Adult_Income_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 413,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>final-weight</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num-of-years</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>Numeric_analyser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Male_&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Male_&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>Male_&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>Male_&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>Private</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Female_&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>48837</th>\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>&lt;=50K</td>\n",
       "      <td>Female_&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\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>&gt;50K</td>\n",
       "      <td>Male_&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\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>&lt;=50K</td>\n",
       "      <td>Female_&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\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>&lt;=50K</td>\n",
       "      <td>Male_&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\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>&gt;50K</td>\n",
       "      <td>Female_&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  final-weight     education  \\\n",
       "0       25       Private        226802          11th   \n",
       "1       38       Private         89814       HS-grad   \n",
       "2       28     Local-gov        336951    Assoc-acdm   \n",
       "3       44       Private        160323  Some-college   \n",
       "4       18       Private        103497  Some-college   \n",
       "...    ...           ...           ...           ...   \n",
       "48837   27       Private        257302    Assoc-acdm   \n",
       "48838   40       Private        154374       HS-grad   \n",
       "48839   58       Private        151910       HS-grad   \n",
       "48840   22       Private        201490       HS-grad   \n",
       "48841   52  Self-emp-inc        287927       HS-grad   \n",
       "\n",
       "       educational-num-of-years      marital-status         occupation  \\\n",
       "0                             7       Never-married  Machine-op-inspct   \n",
       "1                             9  Married-civ-spouse    Farming-fishing   \n",
       "2                            12  Married-civ-spouse    Protective-serv   \n",
       "3                            10  Married-civ-spouse  Machine-op-inspct   \n",
       "4                            10       Never-married       Craft-repair   \n",
       "...                         ...                 ...                ...   \n",
       "48837                        12  Married-civ-spouse       Tech-support   \n",
       "48838                         9  Married-civ-spouse  Machine-op-inspct   \n",
       "48839                         9             Widowed       Adm-clerical   \n",
       "48840                         9       Never-married       Adm-clerical   \n",
       "48841                         9  Married-civ-spouse    Exec-managerial   \n",
       "\n",
       "      relationship   race  gender  capital-gain  capital-loss  hours-per-week  \\\n",
       "0        Own-child  Black    Male             0             0              40   \n",
       "1          Husband  White    Male             0             0              50   \n",
       "2          Husband  White    Male             0             0              40   \n",
       "3          Husband  Black    Male          7688             0              40   \n",
       "4        Own-child  White  Female             0             0              30   \n",
       "...            ...    ...     ...           ...           ...             ...   \n",
       "48837         Wife  White  Female             0             0              38   \n",
       "48838      Husband  White    Male             0             0              40   \n",
       "48839    Unmarried  White  Female             0             0              40   \n",
       "48840    Own-child  White    Male             0             0              20   \n",
       "48841         Wife  White  Female         15024             0              40   \n",
       "\n",
       "      native-country income Numeric_analyser  \n",
       "0      United-States  <=50K       Male_<=50K  \n",
       "1      United-States  <=50K       Male_<=50K  \n",
       "2      United-States   >50K        Male_>50K  \n",
       "3      United-States   >50K        Male_>50K  \n",
       "4      United-States  <=50K     Female_<=50K  \n",
       "...              ...    ...              ...  \n",
       "48837  United-States  <=50K     Female_<=50K  \n",
       "48838  United-States   >50K        Male_>50K  \n",
       "48839  United-States  <=50K     Female_<=50K  \n",
       "48840  United-States  <=50K       Male_<=50K  \n",
       "48841  United-States   >50K      Female_>50K  \n",
       "\n",
       "[48842 rows x 16 columns]"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "import plotly.express as px\n",
    "plt.style.use(\"ggplot\")\n",
    "sns.set_palette(\"mako\")\n",
    "from scipy.stats import boxcox\n",
    "from scipy.stats import spearmanr as sr,chi2_contingency\n",
