FinalProject Marcelo Villalba.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marcelo Villalba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS381 Data Analytics Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Due on 12/11/2019 23:59 pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset is based on an census income data\n",
    "https://archive.ics.uci.edu/ml/datasets/census+income\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Set Information:\n",
    "\n",
    "Extraction was done by Barry Becker from the 1994 Census database. \n",
    "\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "Listing of attributes:\n",
    "\n",
    "* The last column >50K, <=50K is the target variable indicating whether the people earn less than or larger than 50K per year\n",
    "\n",
    "* age: continuous.\n",
    "* workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "* fnlwgt: continuous.\n",
    "* education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "* education-num: continuous.\n",
    "* marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "* occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "* relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "* race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "* sex: Female, Male.\n",
    "* capital-gain: continuous.\n",
    "* capital-loss: continuous.\n",
    "* hours-per-week: continuous.\n",
    "* native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### However, I have modified the dataset and so you will need to load the dataset by reading a csv file I provided. In particular, I changed 50K to 80K just to reflect the inflation "
   ]
  },
  {
   "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>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>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>32565</td>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32566</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32567</td>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32568</td>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32569</td>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  fnlwgt   education  education-num  \\\n",
       "32565   27       Private  257302  Assoc-acdm             12   \n",
       "32566   40       Private  154374     HS-grad              9   \n",
       "32567   58       Private  151910     HS-grad              9   \n",
       "32568   22       Private  201490     HS-grad              9   \n",
       "32569   52  Self-emp-inc  287927     HS-grad              9   \n",
       "\n",
       "           marital-status         occupation relationship   race     sex  \\\n",
       "32565  Married-civ-spouse       Tech-support         Wife  White  Female   \n",
       "32566  Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
       "32567             Widowed       Adm-clerical    Unmarried  White  Female   \n",
       "32568       Never-married       Adm-clerical    Own-child  White    Male   \n",
       "32569  Married-civ-spouse    Exec-managerial         Wife  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country  salary  \n",
       "32565             0             0              38  United-States       0  \n",
       "32566             0             0              40  United-States       1  \n",
       "32567             0             0              40  United-States       0  \n",
       "32568             0             0              20  United-States       0  \n",
       "32569         15024             0              40  United-States       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"adult_income2.csv\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your task is to build a model that can predict whether a people will earn <= 80K or > 80K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Use any one of the models (Logistic, SVM, Decision Tree and Random Forecast) that we have covered in class.\n",
    "* Do not use any models that we have not covered in class.\n",
    "*\n",
    "* The best performance model will have an extra 5 points, but the whole project will still be capped at 20 points for the whole final project\n",
    "* The second and third performance model with have 2 extra points capped at 20 points for the whole final project.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Make sure your work include the following steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* EDA (chekcing missing values, removing outliers)\n",
    "* performed basic exploration of relationship, with plots and graphs\n",
    "* separated data set into training and testing\n",
    "* setup dummy variables to take care categorical variables\n",
    "* normalize numerical features if needed\n",
    "* tried at least two models and checked their model performance\n",
    "* performed cross-validations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First change the target variable salary to 0 and 1.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ^ Already done in excel before reading the csv file with jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aa13b10>"
      ]
     },
     "execution_count": 5,
     "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.countplot(df['salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Good Luck !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show all your work below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First we check for null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               False\n",
       "workclass         False\n",
       "fnlwgt            False\n",
       "education         False\n",
       "education-num     False\n",
       "marital-status    False\n",
       "occupation        False\n",
       "relationship      False\n",
       "race              False\n",
       "sex               False\n",
       "capital-gain      False\n",
       "capital-loss      False\n",
       "hours-per-week    False\n",
       "native-country    False\n",
       "salary            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## There are no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we check for outliers in numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cc8d910>"
      ]
     },
     "execution_count": 9,
     "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": [
    "df.boxplot(column=['age', 'hours-per-week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    32570.000000\n",
      "mean        38.592662\n",
      "std         13.694998\n",
      "min          0.000000\n",
      "25%         28.000000\n",
      "50%         37.000000\n",
      "75%         48.000000\n",
      "max        205.000000\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.age.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>EducationNum</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>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17604</td>\n",
       "      <td>110</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>101475</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17605</td>\n",
       "      <td>111</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>101475</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>112</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>101475</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32548</td>\n",
       "      <td>105</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>252208</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt   education  EducationNum marital-status  \\\n",
       "17604  110  State-gov  101475  Assoc-acdm            12        Widowed   \n",
       "17605  111  State-gov  101475  Assoc-acdm            12        Widowed   \n",
       "17606  112  State-gov  101475  Assoc-acdm            12        Widowed   \n",
       "32548  105  State-gov  252208     HS-grad             9      Separated   \n",
       "\n",
       "           occupation   relationship   race     sex  capital-gain  \\\n",
       "17604  Prof-specialty  Not-in-family  White  Female             0   \n",
       "17605  Prof-specialty  Not-in-family  White  Female             0   \n",
       "17606  Prof-specialty  Not-in-family  White  Female             0   \n",
       "32548    Adm-clerical      Own-child  White  Female             0   \n",
       "\n",
       "       capital-loss  HoursPerWeek native-country  salary  \n",
       "17604             0            40  United-States       0  \n",
       "17605             0            40  United-States       0  \n",
       "17606             0            40  United-States       0  \n",
       "32548             0            40  United-States       0  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.age > 90]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The above shows people of age >90.  205 is deffinetly an outlier.  The rest, although on the extreme could be true. We will replace 205 with the mean age. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[32549,\"age\"]= 38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    32570.000000\n",
      "mean        38.587535\n",
      "std         13.663920\n",
      "min          0.000000\n",
      "25%         28.000000\n",
      "50%         37.000000\n",
      "75%         48.000000\n",
      "max        112.000000\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.age.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cba6790>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df.boxplot(column=['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>216851</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  workclass  fnlwgt  education  education-num      marital-status  \\\n",
       "26    0  Local-gov  216851  Bachelors             13  Married-civ-spouse   \n",
       "\n",
       "      occupation relationship   race   sex  capital-gain  capital-loss  \\\n",
       "26  Tech-support      Husband  White  Male             0             0   \n",
       "\n",
       "    hours-per-week native-country  salary  \n",
       "26              40  United-States       1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.age < 17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[26,\"age\"]= 38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    32570.000000\n",
      "mean        38.588701\n",
