classification based on census data with more plots and analysis..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"adult.data\")\n",
    "df1=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
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      "text/plain": [
       "       39          State-gov   77516    Bachelors   13        Never-married  \\\n",
       "0      50   Self-emp-not-inc   83311    Bachelors   13   Married-civ-spouse   \n",
       "1      38            Private  215646      HS-grad    9             Divorced   \n",
       "2      53            Private  234721         11th    7   Married-civ-spouse   \n",
       "3      28            Private  338409    Bachelors   13   Married-civ-spouse   \n",
       "4      37            Private  284582      Masters   14   Married-civ-spouse   \n",
       "...    ..                ...     ...          ...  ...                  ...   \n",
       "32555  27            Private  257302   Assoc-acdm   12   Married-civ-spouse   \n",
       "32556  40            Private  154374      HS-grad    9   Married-civ-spouse   \n",
       "32557  58            Private  151910      HS-grad    9              Widowed   \n",
       "32558  22            Private  201490      HS-grad    9        Never-married   \n",
       "32559  52       Self-emp-inc  287927      HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "             Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0         Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1       Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2       Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3          Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4         Exec-managerial            Wife   White   Female      0   0   40   \n",
       "...                   ...             ...     ...      ...    ...  ..  ...   \n",
       "32555        Tech-support            Wife   White   Female      0   0   38   \n",
       "32556   Machine-op-inspct         Husband   White     Male      0   0   40   \n",
       "32557        Adm-clerical       Unmarried   White   Female      0   0   40   \n",
       "32558        Adm-clerical       Own-child   White     Male      0   0   20   \n",
       "32559     Exec-managerial            Wife   White   Female  15024   0   40   \n",
       "\n",
       "        United-States   <=50K  \n",
       "0       United-States   <=50K  \n",
       "1       United-States   <=50K  \n",
       "2       United-States   <=50K  \n",
       "3                Cuba   <=50K  \n",
       "4       United-States   <=50K  \n",
       "...               ...     ...  \n",
       "32555   United-States   <=50K  \n",
       "32556   United-States    >50K  \n",
       "32557   United-States   <=50K  \n",
       "32558   United-States   <=50K  \n",
       "32559   United-States    >50K  \n",
       "\n",
       "[32560 rows x 15 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.understanding the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32560 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       39          State-gov   77516    Bachelors   13        Never-married  \\\n",
       "0      50   Self-emp-not-inc   83311    Bachelors   13   Married-civ-spouse   \n",
       "1      38            Private  215646      HS-grad    9             Divorced   \n",
       "2      53            Private  234721         11th    7   Married-civ-spouse   \n",
       "3      28            Private  338409    Bachelors   13   Married-civ-spouse   \n",
       "4      37            Private  284582      Masters   14   Married-civ-spouse   \n",
       "...    ..                ...     ...          ...  ...                  ...   \n",
       "32555  27            Private  257302   Assoc-acdm   12   Married-civ-spouse   \n",
       "32556  40            Private  154374      HS-grad    9   Married-civ-spouse   \n",
       "32557  58            Private  151910      HS-grad    9              Widowed   \n",
       "32558  22            Private  201490      HS-grad    9        Never-married   \n",
       "32559  52       Self-emp-inc  287927      HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "             Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0         Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1       Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2       Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3          Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4         Exec-managerial            Wife   White   Female      0   0   40   \n",
       "...                   ...             ...     ...      ...    ...  ..  ...   \n",
       "32555        Tech-support            Wife   White   Female      0   0   38   \n",
       "32556   Machine-op-inspct         Husband   White     Male      0   0   40   \n",
       "32557        Adm-clerical       Unmarried   White   Female      0   0   40   \n",
       "32558        Adm-clerical       Own-child   White     Male      0   0   20   \n",
       "32559     Exec-managerial            Wife   White   Female  15024   0   40   \n",
       "\n",
       "        United-States   <=50K  \n",
       "0       United-States   <=50K  \n",
       "1       United-States   <=50K  \n",
       "2       United-States   <=50K  \n",
       "3                Cuba   <=50K  \n",
       "4       United-States   <=50K  \n",
       "...               ...     ...  \n",
       "32555   United-States   <=50K  \n",
       "32556   United-States    >50K  \n",
       "32557   United-States   <=50K  \n",
       "32558   United-States   <=50K  \n",
       "32559   United-States    >50K  \n",
       "\n",
       "[32560 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       39          State-gov   77516    Bachelors   13        Never-married  \\\n",
       "0      50   Self-emp-not-inc   83311    Bachelors   13   Married-civ-spouse   \n",
       "1      38            Private  215646      HS-grad    9             Divorced   \n",
       "2      53            Private  234721         11th    7   Married-civ-spouse   \n",
       "3      28            Private  338409    Bachelors   13   Married-civ-spouse   \n",
       "4      37            Private  284582      Masters   14   Married-civ-spouse   \n",
       "...    ..                ...     ...          ...  ...                  ...   \n",
       "32555  27            Private  257302   Assoc-acdm   12   Married-civ-spouse   \n",
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       "32557  58            Private  151910      HS-grad    9              Widowed   \n",
       "32558  22            Private  201490      HS-grad    9        Never-married   \n",
       "32559  52       Self-emp-inc  287927      HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "             Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0         Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1       Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2       Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3          Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4         Exec-managerial            Wife   White   Female      0   0   40   \n",
       "...                   ...             ...     ...      ...    ...  ..  ...   \n",
       "32555        Tech-support            Wife   White   Female      0   0   38   \n",
       "32556   Machine-op-inspct         Husband   White     Male      0   0   40   \n",
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       "32558        Adm-clerical       Own-child   White     Male      0   0   20   \n",
       "32559     Exec-managerial            Wife   White   Female  15024   0   40   \n",
       "\n",
       "        United-States   <=50K  \n",
       "0       United-States   <=50K  \n",
       "1       United-States   <=50K  \n",
       "2       United-States   <=50K  \n",
       "3                Cuba   <=50K  \n",
       "4       United-States   <=50K  \n",
       "...               ...     ...  \n",
       "32555   United-States   <=50K  \n",
       "32556   United-States    >50K  \n",
       "32557   United-States   <=50K  \n",
       "32558   United-States   <=50K  \n",
       "32559   United-States    >50K  \n",
       "\n",
       "[32560 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32560, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <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>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</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>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   39          State-gov   77516      Bachelors   13           Never-married  \\\n",
       "0  50   Self-emp-not-inc   83311      Bachelors   13      Married-civ-spouse   \n",
       "1  38            Private  215646        HS-grad    9                Divorced   \n",
       "2  53            Private  234721           11th    7      Married-civ-spouse   \n",
       "3  28            Private  338409      Bachelors   13      Married-civ-spouse   \n",
       "4  37            Private  284582        Masters   14      Married-civ-spouse   \n",
       "5  49            Private  160187            9th    5   Married-spouse-absent   \n",
       "6  52   Self-emp-not-inc  209642        HS-grad    9      Married-civ-spouse   \n",
       "7  31            Private   45781        Masters   14           Never-married   \n",
       "8  42            Private  159449      Bachelors   13      Married-civ-spouse   \n",
       "9  37            Private  280464   Some-college   10      Married-civ-spouse   \n",
       "\n",
       "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
       "5       Other-service   Not-in-family   Black   Female      0   0   16   \n",
       "6     Exec-managerial         Husband   White     Male      0   0   45   \n",
       "7      Prof-specialty   Not-in-family   White   Female  14084   0   50   \n",
       "8     Exec-managerial         Husband   White     Male   5178   0   40   \n",
       "9     Exec-managerial         Husband   Black     Male      0   0   80   \n",
       "\n",
       "    United-States   <=50K  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3            Cuba   <=50K  \n",
       "4   United-States   <=50K  \n",
       "5         Jamaica   <=50K  \n",
       "6   United-States    >50K  \n",
       "7   United-States    >50K  \n",
       "8   United-States    >50K  \n",
       "9   United-States    >50K  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['39', ' State-gov', ' 77516', ' Bachelors', ' 13', ' Never-married',\n",
      "       ' Adm-clerical', ' Not-in-family', ' White', ' Male', ' 2174', ' 0',\n",
      "       ' 40', ' United-States', ' <=50K'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.rename(columns = {' <=50K':'salary'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
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       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32560 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       39          State-gov   77516    Bachelors   13        Never-married  \\\n",
       "0      50   Self-emp-not-inc   83311    Bachelors   13   Married-civ-spouse   \n",
       "1      38            Private  215646      HS-grad    9             Divorced   \n",
       "2      53            Private  234721         11th    7   Married-civ-spouse   \n",
       "3      28            Private  338409    Bachelors   13   Married-civ-spouse   \n",
       "4      37            Private  284582      Masters   14   Married-civ-spouse   \n",
       "...    ..                ...     ...          ...  ...                  ...   \n",
       "32555  27            Private  257302   Assoc-acdm   12   Married-civ-spouse   \n",
       "32556  40            Private  154374      HS-grad    9   Married-civ-spouse   \n",
       "32557  58            Private  151910      HS-grad    9              Widowed   \n",
       "32558  22            Private  201490      HS-grad    9        Never-married   \n",
       "32559  52       Self-emp-inc  287927      HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "             Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0         Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1       Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2       Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3          Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4         Exec-managerial            Wife   White   Female      0   0   40   \n",
       "...                   ...             ...     ...      ...    ...  ..  ...   \n",
       "32555        Tech-support            Wife   White   Female      0   0   38   \n",
       "32556   Machine-op-inspct         Husband   White     Male      0   0   40   \n",
       "32557        Adm-clerical       Unmarried   White   Female      0   0   40   \n",
       "32558        Adm-clerical       Own-child   White     Male      0   0   20   \n",
       "32559     Exec-managerial            Wife   White   Female  15024   0   40   \n",
       "\n",
       "        United-States  salary  \n",
       "0       United-States   <=50K  \n",
       "1       United-States   <=50K  \n",
       "2       United-States   <=50K  \n",
       "3                Cuba   <=50K  \n",
       "4       United-States   <=50K  \n",
       "...               ...     ...  \n",
       "32555   United-States   <=50K  \n",
       "32556   United-States    >50K  \n",
       "32557   United-States   <=50K  \n",
       "32558   United-States   <=50K  \n",
       "32559   United-States    >50K  \n",
       "\n",
       "[32560 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    24719\n",
       " >50K      7841\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"salary\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Private             22696\n",
       " Self-emp-not-inc     2541\n",
       " Local-gov            2093\n",
       " ?                    1836\n",
       " State-gov            1297\n",
       " Self-emp-inc         1116\n",
       " Federal-gov           960\n",
       " Without-pay            14\n",
       " Never-worked            7\n",
       "Name:  State-gov, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\" State-gov\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32560.000000</td>\n",
       "      <td>3.256000e+04</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581634</td>\n",
       "      <td>1.897818e+05</td>\n",
       "      <td>10.080590</td>\n",
       "      <td>1077.615172</td>\n",
       "      <td>87.306511</td>\n",
       "      <td>40.437469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640642</td>\n",
       "      <td>1.055498e+05</td>\n",
       "      <td>2.572709</td>\n",
       "      <td>7385.402999</td>\n",
       "      <td>402.966116</td>\n",
       "      <td>12.347618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178315e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783630e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370545e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 39         77516            13          2174             0  \\\n",
       "count  32560.000000  3.256000e+04  32560.000000  32560.000000  32560.000000   \n",
       "mean      38.581634  1.897818e+05     10.080590   1077.615172     87.306511   \n",
       "std       13.640642  1.055498e+05      2.572709   7385.402999    402.966116   \n",
       "min       17.000000  1.228500e+04      1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178315e+05      9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783630e+05     10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370545e+05     12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06     16.000000  99999.000000   4356.000000   \n",
       "\n",
       "                 40  \n",
       "count  32560.000000  \n",
       "mean      40.437469  \n",
       "std       12.347618  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%       40.000000  \n",
       "75%       45.000000  \n",
       "max       99.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36    898\n",
      "31    888\n",
      "34    886\n",
      "23    877\n",
      "35    876\n",
      "     ... \n",
      "83      6\n",
      "85      3\n",
      "88      3\n",
      "87      1\n",
      "86      1\n",
      "Name: 39, Length: 73, dtype: int64\n",
      "********************\n",
      " Private             22696\n",
      " Self-emp-not-inc     2541\n",
      " Local-gov            2093\n",
      " ?                    1836\n",
      " State-gov            1297\n",
      " Self-emp-inc         1116\n",
      " Federal-gov           960\n",
      " Without-pay            14\n",
      " Never-worked            7\n",