    "def getPlotLabels(df,column,splot):\n",
    "    for p in splot.patches:\n",
    "        splot.annotate(format(p.get_height(), '.0f'), \n",
    "                       (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                       ha = 'center', va = 'center', \n",
    "                       size=15,\n",
    "                       xytext = (0, -12), \n",
    "                       textcoords = 'offset points',\n",
    "                       fontsize=\"20\",\n",
    "                       fontweight=\"bold\",\n",
    "                       color=\"#e9e9e6\")\n",
    "warnings.filterwarnings('ignore')\n",
    "df=pd.read_csv(\"Adult_income.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 414,
     "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so After Looking Over All the boxplots,we know that \n",
    "- Age\n",
    "- final-weight\n",
    "- capital gain\n",
    "- capital loss\n",
    "- hours per week\n",
    "\n",
    "Lets Transform Age using log and remove the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9459101490553132"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.999999999999999"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.718281828459045**1.9459101490553132"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"age\"]=np.log(df[\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 418,
     "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": [
    "df[[\"age\"]].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets Now Treat outliers In final-weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 419,
     "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": [
    "df[\"final-weight\"].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "limit=np.percentile(df['final-weight'],95)\n",
    "df=df[df['final-weight'] <= limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets treat outliers in capital gain and Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 421,
     "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": [
    "df[[\"capital-gain\",\"capital-loss\"]].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We See that two colums have weird distribution,Lets calculate in one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total-capital\"]=df[\"capital-gain\"]-df[\"capital-loss\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "df=df[(stats.zscore(df['capital-loss'])>-3) & (stats.zscore(df['capital-loss'])<3)]\n",
    "df=df[(stats.zscore(df['capital-gain'])>-3) & (stats.zscore(df['capital-gain'])<3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 424,
     "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": [
    "df[[\"capital-gain\",\"capital-loss\",\"total-capital\"]].plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After merging we understand that,the two colums now have been calculated based on some assumption and now looks little bit good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets Now treat Outliers in hours-per-week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[(stats.zscore(df['hours-per-week'])>-3) & (stats.zscore(df['hours-per-week'])<3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets Encode Our Traget Data but here we will drop the Numeric_analyser bcoz that column was meant for EDA and has no significance and capital gain and loss too as we have calculated the the total capital using profit-loss i.e total price capital."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Numeric_analyser\",\"capital-gain\",\"capital-loss\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age vs Target => Ttest_indResult(statistic=-15.548741551529652, pvalue=1.5002767700219344e-51)\n",
      "final-weight vs Target => Ttest_indResult(statistic=-0.22386366116736497, pvalue=0.8228862796107866)\n",
      "educational-num-of-years vs Target => Ttest_indResult(statistic=-17.972709325866656, pvalue=4.5760076737473646e-67)\n",
      "hours-per-week vs Target => Ttest_indResult(statistic=-12.925174041163002, pvalue=9.241113615703657e-37)\n",
      "total-capital vs Target => Ttest_indResult(statistic=-13.219444108282726, pvalue=2.61717075558987e-38)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "np.random.seed(1)\n",
    "data=df.groupby([\"income\"]).sample(1000)\n",
    "for i in df.select_dtypes([\"int64\",\"float64\"]):\n",
    "    target_yes = data.loc[data['income']=='<=50K',i]\n",
    "    target_no = data.loc[data['income']=='>50K',i]\n",
    "    ttest_results = ttest_ind(target_yes,target_no)\n",
    "    print(f'{i} vs Target => {ttest_results}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From aboove we see that final-weight is having low pv,so we drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"final-weight\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets Transform Our Target variables and gender as they have only two categories.Rest we will One hot encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['gender'] = le.fit_transform(df['gender'])\n",
    "df['income'] = le.fit_transform(df['income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets Transform categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['workclass', 'education', 'marital-status', 'occupation',\n",
       "       'relationship', 'race', 'native-country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes([\"object\"]).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       workclass_Local-gov  workclass_Never-worked  workclass_Private  \\\n",
