Practice Project 16 (Student Intervention System).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Student Intervention System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Downloading DataSet (https://github.com/shubh2002/DataSets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal for this project is to identify students who might need early intervention before they fail to graduate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,roc_curve\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Student Intervention System.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>passed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  internet romantic  famrel  freetime  goout Dalc Walc health absences passed  \n",
       "0       no       no       4         3      4    1    1      3        6     no  \n",
       "1      yes       no       5         3      3    1    1      3        4     no  \n",
       "2      yes       no       4         3      2    2    3      3       10    yes  \n",
       "3      yes      yes       3         2      2    1    1      5        2    yes  \n",
       "4       no       no       4         3      2    1    2      5        4    yes  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{dtype('int64'): Index(['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel',\n",
       "        'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences'],\n",
       "       dtype='object'),\n",
       " dtype('O'): Index(['school', 'sex', 'address', 'famsize', 'Pstatus', 'Mjob', 'Fjob',\n",
       "        'reason', 'guardian', 'schoolsup', 'famsup', 'paid', 'activities',\n",
       "        'nursery', 'higher', 'internet', 'romantic', 'passed'],\n",
       "       dtype='object')}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.groupby(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_dtype_columns=df.columns[df.dtypes=='object']\n",
    "len(obj_dtype_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6192ec18>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f61ce83c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_heatmap=df.corr()\n",
    "plt.figure(figsize=(14,12))\n",
    "sns.heatmap(corr_heatmap,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GP    349\n",
      "MS     46\n",
      "Name: school, dtype: int64\n",
      "\n",
      "\n",
      "F    208\n",
      "M    187\n",
      "Name: sex, dtype: int64\n",
      "\n",
      "\n",
      "U    307\n",
      "R     88\n",
      "Name: address, dtype: int64\n",
      "\n",
      "\n",
      "GT3    281\n",
      "LE3    114\n",
      "Name: famsize, dtype: int64\n",
      "\n",
      "\n",
      "T    354\n",
      "A     41\n",
      "Name: Pstatus, dtype: int64\n",
      "\n",
      "\n",
      "other       141\n",
      "services    103\n",
      "at_home      59\n",
      "teacher      58\n",
      "health       34\n",