      "std         13.662248\n",
      "min         17.000000\n",
      "25%         28.000000\n",
      "50%         37.000000\n",
      "75%         48.000000\n",
      "max        112.000000\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.age.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## on the lower extreme person with age of zero @ position was replaced with the mean age. The rest are 17 years or older, which is valid. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "##rename hours-per-week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'education-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital-gain', 'capital-loss', 'HoursPerWeek', 'native-country',\n",
       "       'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'hours-per-week': 'HoursPerWeek'}, inplace = True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17701</td>\n",
       "      <td>21</td>\n",
       "      <td>Private</td>\n",
       "      <td>540712</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1719</td>\n",
       "      <td>200</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17702</td>\n",
       "      <td>21</td>\n",
       "      <td>Private</td>\n",
       "      <td>540712</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1719</td>\n",
       "      <td>201</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17703</td>\n",
       "      <td>21</td>\n",
       "      <td>Private</td>\n",
       "      <td>540712</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1719</td>\n",
       "      <td>202</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass  fnlwgt education  education-num marital-status  \\\n",
       "17701   21   Private  540712   HS-grad              9  Never-married   \n",
       "17702   21   Private  540712   HS-grad              9  Never-married   \n",
       "17703   21   Private  540712   HS-grad              9  Never-married   \n",
       "\n",
       "          occupation    relationship   race   sex  capital-gain  capital-loss  \\\n",
       "17701  Other-service  Other-relative  Black  Male             0          1719   \n",
       "17702  Other-service  Other-relative  Black  Male             0          1719   \n",
       "17703  Other-service  Other-relative  Black  Male             0          1719   \n",
       "\n",
       "       HoursPerWeek native-country  salary  \n",
       "17701           200  United-States       0  \n",
       "17702           201  United-States       0  \n",
       "17703           202  United-States       0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.HoursPerWeek > 133]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## the above are outliers, replce with mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    32570.000000\n",
      "mean        40.452165\n",
      "std         12.441520\n",
      "min          1.000000\n",
      "25%         40.000000\n",
      "50%         40.000000\n",
      "75%         45.000000\n",
      "max        202.000000\n",
      "Name: HoursPerWeek, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.HoursPerWeek.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[17701,\"HoursPerWeek\"]= 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[17702,\"HoursPerWeek\"]= 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[17703,\"HoursPerWeek\"]= 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <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",
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       "      <th>sex</th>\n",
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       "      <th>capital-loss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>273</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>30653</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>2407</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>936</td>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>176900</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1173</td>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>404616</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</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>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1418</td>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>179186</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1731</td>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>180060</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>6849</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32398</td>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>199763</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Protective-serv</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>81</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32409</td>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>288154</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32476</td>\n",
       "      <td>58</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>181974</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32483</td>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>30673</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32538</td>\n",
       "      <td>30</td>\n",
       "      <td>?</td>\n",
       "      <td>33811</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  education-num  \\\n",
       "273     50  Self-emp-not-inc   30653       Masters             14   \n",
       "936     37           Private  176900       HS-grad              9   \n",
       "1173    25           Private  404616       Masters             14   \n",
       "1418    31           Private  179186     Bachelors             13   \n",
       "1731    24           Private  180060       Masters             14   \n",
       "...    ...               ...     ...           ...            ...   \n",
       "32398   55           Private  199763       HS-grad              9   \n",
       "32409   42           Private  288154  Some-college             10   \n",
       "32476   58      Self-emp-inc  181974     Doctorate             16   \n",
       "32483   35           Private   30673          12th              8   \n",
       "32538   30                 ?   33811     Bachelors             13   \n",
       "\n",
       "           marital-status       occupation   relationship                race  \\\n",
       "273    Married-civ-spouse  Farming-fishing        Husband               White   \n",
       "936    Married-civ-spouse     Craft-repair        Husband               White   \n",
       "1173   Married-civ-spouse  Farming-fishing  Not-in-family               White   \n",
       "1418   Married-civ-spouse            Sales        Husband               Black   \n",
       "1731        Never-married  Exec-managerial      Own-child               White   \n",
       "...                   ...              ...            ...                 ...   \n",
       "32398           Separated  Protective-serv  Not-in-family               White   \n",
       "32409  Married-civ-spouse     Craft-repair        Husband               White   \n",
       "32476       Never-married   Prof-specialty  Not-in-family               White   \n",
       "32483  Married-civ-spouse     Craft-repair        Husband               White   \n",
       "32538       Never-married                ?  Not-in-family  Asian-Pac-Islander   \n",
       "\n",
       "          sex  capital-gain  capital-loss  HoursPerWeek native-country  salary  \n",
       "273      Male          2407             0            98  United-States       0  \n",
       "936      Male             0             0            99  United-States       1  \n",
       "1173     Male             0             0            99  United-States       1  \n",
       "1418     Male             0             0            90  United-States       1  \n",
       "1731     Male          6849             0            90  United-States       0  \n",
       "...       ...           ...           ...           ...            ...     ...  \n",
       "32398    Male             0             0            81  United-States       0  \n",
       "32409    Male             0             0            89  United-States       1  \n",
       "32476  Female             0             0            99              ?       0  \n",
       "32483    Male             0             0            84  United-States       0  \n",
       "32538  Female             0             0            99  United-States       0  \n",
       "\n",
       "[208 rows x 15 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.HoursPerWeek > 80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  education-num  \\\n",
       "78      67                 ?  212759          10th              6   \n",
       "158     71  Self-emp-not-inc  494223  Some-college             10   \n",
       "190     58         State-gov  109567     Doctorate             16   \n",
       "347     67                 ?   36135          11th              7   \n",
       "388     44           Private  162028  Some-college             10   \n",
       "...    ...               ...     ...           ...            ...   \n",
       "32324   63                 ?  126540  Some-college             10   \n",
       "32348   74  Self-emp-not-inc  199136     Bachelors             13   \n",
       "32466   85           Private   98611     Bachelors             13   \n",
       "32501   82                 ?  403910       HS-grad              9   \n",
       "32532   81                 ?  120478     Assoc-voc             11   \n",
       "\n",
       "           marital-status       occupation   relationship   race     sex  \\\n",
       "78     Married-civ-spouse                ?        Husband  White    Male   \n",
       "158             Separated            Sales      Unmarried  Black    Male   \n",
       "190    Married-civ-spouse   Prof-specialty        Husband  White    Male   \n",
       "347    Married-civ-spouse                ?        Husband  White    Male   \n",
       "388    Married-civ-spouse     Adm-clerical           Wife  White  Female   \n",
       "...                   ...              ...            ...    ...     ...   \n",
       "32324            Divorced                ?  Not-in-family  White  Female   \n",
       "32348             Widowed     Craft-repair  Not-in-family  White    Male   \n",