       "0                      0.0                     0.0                1.0   \n",
       "1                      0.0                     0.0                1.0   \n",
       "2                      1.0                     0.0                0.0   \n",
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       "...                    ...                     ...                ...   \n",
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       "43349                  0.0                     0.0                0.0   \n",
       "\n",
       "       workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "...                       ...                         ...   \n",
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       "\n",
       "       workclass_State-gov  workclass_Without-pay  education_11th  \\\n",
       "0                      0.0                    0.0             1.0   \n",
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       "2                      0.0                    0.0             0.0   \n",
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       "...                    ...                    ...             ...   \n",
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       "\n",
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       "0                 0.0                0.0  ...                      0.0   \n",
       "1                 0.0                0.0  ...                      0.0   \n",
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       "\n",
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       "0                             0.0                      0.0   \n",
       "1                             0.0                      0.0   \n",
       "2                             0.0                      0.0   \n",
       "3                             0.0                      0.0   \n",
       "4                             0.0                      0.0   \n",
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       "\n",
       "       native-country_South  native-country_Taiwan  native-country_Thailand  \\\n",
       "0                       0.0                    0.0                      0.0   \n",
       "1                       0.0                    0.0                      0.0   \n",
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       "...                     ...                    ...                      ...   \n",
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       "\n",
       "       native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "0                                 0.0                           1.0   \n",
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       "2                                 0.0                           1.0   \n",
       "3                                 0.0                           1.0   \n",
       "4                                 0.0                           1.0   \n",
       "...                               ...                           ...   \n",
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       "43347                             0.0                           1.0   \n",
       "43348                             0.0                           1.0   \n",
       "43349                             0.0                           1.0   \n",
       "\n",
       "       native-country_Vietnam  native-country_Yugoslavia  \n",
       "0                         0.0                        0.0  \n",
       "1                         0.0                        0.0  \n",
       "2                         0.0                        0.0  \n",
       "3                         0.0                        0.0  \n",
       "4                         0.0                        0.0  \n",
       "...                       ...                        ...  \n",
       "43345                     0.0                        0.0  \n",
       "43346                     0.0                        0.0  \n",
       "43347                     0.0                        0.0  \n",
       "43348                     0.0                        0.0  \n",
       "43349                     0.0                        0.0  \n",
       "\n",
       "[43350 rows x 89 columns]"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder as ohc\n",
    "enc=ohc(drop=\"first\",sparse=False)\n",
    "encD=enc.fit_transform(df[['workclass', 'education', 'marital-status', 'occupation','relationship', 'race', 'native-country']])\n",
    "encD=pd.DataFrame(encD,columns=enc.get_feature_names(df[['workclass', 'education', 'marital-status', 'occupation','relationship', 'race', 'native-country']].columns))\n",
    "encD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['workclass', 'education', 'marital-status', 'occupation','relationship', 'race', 'native-country'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True)\n",
    "encD.reset_index(inplace=True)\n",
    "df=pd.concat([encD,df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"index\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Have encoded the String values,Lets Now scale numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>workclass_Local-gov</th>\n",
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       "      <th>workclass_State-gov</th>\n",
