Adult Census.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>?</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   90         ?   77053       HS-grad              9        Widowed   \n",
       "1   82   Private  132870       HS-grad              9        Widowed   \n",
       "2   66         ?  186061  Some-college             10        Widowed   \n",
       "3   54   Private  140359       7th-8th              4       Divorced   \n",
       "4   41   Private  264663  Some-college             10      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0                  ?  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                  ?      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              40  United-States  <=50K  \n",
       "1          4356              18  United-States  <=50K  \n",
       "2          4356              40  United-States  <=50K  \n",
       "3          3900              40  United-States  <=50K  \n",
       "4          3900              40  United-States  <=50K  "
      ]
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     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py:1649: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  result = method(y)\n"
     ]
    }
   ],
   "source": [
    "data=data[(data!='?').all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30162 entries, 1 to 32560\n",
      "Data columns (total 15 columns):\n",
      "age               30162 non-null int64\n",
      "workclass         30162 non-null object\n",
      "fnlwgt            30162 non-null int64\n",
      "education         30162 non-null object\n",
      "education.num     30162 non-null int64\n",
      "marital.status    30162 non-null object\n",
      "occupation        30162 non-null object\n",
      "relationship      30162 non-null object\n",
      "race              30162 non-null object\n",
      "sex               30162 non-null object\n",
      "capital.gain      30162 non-null int64\n",
      "capital.loss      30162 non-null int64\n",
      "hours.per.week    30162 non-null int64\n",
      "native.country    30162 non-null object\n",
      "income            30162 non-null int64\n",
      "dtypes: int64(7), object(8)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['income']=data['income'].map({'<=50K':0,'>50K':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
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       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>216864</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>150601</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "1   82   Private  132870       HS-grad              9        Widowed   \n",
       "3   54   Private  140359       7th-8th              4       Divorced   \n",
       "4   41   Private  264663  Some-college             10      Separated   \n",
       "5   34   Private  216864       HS-grad              9       Divorced   \n",
       "6   38   Private  150601          10th              6      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "5      Other-service      Unmarried  White  Female             0   \n",
       "6       Adm-clerical      Unmarried  White    Male             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country  income  \n",
       "1          4356              18  United-States       0  \n",
       "3          3900              40  United-States       0  \n",
       "4          3900              40  United-States       0  \n",
       "5          3770              45  United-States       0  \n",
       "6          3770              40  United-States       0  "
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     "execution_count": 42,
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     "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.countplot(x='education.num',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16770ac12e8>"
      ]
     },
     "execution_count": 47,
     "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": [
    "data['education.num'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16770e8b208>"
      ]
     },
     "execution_count": 50,
     "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.barplot(x='education.num',y='income',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['native.country'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1677290c390>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "sns.barplot(x='marital.status',y='income',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['marital.status']=data['marital.status'].map({'Married-civ-spouse':'Married', 'Divorced':'Single', 'Never-married':'Single', 'Separated':'Single', \n",
    "'Widowed':'Single', 'Married-spouse-absent':'Married', 'Married-AF-spouse':'Married'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Single</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Single</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>216864</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Single</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>150601</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Single</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "1   82   Private  132870       HS-grad              9         Single   \n",
       "3   54   Private  140359       7th-8th              4         Single   \n",
       "4   41   Private  264663  Some-college             10         Single   \n",
       "5   34   Private  216864       HS-grad              9         Single   \n",
       "6   38   Private  150601          10th              6         Single   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "5      Other-service      Unmarried  White  Female             0   \n",
       "6       Adm-clerical      Unmarried  White    Male             0   \n",
       "\n",
       "   capital.loss  hours.per.week  income  \n",
       "1          4356              18       0  \n",
       "3          3900              40       0  \n",
       "4          3900              40       0  \n",
       "5          3770              45       0  \n",
       "6          3770              40       0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x167727e3e80>"
      ]
     },
     "execution_count": 63,
     "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(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in data:\n",
    "    if data.dtypes[col]==np.object:\n",
    "        data[col]=le.fit_transform(data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>132870</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>140359</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>264663</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>216864</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>150601</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age  workclass  fnlwgt  education  education.num  marital.status  \\\n",
       "1   82          2  132870         11              9               1   \n",
       "3   54          2  140359          5              4               1   \n",
       "4   41          2  264663         15             10               1   \n",
       "5   34          2  216864         11              9               1   \n",
       "6   38          2  150601          0              6               1   \n",
       "\n",
       "   occupation  relationship  race  sex  capital.gain  capital.loss  \\\n",
       "1           3             1     4    0             0          4356   \n",
       "3           6             4     4    0             0          3900   \n",
       "4           9             3     4    0             0          3900   \n",
       "5           7             4     4    0             0          3770   \n",
       "6           0             4     4    1             0          3770   \n",
       "\n",
       "   hours.per.week  income  \n",
       "1              18       0  \n",
       "3              40       0  \n",
       "4              40       0  \n",
       "5              45       0  \n",
       "6              40       0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop('education',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
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       "   age  workclass  fnlwgt  education.num  marital.status  occupation  \\\n",
       "1   82          2  132870              9               1           3   \n",
       "3   54          2  140359              4               1           6   \n",
       "4   41          2  264663             10               1           9   \n",
       "5   34          2  216864              9               1           7   \n",
       "6   38          2  150601              6               1           0   \n",
       "\n",
       "   relationship  race  sex  capital.gain  capital.loss  hours.per.week  income  \n",
       "1             1     4    0             0          4356              18       0  \n",
       "3             4     4    0             0          3900              40       0  \n",
       "4             3     4    0             0          3900              40       0  \n",
