credit-card-default-a-comparative-study.ipynb

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    "**Welcome to my Kernel**\n",
    "\n",
    "In this dataset, we have to predict default payment of credit and to find which variables are the strongest predictors of default payment? and How does the probability of default payment vary by categories of different demographic variables?\n",
    "\n",
    "So, first we will see what are features and how many features are available to predict default payment.\n",
    "\n",
    "There are 25 variables:\n",
    "\n",
    "**1. ID:** ID of each client <br>\n",
    "**2. LIMIT_BAL:** Amount of given credit in NT dollars (includes individual and family/supplementary credit<br>\n",
    "**3. SEX:** Gender (1=male, 2=female)<br>\n",
    "**4. EDUCATION:** (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)<br>\n",
    "**5. MARRIAGE:** Marital status (1=married, 2=single, 3=others)<br>\n",
    "**6. AGE:** Age in years<br>\n",
    "**7. PAY_0:** Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months,8=payment delay for eight months, 9=payment delay for nine months and above)<br>\n",
    "**8. PAY_2:** Repayment status in August, 2005 (scale same as above)<br>\n",
    "**9. PAY_3:** Repayment status in July, 2005 (scale same as above)<br>\n",
    "**10. PAY_4:** Repayment status in June, 2005 (scale same as above)<br>\n",
    "**11. PAY_5:** Repayment status in May, 2005 (scale same as above)<br>\n",
    "**12. PAY_6:** Repayment status in April, 2005 (scale same as above)<br>\n",
    "**13. BILL_AMT1:** Amount of bill statement in September, 2005 (NT dollar)<br>\n",
    "**14. BILL_AMT2:** Amount of bill statement in August, 2005 (NT dollar)<br>\n",
    "**15. BILL_AMT3:** Amount of bill statement in July, 2005 (NT dollar)<br>\n",
    "**16. BILL_AMT4:** Amount of bill statement in June, 2005 (NT dollar)<br>\n",
    "**17. BILL_AMT5:** Amount of bill statement in May, 2005 (NT dollar)<br>\n",
    "**18. BILL_AMT6:** Amount of bill statement in April, 2005 (NT dollar)<br>\n",
    "**19. PAY_AMT1:** Amount of previous payment in September, 2005 (NT dollar)<br>\n",
    "**20. PAY_AMT2:** Amount of previous payment in August, 2005 (NT dollar)<br>\n",
    "**21. PAY_AMT3:** Amount of previous payment in July, 2005 (NT dollar)<br>\n",
    "**22. PAY_AMT4:** Amount of previous payment in June, 2005 (NT dollar)<br>\n",
    "**23. PAY_AMT5:** Amount of previous payment in May, 2005 (NT dollar)<br>\n",
    "**24. PAY_AMT6:** Amount of previous payment in April, 2005 (NT dollar)<br>\n",
    "**25. default.payment.next.month:** Default payment (1=yes, 0=no)<br>\n",
    "\n",
    "Now, we know about the overall structure of a dataset . So let's apply some of the steps that we should generally do while applying machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "630529a1d9f2a0033c71153f2d55615a4367ebee"
   },
   "source": [
    "**STEP 1 : IMPORTING LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve as curves\n",
    "from time import time\n",
    "import os\n",
    "# print(os.listdir(\"../input\"))\n",
    "from IPython.display import display\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('/Users/nic/Documents/学校论文/代码/datasets/UCI_Credit_Card.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
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       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
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       "\n",
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       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default.payment.next.month  \n",
       "0       0.0       0.0       0.0                           1  \n",
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     "metadata": {},
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   "source": [
    "# Now lets see how the data looks like\n",
    "dataset.head()"
   ]
  },
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       "\n",
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Checking the last few entries of dataset to see the distribution of data\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a76aa515ea166affeaf00b535c60b87e19630407"
   },
   "source": [
    "**Step 2 : Preprocessing & Cleaning of Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "3a66334e4dd4cb9d8030ea26d3630764621006fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1      -1\n",
       "2       0\n",
       "9      -2\n",
       "13      1\n",
       "129     3\n",
       "360     4\n",
       "649     8\n",
       "2324    7\n",
       "3537    5\n",
       "4801    6\n",
       "Name: PAY_0, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['PAY_0'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2919414a4da3230f84db9b216ffc412d6b4812a0"
   },
   "source": [
    "Means there are 30,000 entries with 25 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "eeefe576c75291a24521f9ebf854f70fdc21670b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                              int64\n",
      "LIMIT_BAL                     float64\n",
      "SEX                             int64\n",
      "EDUCATION                       int64\n",
      "MARRIAGE                        int64\n",
      "AGE                             int64\n",
      "PAY_0                           int64\n",
      "PAY_2                           int64\n",
      "PAY_3                           int64\n",
      "PAY_4                           int64\n",
      "PAY_5                           int64\n",
      "PAY_6                           int64\n",
      "BILL_AMT1                     float64\n",
      "BILL_AMT2                     float64\n",
      "BILL_AMT3                     float64\n",
      "BILL_AMT4                     float64\n",
