKB16elRaanRyhorIf55XnMArUVJX8A7DfcH/rUriAZmGLAx3MG4KqlPTCh9CwUNJVh1vHNmH18E06u3e03wtIeam4fOM/7c/MOoRDaDYdYiqQAzWki26pYJ2WC13HneX9iPgPS9nUQDuyG2n9IBK3kcOITcc9WRjhEze2DIjEFgCcCHI5Ur1Zyrew6YnvmUJwkSiBK83NMOFYKUlEGmpkTNhs4HYM7KRw3sdQjpRVtJIXXpEQGm0zxhx+PY28tuxh9YFwKhqdFd0EaKORoCSwL5kIoO6w75pxxEZy/uw4wxkbKWqpJwMIZ6bhnbS1e3ZWD56w5eK732UiWm3BG1TbMrNqKwbYy5DuOI89ZDUHVRIREETQ7r1kmOScfysDhUEZODJuKVazID7eiVfgqaWQ/t+qgkVAGjYS4d6t7zvj1e7Df/EhE7ONw4hlx1yZmLA8d41HhayGcTor23kWyAerA4YzsvLhzI+Tps8NmA6djcCeF40bfbT766V68JiXyKCrFX5dXYfUxttv6hX0tuH5YQpSsCg5x50aYX3wApLGemacJybBfcweUsVOjZJl/JIHgiUmpGJ1hxLwNtShtUlEnWfFZ1kR8luXR8SdUxWxrNRaMEJCt2qBmZINm5oYslas9quwKyr1SQ40Coi4LrnVSvGXLW3Ge9ydYnr7DPZY2roBQcgBqXv+w28fhxDPaehRl6GiUapyUcPRIcd/bh0y5PGwsd1K6ANHfKufEDNp0r2g2cmxFq+6lrZvhhBZKKe5eW4uvi9hGhFN6GvHKtB5hyxkOJdIvX8H89O06B0UZMAxNj7wRkw6KN38YaMXWS3rig9PTcGaeCdrfOCUCFtnSMWZTGl43DIPSs3fEHBQA2FXDRikGpkiQguxRE2r6JLGLkEP1su4c5YQJUPoOYuYMiz4Iq10cTtzTWA+hmJVdV4aMRmmTxkkJa7qXDwXAYWOZOXHXRsBPvyxO9Ij+KpQTM1SEON2LHC1BztIvYHrnGQj7dnToHqkmAd7lD3UuCrvMHyTh4sXtDXh1F9s7ZGiqhPdPS4/9OhRFhvH9F2B+51ldgbzrpBmw3TUfNM2HfG8MIgkEs/ItWDgjA1suycatIxOhDWzWuyhuXVWDi344jgZX5Jx3barXsCjXowBAP02vnAP1PiKuhMB57p+ZKWn1zyDlR8JpGocT14h7tjDNcpW8/kBSqi7dK5yRFF8y5Wr/oaBmi3tOqK2GcORg2GzgdAzupHDc6LvNd+DjoSoQN/0G89O3I+HOP6HnisUw/PI1rPNuhOn1x0HqqoO6nUCIvnjezlO+wsHCUgn3ra9j5nKsAj6ekR77/VCaGmCePxfGHz7VHXJcfC0cf707ZupPgiU/UcL941Kw4vwsTM7WN19cWurA336tBo3QLmCsdJr3pp8m3exQvQxF1f8+lLFToOT2dY8JVWFc/GG4zeNw4hZtPYoydAwA6JwUbcpmKNE5KU0KIElQBo9i5sWdbC8XTvSJ8ZUHJ5Lo072CeGg4HTAs/hDW2y+DZcHdkLat051iWPEdrHf+GdJPXwBq4I6Grniep3yFnJd3NOCpA+wCOMlA8PGMDOQlxnbpGik/Auu8G3WfOWo0w3bTPLjOvaxbNO4bmGLA4rMz8PSkFFhFdgH+VZEdC7Y1RMQOrfxwtIvmASDFKCDdy5F2qcCRJh/PGEGA65w/MlPSiu9Aqo6F20QOJy7xVY8CIKLpXmkmASav29e7KOqcKpRh45jzxB3cSYk1uJPCcaOt99BGMPzisMPy2M0wLXwVQuXRNk8lTQ0wv7sAlodugHBgd0C31xXP80hKSHlhWz3mrq1l5owC8N5paTghxpW8hN2bYX3oBgilbG8RNS0TtntfgDL+5ChZFh4EQnDN0ET8b4wdw3uwzsHDG+rwY4ndz5WhgVIac/LDrfRPZhc5B+v0dSkAIE86DWpmrntMFBmGbz8Kq20cTlxSXwPx8H73kBICZchoNMmqu/kqAEgkiPVGByCE6OtSmnzUpezeDCi+nxuc6MCdFA6AZkWnKkfHCueNH70C8aBvh6Mxtx+TXtGKeGgvLI/epAsF+0JbG3OMR1JCxrNb63UpXiYR+OD0dEzPNUfJqsCQli2G5cl/gTSy9isDhsL2wCtQ+xREybLwk2OmeP/0dPQweSJEFMDVy6r8Ls5DQYVdZZ4TZhHokxh9FUBAn/J1oM7PZoYowTn7D8yUYekikKqKcJnG4cQl4u4tzFjNHwgkJOmUvXpaRYhhFt/wpfCl5vWDmpTqniP2JggH92gv5UQR7qRwAABVDhXeCSQpRgJDAA8NcfNvMP70BTNHDQa4pp6Fpgdfwd6r7oZt3htw/P56UBO76CWyC6Y3HgdsTW2+Rpa26zx3UkLCE5vr8PAGdoFvEQk+OiMdZ+TFsIOiKjB++BLMbz2lL5CfdDpsdy0ATU2PknGRo2+ShLemp8H7z7TWSXHZz8fRGKZCem0UZXCqIeyLi0DRF8/7d9bkqTOhpma4x8TpgPHzt8NmG4cTj/ivR2GfT+GsR2mll1XvpEAQoAwbw8xr09M40YU7KRwAPormA6hHIbVVML3xJDOnZuWi6dmFcFx7F9R+Ld2cJQmuWZei6fF34TrxVOZ8obIcxo9fa/N1dDLEPN2r0zyzpR7/3sRK9JoFioUz0nFKLEdQbI0wL7gHxu8W6g45LroKjuvv7bIF8h3h1F5mPDgumZnbWS3jppU1YSmk19ajDImBepRWtL1a2owoGYxwnq9R+vr1WwiHD4TDNA4nLomFepRWfCl8NdukcVK8Gr5yog93UjgA9E5Ku/LDlML05pMQ6ms8U4IA+/X3gib38H1JWhYcNz4A58xLmHnjT1+0mfalLZznkZTO8cL2eszbyEZQEiWC54c7cHJO7C7wSUUZLPNuhLRlNTNPjSbY/v4gXOdf3i0K5IPlphMScWFfCzP32UEbvjwU+vqU3dXaovnYqEcBgP5BRFIAQJ4+G2pOvntMKIXxo1fCYhuHE2+Q2iqIpYfcY0oEKINGAtAre4VTftj9Gr4UvgC9wtfebUEJ+3DCC3dSOACAyiB7pEg/f6lbLDovuBLqgGHtvpZzztVQs3sxc6Y3nwIcNp/na6WQedf5jvPqzgbct07voHw2Mx1jUmLX+RP2bIX1oeshHjnEzKupGbDd/TyUCadExa5YgBCCF6emYpimkP6B9bVwKKGNpuyuic2ieQDol6xt6Ki0HU0SJTh+fz0zJW1bC3H7+nCYx+HEFeJuNoqi9h0EWBMBQN9tPgKRFF8NHQGA5uSDJqW454m9CULxfnBiA+6kcAAEF0khpUUwffgSM6cMPEEn7ekXkxn2q+5gpoSKUhg/fsPn6VrVjwp77C6mY5m3dzfizjWsipdVIvj4zHScmBW7ERTp129heeJWkHrWdqXfYNgefAVqv8FRsix2SDAIePuUNKbxaVGDgtd2hU6WmFKKnTGc7pVuEpBs8PwCmmSK8nairsroyVCGsDupxo9e5jupHE4n8VePAujlwbWpWOHAX7oXCHFHeFoR97AF/5zowZ0UDgD9wj/DX02K7IL5lUdAXE73FDVbYb/+HkAMfMGiDhkF54yLmDnDj59B8JEPygvnO8/7hY24ZVUNM2cWgf+dkY7J2THqoMgyjO89D/MbT4BoZCFdJ54K29znQHtk+Lk4/hicasBVgxOYuae21ON4iGq4yppU1Dk9kYlEiaB3jCh7Ac0RJb3CVztKZ4TAcekNzJRYvB/Sb0tCbR6HE1f4q0cB9JGUSDgp2mhNiZejpAzWOim8LiVW4E4KB4C+kWOGn27zhh8/h1hUyMw5Lv8naGZO0K/pvORaqF7XEUphfuNJwMHm0mujOlUOFbKPbtIc3/xyxI5/rGQdFKPQLDM8LUZrUEjNcVieuBXGJZ/pjjkuuBKOv90PmGK4wD9K3DkmCclGTzShzknxxOb6Nq4InO1VbBRlcKoEIcZqgIJR+GpF7TcErslnMHPGT97QPYc4HE5gkOpKCEcPu8dUFKEMGuEeR6MmJcMswOC1lKhzUtS3qCDq61K2AmEQHuEET0ScFELIW4SQY4SQ7X6OE0LI84SQfYSQrYSQsb7O44SPgGpSZBmG7z9mplwTT4V80oyOvajJAsfVmrSv8hKdFKgkEKR5dZOm0KencXyzu8aFK5ZWwbs0wSAA/3daOk7rFZuLfGHfDlgeuE6nskINRtj/dj9cF14ZlwXygZBhFnHbyCRm7q3djdhX6/JzReD8oGkUOSIGG31qGzoe8tcrRYNzztWgkufnEaorYfjhk5DaxuHEC9pUL7XfEMBsBQDYZLYnm0iAbD+boqFE8NHQsazFWVLzB4C22AcApL4WpKw47DZx2idSkZR3AJzVxvGzARS0/PsrgJcjYBPHi0C6zUvrlkHwanhGjSY4/nxzpxaMytAxcJ16HjNn+PEzkNoqZk7XdZ4Xz7dLpV3B75ccZ1J0CIA3pqdhZu/YdFCkX76G5bGbIdRUMvNqejZs974IeeJpUbKs6/DXoYnI90rDkilwv6ZhZ7ColGJxMStsEYufoY5EUgCAZubAdeYcZs646APdc4jD4bSPtmjeux6lTFOPkhOBRo6t+FP4gsBGegBelxIrRMRJoZQuB9DW0/58AO/SZlYDSCWEBJ8/xOkw2nSvdG1NCqUwfPcRMyVPPQvw6tbaURy/vx5qWqZ7TFwuGDRpPlqniUdS2sYuU1z2UxWKGtgvhIcnJON8jVxtTOBywvTWUzC/84yu/kQeNhZND73arA7DaRezRHS9U74ptuPXMkeH77mp0oWyJs/fXIJEYrKfTtA1KV44z7kMNMHzeyP2JpjeeZanfXA4QaLtNK8M8dSjRCPVqxVt7Yu3LbwuJTaJlZqUXgAOe41LWuY4EUJbOK91CoQ9WyEe2useU0LgnHlxaF7cYoXr7EuZKcPPXwJ2Tyd6bfH8MV487xdKKW5aWY01x5zM/BWDrPj78MQoWeUfUnUMlsduhmHZYt0x56xLYb/tyZA4w/HEhf0smJDJpmPdu64WagcX3IuK2CjK6b1MsEixl3Lnq1dKwE0tE5LgvOByZkrauALSr9+GyjwOp9tDao5DKC9xj6koQikY7h7rnJQIFM27X8uPDDEAvcLX7s18gyIGiBX9SF/fdm1+OgoLC9s6zAkChQLVDgu834aqwwdQ5+Wn9Pv0bVi9rqktGImD9Xagvv33IZD3Sug1CMPNVkgtjglprEfNp/9FxYnNBa2S3QDAs+jaebgchTTwXdJ44s1iCR8XG5m5CSkKrs+oxL59lX6uaibSf1eJRXvQ97NXITayxd2KwYjic69EGSZFDwAAIABJREFUzbAJwIGDEbWpq9Dee3V9joB1FZ5ox5bjLjy/8iBmZwefKvn5PjO897TGm2pRWBh7qVAqBUyCBQ61+VlW56RYv2sfAu45mT8cg3L7IaHU85kzvPsc9ptS4EzL6rBd/Puq68Dfq86RumMt+nmNm3r2QWGxx2nZelgC4Pl+sjrrUFh4vEOvFex7ZWhiX3tnWRUKE8oBAEQRMVIyQJCb6/eE6koUrVsFZ49MX7fiBEFBQUGHr40VJ6UEQG+vcR6A0rYu6MwPzWGpsCmgOOoepxoJhg72/H7J0RJYC9nwrXHOVQG9B4WFhQG/V+qZc4Cv/s89zt24FKmXXguIEgY11QNlnrx6mpiGgoIUX7eJa5aW2vFqMfvAL0iR8PHsHKSa2g6cBvNedRpKYVjyKYwfvgSislExNSsXjpsfQWZef/CvB98E8l4VAFhUV4XPD3miIK8dseCvk7JhlQIPou+tceGQ7Zh7LBHg8vF92/08RYsBO8qx06vpJMnsg4JMYxtXaLh5Huh914A4m4UCRJcDQ75/H7Z7ng9KZr2ViP5dcToFf686j2nl18zYMHoi8zt1VtYAaHSPh+dloKAg+Ah/R96rUQYbcMCzudIoJaKgwCNjrw4cDsGrnmaAsw5ywUlB28YJHbHyLfMVgMtbVL4mAaillJZF26h4QZfqpUmtMvzwCYhX2FPpOwiqJn8zFLjOuBDU4KWwU1kOae2yFps0DR154byOo00Krl1WzYQg00wCFp6RHlsLSocdplcfhen9F3UOijxqEpoefBVqXv8oGde9eGB8Moxeb31pk4oXtwfX4HFRMavqdXKOKbY+Txr6dqIuBQBozzw4Lvs7Myfu3wnD1+932jYOp7ujLThXBo9mxtp0r14RrEnJa6MmBQBUrRQxr0uJOpGSIP4QwCoAgwkhJYSQqwkh1xNCrm855RsABwDsA/A6gL9Fwi5OM212m2+ohUGTk+0663dhkYClKWmQp7AicIZvPgQo1al78a7zLLJKcc2yKub3QgC8fUoPXTFxNCHlR2CZdyMMq37UHXOefwXs/3wMSEjycSWnI/RNknD9MHaX8rltDTjaFLiTv1hTj3JOn9grmPdGW5dyMECFL2/k6bMhj5nCzBm//C+E/Ts7ZRuH050hddUQSovcY0oEKINOYM7R16REbsPDr7pXC7x4PvaIlLrXHyilOZRSA6U0j1L6JqX0FUrpKy3HKaX0RkrpAErpCErp+kjYxWmm0ua/R4rhl69BnB5VIDUtE/KEU8Jmi/Ps34F6OUBi8T6IOzcg08wL59viic31WHGULZS/c3QSpseQApO4ZTWsD14H8fB+Zp6arbDd/CicF/0FEGJ3h76rcuvIJKbPUKNM8dimwCSJSxsVbKhke6yc3TsG1eG86N/JSAoAgBDYr7odakoPz5Sqwvzqo4ygB4fD8SBoFvVq3wLAksDMaR2DSKp7ZZoFeOt9VDsommTPWkIZOAxU9NgjlJeA1HSsXoYTGviKgOM/kuJy6qSAXTPmAFL4duZpz95Qxk5l5gzffMTTvdrg5yN2PL2FLTyfnmPC7aNiJCKhqjB8/g7M8+eCNLGpRmpuHzQ9+AqUsVP8XMzpLKkmAXeNZj8L7xU26TrI+0LbG2V8piGiajwdoV8Sa9/BABs66khOhePqO5kpofwIzK8/DshctIPD0aJP9WLTp+wyZdYbAgF6RtBJEQWie72yRq/1j8mik7rn0ZTowp0UDiodvnukSGt+huDVzIyaLXBNnx12e5yzWDliafs65BxjFZ4q7WqH5VS7E2VNCv66nK1DybYIeH16j4g1yGqTxnqYF9wN0xfvMHVNACBPmI6m+18GzcmPknHxw1+GJKAgxbO5oFLgvnW17crzLtbUo5yTH9tRFEDfK6Uj6V6tKKMmwXXa+cyctH45zC8/zB0VDkeDrj+Kxkk5qtlc7GkRIEX4e0rbK6WkUZvyxdos8KaOUYU7KRxU+uk2b/jhU2beNW1WROoF1IHDoRSweayJP3yEZIPnYSZToMYR3ylfKqW4bnm1bmfq9elpur4y0UAo3g/rg9dB2rKamadEgOP318N+44OAxer7Yk5IMQgED49nGzz+UurAj0f8N3iscahYoWkAOTvG61GA5uJYg9c3W4VdRZ2z488Kx6U3QM3tw8xxR4XD0dBQB6HkgHtICdHVeGgdgmhEZXW9UtqrS9m7Lew2cfzDnRQOKu36mhTh8AGIRR4NckoEuM4MUfPGAHDO+gMzltb8jBEq25fhWJwXz7+ysxHLNYvIu0YnYVqOKUoWeZB+WwLLvL9BOMYqiatJqbDf+Qxcsy4Ni/gCxz9n9Tbj5J6sFO/tq2twzE/q5PcldshegZbBKRIKUgJtOBI9JIEgP1GT8tWJaApMZthuexJqZi77OuuXw/zSQ4Dcftoch9PdEfduY6Llau/+uk3N0ih2m29FG0nR2qQUjGDqYoWSA0BDYDV8nNDDnRSOTikrwyxCWrWEmVNOGA+amRMxm5TRk6F6pQERVcUVZcuYc+K5eH5XtQsPbahl5qblmPCvkVGuQ5FlGN97AeZXH2UEFwBA6T8UtodegzJ0TJSMi28IIXjkxBSmc+6hegUXfl/pMyqp7TIf66pe3mgVvg7Vd66GjaZnwzZ3gd5R2fArzP95kDsqnLinPelhQO8QaB2GSNCewhcSkpodrBYIpRALt0fCNI4PuJPCwXGtk2IEpFU/MXPy5DMiaRIgCHDOmMNMnVv0CwTqsVWrShYvOJXmNC+H14+fYiR4+eTo1qGQmuOwPHELjEs+1R1znXoubHc/B5re8a7dnM4zKt2Ivwxm1XZ2VMu4eEkl6l3Nf1uKSvHSjgZ8f5itR5ndBepRWtHWpXRI4UsDTc+C7e4FULM0jsrGlTA/dTtIWXGnX4PD6aqIXk0QAX3aFAAc0Sp7RcFJ0TpGWklkQF+XonXAOJGDOykcnbpXXskOCFWeDtPUaIY8bqr2srAjTz4d1OjZvc1srMSZVR6ljXhN93picx22apSZnp6UGpVdqVaEQ3thefA6Xf4uNRhgv/pOOK78F2AIous3J2w8PjEFM3qxKYHrK1z444/HsanSiZnfVODutbXwLuPoZRUxJiP2U71a6ZcUeicFAGhaFmxzn4OancfMS7s3w3rPX2D88CXA1ujn6hDisIEcOQRxy2pISxdBWv4tpFU/QVz/K8StayDu2gRytATg4iKcSNDUAKFoHzOlXegD0W3k2Io2xcynkzKI90uJFWKnyxsnKsgqRZUm1SNjgyaKMm4qYI5CgbM1EfKJp8Cw4jv31FVlS/FdenMYOR5liNeUOzB/Gyvje1E/Cy7uH71dbnHdMphfe0yX3qVmZMP+94eh9hscJcs4vjCKBO+elo6Ll1RipVdvnV+POnHq1xU+r7luWAJIF6oh0vZK6VRNigaalgnbXfNheeJWCEcPu+eJosD43UJIq5bAefFfIU+d2fm+P7ZGCEX7IB7aC6FoL4SywyCVRyHU1wR0uZrSA8qgUVCHjIIyZBTU3L68FxEn5IiF20G8shyU3L5AcqruPF1NSgxEUkoa9c8GddAIZiwc2tPcHyka66A4hzspcY7WQekpuWD8bSkzJ0+eETF7tLimz2aclHOPb0SWsxbHjClxV5PS4FJx3a/VUL02R3OsAp6ZnBqdBSSlMHz9Hkyfvqk7JA8fD/sN9wJJ+i8qTvSxSAQfnp6OC76vxMZK//UUyUaCRyak4M8FXevLuX9yiHql+IGmZcI2dwFML8+DpElzEWqrYX7zCaiL3ocybCx6JGeCpCWDpmf7v6Esgxw7AqG0CMKRQxCOHIRYtI9xgjqCUFsNYd1SYN1SAECVMQmf5kzB3ilzMPeMgTCKXcfx5MQuWulhdYg+igLo6z+iEf3vaRVgEICW7FZUOyjqnCqSjR7nnaamQ+3Z2/33R1QV4v6dUIaPj7i98Q53UuKcCs1C/6LarSBNnnQFNSkVygnjIm2W5/ULToCakw+hJd/bQBX8+eiveCb/HF3Bf3fngfV1ugLgl6b2QA9TFHZGnQ6Y3noKhlU/6g/NvATOS68HhOjLIHP8k2wU8MmMdJzzbSV21uh3E8/JN+OpyanIiUJKRmfJT5RAAHf/oCNNCmwyhUUK3aKcpqbDftd8SGuXwvi/l5kUWaC5W7VQXoK+APDlm1DTs5uljCkFFBlEUQBVAWlqACk/AqKEX844zVmPa4u+g6t4CTZvmo5hl18BqpFX5nCCRVc078NJcSqU2VgkiGwjx1YEQtA7QcQBr+/Sww0Khqex36PK4JHMJoG4Zyt3UqIAd1LiHG09ypzSFcxYnnQaIEbxY0IIXNNnw/S/l91TV5UtxTO9Z8dVutevZQ68uZvNdb92aAJO7RV5xSVSVw3zgrsh7t/FzFNRhOPyWyCfck7EbeJ0jDSziM9nZmDWtxXY3xJtyLYIeHJSKs7v23UK5bWYRIK8RBGHGzzPiKIGGUNSQ1xXQwjkiadCHj0Zxm8+hGHxhyAup89ThePlEI6Xh+RlqSCApmdDzejpEaNwOUGcTsDlRFVtAyylB5Gk2H1eb6AKJuz6GfTuX6CMOxnOcy7jaZmcjuGwNadDeeGrHkUbRcm2CDBESeglP0linJTiBhnD09hngzJ4FAzLFrvHvHg+OnAnJc457tUjJdXViJPKNjLHo5nq1YprykwYP37dvdM42FaGk2t341DSCe1c2T1odKm4aWU1MzcgWcRDmuZ8kYCUl8Dy9B26/ic0IRn2mx7i8sJdkGyriB/PycKbuxthEoA/D0pAajSicyGmf5LEOCkH6sLgpLRiMsN54V/gOvlsGBe+CmntMiZHv6NQQkBz8qH0KYDadzDUPgOhZuWCpqb73Tyqd6k48bNyHOvnwtiGQ5hWswvTanZhau0epCisrDShFNL65ZDWL4dzxhw4L7kWMHUdqWlO9BELdzRHBVtQs/OaP58aihtYJyUvMXoRWm0fJa1tgF6dTNi/E3A5uQBMhOFOSpzjnTI1p2INDKon5UDN7gW1/5BomMWSnAp57FQYWvKqgeZoyg3pQ0Ep7VIFvR3h0U1smhcB8OLUHrBKkV1ICvt3wjJ/Lkg9259FzcmH7ZZ/g2b3iqg9nNDRwyTgtlFR7rETYvoni1hW5hnvqHJhVphllGlGTzj+9gAcVzZA3LcD4t5tcG5eg4Sjh0BcbfdSUVMzoPbqAzW35V9eP6j5A4Mu1n1ycz3KmlRAELEueQA2pAzAbxMuwtR0gqn7l6Fg+ccYZDuqu8645FNIW1fDfu1cqAXxsQHE6Tw66WE/9ShFGvGKPonRW37ma17bl5NCM3pCTctyp3ESlwvCwd1QB+mllTnhgzspcY53utdl5SuZY67JM2KmK7g8fTbjpFxcsQb/tF+OehdFsjE2bAwH64458fIONs3rmqEJmJwd2a7y4saVML/8sE7BSx4+Dva/PwRYEyNqD4fTHqPSjQCa3OP1bQgEhBxrIpSRE6GMnIjCUdNR0LcPhKJCkIba5giIKIEKIiCKgMEINTNH1527I+yqduHlHaz63y0jEnHfuJTmwcQ5uLHXdDhWL8OdxV9hTEMRc65QfgSWR2+C66zfwXnRVYAxss8ZTtdD38TRt5OidQS00YxIoo+k+K4HUwaPhOBVdynu2cqdlAjDnZQ4p7Il3SvfXoFptbuZY/JJEW7g2AbK8HFQM7IhVDbndFtUF/5w7DccaezHqHJ0J+wyxd9XVMO700HvRBEPjItsmpf005cw/d9zuvQV15SZcFx1GyB1nf4ZnPhhfCablrGhwhm9yKvBCHXg8LC+BKUUt62ugez1wMhLEHHrSNb5eXRSGiYfPQkTMifi7KoteK7wHfS3e6SnCaUwfvsRpM2rYL/hPqh9CsJqN6cL47BDOMCuG5Qh+k7zQHNNmDd9kqIZSWk/3QtoqUvROCmuc8NqGkdD91zdcQKmvEVt49LyVcy8MmAoqKZhWVQRBLimzWamri77BUX1EdwdjTBPbanDnlr2wf78SalINEToz5ZSGD97C+Z35+scFOd5f4bj2ru4g8KJWYamSkjwUvOqtKso8rMY6Q58csDG9L0BgH9PTEGC5nmRahKw4KQeACH4Nn00xox/HC/n6jekhLJiWObdCGn1T7pjHA7Q0h9F9nwHq5k5HiEHDcUaZco+UY2kaNO9/EdSvBELtwMRUOHjeOBOSpxT0qAAlOKyco2qVwwUzGuRp54FlXg+smMaimDbtzeKFoWPLcedWKBp2vjnAmvk1LxUFcb3X4Dxy3eZaUoE2K+8Fc45V8dMKiCH4wtRIBibwTrR6475Vt7q6tQ5Vdy7jq0VO6OXCefk+35ezOxtxh8GNte6NEpm3DToLzh71Fw4e2Qy5xGXE+aX58H48euAGl+S75z2EXduYMbKsLF+z9VGUrSOQiRp7ZXSSmuvFC00Jx+qV68vYm+CULw/EiZyWuBOSpxzpFHByMZiDG864p6jggB54qlRtMo3ND0L+/qyD8F+67/zc3bXRVEp/vlbDRRN08Z5E1IiZIAM0+uPw7jkM2aaGs2w//MRyKeeFxk7OJxOok35WlfRPZ2UxzfXuaPiAGAUgCcmtt3k9d8npiDH6lkCLOlxAh77/fNwTZulO9e46H2Yn7sHsDXqjnHiF3Enqwbqz0lxKLRZzKEFguiqe7X2SvHmsK8oKyFQtdGUPVvDaRpHA3dS4pgmWcVxh4pLjq1h5pUTJoAm94iSVW1zeMJZzHjM3uWAw+bn7K7JW3sasUlT5PvM5NTIyMI6HTC/+AAMv/3ATNOEJNjufAbK6JPCbwOHEyK0Tsr6buikHKiT8epO1nn4x4gkDEhpe6c61SRg7hi2vu29IwT2q26H/Zo7QTWpnNLmVbA+/DeQ8pLQGM7p2jTWQzjEZjL4c1IOa6IouVYRJjG6kfj8pEBTvlghAN4vJbJwJyWOKW1sTvWaU8E6KfLE06JkUfsIYyfjqMETUUhwNUFauzR6BoWYsiYF8zbUMXPn9TGHXToVAASHHeb5cyFtZFXe1JQ02OY+F/bCXw4n1GidlG1VLti9K8u7AS/taGCirr0TRdw6MjC1vfP7WmDy2lA+VK9gfYUL8slnwzZ3AdSUNOZ8obQI1odugLCX7ybHO+KuzSDU88FT8vr73dzU1oLlJ0UviuK2IeDieU0kZe9WgHavZ0gsw52UOKY11ctbM5+KEuQxsbtbnp9ixrs9pzFzkldX2K7O3WtqUefyPACTDASPT0xt44oQYWvEwA/mQ9KE79XMHNjueQFq7/7ht4HDCTHZVpFZjLhUYGtV94mmHLcreL+wiZm7d2xywD2UUowCZuaxdSsLDzTfTx04HLYHX4HSdxBznDTWw/LkbRDX/9oJyzldnWDqUbRF89GUH/bYwEZSfKZ7AVB79we1JLjHpKEOpLTI57mc0MOdlDjmcKOCi3WpXuNDotcfLrIsAj7IO4WZkwq3d4uHxo8ldnx+iE1du2dsMnITwvxAtzXB8vQdSDhygJlWcvvCdvfzvEkjp0ujr0vpPoqAb+xuhM0rjJKXIOKifsFFXS8ZwDaL/PygDS61+Z40LQu2e16AazKr/kVcTphfvB+GHz/voOWcro52Q0sZHnjRfDTlh1sJtFcKBBHKoBHMFE/5ihzcSYljjjTImFOxlpmTTzwlOsYECCEErqw8LEsZyswbung0pUlW8a9VNczc6HQDrh2S4OeKEGFvguWZOyDu28FMK/0Gw3bPc6BpmX4u5HC6Brq6lG6i8GWTKV7fxdaiXD8sAQYhuFz/M/PMSDGyUs1LS72athpNcFx3DxwXXcVcRyiF6f+ea1b+4ukvcQWpqoBQVuweU0Hw28QRAIpiSH64lUDTvQAfKV+8eD5icCclnik5iMG2MvdQESTIY6ZE0aDA6JMk4o1cVn3MsPJ7wNV1Fx/PbKln8nYFAiw4KRVikAuOoHDYYHl2brP2uxfKgGGw3fkskBghNTEOJ4xM6KYKX//b14RKu0cxKdlAcPmg4Dc1TCLB+X3Z6MvC/WwKGQiB6/zLYb/6DlCBXTYYF70P0+uPAzLvHxEviLs2MWO1/1DA4v+zp41SxEYkRVs435aT4qN4njvmEYE7KXHM4N1sb5TyAaNjOtWrlT6JEj7PmIBqyZOmQOprIW76LYpWdZw9NS48v53tiXLNkASMzjD6uSIEOOwwPztXF7ZW+g+F7bYn2/zC4XC6EiPTDTB6fdOVNCo42tS1mzqqlOLFHfXM3JWDE5Bs7NhX+iX92ZSvxcV2NLj0fSPkabNg/+djoEa2jsWw8nuY588FbE26azjdj0Clh1vRFc7HQCRF2yulyqGi3sdnHgDUvoNAjSb3WKiuBKko83kuJ7RwJyVeoRSTD7GL+sZx06NkTHDkJ4mwi0a8nz2Vme+KKV+UUty5phbez8Ycq4B7xyb7v6izOB0wL7gb0u7NzHRjTp9mB8UamDIQh9MVMIkEI9JYOd2uLkX8bbEd++s8Cz+JANcN6/jf7ZSeRvSyehaOTTLFN8V2n+cqoyY1K38lsYIe0vZ1sPz7nyA1xztsB6cLQGlQTkqDS2UifhIB81mLFgH3SgEAyQBlwDBmSuQKdxGBOylxCik5iAENpe6xi4gwnzi1jStihz4tYdo3c9iUL3HH+i63u/F1kZ3N/wbw7xNTO7wj2i5OB8wL7tEXPfYZhP1/vKVLRNI4nGDpbv1SXtBEXi/ub0GvTghsCIRgTn825etjbcqXF2r/IbDd9yLUrFxmXizaC8u8G0GOHu6wLZzYhpQfgVB1zD2mBqNuAe+NNo0qL1EMbxpzEATaKwUAb+oYJbiTEqcoq5cy41/ShiM1LQJStyGgT4vG+rbEfKxL8kjjEkph+LXrdKBvklXcs66WmZueY8L5fc1+rugkTgfMz98Hacd6ZlrJHwjbHU9D4SlenG7KhKzuU5ey9pgDqzXF/38/ofObC1qVr59LHaiw+U+Lo9l5sN33Hyj9BjPzQuVRWOfdCEEjxsHpHuikhweNALxSobRoF/7aWpBooiuerw+yLoUTdriTEqdI65Yx42V5k0FIbOxutEcfr4ecNpoi/foNoHaNfPPntjUw4WWRAE9MSgnP++BywvziA5C2sWpuSl5/2O54GkgMY3oZhxNltJGUTZUuyGrXLHx9URNFOS3XhBM06Wwd4YQeEoalep6tCm2WI24LmtwDtrvmQx45kZknDXWwPHErRE1jWE7XRxeFb68eJQaVvVoJqnh+wDBQ0WO7UH4EpKoibLZxmuFOShxCjhyCtdzTV0SGgJ0DJkXRouBINQluycyPsiahUfAqaKuqgLhtXbRMC5iiehnPbWMLX/86NAFDUju/2NAhu2B+8UFIW1Yz00pev2YVr6SuEUHjcDpKn0QRGWbP112TTLGrpuupUW2rcuHrIrZW5KYTQlNDRgjRRVM+PhBAIbzZCvvNj8I1bRZ7v5bIreH7j7kSUndBVXXKXsqwcW1eEos9UloJuFcKAJjMUPsNYaa0vwtO6OFOShwirV3KjH/uMRxJXSTVq5XWaEq9ZMVHWayD1RUK6O9ZWwu716ZNplnAXWPCEM2QZZj/8yCkzaxIgpLbF/Y7nwWSu9b7zuF0BEJIt+iXcv+6Wngv909IM+CUXP+pNsGirUtZV+HCofoAnDlJguOq2+E8/3JmmlAVpg/+A9N/53OJ4m6AcHg/SEOde0ytiVD7FrR5jTaFKrYiKYH3SgEAZTjrkInb1/s5kxMquJMSh0jrljLjTzInIi+GHhyB0FqXAgBvaQvoN62M6TDsL0fsWKRRznlgfDJSQl0sL8swv/wwJE3KhZqTD/udz4Am9wjt63E4MUxX75fy0xE7ftGIbNw3Njmk6aH5iRImZ7O/p8V+VL50EALnRVfBfuW/QAn7LDP88hXMz94FNNb7uZjTFRB3aOpRhowGhLbXDrEoP9xKMOleACBrokbizg08ShhmuJMSZ5DSIohHDrnHMgR8mTEeeZ1QhokG3nUpq5MHojw93z0mqgrDz19Gw6x2canNksPejM804I8DrX6u6CCKDNMrj0Bav5yZVnv2hu2u+aCp6aF9PQ4nxunKCl+KSnGfRmTj5J5GnJkXuihKK+f2YaMpi4varkvRIp96Luy3/hvUzD7TpB3rYX3k7yDHSv1cyYl1gu2PAsR2ulcwvVIAQB04DNTkEbYRao5D8FpPcUIPd1LiDG3B/NIew3DcmNT1nBSvSAoIwXdDzmaOG5Z+DTgdiDXe2t2IvbWehzYB8OTEVAihLJZXZJhefQwGTcRMze4F253PcgeFE5eMzTTA+69sb62MGof/BUks8cG+JuysZhd78yaER2RjVj6rLrj6mBOV9uDESJSRE5slijOymXmhtAjWh2/QLXY5XQDZpZPdlYe3XY9S41BR5/REGswikG2JnWWnQIhu7eO3VwrQ3C9lyGhmSqt2xgktsfNp4UQEbT3KJ5nNqiyd0diPBn00YdoPc6aCWj0SuqS+FtLqnyNtVpvUOFQ8sZlNd7iswIqxmSHsLK8qML3+OAxr2J9dzcxtjqCkZYbutTicLkSSQcDQVPa58VWQUYJo0OhS8dimOmbudwMsGJ0RwueGF32TJEYtTKXNzSODRc3rD9v9L+t6aJD6WpifvA2GxR/yVJkuhLB/F4jT8zlQU9NBc/LbuEIfRclPlGJORVSf8tV27RSvS4ks3EmJI0hZMcSSA+6xDAFfZIwH0PWclPwk1t49dgNc02Yzc4Yln8bUl+D8rfWo8tq5TZAI7gtlZ3lVgen1J2BY9SM7ndETtrnzQdOyQvdaHE4X5LRebJTg+W0NUGJcivilHQ0oa/I8N0wicG8onxs+OEcTTdHW0AUKTUmD7a75cE06nZknVIVp4aswv/gAYGvssJ2cyCFtXcOMlaFjgHYcDq38cCzVo7QSTK8UwIeTsnszF4UII9xJiSO0UZRlqUNRaUy5XEASAAAgAElEQVRGqpEg0dC1PgraB8uRRgW20y4A9XpoisX7IOzdFmnTfFJUL+PlnWx/g5tHJCLbGqKHtqrC9OZTMPz2Azudnt0cQUnP9nMhhxM//HVYAiSvddW+OrnDC/BIcMym4Llt7HPj+qGJYW+IN1tTl7K01I6GNnL128RoguP6e+G46Crm+QwA0vrlsD54PYSSgx01lRMhxE2sAIsyqv22BbFcj9JKsApfaq9+UFPS3GPisEM4sDMstnG4kxJXaOtRPsnqmqleAGCVBGR55baqFDhszYIy5iTmPOMPn0TaNJ/M21gHp9d3fI5VwN9D1N8AqgrT20/DsOI7djotC7a5C0Azc0LzOhxOFyc/UcLFGpndBdvqQWMo4tqKolLMXVOLBtljW5pJwC0jO99dvj1O6CExUrEOBfjpSCdq/AiB6/zLYb/1cdAE1n7h6GFYHr4B0sof/FzMiTbkWCkjuEMFQdfA0xfaBX8syQ+3kp8UXLoXCNFFU6TtvC4lXHAnJU4gRw9DPLzfPVZA3KleXa1ovhXtA6+oXobrzIuZOXHDCpDj5ZE0S8eGCic+OcDmvt87NhlWKQR/fqoK03/nw7D8G3a6R0ZzBIU7KBwOwz81i/xNlS4sL4stkQ2HQnH1smp8qun4fsfoJKSawv+1TQjB7D5sylewKl++UEZORNNDr0Hpw/bWIA47zK89BtMrj/D0rxhE12dr0EggoX1nubheX5MSawQbSQF8pHzt4E5KuOBOSpygjaIsTx2KCmMKACAvBh8cgaANHRc1KFCGjIaS1889R6gKw09fRNo0N5RS3KuRDj0hzYBLB4RAcphSmP7vuWYlMy/U1AzY7loAmt2r86/B4XQzhqQadApW8zUpVR2lSVax4qgD7+xpxFeHbNhV7WIiqIFQ71LxuyXH8cUh1ikYmCzhqsEJfq4KPefksxGn70rscIWgfodm5sB274twnXy27phh1Y+w3ncNhH07Ov06nNAhbtI4KWOmBHSdtkdKn6TY2xANtlcKACiafinCgZ1AU2ieIRyWrrk65QSNP1UvoGumewH6SEpxvdKcVjBjDsS3n3bPG5YugvP8KwCTWXuLsLOo2I5V5Ww/hkcnJEMUOqlwQimM7z2v6wejpqTBdtezoD3zOnd/Dqcbc8uIJHzjVYuytNSBTZVOjAlSMavRpWJJiQOrjzmw5pgT2467IGvW8QIs6LPtKAalSJiYbcJpuSaMTDf4lB2vtCu4ZMlxbKp0MfO9rCI+OD0NRjFyykgTs4zIMAuotDd7WXVOihVlDpzaKwTPUaMJjqvvgDJwOEzvPQ/i8jwjhYoyWB69Cc4LroTr3MvabRbICTON9RD3bGGm5DGT272MUtol0r16Wpp7pbSWXLX2Sklqo06XpmVCze0DobQIQHNvNnH3FihjA3PeOIHDnZQ4gJSXQCze5x4rRMDnmRPc4y7rpOgiKc2hZXnyGaALXwNpbJbtJI31kFb9CPmUcyJqn1OheEATRTkzz4TpuZ38kqcUxg9ehPHHz5lpNaVHc4pXO7KQHE68MyHLiCk9jVh51LM4XrCtHv89NbAeQg6F4u09jXhmSz0q7G2HSlQQHKxXcLBewfclDjy8AcgwCzg114QTs4yod1GUNyk4ZlOxrsKJkkZ2YVeQIuGzM9PRO8IRb1EgOLu3Gf9X2OSeW1xsD42TAgCEQD7lHKgDh8H08jyIXsXzRFVh+uwtSNvXw/7XuTxtNYpI29aCKJ7PpJrbBzS7/U2wSruKJi+PPclA0CMCqYrBIgrNvVIOeql6HW5QMKxH27bKw8fD2OKkAIC4Yz13UsJA7H1iOCFHWsumem3OGIpjLaleQHeqSWl5yJjMcJ0SfTnit/Y04oDXg08gwEPjU9q4IgAohfF/L8P4w6fMtJqUCtud80Fz+3Tu/hxOnHDLCDan/qtDduyrdfk5uxlZpXh3byPGfVqOu9bUtuug+KPSruLjAzbcvroWD2+ow6u7GvH5IZvOQRmTYcC3szIi7qC0oqtLKbZBDfFzVM3rD9sDr8A54yLdMXHvVljvvQrS0kUxJScfT2hTvWSNOI0/tKlevRPFmOuR0kqwvVIAfV2KxOtSwgJ3UuIAbT3Kp5knMuPuFkkBANfpF4ASz8dbLDkY0eK25saNbAO2KwZZMbSHwc8VAUApjAtfg/G7hex0Ugrsdz0L2qtvx+/N4cQZp/cyMU0LKYB71taixMcCpd6l4u3djZj0+TH8Y2WNzpnwpn+SiIv6WXBqrqlTG0DTc0z46qwMZJij93w+JceMBC/N5rImVZeKFhKMJjj/9A/YbnkMNIndyCF2G8xvPw3zs3eBVFeG/rU5/pFlSFtXs1OjA3RSNEXz2gbMsUSwvVIAQBkyGlTwrDGEsmKQqmMhty3e4U5KN4ccK4VYtNc9poTgvynjPccB5IaqV0eE6ZUgwru0o9ymwtYSXqbp2VDGTWXON376ZsR2457ZWo9qh+e1EiWCu0Z3ogEbpTB++iaM33zITickw3bHs1Dz+nf83hxOHEIIwS0jWBnw70scGPVJOa78pQqrypvrVG5eWY0h/zuKW1bVYF+d3oFJkAiuG5qA905Lw95Le2LjxT3x1ilp+HxmBrb/rieWT27CsvMyMX9yKs7JNyPZ0PZuskCAPxVYsXBGept58ZHALBGckWdi5haFQOXLH8rok9D0yFuQR0zQHZO2roH1nr9AWv1T2F6fwyLu3QrS5FFbo0kpUAcOC+haXT1KDBbNt9IRhS9YrFAHsL8LrvIVemLXteWEBGndUmbcNGAEyk2p7nG2RYhoMWYoMQgEvRJEHPZ6oBQ3yBic2rw76px1KaT1y93HxAO7IG74Fcr4aWG161C9jFdD3LjR+PnbMH79HjNHE5Jgu/MZqPkDOnxfDieeOb+vBfM21uGQ186pQoEvDtl06lpajAJw9ZAE3DoyCZkW/3/bFhEoSDdiVLoRfxmSAJdKsb7CiZ+POHCkUUGaSUC2RUCWVUS2RUBBiiGmotvn5Fvw5SGPyMCiYjvuH5ccttQdmpoO+7+ehLT0a5g+fAnE4Xlt0lgP88vz4NqwAo7LbwaSUtu4E6ez6FK9Rk0OWMigS0VS2sjKaAtl+DiIhdvdY3HHBsg+VOs4HSd2PzWckKBV9SoaPhXwquWOpS/DjtAnkXVSiuoVt5OiDhgGeexUSBtXuI+bPnkdTWNOAsTwffQf3sA2bsy1CrixE40bDV/8F8Yv32XmqDURtjuegarpN8DhcAJHEghenNoDV/xcheOOwOpLRAJcVmDF7aOSOlQrYhAIJmebMDnb1P7JMcCMPDMkArdqWWGtjE2VLozNDE4JLSgIgXzqeVCGjYP5jcch7t3GHDas/QXins1wXHU7lADTjzhBQikkTZf5QOtRAH1NijZaEUv000R5CmsDc1Lk4eNh/OK/7rG4c0NztkaM1t50RXi6VzeGHCuFeIhN9drcn33IdHknpZ0dEMfF1zC1KULZYUi/sp3ZQ8m6Y058djB0jRsNX74L0+dvM3PUmgDbHU9D7Tuow3ZyOJxmpvY0Yesl2VhwUiqGpvp3OnKsAu4YnYTNF2fj+Sk9olbMHmlSTQLOyGML6N/zUvwKJzS7F2xzF8Dx++tBJbaeT6ithmX+3TC9+SRvABkGhCOHIFSUucdUMkA5YXwbV7DoIilJsfv30rqx2cq+WhkOpf3UcLX/UFCzp+eZUFsNwUuljtN5IuakEELOIoTsIYTsI4Tc5eP4lYSQCkLI5pZ/10TKtu6Kd6oTAKgFI1BI2KLEvBje3QgEvwpfLdBefSFPncnMGb94B3CGvsM0pRT3aSSHR6QZcOnADjRupBTGz96C6bO32GlLAmy3PQW135DOmMrhcLxIMAi4cnACfrsgC1/OzMCsfDMImmv2ZuaZ8MHpadh2SU/cPSY5bpwTb/5cwD7DPjnQhCa5Y8pmQSOIcM26FLaHXoPSR78xY1j+Daz3XgVx16bI2BMn6Bo4DhsLmAP7LlNUqhOXiOVISopRYEQuZNrsqLSLJEEZMpqZErevC7V5cU1EnBRCiAjgPwDOBjAMwB8IIb6qrz6ilI5u+fdGJGzrzmhTveQJ03FE8+DoldC1v3Dbi6QAgPPCv4AaPDslQnUlDJoeI6HgqyI7Vh9jGzc+MiHFZ9O2NqEUxo9f06d4mS2w3fakrliPw+GEBkIIpuea8MHp6Tj4xxwc/GMOPpqRgVn5FkidbcDahTmztxmZZs9yoc5F8XWRvY0rQo+a1w+2+1+C84IrGFUlABAqy2F5/BYY338hLBtQ8Yi0WVOPEkRa3YF6mUl5zjQLSDbGduKONoq6qyYwFTttdEnauNLPmZyOEKlPzYkA9lFKD1BKnQD+B+D8CL12XELKSyAe3M3MyeOn4Ugju4jvqj1SWmkvkgIAND0LrtMvZOaMi94HGutDZodToXhwPRtFmZlnwvTcIPPOKYXxw5dgXKxR8TJbYfvXk1AHDu+sqRwOJwBSTQJSY7D5XDQwCEQXEf6/vVFIsZIkOC/8C2z3vwTVR08o4w+fwnr/NRD274q8bd0IUlsFYf9OZk4JoMt8Kzuq2HXG8LROSO9HCG17gF3VgTkp8mj29yIUbgOpqw6ZXfFOpLbRewE47DUuATDRx3lzCCHTAOwFcAul9LCPcwAAhYWFobWwm9Fz2ZdI8Bo39B6IwuM12F9lhrdvSqtKUegKb9g+nO+V6iAALO7x/lon9uwthHbTUxw2GcN/+Rqio7lehDTWo+G9l1B2mr6BWEf48IiEg/WeQlIRFFdn1aCwMIiHFVWR9/3/kLj+F2ZaNlmw/w83o4mYgTB/7vnfVdeBv1ddh+7wXp1sJHjB61m74qgTv2zdhzxLNJosiiB/vgO5S79A5pofQeCxQSg7DMu8G1E+5WwcPfkc0CBFUrrDe9VZ0javQB8vuf6mnvnYe7wWOF7bxlUefi0yAPAs+nPRENx3YYCE8r1Kc4gAPJuK60pqUJgcWN+Twdm9YS1vXq4SSnH8u89xfMzJIbOtq1NQ0HGBn0g5Kb7i5Non29cAPqSUOggh1wP4L4DT/N2wMz90t0dVYX1lPTMlnXEBCgoKULG2FN6/+klD+qJnGPukFBYWhvW9GkgpUreUocbZ/DM1KgRKZl8M9tE0UT73MoifeLIIs9f/jKTfXQ3aI6NTNtQ4VLy97ii8f69XDE7EmaPyAr+JqsD0znwYNA4KTUiC8/an0avf4E7ZGAjhfq84oYO/V12H7vJeFQA48XAF1lZ4UlpXurJw78hO9H/qLMOGw3babJhffxxC5VH3NKEqeq5YjMziPXD89W6ovQPrI9Vd3qvOYl7EirVIk04N6vdSVnwcgCcdcEr/TBQUJPi/oAOE+r06pYcTKKxwj4tdJhQU6KN1vhBPOgPwErjJLdmLtN9dFTLb4plIxbJLAPT2GucBKPU+gVJ6nFLamkz6OoBxEbKt2yEUbmce2NRggHziKah3qah1ehbSBgHIsnTtdAZCCCZmsVKYq8udPs91nTkHakqa51qnA8aFr3bahqe3+GjcOCYp8Bs4HTD/5yEYli1ipmlSCmx3zYcaAQeFw+Fw2uNPg9iUrw/2NUJRoxFJ8aAOGY2mR96C65RzdcfE4n2wPHgdDIs/ANQAGvRxQGqOQ9R2mR8zJah77KhiU6WG+9g0jDUGpUrMbnpRvYLGALNMlHFs1ETcsQGwRUYBr7sTqRXqOgAFhJB+hBAjgEsBfOV9AiEkx2t4HgCeVNpBDCu/Z8by2KmANVFXNJ9rFYMv6o5BJmn6Dawu91M4abLAecEVzJThtyWQfv22w699qF7Ga7vYxo23jExCVhvN3Rga6mB56ja9EltKD9juWgA1f2CHbeNwOJxQcmE/CxIkz3dGaZOKn0tjoFDdYoXjL/+C7dbHoaamM4eI7IJp4WuwPHozSHlJlAzsOki/LQFRPYtzJbdvUP246l0q0yNFIHqJ31jEKglMvxQKYG+A/VLUvH5Qs3LdYyK7IG1dE2oT45KIOCmUUhnA3wF8j2bnYyGldAch5GFCyHktp/2DELKDELIFwD8AXBkJ27odTode1WtKswSvXtmraxfNtzIpm42krDrmO5ICAPK02VBy+zJzpncXQDh8oEOv/dB6tnFjL6uIG4YHFtYmx8thffQmXaMytUdGs4OS169DNnE4HE44SDIIuKCfhZl7rzB2epQooyah6dG34Zp0uu6YuG87rPdeA+mnL5ob7nH0UAqDZtNOnnZ2UM0JtQXnA5MlWKSusRmqLZ7fGWDxPAiBrI2mbPg1VGbFNRHL9aGUfkMpHUQpHUApfbRl7n5K6Vct/59LKR1OKR1FKT2VUrq77TtyfCFtWgni1dhKTe7hlsgr0XSA7eo9UloZk26Et7rh4QZF55C5kSQ4brwf1OiJvhCnA+b/PBB0eHbtMQc+P6Rp3DgusMaNQvF+WB6+EUJpETOv9OoL230vgfpQruFwOJxo8ydNz5Rviu2otMdQKlViMhw33AfbjQ+CJrL1MsRph/ndBTAvuBtoCKwIPJ4Q9u9kvpOoKEI+aUZQ99hZzUYfhnWBVK9W9ApfgUVSgJaMFS+kLasBl/8NU05gdO2CBI4OaeUPzFiefAbQom6i7SHS1eWHWzFLBGMy2GjKGn8pXwDUvP5wXHELMyeUHYbp7acD3mGjlOLetXXM3Mg0A34/wOLnCg/iltWwPPYPCDWVzLwyeBRs97wAmp4VkA0cDocTaSZlGTEw2aO541KBhfttbVwRHZQTT0HTo2/rJGIBQNq8Ctb7roVQuD0KlsUuhuXfMGNl1GRQrzrOQNDXo3SdXmzDOtgrBQDUgcOhpvRwj4m9CeJO3mC0s3AnpRtBao5D3LaWmZOnnOn+v/bh4f1F09WZlBV4yhcAyFPPgmvaLGbOsOZnSD9/5ecKlq+K7IzKDQA8cmI7jRtlGcaPXoHl2buYaBfQ3GjTdtuTQEIQBfccDocTYQghumjKSzsaUOuMUAf6IKCp6bD/8zHYr74DVNMtXag6Bstj/4Bh8YeAGnu2RxyHDdKan5kp1/RZfk72zw5NilTXjqQE7qRAEKCM0URTeMpXp+FOSjdCWv0TW/CW158pvN6mcVJGpLML+66Mti7Fn8KXN44//QNKHitNafrgRQgH97R5nU2muH8dmypwVm8zpuX4b9xIjpfD8u+bYfzmf7pjzhlzYP/b/YAxyMaPHA6HEwUuHWiFd5lBSaOCu9fGaPoUIZCnzULTI29CGTCUPaSqMC18Feb5c4H6migZGBtI65aB2D0RMTUlDcqIE4O6B6VU56R0hUaOrQxIlmDwWhWXNqmocQTuwMrjWCdF3LSSq8p1Eu6kdCMkrarX/7d33/FRVenjxz/P9IQUIPSugg2kKFVFUYoouFZYlbWsirqu37Wsu2sXu+uube267k9du9g7griiIBYUFEUERDohCZA6kynn98e9IXMnbVIMmeR5v155wZzbzszN3Nznnuecc8ikXR3eCoJRNpVWftm8Ltgnu/W0pCQOQ7x8e5jCup7s+QME/+8GTKAyRUsiYQL3XIXrx2U1bnb/d0WO0UvcAjcMr3muAPeST0m/9lzcq5Y7yo3LRejUCymfcRG4WkfqnVKq9euW7uYPAzMcZc/8VMq76xqX9lUQjPLhxiB3LSvijA/zGfLSFg54aQt//GQ7C7eEMI3o8G46d6fsqn9RPnl6lWWeZYtJv/Zc2q1b2ZjqpzTvxwkd5g89aleqeLI2lkQd0xxkeoU+KdT31ecWBmQ1POUruv+BmLTKgXNchdtxJfzdV/WjQUor4Vq3Cve61bteG3E5Orx9W+Dsj7JPey8+d2qMuJGMjgG3I+iKGfhyW92tKaZbb0Jn/8VR5tqRT9ptl+B98+kqaQAbiiPctcw55PDZ+7ardohFyc/F//gdpN17NVJS5FgW69iZsivvITx5er1GTlFKqZbg6mFZ7JuQw3/xwh3kN6AT/fyNQca/mcuez23hxDn53PhVIW/8EuSX4ijri6M881Mpx7ybx0Evb+WfS4tqHhilLh4v5adeSNmlt2LaOR8subbnMeC/d1Z73W/tZMsG3D8udZSFxx5d7/0kdprfr7035aY52LcRnefxeIkMGe0s+uqTpqhWm6VBSiuR2GE+Omg4Jm68+G8LnDfsg1KoM1uyqgxFnETKF0Bk1JGUTzzRUSYmhn/2vwnc+TekcPuu8uu+LKQsWvmkqKPfxdXDEkaQ2VmA75n7SP/bjCodEQEiQ0ZTeuNjxPYenFT9lFKqpQl4hIfHdiD+WVduWYzLFyWf9rViR5jpH+Rxwpx8vsqr+4n1mqIoNy8pZPBLW7j5q0JiDWxZiQ49mNKbHiPaf5CjvKbrfmuXOOxwdMAgTPc+9d5P1VSv1LvP2K8RneeBKkMRe75aoENeN4IGKa1BNIJn0VxHUcXcKBW+a8X9USpUnXk++UnGyk/7I+VTTqtS7vnuC9KuPRf31wtZtL6QV352pjNcd1AW7f3W10gKt+Ob/W/SLz8N35yXkbDzMzduN6HfXkDwklshs33SdVNKqZZoaCcflw9xDvbx6toyXl5T+3DuuWVRLlu4g0Ney2XOhvpPBhk18M9lRZw1v4DSSMNaPUxOV8quvIfyY06tsqziuu9a8U2D9p1SohE8nzhTxcOHTWnQrhLnFUmlTvMVGtV5HogOHonxVu7DtW1zg+dhU5B6Ya6qwv3dl7h2Fux6bQLpRA48xLFOYqf5QSl48ajLmISZ57/KCxOOGbyuJJqbXW7Kp59HdN8hBB65BSmuHF7YtSOftHuuYpy4+TSjHwuz9+bT7H3olp3GuSty8cz5CdfaH3Hlba1x97GuPQmedxWx/gMb/P6UUqqluXxIJu+tD7I0v/JvzJ8X7SBqYFKvwK6HOABfbSvnsR+KeXVtGaEaMrYGdfQyLMfL0E5ehub4KAjFeOanUt5ZV0ZiN8M3fgmy4d08nh2fQ7f0BvR98Hgo/+351nX/0VurXvdvv4zyk84mPOU0cLXOZ7ru7750DIdv/AEiIw9v0L6qDj+cevcZiYHV99sjGGOQZNPWAulEBw7H882iXUXurxYQ67NXU1azzdAgpRXwzn3V8Toychz