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       "      <th>education_11th</th>\n",
       "      <th>education_12th</th>\n",
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       "      <th>native-country_Vietnam</th>\n",
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       "      <th>educational-num-of-years</th>\n",
       "      <th>gender</th>\n",
       "      <th>hours-per-week</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43350 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       workclass_Local-gov  workclass_Never-worked  workclass_Private  \\\n",
       "0                      0.0                     0.0                1.0   \n",
       "1                      0.0                     0.0                1.0   \n",
       "2                      1.0                     0.0                0.0   \n",
       "3                      0.0                     0.0                1.0   \n",
       "4                      0.0                     0.0                1.0   \n",
       "...                    ...                     ...                ...   \n",
       "43345                  0.0                     0.0                1.0   \n",
       "43346                  0.0                     0.0                1.0   \n",
       "43347                  0.0                     0.0                1.0   \n",
       "43348                  0.0                     0.0                1.0   \n",
       "43349                  0.0                     0.0                0.0   \n",
       "\n",
       "       workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "...                       ...                         ...   \n",
       "43345                     0.0                         0.0   \n",
       "43346                     0.0                         0.0   \n",
       "43347                     0.0                         0.0   \n",
       "43348                     0.0                         0.0   \n",
       "43349                     1.0                         0.0   \n",
       "\n",
       "       workclass_State-gov  workclass_Without-pay  education_11th  \\\n",
       "0                      0.0                    0.0             1.0   \n",
       "1                      0.0                    0.0             0.0   \n",
       "2                      0.0                    0.0             0.0   \n",
       "3                      0.0                    0.0             0.0   \n",
       "4                      0.0                    0.0             0.0   \n",
       "...                    ...                    ...             ...   \n",
       "43345                  0.0                    0.0             0.0   \n",
       "43346                  0.0                    0.0             0.0   \n",
       "43347                  0.0                    0.0             0.0   \n",
       "43348                  0.0                    0.0             0.0   \n",
       "43349                  0.0                    0.0             0.0   \n",
       "\n",
       "       education_12th  education_1st-4th  ...  native-country_Trinadad&Tobago  \\\n",
       "0                 0.0                0.0  ...                             0.0   \n",
       "1                 0.0                0.0  ...                             0.0   \n",
       "2                 0.0                0.0  ...                             0.0   \n",
       "3                 0.0                0.0  ...                             0.0   \n",
       "4                 0.0                0.0  ...                             0.0   \n",
       "...               ...                ...  ...                             ...   \n",
       "43345             0.0                0.0  ...                             0.0   \n",
       "43346             0.0                0.0  ...                             0.0   \n",
       "43347             0.0                0.0  ...                             0.0   \n",
       "43348             0.0                0.0  ...                             0.0   \n",
       "43349             0.0                0.0  ...                             0.0   \n",
       "\n",
       "       native-country_United-States  native-country_Vietnam  \\\n",
       "0                               1.0                     0.0   \n",
       "1                               1.0                     0.0   \n",
       "2                               1.0                     0.0   \n",
       "3                               1.0                     0.0   \n",
       "4                               1.0                     0.0   \n",
       "...                             ...                     ...   \n",
       "43345                           1.0                     0.0   \n",
       "43346                           1.0                     0.0   \n",
       "43347                           1.0                     0.0   \n",
       "43348                           1.0                     0.0   \n",
       "43349                           1.0                     0.0   \n",
       "\n",
       "       native-country_Yugoslavia       age  educational-num-of-years  gender  \\\n",
       "0                            0.0 -1.014282                 -1.198384       1   \n",
       "1                            0.0  0.138630                 -0.408331       1   \n",
       "2                            0.0 -0.702233                  0.776749       1   \n",