      "BILL_AMT5                     float64\n",
      "BILL_AMT6                     float64\n",
      "PAY_AMT1                      float64\n",
      "PAY_AMT2                      float64\n",
      "PAY_AMT3                      float64\n",
      "PAY_AMT4                      float64\n",
      "PAY_AMT5                      float64\n",
      "PAY_AMT6                      float64\n",
      "default.payment.next.month      int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Checking the object type of all the columns to see if there is not a object type mismatch in any column \n",
    "print(dataset.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e4d71ec8d5fcff2858c0efad9dbbb9d3679beb34"
   },
   "source": [
    "From the above output it is clear that there is no object type mismatch in any column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "08ab928ac5ab9580773decf399813209b5ff89a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            0\n",
       "LIMIT_BAL                     0\n",
       "SEX                           0\n",
       "EDUCATION                     0\n",
       "MARRIAGE                      0\n",
       "AGE                           0\n",
       "PAY_0                         0\n",
       "PAY_2                         0\n",
       "PAY_3                         0\n",
       "PAY_4                         0\n",
       "PAY_5                         0\n",
       "PAY_6                         0\n",
       "BILL_AMT1                     0\n",
       "BILL_AMT2                     0\n",
       "BILL_AMT3                     0\n",
       "BILL_AMT4                     0\n",
       "BILL_AMT5                     0\n",
       "BILL_AMT6                     0\n",
       "PAY_AMT1                      0\n",
       "PAY_AMT2                      0\n",
       "PAY_AMT3                      0\n",
       "PAY_AMT4                      0\n",
       "PAY_AMT5                      0\n",
       "PAY_AMT6                      0\n",
       "default.payment.next.month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of Null entries in the data columnwise.\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6489c10d5fa3f8a7fa874db2f4d71501a8bec96b"
   },
   "source": [
    "**STEP 3. Data Visualization & Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "829e802479a9c3c3872dee6ea97ef24ce970e6f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the number of counts of defaulters and non defaulters sexwise\n",
    "g=sns.countplot(x=\"SEX\", data=dataset,hue=\"default.payment.next.month\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "244efbfc628d926438f8ec2b3f7077d110c5deb0"
   },
   "source": [
    "It is evident from the above output that females have overall less default payments wrt males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "c0869ac842763b3f6f6df154084b7adbcc51d81f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.countplot(x=\"MARRIAGE\", data=dataset,hue=\"default.payment.next.month\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "78842adb5d81352c8ec5a26c591d9c8ffd37a31c"
   },
   "source": [
    "From the above plot it is clear that those people who have marital status single have less default payment wrt married status people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "5b249152180c6bb93b2c36b19f950cc42b17e742"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142deeeb8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='default.payment.next.month',y='AGE',data=dataset,palette='Set2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "fc720bffec89d9184f281c62ce887370e243a3cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143340780>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='default.payment.next.month',hue='MARRIAGE', y='AGE',data=dataset,palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "d1df49eeeb000e06a0b7ec19ca27898aea26cf6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x14341c390>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1035.05x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dataset, hue = 'default.payment.next.month', vars = ['AGE', 'MARRIAGE', 'SEX', 'EDUCATION', 'LIMIT_BAL'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "118c0ec2741659c79d114d827ed0e44711063d83"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x142751908>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='LIMIT_BAL',y='AGE',data=dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "34e5de850e268c3e72d6686a1149b5bbcab096b8"
   },
   "source": [
    "**Distribution of Male and Female according to their age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "fb2192e3b711110e365c7503951235da0c6b698d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14399ba58>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=dataset,col='SEX')\n",
    "g.map(plt.hist,'AGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "7dc56ae254d4132f6c12bf1bde0611d79339e3cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143a2ec50>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['LIMIT_BAL'].plot.density(lw=5,ls='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "eaff94037472c7cea1c0eb427a04d842da3bf49c"
   },
   "source": [
    "**STEP 4. Finding Correlation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "f2b845ff70407285aa5c98c4e150dc8a20290fb6"
   },
   "outputs": [],
   "source": [
    "X = dataset.drop(['default.payment.next.month'],axis=1)\n",