       "32466  Married-civ-spouse  Exec-managerial        Husband  White    Male   \n",
       "32501       Never-married                ?  Not-in-family  White    Male   \n",
       "32532            Divorced                ?      Unmarried  White  Female   \n",
       "\n",
       "       capital-gain  capital-loss  HoursPerWeek native-country  salary  \n",
       "78                0             0             2  United-States       0  \n",
       "158               0          1816             2  United-States       0  \n",
       "190               0             0             1  United-States       1  \n",
       "347               0             0             8  United-States       0  \n",
       "388               0          2415             6  United-States       1  \n",
       "...             ...           ...           ...            ...     ...  \n",
       "32324             0             0             5  United-States       0  \n",
       "32348         15831             0             8        Germany       1  \n",
       "32466             0             0             3         Poland       0  \n",
       "32501             0             0             3  United-States       0  \n",
       "32532             0             0             1              ?       0  \n",
       "\n",
       "[458 rows x 15 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.HoursPerWeek < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## considering the eldery age of these poeople it is likely for them to work less than 10 hours a day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will now see if we can determine some relationships between our columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077186</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>0.077464</td>\n",
       "      <td>0.057013</td>\n",
       "      <td>0.068618</td>\n",
       "      <td>0.233317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fnlwgt</td>\n",
       "      <td>-0.077186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043361</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>-0.008992</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>-0.009592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>education-num</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>-0.043361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122617</td>\n",
       "      <td>0.079684</td>\n",
       "      <td>0.148113</td>\n",
       "      <td>0.335164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>capital-gain</td>\n",
       "      <td>0.077464</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.122617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031640</td>\n",
       "      <td>0.078410</td>\n",
       "      <td>0.223321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>capital-loss</td>\n",
       "      <td>0.057013</td>\n",
       "      <td>-0.008992</td>\n",
       "      <td>0.079684</td>\n",
       "      <td>-0.031640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054203</td>\n",
       "      <td>0.150196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HoursPerWeek</td>\n",
       "      <td>0.068618</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>0.148113</td>\n",
       "      <td>0.078410</td>\n",
       "      <td>0.054203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>salary</td>\n",
       "      <td>0.233317</td>\n",
       "      <td>-0.009592</td>\n",
       "      <td>0.335164</td>\n",
       "      <td>0.223321</td>\n",
       "      <td>0.150196</td>\n",
       "      <td>0.229672</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    age    fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "age            1.000000 -0.077186       0.036812      0.077464      0.057013   \n",
       "fnlwgt        -0.077186  1.000000      -0.043361      0.000392     -0.008992   \n",
       "education-num  0.036812 -0.043361       1.000000      0.122617      0.079684   \n",
       "capital-gain   0.077464  0.000392       0.122617      1.000000     -0.031640   \n",
       "capital-loss   0.057013 -0.008992       0.079684     -0.031640      1.000000   \n",
       "HoursPerWeek   0.068618 -0.018768       0.148113      0.078410      0.054203   \n",
       "salary         0.233317 -0.009592       0.335164      0.223321      0.150196   \n",
       "\n",
       "               HoursPerWeek    salary  \n",
       "age                0.068618  0.233317  \n",
       "fnlwgt            -0.018768 -0.009592  \n",
       "education-num      0.148113  0.335164  \n",
       "capital-gain       0.078410  0.223321  \n",
       "capital-loss       0.054203  0.150196  \n",
       "HoursPerWeek       1.000000  0.229672  \n",
       "salary             0.229672  1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Education-num has the highest collelation to salary. Followed by age and hours worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## since educatoin-num has highest correlation lets check for outliers there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cb54790>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T+vWdBpwypL80MgNcc1dVXwZ+D7gqyReBq4HHcmQWfj3wKeDGgY/8BfCiJJ8Fngcc7h/nSo6sa6/0l2IeWHd+B3BD/7i3DRznr4C39H9Z+cSBer4DvA74WH/Z5X7gA1P6cT8BPKpf3xuA24f0l0bmrfSS1Chn4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNep/AXYtIIvM+u5MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=['education-num'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "##lets rename the column education-num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'EducationNum',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital-gain', 'capital-loss', 'HoursPerWeek', 'native-country',\n",
       "       'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'education-num': 'EducationNum'}, inplace = True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>EducationNum</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>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>225</td>\n",
       "      <td>53</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>140359</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>933</td>\n",
       "      <td>51</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>241843</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2885</td>\n",
       "      <td>71</td>\n",
       "      <td>Private</td>\n",
       "      <td>235079</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2947</td>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>452405</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3447</td>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>239781</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</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>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3593</td>\n",
       "      <td>39</td>\n",
       "      <td>?</td>\n",
       "      <td>362685</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>20</td>\n",
       "      <td>El-Salvador</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4108</td>\n",
       "      <td>52</td>\n",
       "      <td>Private</td>\n",
       "      <td>416129</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</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>40</td>\n",
       "      <td>El-Salvador</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6434</td>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>211032</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>41310</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6865</td>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>286989</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</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>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7071</td>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>193598</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</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>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7174</td>\n",
       "      <td>64</td>\n",
       "      <td>?</td>\n",
       "      <td>140237</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7895</td>\n",
       "      <td>26</td>\n",
       "      <td>Private</td>\n",
       "      <td>322614</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1719</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8081</td>\n",
       "      <td>21</td>\n",
       "      <td>Private</td>\n",
       "      <td>243368</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</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>50</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8089</td>\n",
       "      <td>54</td>\n",
       "      <td>?</td>\n",
       "      <td>148657</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8097</td>\n",
       "      <td>52</td>\n",
       "      <td>?</td>\n",
       "      <td>248113</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8776</td>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>277700</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10311</td>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>157305</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</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>40</td>\n",
       "      <td>Dominican-Republic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11135</td>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>112137</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>4508</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11361</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>188644</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</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>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11735</td>\n",
       "      <td>65</td>\n",
       "      <td>?</td>\n",
       "      <td>293385</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13249</td>\n",
       "      <td>68</td>\n",
       "      <td>Private</td>\n",
       "      <td>168794</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</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>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15060</td>\n",
       "      <td>21</td>\n",
       "      <td>Private</td>\n",