      "Name:  State-gov, dtype: int64\n",
      "********************\n",
      "123011    13\n",
      "203488    13\n",
      "164190    13\n",
      "121124    12\n",
      "148995    12\n",
      "          ..\n",
      "68318      1\n",
      "284211     1\n",
      "312881     1\n",
      "177711     1\n",
      "229376     1\n",
      "Name:  77516, Length: 21647, dtype: int64\n",
      "********************\n",
      " HS-grad         10501\n",
      " Some-college     7291\n",
      " Bachelors        5354\n",
      " Masters          1723\n",
      " Assoc-voc        1382\n",
      " 11th             1175\n",
      " Assoc-acdm       1067\n",
      " 10th              933\n",
      " 7th-8th           646\n",
      " Prof-school       576\n",
      " 9th               514\n",
      " 12th              433\n",
      " Doctorate         413\n",
      " 5th-6th           333\n",
      " 1st-4th           168\n",
      " Preschool          51\n",
      "Name:  Bachelors, dtype: int64\n",
      "********************\n",
      "9     10501\n",
      "10     7291\n",
      "13     5354\n",
      "14     1723\n",
      "11     1382\n",
      "7      1175\n",
      "12     1067\n",
      "6       933\n",
      "4       646\n",
      "15      576\n",
      "5       514\n",
      "8       433\n",
      "16      413\n",
      "3       333\n",
      "2       168\n",
      "1        51\n",
      "Name:  13, dtype: int64\n",
      "********************\n",
      " Married-civ-spouse       14976\n",
      " Never-married            10682\n",
      " Divorced                  4443\n",
      " Separated                 1025\n",
      " Widowed                    993\n",
      " Married-spouse-absent      418\n",
      " Married-AF-spouse           23\n",
      "Name:  Never-married, dtype: int64\n",
      "********************\n",
      " Prof-specialty       4140\n",
      " Craft-repair         4099\n",
      " Exec-managerial      4066\n",
      " Adm-clerical         3769\n",
      " Sales                3650\n",
      " Other-service        3295\n",
      " Machine-op-inspct    2002\n",
      " ?                    1843\n",
      " Transport-moving     1597\n",
      " Handlers-cleaners    1370\n",
      " Farming-fishing       994\n",
      " Tech-support          928\n",
      " Protective-serv       649\n",
      " Priv-house-serv       149\n",
      " Armed-Forces            9\n",
      "Name:  Adm-clerical, dtype: int64\n",
      "********************\n",
      " Husband           13193\n",
      " Not-in-family      8304\n",
      " Own-child          5068\n",
      " Unmarried          3446\n",
      " Wife               1568\n",
      " Other-relative      981\n",
      "Name:  Not-in-family, dtype: int64\n",
      "********************\n",
      " White                 27815\n",
      " Black                  3124\n",
      " Asian-Pac-Islander     1039\n",
      " Amer-Indian-Eskimo      311\n",
      " Other                   271\n",
      "Name:  White, dtype: int64\n",
      "********************\n",
      " Male      21789\n",
      " Female    10771\n",
      "Name:  Male, dtype: int64\n",
      "********************\n",
      "0        29849\n",
      "15024      347\n",
      "7688       284\n",
      "7298       246\n",
      "99999      159\n",
      "         ...  \n",
      "4931         1\n",
      "1455         1\n",
      "6097         1\n",
      "22040        1\n",
      "1111         1\n",
      "Name:  2174, Length: 119, dtype: int64\n",
      "********************\n",
      "0       31041\n",
      "1902      202\n",
      "1977      168\n",
      "1887      159\n",
      "1848       51\n",
      "        ...  \n",
      "1411        1\n",
      "1539        1\n",
      "2472        1\n",
      "1944        1\n",
      "2201        1\n",
      "Name:  0, Length: 92, dtype: int64\n",
      "********************\n",
      "40    15216\n",
      "50     2819\n",
      "45     1824\n",
      "60     1475\n",
      "35     1297\n",
      "      ...  \n",
      "92        1\n",
      "94        1\n",
      "87        1\n",
      "74        1\n",
      "82        1\n",
      "Name:  40, Length: 94, dtype: int64\n",
      "********************\n",
      " United-States                 29169\n",
      " Mexico                          643\n",
      " ?                               583\n",
      " Philippines                     198\n",
      " Germany                         137\n",
      " Canada                          121\n",
      " Puerto-Rico                     114\n",
      " El-Salvador                     106\n",
      " India                           100\n",
      " Cuba                             95\n",
      " England                          90\n",
      " Jamaica                          81\n",
      " South                            80\n",
      " China                            75\n",
      " Italy                            73\n",
      " Dominican-Republic               70\n",
      " Vietnam                          67\n",
      " Guatemala                        64\n",
      " Japan                            62\n",
      " Poland                           60\n",
      " Columbia                         59\n",
      " Taiwan                           51\n",
      " Haiti                            44\n",
      " Iran                             43\n",
      " Portugal                         37\n",
      " Nicaragua                        34\n",
      " Peru                             31\n",
      " France                           29\n",
      " Greece                           29\n",
      " Ecuador                          28\n",
      " Ireland                          24\n",
      " Hong                             20\n",
      " Trinadad&Tobago                  19\n",
      " Cambodia                         19\n",
      " Laos                             18\n",
      " Thailand                         18\n",
      " Yugoslavia                       16\n",
      " Outlying-US(Guam-USVI-etc)       14\n",
      " Hungary                          13\n",
      " Honduras                         13\n",
      " Scotland                         12\n",
      " Holand-Netherlands                1\n",
      "Name:  United-States, dtype: int64\n",
      "********************\n",
      " <=50K    24719\n",
      " >50K      7841\n",
      "Name: salary, dtype: int64\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "#finding unique values\n",
    "for column in df2.columns:\n",
    "    print(df2[column].value_counts())\n",
    "    print(\"*\"*20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32560 entries, 0 to 32559\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   39              32560 non-null  int64 \n",
      " 1    State-gov      32560 non-null  object\n",
      " 2    77516          32560 non-null  int64 \n",
      " 3    Bachelors      32560 non-null  object\n",
      " 4    13             32560 non-null  int64 \n",
      " 5    Never-married  32560 non-null  object\n",
      " 6    Adm-clerical   32560 non-null  object\n",
      " 7    Not-in-family  32560 non-null  object\n",
      " 8    White          32560 non-null  object\n",
      " 9    Male           32560 non-null  object\n",
      " 10   2174           32560 non-null  int64 \n",
      " 11   0              32560 non-null  int64 \n",
      " 12   40             32560 non-null  int64 \n",
      " 13   United-States  32560 non-null  object\n",
      " 14  salary          32560 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39                0\n",
       " State-gov        0\n",
       " 77516            0\n",
       " Bachelors        0\n",
       " 13               0\n",
       " Never-married    0\n",
       " Adm-clerical     0\n",
       " Not-in-family    0\n",
       " White            0\n",
       " Male             0\n",
       " 2174             0\n",
       " 0                0\n",
       " 40               0\n",
       " United-States    0\n",
       "salary            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum() #checking missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32560 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       39          State-gov   77516    Bachelors   13        Never-married  \\\n",
       "0      50   Self-emp-not-inc   83311    Bachelors   13   Married-civ-spouse   \n",
       "1      38            Private  215646      HS-grad    9             Divorced   \n",
       "2      53            Private  234721         11th    7   Married-civ-spouse   \n",
       "3      28            Private  338409    Bachelors   13   Married-civ-spouse   \n",
       "4      37            Private  284582      Masters   14   Married-civ-spouse   \n",
       "...    ..                ...     ...          ...  ...                  ...   \n",
       "32555  27            Private  257302   Assoc-acdm   12   Married-civ-spouse   \n",
       "32556  40            Private  154374      HS-grad    9   Married-civ-spouse   \n",
       "32557  58            Private  151910      HS-grad    9              Widowed   \n",
       "32558  22            Private  201490      HS-grad    9        Never-married   \n",
       "32559  52       Self-emp-inc  287927      HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "             Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0         Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1       Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2       Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3          Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4         Exec-managerial            Wife   White   Female      0   0   40   \n",
       "...                   ...             ...     ...      ...    ...  ..  ...   \n",
       "32555        Tech-support            Wife   White   Female      0   0   38   \n",
       "32556   Machine-op-inspct         Husband   White     Male      0   0   40   \n",
       "32557        Adm-clerical       Unmarried   White   Female      0   0   40   \n",
       "32558        Adm-clerical       Own-child   White     Male      0   0   20   \n",
       "32559     Exec-managerial            Wife   White   Female  15024   0   40   \n",
       "\n",
       "        United-States  salary  \n",
       "0       United-States   <=50K  \n",
       "1       United-States   <=50K  \n",
       "2       United-States   <=50K  \n",
       "3                Cuba   <=50K  \n",
       "4       United-States   <=50K  \n",
       "...               ...     ...  \n",
       "32555   United-States   <=50K  \n",
       "32556   United-States    >50K  \n",
       "32557   United-States   <=50K  \n",
       "32558   United-States   <=50K  \n",
       "32559   United-States    >50K  \n",
       "\n",
       "[32560 rows x 15 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analysis of outliars and removing them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.visualization of data-finding outliars-removing outliars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c0e174490>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# Box Plot visualization\n",
    "import seaborn as sns\n",
    "sns.boxplot(df2['39'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that the value above 80 in column named 39 is outliar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PiXA7lJAxJzOBudmJvL2nkuJjdW6HY0zQsgQwjI7WnuJYXTMz0u2G5Ei7ck4GkeFh/NvqbXR02khhY3piCWAYve00QUyzBDDiYnxelp09ga1ltfzinf1uh2NMULIEMIze3FNBQlQ4aXE+t0MJSWdlJnDpjFQefG0vx2qb3Q7HmKBjCWCYtLZ3sv6DKqanxdnIVJeICP/7qlm0dyrfW7vb7XCMCToBJQARWSIie0SkRETu7uF5n4g87zy/QURynP2Xi8gmEdnu/Lu4yzELnP0lIvLfMsY+JYsOVNPY2mEDklw2MSma2z85hZe2ltsaAsZ002cCEBEP8AiwFMgHbhKR/G7FbgNqVHUq8BDwgLO/CrhKVecAK4FnuhzzKLAKyHN+lgziOoLOW3srCfcIU1IHvnKVGRq3XzyF7PFR/O/f77QlJY3pIpAawEKgRFVLVbUVeA5Y3q3McuApZ3s1cKmIiKpuVtVyZ/9OINKpLWQA8ar6rvonc38auGbQVxNE3iyuYFFuEj6vx+1QQl5kuId7l83ig4oGfvnOAbfDMSZoBDIXUCZwuMvjMmBRb2VUtV1EaoEk/DWA0z4NbFbVFhHJdM7T9ZyZPf1yEVmFv6bAxInBMw3vmaYJPl7XzAcVDXymIHukwzK9uGxmKotnpPKjdXv58uI84qNsSm5jAqkB9NQ2371j9RnLiMgs/M1C/9yPc/p3qj6mqgWqWpCSMjqmUt58qAaAghwb/Rss/DeE82nrVP6446jb4RgTFAJJAGVA16+yWUB5b2VExAskANXO4yxgDXCrqu7rUj6rj3OOWu8fOkmEJ4z8CfFuh2K6mJQUw5cumszWslpKKxvcDscY1wWSADYCeSKSKyIRwAqgsFuZQvw3eQGuB95QVRWRROAPwD2q+s7pwqp6FKgXkXOd3j+3Ar8f5LUEjc2HapiVGW/t/0Ho9ounMi46nDWbj9DabjeETWjrMwGoajtwF/AKsBt4QVV3isj9InK1U+wJIElESoCvA6e7it4FTAW+IyJbnJ9U57nbgceBEmAf8Mehuig3tXV0sq2slnnZ1vwTjKIiPFw3P4sTja2s233c7XCMcVVAC8Ko6lpgbbd993bZbgZu6OG47wLf7eWcRcDs/gQ7GhQfraelvZN5ExPdDsX0YkpKLItyx/NOSRWzJsQzKcm66prQZCOBh9jmw/4bwJYAgtuSWekkRIezelMZLW0dbodjjCtsScghtvnQSVLifGQmRrkdSq966sLan7Knu7uOZr5wD9cvyOKJv+xnzZYjfP78HJuyw4QcSwBDbPOhGuZlJ47pD5P+JJBgNjk5lsvy03ht13Ge23iYmxaO/sRmTH9YE9AQqm5s5cCJJuZNtBvAo8Unp6UwNTWW+wp3sqvcFo8xocVqAENoi9P+P9+l9v+x8s18JIWJ8JmCbJ5YX8rtz26i8K4LSLBRwiZEWA1gCG09XEuYwJysBLdDMf0Q6/Py01vmc6TmFN94YQudtoKYCRGWAIZQ8TH/+r/REVaxGm0WTBrPt6+cybrdFTz69r6+DzBmDLBPqiG093jDkK//a806I2fleTlsPnyS//PqHs7KSuDCvNEx95QxA2UJYIi0tndy4EQjV589we1QgspoSmAiwvevm0Px0Xq+8pvNvPyVC90OyZhhZU1AQ6SivhlVhrwGYEZWdISXRz87n/YO5Y5fbaLD7geYMcwSwBA5XudfdHy6JYBRb3JKLD+8/iy2ltXy5p4Kt8MxZthYE9AQOVbbTGR4mM0rM0DB1lS0dE4G187L5PdbjjAzIz6oR3YbM1BWAxgix+tayEuNwxM2dkcAh5r7rppFjM/L6k2Habe1hM0YZDWAIXKsrpkls9PdDsMMoYTocK6dl8nT7x7krb2VXDYzrV/H91arGQtzKZmxwWoAQ6ChpZ2Glna7ATwGzUiPZ05mAn/eW0lNY6vb4RgzpCwBDAG7ATy2LZ2djgistbWEzRhjTUBD4Fht/xNAsN30NL1LjI7g4umpvLbrOCUVtpawGTusBjAEjtc1Ex3hISXW53YoZphcMDWZ8TERvLStnDa7IWzGCEsAQ+B4XTPp8ZFjeg2AUBfuCePKORlU1rfw1F8PuB2OMUMioAQgIktEZI+IlIjI3T087xOR553nN4hIjrM/SUTeFJEGEXm42zFvOefsvlj8qKKqVNS3kBpv3/7HuhnpcUxLi+XH6z6gsr7F7XCMGbQ+7wGIiAd4BLgcKAM2ikihqu7qUuw2oEZVp4rICuAB4EagGfgO/sXfe1oA/hZncfhRq76lnZb2TpKt+WfEuHX/RERYNmcCP3nzA374p2L+64azXYnDmKESyE3ghUCJqpYCiMhzwHKgawJYDtznbK8GHhYRUdVGYL2ITB26kINLlfNN0Nr/Q0NynI8vXJDLz98u5eZFE4ds9bexuvayCW6BJIBM4HCXx2XAot7KqGq7iNQCSUBVH+f+hYh0AC8C31XVj828JSKrgFUAEycG3x9EZYM/ASTH+axnTxAajv+TLy/OY837R7jvpV2suf08wmz0txmlArkH0NO7u/sHdSBlurtFVecAFzo/n+upkKo+pqoFqlqQkhJ887NX1bcQ7hFbRjCExPq83L10BlsPn+TF98vcDseYAQskAZQB2V0eZwHlvZURES+QAFSf6aSqesT5tx74Nf6mplGnqqGVpBgfYdYDKKRcMzeT+RMTeeBPe6hvbnM7HGMGJJAEsBHIE5FcEYkAVgCF3coUAiud7euBN3pqzjlNRLwikuxshwPLgB39DT4YVDa0kBxn7f+hJixMuO/qWZxobOHH6z5wOxxjBqTPBKCq7cBdwCvAbuAFVd0pIveLyNVOsSeAJBEpAb4OfNhVVEQOAA8CnxeRMhHJB3zAKyKyDdgCHAH+39Bd1sho7+ikprGVlNgIt0MxLjgrK5EV50zkyXf2s+NIrdvhGNNvAU0FoaprgbXd9t3bZbsZuKGXY3N6Oe2CwEIMXicaW1GwLqAh7O6lM1i3+zj3/G47a+44D6/Hxlaa0cPerYNQ5fQASrEmoJCVEBXOfVfNYvuRWn5pI4TNKGMJYBBOjwGwGkBo+4c56Vw6I5X/++pe9lc1uh2OMQGzBDAIlQ2txPm8RIZ73A7FuEhE+N61c4jwhvHV5zbbZHFm1LAEMAhV1gPIONITIvnBdXPYVlbLj9btdTscYwJiCWAQKutbrPnHfGjpnAxuLMjmp2/t46/7+hoEb4z7LAEMUGNLO6faOqwLqPmIe6/KZ3JyDHc++z7VtoSkCXKWAAaoqsscQMacFuPz8vjKc+hUeOZvB2hp63A7JGN6ZUtCDlBVg//bnTUBjQ1DOWlcbnIMj9w8n1uf3MDzRYe5ZdEkPDZhnAlCVgMYoKqGFsIExkVbE5D5uAvykll21gSKj9Xz202H6ex9ZhRjXGM1gAE60dDCuOgI+2ZnenXu5CRa2jp4ZddxIjxhXDMv0yYNNEHFEsAAnWhsteYf06dPTk+ltaOTN/dU0t6pXDc/0+2QjPmQJYABUFVONLSSmxzjdihmFLhsZhrhnjBe3XWchpZ2rpufRazP/vSM++xdOAAV9S20dnSSZDUAEwAR4eLpqcRFelmz+QjXP/pXHvtcAROTogd0Pls+0gwVuwk8AKfne0mOsRvAJnALJo1n5Xk5HK1t5qqH1/OXDyrdDsmEOEsAA3DASQBWAzD9lZcaR+Fd55OREMnKJ9/jsT/v4wxrJxkzrCwBDMD+qkY8YUJitK0DbPpvUlIML95+Hktmp/Ofa4v56nNbaG23CeTMyLMEMAD7qxoZHxNhXfrMgMX4vDxy83z+dcl0XtpWzs//vI8amzrCjDBLAANw4ESjtf+bQRMR7rh4Kk9+/hxqmlp55K0S9lU2uB2WCSGWAPqps1M5eKLJ2v/NkLlkeip3XjyVWJ+XX7yzn3dKquy+gBkRASUAEVkiIntEpERE7u7heZ+IPO88v0FEcpz9SSLypog0iMjD3Y5ZICLbnWP+W2R0tKccrWumpb2TJJsF1AyhpFgft39yCjPS4/nD9qP8dlMZLe02kZwZXn0mABHxAI8AS4F84CYRye9W7DagRlWnAg8BDzj7m4HvAP/Sw6kfBVYBec7PkoFcwEg73QPIRgGboeYL93DzoolcNjONLYdPcsev3rckYIZVIDWAhUCJqpaqaivwHLC8W5nlwFPO9mrgUhERVW1U1fX4E8GHRCQDiFfVd9Vf130auGYwFzJSTo8BSLJ7AGYYhImweEYqy+dO4PXiCu58drP1EDLDJpAEkAkc7vK4zNnXYxlVbQdqgaQ+zlnWxzkBEJFVIlIkIkWVle4PnNlf1YjPG0Z8lHUBNcNnUW4S/3HNbNbtPs7Xnt9CZ6fdEzBDL5CpIHpqm+/+bgykzIDKq+pjwGMABQUFrv8VHKhqJDc5xrqAhrCRmorhc+dOorm1g++t3U1GQiTfXta95dWYwQkkAZQB2V0eZwHlvZQpExEvkABU93HOrD7OGZRKqxqZmRHndhgmRHzxwlyOnDzF4+v3MyExii9ckOt2SGYMCaQJaCOQJyK5IhIBrAAKu5UpBFY629cDb+gZ+rGp6lGgXkTOdXr/3Ar8vt/Rj7DW9k4OVTcxOTnW7VBMiBARvrMsnyWz0vmPP+zi7b3uN4OasaPPBOC06d8FvALsBl5Q1Z0icr+IXO0UewJIEpES4OvAh11FReQA8CDweREp69KD6HbgcaAE2Af8cWguafgcqm6io1OZnGLTQJuR4wkTHrzxbKanxfGV32y2xebNkAloOmhVXQus7bbv3i7bzcANvRyb08v+ImB2oIEGg1JnlObklFh2lde5HI0JJdERXn7+uQVc9ZP1PLvhIP980RQivDaO0wyOvYP6odTpAmo1AOOGSUkx/PimeRyrbeZ/thyx0cJm0GxBmH4orWwgOdZHfKR1ATUf1VPPoOFwyfRULp2ZyrrdFWSNi+K8Kckj8nvN2GQ1gH7YV9lo3/6N6y6ensrM9DjWbj/64cBEYwbCEkA/lFY2MMUSgHFZmAg3FGQzPiaC37x3iLrmNrdDMqOUNQEFqKaxlZqmNusCaoJCZLiHWxZN4qdvlfCb9w5x2wW5hHs++n3O1g42fbEaQIBKq073ALIagAkOafGRXDsvi4Mnmnjgj8Vuh2NGIUsAAdpXeboHkNUATPCYm53IuZOTeHz9ftZuP+p2OGaUsSagAJVWNhLuEbLHRbkdijEf8Q9z0mlu6+Cbv93K9PQ4ptiXFBMgSwABKq1sYOL4aLweqzSZwRnqLqPesDB+est8lv1kPV96ZhP/c+f5xPjsT9v0zT7NAlRa1WjNPyZoTUiM4ic3zWNfZQP3/G67DRIzAbEEEID2jk4OnrAxACa4nT81mW9cMZ3CreU8/e5Bt8Mxo4AlgAAcrG6irUOZajUAE+Ru/+QULp2Rynf/sItDJ2yQmDkzSwABKD5aD8DMjHiXIzHmzMLChAc/M5eMhCh+bYPETB/sTlEAio/V4QkTpqZaDcCMnIHeLE6IDudnn13A8kfW86u/HeSfLpz8sUFixoDVAAKy+2g9uckxRIZ73A7FmIDkT4jnxoJsympO8eL7ZXZT2PTIEkAA9hyvY0a6LQNpRpf8CQl8Kj+NbWW1rNt93O1wTBCyBNCH+uY2DlefsvZ/MypdNC2FBZPG8eaeSjbsP+F2OCbIWALow97j/hvA09OsBmBGHxHhmrmZTE+Lo3BLOX/accztkEwQsZvAfSg+5k8AMzIsAZjRyRMm3LRwIk+sL+Urv9nM4ysLuGhaykfK2MyhoSmgGoCILBGRPSJSIiJ39/C8T0Sed57fICI5XZ67x9m/R0Q+1WX/ARHZLiJbRKRoKC5mOBQfrSfO5yUz0eYAMqNXhDeMleflMDklhlXPFPG3UmsOMgEkABHxAI8AS4F84CYRye9W7DagRlWnAg8BDzjH5gMrgFnAEuCnzvlOu0RV56pqwaCvZJgUH6tjRkYcIuJ2KMYMSnSEl199cRFZ46K57ZcbeXefJYFQF0gNYCFQoqqlqtoKPAcs71ZmOfCUs70auFT8n5jLgedUtUVV9wMlzvlGBVWl+Fg9060HkBkjkmN9/PqLi5iQGMXKX7zHul3WOyiUBZIAMoHDXYyiZTUAAA2ySURBVB6XOft6LKOq7UAtkNTHsQq8KiKbRGRVb79cRFaJSJGIFFVWVgYQ7tApr22mvrmdGenWA8iMHanxkbzwz59gZnoc//yrTazZXOZ2SMYlgdwE7qnto/uokt7KnOnY81W1XERSgddEpFhV//yxwqqPAY8BFBQUjOholuKjdQDMtBvAJsj1d9TwuJgInv2nc1n1dBFfe34rV52VwSemJA9TdCZYBVIDKAOyuzzOAsp7KyMiXiABqD7Tsap6+t8KYA1B2DS0s7wOEZhmXUDNGBTr8/Lk58/h8vw0Xtp2lNeLj9uI4RATSALYCOSJSK6IROC/qVvYrUwhsNLZvh54Q/3vpEJghdNLKBfIA94TkRgRiQMQkRjgCmDH4C9naBUdrGF6WhxxkeFuh2LMsIgM9/DoLfOZPzGR13dXsHpTGe0dnW6HZUZIn01AqtouIncBrwAe4ElV3Ski9wNFqloIPAE8IyIl+L/5r3CO3SkiLwC7gHbgTlXtEJE0YI3Ts8YL/FpV/zQM1zdgHZ3K+wdruGbeBLdDMWZYeT1hfHp+FuNjIli3u4LqxlZuOXeS22GZERDQQDBVXQus7bbv3i7bzcANvRz7PeB73faVAmf3N9iRVHysjoaWds7JGe92KMYMOxFh8Yw0kmN9rN5UxqNvlfDJaSnWA26Ms6kgelF0oAaABZPGuRyJMSPnrKxEVl00mfZO5dOP/pU3iq2b6FhmCaAXGw9Uk5EQaSOATcjJGhfNHRdPJSc5mi/8sogf/qnY7guMUZYAeqCqbDxQTUHOeBsBbEJSQlQ4q790HjctzOanb+3j5sc3cLyu2e2wzBCzyeB6UFZziuN1LZyTY80/JnRFhnv4/nVnsTB3PN/63Q7+4cd/YfnczB5XxrOJ40YnSwA92HTQ3/5fMMluAJuxZSDLTF47L4vZExK449n3+cU7+7loWgqXzkjFa8tMjnr2P9iDjQeqifN5rQeEMY68tDh+f9f5LJg0jrf3VvLwmyWU1TS5HZYZJEsAPfhb6QnmTRqHJ8za/405LTrCy3Xzs/j8eTm0tHfy6Fv7WLP5CI0t7W6HZgbImoC62XOsnn2VjXz+vBy3QzEmKE1Li+Orl+bx+u7jvFt6gh1HaulQ5dZPTCI6wj5SRhOrAXTz0tZywgSWzslwOxRjglZkuIcrz5rAlxfnkT0+ih/8sZiLfvgWP397HyebWt0OzwTI0nUXqspL28o5f2oyybE+t8MxxlWB3DBOi4/k8+flMi0tlofW7eX7fyzmoXV7WX52JreeN4lZExJGIFIzUJYAuth+pJaDJ5q48+KpbodizKhSkDOeZ794LrvK63jmbwdYs/kIzxcdpmDSOG48J5sls9OJiwzvNalYN1J3WALoonBLOeEe4VOz0t0OxZhRKX9CPN+/7izuXjKT3246zDN/O8g3V2/j2/+zg8tmpjEuOoJp6bF4w6z1ORhYAnB0diovbzvKJ6elkBBt0z8bMxgJ0eF88cLJ3HZBLu8fOsnvtxzh5W1HqW5sJSrcQ35GPLMz45mSEmvjCVxkCcDx9t5KjtU1c/fSGW6HYsyYISIsmDSOBZPG8Z1l+Xz35d1sKzvJzqO1bDpUg88bxsyMeJJjI7hoWgqR4R63Qw4plgCA9o5O/nPtbiaOj2bpHGv+Maa/Ah1hPD09junpcbR3dLKvsoEd5XXsKq9j1TObiI7wsHhGKpfnp3FRXgrjYiKGOWpjCQD4zXuH+KCigZ99dgE+r30DMWa4eT1hTE+PZ3p6PNfMVXKTY1i74yiv7jzGy9uOEiZwdnYil0xP5eLpKeRnxFtT0TAI+QRQ29TGg6/t5ROTk/jUrDS3wzEm5HjChEPVTcyekEB+RjxHak6x93g9e47X89Bre3nwtb1EeMLIHBfFp2alMzc7kbnZiaTF+2y23kEK6QTQ0ancW7iDk6fa+M6yfHszGeOyMBGyx0eTPT6aS2em0dDSzr6KBg5VN3G4pokn1pfS1uFfuD4+0kteWhxhAqlxkaTG+UiNjyQ+0ouIjFjX0tHctTVkE0BLewdfe34La7cf4+uXTyN/QrzbIRljuon1eTk7O5GzsxMBuG5+JjvL69hedpIPKhr4oKKBHUdq2dha8+ExPm8YqXE+Nh+qIS8tlry0OKamxJKZGEWYze/1EaKqfRcSWQL8GP+i8I+r6g+6Pe8DngYWACeAG1X1gPPcPcBtQAfwFVV9JZBz9qSgoECLiooCvrjebCs7yffXFvNu6Qm+feVMvnjh5H6fYyDT6hpjhkdDSzsV9c1U1LVQUd9CRV0zdc3tVDW0fFjG5w0jJymG3OQYclNimDQ+mozEKCYkRJKRGEWsL/Dvw63tnZxobKGiroXfFpVR39JGfXM79c3tdHR2ogp5abEkRkcwLjqCCYmRTHRqNkkxESPe2iAim1S1oPv+Pq9YRDzAI8DlQBmwUUQKVXVXl2K3ATWqOlVEVgAPADeKSD6wApgFTADWicg055i+zjlkymqaKD5aT/GxOt7cU8mmgzXE+rz81/VncUNB9nD8SmPMCIr1eYn1xTI5+aOL1TS1tFNR30JlfQtVDf6fooM1vLrrGJ3dvvvGRXpJiokgOsJLrM9LjM9DtM8LCm0dnTS0+BNKZX0LNU1tH4tBgGifl3CPIEB57SlONrXR0v7R5TSjwj1kj48ie5w/IWSNiyJ7fDRp8ZFER3iICvcQFeEhOsJDmJMoOlWJCvcMeeIIJOUtBEpUtRRARJ4DlgNdP6yXA/c526uBh8Uf6XLgOVVtAfaLSIlzPgI455C55fENHDzhn7t8SkoM31mWz2cKsoiLtAFfxoxl0T4vOT4vOckxH9nf0anUnWrj5Kk2ak+1fbjd1NpOa3snlQ0tNLd7OVjdRJgI4Z4woiM8hHvCmJYWR1ykl1hfOHGRXucnnFif9yNTyJ++B9DY0s6Rk6c4dMJ/H+Nw9Snn3yY27K+mIcDptIv/Y8mQj5MIJAFkAoe7PC4DFvVWRlXbRaQWSHL2/63bsZnOdl/nBEBEVgGrnIcNIrIngJh7dRB4A/jiYE7SP8lA1cj9ulHJXqO+2Wt0ZkH3+twyxOeLemBQh0/qaWcgCaCnOkf3Gwe9leltf08denu8GaGqjwGPnSnAYCYiRT21vZm/s9eob/YanZm9PgMTyMiKMqBrQ3kWUN5bGRHxAglA9RmODeScxhhjhlEgCWAjkCciuSISgf+mbmG3MoXASmf7euAN9XcvKgRWiIhPRHKBPOC9AM9pjDFmGPXZBOS06d8FvIK/y+aTqrpTRO4HilS1EHgCeMa5yVuN/wMdp9wL+G/utgN3qmoHQE/nHPrLCwqjtvlqBNlr1Dd7jc7MXp8BCGgcgDHGmLHHZlcyxpgQZQnAGGNClCWAISQi2SLypojsFpGdIvJVZ/94EXlNRD5w/h3ndqxuEhGPiGwWkZedx7kissF5fZ53OgaELBFJFJHVIlLsvJc+Ye+hjxKRrzl/YztE5DciEmnvo/6zBDC02oFvqOpM4FzgTmc6jLuB11U1D3jdeRzKvgrs7vL4AeAh5/WpwT+1SCj7MfAnVZ0BnI3/tbL3kENEMoGvAAWqOht/R5LTU9DY+6gfLAEMIVU9qqrvO9v1+P9wM/FPc/GUU+wp4Bp3InSfiGQBVwKPO48FWIx/ChGw1yceuAh/zzpUtVVVT2Lvoe68QJQz7igaOIq9j/rNEsAwEZEcYB6wAUhT1aPgTxJAqnuRue5HwL8Cp2fISgJOqurpCVG6ThcSiiYDlcAvnGayx0UkBnsPfUhVjwD/BziE/4O/FtiEvY/6zRLAMBCRWOBF4H+pap3b8QQLEVkGVKjqpq67eygayn2TvcB84FFVnQc0EsLNPT1x7n8sB3LxzzIcAyztoWgov48CYglgiIlIOP4P/2dV9XfO7uMikuE8nwFUuBWfy84HrhaRA8Bz+KvsPwISnao82LQgZUCZqm5wHq/GnxDsPfR3lwH7VbVSVduA3wHnYe+jfrMEMISc9uwngN2q+mCXp7pOlbES+P1IxxYMVPUeVc1S1Rz8N+3eUNVbgDfxTyECIfz6AKjqMeCwiEx3dl2KfyS9vYf+7hBwrohEO39zp18jex/1k40EHkIicgHwF2A7f2/j/hb++wAvABPxv3lvUNVqV4IMEiJyMfAvqrpMRCbjrxGMBzYDn3XWkAhJIjIX/03yCKAU+Ef8X9bsPeQQkX8HbsTf824z/hneM7H3Ub9YAjDGmBBlTUDGGBOiLAEYY0yIsgRgjDEhyhKAMcaEKEsAxhgToiwBGNNPzsyT74nIVmdGyn939i8WkfedGSqf6jIoyZigZAnAmP5rARar6tnAXGCJiJyHfwKyFc4MlQf5+8AtY4KSJQBj+kn9GpyH4c5PB9Ciqnud/a8Bn3YjPmMCZQnAmAFwFrXZgn9OnteA94BwESlwilwPZLsVnzGBsARgzACoaoeqzsU/6dhCYBb++Y0eEpH3gHr80xQYE7QsARgzCM5iLW8BS1T1XVW9UFUXAn8GPnA1OGP6YAnAmH4SkRQRSXS2o/BPT1wsIqnOPh/wb8DP3IvSmL5ZNzVj+i8DeEpEPDizdKrqyyLyX86iN2H4F3R5w9UojemDzQZqjDEhypqAjDEmRFkCMMaYEGUJwBhjQpQlAGOMCVGWAIwxJkRZAjDGmBBlCcAYY0LU/wdgpg0YLqXvTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting density plot for carat using distplot()\n",
    "sns.distplot(a=df2['39'], hist=True)\n",
    " \n",
    "# visualizing plot using matplotlib.pyplot library\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([  221,   917,  1039,  1167,  1934,  2302,  2890,  2905,  3210,\n",
      "        3536,  3962,  4069,  4108,  4833,  5103,  5271,  5369,  5405,\n",
      "        6213,  6231,  6623,  6747,  7480,  7719,  7871,  8380,  8430,\n",
      "        8805,  8962,  8972,  9470, 10209, 10544, 11237, 11511, 11730,\n",
      "       11995, 12450, 12491, 12829, 12974, 13024, 13294, 13695, 13927,\n",
      "       14158, 14710, 14755, 15355, 15661, 15891, 16301, 16761, 18276,\n",
      "       18412, 18724, 18831, 19044, 19171, 19179, 19211, 19488, 19494,\n",
      "       19746, 20420, 20462, 20609, 20825, 21500, 21811, 21834, 22219,\n",
      "       22480, 22894, 22897, 23458, 24026, 24042, 24237, 24279, 24394,\n",
      "       24559, 25302, 26011, 26730, 27794, 28462, 28947, 29593, 29723,\n",
      "       31029, 31431, 31695, 31854, 32276, 32366, 32458, 32493, 32524]),)\n"
     ]
    }
   ],
   "source": [
    "#finding outliars\n",
    "print(np.where(df2['39']>80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping only those values less than 80 i.e removing the outliar\n",
    "df2 = df2[df2[\"39\"]<80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting density plot for carat using distplot()\n",
    "sns.distplot(a=df2['39'], hist=True)\n",
    " \n",
    "# visualizing plot using matplotlib.pyplot library\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c07929250>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAALeElEQVR4nO3df6zdd13H8ddn/UU7x11rx1LviGW5BFAjA5s5QM2cbgJhNIv8MeMf/GHCP6YWY+KPmJjwp9EIS//QLBqzGIM/kFm3EG0zaEwI2dKOocN2cpUNVmArbHTVzrUbH/8439u1Xbfeci/3fb69j0dyc8/53LOdz2fnfJ/53k9Pv2u99wCw8q6ongDAaiXAAEUEGKCIAAMUEWCAImsv5cFbt27t27dv/wFNBeDydOjQoW/33q85f/ySArx9+/YcPHhw+WYFsAq01p640LgtCIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYpc0v8TjnPt2bMn8/Pz1dNYtKNHjyZJZmdni2eyNHNzc9m1a1f1NGDJBHgJ5ufn88ijh/PSpi3VU1mUNSePJ0m+9cJ4X/Y1J5+pngIsm/EeiVPipU1b8vxb3189jUXZeOQzSTKa+V7IwhrgcmAPGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigyIoEeM+ePdmzZ89KPBXAaJqzdiWeZH5+fiWeBiDJeJpjCwKgiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMECRtdUTAFgJN99885nbBw4cyK233prTp09n/fr12bdvX5Lk9ttvz4kTJzIzM5O9e/cmSXbu3Jnjx49n8+bNuffee5d1Ts6AgVXp9OnTSZJTp06dGTtx4kSS5Pjx42fGFm4/++yzyz4HZ8DAZe/ss98L3b/tttuyYcOGc8Z27tz5in/PHXfcsaxnwSsS4KNHj+b555/P7t27V+LpVsz8/HyuONWrp7GqXPF/z2V+/sRl915iec3Pz2fjxo2LfvypU6fOORNOzj0LXrDcZ8EX3YJorX2ktXawtXbw2LFjy/rkAKvZRc+Ae+93J7k7SXbs2PF9ne7Nzs4mSe66667v5x+fWrt3786h/36qehqryvde9/rMXX/tZfdeYnmN5TckfwgHrHrr16/PVVdddc7YzMxMZmZmzhnbvHnzsj6vAAOXvQMHDrzm/X379uW+++47Z2zv3r1nPoq2wMfQAJbBunXrkkzOfhcsnAWffea7cHu5z34TH0MDVonzz3r379//isecfxac5BVnwcvJGTBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAia1fiSebm5lbiaQCSjKc5KxLgXbt2rcTTACQZT3NsQQAUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigyNrqCYzdmpPPZOORz1RPY1HWnPxOkoxmvhey5uQzSa6tngYsCwFegrm5ueopXJKjR19MkszOjjlg147uvzu8GgFegl27dlVPARgxe8AARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIq33vvgHt3YsyRM/uOm8qq1Jvl3wvMvJGqaDNUyH1baGH+29X3P+4CUFuEpr7WDvfUf1PJbCGqaDNUwHa5iwBQFQRIABiowlwHdXT2AZWMN0sIbpYA0ZyR4wwOVoLGfAAJcdAQYoMlUBbq29sbX2udba4dbal1tru4fxLa21/a21rwzfN1fP9dW01l7XWnuotfalYQ0fG8bf1Fp7cFjD37bW1lfP9WJaa2taa19srd0/3B/VGlprj7fW/r219khr7eAwNpr3UpK01q5urX2qtXZkOC7eNcI1vGV4DRa+nmutfXSE6/jN4Zh+tLX2yeFYX9IxMVUBTvJikt/qvb8tyU1Jfr219mNJfjfJA733Nyd5YLg/rV5Ickvv/e1Jbkjy3tbaTUn+MMnHhzU8m+TXCue4WLuTHD7r/hjX8PO99xvO+rzmmN5LSXJXkn/uvb81ydszeT1GtYbe+2PDa3BDkp9KcjLJvRnROlprs0l+I8mO3vtPJFmT5M4s9ZjovU/tV5K9SW5N8liSbcPYtiSPVc9tkfPflOThJD+dyd+YWTuMvyvJv1TP7yJzvy6Tg+KWJPcnaSNcw+NJtp43Npr3UpLXJ/lqhj8sH+MaLrCm25J8fmzrSDKb5OtJtiRZOxwTv7TUY2LazoDPaK1tT/KOJA8mubb3/s0kGb6/oW5mFzf86v5IkqeT7E/yX0m+23t/cXjIk5m8oNPsE0l+O8n3hvs/nPGtoSfZ11o71Fr7yDA2pvfS9UmOJfnLYSvoz1trV2ZcazjfnUk+OdwezTp670eT/HGSryX5ZpLjSQ5licfEVAa4tfZDSf4hyUd7789Vz+dS9d5f6pNft65LcmOSt13oYSs7q8VrrX0gydO990NnD1/goVO7hsF7eu/vTPK+TLazfq56QpdobZJ3JvnT3vs7kvxvpvjX9IsZ9kc/mOTvq+dyqYb96Z1J3pTkR5Jcmcn76nyXdExMXYBba+syie9f994/PQw/1VrbNvx8WyZnllOv9/7dJAcy2c++urW2dvjRdUm+UTWvRXhPkg+21h5P8jeZbEN8IuNaQ3rv3xi+P53JnuONGdd76ckkT/beHxzufyqTII9pDWd7X5KHe+9PDffHtI5fTPLV3vux3vvpJJ9O8u4s8ZiYqgC31lqSv0hyuPf+J2f96J+SfHi4/eFM9oanUmvtmtba1cPtjZm8cIeTfC7Jh4aHTfUaeu+/13u/rve+PZNfGT/be//VjGgNrbUrW2tXLdzOZO/x0YzovdR7/1aSr7fW3jIM/UKS/8iI1nCeX8nL2w/JuNbxtSQ3tdY2DZ1aeC2WdkxUb26ft9H9M5mcwv9bkkeGr/dnsv/4QJKvDN+3VM/1Ndbwk0m+OKzh0SR/MIxfn+ShJPOZ/Aq2oXqui1zPzUnuH9sahrl+afj6cpLfH8ZH814a5ntDkoPD++kfk2we2xqGdWxK8p0kM2eNjWodST6W5MhwXP9Vkg1LPSb8VWSAIlO1BQGwmggwQBEBBigiwABFBBigiAAzOq9xxblbWmsPD1eruuesD8jDVBJgxuhCV5x7d5J7ktzZJ1ereiIvf8gfppIAMzp94n+Gu+uGr5eSvNB7/89hfH+SX66YHyyWADNKF7ji3ENJ1rXWFq77+6Ekb6yaHyyGADNK/ZVXnPvxTK5b8fHW2kNJTmRygX+YWgLMqPWXrzj33t77F3rvP9t7vzHJv2ZyjQGYWgLM6LzKFeeOtNbeMIxtSPI7Sf6sbpZwcT6mwxhtS3JPa21NJicRf9d7v7+19kfDxeSvyOQi5p8tnSVchKuhARSxBQFQRIABiggwQBEBBigiwABFBBigiAADFPl/h/WgAgimxAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df2['39'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "see the values above 80 is removed from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32439, 15)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape# see the reducing values of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c07982cd0>"
      ]
     },
     "execution_count": 26,
     "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": [
    "# Box Plot visualization\n",
    "sns.boxplot(df2[' 77516'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting density plot for carat using distplot()\n",
    "sns.distplot(a=df2[' 77516'], hist=True)\n",
    " \n",
    "# visualizing plot using matplotlib.pyplot library\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([   36,    39,   181,   191,   214,   228,   310,   412,   505,\n",
      "         648,   824,   911,   959,  1008,  1146,  1285,  1308,  1448,\n",
      "        1591,  1841,  1877,  1964,  2011,  2058,  2334,  2339,  2361,\n",
      "        2391,  2667,  2751,  2761,  2815,  2841,  2867,  2890,  2925,\n",
      "        2966,  3182,  3291,  3397,  3465,  3490,  3556,  3558,  3599,\n",
      "        3706,  3725,  3731,  3734,  3784,  3856,  3892,  3949,  3968,\n",
      "        4045,  4107,  4261,  4556,  4680,  4712,  4955,  5177,  5258,\n",
      "        5329,  5348,  5400,  5482,  5507,  5530,  5599,  5704,  5991,\n",
      "        5995,  6121,  6144,  6194,  6233,  6262,  6496,  6622,  6675,\n",
      "        6705,  6755,  6824,  6851,  7005,  7093,  7129,  7304,  7323,\n",
      "        7427,  7475,  7634,  7690,  7903,  8005,  8206,  8227,  8245,\n",
      "        8256,  8314,  8400,  8452,  8456,  8472,  8522,  8625,  8695,\n",
      "        8886,  8961,  8981,  9009,  9211,  9285,  9318,  9578,  9662,\n",
      "        9677,  9698,  9710,  9716,  9718,  9762,  9772,  9969, 10360,\n",
      "       10402, 10486, 10678, 10712, 10873, 11015, 11286, 11294, 11419,\n",
      "       11652, 11792, 11840, 11933, 11983, 12015, 12033, 12062, 12108,\n",
      "       12218, 12287, 12336, 12342, 12440, 12464, 12655, 12713, 12783,\n",
      "       12799, 12851, 12918, 12968, 13044, 13059, 13067, 13096, 13137,\n",
      "       13180, 13190, 13256, 13317, 13469, 13628, 13998, 14106, 14359,\n",
      "       14394, 14715, 14735, 14782, 14986, 15029, 15096, 15146, 15284,\n",
      "       15292, 15450, 15510, 15655, 15668, 15676, 15838, 15892, 15896,\n",
      "       16139, 16389, 16609, 16677, 16927, 17120, 17232, 17278, 17325,\n",
      "       17361, 17372, 17415, 17471, 17632, 17651, 17679, 17719, 18073,\n",
      "       18077, 18129, 18174, 18209, 18247, 18561, 18694, 18788, 18789,\n",
      "       18803, 18835, 18918, 18936, 19097, 19149, 19177, 19242, 19552,\n",
      "       19574, 19793, 20173, 20213, 20237, 20404, 20447, 20452, 20512,\n",
      "       20874, 20938, 20995, 21206, 21299, 21391, 21433, 21702, 21716,\n",
      "       21863, 21871, 21879, 21992, 22050, 22157, 22332, 22478, 22561,\n",
      "       22739, 22768, 22838, 22898, 22938, 23059, 23062, 23085, 23279,\n",
      "       23338, 23428, 23505, 23544, 23568, 23672, 23710, 23722, 23788,\n",
      "       23808, 23839, 23921, 23993, 23999, 24002, 24103, 24116, 24254,\n",
      "       24421, 24431, 24458, 24462, 24974, 25445, 25700, 25748, 25831,\n",
      "       25835, 26077, 26082, 26122, 26170, 26400, 26469, 26668, 26685,\n",
      "       26775, 26854, 26934, 27062, 27128, 27190, 27260, 27446, 27472,\n",
      "       27503, 27577, 27637, 27725, 27809, 27867, 27938, 28002, 28048,\n",
      "       28115, 28214, 28239, 28318, 28467, 28489, 28534, 28614, 28637,\n",
      "       28788, 28848, 28968, 28984, 29041, 29055, 29213, 29238, 29298,\n",
      "       29322, 29328, 29338, 29376, 29489, 29519, 29585, 29781, 29803,\n",
      "       29806, 29952, 30005, 30006, 30183, 30249, 30262, 30387, 30409,\n",
      "       30607, 30649, 30695, 30720, 30821, 31004, 31138, 31162, 31163,\n",
      "       31278, 31316, 31323, 31562, 31648, 31678, 31697, 31918, 32088,\n",
      "       32091, 32390]),)\n"
     ]
    }
   ],
   "source": [
    "#finding outliars\n",
    "print(np.where(df2[' 77516']>500000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping only those values less than 500000i.e removing the outliar\n",