    "y = dataset['default.payment.next.month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "7b9725bb35bb31b659b3d89dabc9bf59680b09af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143b07400>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 29305 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 24449 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20540 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 19982 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26631 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 31614 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20043 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 30340 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 30456 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20851 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 31995 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 29305 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 24449 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20540 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 19982 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26631 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 31614 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20043 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 30340 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 30456 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20851 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 31995 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.corrwith(dataset['default.payment.next.month']).plot.bar(\n",
    "        figsize = (20, 10), title = \"特征值与标签值（Default）之间的相关系数\", fontsize = 20,\n",
    "        rot = 90, grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3aaea3be517d488e4a57754315a99398b9cd73c6"
   },
   "source": [
    "It seems from the above graph is that most negatively correlated feature is LIMIT_BAL but we cannot blindly remove this feature because according to me it is very important feature for prediction. ID is unimportant and it has no role in prediction so we will remove it later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "a3952cf1ed3f36ef07ac80453425469eced3b412"
   },
   "outputs": [],
   "source": [
    "dataset2 = dataset.drop(columns = ['default.payment.next.month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "194afecf587b80d0f3a901560a268b148e410ee2"
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"white\")\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = dataset2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "33008b7162f52862c577cae5d0d445ca80e080f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13e5a1be0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(18, 15))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(250, 15, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e0114096280c711fff936cb092e857ce46e05cbc"
   },
   "source": [
    "**STEP 5 : SPLITTING DATA INTO TRAINING AND TESTING SET** <br>\n",
    "\n",
    "The training dataset and test dataset must be similar, usually have the same predictors or variables. They differ on the observations and specific values in the variables. If you fit the model on the training dataset, then you implicitly minimize error or find correct responses. The fitted model provides a good prediction on the training dataset. Then you test the model on the test dataset. If the model predicts good also on the test dataset, you have more confidence. You have more confidence since the test dataset is similar to the training dataset, but not the same nor seen by the model. It means the model transfers prediction or learning in real sense.\n",
    "\n",
    "So,by splitting dataset into training and testing subset, we can efficiently measure our trained model since it never sees testing data before.Thus it's possible to prevent overfitting.\n",
    "\n",
    "I am just splitting dataset into 20% of test data and remaining 80% will used for training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "dcfae5267b8ad2ab131bcdb993fc985ef1aed2c0"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "65150d860171a93f97557abc2b4975a57d63b950"
   },
   "source": [
    "**STEP 6: Normalizing the data : Feature Scaling** <br>\n",
    "\n",
    "Feature scaling through standardization (or Z-score normalization) can be an important preprocessing step for many machine learning algorithms. Standardization involves rescaling the features such that they have the properties of a standard normal distribution with a mean of zero and a standard deviation of one.\n",
    "\n",
    "While many algorithms (such as SVM, K-nearest neighbors, and logistic regression) require features to be normalized, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "79804d884b3771817c0de6e635117b04041dff3e"
   },
   "outputs": [],
   "source": [
    "min_train = X_train.min()\n",
    "range_train = (X_train - min_train).max()\n",
    "X_train_scaled = (X_train - min_train)/range_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "0dedf8af91d4d6eb78c6f8d0bbbf9f705f1fc051"
   },
   "outputs": [],
   "source": [
    "min_test = X_test.min()\n",
    "range_test = (X_test - min_test).max()\n",
    "X_test_scaled = (X_test - min_test)/range_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c45ad81f6ee1f7d6c5bc7393acac5afe9866ad67"
   },
   "source": [
    "**STEP 7: Applying Machine Learning Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "e80a90da3aacc28f26bcc60afeb305410387d903"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble  import AdaBoostClassifier\n",
    "adaboost =AdaBoostClassifier()\n",
    "\n",
    "start = time()\n",
    "adaboost.fit(X_train_scaled, y_train)\n",
    "end = time()\n",
    "train_time_ada=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "bd2fcd4c1b24463c2bc3a2b43fb7a897ba67b01e"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.696517</td>\n",
       "      <td>0.323824</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>0.642456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model  Accuracy  Precision    Recall  F1 Score       ROC\n",