       "      <td>243368</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</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>50</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16498</td>\n",
       "      <td>75</td>\n",
       "      <td>Private</td>\n",
       "      <td>71898</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Priv-house-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16563</td>\n",
       "      <td>46</td>\n",
       "      <td>Private</td>\n",
       "      <td>225065</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18422</td>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>243368</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</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>36</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20447</td>\n",
       "      <td>63</td>\n",
       "      <td>Private</td>\n",
       "      <td>440607</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21377</td>\n",
       "      <td>61</td>\n",
       "      <td>Private</td>\n",
       "      <td>98350</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>China</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21395</td>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>196678</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21729</td>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>149809</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</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>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21801</td>\n",
       "      <td>41</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>160893</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Handlers-cleaners</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22174</td>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>341741</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22538</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>182268</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22947</td>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>266820</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</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>35</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24182</td>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>349340</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24565</td>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>572751</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</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>Nicaragua</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24705</td>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>223212</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</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>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25107</td>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>209182</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>El-Salvador</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25120</td>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>69911</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25234</td>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>240049</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Laos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25659</td>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>144995</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Handlers-cleaners</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>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25950</td>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>277695</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</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>36</td>\n",
       "      <td>Hong</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26508</td>\n",
       "      <td>52</td>\n",
       "      <td>Private</td>\n",
       "      <td>370552</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</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>El-Salvador</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26613</td>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>175262</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>China</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27159</td>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>236879</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Priv-house-serv</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27246</td>\n",
       "      <td>34</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>144182</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28402</td>\n",
       "      <td>36</td>\n",
       "      <td>Private</td>\n",
       "      <td>252231</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Puerto-Rico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31805</td>\n",
       "      <td>31</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77634</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</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>24</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32042</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>566537</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1672</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32231</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>70645</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32366</td>\n",
       "      <td>46</td>\n",
       "      <td>Private</td>\n",
       "      <td>139514</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>Dominican-Republic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32439</td>\n",
       "      <td>36</td>\n",
       "      <td>Private</td>\n",
       "      <td>208068</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt  education  EducationNum         marital-status  \\\n",
       "225     53  Local-gov  140359  Preschool             1          Never-married   \n",
       "933     51  Local-gov  241843  Preschool             1     Married-civ-spouse   \n",
       "2885    71    Private  235079  Preschool             1                Widowed   \n",
       "2947    31    Private  452405  Preschool             1          Never-married   \n",
       "3447    33    Private  239781  Preschool             1     Married-civ-spouse   \n",
       "3593    39          ?  362685  Preschool             1                Widowed   \n",
       "4108    52    Private  416129  Preschool             1     Married-civ-spouse   \n",
       "6434    27    Private  211032  Preschool             1     Married-civ-spouse   \n",
       "6865    54    Private  286989  Preschool             1     Married-civ-spouse   \n",
       "7071    30    Private  193598  Preschool             1          Never-married   \n",
       "7174    64          ?  140237  Preschool             1     Married-civ-spouse   \n",
       "7895    26    Private  322614  Preschool             1  Married-spouse-absent   \n",
       "8081    21    Private  243368  Preschool             1          Never-married   \n",
       "8089    54          ?  148657  Preschool             1     Married-civ-spouse   \n",
       "8097    52          ?  248113  Preschool             1  Married-spouse-absent   \n",
       "8776    20    Private  277700  Preschool             1          Never-married   \n",
       "10311   59    Private  157305  Preschool             1          Never-married   \n",
       "11135   32    Private  112137  Preschool             1     Married-civ-spouse   \n",
       "11361   53    Private  188644  Preschool             1     Married-civ-spouse   \n",
       "11735   65          ?  293385  Preschool             1     Married-civ-spouse   \n",
       "13249   68    Private  168794  Preschool             1          Never-married   \n",
       "15060   21    Private  243368  Preschool             1          Never-married   \n",
       "16498   75    Private   71898  Preschool             1          Never-married   \n",
       "16563   46    Private  225065  Preschool             1     Married-civ-spouse   \n",
       "18422   24    Private  243368  Preschool             1          Never-married   \n",
       "20447   63    Private  440607  Preschool             1     Married-civ-spouse   \n",
       "21377   61    Private   98350  Preschool             1  Married-spouse-absent   \n",
       "21395   24    Private  196678  Preschool             1          Never-married   \n",
       "21729   49    Private  149809  Preschool             1     Married-civ-spouse   \n",
       "21801   41  Local-gov  160893  Preschool             1          Never-married   \n",
       "22174   39    Private  341741  Preschool             1          Never-married   \n",
       "22538   40    Private  182268  Preschool             1  Married-spouse-absent   \n",
       "22947   25    Private  266820  Preschool             1          Never-married   \n",
       "24182   54    Private  349340  Preschool             1     Married-civ-spouse   \n",
       "24565   42    Private  572751  Preschool             1     Married-civ-spouse   \n",
       "24705   32    Private  223212  Preschool             1     Married-civ-spouse   \n",
       "25107   48    Private  209182  Preschool             1              Separated   \n",
       "25120   23    Private   69911  Preschool             1          Never-married   \n",
       "25234   23    Private  240049  Preschool             1          Never-married   \n",
       "25659   42    Private  144995  Preschool             1          Never-married   \n",
       "25950   19    Private  277695  Preschool             1          Never-married   \n",
       "26508   52    Private  370552  Preschool             1     Married-civ-spouse   \n",
       "26613   54    Private  175262  Preschool             1     Married-civ-spouse   \n",
       "27159   66    Private  236879  Preschool             1                Widowed   \n",
       "27246   34  Local-gov  144182  Preschool             1          Never-married   \n",
       "28402   36    Private  252231  Preschool             1          Never-married   \n",
       "31805   31  State-gov   77634  Preschool             1          Never-married   \n",
       "32042   40    Private  566537  Preschool             1     Married-civ-spouse   \n",