    "df2 = df2[df2[\" 77516\"]<500000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eKB8xxvD7vdUA3Lg088LYxqzUWJ74UhHldR38n9cOjTnmb/5WSV5KDKtmpfi8z2OVkRjN+b5BWrrGnhSVCmWjJhNr/OI+YAtwBNhkjCkVkR+JyA1WtadwjF9UAPcDD1r7lgKbcAzWvwHca4wZcBfTivUAcL8VK8WK7bYNFTjK6joor+/k2gUzSIr56OWoq+akcd/VBfx+31nePFznccy/HK5j/5lWvrm6AJvNPwPvzjISHeM+Z1u7/dwTpQKLJwPwGGNeB14fVvYDp9fngc+72fcnwE88iWmVn+DDGV/O5W7bcKrz8Ejb1cQ6UNVGdLidlXnJLrd/c3UBbxyq5buvHmRFXhKJMSOPfwwMGv5jSxmz06bxuWWBMeA9PT4Km8DZNk0mSjnzKJkoNZr+gUGO1LSzMDMBu5sziIgwG//x+YtZ++gHPLy5lA23Lhlxmu+re6spr+/kl19YxqaSqonq+piE222kx0VR3aLJRClnupyK8omKhk56+gdZmJHgcvvQAPaBqjZWz03j9/vO8tKuMy7rAnT19rPhzWMszkqY9EUdR5OVFE1VSzeOIT6lFGgyUT5yqLqdqHAbs9OnjVr3k3PTKUiP5QebS10unDgwaPjWi3upaevmu9f77yZFd7KTYujuG+BUU5e/u6JUwNBkorzWPzjI4Zo25s2IJ8w2+kfKJsItRdkkx0Twjef3UOH0jBBjDD/8Qyl/PVLPwzcsCIgZXMNlJTueCb//TKufe6JU4NBkorx2ouEc5/sGLzzzwxOxkWE89sVltHX3cd1/vcf/23KU57af4q5f7+LZ7ZXcc9Us7rg0b8L67I3p8VFE2G3s02Si1AU6AK+8Vnq2jYgw25jXq1qWk8Rb//oJfvr6ER7ddhyA7ORoPjk3jZzkmEld+2ssbCJkJkWzV5OJUhdoMlFeO95wjoK0WMLtYz/RTY2N5Oe3LOFrV80m3C7kp07jxZ3uB+YDRXZSNDtONNPTP0BkmN3f3VHK7/Qyl/JK+/k+ms/1kpcS41WcuTPimJUWG3CD7e5kJcXQOzDIYV2nSylAk4nyUqU1oyk3ZfRZXKEkO9mRPHXcRCkHTSbKK6eazhFuFzISo/3dlUmVEB3O9PhITSZKWTSZKK9UNp0jOznG7V3voWxJdqImE6UsmkzUuJ3vG6Cm9Tx5U+wS15Al2UlUNnXRfK7X311Ryu80mahxO9PchQFyvRx8D1Yr8pIA2Hmy2c89Ucr/NJmocTvV1IUAOUlTM5kszkokMsymyUQpNJkoL1Q2nSMjMZrI8Kl5n0VEmI1lOUkUn2zyd1eU8jtNJmpc+gYGOdPSNWUvcQ25ZFYyh2vaaT+vT15UU5smEzUuR2s66Bsw5CRP7WSyMj8ZY6DklF7qUlObJhM1LgeqHVNis6boeMmQZTlJRNhtFJ/QZKKmNk0malwOVrURE2EnKSbc313xq6hwOxdnJ1Csg/BqitNkosZlf1UbmYnRQbOW1kRamZ/Mweo2zvX0+7srSvmNrhqsxux83wDH6jq4sjDVqziBusT8WF2Sn8Kj246zu7KFq+ak+bs7SvmFnpmoMTtc087AoCFriq3H5c7y3CTCbML2EzpFWE1dmkzUmB2w1qPKnOKD70OmRYaxPDeJd8oa/N0VpfxGk4kaswPVbaTFRRIfpVdJh6yem87hmnbq2s/7uytK+YUmEzVmB6raWJyZoIPvTlbPdYyV6NmJmqr0q2WQcDVYffslOZPej86efo43dPLZxRmT3nYgu2hGHDPio3j7WD23rMj2d3eUmnR6ZqLGpLS6DWNgcVaCv7sSUESE1XPTeO9YI30Dg/7ujlKTzqNkIiJrRKRMRCpE5EEX2yNF5CVre7GI5Dlte8gqLxORa0eLKSL5VoxyK2bESG2IyEoR2Wf97BeRfxjvm6FGd6CqDYBFmkw+ZvXcNDp6+tlT2eLvrig16UZNJiJiBx4FrgPmA7eJyPxh1e4GWowxBcAGYL2173xgHbAAWAM8JiL2UWKuBzYYYwqBFiu22zaAQ0CRMWaJ1cbjIqKX7ybIgWrHzYqpsZH+7krAubwglTCb8PYxHTdRU48nZyYrgQpjzAljTC+wEVg7rM5a4Bnr9cvAp8QxOrsW2GiM6THGnAQqrHguY1r7XG3FwIp540htGGO6jDFDtx5HAcbTg1djd6CqlUWZelbiSlxUOMtzk9h2tN7fXVFq0nnyDT4TOOP0exVwibs6xph+EWkDUqzyHcP2zbReu4qZArQ6JQfn+u7aaBSRS4CngVzgS077Kx9q6+qjsqmLW3WAGXA9KSIlNpLik82cbDxHfurUfJyxmpo8OTNxNf9z+Ld/d3V8VT5iP4wxxcaYBcAK4CERiRpeUUTuEZESESlpaNDLEONxsNoxXrI4M9HPPQlcCzPiAXj9YI2fe6LU5PIkmVQBzl9Fs4Cz7upY4xUJQPMI+7orbwQSncY8nNty18YFxpgjwDlg4fCDMMY8YYwpMsYUpaXp+knjsb/Kcee7XuZyLzEmgmU5ifzpgCYTNbV4kkx2AYXWLKsIHAPqm4fV2Qzcab2+GdhqjDFW+TprJlY+UAjsdBfT2mebFQMr5msjtWHFCAMQkVxgLnDK43dAeexgVRt5KTEkTPFl50dz/aKZHK5p50RDp7+7otSkGTWZWOMP9wFbgCPAJmNMqYj8SERusKo9BaSISAVwP/CgtW8psAk4DLwB3GuMGXAX04r1AHC/FSvFiu22DeAKYL+I7ANeBb5pjGkc39uhRnKwuo1FWXqJazTXL5oJ6KUuNbV4NIXWGPM68Pqwsh84vT4PfN7Nvj8BfuJJTKv8BI7ZXsPLXbZhjHkOeG7Ug1Beaezsobq1my9fnufvrgS8jMRolucm8aeDtdx3daG/u6PUpNA74JVHDg7drKjjJR65ftFMjtS0c1wvdakpQpOJ8sj+qlZEYIEmE498ZtFMbAK/31vt764oNSn0TnHlkTcO1ZIWG8nmfcMn8qnhhu4/KUiP5dntlUyPj+KLq3L93CulJpaemahRGWOoaukmU5+sOCbLcpJo6+7jeL1e6lKhT5OJGtXp5i46e/rJSdEnK47FvJnxRIfb2X1aF35UoU+TiRpVySnHH8PcZF0eZCzC7TYuzk7g8Nl22rr6/N0dpSaUJhM1qpLKFqLCbaTH60rBY7U8J5n+QcPmAzrWpEKbJhM1qt2VzeQkx2DTx/SOWUZiFDPio9i48zSOBR6UCk2aTNSI2rr6OFbXSY5e4hoXEWFlfjKlZ9vZe6bV391RasJoMlEj2nPGGi/RwfdxW5qdSGxkGM9vr/R3V5SaMJpM1Ih2n2rBbhOykzSZjFdkuJ3PLcvkjwdqaOrs8Xd3lJoQmkzUiEoqm5k/M56IMP2oeONLq3LpHRhkU0mVv7ui1ITQvxDKrb6BQfadaWV5bpK/uxL0CqfHsWpWMs/vqGRgUAfiVejRZKLcOny2nfN9gxTlaTLxhTsvzaO6tZs3D9f5uytK+ZwmE+XW+xWOx8KszE/2c0+C3wvFp2ns7CUpJpz/+/oRl8+PVyqYaTJRbr1dVs/CzHjS46L83ZWQYLcJl81OpbK5izPNXf7ujlI+pclEudTW3cee062snpPu766ElKLcJKLCbRfO+pQKFZpMlEvvlzcyMGhYPTfN310JKZHhdlbkJXOouk3PTlRI0WSiXHq7rJ74qDCWZOsz333tstmpiMBT75/0d1eU8hlNJkFoW1k9z24/xfvljROy3pMxhneONXDlnDTC7PoR8bWE6HCWZifx4s7TNHToTYwqNOhfiiCz40QTbx6u43hDJ198qpgbHvmAs63dPm3jcE079R09rJ6jl7gmyifmptE3MMiT75/wd1eU8glNJkHkSE07f9h/lotmxPG96+ez/qZFnGjo5Fsv7qV/YNBn7bxd1gA4/uCpiZEaG8lnL87gue2VtJzr9Xd3lPKaPgM+ALm6B6G7d4CXdp0hIzGadStyiAizceuKHKLC7Xx74z5+8VY5918z1yftv3GoVqcET4J7P1nAa/vO8usPTvrsv51S/qJnJkHiaG07vQODfHbxzI+sk7V2SSafX57Ff2+r4G/HvZ9uevhsOwer2/jc0iyvY6mRzZkex3ULZ/D0B6d0AUgV9DSZBInDNe3ERYWRlfzx1Xt/uHYB+SnT+M7LB+juHfCqnU0lZ7DbhMFBwwvFpy/8qInxr9fMoau3n//eWuHvrijlFU0mQaC3f5BjdR3Mnxnv8mmHMRFh/PRzi6hq6ea/t5aPu53zfQO8urea+TPjiYnUK6CToSA9jltX5PDb4koqm875uztKjZsmkyBQUd9B34BhQUaC2zqrZqVw07Isnnj3BMfqOsbVzl8O19HW3ceKPF2LazL9y6cLCbPZ+H9byvzdFaXGzaNkIiJrRKRMRCpE5EEX2yNF5CVre7GI5Dlte8gqLxORa0eLKSL5VoxyK2bESG2IyN+JyG4ROWj9e/V434xAVXq2nehwO/mpIz8697vXX0RsVBjfe/Ugg+NY5nzTrjNkJUUzK00f0TuZ0uOj+OqV+fzxQA27K1v83R2lxmXUZCIiduBR4DpgPnCbiMwfVu1uoMUYUwBsANZb+84H1gELgDXAYyJiHyXmemCDMaYQaLFiu20DaAQ+a4xZBNwJPDe2tyCwDQwajtZ2cNGMOOy2j1/icpYSG8l3r5/HrlMtPP3B2O6uLj3bxvsVjXx+ebbLS2nK95zHpJJiIkiIDucbz++mt99307yVmiyenJmsBCqMMSeMMb3ARmDtsDprgWes1y8DnxIRsco3GmN6jDEngQornsuY1j5XWzGwYt44UhvGmL3GmLNWeSkQJSKRnr4Bge5k4zm6+wZYkBHvUf3PL8/i0/Om87MtZZTVena5yxjDw5tLSZ4WwV2X5XnRWzVekeF21l6cQX1HD79657i/u6PUmHmSTDKBM06/V1llLusYY/qBNiBlhH3dlacArVaM4W25a8PZTcBeY0zIzLM83tCJTRwDtZ4QEVbmJxNut3Hn0zt59m+nRp2N9dq+s+w61cJ3rp1LQky4L7qtxuGimfEsykzgka0VVNR3+rs7So2JJ8nE1TWP4Rfk3dXxVfmo/RCRBTgufX3NRT1E5B4RKRGRkoaGBldVAlJ1azcz4qPG9Az22MgwblqaSW37eV7ZW83gCOt3dfb089PXj7A4K4FbirJ90WXlhb9fPJPoCDv/umkfPf3eTfNWajJ5Mv+zCnD+K5MFnHVTp0pEwoAEoHmUfV2VNwKJIhJmnX0413fXBiKSBbwK3GGMcXmNwBjzBPAEQFFRUVA8hNsYQ3VLNwszXV/iGumM46KZ8Vwzfzp/OVyHTYTbV+ZgGzbm0t07wLdf3Et9Rw+Pf2n5x7aryRcXFc76mxbx9ef38OM/HuHfblzo7y4p5RFPvu7uAgqtWVYROAbUNw+rsxnH4DfAzcBW41jOdjOwzpqJlQ8UAjvdxbT22WbFwIr52khtiEgi8CfgIWPMB2M5+EDX0tVHd98AGYnR49p/9dx0PnVROntOt3Dfi3s41fjhfQzN53q5/ckdbC2r59/WLmBpjj7nPVCsWTiTe66axXM7Kvn93mp/d0cpj4x6ZmKM6ReR+4AtgB142hhTKiI/AkqMMZuBp4DnRKQCx9nCOmvfUhHZBBwG+oF7jTEDAK5iWk0+AGwUkR8De63YuGsDuA8oAL4vIt+3yq4xxtSP7y0JHNXWasBZiR+/691TV1+Ujt0m/PVIPW8cquWqOWm0d/dxrK6T3oFBfvmFZaxZONNXXVY+8p1r57LvTCsPvnKA7ORolufqvT8qsMlEPA8j0BUVFZmSkhJ/d8OtoctXbxyq5YOKRv7PZ+d7/VyRT89L56kPTvLng7XMSIhizvRYbl6e7fLhV7p8in/dfkkOAA0dPdzy+HYaO3t48aurWJjp/qZVpSaDiOw2xhS52qZrZgSw6tYupidE+uQBVenxUTx03Tweum6eD3qmJkNaXCTP/+Ml3PKr7dzx9E5e/Ooq5s7wbFafUpNNk0mAMsZQ3drNokx9bO5UM/zM8NYV2Ty/o5Kbf/U3nvhSEZfOHj4jXin/07W5AlTzuV7O9w2SOc7BdxU6UmMjeeWblzE9Poo7n96pg/IqIOmZSYAaGmt4lUUAABbfSURBVHzPTJq4ZKJjI8Hj3WON3LYih+eLK/nnl/bx8u4qfvPlFT65BKqUL2gyCVDVrd3YbcL0+JBZGUZ5KTrCzpcvz+P1g7W8X9HINRveZd3KHGKHPS5gaABfqcmkX2sC1NCd72E2/U+kPhRms3HDxRncvCyL081dPLqtgqqWLn93Syk9MwlExhhqWs97vLijJ/SSVmhZlpvE9IQofltcyRPvnuDGJZksy9UbT5X/6NfeANTZ00933wDT46P83RUVwDITo7l3dQG5KTG8vKeKNw/XMhXvG1OBQZNJAKrvcCx6rMlEjWZaZBh3XZZPUW4S28oaeKnkjD4PRfmFJpMAVNd+HoD0OB18V6Oz24R/WJrJtfOnc6CqjX98toSu3v7Rd1TKhzSZBKD69h6iwm3ERemQlvKMiPCJuel8bmkm75c3cMdTO2nr7vN3t9QUoskkANV1nGd6XBSij89VY1SUl8wjty9jf1Urtz2xg4aOkHlOnApwmkwCjDGG+vYe0nW8RI3T9Ytm8uSdKzjZeI5bHt9+4QZYpSaSJpMA09DZY83k0vESNT4vFJ+muqWbOy7Npaatm+v/6z19DLCacJpMAkx5neN/+vQ4PTNR3slNmcZXr5xF/6Dhlse3c6i6zd9dUiFMk0mAOVbXAUC6npkoH5iZEM3XrppFdLid257YwbayoH9mnApQmkwCzLG6TqLD7cRF6kwu5RupsZG8/I1LyUqO4Su/2cUjW8sZHNSbG5VvaTIJMOV1HaTHR+pMLuVTMxOieeUbl7H24gz+4y/H+PJvdlHTpgPzynf0628AMcZQXt/J3On6ND3le9ERdjbcuoTluUn89PWjXPPzd/neZ+ZxS1E2NtvYv7y4W+9NVy2emjSZBJCGjh7auvt0vET5nPMffrvNxjdXz+b9ikYefOUgz+2o5HvXz+OyglQ/9lAFO73MFUCOWTO5dE0uNdFSYiN58aur+K91S2jt6uP2J4tZ98R2tpXV62KRalz0zCSAXJjJpWtyqUmwcdcZAO65ahbFJ5t5v7yBL/96FwXpsdy0LIt/WJrJjAT9YqM8o8kkgJTXd5AYE/6xJ+cpNZHC7TauKEhl1axkDpxp42TTOda/cZT1bxzlohlxrJqVwqLMBHJTYshKiiFpWjiRYXZ/d1sFGP2rFUCO1XUyJz1OZ3Ipvwiz2ViWm8Sy3CQ+MSeNQ9VtnGg4x2+LK+kb+Oilr9jIMGIi7KRMi2BmYjQLMxKYrrMQpzRNJgHCGEN5XQefvTjD311RitTYSFbPTWf1XOgfHOTyglRON3VR3dpNy7lems71UnyiiaZzvRyt7WDr0XrS4iJZs2CGv7uu/ESTSYCo7+ih/Xw/c3RasAowYTYbs9NimZ0W+5HyoRliHef7KD3bzo4TTTy3o5K69vP8aO1CHW+ZYjSZBIihwffC6bGcauzyc2+U8lxcVDirZqWwIi+ZDyoaeetoHddseIe7Lsu/kFD03pPQ59HUYBFZIyJlIlIhIg+62B4pIi9Z24tFJM9p20NWeZmIXDtaTBHJt2KUWzEjRmpDRFJEZJuIdIrII+N9I/xtaFqwnpmoQPRC8emP/QxntwlXzUnjG58oAOCJ945zolFXK54qRk0mImIHHgWuA+YDt4nI/GHV7gZajDEFwAZgvbXvfGAdsABYAzwmIvZRYq4HNhhjCoEWK7bbNoDzwPeB/zXGYw8o5XUdJMWEkzItwt9dUcorMxKi+NonZhMXGc5vPjjFycZz/u6SmgSenJmsBCqMMSeMMb3ARmDtsDprgWes1y8DnxLHtI61wEZjTI8x5iRQYcVzGdPa52orBlbMG0dqwxhzzhjzPo6kErSO1XVQOF1ncqnQkBQTwVevmkVSTATPbj+ly99PAZ4kk0zgjNPvVVaZyzrGmH6gDUgZYV935SlAqxVjeFvu2gh6Q2tyzZkeO3plpYJEbGQYX748j6hwO3f9eqeeoYQ4T5KJq6/Kw9dbcFfHV+We9sMtEblHREpEpKShocHT3SZFXXsPHTqTS4WgxJgIvnJ5PsbAF58s1pWKQ5gnyaQKyHb6PQs4666OiIQBCUDzCPu6K28EEq0Yw9ty14ZHjDFPGGOKjDFFaWlpnu42KS7M5ErXZKJCT1pcJM98ZSVt3X3c8dROWs71+rtLagJ4kkx2AYXWLKsIHAPqm4fV2Qzcab2+GdhqHKvFbQbWWTOx8oFCYKe7mNY+26wYWDFfG6WNoDeUTPQylwpVCzMTePLOIiqbu7jj6Z20dfX5u0vKx0ZNJtb4xH3AFuAIsMkYUyoiPxKRG6xqTwEpIlIB3A88aO1bCmwCDgNvAPcaYwbcxbRiPQDcb8VKsWK7bQNARE4BPwfuEpEqF7PNAlp5XSfJ0yJIidUFHlXoWjUrhce/uJyy2g5uf3KHnqGEGAmRL/djUlRUZEpKSvzdjQs+84v3SIqJ4Pl/vARw/9AhpULBsboOnt9RSUF6LL/58kq9Uz6IiMhuY0yRq236PBM/6+kf4FhdBwszE/zdFaUmxZzpcdxxaR5nmru44ZH32Xu6xd9dUj6gycTPjtV20jdgWKTJRE0hBemxvPLNy4kMt3HrEzt4ofi0PpQryOnaXH520LqZS5OJmmrmzohj871X8E8v7uW7rx7kzcO1/PtNi3nrSP3H6uraXoFPz0z87GB1GwnR4WQnR/u7K0pNuqRpETz7lZU8/Nn5bD/RxKd//g7bjzcyMKhnKcFGk4mfHapuY2FmvC6joqYsm0246/J8Xv/WlSzOSuAPB2p47O0KKpv0jvlgosnEj3r7Bymr1cF3pQBmpcXy/N2XcNvKHLp6B3j83RP8T8kZOs7rPSnBQMdM/OhYXQe9A4M6XqKURURYlJnA3OlxbCur5/3yRkpr2unuG+CrV85iWqT+yQpU+l/Gj4YG3xdmaDJRU89I91NFhNm4dsEMlucmsaW0lv/8aznP76jk258qZN3KHMLtelEl0Oh/ET86WN1GXFQYuSkx/u6KUgEpNTaSL1ySyyvfvIxZqbF8/7VS/u7n7/DKnip6+wf93T3lRM9M/OhQdRsLMxJ08F2pUSzLSeKlr61i69F6fvZGGfdv2s+///koX1yVy03Ls8hM/HA2pKszHp1aPPE0mfhJT/8AR2s6uOvyPH93RamA55wg7rg0l/L6To43dPLzN4+x4a/HuGx2Cjcty2LNwhl+7OXUpsnET/ZUttI7MMiKvGR/d0WpoCIizJkex8M3LOB0Uxev7K3id3uquH/Tfr7/+0PMnRHPstxE8lKmYdOz/kmjycRP3itvIMwmrJqlyUSp8cpJieGfPz2Hb11dyK5TzfxuTxWv7TvLntMtJMWEszQniaXZif7u5pSgycRP3itvZFlOEnFR4f7uilJBydXYyJLsJObPTOBwTRt7KlvZdrSerUfr2X26hX+6uoDlufrlbaJoMvGD5nO9HDrbxr98eo6/u6JUyIkIs7EkO4kl2Um0dvWy53QLe063ctMvt3PprBT+6eoCLp2dohNffEynBvvBBxWNGANXFqb6uytKhbTEmAiuvmg67z/wSf73Z+ZxvKGT258s5qZf/o1tR+t1pWIf0mTiB++VNxAfFcbiLL2Wq9RkiIkI4x+vnMW73/kkP75xIXXtPXz5N7v47CPv88ahWgZ1YUmv6WWuSWaM4b3yRq4oTMVu09NspSaD8/iKTYSvf2I2keE2HttWwdef383c6XF885OzuX7RTL27fpz0XZtkxxs6qWk7zxUFaf7uilJTlt0m9A8Y7r5iFrcUZdPS1cu3N+5j+b+9yS/eKqe27by/uxh09Mxkkr152PHgHx0vUcr/7DZhSXYii7MSKKvtYMeJpgs3Ql4+O5UbLs5g9UVppMfpc+pHo8lkEg0MGp7fUckl+clkJ+t6XEoFCpsI82bGM29mPJfOTuHVPVW8uq+a7/zuAAALM+NZPSedT16UxsVZiYTppbCP0WQyif56pI7q1m6+//fz/N0VpZQb2483MSMhmq9fNZuatvMcq+ugrK6Dx96u4JFtFUSE2chKiuYzi2ayLCeJpTmJJMZE+LvbfqfJZBL9+oOTZCZG8+l50/3dFaXUKESEjMRoMhKjWT03ne7eAcrrOzjVdI7TzV089vbxC48Xnp02zUosSSzOSmDO9DgiwqbW2Ysmk0lypKadHSeaefC6iy6cIo/0PAelVGCJjrCzOCvxwpT+3v5Bqlq6ON3s+PnTwRr+Z3cVABF2GxfNjGNRZoLjx0owoTxTTJPJJPn1ByeJCrexbkW2v7uilPKBiDAbs9JimZUWCzim/Tef66W6tZuzrd1UtXbzuz1V/Nb60hgRZmPezHgWZcazODORRVkJFKbHhsz4iyaTSbCtrJ5NJVXcdVmeXltVKkSJCCmxkaTERl44e3FOMNUt3VS3dvM/JVU8v8ORYCLDbMzPiGdxZgKXzk7lsoIU4oN0vT5NJhPsTHMX//LSPubNjOeBNRf5uztKqUnkKsEMDiUYK7lUt3bz4q4zPLO9EptAdnIMhelxzJkeS0ZiNDaRoHi4l0fJRETWAP8F2IEnjTH/Pmx7JPAssBxoAm41xpyytj0E3A0MAN8yxmwZKaaI5AMbgWRgD/AlY0zveNrwt7buPu59YQ8DA4ZffmEZ0RF2f3dJKeVnNhFSYyNJjY3kYmt5/IFBw+nmLsrrOiiv7+SvR+r465E64iLDmDsjjrS4SK4oSA3ovyGjJhMRsQOPAn8HVAG7RGSzMeawU7W7gRZjTIGIrAPWA7eKyHxgHbAAyAD+KiJDS+W6i7ke2GCM2Sgiv7Ji/3KsbRhjBrx5Y7xhjOFPB2v44R8O09TZw6++uJy81Gn+6o5SKsDZbUJ+6jTyU6dxzQLo7OmnvK6DI7UdHKxu46vPlhAZZuOy2SlcXpDKpbNTuGhGfEAtyeTJmclKoMIYcwJARDYCawHnZLIWeNh6/TLwiDjWd14LbDTG9AAnRaTCioermCJyBLgauN2q84wV95fjaGO7h+/BuAwOGvoGB+kfMHT1DlDXfp6zrd3sPNnMe+WNlNV1sDAznqfvXMGirISJ7IpSKsTERoY5HuyVk0T/4CCzUmP565E63i1vYNufjgAQHW5nYWY8c2fEkZs8jaykaBJiwomPCich2vFvdISdMJtgm4Sk40kyyQTOOP1eBVziro4xpl9E2oAUq3zHsH0zrdeuYqYArcaYfhf1x9OGTx2oauXzv9pO/6C5ML98uAi7jRX5SXzp0oWsW5EdMjM1lFL+EWazcUVhKldYSzDVtHWz40QT+8+0cbC6jT/sr6Gtu8+DOI6k8veLZvLzW5f4vp8e1HGV0ob/JXVXx125q7+wI9UfTxsf7aDIPcA91q+dIlLmYj+fKAdeAL70YVEq0DhR7flRqB4X6LEFo1A9Lr7gw2PbAGxYN+7dc91t8CSZVAHON0dkAWfd1KkSkTAgAWgeZV9X5Y1AooiEWWcnzvXH08YFxpgngCc8OF6fE5ESY0yRP9qeSKF6XKDHFoxC9bggOI7Nk2swu4BCEckXkQgcg92bh9XZDNxpvb4Z2GocjzDbDKwTkUhrllYhsNNdTGufbVYMrJivjbMNpZRSk2TUMxNrfOI+YAuOabxPG2NKReRHQIkxZjPwFPCcNfjdjCM5YNXbhGOwvh+4d2iWlauYVpMPABtF5MfAXis242lDKaXU5BB9BvLEE5F7rMtsISVUjwv02IJRqB4XBMexaTJRSinlNZ23qpRSymuaTCaQiKwRkTIRqRCRB/3dH2ci8rSI1IvIIaeyZBF5U0TKrX+TrHIRkV9Yx3FARJY57XOnVb9cRO50Kl8uIgetfX5h3WDqtg0fHle2iGwTkSMiUioi3w6hY4sSkZ0ist86th9a5fkiUmy1+5I1qQVrUspLVj+LRSTPKdZDVnmZiFzrVO7yM+uuDR8fn11E9orIH0PsuE5Zn5d9IlJilQX95/FjjDH6MwE/OCYWHAdmARHAfmC+v/vl1L+rgGXAIaeynwEPWq8fBNZbr68H/ozjnp5VQLFVngycsP5Nsl4nWdt2Apda+/wZuG6kNnx4XDOBZdbrOOAYMD9Ejk2AWOt1OFBs9XkTsM4q/xXwDev1N4FfWa/XAS9Zr+dbn8dIIN/6nNpH+sy6a8PHx3c/jlu0/jhSm0F4XKeA1GFlQf95/NhxTmTwqfxj/cfd4vT7Q8BD/u7XsD7m8dFkUgbMtF7PBMqs148Dtw2vB9wGPO5U/rhVNhM46lR+oZ67NibwGF/DsQZcSB0bEINjIdRLcNyfFTb8c4djtuSl1uswq54M/ywO1XP3mbX2cdmGD48nC3gLx3JKfxypzWA6LivuKT6eTELq82iM0ctcE8jVMjQTssyLD003xtQAWP+mW+XujmWk8ioX5SO14XPW5Y+lOL7Bh8SxWZeC9gH1wJs4vnF7tAQR4LwE0ViOeaRljnzlP4HvAIPW7x4vrURgHxc4VuT4i4jsFsdKHBAin0dn+jyTiePRMi9BYqxL2fj92EUkFvgd8M/GmHbrMrLLqi7KAvbYjOMeqiUikgi8CswboT+TscyR10Tk74F6Y8xuEVk9VDxCm0FxXE4uN8acFZF04E0ROTpC3aD6PDrTM5OJ49EyLwGmTkRmAlj/1lvl7o5lpPIsF+UjteEzIhKOI5H81hjzyijtBtWxDTHGtAJv47iuniiOJYaG9+fCMYhnSxC5K7+wzJGLNnzhcuAGETmF41lGV+M4Uwn24wLAGHPW+rcexxeAlYTY5xE0mUwkT5ahCTTOS9YMX8rmDmumySqgzTpt3gJcIyJJ1kyRa3Bcc64BOkRklTWz5A5cL4vj3IZPWO09BRwxxvw8xI4tzTojQUSigU8DR/DdEkTjWebIa8aYh4wxWcaYPKvNrcaYLwT7cQGIyDQRiRt6jeNzdIgQ+Dx+zEQOyEz1HxwzM47huK79PX/3Z1jfXgRqgD4c327uxnEN+S0cCx+/BSRbdQXHw8yOAweBIqc4XwEqrJ8vO5UX4fif5jjwCB/eIOuyDR8e1xU4TvMPAPusn+tD5NgW41hi6IDV/g+s8lk4/mhWAP8DRFrlUdbvFdb2WU6xvmf1vwxr9s9In1l3bUzA53I1H87mCvrjsuLvt35Kh9oOhc/j8B+9A14ppZTX9DKXUkopr2kyUUop5TVNJkoppbymyUQppZTXNJkopZTymiYTpSaAiMRZq8QO/TSKyH9a2zY4lR8TkVan/Qactm12Kr/PWhXWiEjqsLZWW/VLReSdyTtKpT6kU4OVmgQishv4F2PMu8PK/wlYaoz5ivV7pzEm1sX+S4EWHHe9FxljGq3yROBvwBpjzGkRSTeOO62VmlR6ZqLUBBORQhyL7L3nYvNtOG4gHZExZq8x5pSLTbcDrxhjTlv1NJEov9BkotTEuw3HMzc+chlARHJxPHdjq1NxlIiUiMgOEbnRg9hzgCQRedtalfYO33VbKc/pqsFKTbx1wJfclL9sHCsBD8kxjhVmZwFbReSgMeb4CLHDgOXAp4BoYLuI7DDGHPNV55XyhJ6ZKDWBRORiHA9f2u1i8zqGXeIyH64wewLH+MjSUZqoAt4wxpyzxlHeBS72tt9KjZUmE6UmlssxERGZi+Pxq9udypJEJNJ6nYpjafbDo8R/DbhSRMJEJAbHkxeP+KjvSnlMk4lSE+sWXA+w3wZsHDaOMg8oEZH9OJZG/3djzGEAEfmWiAw9u+KAiDwJYIw5AryBYyXhncCTxphDE3Y0SrmhU4OVUkp5Tc9MlFJKeU2TiVJKKa9pMlFKKeU1TSZKKaW8pslEKaWU1zSZKKWU8pomE6WUUl7TZKKUUspr/x80ylRKVMRKLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting density plot for carat using distplot()\n",
    "sns.distplot(a=df2[' 77516'], hist=True)\n",
    " \n",
    "# visualizing plot using matplotlib.pyplot library\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32068, 15)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c0bf6ec10>"
      ]
     },
     "execution_count": 32,
     "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": [
    "# Box Plot visualization\n",
    "sns.boxplot(df2[' 13'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting density plot for carat using distplot()\n",
    "sns.distplot(a=df2[' 13'], hist=True)\n",
    " \n",
    "# visualizing plot using matplotlib.pyplot library\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([   53,   157,   190,   215,   217,   308,   326,   372,   406,\n",
      "         452,   475,   486,   499,   524,   626,   758,   860,   906,\n",
      "         913,   916,  1120,  1273,  1285,  1331,  1520,  1608,  1609,\n",
      "        1764,  1886,  2093,  2128,  2182,  2410,  2466,  2545,  2548,\n",
      "        2580,  2603,  2631,  2635,  2744,  2775,  2777,  2819,  2841,\n",
      "        2899,  2997,  3070,  3147,  3182,  3236,  3296,  3353,  3372,\n",
      "        3394,  3535,  3610,  3617,  3814,  3865,  3875,  4036,  4057,\n",
      "        4229,  4252,  4450,  4506,  4642,  4753,  4916,  4932,  4940,\n",
      "        4986,  5011,  5102,  5233,  5256,  5280,  5352,  5420,  5437,\n",
      "        5449,  5530,  5543,  5572,  5623,  5640,  5647,  5740,  5748,\n",
      "        5783,  5864,  5877,  5881,  5912,  6035,  6330,  6381,  6515,\n",
      "        6752,  6956,  7009,  7057,  7184,  7203,  7320,  7394,  7583,\n",
      "        7606,  7615,  7644,  7726,  7769,  7942,  7953,  7961,  7969,\n",
      "        8084,  8144,  8152,  8179,  8331,  8361,  8611,  8633,  8741,\n",
      "        8743,  8744,  8832,  8837,  8849,  8853,  8986,  9102,  9135,\n",
      "        9238,  9294,  9442,  9473,  9491,  9504,  9597,  9625,  9677,\n",
      "        9691,  9836,  9975, 10003, 10099, 10142, 10145, 10179, 10180,\n",
      "       10238, 10332, 10350, 10390, 10495, 10545, 10667, 10734, 10769,\n",
      "       10799, 10860, 10945, 10961, 11120, 11123, 11164, 11184, 11230,\n",
      "       11263, 11301, 11389, 11423, 11463, 11552, 11554, 11656, 11693,\n",
      "       11723, 11755, 11784, 11799, 11877, 11930, 11942, 12046, 12065,\n",
      "       12080, 12231, 12282, 12419, 12422, 12584, 12668, 12877, 12912,\n",
      "       12932, 13011, 13034, 13072, 13087, 13114, 13287, 13324, 13382,\n",
      "       13446, 13463, 13475, 13539, 13597, 13614, 13630, 13735, 13862,\n",
      "       13866, 13875, 13900, 14021, 14026, 14090, 14102, 14232, 14258,\n",
      "       14275, 14318, 14355, 14492, 14555, 14644, 14660, 14662, 14676,\n",
      "       14680, 14702, 14739, 14747, 14775, 14779, 14787, 14825, 14893,\n",
      "       14935, 14940, 15014, 15123, 15306, 15318, 15332, 15591, 15815,\n",
      "       15826, 15853, 15892, 16112, 16115, 16241, 16244, 16252, 16309,\n",
      "       16321, 16557, 16700, 16763, 16833, 16996, 17083, 17123, 17128,\n",
      "       17181, 17225, 17295, 17370, 17425, 17441, 17484, 17525, 17540,\n",
      "       17599, 17639, 17667, 17780, 18024, 18029, 18061, 18107, 18135,\n",
      "       18256, 18364, 18406, 18474, 18556, 18569, 18617, 18683, 18720,\n",
      "       18778, 18820, 18876, 18885, 19109, 19259, 19269, 19274, 19282,\n",
      "       19576, 19673, 19708, 19864, 20031, 20093, 20128, 20189, 20194,\n",
      "       20397, 20401, 20483, 20508, 20707, 20725, 20773, 20779, 20854,\n",
      "       20998, 21045, 21062, 21181, 21212, 21307, 21393, 21417, 21447,\n",
      "       21449, 21464, 21468, 21530, 21538, 21573, 21659, 21727, 21829,\n",
      "       21960, 21986, 21999, 22005, 22008, 22033, 22170, 22189, 22228,\n",
      "       22274, 22520, 22538, 22591, 22627, 22651, 22662, 22719, 22721,\n",
      "       22774, 22816, 22821, 22932, 23138, 23153, 23256, 23286, 23312,\n",
      "       23361, 23426, 23441, 23555, 23561, 23563, 23802, 23919, 24043,\n",
      "       24314, 24586, 24715, 24728, 24796, 24842, 24897, 24931, 24961,\n",
      "       24965, 25123, 25157, 25160, 25181, 25264, 25373, 25471, 25482,\n",
      "       25526, 25551, 25702, 25792, 25871, 25882, 26010, 26033, 26104,\n",
      "       26207, 26343, 26373, 26390, 26403, 26432, 26494, 26499, 26522,\n",
      "       26591, 26667, 26698, 26747, 26787, 26832, 26922, 26960, 26993,\n",
      "       26996, 27012, 27079, 27096, 27103, 27122, 27136, 27300, 27312,\n",