       "0  Adaboost  0.823333   0.696517  0.323824  0.442105  0.642456"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = adaboost.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "results = pd.DataFrame([['Adaboost', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "e288c030e6ed056a487fe56e5dbe312918fae1d9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nic/Library/Python/3.7/lib/python/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:12:15] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_classifier = XGBClassifier()\n",
    "start = time()\n",
    "\n",
    "xgb_classifier.fit(X_train_scaled, y_train,verbose=True)\n",
    "end=time()\n",
    "train_time_xgb=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "112761889cde92cbb6e4d5ec6d3ee6021f5014d0"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>ROC</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.696517</td>\n",
       "      <td>0.642456</td>\n",
       "      <td>0.323824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.791167</td>\n",
       "      <td>0.248350</td>\n",
       "      <td>XGboost</td>\n",
       "      <td>0.559459</td>\n",
       "      <td>0.562470</td>\n",
       "      <td>0.159599</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Accuracy  F1 Score     Model  Precision       ROC    Recall\n",
       "0  0.823333  0.442105  Adaboost   0.696517  0.642456  0.323824\n",
       "0  0.791167  0.248350   XGboost   0.559459  0.562470  0.159599"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = xgb_classifier.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "model_results = pd.DataFrame([['XGboost', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results,sort=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Python/3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lg = LogisticRegression(C=1.0)  # 默认使用L2正则化避免过拟合，C=1.0表示正则力度(超参数，可以调参调优)\n",
    "lg.fit(X_train_scaled, y_train)\n",
    "end=time()\n",
    "train_time_sgd=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>ROC</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.696517</td>\n",
       "      <td>0.642456</td>\n",
       "      <td>0.323824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.791167</td>\n",
       "      <td>0.248350</td>\n",
       "      <td>XGboost</td>\n",
       "      <td>0.559459</td>\n",
       "      <td>0.562470</td>\n",
       "      <td>0.159599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.821167</td>\n",
       "      <td>0.406198</td>\n",
       "      <td>LR</td>\n",
       "      <td>0.719608</td>\n",
       "      <td>0.626277</td>\n",
       "      <td>0.282961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy  F1 Score     Model  Precision       ROC    Recall\n",
       "0  0.823333  0.442105  Adaboost   0.696517  0.642456  0.323824\n",
       "0  0.791167  0.248350   XGboost   0.559459  0.562470  0.159599\n",
       "0  0.821167  0.406198        LR   0.719608  0.626277  0.282961"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lg.predict(X_test_scaled)\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "model_results = pd.DataFrame([['LR', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results,sort=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>15000.500000</td>\n",
       "      <td>8660.398374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7500.75</td>\n",
       "      <td>15000.5</td>\n",
       "      <td>22500.25</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>167484.322667</td>\n",
       "      <td>129747.661567</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>240000.00</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARRIAGE</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>34.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_0</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_2</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_5</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_6</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.291100</td>\n",
       "      <td>1.149988</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>51223.330900</td>\n",
       "      <td>73635.860576</td>\n",
       "      <td>-165580.0</td>\n",
       "      <td>3558.75</td>\n",
       "      <td>22381.5</td>\n",
       "      <td>67091.00</td>\n",
       "      <td>964511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>49179.075167</td>\n",
       "      <td>71173.768783</td>\n",
       "      <td>-69777.0</td>\n",
       "      <td>2984.75</td>\n",
       "      <td>21200.0</td>\n",
       "      <td>64006.25</td>\n",
       "      <td>983931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>47013.154800</td>\n",
       "      <td>69349.387427</td>\n",
       "      <td>-157264.0</td>\n",
       "      <td>2666.25</td>\n",
       "      <td>20088.5</td>\n",
       "      <td>60164.75</td>\n",
       "      <td>1664089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>-170000.0</td>\n",
       "      <td>2326.75</td>\n",
       "      <td>19052.0</td>\n",
       "      <td>54506.00</td>\n",
       "      <td>891586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>-81334.0</td>\n",
       "      <td>1763.00</td>\n",
       "      <td>18104.5</td>\n",
       "      <td>50190.50</td>\n",
       "      <td>927171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>-339603.0</td>\n",
       "      <td>1256.00</td>\n",
       "      <td>17071.0</td>\n",
       "      <td>49198.25</td>\n",
       "      <td>961664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>5006.00</td>\n",
       "      <td>873552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5921.163500</td>\n",
       "      <td>23040.870402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>833.00</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>1684259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5225.681500</td>\n",