      "Name: Mjob, dtype: int64\n",
      "\n",
      "\n",
      "other       217\n",
      "services    111\n",
      "teacher      29\n",
      "at_home      20\n",
      "health       18\n",
      "Name: Fjob, dtype: int64\n",
      "\n",
      "\n",
      "course        145\n",
      "home          109\n",
      "reputation    105\n",
      "other          36\n",
      "Name: reason, dtype: int64\n",
      "\n",
      "\n",
      "mother    273\n",
      "father     90\n",
      "other      32\n",
      "Name: guardian, dtype: int64\n",
      "\n",
      "\n",
      "no     344\n",
      "yes     51\n",
      "Name: schoolsup, dtype: int64\n",
      "\n",
      "\n",
      "yes    242\n",
      "no     153\n",
      "Name: famsup, dtype: int64\n",
      "\n",
      "\n",
      "no     214\n",
      "yes    181\n",
      "Name: paid, dtype: int64\n",
      "\n",
      "\n",
      "yes    201\n",
      "no     194\n",
      "Name: activities, dtype: int64\n",
      "\n",
      "\n",
      "yes    314\n",
      "no      81\n",
      "Name: nursery, dtype: int64\n",
      "\n",
      "\n",
      "yes    375\n",
      "no      20\n",
      "Name: higher, dtype: int64\n",
      "\n",
      "\n",
      "yes    329\n",
      "no      66\n",
      "Name: internet, dtype: int64\n",
      "\n",
      "\n",
      "no     263\n",
      "yes    132\n",
      "Name: romantic, dtype: int64\n",
      "\n",
      "\n",
      "yes    265\n",
      "no     130\n",
      "Name: passed, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in obj_dtype_columns:\n",
    "    print(df[i].value_counts())\n",
    "    print('\\n')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(16,14))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>...</th>\n",
       "      <th>guardian_other</th>\n",
       "      <th>schoolsup_yes</th>\n",
       "      <th>famsup_yes</th>\n",
       "      <th>paid_yes</th>\n",
       "      <th>activities_yes</th>\n",
       "      <th>nursery_yes</th>\n",
       "      <th>higher_yes</th>\n",
       "      <th>internet_yes</th>\n",
       "      <th>romantic_yes</th>\n",
       "      <th>passed_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  Medu  Fedu  traveltime  studytime  failures  famrel  freetime  goout  \\\n",
       "0   18     4     4           2          2         0       4         3      4   \n",
       "1   17     1     1           1          2         0       5         3      3   \n",
       "2   15     1     1           1          2         3       4         3      2   \n",
       "3   15     4     2           1          3         0       3         2      2   \n",
       "4   16     3     3           1          2         0       4         3      2   \n",
       "\n",
       "   Dalc  ...  guardian_other  schoolsup_yes  famsup_yes  paid_yes  \\\n",
       "0     1  ...               0              1           0         0   \n",