      "       27334, 27361, 27405, 27429, 27521, 27569, 27584, 27590, 27597,\n",
      "       27637, 27712, 27797, 27799, 27813, 27826, 27874, 27971, 27992,\n",
      "       28016, 28017, 28057, 28081, 28138, 28159, 28169, 28175, 28177,\n",
      "       28285, 28310, 28317, 28343, 28380, 28410, 28439, 28640, 28831,\n",
      "       28845, 28899, 28940, 28960, 28971, 28976, 28998, 29012, 29127,\n",
      "       29231, 29236, 29295, 29345, 29402, 29404, 29425, 29659, 29828,\n",
      "       29867, 29892, 29902, 29952, 29999, 30023, 30049, 30096, 30125,\n",
      "       30340, 30375, 30390, 30391, 30524, 30576, 30613, 30620, 30672,\n",
      "       30773, 30897, 30900, 30904, 31033, 31042, 31074, 31079, 31318,\n",
      "       31381, 31389, 31524, 31559, 31668, 31720, 31727, 31737, 31818,\n",
      "       31846, 31871, 31914, 31924, 31936, 31943]),)\n"
     ]
    }
   ],
   "source": [
    "#finding outliars\n",
    "print(np.where(df2[' 13']<4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping only those values more than 4 i.e removing the outliar\n",
    "df2 = df2[df2[\" 13\"]>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting density plot for carat using distplot()\n",
    "sns.distplot(a=df2[' 13'], hist=True)\n",
    " \n",
    "# visualizing plot using matplotlib.pyplot library\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30911, 15)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c07841c50>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPlklEQVR4nO3dfWxcVX7G8ecXO3ZcW8s6ZoNmk6izyHQ3qybZdSIU0lUUUSLyUhEJGtQoWgexaCWEgosqwQYCwfSfVLx0abRAUUohFWxLt6G8KNoK6EZohWDXoSWlTVhcbZZ1yTZvjrcOebGd0z/mztW1M5OM7bn+jWe+H2k0nuN77znnnutn7py5M7YQggAAU2+GdwMAoFYRwADghAAGACcEMAA4IYABwEn9eBa+8sorQzabTakpAFCd9u/ffzyE8KWx5eMK4Gw2q56envK1CgBqgJn9qlA5UxAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADgZ1/+EA4q54447dOrUKc2dO3fC22hvb9eWLVvK2CqgshHAKIsjR45o8PTn+s25iR1SdZ+fLHOLgMpHAKN86up15mtrJ7Rq06G9ZW4MUPmYAwYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEcI3ZuXOndu7c6d2MKVFLfcX0VO/dAEyt3t5e7yZMmVrqK6YnzoABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4KR+Kiq59dZbdfToUWUyGZ08eVLnzp3TrFmzNDQ0pJGREdXX12t4ePiy29m3b5/WrFmjM2fOqLm5WadPn45/l8lkdOTIEc2bN099fX1x+fLly/Xuu+9qxYoVeuedd+Ly1tZW9ff3q62tTf39/bpw4YLq6uo0MjIyqr6VK1fGjxsaGnT+/Hk1Njbq3Llz46rjxIkTcXn+8Zw5c3T06NGifb3xxhvjfTU8PKzh4WHNnDlTQ0ND8XKbNm3Siy++qM7OTu3evTsuf+ihh/TII49o+/bt6u7ujssXL1582f1cjdavX6+BgQG1trbqlVdeGTWuxeSPpWw2q88++ywe+/vuu6/gvt21a5e6urr05JNP6sEHH4yPx2uvvVZ79uzRhg0bdNddd+nuu+/WgQMH1NHRoQ8++CBef8GCBTp48KAWLlyorq6ueFvPPPOMenp6tGzZMu3YsSNePtmHffv2FS3v7e2Nt9Xa2qru7m5t375dt9xyS7zcY489pnvvvVePPvqoBgYG4v719vbGx9ftt99esI7kutlsNt5+W1vbqP2ZXCf5N9PZ2Vmwff39/XG5pPjnO++8U0NDQ2poaNBTTz0Vl7e3t+vEiRMF60+WJ7f76aefxn2dP39+XP7cc88V/JtO7udysBBCyQsvXbo09PT0jLuSUg72UowNxLRNdX3lrDv/pDb2yS0fwPmDulzWrVunwbPnNdjx7Qmt33Ror5ZcfVVZ29XV1SUp19exoZTGvs1mszp8+HB8X0ipdRfb1qWCtlj5bbfdFm9r0aJFev3113XTTTfp1VdfjZdraWnR4OCgWlpadPbs2YL9K1ZHct3rr78+3v4999wzqk/F+p3sa7J9H374YVwuqeD+SK77/PPP64knnihYf7I8ud2+vr64r/PmzStp/CbCzPaHEJZeVJ52AOfPflE5Fi9eXFMBfPjwYQ0MDJRtu5Mxe/ZsnTx5csLr58+CC4VZKeE+9hXUeOTPgi9VR/5VZGNjo1566aX4LLTUJ7x86E+knY8//ri2bt0av1LJ13/ixAlt3LhR58+fn1T/8yYSwsUCOPUpCMK38uRfkpbTmTNnpNKfyy8y4+xv1dv7f2VtV29vr5qamiomfCVNKnwl6b333pvU+pMJn927d4+ahigkP4U3MjKi3bt3X3QWfDn5M+6JtHP79u26cOHCRfW/8MILcflkw7fcLvsmnJl918x6zKzn2LFjU9EmANPc8PCw3nzzzSmtc3BwMA7wZP1vvfVWSe8xebjsGXAI4VlJz0q5KYjUW4TUtbe3pzYFMVEXZn1B7SlNQRw/frxs20Rp6uvrtWrVqimtc+z8db7+G264QXv37q3IEE79MrQ5c+akXQVwSVdccYV3E2KzZ8+e1PrLli2b1PozZ86c8LqdnZ2XXaauri6+L2X5serrc+eEE2lnd3e3ZsyYcVH9mzdvjssn0/80pB7AL7/8ctm2Ve5LQCqtvnLWnT+Q8/d5tXgZWvLdfim9fZt/tz5/X8iePXtKqqPYtvKXoY3tQ/5xofLkttasWSMz0/r160ct19LSEt8X619+/ndsHcl1161bJzPT6tWrR10Gdql9nmzf2rVrZWZau3btqPJi+yNZvmTJEq1evfqi+tva2uLysdtN9rWU8St3JkzJBzHyZ8GZTEaNjY2SpFmzZsXPlmMH+lKampokSc3NzaPKM5mMpNy1m0nLly+XJK1YsWJUeWtrq6Tc4CSfNS+loaFBkuI+jKeOpPzjy706SO6rYmcGmzZtknTx2cn9998vSXrggQcuWUetyJ8F58ekFPljKZvNjhr7Yvt227Ztam5u1rZt20YdjzfffLMkacOGDZKkRYsWSZI6OjpGrb9gwQJJ0sKFC0dta+nS3JvnEz37TW5r8+bNWrhw4UXHy8MPP6wZM2aou7t7VP+KHV/F1i22/bGSfzPF2pcsT/6c/xtoaGgYVS6paP3Ftpvsa7K82N90uU3JdcCoHMlrY8upki9DK3dfgfEqdhkaH0UGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgJN67wZgarW3t3s3YcrUUl8xPRHANWbLli3eTZgytdRXTE9MQQCAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwUu/dAFSRkWE1Hdo7oVXrPj8p6arytgeocAQwyiKTyejUqVOaO3eiIXqV2tvby9omoNIRwCiLXbt2eTcBmHaYAwYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOLIRQ+sJmxyT9ahzbv1LS8fE2qgrQ79pCv2vLRPr9uyGEL40tHFcAj5eZ9YQQlqZWQYWi37WFfteWcvabKQgAcEIAA4CTtAP42ZS3X6nod22h37WlbP1OdQ4YAFAcUxAA4IQABgAnqQSwma02s4/NrNfMvpdGHZXAzOab2U/M7KCZ/aeZdUXls83sTTP7JLpv9W5rGsyszsz+zczeiB5/xczej/r9D2bW4N3GNJjZF83sR2Z2KBr762phzM3snug4/8jMfmhms6pxzM3sOTM7amYfJcoKjq/l/FWUdQfMrGM8dZU9gM2sTtIPJK2R9HVJG83s6+Wup0IMS/qzEMICScsk3RX19XuS3g4hXCPp7ehxNeqSdDDx+C8k/WXU735J33FpVfqelPTjEMLXJC1Wbh9U9Zib2VxJd0taGkL4fUl1kv5E1Tnmz0taPaas2PiukXRNdPuupKfHVVMIoaw3SddJ+pfE462Stpa7nkq8SXpV0ipJH0vKRGUZSR97ty2Fvs6LDsTrJb0hyZT7dFB9oeOgWm6SviDpl4rewE6UV/WYS5or6deSZkuqj8b8xmodc0lZSR9dbnwl/bWkjYWWK+WWxhREfqDy+qKyqmZmWUnflPS+pKtCCEckKbqf49ey1Hxf0r2SLkSP2ySdCiEMR4+rddyvlnRM0t9G0y+7zKxZVT7mIYT/kfSYpE8lHZE0IGm/amPMpeLjO6m8SyOArUBZVV/rZmYtkv5J0p+GEH7r3Z60mdkfSToaQtifLC6waDWOe72kDklPhxC+Kem0qmy6oZBoznO9pK9I+rKkZuVefo9VjWN+KZM67tMI4D5J8xOP50n6LIV6KoKZzVQufF8MIeyJiv/XzDLR7zOSjnq1LyV/IOkmMzss6e+Vm4b4vqQvmll9tEy1jnufpL4QwvvR4x8pF8jVPuY3SPplCOFYCGFI0h5Jy1UbYy4VH99J5V0aAfxzSddE7442KDdR/1oK9bgzM5P0N5IOhhCeSPzqNUmbo583Kzc3XDVCCFtDCPNCCFnlxvdfQwibJP1E0h9Hi1VdvyUphPAbSb82s69GRX8o6b9U5WOu3NTDMjP7nei4z/e76sc8Umx8X5PUGV0NsUzSQH6qoiQpTWCvlfQLSf8t6QHvCfUUJ+q/pdzLjQOS/j26rVVuPvRtSZ9E97O925riPlgp6Y3o56sl/UxSr6R/lNTo3b6U+vwNST3RuP+zpNZaGHNJ3ZIOSfpI0t9JaqzGMZf0Q+XmuYeUO8P9TrHxVW4K4gdR1v2HcleJlFwXH0UGACd8Eg4AnBDAAOCEAAYAJwQwADghgAHACQGMac3MXivlW6uASkQAY9oys5slDY4prupvJUN14TpgTEvR92/8WLmvAHw55L4iUWb2saSVIYQj0UdG94UQvnqJTQFuOAPGdPXnkh6X9PmY8qr+VjJUFwIY046ZfUNSewjhFe+2AJNBAGM6uk7Skujb2H4q6ffMbF/0u2r/VjJUEQIY004I4ekQwpdD7tvYviXpFyGEldGvq/1byVBFCGBUmx2SVpnZJ8r9e6gdzu0BiuIqCABwwhkwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4OT/Ae3c+tmQ5HfYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot visualization\n",
    "sns.boxplot(df2[' 40'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([    0,     5,    19, ..., 30895, 30903, 30909]),)\n"
     ]
    }
   ],
   "source": [
    "print(np.where(df2[' 40']<30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping only those values more than 30 i.e removing the outliar\n",
    "df2 = df2[df2[\" 40\"]>30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26003, 15)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([    7,    15,    21, ..., 25980, 25984, 25992]),)\n"
     ]
    }
   ],
   "source": [
    "print(np.where(df2[' 40']>50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c077ca410>"
      ]
     },
     "execution_count": 43,
     "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": [
    "# Box Plot visualization\n",
    "sns.boxplot(df2[' 0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the column 2174 and 0 are highly fragmented so we need to drop it,which we have\n",
    "done at later stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26003, 15)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c0779ecd0>"
      ]
     },
     "execution_count": 46,
     "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": [
    "# making a count plot for \"salary\" column which is our target variable\n",
    "sns.countplot('salary',data=df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    18668\n",
       " >50K      7335\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"salary\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding the categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['39', ' State-gov', ' 77516', ' Bachelors', ' 13', ' Never-married',\n",
      "       ' Adm-clerical', ' Not-in-family', ' White', ' Male', ' 2174', ' 0',\n",
      "       ' 40', ' United-States', 'salary'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
       "1  38            Private  215646     HS-grad    9             Divorced   \n",
       "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
       "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
       "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
       "6  52   Self-emp-not-inc  209642     HS-grad    9   Married-civ-spouse   \n",
       "\n",
       "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
       "6     Exec-managerial         Husband   White     Male      0   0   45   \n",
       "\n",
       "    United-States  salary  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3            Cuba   <=50K  \n",
       "4   United-States   <=50K  \n",
       "6   United-States    >50K  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_numeric = df2[[\"39\",\" 77516\",\" 13\",\" 2174\",\" 0\",\" 40\",]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   39   77516   13   2174   0   40\n",
       "1  38  215646    9      0   0   40\n",
       "2  53  234721    7      0   0   40\n",
       "3  28  338409   13      0   0   40\n",
       "4  37  284582   14      0   0   40\n",
       "6  52  209642    9      0   0   45"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_numeric.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_categorical = df2[[' State-gov', ' Bachelors', ' Never-married', ' Not-in-family', ' White',' Male',' United-States',\"salary\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>State-gov</th>\n",
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       "      <th>Not-in-family</th>\n",
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       "      <td>Wife</td>\n",
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       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
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       "      <td>Wife</td>\n",
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       "    </tr>\n",
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       "      <td>HS-grad</td>\n",
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       "      <td>Husband</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State-gov   Bachelors        Never-married   Not-in-family   White  \\\n",
       "1            Private     HS-grad             Divorced   Not-in-family   White   \n",
       "2            Private        11th   Married-civ-spouse         Husband   Black   \n",
       "3            Private   Bachelors   Married-civ-spouse            Wife   Black   \n",
       "4            Private     Masters   Married-civ-spouse            Wife   White   \n",
       "6   Self-emp-not-inc     HS-grad   Married-civ-spouse         Husband   White   \n",
       "\n",
       "      Male   United-States  salary  \n",
       "1     Male   United-States   <=50K  \n",
       "2     Male   United-States   <=50K  \n",
       "3   Female            Cuba   <=50K  \n",
       "4   Female   United-States   <=50K  \n",
       "6     Male   United-States    >50K  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State-gov_ Federal-gov   State-gov_ Local-gov   State-gov_ Never-worked  \\\n",
       "1                         0                      0                         0   \n",
       "2                         0                      0                         0   \n",
       "3                         0                      0                         0   \n",
       "4                         0                      0                         0   \n",
       "6                         0                      0                         0   \n",
       "7                         0                      0                         0   \n",
       "8                         0                      0                         0   \n",
       "9                         0                      0                         0   \n",
       "10                        0                      0                         0   \n",
       "12                        0                      0                         0   \n",
       "13                        0                      0                         0   \n",
       "15                        0                      0                         0   \n",
       "16                        0                      0                         0   \n",
       "17                        0                      0                         0   \n",
       "18                        0                      0                         0   \n",
       "19                        0                      0                         0   \n",
       "21                        1                      0                         0   \n",
       "22                        0                      0                         0   \n",
       "23                        0                      0                         0   \n",
       "24                        0                      1                         0   \n",
       "\n",
       "     State-gov_ Private   State-gov_ Self-emp-inc  \\\n",
       "1                     1                         0   \n",
       "2                     1                         0   \n",
       "3                     1                         0   \n",
       "4                     1                         0   \n",
       "6                     0                         0   \n",
       "7                     1                         0   \n",
       "8                     1                         0   \n",
       "9                     1                         0   \n",
       "10                    0                         0   \n",
       "12                    1                         0   \n",
       "13                    1                         0   \n",
       "15                    0                         0   \n",
       "16                    1                         0   \n",
       "17                    1                         0   \n",
       "18                    0                         0   \n",
       "19                    1                         0   \n",
       "21                    0                         0   \n",
       "22                    1                         0   \n",
       "23                    1                         0   \n",
       "24                    0                         0   \n",
       "\n",
       "     State-gov_ Self-emp-not-inc   State-gov_ State-gov  \\\n",
       "1                              0                      0   \n",
       "2                              0                      0   \n",
       "3                              0                      0   \n",
       "4                              0                      0   \n",
       "6                              1                      0   \n",
       "7                              0                      0   \n",
       "8                              0                      0   \n",
       "9                              0                      0   \n",
       "10                             0                      1   \n",
       "12                             0                      0   \n",
       "13                             0                      0   \n",
       "15                             1                      0   \n",
       "16                             0                      0   \n",
       "17                             0                      0   \n",
       "18                             1                      0   \n",
       "19                             0                      0   \n",
       "21                             0                      0   \n",
       "22                             0                      0   \n",
       "23                             0                      0   \n",
       "24                             0                      0   \n",
       "\n",
       "     State-gov_ Without-pay   Bachelors_ 11th   Bachelors_ 12th  ...  \\\n",
       "1                         0                 0                 0  ...   \n",
       "2                         0                 1                 0  ...   \n",
       "3                         0                 0                 0  ...   \n",
       "4                         0                 0                 0  ...   \n",
       "6                         0                 0                 0  ...   \n",
       "7                         0                 0                 0  ...   \n",
       "8                         0                 0                 0  ...   \n",
       "9                         0                 0                 0  ...   \n",
       "10                        0                 0                 0  ...   \n",
       "12                        0                 0                 0  ...   \n",