       "32231   40    Private   70645  Preschool             1          Never-married   \n",
       "32366   46    Private  139514  Preschool             1     Married-civ-spouse   \n",
       "32439   36    Private  208068  Preschool             1               Divorced   \n",
       "\n",
       "              occupation    relationship                race     sex  \\\n",
       "225    Machine-op-inspct   Not-in-family               White  Female   \n",
       "933        Other-service         Husband               White    Male   \n",
       "2885        Craft-repair       Unmarried               Black    Male   \n",
       "2947       Other-service  Other-relative               White  Female   \n",
       "3447     Farming-fishing         Husband               White    Male   \n",
       "3593                   ?   Not-in-family               White  Female   \n",
       "4108       Other-service   Not-in-family               White    Male   \n",
       "6434     Farming-fishing  Other-relative               White    Male   \n",
       "6865     Farming-fishing         Husband               White    Male   \n",
       "7071     Farming-fishing   Not-in-family               White    Male   \n",
       "7174                   ?         Husband               White    Male   \n",
       "7895   Machine-op-inspct   Not-in-family               White    Male   \n",
       "8081     Farming-fishing   Not-in-family               White    Male   \n",
       "8089                   ?            Wife               White  Female   \n",
       "8097                   ?  Other-relative               White    Male   \n",
       "8776       Other-service       Own-child               White    Male   \n",
       "10311  Machine-op-inspct   Not-in-family               White    Male   \n",
       "11135  Machine-op-inspct            Wife  Asian-Pac-Islander  Female   \n",
       "11361      Other-service         Husband               White    Male   \n",
       "11735                  ?         Husband               Black    Male   \n",
       "13249  Machine-op-inspct   Not-in-family               White    Male   \n",
       "15060    Farming-fishing   Not-in-family               White    Male   \n",
       "16498    Priv-house-serv   Not-in-family  Asian-Pac-Islander  Female   \n",
       "16563  Machine-op-inspct            Wife               White  Female   \n",
       "18422    Farming-fishing   Not-in-family               White    Male   \n",
       "20447     Prof-specialty         Husband               Other    Male   \n",
       "21377      Other-service   Not-in-family  Asian-Pac-Islander    Male   \n",
       "21395  Machine-op-inspct       Own-child               White  Female   \n",
       "21729      Other-service         Husband               White    Male   \n",
       "21801  Handlers-cleaners       Own-child               White  Female   \n",
       "22174      Other-service   Not-in-family               White  Female   \n",
       "22538       Adm-clerical       Own-child               White    Male   \n",
       "22947    Farming-fishing   Not-in-family               White    Male   \n",
       "24182       Craft-repair         Husband  Asian-Pac-Islander    Male   \n",
       "24565       Craft-repair         Husband               White    Male   \n",
       "24705  Machine-op-inspct         Husband               White    Male   \n",
       "25107      Other-service       Unmarried               White  Female   \n",
       "25120      Other-service       Own-child               White  Female   \n",
       "25234      Other-service   Not-in-family  Asian-Pac-Islander  Female   \n",
       "25659  Handlers-cleaners   Not-in-family               White    Male   \n",
       "25950    Farming-fishing   Not-in-family               White    Male   \n",
       "26508  Machine-op-inspct         Husband               White    Male   \n",
       "26613       Craft-repair         Husband  Asian-Pac-Islander    Male   \n",
       "27159    Priv-house-serv  Other-relative               White  Female   \n",
       "27246       Adm-clerical       Own-child               Black  Female   \n",
       "28402  Machine-op-inspct   Not-in-family               Black    Male   \n",
       "31805      Other-service   Not-in-family               White    Male   \n",
       "32042      Other-service         Husband               White    Male   \n",
       "32231      Other-service   Not-in-family               White  Female   \n",
       "32366  Machine-op-inspct  Other-relative               Black    Male   \n",
       "32439      Other-service   Not-in-family               Other    Male   \n",
       "\n",
       "       capital-gain  capital-loss  HoursPerWeek      native-country  salary  \n",
       "225               0             0            35       United-States       0  \n",
       "933               0             0            40       United-States       0  \n",
       "2885              0             0            10       United-States       0  \n",
       "2947              0             0            35              Mexico       0  \n",
       "3447              0             0            40              Mexico       0  \n",
       "3593              0             0            20         El-Salvador       0  \n",
       "4108              0             0            40         El-Salvador       0  \n",
       "6434          41310             0            24              Mexico       0  \n",
       "6865              0             0            60       United-States       0  \n",
       "7071              0             0            40              Mexico       0  \n",
       "7174              0             0            40       United-States       0  \n",
       "7895              0          1719            40              Mexico       0  \n",
       "8081              0             0            50              Mexico       0  \n",
       "8089              0             0            40              Mexico       0  \n",
       "8097              0             0            40              Mexico       0  \n",
       "8776              0             0            32       United-States       0  \n",
       "10311             0             0            40  Dominican-Republic       0  \n",
       "11135          4508             0            40            Cambodia       0  \n",
       "11361             0             0            40              Mexico       0  \n",
       "11735             0             0            30       United-States       0  \n",
       "13249             0             0            10       United-States       0  \n",
       "15060             0             0            50              Mexico       0  \n",
       "16498             0             0            48         Philippines       0  \n",
       "16563             0             0            40              Mexico       0  \n",
       "18422             0             0            36              Mexico       0  \n",
       "20447             0             0            30              Mexico       0  \n",
       "21377             0             0            40               China       0  \n",
       "21395             0             0            30       United-States       0  \n",
       "21729             0             0            40                   ?       0  \n",
       "21801             0             0            30       United-States       0  \n",
       "22174             0             0            12       United-States       0  \n",
       "22538             0             0            40       United-States       0  \n",
       "22947             0             0            35              Mexico       0  \n",
       "24182             0             0            40               India       0  \n",
       "24565             0             0            40           Nicaragua       0  \n",
       "24705             0             0            40              Mexico       0  \n",
       "25107             0             0            40         El-Salvador       0  \n",
       "25120             0             0            15       United-States       0  \n",
       "25234             0             0            40                Laos       0  \n",
       "25659             0             0            25       United-States       0  \n",
       "25950             0             0            36                Hong       0  \n",
       "26508             0             0            40         El-Salvador       0  \n",
       "26613             0             0            40               China       0  \n",
       "27159             0             0            40           Guatemala       0  \n",
       "27246             0             0            25       United-States       0  \n",
       "28402             0             0            40         Puerto-Rico       0  \n",
       "31805             0             0            24       United-States       0  \n",
       "32042             0          1672            40              Mexico       0  \n",
       "32231             0             0            20       United-States       0  \n",
       "32366             0             0            75  Dominican-Republic       0  \n",
       "32439             0             0            72              Mexico       0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.EducationNum <=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## althoug <1 years of education is extreme. There are 552 rows which in my opinoon is significant enought to not be\n",
    "## outliers and makes sense with the relative data in education column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ca91710>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='race',hue='salary',data=df,palette='rainbow')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c16c050>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',hue='salary',data=df,palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see the population for whites is much greater than the population for other races. But clearly the ratio within each race for >80k or not is.\n",
    "### ratio of females that make more than 80k is small compared to males. \n",