4A7tel0cNK3c68yoPSMFm2Lr0y3TTNc3F1jLrL1lpxPBtfpgDOyffahQdPIrSm/5N4KGbqswE7zVRRhWtZlTRai7d8G4Ne3CKdexM+XFnEjl0Mnha32eulGrbvC4r7Wvcm7m7Ao8d5YbzPt6O1wWHdfczpquft9eV8XUtKV1juvq4eUQ2B1VzvZ7YK0BBMMrsNWX8c1kRuWWV0cqSvDAT3trG8xNyGNTAkaSiQ0ZTetO/MXdeRcaGVbvKK9K/3KuWEzzvqlY5RHxiSnJk1JGQMFxzMqq7z0jFlpS+GW7S3LIrrbsgFGNbMEaXtOSD4MiBhzqCFM+X/yN8/Jna/7QBWuejgTZENq/Ds2yxoyx8uLOpdsWOMOG4J1A90910DKTOiBvJEpGq/VLqmC+lOqZjF8quuJvyqTMaXJdYVgdCMy6i9O9PExk3VQMUpVSrtV8HL9cMqzrCYTgG8zaGuHlJYY0Byp6Zbp46oiPvHN2p2gClQseAm/P2z2Du1M5VJt3bUBJl8tvb+GhTsMHvwXTswk9nXF7tdd/zzSLSr5tZ5/D0qUbyt+L+eqGjrCEd5gFW7owQiet60aud29GKlircLmGfxH4p9Uz5igw7BCOV79294Wdca1Y0Sf3amtT7DVIO3g9ecbyO7rlflZSixP4og3JS7+lGskZ1caZ81adfioPbQ/m0mZRefR+f9h/HmkDnOjcxbjfRPnsRmjaT0n88Q3jSyTr3iVKqTbhwYAanD0j+CXzfDDe3j8rmsxO68pt+aUmn0/TJ8PDelM5M6Om8thZHDKfMzWfB5gZe88FK+502k7I//73q6F95W0i7+SI8899oNR2hfa89iUQrWz9i3XoTGzColi1qVqXTfAoPzpPYLyVx1LI6ZbUnOmSUo8j70ZuNrVablLq/RQpKivB+8p6jKDzp5CqrfZdw8TggBZtgkzUmoSVlcW55/fJJE7yXNoDpvWZCL+gW2s7BO1dyWWAtIwpXg4kR692faL8BxPrtTaznHhqUKKXaJLdLuO/QDpy9bzve/KWMN9YGWVXovLkTYFIvP+fsm8H4nn7cyfQXrEaWz8XzE3K4cvFOHltRsqs8GIVT5ubz8qQcRndt+LXYSvt9jMD9s3Cv+aGy/pEwgSfuIrzyO0JnXQr+tFr20rLJxrV4FjjvH8qPOaXBKUnfF6R+p/kKiS119R3hCyB8xLHOlK/PPiR06oWQnlHLViqRBikpzPvxO0iosnk71r4TkRFVO7x9m584slfqXjzqckBHL+08Qond7pxbFuPnoih7ZtX/V317KMafPq0chnKLvwPfDTiEAcedQFkrmmNGKaWayrBOPoZ18nHtgVn8uDPCW78E+XJbOfu193DmPu3ol9k0tx0el/CPMe3pm+nmmi8qO7yXRAzTPsjn1aM6Mbwe/RETmZyulF11L77nH8KX0O/Tu3AOrnU/EbzohgYN1dsS+F9+HDGVeeCx7n2sCRwbKJVnmk/U2BG+AKIHjCTWsTOugm0ASHkQz6J5RMYf1yR1bCs03StVRSN45zpTvcITjq/S98EYU6UlpTWO7FXB45Iqec2LGpjy9dfPdrAlroOmW+ChsR3wa4CilFK1EhH2be/l8iGZPD8hh+uHZzdZgBLvokGZ3DTCmZpVFDacOCePb/Lq3yfRweuj/PSLCf7hWkzcYDRg9TNIn3UB7i8+atwxdgPX6u+t+TvihE4+t94zzMdrDcMPV0icpPQHe4SvenF7iBx2jKPI+1HrSRVsLhqkpCj31wsdQ94ar4/wuKlV1ttYEmV7qPJL0c4j7JHV+jrNx2vopI7xXl9bxktrnHOiXDo4s9aOnUoppZrf/w3K5JoDnXNMytkAACAASURBVIFKYbnhhDl5LMtvZKACREaPp3TWI8R69HWUS7CUtPtn4XvmfojU/2n7bmEMvhcfdRRF99yPaMIkhPWxPRRjU2nlAz2vCwZkp26iTs92brK8lQ8jiyOG9SU1jJldi/BhU5wd6NetbnWDL/zaNEhJUb73ZzteRw6eWO0EgYmtKAM7eHG18mHwxiRM6jh7TSlri5Lv+LbNnmgs3qCOXv46pPUNP6mUUq3B5UMy+UvCNXp7yHDse3ksbugAKnFMj76UXv8Q4TETqizzzZlN2m2XIHlbGn2cX5v7uy/wJExSWT79vEYNj5uY6rV3tie5+claKBGpkvK1or6d5wGT00U70DeSBikpyLV2Je6Vyxxl4YknVbtuW+qPUmF0Vz85cUMfBqPwt892JNVca4zhkoU7yA85nwo9PLYDPk3zUkqpFuuqYZn8aZCzY/LOcsMJc/IbNTzxLoF0QudfTfCMSzEe599S96rlpF97Lu4vF9SwcQsQi+F76TFHUWTQCKL7DWvUbqtM4pjC/VEq7JeQ8pWYzpas8LhjHa89n82DspIa1laJNEhJQd45LzteR/Y/kFjvPatdty31R6mQ5hFmDXc2/b+/IcTb6+r+I3XPt8VV1rtyWFaDJwlTSinVPESEG4Zn8ceBzkClNGKY/kE+b/1SVsOW9ToIkfHHUXb1fcRyujoXlRaTdt+1+J66B8ob33rT1Dyfz8f9y0+OsvJpMxu936rDD6f+38vElpTFDZhzDSA6eCSxDp12vZZQEM+iuY2qW1uiQUqKkZ0FeBZ/6CirbtjhCm2xJQVgxoB0RiWkfV2xeCfF8bNaJrhzaRE3fFXoKBve2VvlyZxSSqmWSUS4eUQWVwx1pn6Vx+DM+QU8v6q0SY4T23NfSm98lMiQ0VWW+ea9RtpNf0S2rG+SYzWJSATfy487isKjjiDWb+9G77pKS0orCFISpzNYsDlEebQBnd7dHiKHOSfY9s5/UzvQJ0mDlBTj/fB1JK6DXqxLD6LVXCQBisPW8LsVhKpNmK2VS4Q7x7QnPkNrQ0mUO74pqnb9f3xTyE1LnAFKO4/w0NgOeFI4t1YppdoaEeGKYVncOjLbUR41cMGC7cz6cifhWBPcJGZkE7z0NkKnXohJGBnLvW4V6dfNxNNCbkh9rz2BK3fTrtfG5aL8xHMavd8doRjfJDwMbQ2ZB4M6eumSVnmLXBwxfL6tYa0p4cOPSehAvwrXWu1AnwwNUlJJ8U68c5wd5sMTTwJX9afx++1h4i+N/bM9tPO2nVM+qKOXP+zvbAV5cHlxldzS278u5JavncFLO4/w4sQcBmSn/sVWKaXaogsHZnDfIe1JfM50z7fFTH03jw3F9e8MXYUI4cnTKbvmfmKduzsXhYIEnriTwJ1/Q+z5MnYHz8IP8L35tKMscvgUTLdejd73hxuDxDcw7NfeQ7f01B9B1CXCkT2cE4LO29Cwfk0mpyvRwSMdZd75bzW4bm1J27ljbQV8r/8XKa3scGXSMwiPnVzj+t8WtL3+KImuGJZJz7gLZsTA+R9v5+rPd/KnT7czbU4etye0rmR4hNmTcjikm84er5RSqez0vdvxn8M7kvh8bnFuOWPfyOW99U3QT4WK9K/HCI88osoyz7efk3717/F8OqfZW1Vcq5bj/88djrJYVgfKjz+rSfY/J+HGfVKvQA1rpp7xPZ3vZd7GhvczqtqBfq52oE+CBikpQrZuxDvvNUdZ+bG/g7R2NW7zXUHb7I8SL8Pr4rZRzib/bwvCPLC8mKdWlvJBwkWnIkAZ01UDFKWUag2O3yON14/qRI905y3P9pDhlLkFXLl4R639FZOWnkHowusI/v7yKpM/SmkxgUdvJXDfdcjOgsYfKwmSv5XAvdcg4cp7AePxErz4Zkz7nEbvP2YMcxP+hk7s3XqClCN6+olvhFtWECa3rP7zpQBEh4yq0oHe+95Ljaxh66dBSorwzf43Eq1smo516kp4wgm1bqMtKZZj+waY1KvuoCPTK7w8KYfRGqAopVSrcnA3Px8f14WJPate3x/6voRRr+QyN89d/5nFE4kQGTeV0pv/Q3TvwVUWe75aQPrfTsf73ou/7gSQwVIC91yFq3C7ozh09l+I9R/YJIdYkhcmL1gZ3GX5pMqANamsU8DNkISHu/M3NbA1xe0hfMRvHEW+915AEs6PctIgJQW4Vn+P9/P5jrLyk84FX80309GY4fuEyYfaYksKWJ0o7xjdng7+mjvAd/BbAcooDVCUUqpV6hRw88LEHG4YnkXitFcbS6NcucLP8e/n8+OOxgcPpksPyq68h9Cpf8R4nX97pawE/3MPkn7N2biXLm70saqIxQg8cgvudasdxeXH/o7IIZOa7DDvr3emeo3vEUjpSRyrMz4hqJ23seHz7YQnnYTJrMzskGAZ3jf+2+D9tQVtY6inVGYM/ucfchRF++5NZPT4WjdbUxShNFL5RCjH76JbWtuNSftlevhgSmde/bmMUAyyvEKm10WmT8j2uRje2UcHf9v9fJRSqi1wiXDxAZmM7uLjnP9tZ0OJM33nf5tDHPJaLhfsn8HlQzJp35i/Cy4X4cnTiAweSeDR23D/vMK5ePN60u76G5Ehowmd8gdMj74NP5ZNCnLx//dePEs+dZRHDhpL+YlnN3r/8ar0R2lFqV4VjuwZ4M5lxbtez98YImYMLmlAMJbWjvJjf4f/2Qd2FXk/fIPwpJMxXXo0RXVbHQ1SWjj3kk9wr/zWUVZ+ygU1juhV4YuEiYcOyPEiDflStSL9s738ZWjbbE1SSilVaVRXP5+d0IV/Li3i/u+KiXumR8TA/cuLeXZVKX8bmsnZ+7ZrVAuB6dGXsmvvx/vBq/heewJJ6DDtWfoZnqWfETlgJOEJJ1gjQbnqOUJWNGLt/9X/IEHnYADRPv0JnndlnfcN9bGlNMrSuKGHBZiYRFp1qhnZxUemVygKW78g24Ixvi0IMySnYWlt4SOPwztnNq68rQBINILvlf8QuuCaJqtza6KPjluySAT/i486i4aMJrr/gXVu+urPzovU8E6tJ09UKaWUaqwMr4tZw7NZeHwXxvWoeoNdEIrxt8U7GfNqLm//Uta4/ipuD+HJ0yi942nCh0/FVPPQ0PPt56TdfSXpf/0d3neeh+KdSe3atfp70madj/+5B6oEKLHsDgQvuRUC6Q2vezUSW1EO6uylUyD1hx5O5HUJY7s7fzc+bMQoX3h9Vean8S6ai+uXnxq+z1ZMW1JaMM//3sIVN2OtERfl08+rc7ttZVE+TOjcdcIeaU1eP6WUUirV7d3ey6uTcnjks5+5b106G0udKWCrCiPM+LCAQ7v5uHlENkMb8dDPZHUgdPblhMcfh//p+3CvXFZlHde2zfhfeBjfi49guvYi1ntPor33ItZ7L0zHzri2bkQ2/YJr8zpcm3/BtX4NUk0AFd1zP4LnXYnJ6dLg+tbkg4QgZWIrGno40fieft5ZV/l+520McungzAbvLzJmPNF3nse9Yc2uMt9LjxG8/I5atmqbNEhpqQp34Hv1CUdRZOxkYr32rHPT19aWOSZX2r+9h4GtYAZYpZRS6tcgIozvFOWMEV14cHkJdy8roiTivPH/ZEs5R7y5jd/ulca1B2XTs13DWw5ifQdQdtW9uL/8H753XsC95oeqdTIG2bIe15b1eL74X9L7NuntCE07j8i4qfVPG0tCedTwUcKD0KNadZASACpbtRbnllMUjpHZ0MmxXW7Kp80k7e4rdxV5vv0c9w9fE91vWCNr27pouldLFIsReOw2XEU7dhUZXyDpTm+z1zibe0/eq2mbeZVSSqnWKN3j4vIhmSw5qStn7p1eZbZ6Azy/uozhL2/l5iWFjZtfRYToiHGUXf8Qpdc/TPjQo6qMBFZf4TETKL3tKSJHHverBCgAi7aGdvXRAOia5mJwKx49tF+mh72yKj/LcAwWbG5EyhcQHTK6yhDVvhcfbfbJPls6DVJaIO97L+JZ5hyWsHzKqZi4iYBqsrYowuKETvMnaaqXUkoplbSu6W7uPaQDC37TpcowtABlUcM/lxYx/OWtPPtTCbFG3lzG9tyX0MwrKbnrJULTZhLr0bfafis1ifbZi7K/3knogmuaZKLG2rxfTapXg0a7SiFHJsw+36h+KQAihH57vqPIveYHPAnTTbR1mu7VwrhWLcc3+zFHWbT/IMLHzkhq+1cSOsyP7uKjb6aeZqWUUqq+Bnb08vKkTszdEOTaL3byww7n/GNbymJc+MkOHltRwm0jsxs/GXBWe8JTZxCeOgOCpbg2/Ixr/Rpc61fjXrcKKd5JrFN3Yt37EOvR1/rp3gey2jfuuPUwZ73zBn1SK071qjC+p5/Hfqgcla0x86VUiPUfSOTAQ/Es+WRXmf+Ju4jusa8OSWzTu9eWpKSIwEM3ItHKTnumXSbBC68Fd3KnavbqUsfrk/fUVhSllFKqMSb0CjCuh5+nfyrlliWFbAs607y+zgsz+Z08TtojjVnDs+id0QS3V4F0Yv0HNtkM8U1hTWGEVYWVgZrXRbUjo7U2h3bz43VZqV4APxdF+bkwwh5ZjTvPoWkzcS9dtOu+T0qLCdw/i7Jr7qt1wu62QtO9WgpjCDx+x66xsysEz70Ck9M1qV0sLwjzfdxTHrfA8ZrqpZRSSjWaxyWctU87lpzclcsGZ+CvpsvHyz+XMeKVrdyypJCSxvRXaaEShx4e09VPlq/130pmeF2M7uIc1e3ZVaU1rJ0806Mv5dMvcJS5f1mJ/9n7G73v1qD1/2alCO+81/B8tcBRVj7pJKIHHpL0Pl5a4/zCHNnD3yrHLVdKKaV2l0yvi+sOymbxCV05rl/VVKdgFP6xtIgRr2zlhdWlje6v0lJEYoYnfnRORDmpFU7gWJPJfZwPfR9cXkxuWbSGtZMXPupkIsMPc5R557+J59M5jd53qtMgpQVw/bgM33MPOsqi/famfPr5NWxRVcwYHdVLKaWUaib9Mj08eUQObx/dicHVDPO/qTTG+R9vZ+Jb2/i4kaNBtQRPrSxlRVy2hgBT+rSdbI3TB6TTKVB521wSMfxjaVHjdyxC8Jy/Euva01Hsf+IuXHFzqbRFGqTsZu5li0n751+QSHhXmQmkE7zwevAmP2HU4txyNpRURvRpbmFKn9bfmU0ppZTanQ7p5mf+sZ2575D2dEmrelv1VV6Y37yXx3Hv5fHltvJq9tDy7SyPcevXhY6yU/unN7pPRirJ8lnDU8d74scS1hZFatiiHtIzCF50Aybuvk/KgwTuvx7KGp9Wlqo0SNmN3F98ROCeq5Fy5xOW0NmXYxIi6roktqIc0ydARkMnGlJKKaVU0twu4fS92/HliV255IAMquum8b/NISa8tY1T5ubzbUG46got2N3LisiLGywg3SNcc2DWbqzR7vH7fdrRJ8M5Z8otSwpr2SJ5sT79CZ1xqaPMtXk9gQdvgFBZDVu1bnoXu5t4FrxL4IEbkagzAg+dPJPIqCPrta/icIxXE4Ye1lG9lFJKqeaV5XMxa3g2n5/YlWP7Vp/N8N76IGNfz2X6B3l8siWEaeF9VtYWRXhwebGj7E+DMujRru31efW7hasTgrOX1pSxLL9pWsgihx1N+LBjHGWeZYtJu/ViZHtekxwjlWiQsht457xM4N9/R4xz5I/Q6RcnPR9KhZgxnP/xdgpClftq7xPG99RUL6WUUmp36Jfp4b9H5vDBlM4c3r36zuVzNoSY+m4eE97axutry4jGWmawcuNXhZTH3a50T3fxf4Mydl+FdrNpe6YxsIMzze3Gr5qmNQWse8Fon70cZe61K0m78Q+41q1usuOkAg1SmlNJEf4n7sL/zH2OYiMugjOvJDzhhHrv8tYlRby9zjkk4IwB7fC5W/fsr0oppVRLN6KLj9cnd+L1ozoxonPVzvVg9Vk5c34Bw17eyp1Li9hS2vgRo5rK57mhKpNEX3tgFu3acDq5S4TrD8p2lM3dGGJBUw2O4PMTvPT2KoGKq2AbabdchHvpZ01znBTQdn/LmpMxeD6dQ/oVZ+Cd/4ZzkcdL8KIbiBx6VL13+9LqUv65zDmyxAEdvVw1LLOGLZRSSinV3A7v4WfOlM48P6FjjcHKuuIoNy0pZOCLW5gxL58PNgR3a+uKMYarPt/pKBuS4+WU/jpy6MRefg7u6hzc6NovdrKzvGnmxjEdO1N21X1Ehox2lEuwjMDdV+F99wWINEGH/RZOg5RfmWxcS9rtlxB49FZchdsdy4wvQPDS24gOH1vv/X65rZyLPnXur0uai2fHd2zTTziUUkqplkhEmNw7jTlTOvPO0Z04qnf1adlRA2+vCzLtg3z2e3ELf160gwWbQ80asISihssW7eDLbc4O/jePyMYlmqkhItww3Nma8k1+mENfz2XhliZqUUlLJ3jxzZRPPNF5bBPD//xDpF9xBp5P3odYy2l5a2ptZ+y45hSL4l6xFM+iuXg+fR+JVv0FinXpQfD8q4n1H1jv3W8siTJjXj6huN36XPD0kR3pnaGnVCmllGqpRISDu/k5uJufH7aHue+7Yl7+udTxN71CblmMx1eU8PiKErqkuTi2bxoTe/k5tJv/VxvBc11xhLPmF