       "3                            0.0  0.542300                 -0.013304       1   \n",
       "4                            0.0 -1.918812                 -0.013304       0   \n",
       "...                          ...       ...                       ...     ...   \n",
       "43345                        0.0 -0.802371                  0.776749       0   \n",
       "43346                        0.0  0.279865                 -0.408331       1   \n",
       "43347                        0.0  1.302958                 -0.408331       0   \n",
       "43348                        0.0 -1.366269                 -0.408331       1   \n",
       "43349                        0.0  1.002280                 -0.408331       0   \n",
       "\n",
       "       hours-per-week  income  total-capital  \n",
       "0            0.015825       0      -0.242586  \n",
       "1            0.906994       0      -0.242586  \n",
       "2            0.015825       1      -0.242586  \n",
       "3            0.015825       1       3.108324  \n",
       "4           -0.875344       0      -0.242586  \n",
       "...               ...     ...            ...  \n",
       "43345       -0.162409       0      -0.242586  \n",
       "43346        0.015825       1      -0.242586  \n",
       "43347        0.015825       0      -0.242586  \n",
       "43348       -1.766513       0      -0.242586  \n",
       "43349        0.015825       1       6.305810  \n",
       "\n",
       "[43350 rows x 95 columns]"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "df[[\"educational-num-of-years\",\"hours-per-week\",\"total-capital\",\"age\"]] = sc.fit_transform(df[[\"educational-num-of-years\",\"hours-per-week\",\"total-capital\",\"age\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Have Scaled and Transformed our Data but one thing keep in mind we have an imbalanced data,so we will have to use undersampling or over sampling or any other technique to make it good. So we will use SMOTE bcoz it provides some add info about minority class compared to Under and OverSampler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "X=df.drop([\"income\"],axis=1)\n",
    "Y=df[\"income\"]\n",
    "sm = SMOTE(random_state = 42)\n",
    "X,Y=sm.fit_resample(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "ax=sns.countplot(x=\"income\",data=pd.DataFrame(Y))\n",
    "getPlotLabels(pd.DataFrame(Y),\"income\",ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So Now we see that Using Smote, Our Class Counts have been Balanced.Now Lets Split Data using Train test split and then train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "XT,XTS,YT,YTS=tts(X,Y,random_state=42,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,roc_curve,precision_score,recall_score,plot_confusion_matrix,classification_report\n",
    "models=pd.DataFrame({})\n",
    "lor=LogisticRegression()\n",
    "lor.fit(XT,YT)\n",
    "lor_pred_train=lor.predict(XT)\n",
    "lor_pred_test=lor.predict(XTS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bestModelCV(model):\n",
    "    cvs=cross_validate(model,X=X,y=Y,cv=10,scoring=[\"accuracy\",\"roc_auc\"],return_estimator=True)\n",
    "    return cvs[\"estimator\"][cvs[\"test_accuracy\"].tolist().index(max(cvs[\"test_accuracy\"].tolist()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BEST_MODEL_SELECTED  TEST_ACCURACY  TEST_FSCORE  \\\n",
       "0  LogisticRegression(random_state=2)       0.833987     0.839774   \n",
       "\n",
       "   TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0        0.828019      0.832643  "
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=LogisticRegression(random_state=2)\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decesion Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree=DecisionTreeClassifier(criterion=\"gini\")\n",
    "dtree.fit(XT,YT)\n",
    "dtree_pred_train=dtree.predict(XT)\n",
    "dtree_pred_test=dtree.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      BEST_MODEL_SELECTED  TEST_ACCURACY  TEST_FSCORE  \\\n",
       "0      LogisticRegression(random_state=2)       0.833987     0.839774   \n",
       "1  DecisionTreeClassifier(random_state=2)       0.971431     0.971580   \n",
       "\n",
       "   TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0        0.828019      0.832643  \n",
       "1        0.968968      0.968890  "
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=DecisionTreeClassifier(criterion=\"gini\",random_state=2)\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NaiveBayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "nby=GaussianNB()\n",
    "nby.fit(XT,YT)\n",
    "nby_pred_train=nby.predict(XT)\n",
    "nby_pred_test=nby.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      BEST_MODEL_SELECTED  TEST_ACCURACY  TEST_FSCORE  \\\n",
       "0      LogisticRegression(random_state=2)       0.833987     0.839774   \n",
       "1  DecisionTreeClassifier(random_state=2)       0.971431     0.971580   \n",
       "2                            GaussianNB()       0.720746     0.772286   \n",
       "\n",
       "   TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0        0.828019      0.832643  \n",
       "1        0.968968      0.968890  \n",
       "2        0.716993      0.768546  "
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=nby\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=3,n_jobs=-1)\n",
    "knn.fit(XT,YT)\n",