    "# We will now create dummy variables for categorical values race and sex before building out model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>EducationNum</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>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</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>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  EducationNum      marital-status  \\\n",
       "0   39         State-gov   77516  Bachelors            13       Never-married   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors            13  Married-civ-spouse   \n",
       "2   38           Private  215646    HS-grad             9            Divorced   \n",
       "3   53           Private  234721       11th             7  Married-civ-spouse   \n",
       "4   28           Private  338409  Bachelors            13  Married-civ-spouse   \n",
       "\n",
       "          occupation   relationship   race     sex  capital-gain  \\\n",
       "0       Adm-clerical  Not-in-family  White    Male          2174   \n",
       "1    Exec-managerial        Husband  White    Male             0   \n",
       "2  Handlers-cleaners  Not-in-family  White    Male             0   \n",
       "3  Handlers-cleaners        Husband  Black    Male             0   \n",
       "4     Prof-specialty           Wife  Black  Female             0   \n",
       "\n",
       "   capital-loss  HoursPerWeek native-country  salary  sex_Female  sex_Male  \n",
       "0             0            40  United-States       0           0         1  \n",
       "1             0            13  United-States       0           0         1  \n",
       "2             0            40  United-States       0           0         1  \n",
       "3             0            40  United-States       0           0         1  \n",
       "4             0            40           Cuba       0           1         0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = ['race', 'sex']\n",
    "for col in categorical_features:\n",
    "    dummies = pd.get_dummies(df[col], prefix=col)\n",
    "    newdf = pd.concat([df, dummies], axis=1)\n",
    "newdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf = newdf.drop([\"sex\"], axis =1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
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       "      <td>Never-married</td>\n",
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       "      <td>Not-in-family</td>\n",
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       "      <td>Bachelors</td>\n",
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       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  EducationNum      marital-status  \\\n",
       "0   39         State-gov   77516  Bachelors            13       Never-married   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors            13  Married-civ-spouse   \n",
       "2   38           Private  215646    HS-grad             9            Divorced   \n",
       "3   53           Private  234721       11th             7  Married-civ-spouse   \n",
       "4   28           Private  338409  Bachelors            13  Married-civ-spouse   \n",
       "\n",
       "          occupation   relationship   race  capital-gain  capital-loss  \\\n",
       "0       Adm-clerical  Not-in-family  White          2174             0   \n",
       "1    Exec-managerial        Husband  White             0             0   \n",
       "2  Handlers-cleaners  Not-in-family  White             0             0   \n",
       "3  Handlers-cleaners        Husband  Black             0             0   \n",
       "4     Prof-specialty           Wife  Black             0             0   \n",
       "\n",
       "   HoursPerWeek native-country  salary  sex_Female  sex_Male  \n",
       "0            40  United-States       0           0         1  \n",
       "1            13  United-States       0           0         1  \n",
       "2            40  United-States       0           0         1  \n",
       "3            40  United-States       0           0         1  \n",
       "4            40           Cuba       0           1         0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "race = pd.get_dummies(newdf['race'],drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <th>Black</th>\n",
       "      <th>Other</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32565</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32567</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32568</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32570 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Asian-Pac-Islander  Black  Other  White\n",
       "0                       0      0      0      1\n",
       "1                       0      0      0      1\n",
       "2                       0      0      0      1\n",
       "3                       0      1      0      0\n",
       "4                       0      1      0      0\n",
       "...                   ...    ...    ...    ...\n",
       "32565                   0      0      0      1\n",
       "32566                   0      0      0      1\n",
       "32567                   0      0      0      1\n",
       "32568                   0      0      0      1\n",
       "32569                   0      0      0      1\n",
       "\n",
       "[32570 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf = pd.concat([newdf,race], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf = finaldf.drop([\"race\"], axis =1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "## also decided to drop education since it is non-numerical and education Num is its equivalent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf = finaldf.drop([\"education\"], axis =1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Other</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32565</td>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32566</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32567</td>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32568</td>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32569</td>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  EducationNum      marital-status  \\\n",
       "0       39         State-gov   77516            13       Never-married   \n",
       "1       50  Self-emp-not-inc   83311            13  Married-civ-spouse   \n",
       "2       38           Private  215646             9            Divorced   \n",
       "3       53           Private  234721             7  Married-civ-spouse   \n",
       "4       28           Private  338409            13  Married-civ-spouse   \n",
       "...    ...               ...     ...           ...                 ...   \n",
       "32565   27           Private  257302            12  Married-civ-spouse   \n",
       "32566   40           Private  154374             9  Married-civ-spouse   \n",
       "32567   58           Private  151910             9             Widowed   \n",
       "32568   22           Private  201490             9       Never-married   \n",
       "32569   52      Self-emp-inc  287927             9  Married-civ-spouse   \n",
       "\n",
       "              occupation   relationship  capital-gain  capital-loss  \\\n",
       "0           Adm-clerical  Not-in-family          2174             0   \n",
       "1        Exec-managerial        Husband             0             0   \n",
       "2      Handlers-cleaners  Not-in-family             0             0   \n",
       "3      Handlers-cleaners        Husband             0             0   \n",
       "4         Prof-specialty           Wife             0             0   \n",
       "...                  ...            ...           ...           ...   \n",
       "32565       Tech-support           Wife             0             0   \n",
       "32566  Machine-op-inspct        Husband             0             0   \n",
       "32567       Adm-clerical      Unmarried             0             0   \n",
       "32568       Adm-clerical      Own-child             0             0   \n",
       "32569    Exec-managerial           Wife         15024             0   \n",
       "\n",
       "       HoursPerWeek native-country  salary  sex_Female  sex_Male  \\\n",
       "0                40  United-States       0           0         1   \n",
       "1                13  United-States       0           0         1   \n",
       "2                40  United-States       0           0         1   \n",
       "3                40  United-States       0           0         1   \n",
       "4                40           Cuba       0           1         0   \n",
       "...             ...            ...     ...         ...       ...   \n",
       "32565            38  United-States       0           1         0   \n",
       "32566            40  United-States       1           0         1   \n",
       "32567            40  United-States       0           1         0   \n",
       "32568            20  United-States       0           0         1   \n",
       "32569            40  United-States       1           1         0   \n",
       "\n",
       "       Asian-Pac-Islander  Black  Other  White  \n",
       "0                       0      0      0      1  \n",
       "1                       0      0      0      1  \n",
       "2                       0      0      0      1  \n",
       "3                       0      1      0      0  \n",
       "4                       0      1      0      0  \n",
       "...                   ...    ...    ...    ...  \n",
       "32565                   0      0      0      1  \n",
       "32566                   0      0      0      1  \n",
       "32567                   0      0      0      1  \n",
       "32568                   0      0      0      1  \n",
       "32569                   0      0      0      1  \n",
       "\n",
       "[32570 rows x 18 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We still have many non numerical features that may prove to be beneficial if removed. Will come back to this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOW We sample out data into training and testing sets for our models.   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26056, 18)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
