logistic_regression_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n0   25    Private  226802          11th                7       Never-married   \n1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n3   44    Private  160323  Some-college               10  Married-civ-spouse   \n4   18          ?  103497  Some-college               10       Never-married   \n\n          occupation relationship   race  gender  capital-gain  capital-loss  \\\n0  Machine-op-inspct    Own-child  Black    Male             0             0   \n1    Farming-fishing      Husband  White    Male             0             0   \n2    Protective-serv      Husband  White    Male             0             0   \n3  Machine-op-inspct      Husband  Black    Male          7688             0   \n4                  ?    Own-child  White  Female             0             0   \n\n   hours-per-week native-country income  \n0              40  United-States  <=50K  \n1              50  United-States  <=50K  \n2              40  United-States   >50K  \n3              40  United-States   >50K  \n4              30  United-States  <=50K  ",
      "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>educational-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>gender</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>25</td>\n      <td>Private</td>\n      <td>226802</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Never-married</td>\n      <td>Machine-op-inspct</td>\n      <td>Own-child</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</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>38</td>\n      <td>Private</td>\n      <td>89814</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Farming-fishing</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>Local-gov</td>\n      <td>336951</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Protective-serv</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>44</td>\n      <td>Private</td>\n      <td>160323</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>7688</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>18</td>\n      <td>?</td>\n      <td>103497</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Never-married</td>\n      <td>?</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('adult.csv')\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n0   25    Private  226802          11th                7       Never-married   \n1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n3   44    Private  160323  Some-college               10  Married-civ-spouse   \n5   34    Private  198693          10th                6       Never-married   \n\n          occupation   relationship   race gender  capital-gain  capital-loss  \\\n0  Machine-op-inspct      Own-child  Black   Male             0             0   \n1    Farming-fishing        Husband  White   Male             0             0   \n2    Protective-serv        Husband  White   Male             0             0   \n3  Machine-op-inspct        Husband  Black   Male          7688             0   \n5      Other-service  Not-in-family  White   Male             0             0   \n\n   hours-per-week native-country income  \n0              40  United-States  <=50K  \n1              50  United-States  <=50K  \n2              40  United-States   >50K  \n3              40  United-States   >50K  \n5              30  United-States  <=50K  ",
      "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>educational-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>gender</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>25</td>\n      <td>Private</td>\n      <td>226802</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Never-married</td>\n      <td>Machine-op-inspct</td>\n      <td>Own-child</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</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>38</td>\n      <td>Private</td>\n      <td>89814</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Farming-fishing</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>Local-gov</td>\n      <td>336951</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Protective-serv</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>44</td>\n      <td>Private</td>\n      <td>160323</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>7688</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>34</td>\n      <td>Private</td>\n      <td>198693</td>\n      <td>10th</td>\n      <td>6</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['workclass'] != '?']\n",
    "df = df[df['occupation'] != '?']\n",
    "df = df[df['native-country'] != '?']\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational-num',\n       'marital-status', 'occupation', 'relationship', 'race', 'gender',\n       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n       'income'],\n      dtype='object')"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "   age  educational-num  hours-per-week  workclass_Federal-gov  \\\n0   25                7              40                      0   \n1   38                9              50                      0   \n2   28               12              40                      0   \n3   44               10              40                      0   \n5   34                6              30                      0   \n\n   workclass_Local-gov  workclass_Private  workclass_Self-emp-inc  \\\n0                    0                  1                       0   \n1                    0                  1                       0   \n2                    1                  0                       0   \n3                    0                  1                       0   \n5                    0                  1                       0   \n\n   workclass_Self-emp-not-inc  workclass_State-gov  workclass_Without-pay  \\\n0                           0                    0                      0   \n1                           0                    0                      0   \n2                           0                    0                      0   \n3                           0                    0                      0   \n5                           0                    0                      0   \n\n   ...  native-country_Portugal  native-country_Puerto-Rico  \\\n0  ...                        0                           0   \n1  ...                        0                           0   \n2  ...                        0                           0   \n3  ...                        0                           0   \n5  ...                        0                           0   \n\n   native-country_Scotland  native-country_South  native-country_Taiwan  \\\n0                        0                     0                      0   \n1                        0                     0                      0   \n2                        0                     0                      0   \n3                        0                     0                      0   \n5                        0                     0                      0   \n\n   native-country_Thailand  native-country_Trinadad&Tobago  \\\n0                        0                               0   \n1                        0                               0   \n2                        0                               0   \n3                        0                               0   \n5                        0                               0   \n\n   native-country_United-States  native-country_Vietnam  \\\n0                             1                       0   \n1                             1                       0   \n2                             1                       0   \n3                             1                       0   \n5                             1                       0   \n\n   native-country_Yugoslavia  \n0                          0  \n1                          0  \n2                          0  \n3                          0  \n5                          0  \n\n[5 rows x 85 columns]",
      "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>educational-num</th>\n      <th>hours-per-week</th>\n      <th>workclass_Federal-gov</th>\n      <th>workclass_Local-gov</th>\n      <th>workclass_Private</th>\n      <th>workclass_Self-emp-inc</th>\n      <th>workclass_Self-emp-not-inc</th>\n      <th>workclass_State-gov</th>\n      <th>workclass_Without-pay</th>\n      <th>...</th>\n      <th>native-country_Portugal</th>\n      <th>native-country_Puerto-Rico</th>\n      <th>native-country_Scotland</th>\n      <th>native-country_South</th>\n      <th>native-country_Taiwan</th>\n      <th>native-country_Thailand</th>\n      <th>native-country_Trinadad&amp;Tobago</th>\n      <th>native-country_United-States</th>\n      <th>native-country_Vietnam</th>\n      <th>native-country_Yugoslavia</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>25</td>\n      <td>7</td>\n      <td>40</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>38</td>\n      <td>9</td>\n      <td>50</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>12</td>\n      <td>40</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>44</td>\n      <td>10</td>\n      <td>40</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>34</td>\n      <td>6</td>\n      <td>30</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 85 columns</p>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selectedColumns = df[['age', 'workclass', 'educational-num', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'hours-per-week', 'native-country']]\n",
    "X = pd.get_dummies(selectedColumns, columns = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country'])\n",
    "X.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "LabelEncoder()"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit(df['income'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "0    0\n1    0\n2    1\n3    1\n4    0\ndtype: int64"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.Series(le.transform(df['income']))\n",
    "y.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train)\n",
    "predictions = model.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8336404510945677"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "Pipeline(steps=[('standardscaler', StandardScaler()),\n                ('svc', SVC(gamma='auto'))])"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
    "clf.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8286282892312228"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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   "display_name": "Python 3",
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