       "13                        0                 0                 0  ...   \n",
       "15                        0                 0                 0  ...   \n",
       "16                        0                 0                 0  ...   \n",
       "17                        0                 1                 0  ...   \n",
       "18                        0                 0                 0  ...   \n",
       "19                        0                 0                 0  ...   \n",
       "21                        0                 0                 0  ...   \n",
       "22                        0                 1                 0  ...   \n",
       "23                        0                 0                 0  ...   \n",
       "24                        0                 0                 0  ...   \n",
       "\n",
       "     United-States_ Puerto-Rico   United-States_ Scotland  \\\n",
       "1                             0                         0   \n",
       "2                             0                         0   \n",
       "3                             0                         0   \n",
       "4                             0                         0   \n",
       "6                             0                         0   \n",
       "7                             0                         0   \n",
       "8                             0                         0   \n",
       "9                             0                         0   \n",
       "10                            0                         0   \n",
       "12                            0                         0   \n",
       "13                            0                         0   \n",
       "15                            0                         0   \n",
       "16                            0                         0   \n",
       "17                            0                         0   \n",
       "18                            0                         0   \n",
       "19                            0                         0   \n",
       "21                            0                         0   \n",
       "22                            0                         0   \n",
       "23                            0                         0   \n",
       "24                            0                         0   \n",
       "\n",
       "     United-States_ South   United-States_ Taiwan   United-States_ Thailand  \\\n",
       "1                       0                       0                         0   \n",
       "2                       0                       0                         0   \n",
       "3                       0                       0                         0   \n",
       "4                       0                       0                         0   \n",
       "6                       0                       0                         0   \n",
       "7                       0                       0                         0   \n",
       "8                       0                       0                         0   \n",
       "9                       0                       0                         0   \n",
       "10                      0                       0                         0   \n",
       "12                      0                       0                         0   \n",
       "13                      0                       0                         0   \n",
       "15                      0                       0                         0   \n",
       "16                      0                       0                         0   \n",
       "17                      0                       0                         0   \n",
       "18                      0                       0                         0   \n",
       "19                      0                       0                         0   \n",
       "21                      0                       0                         0   \n",
       "22                      0                       0                         0   \n",
       "23                      0                       0                         0   \n",
       "24                      0                       0                         0   \n",
       "\n",
       "     United-States_ Trinadad&Tobago   United-States_ United-States  \\\n",
       "1                                 0                              1   \n",
       "2                                 0                              1   \n",
       "3                                 0                              0   \n",
       "4                                 0                              1   \n",
       "6                                 0                              1   \n",
       "7                                 0                              1   \n",
       "8                                 0                              1   \n",
       "9                                 0                              1   \n",
       "10                                0                              0   \n",
       "12                                0                              1   \n",
       "13                                0                              0   \n",
       "15                                0                              1   \n",
       "16                                0                              1   \n",
       "17                                0                              1   \n",
       "18                                0                              1   \n",
       "19                                0                              1   \n",
       "21                                0                              1   \n",
       "22                                0                              1   \n",
       "23                                0                              1   \n",
       "24                                0                              1   \n",
       "\n",
       "     United-States_ Vietnam   United-States_ Yugoslavia  salary_ >50K  \n",
       "1                         0                           0             0  \n",
       "2                         0                           0             0  \n",
       "3                         0                           0             0  \n",
       "4                         0                           0             0  \n",
       "6                         0                           0             1  \n",
       "7                         0                           0             1  \n",
       "8                         0                           0             1  \n",
       "9                         0                           0             1  \n",
       "10                        0                           0             1  \n",
       "12                        0                           0             0  \n",
       "13                        0                           0             1  \n",
       "15                        0                           0             0  \n",
       "16                        0                           0             0  \n",
       "17                        0                           0             0  \n",
       "18                        0                           0             1  \n",
       "19                        0                           0             1  \n",
       "21                        0                           0             0  \n",
       "22                        0                           0             0  \n",
       "23                        0                           0             0  \n",
       "24                        0                           0             1  \n",
       "\n",
       "[20 rows x 77 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_categorical_encoded = pd.get_dummies(df2_categorical, drop_first=True)\n",
    "df2_categorical_encoded.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>State-gov_ Federal-gov</th>\n",
       "      <th>State-gov_ Local-gov</th>\n",
       "      <th>State-gov_ Never-worked</th>\n",
       "      <th>State-gov_ Private</th>\n",
       "      <th>...</th>\n",
       "      <th>United-States_ Puerto-Rico</th>\n",
       "      <th>United-States_ Scotland</th>\n",
       "      <th>United-States_ South</th>\n",
       "      <th>United-States_ Taiwan</th>\n",
       "      <th>United-States_ Thailand</th>\n",
       "      <th>United-States_ Trinadad&amp;Tobago</th>\n",
       "      <th>United-States_ United-States</th>\n",
       "      <th>United-States_ Vietnam</th>\n",
       "      <th>United-States_ Yugoslavia</th>\n",
       "      <th>salary_ &gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "      <td>40</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>284582</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   39   77516   13   2174   0   40   State-gov_ Federal-gov  \\\n",
       "1  38  215646    9      0   0   40                        0   \n",
       "2  53  234721    7      0   0   40                        0   \n",
       "3  28  338409   13      0   0   40                        0   \n",
       "4  37  284582   14      0   0   40                        0   \n",
       "6  52  209642    9      0   0   45                        0   \n",
       "\n",
       "    State-gov_ Local-gov   State-gov_ Never-worked   State-gov_ Private  ...  \\\n",
       "1                      0                         0                    1  ...   \n",
       "2                      0                         0                    1  ...   \n",
       "3                      0                         0                    1  ...   \n",
       "4                      0                         0                    1  ...   \n",
       "6                      0                         0                    0  ...   \n",
       "\n",
       "    United-States_ Puerto-Rico   United-States_ Scotland  \\\n",
       "1                            0                         0   \n",
       "2                            0                         0   \n",
       "3                            0                         0   \n",
       "4                            0                         0   \n",
       "6                            0                         0   \n",
       "\n",
       "    United-States_ South   United-States_ Taiwan   United-States_ Thailand  \\\n",
       "1                      0                       0                         0   \n",
       "2                      0                       0                         0   \n",
       "3                      0                       0                         0   \n",
       "4                      0                       0                         0   \n",
       "6                      0                       0                         0   \n",
       "\n",
       "    United-States_ Trinadad&Tobago   United-States_ United-States  \\\n",
       "1                                0                              1   \n",
       "2                                0                              1   \n",
       "3                                0                              0   \n",
       "4                                0                              1   \n",
       "6                                0                              1   \n",
       "\n",
       "    United-States_ Vietnam   United-States_ Yugoslavia  salary_ >50K  \n",
       "1                        0                           0             0  \n",
       "2                        0                           0             0  \n",
       "3                        0                           0             0  \n",
       "4                        0                           0             0  \n",
       "6                        0                           0             1  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_new = pd.concat([df2_numeric, df2_categorical_encoded], axis=1)\n",
    "df2_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# re-shaping the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26003 entries, 1 to 32559\n",
      "Data columns (total 83 columns):\n",
      " #   Column                                      Non-Null Count  Dtype\n",
      "---  ------                                      --------------  -----\n",
      " 0   39                                          26003 non-null  int64\n",
      " 1    77516                                      26003 non-null  int64\n",
      " 2    13                                         26003 non-null  int64\n",
      " 3    2174                                       26003 non-null  int64\n",
      " 4    0                                          26003 non-null  int64\n",
      " 5    40                                         26003 non-null  int64\n",
      " 6    State-gov_ Federal-gov                     26003 non-null  uint8\n",
      " 7    State-gov_ Local-gov                       26003 non-null  uint8\n",
      " 8    State-gov_ Never-worked                    26003 non-null  uint8\n",
      " 9    State-gov_ Private                         26003 non-null  uint8\n",
      " 10   State-gov_ Self-emp-inc                    26003 non-null  uint8\n",
      " 11   State-gov_ Self-emp-not-inc                26003 non-null  uint8\n",
      " 12   State-gov_ State-gov                       26003 non-null  uint8\n",
      " 13   State-gov_ Without-pay                     26003 non-null  uint8\n",
      " 14   Bachelors_ 11th                            26003 non-null  uint8\n",
      " 15   Bachelors_ 12th                            26003 non-null  uint8\n",
      " 16   Bachelors_ 9th                             26003 non-null  uint8\n",
      " 17   Bachelors_ Assoc-acdm                      26003 non-null  uint8\n",
      " 18   Bachelors_ Assoc-voc                       26003 non-null  uint8\n",
      " 19   Bachelors_ Bachelors                       26003 non-null  uint8\n",
      " 20   Bachelors_ Doctorate                       26003 non-null  uint8\n",
      " 21   Bachelors_ HS-grad                         26003 non-null  uint8\n",
      " 22   Bachelors_ Masters                         26003 non-null  uint8\n",
      " 23   Bachelors_ Prof-school                     26003 non-null  uint8\n",
      " 24   Bachelors_ Some-college                    26003 non-null  uint8\n",
      " 25   Never-married_ Married-AF-spouse           26003 non-null  uint8\n",
      " 26   Never-married_ Married-civ-spouse          26003 non-null  uint8\n",
      " 27   Never-married_ Married-spouse-absent       26003 non-null  uint8\n",
      " 28   Never-married_ Never-married               26003 non-null  uint8\n",
      " 29   Never-married_ Separated                   26003 non-null  uint8\n",
      " 30   Never-married_ Widowed                     26003 non-null  uint8\n",
      " 31   Not-in-family_ Not-in-family               26003 non-null  uint8\n",
      " 32   Not-in-family_ Other-relative              26003 non-null  uint8\n",
      " 33   Not-in-family_ Own-child                   26003 non-null  uint8\n",
      " 34   Not-in-family_ Unmarried                   26003 non-null  uint8\n",
      " 35   Not-in-family_ Wife                        26003 non-null  uint8\n",
      " 36   White_ Asian-Pac-Islander                  26003 non-null  uint8\n",
      " 37   White_ Black                               26003 non-null  uint8\n",
      " 38   White_ Other                               26003 non-null  uint8\n",
      " 39   White_ White                               26003 non-null  uint8\n",
      " 40   Male_ Male                                 26003 non-null  uint8\n",
      " 41   United-States_ Cambodia                    26003 non-null  uint8\n",
      " 42   United-States_ Canada                      26003 non-null  uint8\n",
      " 43   United-States_ China                       26003 non-null  uint8\n",
      " 44   United-States_ Columbia                    26003 non-null  uint8\n",
      " 45   United-States_ Cuba                        26003 non-null  uint8\n",
      " 46   United-States_ Dominican-Republic          26003 non-null  uint8\n",
      " 47   United-States_ Ecuador                     26003 non-null  uint8\n",
      " 48   United-States_ El-Salvador                 26003 non-null  uint8\n",
      " 49   United-States_ England                     26003 non-null  uint8\n",
      " 50   United-States_ France                      26003 non-null  uint8\n",
      " 51   United-States_ Germany                     26003 non-null  uint8\n",
      " 52   United-States_ Greece                      26003 non-null  uint8\n",