       "      <td>17606.961470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>390.00</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>4505.00</td>\n",
       "      <td>896040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>296.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>4013.25</td>\n",
       "      <td>621000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.50</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>4031.50</td>\n",
       "      <td>426529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.75</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>528666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default.payment.next.month</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.221200</td>\n",
       "      <td>0.415062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count           mean            std       min  \\\n",
       "ID                          30000.0   15000.500000    8660.398374       1.0   \n",
       "LIMIT_BAL                   30000.0  167484.322667  129747.661567   10000.0   \n",
       "SEX                         30000.0       1.603733       0.489129       1.0   \n",
       "EDUCATION                   30000.0       1.853133       0.790349       0.0   \n",
       "MARRIAGE                    30000.0       1.551867       0.521970       0.0   \n",
       "AGE                         30000.0      35.485500       9.217904      21.0   \n",
       "PAY_0                       30000.0      -0.016700       1.123802      -2.0   \n",
       "PAY_2                       30000.0      -0.133767       1.197186      -2.0   \n",
       "PAY_3                       30000.0      -0.166200       1.196868      -2.0   \n",
       "PAY_4                       30000.0      -0.220667       1.169139      -2.0   \n",
       "PAY_5                       30000.0      -0.266200       1.133187      -2.0   \n",
       "PAY_6                       30000.0      -0.291100       1.149988      -2.0   \n",
       "BILL_AMT1                   30000.0   51223.330900   73635.860576 -165580.0   \n",
       "BILL_AMT2                   30000.0   49179.075167   71173.768783  -69777.0   \n",
       "BILL_AMT3                   30000.0   47013.154800   69349.387427 -157264.0   \n",
       "BILL_AMT4                   30000.0   43262.948967   64332.856134 -170000.0   \n",
       "BILL_AMT5                   30000.0   40311.400967   60797.155770  -81334.0   \n",
       "BILL_AMT6                   30000.0   38871.760400   59554.107537 -339603.0   \n",
       "PAY_AMT1                    30000.0    5663.580500   16563.280354       0.0   \n",
       "PAY_AMT2                    30000.0    5921.163500   23040.870402       0.0   \n",
       "PAY_AMT3                    30000.0    5225.681500   17606.961470       0.0   \n",
       "PAY_AMT4                    30000.0    4826.076867   15666.159744       0.0   \n",
       "PAY_AMT5                    30000.0    4799.387633   15278.305679       0.0   \n",
       "PAY_AMT6                    30000.0    5215.502567   17777.465775       0.0   \n",
       "default.payment.next.month  30000.0       0.221200       0.415062       0.0   \n",
       "\n",
       "                                 25%       50%        75%        max  \n",
       "ID                           7500.75   15000.5   22500.25    30000.0  \n",
       "LIMIT_BAL                   50000.00  140000.0  240000.00  1000000.0  \n",
       "SEX                             1.00       2.0       2.00        2.0  \n",
       "EDUCATION                       1.00       2.0       2.00        6.0  \n",
       "MARRIAGE                        1.00       2.0       2.00        3.0  \n",
       "AGE                            28.00      34.0      41.00       79.0  \n",
       "PAY_0                          -1.00       0.0       0.00        8.0  \n",
       "PAY_2                          -1.00       0.0       0.00        8.0  \n",
       "PAY_3                          -1.00       0.0       0.00        8.0  \n",
       "PAY_4                          -1.00       0.0       0.00        8.0  \n",
       "PAY_5                          -1.00       0.0       0.00        8.0  \n",
       "PAY_6                          -1.00       0.0       0.00        8.0  \n",
       "BILL_AMT1                    3558.75   22381.5   67091.00   964511.0  \n",
       "BILL_AMT2                    2984.75   21200.0   64006.25   983931.0  \n",
       "BILL_AMT3                    2666.25   20088.5   60164.75  1664089.0  \n",
       "BILL_AMT4                    2326.75   19052.0   54506.00   891586.0  \n",
       "BILL_AMT5                    1763.00   18104.5   50190.50   927171.0  \n",
       "BILL_AMT6                    1256.00   17071.0   49198.25   961664.0  \n",
       "PAY_AMT1                     1000.00    2100.0    5006.00   873552.0  \n",
       "PAY_AMT2                      833.00    2009.0    5000.00  1684259.0  \n",
       "PAY_AMT3                      390.00    1800.0    4505.00   896040.0  \n",
       "PAY_AMT4                      296.00    1500.0    4013.25   621000.0  \n",
       "PAY_AMT5                      252.50    1500.0    4031.50   426529.0  \n",
       "PAY_AMT6                      117.75    1500.0    4000.00   528666.0  \n",
       "default.payment.next.month      0.00       0.0       0.00        1.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_desc = dataset.describe().T\n",
    "# dataset_desc.to_excel('/Users/nic/Documents/学校论文/代码/datasets/dataset_desc.xlsx', index=False)\n",
    "dataset_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "d6c07602adbd55c3adf8976f065eb6fb670dc6ee"
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "sgd = linear_model.SGDClassifier(max_iter=1000)\n",
    "start = time()\n",