       "1     1  ...               0              0           1         0   \n",
       "2     2  ...               0              1           0         1   \n",
       "3     1  ...               0              0           1         1   \n",
       "4     1  ...               0              0           1         1   \n",
       "\n",
       "   activities_yes  nursery_yes  higher_yes  internet_yes  romantic_yes  \\\n",
       "0               0            1           1             0             0   \n",
       "1               0            0           1             1             0   \n",
       "2               0            1           1             1             0   \n",
       "3               1            1           1             1             1   \n",
       "4               0            1           1             0             0   \n",
       "\n",
       "   passed_yes  \n",
       "0           0  \n",
       "1           0  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 39)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=14)\n",
    "scaling=MinMaxScaler()\n",
    "x_scaled=scaling.fit_transform(x)\n",
    "x_scaled=pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,-1]\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x_scaled,y,test_size=.20,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7468354430379747\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model1=LogisticRegression()\n",
    "model1.fit(x_train,y_train)\n",
    "y_pred=model1.predict(x_test)\n",
    "acc1=accuracy_score(y_test,y_pred)\n",
    "print(acc1)\n",
    "sns.heatmap(confusion_matrix(y_test,y_pred),annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": [
    "y_pred_prob=model1.predict_proba(x_test)[::,1]\n",
    "auc1=roc_auc_score(y_test,y_pred_prob)\n",
    "fpr,tpr,threshold=roc_curve(y_test,y_pred_prob)\n",
    "plt.plot(fpr,tpr,label='Logistic Regression auc='+str(auc1))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6962025316455697\n",
      "AxesSubplot(0.125,0.125;0.62x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model2=GaussianNB()\n",
    "model2.fit(x_train,y_train)\n",
    "y_pred=model2.predict(x_test)\n",
    "acc2=accuracy_score(y_test,y_pred)\n",
    "print(acc2)\n",
    "print(sns.heatmap(confusion_matrix(y_test,y_pred),annot=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAZc0lEQVR4nO3df3RU5b3v8ffXgKKFCJKo/DTYEiVAABs5taQ3qCBgFdRSCl1cOfdq8QeeWmtr6eptVU5dUrXHela5eChF1EUrYqsEF6f+xF/VWNIlempQFkQJEYRIuRGliJHv/WPGnPyePTCTyTz5vNbKWrP3frL395lJPnnyzJ69zd0REZHsd0ymCxARkdRQoIuIBEKBLiISCAW6iEggFOgiIoHokakD5+XleUFBQaYOLyKSlf76179+4O75bW3LWKAXFBRQWVmZqcOLiGQlM9ve3jZNuYiIBEKBLiISCAW6iEggFOgiIoFQoIuIBCJhoJvZCjPbY2Z/a2e7mdm/m9lWM3vDzM5KfZkiIpJIlBH6SmBqB9unAcPjX/OBpUdfloiIJCvheeju/oKZFXTQZAbwgMeuw1thZn3NbIC770pRjSIiR+13r9awdtN7mS4DgKKBudx88ciU7zcVc+iDgB1Nlmvj61oxs/lmVmlmlXV1dSk4tIhINGs3vUfVrg8zXUZapeKTotbGujbvmuHuy4BlACUlJbqzhoh0qqIBuay+6pxMl5E2qRih1wJDmiwPBnamYL8iIpKEVAR6OXB5/GyXrwD1mj8XEel8CadczOz3wEQgz8xqgZuBngDufi+wHrgQ2AocAP5XuooVEZH2RTnLZU6C7Q4sSFlFIiJyRPRJURGRQGTseugiIi2l81zxql0fUjQgNy377io0QheRLiOd54oXDchlxtg2PyITDI3QRaRLCf1c8XTSCF1EJBAaoYtIWiUzL94d5rnTSSN0EUmrZObFu8M8dzpphC4iaad58c6hQBcRnS4YCE25iIhOFwyERugiAmhaJAQaoYuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKfFBUJlC5b2/1ohC4SKF22tvvRCF0kYLo+S/eiEbqISCA0QhfJIpoXl45ohC6SRTQvLh3RCF0ky2heXNqjEbqISCAU6CIigVCgi4gEQoEuIhKISG+KmtlU4B4gB1ju7otbbB8K3A/0jbdZ6O7rU1yrSFZI5tTCZOlUROlIwhG6meUAS4BpQBEwx8yKWjT7P8DD7j4OmA3831QXKpItkjm1MFk6FVE6EmWEPh7Y6u7VAGb2EDADqGrSxoHPhw0nAjtTWaRIttGphZIJUebQBwE7mizXxtc1dQsw18xqgfXAv7S1IzObb2aVZlZZV1d3BOWKiEh7ogS6tbHOWyzPAVa6+2DgQuBBM2u1b3df5u4l7l6Sn5+ffLUiItKuKIFeCwxpsjyY1lMqVwAPA7j7K0AvIC8VBYqISDRRAn0jMNzMhpnZscTe9Cxv0aYGOB/AzEYQC3TNqYiIdKKEge7uDcB1wBPAZmJns7xpZovMbHq82Y3Ad8zsdeD3wD+7e8tpGRERSaNI56HHzylf32Ldz5o8rgImpLY0ERFJhj4pKiISCAW6iEggFOgiIoHQDS6k20rXNVd0vRXJFI3QpdtK1zVXdL0VyRSN0KVb0zVXJCQaoYuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoHQDS4kKMncVk63ipPQaIQuQUnmtnK6VZyERiN0CY5uKyfdlQJdOl0y0yLJ0jSKdGeacpFOl8y0SLI0jSLdmUbokhGaFhFJPY3QRUQCoUAXEQlEpEA3s6lm9raZbTWzhe20mWVmVWb2ppn9LrVliohIIgnn0M0sB1gCTAZqgY1mVu7uVU3aDAd+DExw931mdnK6ChYRkbZFGaGPB7a6e7W7HwIeAma0aPMdYIm77wNw9z2pLVNERBKJcpbLIGBHk+Va4J9atCkEMLM/AznALe7+p5Y7MrP5wHyAoUOHHkm90kXpI/cimRdlhG5trPMWyz2A4cBEYA6w3Mz6tvom92XuXuLuJfn5+cnWKl2YPnIvknlRRui1wJAmy4OBnW20qXD3T4F3zOxtYgG/MSVVSlbQueUimRVlhL4RGG5mw8zsWGA2UN6izWPAuQBmlkdsCqY6lYWKiEjHEo7Q3b3BzK4DniA2P77C3d80s0VApbuXx7ddYGZVwGfAD919bzoLl/RK9normhcXybxIH/139/XA+hbrftbksQPfj39JAD6fE48a0poXF8k8XctF2qU5cZHsoo/+i4gEQiP0LJeua4trTlwk+2iEnuXSdW1xzYmLZB+N0AOguW4RAY3QRUSCoUAXEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQlEpEA3s6lm9raZbTWzhR20m2lmbmYlqStRRESiSBjoZpYDLAGmAUXAHDMraqNdH+C7wKupLlJERBKLMkIfD2x192p3PwQ8BMxoo92/AncAB1NYn4iIRBQl0AcBO5os18bXNTKzccAQd3+8ox2Z2XwzqzSzyrq6uqSLFRGR9kUJdGtjnTduNDsGuBu4MdGO3H2Zu5e4e0l+fn70KkVEJKEeEdrUAkOaLA8GdjZZ7gOMAp4zM4BTgXIzm+7ulakqtDv53as1rN30XqS2Vbs+pGhAbporEpFsEGWEvhEYbmbDzOxYYDZQ/vlGd6939zx3L3D3AqACUJgfhbWb3qNq14eR2hYNyGXG2EGJG4pI8BKO0N29wcyuA54AcoAV7v6mmS0CKt29vOM9yJEoGpDL6qvOyXQZIpJFoky54O7rgfUt1v2snbYTj74sERFJVqRAl6OTzJw4aF5cRI6MPvrfCZKZEwfNi4vIkdEIvZNoTlxE0k2BfoR0aqGIdDWacjlCOrVQRLoajdCPgqZRRKQr0QhdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQOsulCZ1bLiLZTCP0JnRuuYhkM43QW9C55SKSrTRCFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAhHpBhdmNhW4B8gBlrv74hbbvw9cCTQAdcD/dvftKa71iOi2ciLSXSQcoZtZDrAEmAYUAXPMrKhFs9eAEncvBh4B7kh1oUdKt5UTke4iygh9PLDV3asBzOwhYAZQ9XkDd9/QpH0FMDeVRR4t3VZORLqDKHPog4AdTZZr4+vacwXwn21tMLP5ZlZpZpV1dXXRqxQRkYSiBLq1sc7bbGg2FygB7mxru7svc/cSdy/Jz8+PXqWIiCQUZcqlFhjSZHkwsLNlIzObBPwEKHP3T1JTnoiIRBVlhL4RGG5mw8zsWGA2UN60gZmNA/4DmO7ue1JfpoiIJJIw0N29AbgOeALYDDzs7m+a2SIzmx5vdifQG1hjZpvMrLyd3YmISJpEOg/d3dcD61us+1mTx5NSXJeIiCRJnxQVEQmEAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBCRTlvsSpK5HC7okrgi0n1k3Qg9mcvhgi6JKyLdR9aN0EGXwxURaUvWjdBFRKRtCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJRFZenEuS9+mnn1JbW8vBgwczXYqIRNCrVy8GDx5Mz549I3+PAr2bqK2tpU+fPhQUFGBmmS5HRDrg7uzdu5fa2lqGDRsW+fs05dJNHDx4kP79+yvMRbKAmdG/f/+k/6NWoHcjCnOR7HEkv68KdBGRQCjQpdOYGTfeeGPj8l133cUtt9zS4feUl5ezePHioz72ypUryc/PZ+zYsYwcOZKZM2dy4MCBo95vV/X3v/+dyZMnM3z4cCZPnsy+ffvabFdTU8MFF1zAiBEjKCoq4t133wXgmWee4ayzzmLs2LGUlpaydetWoPnzOHbsWJYvX964r6lTp9K3b18uuuiiZsfQvlrva9OmTZxzzjmMHDmS4uJiVq9e3ebrkzR3z8jXl7/8ZT8Ss+592Wfd+/IRfW93VlVVlekS/LjjjvOCggKvq6tzd/c777zTb7755k459n333ecLFixoXJ4zZ46vWLGiU46dCT/84Q/99ttvd3f322+/3W+66aY225WVlfmTTz7p7u779+/3jz/+2N3dhw8f3vgzs2TJEp83b567t34em3r66ae9vLzcv/71rzdbr3213tfbb7/tW7ZscXf39957z0899VTft29fq3Zt/d4Cld5Oruosl27o1nVvUrUz+o22oygamMvNF4/ssE2PHj2YP38+d999N7fddluzbevWrePnP/85hw4don///qxatYpTTjmFlStXUllZyW233caYMWOorq7mmGOO4cCBA5xxxhlUV1dTU1PDggULqKur44QTTuA3v/kNZ555Zrt1NDQ08PHHH9OvX792j52fn88ZZ5zByy+/TH5+PocPH6awsJCKigrcnauvvpqamhoAfvWrXzFhwgSef/55rr/+eiD238gLL7xAnz59mh37kksuYceOHRw8eJDrr7+e+fPnA9C7d28++ugjAB555BEef/xxVq5cye7du7n66quprq4GYOnSpXz1q19N+HqsXbuW5557DoB58+YxceJEfvGLXzRrU1VVRUNDA5MnT26s4XNmxocfxn5G6uvrGThwYMJjnn/++Y3HbEr7aq2wsLDx8cCBAzn55JOpq6ujb9++CWvoiAJdOtWCBQsoLi7mpptuara+tLSUiooKzIzly5dzxx138Mtf/rJx+4knnsiYMWN4/vnnOffcc1m3bh1TpkyhZ8+ezJ8/n3vvvZfhw4fz6quvcu211/Lss8+2Ovbq1at56aWX2LVrF4WFhVx88cUdHnvu3LmsWrWK733vezz99NOMGTOGvLw8vv3tb3PDDTdQWlpKTU0NU6ZMYfPmzdx1110sWbKECRMm8NFHH9GrV69WNaxYsYKTTjqJf/zjH5x99tl84xvfoH///u0+X9/97ncpKyvj0Ucf5bPPPmsM/a997Wvs37+/Vfu77rqLSZMmsXv3bgYMGADAgAED2LNnT6u2W7ZsoW/fvlx22WW88847TJo0icWLF5OTk8Py5cu58MILOf7448nNzaWioqLx+/7whz/wwgsvUFhYyN13382QIUParR/QvhLs6y9/+QuHDh3ii1/8YofHiKS9oXu6vzTl0rm6wpTLF77wBXd3/+lPf+qLFi1qNuXyxhtv+OTJk33UqFFeWFjoU6ZMcffm/7KuWrXKr7rqKnd3v+SSS/zJJ5/0/fv3e69evXzMmDGNX2eeeWarYzfdz+HDh/2aa65pnJJo79g1NTU+btw4d3f/1re+5evWrXN39/z8/GbHGzhwoH/44Yd+++23+/jx4/2ee+7xHTt2tPkc3HzzzV5cXOzFxcWem5vrr7zySrPnxt19zZo1jf+y5+Xl+cGDB5N+rk888cRmy3379m3VZs2aNZ6bm+vbtm3zTz/91C+77DJfvny5u7tfeumlXlFR4e7ud9xxh19xxRXu7v7BBx801rN06VI/99xzm+1zw4YNraYjtK/297Vz504vLCxs/DloKdkpl0jhC0wF3ga2Agvb2H4csDq+/VWgINE+FeidqysF+t69e/20007zW265pTHQy8rKfO3ate4e+4UpKytz9+ZBvH//fh86dKjv3bvXhwwZ4g0NDV5fX++nnnpqwmO3nMtcv369T5s2rcNju7tPnTrVn3nmGS8oKPCGhgZ3d+/fv78fOHCgzeO88cYbvnjxYh80aJBv3ry52bYNGzb4hAkTGuepy8rKfMOGDe7u3rt378Z2Dz74YMJALy0tbfZH5fOvp556yt3dCwsLfefOne7+36HR0iuvvNKsrw888IBfe+21vmfPHj/99NMb12/fvt1HjBjR6vsbGho8Nze3VR+bhp321f6+6uvrfdy4cf7www+3avu5ZAM94VkuZpYDLAGmAUXAHDMratHsCmCfu38JuBv4BSLtOOmkk5g1axa//e1vG9fV19czaNAgAO6///42v693796MHz+e66+/nosuuoicnBxyc3MZNmwYa9asAWIDlNdffz1hDS+99FLjv7gdHfvKK69k7ty5zJo1i5ycHAAuuOACfv3rXze22bRpEwDbtm1j9OjR/OhHP6KkpIS33nqr2b7q6+vp168fJ5xwAm+99Vazf8tPOeUUNm/ezOHDh3n00Ucb159//vksXboUgM8++6xxnvbFF19k06ZNrb4mTZoEwPTp0xv7cv/99zNjxoxWz8HZZ5/Nvn37qKurA+DZZ5+lqKiIfv36UV9fz5YtWwB46qmnGDFiBAC7du1q/P7y8vLG9e3Rvtre16FDh7j00ku5/PLL+eY3v9nh/pPSXtL7f4++zwGeaLL8Y+DHLdo8AZwTf9wD+ACwjvarEXrn6kojdHf3999/348//vjGEfpjjz3mw4YN89LSUv/BD37Q5gjdPTZNAPhzzz3XuK66utqnTJnixcXFPmLECL/11ltbHfu+++7zvLw8HzNmjI8ePdqnTZvmu3fv7vDY7u6HDh3yPn36NBtt19XV+axZs3z06NE+YsSIxmmg6667zkeOHOnFxcU+e/bsViPrgwcP+tSpU3306NE+c+bMZiP0NWvW+Omnn+5lZWW+YMGCxhH6+++/79OnT/dRo0b5mDFj/OWXo/3sf/DBB37eeef5l770JT/vvPN879697u6+cePGxukAd/cnn3zSR48e7aNGjfJ58+b5J5984u7uf/zjH33UqFFeXFzsZWVlvm3bNnd3X7hwoRcVFXlxcbFPnDix2fNSWlrqeXl53qtXLx80aJD/6U9/0r7a2deDDz7oPXr0aPbf1WuvvdbqdUx2hG6x7e0zs5nAVHe/Mr78P4F/cvfrmrT5W7xNbXx5W7zNBy32NR+YDzB06NAvb9++Pek/QLeuexMg4RkV0tzmzZsTjjSktcrKSm644QZefPHFTJci3VBbv7dm9ld3L2mrfZSzXNr6/GnLvwJR2uDuy4BlACUlJR3/JWmHglw6y+LFi1m6dCmrVq3KdCkikUT5pGgt0PQ8m8HAzvbamFkP4ETg76koUCRTFi5cyPbt2yktLc10KSKRRAn0jcBwMxtmZscCs4HyFm3KgXnxxzOBZz3RXI50Or0kItnjSH5fEwa6uzcA1xF743Mz8LC7v2lmi8xserzZb4H+ZrYV+D6wMOlKJK169erF3r17FeoiWcA9dj30tj6c1pGEb4qmS0lJiVdWVmbk2N2R7lgkkl3au2PR0b4pKgHo2bNnUnc+EZHso8vniogEQoEuIhIIBbqISCAy9qaomdUByX9UNCaP2OUFuhP1uXtQn7uHo+nzae6e39aGjAX60TCzyvbe5Q2V+tw9qM/dQ7r6rCkXEZFAKNBFRAKRrYG+LNMFZID63D2oz91DWvqclXPoIiLSWraO0EVEpAUFuohIILp0oJvZVDN728y2mlmrKzia2XFmtjq+/VUzK+j8KlMrQp+/b2ZVZvaGmT1jZqdlos5UStTnJu1mmpmbWdaf4halz2Y2K/5av2lmv+vsGlMtws/2UDPbYGavxX++L8xEnaliZivMbE/8jm5tbTcz+/f48/GGmZ111Adt7950mf4CcoBtwOnAscDrQFGLNtcC98YfzwZWZ7ruTujzucAJ8cfXdIc+x9v1AV4AKoCSTNfdCa/zcOA1oF98+eRM190JfV4GXBN/XAS8m+m6j7LP/wM4C/hbO9svBP6T2B3fvgK8erTH7Moj9PHAVnevdvdDwENAy1uXzwA+v037I8D5ZtbW7fCyRcI+u/sGdz8QX6wgdgepbBbldQb4V+AOIITr/0bp83eAJe6+D8Dd93RyjakWpc8O5MYfn0jrO6NlFXd/gY7v3DYDeMBjKoC+ZjbgaI7ZlQN9ELCjyXJtfF2bbTx2I456oH+nVJceUfrc1BXE/sJns4R9NrNxwBB3f7wzC0ujKK9zIVBoZn82swozm9pp1aVHlD7fAsw1s1pgPfAvnVNaxiT7+55QV74eespuTp1FIvfHzOYCJUBZWitKvw77bGbHAHcD/9xZBXWCKK9zD2LTLhOJ/Rf2opmNcvf/l+ba0iVKn+cAK939l2Z2DvBgvM+H019eRqQ8v7ryCL073pw6Sp8xs0nAT4Dp7v5JJ9WWLon63AcYBTxnZu8Sm2ssz/I3RqP+bK9190/d/R3gbWIBn62i9PkK4GEAd38F6EXsIlahivT7noyuHOjd8ebUCfscn374D2Jhnu3zqpCgz+5e7+557l7g7gXE3jeY7u7ZfP/CKD/bjxF7AxwzyyM2BVPdqVWmVpQ+1wDnA5jZCGKBXtepVXaucuDy+NkuXwHq3X3XUe0x0+8EJ3iX+EJgC7F3x38SX7eI2C80xF7wNcBW4C/A6ZmuuRP6/DSwG9gU/yrPdM3p7nOLts+R5We5RHydDfg3oAr4L2B2pmvuhD4XAX8mdgbMJuCCTNd8lP39PbAL+JTYaPwK4Grg6iav8ZL48/Ffqfi51kf/RUQC0ZWnXEREJAkKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRUQC8f8BjStE+LKFoBwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_prob=model2.predict_proba(x_test)[::,1]\n",
    "auc2=roc_auc_score(y_test,y_pred_prob)\n",
    "fpr,tpr,threshold=roc_curve(y_test,y_pred_prob)\n",
    "plt.plot(fpr,tpr,label='Naive Bayes auc='+str(auc2))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.620253164556962\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model3=DecisionTreeClassifier(criterion='gini')\n",
    "model3.fit(x_train,y_train)\n",
    "y_pred=model3.predict(x_test)\n",
    "acc3=accuracy_score(y_test,y_pred)\n",
    "print(acc3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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": [
    "y_pred_proba=model3.predict_proba(x_test)[::,1]\n",
    "auc3=roc_auc_score(y_test,y_pred_proba)\n",