7AkzxmgTOkTYGwN/WvaohFdfEztE+CtuJT79cVRprybx6WDM7hiaFbjU+/dHsp/9ydMl574nn3A0ZfZtW0TgcduI/bWM5SfcBaREePA1boeUusdbVMxBteaH/B8Ng/P5x/h2pFf/WpeL+VTZhCecir4GvZl/7agnB0JTYr/OqQDI7voxUMppZRKFft18PLg2A7cMjKbF1aX8uSPJfywo/o0nviAxeuCEZ19HNHDz7geAQbnePE3QV/UeRuDnPu/AraHnK02WV7hphHZNWzVdt19cHvWFOXx/fbKc2aAu5YVM29jiBcn5NA1vfGjoIUnnUSsS3cCD92EBJ19hFyb1xF48EZi3f4fkcGjiO5/INF9BkN66g9uoEFKA0hBLu7vl+DK3YRs3YgrdxOurRuQktpnHo0cMJLQ6RfXew6URJN7p/HW5M7M+DCf3LIYlxyQoTmiSimlVIrq4Hdxwf4ZnL9fOz7PLefJlaW89UsZheHqU7zCMVi4tZyFW8u55esivC4Y2MHLgZ18DOvk5YCOXvplemjvr/vJem5ZlIVbypm3McjTP5WSeMR+mW6ePKIje7ahiRuT1TnNzYdTu3DDVzt56PsSxzKfC3ICTdeyER16MKW3PYnvjafxfPx2lSwd15b1+LashzmzMeIi1m9vwuOPIzL26CarQ3Nrtt84EZkM3Au4gX8bY25PWO4HngIOAvKB3xpj1jZX/erDtXYlgcdur3tFWyynK6HT/kj0oLHQRLmcI7r4+HBqZx79oYTrDmp7EyoppZRSrY2IMKqrn1Fd/dx9cHs+3BjktbVlvLsuWGPAAlbQ8k1+mG/yw/BjZXm2T+iX6aFvhptsnwuXYP8Iwajhi9xyVu6suQP25N4BHh7bIalgp60KeITbRrXnqN4B/rBgO5tLY7TzCI8c1hGPq2n775iOXQiddRnlU07F99qTeD6dU2U6C7D6rbh/XkGkaFyTHr+5NUuQIiJu4AFgIrAB+EJE3jDGfB+32jnAdmNMfxE5Bfg78NvmqF99mS51t4SYQBqRA8cSGT2e6MCDwNP0H3WvDA83avOrUkop1er43cLRfdI4uk8aoahh/qYgczeEmL8pyOrC5DpL7yw3LM0PszS/fjPcu8QaavjiAzK0o3ySxvUIsPD4rly6cAdH9vT/qi1PpnN3QjOvoHzqaVawsnh+tcFKdL+hv1odmkNztaSMBFYZY9YAiMjzwHFAfJByHDDL/v9s4H4REdMCp2KNdelRbbnx+YkOHkV49JFEh4xpcJ8TpZRSSqkKfrc1Mtjk3mmA1bn9o00hPtoU4vPccjaUNN0IT93SXDxyWEcO76H3MPXVwe/i/43r0GzHM937EPrDtYROvxj3iqW4f1iC+4evcW9ci0lvR6zvgGary69BmiMGEJGTgcnGmHPt16cDo4wxF8Wt8529zgb79Wp7nbyKdXbu3NniAhallFJKKaVU7bKzs+vVLNdcSYbVVSox4EhmHaWUUkoppVQr11xBygagd9zrXsCmmtYREQ+QDRQ0S+2UUkoppZRSLUZz9Un5AhggInsAG4FTgNMS1nkDOBNYBJwMfJjYH6W+zURKKaWUUkqp1NMsQYoxJiIiFwHvYw1B/B9jzHIRuRH40hjzBvA48F8RWYXVgnJKc9RNKaWUUkop1bI028DXxph3jDF7G2P2MsbcYpddZwcoGGOCxphpxpj+xpiRFSOBAYhIRxH5QER+sv+tMnSCiAwVkUUislxElolIixy+uLUSkcki8qOIrBKRK6pZ7heRF+zli0WkX/PXUkFS5+oyEfne/h7NE5G+u6Oequ5zFbfeySJiRGR4c9ZPVUrmXInIdPu7tVxEnm3uOipLEtfAPiIyX0S+tq+Dx+yOerZ1IvIfEcm1B1aqbrmIyL/s87hMRA5s7joqSxLnaoZ9jpaJyEIRGZLMflNldp4rgHnGmAHAPPt1olLgDGPMQGAycI+ItG/GOrZZcfPgHA3sD5wqIvsnrLZrHhzgbqx5cFQzS/JcfQ0MN8YMxhoO/I7mraWCpM8VIpIJ/AlY3Lw1VBWSOVciMgC4EjjE/jt1SbNXVCX7vboGeNEYMwwrq+PB5q2lsj2BdT9Xk6OBAfbPecBDzVAnVb0nqP1c/Qwcbt9X3AQ8msxOUyVIOQ540v7/k8DxiSsYY1YaY36y/78JyAU6N1sN27Zd8+AYY8qBinlw4sWfw9nAeBGdIWo3qPNcGWPmG2NK7ZefYQ10oZpfMt8rsC74dwDB5qycckjmXM0EHjDGbAcwxuQ2cx2VJZlzZYAs+//ZVB3oRzUDY8zH1D6A0nHAU8byGdBeRLo3T+1UvLrOlTFmYcW1j3rcV6RKkNLVGLMZwP63S20ri8hIwAesboa6KegJrI97vcEuq3YdY0wE2AnkNEvtVLxkzlW8c4B3f9UaqZrUea5EZBjQ2xjzVnNWTFWRzPdqb2BvEflURD4TkdqeOqpfTzLnahbwOxHZALwD/F/zVE3VU33/nqmWIen7iuYa3atOIjIX6FbNoqvruZ/uwH+BM40xsaaom6qTzoOTOpI+DyLyO2A4cPivWiNVk1rPlYi4sFInz2quCqkaJfO98mClpYzDeoq4QEQGGWN2/Mp1U07JnKtTgSeMMXeKyBisQX0G6T1Fi6P3FSlGRI7AClIOTWb9FhOkGGMm1LRMRLaKSHdjzGY7CKm2mVxEsoC3gWvspj/VPOozD84GnQdnt0rmXCEiE7AeEBxujAk1U92UU13nKhMYBHxkZ052A94Qkd8YY75stloqSP4a+JkxJgz8LCI/YgUtXzRPFZUtmXN1DnZ+vTFmkYgEgE7UcO+hdpuk/p6plkFEBgP/Bo42xuQns02qpHtVzKGC/e/riSuIiA94FSs/8aVmrJuKmwfHPg+nYJ2zePHnsNp5cFSzqPNc2SlEjwC/0bz53arWc2WM2WmM6WSM6WeM6YeV56sByu6RzDXwNeAIABHphJX+tQbV3JI5V+uA8QAish8QALY1ay1VMt4AzrBH+RoN7KzoGqBaFhHpA7wCnG6MWZnsdi2mJaUOtwMvisg5WBePaQD2cJsXGGPOBaYDhwE5InKWvd1ZxphvdkN92xSdByd1JHmu/gFkAC/ZT+jXGWN+s9sq3UYlea5UC5DkuXofmCQi3wNR4C/JPk1UTSfJc/Vn4DERuRQrfegsfajW/ETkOaz0yE52/6DrAS+AMeZhrP5CxwCrsEZ4/f3uqalK4lxdh9UP+UH7viJijKlzyHzR751SSimllFKqJUmVdC+llFJKKaVUG6FBilJKKaWUUqpF0SBFKaWUUkop1aJokKKUUkoppZRqUTRIUUoppZRSSrUoGqQopZRSSimlWhQNUpRSSv3qRGS5iIzb3fVQyRORj0TkXPv/M0RkThPvv5+IGBFJlTnblFLNSIMUpVSLIyKniciXIlIsIptF5F0ROVREZolI2C7fISILRWRM3HbjRCRmL4//GWMv/0hEgiJSJCKFIvKViFwhIv64fcwSkadFpE/CPoyIlMS9HpvE+5hlbzeymmXdReRx+/0VicgKEblBRPrWdVwReUJEbhaRgP05HFnN/u8Wkdn2/9eKSFnCfu9P8lyMs+vw12TWt7d5QkRuji8zxgw0xnyU7D5Sgf259N/d9WgOxphnjDGTdnc9lFJthwYpSqkWRUQuA+4BbgW6An2AB4Hj7FVeMMZkAJ2A+cBLCbvYZIzJSPhZFLf8ImNMJtAda2bpU4B3xJ4Gt4IxZl38PuziIXFlC+p4HwKcDhQAZyYs6wgsAtKAMXZ9JgLtgexkj2uMCQIvAGck7N8NnAo8GVd8bMJnclFt9Y9zZnXvQbUs2hqhlGptNEhRSrUYIpIN3Aj80RjzijGmxBgTNsa8aYz5S/y6xpgI8AzQU0Q61/dY9r4/An4DjAGmNP4dOIwFegAXA6eIiC9u2WVAEfA7Y8xauz7rjTEXG2OW1fM4TwIniUh6XNlRWNf3dxtaeQB7nycDfwQGiMjwhOWH2q1ZO0RkvYicJSLnATOAv9otNm/a664VkQki0sNu1ekYt59hIpInIl779dki8oOIbBeR90WkbxJ1HSgiH4hIgYhsFZGr7HK/iNwjIpvsn3sqWs7s+n6SsJ9drSN2i9ADIvK23dq1WET2spd9bG+y1H6fvxWRTiLylv15FIjIAhGp9e+s/blcLiLLRGSniLwgIoG45TNFZJW9vzdEpEdCXf8oIj8BP8WVXSgiP9l1vklE9hKRRWK1Hr5Y8bsoIh3s+m6zP+u3RKRXDfXc9VmJSMW5rfgJi8gT9rJsqWwh3Gi3+LntZW4R+ad9rtfQ9N85pVQrokGKUqolGQMEgFfr6VWj+AAABvRJREFUWtG+0ToDyAe2N/SAxph1wJdYQUVTOhN4E6ulA2Bq3LIJwCvGmFhjD2KMWQhsBk6MKz4deNYO5BrjJKAYq7XqfeJabESkD1YQdB/QGRgKfGOMeRQreLzDbrE5NqG+m7BakU6KKz4NmG2MCYvI8cBV9vvpDCwAnqutkiKSCcwF3sMKDPsD8+zFVwOj7foNAUYC19TjMzgVuAHoAKwCbrHfx2H28opWrhewWuY22PXuar8Pk8QxpgOTgT2AwcBZ9vs6ErjNXt4d+AV4PmHb44FRwP5xZZOBg7De91+BR7ECx97AIPs9gXUP8P+AvlgtlmVAnWmAxpiKc5sB7AdsA160Fz8JRLDOwTBgEnCuvWwm1vdgGDAcKwBWSqlqaZCilGpJcoC8Om6up4vIDqwbqpnAyQnr97CfZMf/tKvjuJuAjnWskzS7BWIaVqAQBmbjTJfKwQosmspT2AGEiGRhpcY9mbDOawmfycwk9nsmVnpdFHgWOLWitQPrpneuMeY5u7Ur3xjzTZL1fRb7RtlOizvFLgM4H7jNGPODfV5vBYbW0ZoyFdhijLnTGBM0xhQZYxbH1fNGY0yuMWYbVsBxepL1BCuY/Dyu5W5oLeuGsYKJvvZnssAYk0yQ8i9jzCZjTAFWYFtxjBnAf4wxS4wxIeBKYIyI9Ivb9jZjTIExpiyu7O/GmEJjzHLgO2COMWaNMWYnVmA5DMA+Zy8bY0qNMUVYAdjhSdQXABFJA14D7jXGvCMiXYGjgUvslspc4G6s8wtWsHWP3WpYgBWAKaVUtTRIUUq1JPlAJ6k9v/5FY0x7rCfV32E9MY63yRjTPuGnpI7j9sTqd9FUTsB6mvyO/foZ4GipTEvLx7qZbSpPAUeISE+sp9OrjDFfJ6xzfMJn8lhtOxSR3sARdt0BXsdq5apI0ekNrG5gfWdj3Wz3AA7Dam2o6GvTF7i3IpjCOi+CdY5qUltdemC1QFT4xS5L1pa4/5cCGTWtCPwDq7VljoisEZErGnkMR92NMcVYvzvxn8X6ava3Ne7/ZdW8zgArmBaRR0TkFxEpBD4G2lekZyXhceBHY8zf7dd9AS+wOe78PQJ0iXs/8fWNPy9KKeWgQYpSqiVZBASxUlhqZYzJw3rqPktEGnzDb9+MH0TlTXJTOBPrRnCdiGzBSpfyUplmMxc4oa7+CsmyU9YWYD15Px0raGms07H+Rrxpv4c1WEFKRcrXemCvmqpUR313AHOwnqyfBjwX1+KwHjg/IaBKs9PaalJbXTZh3TxX6GOXAZQAu/ryiEi32updF7sF58/GmD2BY4HLRGR8I3bpqLvdIpgDbIw/bCP2/2dgH2CUMSYLK2AEKyislR2A7QOcE1e8HggBneLOXZYxZqC9fDNWQFmhTyPqrpRq5TRIUUq1GHY6ynXAAyJyvP2k1ysiR4vIHdWsvwKrr0TSw+NWsPd9OFYLwedUtno0it2aMR4rBWkolX0h/k5lytddQBbwZEUak4j0FJG7RGRwAw/9JHARcAiVrR+NcQZWatTQuJ+TgCkikmMfY4KITBcRj4jkiEhFmtJWYM869v+sfYyTqEz1AngYuFJEBsKujtjT6tjXW0A3EblErI7ymSIyyl72HHCNiHQWkU5Yv19P28uWAgNFZKjdWX1WHcdJ5HifIjJVRPrbKWyFQNT+aahngd/b9fNjpb4trhhsoQlkYrWs7BBrIIPrk9lIRI4G/oTVOrcrzcwYsxkr+LxTRLJExGV32q9IIXsR+JOI9BKRDkCyLU1KqTZIgxSlVItijLkLa/Sra7A65K7Huvl+rYZN/gGcJyK7Ukqk6jwp8Z207xeRIqwbzHuAl4HJTdGJ3XY6VgfyOcaYLRU/wL+AwSIyyM7HPxirD8Niuz7zgJ1Y6UINMRurc/c8+2Yx0ZsJn0mNgxOIyGigH/BA/Hswxrxh1+9Uu/XmGKyn8QXAN1jBGFhpQPvbKT81nbc3gAHAVmPM0opCY8yrWAHd83YK0ndY/RxqZPenmIjVerEFa6SrI+zFN2MNjLAM+BZYYpdhjFmJNZrcXHsbx0hfSZiFFWjuEJHp9vuZizXYwCLgQdOIuWGMMfOAa7F+RzdjtRadUutG9XMP1jDYecBnWAMPJOO3WIMD/BD3+/SwvewMwAd8jzWgxWwqUxsfw3qosBTrPLzSFG9CKdU6SXJ9+pRSSimllFKqeWhLilJKKaWUUqpF0SBFKaUaQESWV5NWViwiM3Z33ZIlIg/X8B4ernvr5iUiY2uoa/HurlttRKRPTfUWa64ZpZRS1dB0L6WUUkoppVSLoi0pSimllFJKqRZFgxSllFJKKaVUi6JBilJKKaWUUqpF0SBFKaWUUkop1aJokKKUUkoppZRqUf4/zGnnCPF387gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_target(var_name='CREDIT_ACTIVE_Active_counts_normalized', df=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well this distribution is all over the place. This variable represents the number of previous loans with a `CREDIT_ACTIVE` value of `Active` divided by the total number of previous loans for a client. The correlation here is so weak that I do not think we should draw any conclusions here! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collinear Variables\n",
    "\n",
    "We can calculate not only the correlations of the variables with the target, but also the correlation of each variable with every other variable. This will allow us to see if there are highly collinear variables that should perhaps be removed from the data. \n",
    "\n",
    "Let's look for any variables that have a greather than 0.8 correlation with other variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "7330e7311864a70afa75b7e7c33ad4b3d77df040",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set the threshold\n",
    "threshold = 0.8\n",
    "\n",
    "# Empty dictionary to hold correlated variables\n",
    "above_threshold_vars = {}\n",
    "\n",
    "# For each column, record the variables that are above the threshold\n",
    "for col in corrs:\n",
    "    above_threshold_vars[col] = list(corrs.index[corrs[col] > threshold])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of these pairs of highly correlated variables, we only want to remove one of the variables. The following code creates a set of variables to remove by only adding one of each pair. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns to remove:  101\n"
     ]