    "sgd.fit(X_train_scaled, y_train)\n",
    "end=time()\n",
    "train_time_sgd=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "5abd6f8b88744c27909e849ec6e54b62739a07c3"
   },
   "outputs": [],
   "source": [
    "y_pred = sgd.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "model_results = pd.DataFrame([['SGD 1000 iter', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results,sort=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "3886781964c2e3c64e69bb641d3c98fbc3c082f1"
   },
   "outputs": [],
   "source": [
    "from sklearn  import ensemble\n",
    "gboost =ensemble.GradientBoostingClassifier()\n",
    "start = time()\n",
    "gboost.fit(X_train_scaled, y_train)\n",
    "end=time()\n",
    "train_time_g=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "042bea48b22050580b4bacbeecd3502d24d8f02a"
   },
   "outputs": [],
   "source": [
    "y_pred = gboost.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "model_results = pd.DataFrame([['Gboost', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results,sort=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "edf8cfe6fae89418d9f522a333ff6005226dd9bd"
   },
   "source": [
    "**Applying Random Forest with 100 trees and criterion entropy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "7098ae734d8d83621b6458f3d4d81417a8d071a3"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(random_state = 47, \n",
    "                                    criterion = 'entropy',n_estimators=100)\n",
    "start = time()\n",
    "classifier.fit(X_train_scaled, y_train)\n",
    "end=time()\n",
    "train_time_r100=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "937695136fea2c18ee95cf8c4467d82ca49b631d"
   },
   "outputs": [],
   "source": [
    "y_pred_r = classifier.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_r)\n",
    "acc = accuracy_score(y_test, y_pred_r)\n",
    "prec = precision_score(y_test, y_pred_r)\n",
    "rec = recall_score(y_test, y_pred_r)\n",
    "f1 = f1_score(y_test, y_pred_r)\n",
    "\n",
    "model_results = pd.DataFrame([['Random_forest_ent100 ', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "8ee36611ef8e6925f9af50e269860b552aa73535"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "\n",
    "svc_model = SVC(kernel='rbf', gamma=0.1,C=100)\n",
    "\n",
    "start = time()\n",
    "svc_model.fit(X_train_scaled, y_train)\n",
    "end=time()\n",
    "train_time_svc=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "643da88b19698c4861004be3be3a923bd42c1db8"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "y_pred_svc = svc_model.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_svc)\n",
    "acc = accuracy_score(y_test, y_pred_svc)\n",
    "prec = precision_score(y_test, y_pred_svc)\n",
    "rec = recall_score(y_test, y_pred_svc)\n",
    "f1 = f1_score(y_test, y_pred_svc)\n",
    "\n",
    "model_results = pd.DataFrame([['SVC ', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "40fae109bffa5a940e07c0177adcdae4ac1ee0b4"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 7)\n",
    "\n",
    "start = time()\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "end=time()\n",
    "train_time_knn=end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "1012c1cb16318c74cd11579f8d9bbb1b3e3aad25"
   },
   "outputs": [],
   "source": [
    "y_pred_g = knn.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_g)\n",
    "acc = accuracy_score(y_test, y_pred_g)\n",
    "prec = precision_score(y_test, y_pred_g)\n",
    "rec = recall_score(y_test, y_pred_g)\n",
    "f1 = f1_score(y_test, y_pred_g)\n",
    "\n",
    "model_results = pd.DataFrame([['KNN 7', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "06ed5deec0a5c4e748067f50f37015e7b15d8124"
   },
   "source": [
    "**STEP 8 : ANALYZING AND COMPARING  TRAINING  TIME OF MACHINE LEARNING MODELS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "cd323c93aa9e78486355eb2b098ae396f2701db9"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "model = ['Adaboost','XGBoost','SGD', 'SVC', 'GBOOST', 'Random forest', 'KNN7']\n",
    "Train_Time = [\n",
    "    train_time_ada,\n",
    "    train_time_xgb,\n",
    "    train_time_sgd,\n",
    "    train_time_svc,\n",
    "    train_time_g,\n",
    "    train_time_r100,\n",
    "    \n",
    "    train_time_knn\n",
    "]\n",
    "index = np.arange(len(model))\n",
    "plt.bar(index, Train_Time)\n",
    "plt.xlabel('Machine Learning Models', fontsize=15)\n",
    "plt.ylabel('Training Time', fontsize=15)\n",
    "plt.xticks(index, model, fontsize=8, )\n",
    "plt.title('Comparison of Training Time of all ML models')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fc906d4b1aac45f6fdf68ffa0e95ee372caf134b"
   },
   "source": [
    "As from the above graph it is evident that Adaboost and XGboost have taken very less time to train in comparison to other models where as SVC has taken maximum time the reason may be we have passed some crucial parameters to SVC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bdb5c96345943d7d182e11d09bcc0241345bd9a7"
   },
   "source": [
    "**STEP 9. Model Optimization**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fce12419123f805aaa2509d33b09fa45a69e8073"
   },
   "source": [
    "Random search outperformed grid search on this dataset across every number of iterations. Also random search seemed to converge to an optimum more quickly than grid search, which means random search with fewer iterations is comparable to grid search with more iterations.\n",
    "\n",
    "In highdimensional parameter space, grid search would perform worse with the same iterations because points become more sparse. Also it is common that one of the hyperparameters is unimportant to finding the optimal hyperparameters, in which case grid search wastes a lot of iterations where as random search does not waste any iteration.\n",