      " 53   United-States_ Guatemala                   26003 non-null  uint8\n",
      " 54   United-States_ Haiti                       26003 non-null  uint8\n",
      " 55   United-States_ Holand-Netherlands          26003 non-null  uint8\n",
      " 56   United-States_ Honduras                    26003 non-null  uint8\n",
      " 57   United-States_ Hong                        26003 non-null  uint8\n",
      " 58   United-States_ Hungary                     26003 non-null  uint8\n",
      " 59   United-States_ India                       26003 non-null  uint8\n",
      " 60   United-States_ Iran                        26003 non-null  uint8\n",
      " 61   United-States_ Ireland                     26003 non-null  uint8\n",
      " 62   United-States_ Italy                       26003 non-null  uint8\n",
      " 63   United-States_ Jamaica                     26003 non-null  uint8\n",
      " 64   United-States_ Japan                       26003 non-null  uint8\n",
      " 65   United-States_ Laos                        26003 non-null  uint8\n",
      " 66   United-States_ Mexico                      26003 non-null  uint8\n",
      " 67   United-States_ Nicaragua                   26003 non-null  uint8\n",
      " 68   United-States_ Outlying-US(Guam-USVI-etc)  26003 non-null  uint8\n",
      " 69   United-States_ Peru                        26003 non-null  uint8\n",
      " 70   United-States_ Philippines                 26003 non-null  uint8\n",
      " 71   United-States_ Poland                      26003 non-null  uint8\n",
      " 72   United-States_ Portugal                    26003 non-null  uint8\n",
      " 73   United-States_ Puerto-Rico                 26003 non-null  uint8\n",
      " 74   United-States_ Scotland                    26003 non-null  uint8\n",
      " 75   United-States_ South                       26003 non-null  uint8\n",
      " 76   United-States_ Taiwan                      26003 non-null  uint8\n",
      " 77   United-States_ Thailand                    26003 non-null  uint8\n",
      " 78   United-States_ Trinadad&Tobago             26003 non-null  uint8\n",
      " 79   United-States_ United-States               26003 non-null  uint8\n",
      " 80   United-States_ Vietnam                     26003 non-null  uint8\n",
      " 81   United-States_ Yugoslavia                  26003 non-null  uint8\n",
      " 82  salary_ >50K                                26003 non-null  uint8\n",
      "dtypes: int64(6), uint8(77)\n",
      "memory usage: 4.5 MB\n"
     ]
    }
   ],
   "source": [
    "df2_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2_new.drop(columns=[' 2174',' 0'],axis=1)#becoz these two columns has very high variation"
   ]
  },
  {
   "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",
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       "    .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>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>40</th>\n",
       "      <th>State-gov_ Federal-gov</th>\n",
       "      <th>State-gov_ Local-gov</th>\n",
       "      <th>State-gov_ Never-worked</th>\n",
       "      <th>State-gov_ Private</th>\n",
       "      <th>State-gov_ Self-emp-inc</th>\n",
       "      <th>State-gov_ Self-emp-not-inc</th>\n",
       "      <th>...</th>\n",
       "      <th>United-States_ Puerto-Rico</th>\n",
       "      <th>United-States_ Scotland</th>\n",
       "      <th>United-States_ South</th>\n",
       "      <th>United-States_ Taiwan</th>\n",
       "      <th>United-States_ Thailand</th>\n",
       "      <th>United-States_ Trinadad&amp;Tobago</th>\n",
       "      <th>United-States_ United-States</th>\n",
       "      <th>United-States_ Vietnam</th>\n",
       "      <th>United-States_ Yugoslavia</th>\n",
       "      <th>salary_ &gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>284582</td>\n",
       "      <td>14</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>22</td>\n",
       "      <td>310152</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>27</td>\n",
       "      <td>257302</td>\n",
       "      <td>12</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>40</td>\n",
       "      <td>154374</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>32557</th>\n",
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       "      <td>151910</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26003 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       39   77516   13   40   State-gov_ Federal-gov   State-gov_ Local-gov  \\\n",
       "1      38  215646    9   40                        0                      0   \n",
       "2      53  234721    7   40                        0                      0   \n",
       "3      28  338409   13   40                        0                      0   \n",
       "4      37  284582   14   40                        0                      0   \n",
       "6      52  209642    9   45                        0                      0   \n",
       "...    ..     ...  ...  ...                      ...                    ...   \n",
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       "32555  27  257302   12   38                        0                      0   \n",
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       "32557  58  151910    9   40                        0                      0   \n",
       "32559  52  287927    9   40                        0                      0   \n",
       "\n",
       "        State-gov_ Never-worked   State-gov_ Private  \\\n",
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       "2                             0                    1   \n",
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       "\n",
       "        United-States_ Puerto-Rico   United-States_ Scotland  \\\n",
       "1                                0                         0   \n",
       "2                                0                         0   \n",
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       "\n",
       "        United-States_ South   United-States_ Taiwan  \\\n",
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       "3                          0                       0   \n",
       "4                          0                       0   \n",
       "6                          0                       0   \n",
       "...                      ...                     ...   \n",
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       "32557                      0                       0   \n",
       "32559                      0                       0   \n",
       "\n",
       "        United-States_ Thailand   United-States_ Trinadad&Tobago  \\\n",
       "1                             0                                0   \n",
       "2                             0                                0   \n",
       "3                             0                                0   \n",
       "4                             0                                0   \n",
       "6                             0                                0   \n",
       "...                         ...                              ...   \n",
       "32554                         0                                0   \n",
       "32555                         0                                0   \n",
       "32556                         0                                0   \n",
       "32557                         0                                0   \n",
       "32559                         0                                0   \n",
       "\n",
       "        United-States_ United-States   United-States_ Vietnam  \\\n",
       "1                                  1                        0   \n",
       "2                                  1                        0   \n",
       "3                                  0                        0   \n",
       "4                                  1                        0   \n",
       "6                                  1                        0   \n",
       "...                              ...                      ...   \n",
       "32554                              1                        0   \n",
       "32555                              1                        0   \n",
       "32556                              1                        0   \n",
       "32557                              1                        0   \n",
       "32559                              1                        0   \n",
       "\n",
       "        United-States_ Yugoslavia  salary_ >50K  \n",
       "1                               0             0  \n",
       "2                               0             0  \n",
       "3                               0             0  \n",
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       "...                           ...           ...  \n",
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       "32556                           0             1  \n",
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       "32559                           0             1  \n",
       "\n",
       "[26003 rows x 81 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling nan values with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>26003 rows × 81 columns</p>\n",
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      ],
      "text/plain": [
       "       39   77516   13   40   State-gov_ Federal-gov   State-gov_ Local-gov  \\\n",
       "1      38  215646    9   40                        0                      0   \n",
       "2      53  234721    7   40                        0                      0   \n",
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       "4      37  284582   14   40                        0                      0   \n",
       "6      52  209642    9   45                        0                      0   \n",
       "...    ..     ...  ...  ...                      ...                    ...   \n",
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       "32557  58  151910    9   40                        0                      0   \n",
       "32559  52  287927    9   40                        0                      0   \n",
       "\n",
       "        State-gov_ Never-worked   State-gov_ Private  \\\n",
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       "32557                         0                                0   \n",
       "32559                         0                                0   \n",
       "\n",
       "        United-States_ United-States   United-States_ Vietnam  \\\n",
       "1                                  1                        0   \n",
       "2                                  1                        0   \n",
       "3                                  0                        0   \n",
       "4                                  1                        0   \n",
       "6                                  1                        0   \n",
       "...                              ...                      ...   \n",
       "32554                              1                        0   \n",
       "32555                              1                        0   \n",
       "32556                              1                        0   \n",
       "32557                              1                        0   \n",
       "32559                              1                        0   \n",
       "\n",
       "        United-States_ Yugoslavia  salary_ >50K  \n",
       "1                               0             0  \n",
       "2                               0             0  \n",
       "3                               0             0  \n",
       "4                               0             0  \n",
       "6                               0             1  \n",
       "...                           ...           ...  \n",
       "32554                           0             0  \n",
       "32555                           0             0  \n",
       "32556                           0             1  \n",
       "32557                           0             0  \n",
       "32559                           0             1  \n",
       "\n",
       "[26003 rows x 81 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['39'].fillna(int(df3['39'].median()), inplace=True)\n",
    "df3[' 77516'].fillna(int(df3[' 77516'].median()), inplace=True)\n",
    "df3[' 13'].fillna(int(df3[' 13'].median()), inplace=True)\n",
    "df3[' 40'].fillna(int(df3[' 40'].median()), inplace=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>39</th>\n",
       "      <th>77516</th>\n",
       "      <th>13</th>\n",
       "      <th>40</th>\n",
       "      <th>State-gov_ Federal-gov</th>\n",
       "      <th>State-gov_ Local-gov</th>\n",
       "      <th>State-gov_ Never-worked</th>\n",
       "      <th>State-gov_ Private</th>\n",
       "      <th>State-gov_ Self-emp-inc</th>\n",
       "      <th>State-gov_ Self-emp-not-inc</th>\n",
       "      <th>...</th>\n",
       "      <th>United-States_ Puerto-Rico</th>\n",
       "      <th>United-States_ Scotland</th>\n",
       "      <th>United-States_ South</th>\n",
       "      <th>United-States_ Taiwan</th>\n",
       "      <th>United-States_ Thailand</th>\n",
       "      <th>United-States_ Trinadad&amp;Tobago</th>\n",
       "      <th>United-States_ United-States</th>\n",
       "      <th>United-States_ Vietnam</th>\n",
       "      <th>United-States_ Yugoslavia</th>\n",
       "      <th>salary_ &gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
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       "      <td>40</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>284582</td>\n",
       "      <td>14</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>22</td>\n",
       "      <td>310152</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>27</td>\n",
       "      <td>257302</td>\n",
       "      <td>12</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>40</td>\n",
       "      <td>154374</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>58</td>\n",
       "      <td>151910</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>52</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26003 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       39   77516   13   40   State-gov_ Federal-gov   State-gov_ Local-gov  \\\n",
       "1      38  215646    9   40                        0                      0   \n",
       "2      53  234721    7   40                        0                      0   \n",
       "3      28  338409   13   40                        0                      0   \n",
       "4      37  284582   14   40                        0                      0   \n",
       "6      52  209642    9   45                        0                      0   \n",
       "...    ..     ...  ...  ...                      ...                    ...   \n",
       "32554  22  310152   10   40                        0                      0   \n",
       "32555  27  257302   12   38                        0                      0   \n",
       "32556  40  154374    9   40                        0                      0   \n",
       "32557  58  151910    9   40                        0                      0   \n",
       "32559  52  287927    9   40                        0                      0   \n",
       "\n",
       "        State-gov_ Never-worked   State-gov_ Private  \\\n",
       "1                             0                    1   \n",
       "2                             0                    1   \n",
       "3                             0                    1   \n",
       "4                             0                    1   \n",
       "6                             0                    0   \n",
       "...                         ...                  ...   \n",
       "32554                         0                    1   \n",
       "32555                         0                    1   \n",
       "32556                         0                    1   \n",
       "32557                         0                    1   \n",
       "32559                         0                    0   \n",
       "\n",
       "        State-gov_ Self-emp-inc   State-gov_ Self-emp-not-inc  ...  \\\n",
       "1                             0                             0  ...   \n",
       "2                             0                             0  ...   \n",
       "3                             0                             0  ...   \n",
       "4                             0                             0  ...   \n",
       "6                             0                             1  ...   \n",