    }
   ],
   "source": [
    "# Track columns to remove and columns already examined\n",
    "cols_to_remove = []\n",
    "cols_seen = []\n",
    "cols_to_remove_pair = []\n",
    "\n",
    "# Iterate through columns and correlated columns\n",
    "for key, value in above_threshold_vars.items():\n",
    "    # Keep track of columns already examined\n",
    "    cols_seen.append(key)\n",
    "    for x in value:\n",
    "        if x == key:\n",
    "            next\n",
    "        else:\n",
    "            # Only want to remove one in a pair\n",
    "            if x not in cols_seen:\n",
    "                cols_to_remove.append(x)\n",
    "                cols_to_remove_pair.append(key)\n",
    "            \n",
    "cols_to_remove = list(set(cols_to_remove))\n",
    "print('Number of columns to remove: ', len(cols_to_remove))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove these columns from both the training and the testing datasets. We will have to compare performance after removing these variables with performance keeping these variables (the raw csv files we saved earlier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "0fedcbce9bce99809bf7d4559b6ad93e8ecca8ea",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Corrs Removed Shape:  (307511, 201)\n",
      "Testing Corrs Removed Shape:  (48744, 200)\n"
     ]
    }
   ],
   "source": [
    "train_corrs_removed = train.drop(columns = cols_to_remove)\n",
    "test_corrs_removed = test.drop(columns = cols_to_remove)\n",
    "\n",
    "print('Training Corrs Removed Shape: ', train_corrs_removed.shape)\n",
    "print('Testing Corrs Removed Shape: ', test_corrs_removed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "2f4443a62f6e8987c0be2e3162589444ecd7d275",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_corrs_removed.to_csv('train_bureau_corrs_removed.csv', index = False)\n",
    "test_corrs_removed.to_csv('test_bureau_corrs_removed.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "be48572e88f64f00b4221da7782ac834444c96b0"
   },
   "source": [
    "# Modeling \n",
    "\n",
    "To actually test the performance of these new datasets, we will try using them for machine learning! Here we will use a function I developed in another notebook to compare the features (the raw version with the highly correlated variables removed). We can run this kind of like an experiment, and the control will be the performance of just the `application` data in this function when submitted to the competition. I've already recorded that performance, \n",
    "so we can list out our control and our two test conditions:\n",
    "\n",
    "__For all datasets, use the model shown below (with the exact hyperparameters).__\n",
    "\n",
    "* control: only the data in the `application` files. \n",
    "* test one: the data in the `application` files with all of the data recorded from the `bureau` and `bureau_balance` files\n",
    "* test two: the data in the `application` files with all of the data recorded from the `bureau` and `bureau_balance` files with highly correlated variables removed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(features, test_features, encoding = 'ohe', n_folds = 5):\n",
    "    \n",
    "    \"\"\"Train and test a light gradient boosting model using\n",
    "    cross validation. \n",
    "    \n",
    "    Args:\n",
    "        features (pd.DataFrame): dataframe of training features to use \n",
    "        for training a model. Must include the TARGET column.\n",
    "        test_features (pd.DataFrame): dataframe of testing features to use\n",
    "        for making predictions with the model. \n",
    "        encoding (str, default = 'ohe'): method for encoding categorical \n",
    "        variables. Either 'ohe' for one-hot encoding or 'le' for integer label encoding\n",
    "        n_folds (int, default = 5): number of folds to use for cross validation\n",
    "        \n",
    "    Returns:\n",
    "        submission (pd.DataFrame): dataframe with `SK_ID_CURR` and `TARGET` probabilities\n",
    "        predicted by the model.\n",
    "        feature_importances (pd.DataFrame): dataframe with the feature importances\n",
    "        from the model.\n",
    "        valid_metrics (pd.DataFrame): dataframe with validation metrics (ROC AUC) for \n",
    "        each fold and overall.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    # Extract the ids\n",
    "    train_ids = features['SK_ID_CURR']\n",
    "    test_ids = test_features['SK_ID_CURR']\n",
    "    \n",
    "    # Extract the labels for training\n",
    "    labels = features['TARGET']\n",
    "    \n",
    "    # Remove the ids and target\n",
    "    features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
    "    test_features = test_features.drop(columns = ['SK_ID_CURR'])\n",
    "    \n",
    "    \n",
    "    # One Hot Encoding\n",
    "    if encoding == 'ohe':\n",
    "        features = pd.get_dummies(features)\n",
    "        test_features = pd.get_dummies(test_features)\n",
    "        \n",
    "        # Align the dataframes by the columns\n",
    "        features, test_features = features.align(test_features, join = 'inner', axis = 1)\n",
    "        \n",
    "        # No categorical indices to record\n",
    "        cat_indices = 'auto'\n",
    "    \n",
    "    # Integer label encoding\n",
    "    elif encoding == 'le':\n",
    "        \n",
    "        # Create a label encoder\n",
    "        label_encoder = LabelEncoder()\n",
    "        \n",
    "        # List for storing categorical indices\n",
    "        cat_indices = []\n",
    "        \n",
    "        # Iterate through each column\n",
    "        for i, col in enumerate(features):\n",
    "            if features[col].dtype == 'object':\n",
    "                # Map the categorical features to integers\n",
    "                features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n",
    "                test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n",
    "\n",
    "                # Record the categorical indices\n",
    "                cat_indices.append(i)\n",
    "    \n",
    "    # Catch error if label encoding scheme is not valid\n",
    "    else:\n",
    "        raise ValueError(\"Encoding must be either 'ohe' or 'le'\")\n",
    "        \n",
    "    print('Training Data Shape: ', features.shape)\n",
    "    print('Testing Data Shape: ', test_features.shape)\n",
    "    \n",
    "    # Extract feature names\n",
    "    feature_names = list(features.columns)\n",
    "    \n",
    "    # Convert to np arrays\n",
    "    features = np.array(features)\n",
    "    test_features = np.array(test_features)\n",
    "    \n",
    "    # Create the kfold object\n",
    "    k_fold = KFold(n_splits = n_folds, shuffle = False, random_state = 50)\n",
    "    \n",
    "    # Empty array for feature importances\n",
    "    feature_importance_values = np.zeros(len(feature_names))\n",
    "    \n",
    "    # Empty array for test predictions\n",
    "    test_predictions = np.zeros(test_features.shape[0])\n",
    "    \n",
    "    # Empty array for out of fold validation predictions\n",
    "    out_of_fold = np.zeros(features.shape[0])\n",
    "    \n",
    "    # Lists for recording validation and training scores\n",
    "    valid_scores = []\n",
    "    train_scores = []\n",
    "    \n",
    "    # Iterate through each fold\n",
    "    for train_indices, valid_indices in k_fold.split(features):\n",
    "        \n",
    "        # Training data for the fold\n",
    "        train_features, train_labels = features[train_indices], labels[train_indices]\n",
    "        # Validation data for the fold\n",
    "        valid_features, valid_labels = features[valid_indices], labels[valid_indices]\n",
    "        \n",
    "        # Create the model\n",
    "        model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', \n",
    "                                   class_weight = 'balanced', learning_rate = 0.05, \n",
    "                                   reg_alpha = 0.1, reg_lambda = 0.1, \n",
    "                                   subsample = 0.8, n_jobs = -1, random_state = 50)\n",
    "        \n",
    "        # Train the model\n",
    "        model.fit(train_features, train_labels, eval_metric = 'auc',\n",
    "                  eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\n",
    "                  eval_names = ['valid', 'train'], categorical_feature = cat_indices,\n",
    "                  early_stopping_rounds = 100, verbose = 200)\n",
    "        \n",
    "        # Record the best iteration\n",
    "        best_iteration = model.best_iteration_\n",
    "        \n",
    "        # Record the feature importances\n",
    "        feature_importance_values += model.feature_importances_ / k_fold.n_splits\n",
    "        \n",
    "        # Make predictions\n",
    "        test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\n",
    "        \n",
    "        # Record the out of fold predictions\n",
    "        out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\n",
    "        \n",
    "        # Record the best score\n",
    "        valid_score = model.best_score_['valid']['auc']\n",
    "        train_score = model.best_score_['train']['auc']\n",
    "        \n",
    "        valid_scores.append(valid_score)\n",
    "        train_scores.append(train_score)\n",
    "        \n",
    "        # Clean up memory\n",
    "        gc.enable()\n",
    "        del model, train_features, valid_features\n",
    "        gc.collect()\n",
    "        \n",
    "    # Make the submission dataframe\n",
    "    submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\n",
    "    \n",
    "    # Make the feature importance dataframe\n",
    "    feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n",
    "    \n",
    "    # Overall validation score\n",
    "    valid_auc = roc_auc_score(labels, out_of_fold)\n",
    "    \n",
    "    # Add the overall scores to the metrics\n",
    "    valid_scores.append(valid_auc)\n",
    "    train_scores.append(np.mean(train_scores))\n",
    "    \n",
    "    # Needed for creating dataframe of validation scores\n",
    "    fold_names = list(range(n_folds))\n",
    "    fold_names.append('overall')\n",
    "    \n",
    "    # Dataframe of validation scores\n",
    "    metrics = pd.DataFrame({'fold': fold_names,\n",
    "                            'train': train_scores,\n",
    "                            'valid': valid_scores}) \n",
    "    \n",
    "    return submission, feature_importances, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importances(df):\n",
    "    \"\"\"\n",
    "    Plot importances returned by a model. This can work with any measure of\n",
    "    feature importance provided that higher importance is better. \n",
    "    \n",
    "    Args:\n",
    "        df (dataframe): feature importances. Must have the features in a column\n",
    "        called `features` and the importances in a column called `importance\n",
    "        \n",
    "    Returns:\n",
    "        shows a plot of the 15 most importance features\n",
    "        \n",
    "        df (dataframe): feature importances sorted by importance (highest to lowest) \n",
    "        with a column for normalized importance\n",
    "        \"\"\"\n",
    "    \n",
    "    # Sort features according to importance\n",
    "    df = df.sort_values('importance', ascending = False).reset_index()\n",
    "    \n",
    "    # Normalize the feature importances to add up to one\n",
    "    df['importance_normalized'] = df['importance'] / df['importance'].sum()\n",
    "\n",
    "    # Make a horizontal bar chart of feature importances\n",
    "    plt.figure(figsize = (10, 6))\n",
    "    ax = plt.subplot()\n",
    "    \n",
    "    # Need to reverse the index to plot most important on top\n",
    "    ax.barh(list(reversed(list(df.index[:15]))), \n",
    "            df['importance_normalized'].head(15), \n",
    "            align = 'center', edgecolor = 'k')\n",
    "    \n",
    "    # Set the yticks and labels\n",
    "    ax.set_yticks(list(reversed(list(df.index[:15]))))\n",
    "    ax.set_yticklabels(df['feature'].head(15))\n",
    "    \n",
    "    # Plot labeling\n",
    "    plt.xlabel('Normalized Importance'); plt.title('Feature Importances')\n",
    "    plt.show()\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control\n",
    "\n",
    "The first step in any experiment is establishing a control. For this we will use the function defined above (that implements a Gradient Boosting Machine model) and the single main data source (`application`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_control = pd.read_csv('../input/application_train.csv')\n",
    "test_control = pd.read_csv('../input/application_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, once we have taken the time to write a function, using it is simple (if there's a central theme in this notebook, it's use functions to make things simpler and reproducible!). The function above returns a `submission` dataframe we can upload to the competition, a `fi` dataframe of feature importances, and a `metrics` dataframe with validation and test performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 241)\n",
      "Testing Data Shape:  (48744, 241)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.760536\ttrain's auc: 0.798315\n",
      "Early stopping, best iteration is:\n",
      "[261]\tvalid's auc: 0.76128\ttrain's auc: 0.809175\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.760752\ttrain's auc: 0.798135\n",
      "Early stopping, best iteration is:\n",
      "[279]\tvalid's auc: 0.761455\ttrain's auc: 0.811899\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.751258\ttrain's auc: 0.800548\n",
      "Early stopping, best iteration is:\n",
      "[201]\tvalid's auc: 0.75129\ttrain's auc: 0.800783\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.761401\ttrain's auc: 0.798293\n",
      "[400]\tvalid's auc: 0.76219\ttrain's auc: 0.830079\n",
      "Early stopping, best iteration is:\n",
      "[390]\tvalid's auc: 0.762349\ttrain's auc: 0.828682\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.760926\ttrain's auc: 0.798301\n",
      "[400]\tvalid's auc: 0.761301\ttrain's auc: 0.830225\n",
      "Early stopping, best iteration is:\n",
      "[359]\tvalid's auc: 0.761495\ttrain's auc: 0.824145\n"
     ]
    }
   ],
   "source": [
    "submission, fi, metrics = model(train_control, test_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.809175</td>\n",
       "      <td>0.761280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.811899</td>\n",
       "      <td>0.761455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.800783</td>\n",
       "      <td>0.751290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.828682</td>\n",
       "      <td>0.762349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.824145</td>\n",
       "      <td>0.761495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.814937</td>\n",
       "      <td>0.759588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.809175  0.761280\n",
       "1        1  0.811899  0.761455\n",
       "2        2  0.800783  0.751290\n",
       "3        3  0.828682  0.762349\n",
       "4        4  0.824145  0.761495\n",
       "5  overall  0.814937  0.759588"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The control slightly overfits because the training score is higher than the validation score. We can address this in later notebooks when we look at regularization (we already perform some regularization in this model by using `reg_lambda` and `reg_alpha` as well as early stopping). \n",
    "\n",
    "We can visualize the feature importance with another function, `plot_feature_importances`. The feature importances may be useful when it's time for feature selection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi_sorted = plot_feature_importances(fi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('control.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__The control scores 0.745 when submitted to the competition.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test One\n",
    "\n",
    "Let's conduct the first test. We will just need to pass in the data to the function, which does most of the work for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 421)\n",
      "Testing Data Shape:  (48744, 421)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.766544\ttrain's auc: 0.810646\n",
      "Early stopping, best iteration is:\n",
      "[282]\tvalid's auc: 0.767375\ttrain's auc: 0.826376\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.7685\ttrain's auc: 0.810126\n",
      "[400]\tvalid's auc: 0.770086\ttrain's auc: 0.845741\n",