       "5             4     4    0             0          3770              45       0  \n",
       "6             4     4    1             0          3770              40       0  "
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   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop('relationship',axis=1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   age  workclass  fnlwgt  education.num  marital.status  occupation  race  \\\n",
       "1   82          2  132870              9               1           3     4   \n",
       "3   54          2  140359              4               1           6     4   \n",
       "4   41          2  264663             10               1           9     4   \n",
       "5   34          2  216864              9               1           7     4   \n",
       "6   38          2  150601              6               1           0     4   \n",
       "\n",
       "   sex  capital.gain  capital.loss  hours.per.week  income  \n",
       "1    0             0          4356              18       0  \n",
       "3    0             0          3900              40       0  \n",
       "4    0             0          3900              40       0  \n",
       "5    0             0          3770              45       0  \n",
       "6    1             0          3770              40       0  "
      ]
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16773901198>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['occupation','fnlwgt'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>4356</td>\n",
       "      <td>18</td>\n",
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       "      <th>3</th>\n",
       "      <td>54</td>\n",
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       "      <td>4</td>\n",
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       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
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       "      <td>4</td>\n",
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       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  education.num  marital.status  race  sex  capital.gain  \\\n",
       "1   82          2              9               1     4    0             0   \n",
       "3   54          2              4               1     4    0             0   \n",
       "4   41          2             10               1     4    0             0   \n",
       "5   34          2              9               1     4    0             0   \n",
       "6   38          2              6               1     4    1             0   \n",
       "\n",
       "   capital.loss  hours.per.week  income  \n",
       "1          4356              18       0  \n",
       "3          3900              40       0  \n",
       "4          3900              40       0  \n",
       "5          3770              45       0  \n",
       "6          3770              40       0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop('income',axis=1)\n",
    "y=data['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
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       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  education.num  marital.status  race  sex  capital.gain  \\\n",
       "1   82          2              9               1     4    0             0   \n",
       "3   54          2              4               1     4    0             0   \n",
       "4   41          2             10               1     4    0             0   \n",
       "5   34          2              9               1     4    0             0   \n",
       "6   38          2              6               1     4    1             0   \n",
       "\n",
       "   capital.loss  hours.per.week  \n",
       "1          4356              18  \n",
       "3          3900              40  \n",
       "4          3900              40  \n",
       "5          3770              45  \n",
       "6          3770              40  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.92      0.89      6754\n",
      "           1       0.71      0.59      0.64      2295\n",
      "\n",
      "   micro avg       0.83      0.83      0.83      9049\n",
      "   macro avg       0.79      0.75      0.77      9049\n",
      "weighted avg       0.83      0.83      0.83      9049\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=cross_val_score(rf,X_train,y_train,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8371622971494557"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC(C=1.0,gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=cross_val_score(model,X_train,y_train,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8279735961276773"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "lmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "score=cross_val_score(lmodel,X_train,y_train,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.78722999953"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid={'n_estimator':[20,50,100,200,150]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=RandomForestClassifier()\n",
    "grid=GridSearchCV(estimator=c,param_grid=param_grid,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid parameter n_estimator for estimator RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n            max_depth=None, max_features='auto', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n            oob_score=False, random_state=None, verbose=0,\n            warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-123-36063720ad87>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    720\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 722\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1189\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1191\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1192\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    709\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 711\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    712\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    915\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    757\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 759\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    760\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    761\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    180\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 182\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    183\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    547\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 549\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 225\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 225\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    512\u001b[0m     \u001b[0mtrain_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mparameters\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 514\u001b[1;33m         \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mset_params\u001b[1;34m(self, **params)\u001b[0m\n\u001b[0;32m    213\u001b[0m                                  \u001b[1;34m'Check the list of available parameters '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m                                  \u001b[1;34m'with `estimator.get_params().keys()`.'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 215\u001b[1;33m                                  (key, self))\n\u001b[0m\u001b[0;32m    216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdelim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Invalid parameter n_estimator for estimator RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n            max_depth=None, max_features='auto', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n            oob_score=False, random_state=None, verbose=0,\n            warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`."
     ]
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=RandomForestClassifier()\n",
    "\n",
    "max_features=np.array([1,2,3,4,5])\n",
    "n_estimators=np.array([25,50,100,150,200])\n",
    "min_samples_leaf=np.array([25,50,75,100])\n",
    "param_grid=dict(n_estimators=n_estimators,max_features=max_features,min_samples_leaf=min_samples_leaf)\n",
    "grid=GridSearchCV(estimator=clf,param_grid=param_grid,cv=3)\n",
    "gres=grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8517501065694122"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gres.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 5, 'min_samples_leaf': 25, 'n_estimators': 100}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gres.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=gres.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.94      0.90      6754\n",
      "           1       0.77      0.60      0.67      2295\n",
      "\n",
      "   micro avg       0.85      0.85      0.85      9049\n",
      "   macro avg       0.82      0.77      0.79      9049\n",
      "weighted avg       0.85      0.85      0.85      9049\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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