    "knn_pred_train=knn.predict(XT)\n",
    "knn_pred_test=knn.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</td>\n",
       "      <td>0.914144</td>\n",
       "      <td>0.917555</td>\n",
       "      <td>0.914028</td>\n",
       "      <td>0.916511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              BEST_MODEL_SELECTED  TEST_ACCURACY  TEST_FSCORE  \\\n",
       "0              LogisticRegression(random_state=2)       0.833987     0.839774   \n",
       "1          DecisionTreeClassifier(random_state=2)       0.971431     0.971580   \n",
       "2                                    GaussianNB()       0.720746     0.772286   \n",
       "3  KNeighborsClassifier(n_jobs=-1, n_neighbors=3)       0.914144     0.917555   \n",
       "\n",
       "   TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0        0.828019      0.832643  \n",
       "1        0.968968      0.968890  \n",
       "2        0.716993      0.768546  \n",
       "3        0.914028      0.916511  "
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=knn\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GradientBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc=GradientBoostingClassifier()\n",
    "gbc.fit(XT,YT)\n",
    "gbc_pred_train=gbc.predict(XT)\n",
    "gbc_pred_test=gbc.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</td>\n",
       "      <td>0.914144</td>\n",
       "      <td>0.917555</td>\n",
       "      <td>0.914028</td>\n",
       "      <td>0.916511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingClassifier(random_state=2)</td>\n",
       "      <td>0.867441</td>\n",
       "      <td>0.873365</td>\n",
       "      <td>0.858459</td>\n",
       "      <td>0.863472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              BEST_MODEL_SELECTED  TEST_ACCURACY  TEST_FSCORE  \\\n",
       "0              LogisticRegression(random_state=2)       0.833987     0.839774   \n",
       "1          DecisionTreeClassifier(random_state=2)       0.971431     0.971580   \n",
       "2                                    GaussianNB()       0.720746     0.772286   \n",
       "3  KNeighborsClassifier(n_jobs=-1, n_neighbors=3)       0.914144     0.917555   \n",
       "4      GradientBoostingClassifier(random_state=2)       0.867441     0.873365   \n",
       "\n",
       "   TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0        0.828019      0.832643  \n",
       "1        0.968968      0.968890  \n",
       "2        0.716993      0.768546  \n",
       "3        0.914028      0.916511  \n",
       "4        0.858459      0.863472  "
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=GradientBoostingClassifier(random_state=2)\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "adb=AdaBoostClassifier()\n",
    "adb.fit(XT,YT)\n",
    "adb_pred_train=adb.predict(XT)\n",
    "adb_pred_test=adb.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "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>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</td>\n",
       "      <td>0.914144</td>\n",
       "      <td>0.917555</td>\n",
       "      <td>0.914028</td>\n",
       "      <td>0.916511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingClassifier(random_state=2)</td>\n",
       "      <td>0.867441</td>\n",
       "      <td>0.873365</td>\n",
       "      <td>0.858459</td>\n",
       "      <td>0.863472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoostClassifier(random_state=2)</td>\n",
       "      <td>0.856561</td>\n",
       "      <td>0.861650</td>\n",
       "      <td>0.847875</td>\n",
       "      <td>0.851561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              BEST_MODEL_SELECTED  TEST_ACCURACY  TEST_FSCORE  \\\n",
       "0              LogisticRegression(random_state=2)       0.833987     0.839774   \n",
       "1          DecisionTreeClassifier(random_state=2)       0.971431     0.971580   \n",
       "2                                    GaussianNB()       0.720746     0.772286   \n",
       "3  KNeighborsClassifier(n_jobs=-1, n_neighbors=3)       0.914144     0.917555   \n",
       "4      GradientBoostingClassifier(random_state=2)       0.867441     0.873365   \n",
       "5              AdaBoostClassifier(random_state=2)       0.856561     0.861650   \n",
       "\n",
       "   TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0        0.828019      0.832643  \n",
       "1        0.968968      0.968890  \n",
       "2        0.716993      0.768546  \n",
       "3        0.914028      0.916511  \n",
       "4        0.858459      0.863472  \n",
       "5        0.847875      0.851561  "
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=AdaBoostClassifier(random_state=2)\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:01:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "xgb=XGBClassifier()\n",
    "xgb.fit(XT,YT)\n",
    "xgb_pred_train=xgb.predict(XT)\n",
    "xgb_pred_test=xgb.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:01:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:01:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:01:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:01:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:01:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:01:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:01:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:02:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:03:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:03:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:03:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:03:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:03:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</td>\n",