      "Early stopping, best iteration is:\n",
      "[424]\tvalid's auc: 0.770161\ttrain's auc: 0.849374\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.759529\ttrain's auc: 0.811565\n",
      "Early stopping, best iteration is:\n",
      "[244]\tvalid's auc: 0.760307\ttrain's auc: 0.820461\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.76791\ttrain's auc: 0.809901\n",
      "[400]\tvalid's auc: 0.768199\ttrain's auc: 0.845167\n",
      "Early stopping, best iteration is:\n",
      "[319]\tvalid's auc: 0.768807\ttrain's auc: 0.832395\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.766847\ttrain's auc: 0.810056\n",
      "[400]\tvalid's auc: 0.76817\ttrain's auc: 0.845973\n",
      "Early stopping, best iteration is:\n",
      "[360]\tvalid's auc: 0.768328\ttrain's auc: 0.839578\n"
     ]
    }
   ],
   "source": [
    "submission_raw, fi_raw, metrics_raw = model(train, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.826376</td>\n",
       "      <td>0.767375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.849374</td>\n",
       "      <td>0.770161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.820461</td>\n",
       "      <td>0.760307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.832395</td>\n",
       "      <td>0.768807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.839578</td>\n",
       "      <td>0.768328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.833637</td>\n",
       "      <td>0.766994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.826376  0.767375\n",
       "1        1  0.849374  0.770161\n",
       "2        2  0.820461  0.760307\n",
       "3        3  0.832395  0.768807\n",
       "4        4  0.839578  0.768328\n",
       "5  overall  0.833637  0.766994"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these numbers, the engineered features perform better than the control case. However, we will have to submit the predictions to the leaderboard before we can say if this better validation performance transfers to the testing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi_raw_sorted = plot_feature_importances(fi_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the feature improtances, it looks as if a few of the feature we constructed are among the most important. Let's find the percentage of the top 100 most important features that we made in this notebook. However, rather than just compare to the original features, we need to compare to the _one-hot encoded_ original features. These are already recorded for us in `fi` (from the original data). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Top 100 Features created from the bureau data = 51.00\n"
     ]
    }
   ],
   "source": [
    "top_100 = list(fi_raw_sorted['feature'])[:100]\n",
    "new_features = [x for x in top_100 if x not in list(fi['feature'])]\n",
    "\n",
    "print('%% of Top 100 Features created from the bureau data = %d.00' % len(new_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over half of the top 100 features were made by us! That should give us confidence that all the hard work we did was worthwhile. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_raw.to_csv('test_one.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Test one scores 0.759 when submitted to the competition.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Two\n",
    "\n",
    "That was easy, so let's do another run! Same as before but with the highly collinear variables removed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 320)\n",
      "Testing Data Shape:  (48744, 320)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.765308\ttrain's auc: 0.806595\n",
      "[400]\tvalid's auc: 0.765961\ttrain's auc: 0.841045\n",
      "Early stopping, best iteration is:\n",
      "[307]\tvalid's auc: 0.766223\ttrain's auc: 0.826585\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.764792\ttrain's auc: 0.80661\n",
      "[400]\tvalid's auc: 0.765812\ttrain's auc: 0.841572\n",
      "Early stopping, best iteration is:\n",
      "[345]\tvalid's auc: 0.766213\ttrain's auc: 0.832874\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.758022\ttrain's auc: 0.808142\n",
      "Early stopping, best iteration is:\n",
      "[257]\tvalid's auc: 0.758759\ttrain's auc: 0.819093\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.765141\ttrain's auc: 0.806355\n",
      "[400]\tvalid's auc: 0.766159\ttrain's auc: 0.840982\n",
      "Early stopping, best iteration is:\n",
      "[332]\tvalid's auc: 0.766782\ttrain's auc: 0.830336\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.765172\ttrain's auc: 0.806503\n",
      "Early stopping, best iteration is:\n",
      "[291]\tvalid's auc: 0.76583\ttrain's auc: 0.82323\n"
     ]
    }
   ],
   "source": [
    "submission_corrs, fi_corrs, metrics_corr = model(train_corrs_removed, test_corrs_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.826585</td>\n",
       "      <td>0.766223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.832874</td>\n",
       "      <td>0.766213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.819093</td>\n",
       "      <td>0.758759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.830336</td>\n",
       "      <td>0.766782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.823230</td>\n",
       "      <td>0.765830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.826423</td>\n",
       "      <td>0.764762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.826585  0.766223\n",
       "1        1  0.832874  0.766213\n",
       "2        2  0.819093  0.758759\n",
       "3        3  0.830336  0.766782\n",
       "4        4  0.823230  0.765830\n",
       "5  overall  0.826423  0.764762"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results are better than the control, but slightly lower than the raw features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi_corrs_sorted = plot_feature_importances(fi_corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_corrs.to_csv('test_two.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Test Two scores 0.753 when submitted to the competition.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "After all that work, we can say that including the extra information did improve performance! The model is definitely not optimized to our data, but we still had a noticeable improvement over the original dataset when using the calculated features. Let's officially summarize the performances:\n",
    "\n",
    "| __Experiment__ | __Train AUC__ | __Validation AUC__ | __Test AUC__  |\n",
    "|------------|-------|------------|-------|\n",
    "| __Control__    | 0.815 | 0.760      | 0.745 |\n",
    "| __Test One__   | 0.837 | 0.767      | 0.759 |\n",
    "| __Test Two__   | 0.826 | 0.765      | 0.753 |\n",
    "\n",
    "\n",
    "(Note that these scores may change from run to run of the notebook. I have not observed that the general ordering changes however.)\n",
    "\n",
    "All of our hard work translates to a small improvement of 0.014 ROC AUC over the original testing data. Removing the highly collinear variables slightly decreases performance so we will want to consider a different method for feature selection. Moreover, we can say that some of the features we built are among the most important as judged by the model. \n",
    "\n",
    "In a competition such as this, even an improvement of this size is enough to move us up 100s of spots on the leaderboard. By making numerous small improvements such as in this notebook, we can gradually achieve better and better performance. I encourage others to use the results here to make their own improvements, and I will continue to document the steps I take to help others. \n",
    "\n",
    "## Next Steps\n",
    "\n",
    "Going forward, we can now use the functions we developed in this notebook on the other datasets. There are still 4 other data files to use in our model! In the next notebook, we will incorporate the information from these other data files (which contain information on previous loans at Home Credit) into our training data. Then we can build the same model and run more experiments to determine the effect of our feature engineering. There is plenty more work to be done in this competition, and plenty more gains in performance to be had! I'll see you in the next notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}