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       "      <td>27793</td>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>407425</td>\n",
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       "    <tr>\n",
       "      <td>3457</td>\n",
       "      <td>22</td>\n",
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       "      <td>113175</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15039</td>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>206599</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9948</td>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>362826</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19382</td>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>29762</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "       age workclass  fnlwgt  EducationNum marital-status         occupation  \\\n",
       "27793   41   Private  407425             8       Divorced  Machine-op-inspct   \n",
       "3457    22         ?  113175            10  Never-married                  ?   \n",
       "15039   19   Private  206599            10  Never-married       Adm-clerical   \n",
       "9948    25   Private  362826             9  Never-married  Handlers-cleaners   \n",
       "19382   44   Private   29762             9       Divorced   Transport-moving   \n",
       "\n",
       "         relationship  capital-gain  capital-loss  HoursPerWeek  \\\n",
       "27793   Not-in-family             0             0            40   \n",
       "3457        Own-child             0             0            20   \n",
       "15039       Own-child             0             0            16   \n",
       "9948   Other-relative             0             0            45   \n",
       "19382   Not-in-family             0             0            68   \n",
       "\n",
       "      native-country  salary  sex_Female  sex_Male  Asian-Pac-Islander  Black  \\\n",
       "27793  United-States       0           1         0                   0      1   \n",
       "3457   United-States       0           1         0                   0      0   \n",
       "15039  United-States       0           1         0                   0      0   \n",
       "9948   United-States       0           0         1                   0      0   \n",
       "19382  United-States       0           0         1                   0      0   \n",
       "\n",
       "       Other  White  \n",
       "27793      0      0  \n",
       "3457       0      1  \n",
       "15039      0      1  \n",
       "9948       0      1  \n",
       "19382      0      1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = finaldf.sample(int(finaldf.shape[0] * 0.8))\n",
    "print(train_df.shape)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6514"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_rows = [idx for idx in finaldf.index if idx not in train_df.index]\n",
    "len(testing_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6514, 18)\n"
     ]
    },
    {
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       "      <td>Not-in-family</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>302146</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>76845</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    workclass  fnlwgt  EducationNum         marital-status  \\\n",
       "5    37      Private  284582            14     Married-civ-spouse   \n",
       "6    49      Private  160187             5  Married-spouse-absent   \n",
       "20   40      Private  193524            16     Married-civ-spouse   \n",
       "21   54      Private  302146             9              Separated   \n",
       "22   35  Federal-gov   76845             5     Married-civ-spouse   \n",
       "\n",
       "         occupation   relationship  capital-gain  capital-loss  HoursPerWeek  \\\n",
       "5   Exec-managerial           Wife             0             0            40   \n",
       "6     Other-service  Not-in-family             0             0            16   \n",
       "20   Prof-specialty        Husband             0             0            60   \n",
       "21    Other-service      Unmarried             0             0            20   \n",
       "22  Farming-fishing        Husband             0             0            40   \n",
       "\n",
       "   native-country  salary  sex_Female  sex_Male  Asian-Pac-Islander  Black  \\\n",
       "5   United-States       0           1         0                   0      0   \n",
       "6         Jamaica       0           1         0                   0      1   \n",
       "20  United-States       1           0         1                   0      0   \n",
       "21  United-States       0           1         0                   0      1   \n",
       "22  United-States       0           0         1                   0      1   \n",
       "\n",
       "    Other  White  \n",
       "5       0      1  \n",
       "6       0      0  \n",
       "20      0      1  \n",
       "21      0      0  \n",
       "22      0      0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = finaldf.iloc[testing_rows, :]\n",
    "print(test_df.shape)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train_df.drop(['workclass','marital-status','relationship', 'occupation', 'native-country'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train.drop('salary',axis=1), \n",
    "                                                    train['salary'], test_size=0.20, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOGISTIC REGRESSION MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.96      0.88      3949\n",
      "           1       0.71      0.28      0.40      1263\n",
      "\n",
      "    accuracy                           0.80      5212\n",
      "   macro avg       0.76      0.62      0.64      5212\n",
      "weighted avg       0.78      0.80      0.76      5212\n",
      "\n",
      "0.7985418265541059\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the logfisctic regression model preformance. It is not too bad. I believe that if we add countries as\n",
    "###a feature for the model, we could imporve the score for logistic regression. Lets see.\n",
    "# BUT IT WILL ADD A LOT OF FEATURES, lets see if model performance improves by a lot or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.get_dummies(['native-country'],drop_first = True)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TRIAL two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf2 = pd.concat([finaldf,countries], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <th>Black</th>\n",
       "      <th>Other</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32565</td>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32566</td>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32567</td>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32568</td>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32569</td>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  EducationNum      marital-status  \\\n",
       "0       39         State-gov   77516            13       Never-married   \n",
       "1       50  Self-emp-not-inc   83311            13  Married-civ-spouse   \n",
       "2       38           Private  215646             9            Divorced   \n",
       "3       53           Private  234721             7  Married-civ-spouse   \n",
       "4       28           Private  338409            13  Married-civ-spouse   \n",
       "...    ...               ...     ...           ...                 ...   \n",
       "32565   27           Private  257302            12  Married-civ-spouse   \n",
       "32566   40           Private  154374             9  Married-civ-spouse   \n",
       "32567   58           Private  151910             9             Widowed   \n",
       "32568   22           Private  201490             9       Never-married   \n",
       "32569   52      Self-emp-inc  287927             9  Married-civ-spouse   \n",
       "\n",
       "              occupation   relationship  capital-gain  capital-loss  \\\n",
       "0           Adm-clerical  Not-in-family          2174             0   \n",
       "1        Exec-managerial        Husband             0             0   \n",
       "2      Handlers-cleaners  Not-in-family             0             0   \n",
       "3      Handlers-cleaners        Husband             0             0   \n",
       "4         Prof-specialty           Wife             0             0   \n",
       "...                  ...            ...           ...           ...   \n",
       "32565       Tech-support           Wife             0             0   \n",
       "32566  Machine-op-inspct        Husband             0             0   \n",
       "32567       Adm-clerical      Unmarried             0             0   \n",
       "32568       Adm-clerical      Own-child             0             0   \n",
       "32569    Exec-managerial           Wife         15024             0   \n",
       "\n",
       "       HoursPerWeek native-country  salary  sex_Female  sex_Male  \\\n",
       "0                40  United-States       0           0         1   \n",
       "1                13  United-States       0           0         1   \n",
       "2                40  United-States       0           0         1   \n",
       "3                40  United-States       0           0         1   \n",
       "4                40           Cuba       0           1         0   \n",
       "...             ...            ...     ...         ...       ...   \n",
       "32565            38  United-States       0           1         0   \n",
       "32566            40  United-States       1           0         1   \n",
       "32567            40  United-States       0           1         0   \n",
       "32568            20  United-States       0           0         1   \n",
       "32569            40  United-States       1           1         0   \n",
       "\n",
       "       Asian-Pac-Islander  Black  Other  White  \n",
       "0                       0      0      0      1  \n",
       "1                       0      0      0      1  \n",
       "2                       0      0      0      1  \n",
       "3                       0      1      0      0  \n",
       "4                       0      1      0      0  \n",