    "\n",
    "Now we will optimize models accuracy  using Randomsearch cv.As shown in above table Adaboost performs best in this dataset. So we will try to further optimize adaboost and SVC by fine tuning its hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f46389e3598f5e4b8145a6f9581dedf69d6e3341"
   },
   "source": [
    "**Parameter Tuning using random Searchcv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "029dc2a79f941ac325dbae7c24d8b19ed6c36887"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV, cross_val_score\n",
    "param_dist = {\n",
    "      'n_estimators': [10,20,50,100,120,150,200],  \n",
    "    'random_state':[47],\n",
    "        'learning_rate':[0.1,0.01,0.001,0.0001]}\n",
    "\n",
    "# run randomized search\n",
    "n_iter_search =20\n",
    "random_search = RandomizedSearchCV(adaboost, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search, cv=5)\n",
    "\n",
    "\n",
    "random_search.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "43d13f5cf0355da0e3a9aee8814f47212159a898"
   },
   "source": [
    "Now, lets see what are the best parameters of adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "164c5acf779fd54d840d38f1cc1ed42981101ada"
   },
   "outputs": [],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "221532617c5096c472435fe6ef07aeda4f90e086"
   },
   "outputs": [],
   "source": [
    "y_pred_ada = random_search.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_ada)\n",
    "acc = accuracy_score(y_test, y_pred_ada)\n",
    "prec = precision_score(y_test, y_pred_ada)\n",
    "rec = recall_score(y_test, y_pred_ada)\n",
    "f1 = f1_score(y_test, y_pred_ada)\n",
    "\n",
    "results_tuned = pd.DataFrame([['Adaboost Tuned', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "4f6f7a2d3c15febf9145155be2979f0b95fc922a"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  RandomizedSearchCV, cross_val_score\n",
    "param_dist ={'n_estimators': [50,100,150,200], 'max_depth': [3,5,7,10], 'min_child_weight': [2,3,4,5]} \n",
    "\n",
    "# run randomized search\n",
    "n_iter_search =10\n",
    "random_search = RandomizedSearchCV(xgb_classifier, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search, cv=5)\n",
    "\n",
    "\n",
    "random_search.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "34daf781505a600247a2f179119ef80389b1b886"
   },
   "outputs": [],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "0c0f4913cc5f47160bf503ace85af04f34421ac4"
   },
   "outputs": [],
   "source": [
    "y_pred_xgb = random_search.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_xgb)\n",
    "acc = accuracy_score(y_test, y_pred_xgb)\n",
    "prec = precision_score(y_test, y_pred_xgb)\n",
    "rec = recall_score(y_test, y_pred_xgb)\n",
    "f1 = f1_score(y_test, y_pred_xgb)\n",
    "\n",
    "model =  pd.DataFrame([['XGBoost Tuned', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results_tuned = results_tuned.append(model, ignore_index = True)\n",
    "results_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "cee86b7990009aa71b2da8d19ca77a81937e0f33"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  RandomizedSearchCV, cross_val_score\n",
    "param_dist ={'alpha': [1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3], # learning rate\n",
    "    'n_iter': [1000], # number of epochs\n",
    "    'loss': ['log'], # logistic regression,\n",
    "    'penalty': ['l2'],\n",
    "    'n_jobs': [-1]} \n",
    "\n",
    "# run randomized search\n",
    "n_iter_search =8\n",
    "random_search = RandomizedSearchCV(sgd, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search, cv=5)\n",
    "\n",
    "\n",
    "random_search.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "b5504cb53d8012a1358754e0196e8358c1b3585d"
   },
   "outputs": [],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "eeab5541a767fd04b5aa4540ad17f1011a770fb4"
   },
   "outputs": [],
   "source": [
    "y_pred_sgd = random_search.predict(X_test_scaled)\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
    "roc=roc_auc_score(y_test, y_pred_sgd)\n",
    "acc = accuracy_score(y_test, y_pred_sgd)\n",
    "prec = precision_score(y_test, y_pred_sgd)\n",
    "rec = recall_score(y_test, y_pred_sgd)\n",
    "f1 = f1_score(y_test, y_pred_sgd)\n",
    "\n",
    "model_results = pd.DataFrame([['SGD Tuned', acc,prec,rec, f1,roc]],\n",
    "               columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results_tuned = results_tuned.append(model_results, ignore_index = True)\n",
    "results_tuned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "beca3cbf73fb253d891bacdb34f64ccfc0fdff21"
   },
   "source": [
    "That's great all the metrics parameters accuracy, F1 score Precision, ROC, Recall iof the  three  models adaboost,XGBoost and SGD are optimized now. Further we can also try some other combination of parameters to see if there will be further improvement or not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6654602612f45a996c91d86ce7338a3ba77885aa"
   },
   "source": [
    "**Plotting of ROC Curve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "ba7f68a73ccb6842bfa351a058ce63530f1f2090"
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble  import AdaBoostClassifier,GradientBoostingClassifier,RandomForestClassifier\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Add the models to the list that you want to view on the ROC plot\n",
    "models = [\n",
    "{\n",
    "    'label': 'Adaboost',\n",
    "    'model': AdaBoostClassifier(random_state=47,n_estimators=120,learning_rate=0.01),\n",
    "},\n",
    "{\n",
    "    'label': 'Gradient Boosting',\n",
    "    'model': GradientBoostingClassifier(),\n",
    "},\n",
    "    {\n",
    "    'label': 'XGBoost',\n",