       "      <td>0.914144</td>\n",
       "      <td>0.917555</td>\n",
       "      <td>0.914028</td>\n",
       "      <td>0.916511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingClassifier(random_state=2)</td>\n",
       "      <td>0.867441</td>\n",
       "      <td>0.873365</td>\n",
       "      <td>0.858459</td>\n",
       "      <td>0.863472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoostClassifier(random_state=2)</td>\n",
       "      <td>0.856561</td>\n",
       "      <td>0.861650</td>\n",
       "      <td>0.847875</td>\n",
       "      <td>0.851561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBClassifier(base_score=None, booster=None, c...</td>\n",
       "      <td>0.916142</td>\n",
       "      <td>0.918000</td>\n",
       "      <td>0.912585</td>\n",
       "      <td>0.913943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 BEST_MODEL_SELECTED  TEST_ACCURACY  \\\n",
       "0                 LogisticRegression(random_state=2)       0.833987   \n",
       "1             DecisionTreeClassifier(random_state=2)       0.971431   \n",
       "2                                       GaussianNB()       0.720746   \n",
       "3     KNeighborsClassifier(n_jobs=-1, n_neighbors=3)       0.914144   \n",
       "4         GradientBoostingClassifier(random_state=2)       0.867441   \n",
       "5                 AdaBoostClassifier(random_state=2)       0.856561   \n",
       "6  XGBClassifier(base_score=None, booster=None, c...       0.916142   \n",
       "\n",
       "   TEST_FSCORE  TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0     0.839774        0.828019      0.832643  \n",
       "1     0.971580        0.968968      0.968890  \n",
       "2     0.772286        0.716993      0.768546  \n",
       "3     0.917555        0.914028      0.916511  \n",
       "4     0.873365        0.858459      0.863472  \n",
       "5     0.861650        0.847875      0.851561  \n",
       "6     0.918000        0.912585      0.913943  "
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=XGBClassifier(random_state=2)\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc=RandomForestClassifier()\n",
    "rfc.fit(XT,YT)\n",
    "rfc_pred_train=rfc.predict(XT)\n",
    "rfc_pred_test=rfc.predict(XTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</td>\n",
       "      <td>0.914144</td>\n",
       "      <td>0.917555</td>\n",
       "      <td>0.914028</td>\n",
       "      <td>0.916511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingClassifier(random_state=2)</td>\n",
       "      <td>0.867441</td>\n",
       "      <td>0.873365</td>\n",
       "      <td>0.858459</td>\n",
       "      <td>0.863472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoostClassifier(random_state=2)</td>\n",
       "      <td>0.856561</td>\n",
       "      <td>0.861650</td>\n",
       "      <td>0.847875</td>\n",
       "      <td>0.851561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBClassifier(base_score=None, booster=None, c...</td>\n",
       "      <td>0.916142</td>\n",
       "      <td>0.918000</td>\n",
       "      <td>0.912585</td>\n",
       "      <td>0.913943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RandomForestClassifier(random_state=2)</td>\n",
       "      <td>0.970469</td>\n",
       "      <td>0.970942</td>\n",
       "      <td>0.970763</td>\n",
       "      <td>0.970969</td>\n",
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      "text/plain": [
       "                                 BEST_MODEL_SELECTED  TEST_ACCURACY  \\\n",
       "0                 LogisticRegression(random_state=2)       0.833987   \n",
       "1             DecisionTreeClassifier(random_state=2)       0.971431   \n",
       "2                                       GaussianNB()       0.720746   \n",
       "3     KNeighborsClassifier(n_jobs=-1, n_neighbors=3)       0.914144   \n",
       "4         GradientBoostingClassifier(random_state=2)       0.867441   \n",
       "5                 AdaBoostClassifier(random_state=2)       0.856561   \n",
       "6  XGBClassifier(base_score=None, booster=None, c...       0.916142   \n",
       "7             RandomForestClassifier(random_state=2)       0.970469   \n",
       "\n",
       "   TEST_FSCORE  TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "0     0.839774        0.828019      0.832643  \n",
       "1     0.971580        0.968968      0.968890  \n",
       "2     0.772286        0.716993      0.768546  \n",
       "3     0.917555        0.914028      0.916511  \n",
       "4     0.873365        0.858459      0.863472  \n",
       "5     0.861650        0.847875      0.851561  \n",
       "6     0.918000        0.912585      0.913943  \n",
       "7     0.970942        0.970763      0.970969  "
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=RandomForestClassifier(random_state=2)\n",
    "predictedValuesTrain=bestModelCV(model_2).predict(XT)\n",
    "predictedValuesTest=bestModelCV(model_2).predict(XTS)\n",