       "...                   ...    ...    ...    ...  \n",
       "32565                   0      0      0      1  \n",
       "32566                   0      0      0      1  \n",
       "32567                   0      0      0      1  \n",
       "32568                   0      0      0      1  \n",
       "32569                   0      0      0      1  \n",
       "\n",
       "[32570 rows x 18 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf2 = finaldf2.drop(['workclass', 'marital-status','occupation', 'relationship', 'native-country' ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we converted features to numerical dummmies and we redo logistic regresion using the above sample, eplitting the data etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26056, 13)\n"
     ]
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      "text/plain": [
       "       age  fnlwgt  EducationNum  capital-gain  capital-loss  HoursPerWeek  \\\n",
       "26326   46  175625            15             0             0            40   \n",
       "10089   50  136836             9             0             0            40   \n",
       "5383    26  205428            10             0             0            45   \n",
       "4016    28  356555            10             0             0            48   \n",
       "13349   34  226443            10             0             0            40   \n",
       "\n",
       "       salary  sex_Female  sex_Male  Asian-Pac-Islander  Black  Other  White  \n",
       "26326       0           1         0                   0      0      0      1  \n",
       "10089       0           1         0                   0      1      0      0  \n",
       "5383        0           0         1                   0      0      0      1  \n",
       "4016        0           0         1                   0      0      0      1  \n",
       "13349       1           0         1                   0      0      0      1  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dfCountries = finaldf2.sample(int(finaldf2.shape[0] * 0.8))\n",
    "print(train_dfCountries.shape)\n",
    "train_dfCountries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6514"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_rowsCountries = [idx for idx in finaldf2.index if idx not in train_dfCountries.index]\n",
    "len(testing_rowsCountries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6514, 13)\n"
     ]
    },
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      ],
      "text/plain": [
       "    age  fnlwgt  EducationNum  capital-gain  capital-loss  HoursPerWeek  \\\n",
       "1    50   83311            13             0             0            13   \n",
       "16   25  176756             9             0             0            35   \n",
       "17   32  186824             9             0             0            40   \n",
       "19   43  292175            14             0             0            45   \n",
       "22   35   76845             5             0             0            40   \n",
       "\n",
       "    salary  sex_Female  sex_Male  Asian-Pac-Islander  Black  Other  White  \n",
       "1        0           0         1                   0      0      0      1  \n",
       "16       0           0         1                   0      0      0      1  \n",
       "17       0           0         1                   0      0      0      1  \n",
       "19       1           1         0                   0      0      0      1  \n",
       "22       0           0         1                   0      1      0      0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dfCountries = finaldf2.iloc[testing_rowsCountries, :]\n",
    "print(test_dfCountries.shape)\n",
    "test_dfCountries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(train_dfCountries.drop('salary',axis=1), \n",
    "                                                    train_dfCountries['salary'], test_size=0.20, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = LogisticRegression()\n",
    "model2.fit(X_train2,y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions2 = model2.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.97      0.89      4005\n",
      "           1       0.74      0.27      0.39      1207\n",
      "\n",
      "    accuracy                           0.81      5212\n",
      "   macro avg       0.78      0.62      0.64      5212\n",
      "weighted avg       0.80      0.81      0.77      5212\n",
      "\n",
      "0.8085188027628549\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test2,predictions2))\n",
    "print(accuracy_score(y_test2, predictions2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By adding more features(Countries) my model performance actually decresed. Although not by much. Concludion the first model of logistic regression type was better "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now I will use attempt to use a decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "treeData = finaldf.drop(['workclass','marital-status','occupation','relationship','native-country'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32569</td>\n",
       "      <td>52</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32570 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  fnlwgt  EducationNum  capital-gain  capital-loss  HoursPerWeek  \\\n",
       "0       39   77516            13          2174             0            40   \n",
       "1       50   83311            13             0             0            13   \n",
       "2       38  215646             9             0             0            40   \n",
       "3       53  234721             7             0             0            40   \n",
       "4       28  338409            13             0             0            40   \n",
       "...    ...     ...           ...           ...           ...           ...   \n",
       "32565   27  257302            12             0             0            38   \n",
       "32566   40  154374             9             0             0            40   \n",
       "32567   58  151910             9             0             0            40   \n",
       "32568   22  201490             9             0             0            20   \n",
       "32569   52  287927             9         15024             0            40   \n",
       "\n",
       "       salary  sex_Female  sex_Male  Asian-Pac-Islander  Black  Other  White  \n",
       "0           0           0         1                   0      0      0      1  \n",
       "1           0           0         1                   0      0      0      1  \n",
       "2           0           0         1                   0      0      0      1  \n",
       "3           0           0         1                   0      1      0      0  \n",
       "4           0           1         0                   0      1      0      0  \n",
       "...       ...         ...       ...                 ...    ...    ...    ...  \n",
       "32565       0           1         0                   0      0      0      1  \n",
       "32566       1           0         1                   0      0      0      1  \n",
       "32567       0           1         0                   0      0      0      1  \n",
       "32568       0           0         1                   0      0      0      1  \n",
       "32569       1           1         0                   0      0      0      1  \n",
       "\n",
       "[32570 rows x 13 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treeData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = treeData.drop('salary',axis=1)\n",
    "y = treeData['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <th>Black</th>\n",
       "      <th>Other</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</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",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  EducationNum  capital-gain  capital-loss  HoursPerWeek  \\\n",
       "0   39   77516            13          2174             0            40   \n",
       "1   50   83311            13             0             0            13   \n",
       "2   38  215646             9             0             0            40   \n",
       "3   53  234721             7             0             0            40   \n",
       "4   28  338409            13             0             0            40   \n",
       "\n",
       "   sex_Female  sex_Male  Asian-Pac-Islander  Black  Other  White  \n",
       "0           0         1                   0      0      0      1  \n",
       "1           0         1                   0      0      0      1  \n",
       "2           0         1                   0      0      0      1  \n",
       "3           0         1                   0      1      0      0  \n",
       "4           1         0                   0      1      0      0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainT, X_testT, y_trainT, y_testT = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=8,\n",
       "                       max_features=None, 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, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "modelTree = DecisionTreeClassifier(max_depth=8)\n",
    "modelTree.fit(X_trainT,y_trainT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6993  441]\n",
      " [1173 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.94      0.90      7434\n",
      "           1       0.73      0.50      0.59      2337\n",
      "\n",
      "    accuracy                           0.83      9771\n",
      "   macro avg       0.79      0.72      0.74      9771\n",
      "weighted avg       0.82      0.83      0.82      9771\n",
      "\n",
      "0.8348173165489714\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "predictionsTree = modelTree.predict(X_testT)\n",
    "print(confusion_matrix(y_testT,predictionsTree))\n",
    "print(classification_report(y_testT,predictionsTree))\n",
    "print(accuracy_score(y_testT, predictionsTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great model performance For decision Tree with the given features!\n",
    "# Decistion Treee was the best performing model when compared with logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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