    "    'model': XGBClassifier(),\n",
    "},\n",
    "    {\n",
    "    'label': 'SGD',\n",
    "    'model': SGDClassifier(max_iter=1000,penalty= 'l2', n_jobs= -1, loss= 'log', alpha=0.0001) ,\n",
    "},\n",
    "    \n",
    "    {\n",
    "    'label': 'KNN',\n",
    "    'model': KNeighborsClassifier(n_neighbors = 5),\n",
    "},\n",
    "    {\n",
    "    'label': 'Randomforest',\n",
    "    'model': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
    "            max_depth=3, max_features=10, max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=10,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=1,\n",
    "            oob_score=False, random_state=47, verbose=0, warm_start=False),        \n",
    "    }\n",
    "]\n",
    "\n",
    "# Below for loop iterates through your models list\n",
    "for m in models:\n",
    "    model = m['model'] # select the model\n",
    "    model.fit(X_train_scaled, y_train) # train the model\n",
    "    y_pred=model.predict(X_test_scaled) # predict the test data\n",
    "# Compute False postive rate, and True positive rate\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, model.predict_proba(X_test_scaled)[:,1])\n",
    "# Calculate Area under the curve to display on the plot\n",
    "    auc = metrics.roc_auc_score(y_test,model.predict(X_test_scaled))\n",
    "# Now, plot the computed values\n",
    "    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (m['label'], auc))\n",
    "# Custom settings for the plot \n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('1-Specificity(False Positive Rate)')\n",
    "plt.ylabel('Sensitivity(True Positive Rate)')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "0f6113e93538f730ebfc8e42b89fa98f352ab492"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import validation_curve\n",
    "# Create range of values for parameter\n",
    "param_range = np.arange(1, 250, 2)\n",
    "# Calculate accuracy on training and test set using range of parameter values\n",
    "train_scores, test_scores = validation_curve(AdaBoostClassifier(), \n",
    "                                             X_train_scaled, \n",
    "                                             y_train, \n",
    "                                             param_name=\"n_estimators\", \n",
    "                                             param_range=param_range,\n",
    "                                             cv=3, \n",
    "                                             scoring=\"accuracy\", \n",
    "                                             n_jobs=-1)\n",
    "\n",
    "# Calculate mean and standard deviation for training set scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "# Calculate mean and standard deviation for test set scores\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot mean accuracy scores for training and test sets\n",
    "plt.plot(param_range, train_mean, label=\"Training score\", color=\"black\")\n",
    "plt.plot(param_range, test_mean, label=\"Cross-validation score\", color=\"dimgrey\")\n",
    "\n",
    "# Plot accurancy bands for training and test sets\n",
    "plt.fill_between(param_range, train_mean - train_std, train_mean + train_std, color=\"gray\")\n",
    "plt.fill_between(param_range, test_mean - test_std, test_mean + test_std, color=\"gainsboro\")\n",
    "\n",
    "# Create plot\n",
    "plt.title(\"Validation Curve With ADABOOST\")\n",
    "plt.xlabel(\"Number Of Trees\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.tight_layout()\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "998076201d4fd612a2d40bdfabe4c7fa1c98305c"
   },
   "source": [
    "**Interpretation of the Validation Curve**\n",
    "\n",
    "if the number of trees are around 10, then the  model suffers from high bias. Two scores are quite close,but both the scores are too far from acceptable level so I think it's a high bias problem.In other words, the model is underfitting. \n",
    "\n",
    "At a maximun number of trees of 250, model suffers from high variance since training score is 0.82 but validation score is about 0.81.In other words, a model is overfitting. Again, the data points suggest a sort of graceful curve. However, our model uses a very complex curve to get as close to every data point as possible. Consequently, a model with high variance has very low bias because it makes little to no assumption about the data. In fact, it adapts too much to the data.\n",
    "\n",
    "As we see from the curve, max trees of around  30 to 40 best generalizes the unseen data. As max trees increases, bias becomes lower and variance becomes higher. We should keep the balance between the two. Just after  30 to 40 number of trees training score increase upwards and validation score starts to goes down, so I it begins to suffer from overfitting. So that's why any number of trees between 30 and 40  should be a good choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e7e4729113f9a8e8b56230e1950445da7ef58131"
   },
   "source": [
    "**Conclusion**\n",
    "\n",
    "So, we have seen that accuracy of tuned Adaboost is around 82.95% and also achieved decent  score of all other performance metric such as F1 score, Precision, ROC and Recall.\n",
    "\n",
    "Further we can also perform model optimization by using Randomsearch or Gridsearch to find the appropriate parameters to increase the accuracy of the models.\n",
    "\n",
    "I think all these three models if properly tuned will perform better.\n",
    "\n",
    "Apart from that, your valuable suggestions for further improvement and optimization are always welcome from my side do comment !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "28c6cbe271bb14c81427662654c720a7f5c0874f"
   },
   "outputs": [],
   "source": []
  }
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