    "models=models.append({\"BEST_MODEL_SELECTED\":model_2,\"TRAIN_ACCURACY\":accuracy_score(predictedValuesTrain,YT),\"TRAIN_FSCORE\":f1_score(predictedValuesTrain,YT),\"TEST_ACCURACY\":accuracy_score(predictedValuesTest,YTS),\"TEST_FSCORE\":f1_score(predictedValuesTest,YTS)},ignore_index=True)\n",
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEST_MODEL_SELECTED</th>\n",
       "      <th>TEST_ACCURACY</th>\n",
       "      <th>TEST_FSCORE</th>\n",
       "      <th>TRAIN_ACCURACY</th>\n",
       "      <th>TRAIN_FSCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier(random_state=2)</td>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.971580</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RandomForestClassifier(random_state=2)</td>\n",
       "      <td>0.970469</td>\n",
       "      <td>0.970942</td>\n",
       "      <td>0.970763</td>\n",
       "      <td>0.970969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBClassifier(base_score=None, booster=None, c...</td>\n",
       "      <td>0.916142</td>\n",
       "      <td>0.918000</td>\n",
       "      <td>0.912585</td>\n",
       "      <td>0.913943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</td>\n",
       "      <td>0.914144</td>\n",
       "      <td>0.917555</td>\n",
       "      <td>0.914028</td>\n",
       "      <td>0.916511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingClassifier(random_state=2)</td>\n",
       "      <td>0.867441</td>\n",
       "      <td>0.873365</td>\n",
       "      <td>0.858459</td>\n",
       "      <td>0.863472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoostClassifier(random_state=2)</td>\n",
       "      <td>0.856561</td>\n",
       "      <td>0.861650</td>\n",
       "      <td>0.847875</td>\n",
       "      <td>0.851561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression(random_state=2)</td>\n",
       "      <td>0.833987</td>\n",
       "      <td>0.839774</td>\n",
       "      <td>0.828019</td>\n",
       "      <td>0.832643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB()</td>\n",
       "      <td>0.720746</td>\n",
       "      <td>0.772286</td>\n",
       "      <td>0.716993</td>\n",
       "      <td>0.768546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 BEST_MODEL_SELECTED  TEST_ACCURACY  \\\n",
       "1             DecisionTreeClassifier(random_state=2)       0.971431   \n",
       "7             RandomForestClassifier(random_state=2)       0.970469   \n",
       "6  XGBClassifier(base_score=None, booster=None, c...       0.916142   \n",
       "3     KNeighborsClassifier(n_jobs=-1, n_neighbors=3)       0.914144   \n",
       "4         GradientBoostingClassifier(random_state=2)       0.867441   \n",
       "5                 AdaBoostClassifier(random_state=2)       0.856561   \n",
       "0                 LogisticRegression(random_state=2)       0.833987   \n",
       "2                                       GaussianNB()       0.720746   \n",
       "\n",
       "   TEST_FSCORE  TRAIN_ACCURACY  TRAIN_FSCORE  \n",
       "1     0.971580        0.968968      0.968890  \n",
       "7     0.970942        0.970763      0.970969  \n",
       "6     0.918000        0.912585      0.913943  \n",
       "3     0.917555        0.914028      0.916511  \n",
       "4     0.873365        0.858459      0.863472  \n",
       "5     0.861650        0.847875      0.851561  \n",
       "0     0.839774        0.828019      0.832643  \n",
       "2     0.772286        0.716993      0.768546  "
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.sort_values(['TEST_ACCURACY', 'TRAIN_ACCURACY'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Aboove We See that <font color=\"green\">Decision Tree Classifier</font> has Highest Accuracy in all Models which fit well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<B><I>SO IF WE WANT TO PREDICT VALUE FOR OUR INPUTS THROUGH USERS POINT OF VIEW,THE USER WOULD ENTER BELOW ARRAY, IN REAL WORLD MORE COMPRESSION TECHNIQUE ARE APPLIED TO SELECT RELEVANT FEATURES AS INPUTS USING VARIETY OF FEATURE ELIMINATION TECHNIQUES DEPENING ON NEEDS AND USER WILL HAVE FEW INPUT FIELDS AND GET THE PREDICTION WHEN COLUMNS DECREASE. SO THE BELOW IS A EXAMPLE RANDOMLY TAKEN WHICH WILL BE GIVEN AS INPUT,JUST ASSUME A INPUT IS GIVEN BELOW</I></B>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
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       "         1.17177523,  1.        ,  0.46140961, -0.24258564]])"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_value=np.array([[ 0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
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    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "         0.        ,  1.        ,  0.        ,  0.        ,  0.60417817,\n",
    "         1.17177523,  1.        ,  0.46140961, -0.24258564]]).reshape(1,-1)\n",
    "input_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Candidate Has Income >50K\n"
     ]
    }
   ],
   "source": [
    "model_2=DecisionTreeClassifier(criterion=\"gini\",random_state=2)\n",
    "predictedValuesTest=bestModelCV(model_2)\n",
    "print(f\"The Candidate Has Income {le.inverse_transform(predictedValuesTest.predict(input_value))[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i><b><font color=\"navy\">So aboove Is Our</font><font color=\"blue\"> Income prediction with given Input and Thats Income</font> ><font color=\"green\"><u>50K</u></font></b></i>"
   ]
  }
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