    "fpr,tpr,threshold=roc_curve(y_test,y_pred_proba)\n",
    "plt.plot(fpr,tpr,label='Decision Tree Classifier auc='+str(auc3))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6708860759493671\n",
      "AxesSubplot(0.125,0.125;0.62x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model4=KNeighborsClassifier(n_neighbors=10)\n",
    "model4.fit(x_train,y_train)\n",
    "y_pred=model4.predict(x_test)\n",
    "acc4=accuracy_score(y_test,y_pred)\n",
    "print(acc4)\n",
    "print(sns.heatmap(confusion_matrix(y_test,y_pred),annot=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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": [
    "y_pred_proba=model4.predict_proba(x_test)[::,1]\n",
    "auc4=roc_auc_score(y_test,y_pred_proba)\n",
    "fpr,tpr,threshold=roc_curve(y_test,y_pred_proba)\n",
    "plt.plot(fpr,tpr,label='KNeighbor Classifier auc='+str(auc4))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6962025316455697\n",
      "AxesSubplot(0.125,0.125;0.62x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model5=RandomForestClassifier(n_estimators=300,criterion='gini')\n",
    "model5.fit(x_train,y_train)\n",
    "y_pred=model5.predict(x_test)\n",
    "acc5=accuracy_score(y_test,y_pred)\n",
    "print(acc5)\n",
    "print(sns.heatmap(confusion_matrix(y_test,y_pred),annot=True))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.569620253164557\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model6=AdaBoostClassifier()\n",
    "model6.fit(x_train,y_train)\n",
    "y_pred=model6.predict(x_test)\n",
    "acc6=accuracy_score(y_test,y_pred)\n",
    "print(acc6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7060897435897435\n",
      "0.6855769230769231\n",
      "0.63\n",
      "0.6783333333333333\n",
      "0.7086538461538462\n",
      "0.6277564102564103\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "models=[model1,model2,model3,model4,model5,model6]\n",
    "CV=[]\n",
    "for i in models:\n",
    "    score=cross_val_score(i,x_scaled,y,scoring='accuracy',cv=10)\n",
    "    score=score.mean()\n",
    "    print(score)\n",
    "    CV.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7341772151898734\n",
      "{'criterion': 'gini', 'max_depth': 4, 'max_features': 'auto', 'n_estimators': 100, 'n_jobs': 1, 'random_state': 40}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\"\"\"For Random Forest\"\"\"\n",
    "parameters=({'n_estimators':[50,100,150,200,250],'criterion':['gini','entropy'],'max_depth':[1,2,3,4,5,6],'max_features':\n",
    "            ['auto','sqrt','log2'],'n_jobs':[1,-1],'random_state':[0,10,15,30,35,40,42,45,50,53]})\n",
    "GSrfc=GridSearchCV(estimator=model5,param_grid=parameters,cv=10,scoring='accuracy',n_jobs=-1)\n",
    "GSrfc.fit(x_train,y_train)\n",
    "print(GSrfc.best_score_)\n",
    "print(GSrfc.best_params_)\n"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}