       "...                         ...                           ...  ...   \n",
       "32554                         0                             0  ...   \n",
       "32555                         0                             0  ...   \n",
       "32556                         0                             0  ...   \n",
       "32557                         0                             0  ...   \n",
       "32559                         1                             0  ...   \n",
       "\n",
       "        United-States_ Puerto-Rico   United-States_ Scotland  \\\n",
       "1                                0                         0   \n",
       "2                                0                         0   \n",
       "3                                0                         0   \n",
       "4                                0                         0   \n",
       "6                                0                         0   \n",
       "...                            ...                       ...   \n",
       "32554                            0                         0   \n",
       "32555                            0                         0   \n",
       "32556                            0                         0   \n",
       "32557                            0                         0   \n",
       "32559                            0                         0   \n",
       "\n",
       "        United-States_ South   United-States_ Taiwan  \\\n",
       "1                          0                       0   \n",
       "2                          0                       0   \n",
       "3                          0                       0   \n",
       "4                          0                       0   \n",
       "6                          0                       0   \n",
       "...                      ...                     ...   \n",
       "32554                      0                       0   \n",
       "32555                      0                       0   \n",
       "32556                      0                       0   \n",
       "32557                      0                       0   \n",
       "32559                      0                       0   \n",
       "\n",
       "        United-States_ Thailand   United-States_ Trinadad&Tobago  \\\n",
       "1                             0                                0   \n",
       "2                             0                                0   \n",
       "3                             0                                0   \n",
       "4                             0                                0   \n",
       "6                             0                                0   \n",
       "...                         ...                              ...   \n",
       "32554                         0                                0   \n",
       "32555                         0                                0   \n",
       "32556                         0                                0   \n",
       "32557                         0                                0   \n",
       "32559                         0                                0   \n",
       "\n",
       "        United-States_ United-States   United-States_ Vietnam  \\\n",
       "1                                  1                        0   \n",
       "2                                  1                        0   \n",
       "3                                  0                        0   \n",
       "4                                  1                        0   \n",
       "6                                  1                        0   \n",
       "...                              ...                      ...   \n",
       "32554                              1                        0   \n",
       "32555                              1                        0   \n",
       "32556                              1                        0   \n",
       "32557                              1                        0   \n",
       "32559                              1                        0   \n",
       "\n",
       "        United-States_ Yugoslavia  salary_ >50K  \n",
       "1                               0             0  \n",
       "2                               0             0  \n",
       "3                               0             0  \n",
       "4                               0             0  \n",
       "6                               0             1  \n",
       "...                           ...           ...  \n",
       "32554                           0             0  \n",
       "32555                           0             0  \n",
       "32556                           0             1  \n",
       "32557                           0             0  \n",
       "32559                           0             1  \n",
       "\n",
       "[26003 rows x 81 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26003, 81)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['39', ' 77516', ' 13', ' 40', ' State-gov_ Federal-gov',\n",
      "       ' State-gov_ Local-gov', ' State-gov_ Never-worked',\n",
      "       ' State-gov_ Private', ' State-gov_ Self-emp-inc',\n",
      "       ' State-gov_ Self-emp-not-inc', ' State-gov_ State-gov',\n",
      "       ' State-gov_ Without-pay', ' Bachelors_ 11th', ' Bachelors_ 12th',\n",
      "       ' Bachelors_ 9th', ' Bachelors_ Assoc-acdm', ' Bachelors_ Assoc-voc',\n",
      "       ' Bachelors_ Bachelors', ' Bachelors_ Doctorate', ' Bachelors_ HS-grad',\n",
      "       ' Bachelors_ Masters', ' Bachelors_ Prof-school',\n",
      "       ' Bachelors_ Some-college', ' Never-married_ Married-AF-spouse',\n",
      "       ' Never-married_ Married-civ-spouse',\n",
      "       ' Never-married_ Married-spouse-absent',\n",
      "       ' Never-married_ Never-married', ' Never-married_ Separated',\n",
      "       ' Never-married_ Widowed', ' Not-in-family_ Not-in-family',\n",
      "       ' Not-in-family_ Other-relative', ' Not-in-family_ Own-child',\n",
      "       ' Not-in-family_ Unmarried', ' Not-in-family_ Wife',\n",
      "       ' White_ Asian-Pac-Islander', ' White_ Black', ' White_ Other',\n",
      "       ' White_ White', ' Male_ Male', ' United-States_ Cambodia',\n",
      "       ' United-States_ Canada', ' United-States_ China',\n",
      "       ' United-States_ Columbia', ' United-States_ Cuba',\n",
      "       ' United-States_ Dominican-Republic', ' United-States_ Ecuador',\n",
      "       ' United-States_ El-Salvador', ' United-States_ England',\n",
      "       ' United-States_ France', ' United-States_ Germany',\n",
      "       ' United-States_ Greece', ' United-States_ Guatemala',\n",
      "       ' United-States_ Haiti', ' United-States_ Holand-Netherlands',\n",
      "       ' United-States_ Honduras', ' United-States_ Hong',\n",
      "       ' United-States_ Hungary', ' United-States_ India',\n",
      "       ' United-States_ Iran', ' United-States_ Ireland',\n",
      "       ' United-States_ Italy', ' United-States_ Jamaica',\n",
      "       ' United-States_ Japan', ' United-States_ Laos',\n",
      "       ' United-States_ Mexico', ' United-States_ Nicaragua',\n",
      "       ' United-States_ Outlying-US(Guam-USVI-etc)', ' United-States_ Peru',\n",
      "       ' United-States_ Philippines', ' United-States_ Poland',\n",
      "       ' United-States_ Portugal', ' United-States_ Puerto-Rico',\n",
      "       ' United-States_ Scotland', ' United-States_ South',\n",
      "       ' United-States_ Taiwan', ' United-States_ Thailand',\n",
      "       ' United-States_ Trinadad&Tobago', ' United-States_ United-States',\n",
      "       ' United-States_ Vietnam', ' United-States_ Yugoslavia',\n",
      "       'salary_ >50K'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df3.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18668\n",
       "1     7335\n",
       "Name: salary_ >50K, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[\"salary_ >50K\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c076ca950>"
      ]
     },
     "execution_count": 65,
     "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": [
    "# making a count plot for \"salary\" column which is our target variable\n",
    "sns.countplot('salary_ >50K',data=df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"salary_ >50K\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.rename(columns = {'salary_ >50K':'salary'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18668\n",
       "1     7335\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[\"salary\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# balancing our target variable by upsampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    18668\n",
       "0    18668\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate majority and minority classes\n",
    "df3_majority = df3[df3.salary==0]\n",
    "df3_minority = df3[df3.salary==1]\n",
    " \n",
    "# Upsample minority class\n",
    "df3_minority_upsampled = resample(df3_minority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=18668,    # to match majority class\n",
    "                                 random_state=123) # reproducible results\n",
    " \n",
    "# Combine majority class with upsampled minority class\n",
    "df4=df3_upsampled = pd.concat([df3_majority, df3_minority_upsampled])\n",
    " \n",
    "# Display new class counts\n",
    "df4.salary.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c077747d0>"
      ]
     },
     "execution_count": 71,
     "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": [
    "# making a count plot for balanced \"salary\" column which is our target variable\n",
    "sns.countplot('salary',data=df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37336, 81)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seperating data and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df4.drop(columns=['salary'],axis=1)\n",
    "Y=df4['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1        0\n",
      "2        0\n",
      "3        0\n",
      "4        0\n",
      "12       0\n",
      "        ..\n",
      "16115    1\n",
      "28945    1\n",
      "15036    1\n",
      "11895    1\n",
      "4358     1\n",
      "Name: salary, Length: 37336, dtype: uint8\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spiliting the data into training data & test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y, test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37336, 80) (29868, 80) (7468, 80)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model=LogisticRegression(class_weight={0:2,1:8})\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the logistic regression model with training data\n",
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train data f1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 Score 0.7986596642716758\n",
      "accuracy_score 0.7907794294897549\n",
      "[[11225  3734]\n",
      " [ 2515 12394]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.75      0.78     14959\n",
      "           1       0.77      0.83      0.80     14909\n",
      "\n",
      "    accuracy                           0.79     29868\n",
      "   macro avg       0.79      0.79      0.79     29868\n",
      "weighted avg       0.79      0.79      0.79     29868\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "y_pred = model.predict(X_train)\n",
    "print(\"f1 Score\",f1_score(Y_train,y_pred))\n",
    "print(\"accuracy_score\",accuracy_score(Y_train,y_pred))\n",
    "print(confusion_matrix(Y_train,y_pred))\n",
    "print(classification_report(Y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test data f1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 Score 0.7965323814380418\n",
      "accuracy_score 0.7862881628280665\n",
      "[[2748  961]\n",
      " [ 635 3124]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.74      0.77      3709\n",
      "           1       0.76      0.83      0.80      3759\n",
      "\n",
      "    accuracy                           0.79      7468\n",
      "   macro avg       0.79      0.79      0.79      7468\n",
      "weighted avg       0.79      0.79      0.79      7468\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(\"f1 Score\",f1_score(Y_test,y_pred))\n",
    "print(\"accuracy_score\",accuracy_score(Y_test,y_pred))\n",
    "print(confusion_matrix(Y_test,y_pred))\n",
    "print(classification_report(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 Score 0.6696356996526232 0.0\n",
      "f1 Score 0.6779722172108966 0.01\n",
      "f1 Score 0.6915285451197053 0.02\n",
      "f1 Score 0.7020499859590003 0.03\n",
      "f1 Score 0.7116552771450265 0.04\n",
      "f1 Score 0.7201000673530261 0.05\n",
      "f1 Score 0.7261754112722671 0.06\n",
      "f1 Score 0.7332677165354331 0.07\n",
      "f1 Score 0.7391520206533612 0.08\n",
      "f1 Score 0.7442652509265753 0.09\n",
      "f1 Score 0.7494443321883208 0.1\n",
      "f1 Score 0.7547169811320754 0.11\n",
      "f1 Score 0.7586065152605076 0.12\n",
      "f1 Score 0.7626749611197512 0.13\n",
      "f1 Score 0.7661442006269593 0.14\n",
      "f1 Score 0.7709320695102685 0.15\n",
      "f1 Score 0.7737009544008484 0.16\n",
      "f1 Score 0.7785148726728013 0.17\n",
      "f1 Score 0.7817849068791043 0.18\n",
      "f1 Score 0.784407146724418 0.19\n",
      "f1 Score 0.7873638344226579 0.2\n",
      "f1 Score 0.7894909688013136 0.21\n",
      "f1 Score 0.7908101571946795 0.22\n",
      "f1 Score 0.7933318613380438 0.23\n",
      "f1 Score 0.7942382271468144 0.24\n",
      "f1 Score 0.7952370353883821 0.25\n",
      "f1 Score 0.7973607694028181 0.26\n",
      "f1 Score 0.7985627666741523 0.27\n",
      "f1 Score 0.7993231810490694 0.28\n",
      "f1 Score 0.8001811389109023 0.29\n",
      "f1 Score 0.8008653079813275 0.3\n",
      "f1 Score 0.80215250744218 0.31\n",
      "f1 Score 0.8032654938484535 0.32\n",
      "f1 Score 0.8027761711972238 0.33\n",
      "f1 Score 0.802417480241748 0.34\n",
      "f1 Score 0.8040564168317986 0.35000000000000003\n",
      "f1 Score 0.804920913884007 0.36\n",
      "f1 Score 0.8057486158558135 0.37\n",
      "f1 Score 0.8058781701825076 0.38\n",
      "f1 Score 0.8049565113785297 0.39\n",
      "f1 Score 0.8049802466179815 0.4\n",
      "f1 Score 0.8043373493975904 0.41000000000000003\n",
      "f1 Score 0.8028595662183449 0.42\n",
      "f1 Score 0.8031668696711327 0.43\n",
      "f1 Score 0.803671970624235 0.44\n",
      "f1 Score 0.8033452219899152 0.45\n",
      "f1 Score 0.8028709318153694 0.46\n",
      "f1 Score 0.8013451239257692 0.47000000000000003\n",
      "f1 Score 0.7981916363179706 0.48\n",
      "f1 Score 0.7967603138445964 0.49\n",
      "f1 Score 0.7965323814380418 0.5\n",
      "f1 Score 0.7960890261160428 0.51\n",
      "f1 Score 0.7935191186001297 0.52\n",
      "f1 Score 0.7909648779213996 0.53\n",
      "f1 Score 0.7840084443858029 0.54\n",
      "f1 Score 0.7811376049020913 0.55\n",
      "f1 Score 0.7749999999999999 0.56\n",
      "f1 Score 0.7697949205486894 0.5700000000000001\n",
      "f1 Score 0.7652221612726275 0.58\n",
      "f1 Score 0.7610324729392174 0.59\n",
      "f1 Score 0.7552368902010403 0.6\n",
      "f1 Score 0.7492885600455322 0.61\n",
      "f1 Score 0.7455485353245259 0.62\n",
      "f1 Score 0.7392883079157588 0.63\n",
      "f1 Score 0.7327256689209056 0.64\n",
      "f1 Score 0.723600178226645 0.65\n",
      "f1 Score 0.7157261098570353 0.66\n",
      "f1 Score 0.706259541984733 0.67\n",
      "f1 Score 0.699490662139219 0.68\n",
      "f1 Score 0.6925 0.6900000000000001\n",
      "f1 Score 0.6849921011058452 0.7000000000000001\n",
      "f1 Score 0.673721340388007 0.71\n",
      "f1 Score 0.6624328285295555 0.72\n",
      "f1 Score 0.6531753866403421 0.73\n",
      "f1 Score 0.6463739188290085 0.74\n",
      "f1 Score 0.6383621412988757 0.75\n",
      "f1 Score 0.6266779949022939 0.76\n",
      "f1 Score 0.6145636081941814 0.77\n",
      "f1 Score 0.6037604456824512 0.78\n",
      "f1 Score 0.5917827543643096 0.79\n",
      "f1 Score 0.5774043618162317 0.8\n",
      "f1 Score 0.5611615245009074 0.81\n",
      "f1 Score 0.5427042980999816 0.8200000000000001\n",
      "f1 Score 0.526039715249157 0.8300000000000001\n",
      "f1 Score 0.5084745762711864 0.84\n",
      "f1 Score 0.4876722966414288 0.85\n",
      "f1 Score 0.4637910565888405 0.86\n",
      "f1 Score 0.4345539146267449 0.87\n",
      "f1 Score 0.4086866597724922 0.88\n",
      "f1 Score 0.38225976768743397 0.89\n",
      "f1 Score 0.34996759559300067 0.9\n",
      "f1 Score 0.30990911106184876 0.91\n",
      "f1 Score 0.2683371298405467 0.92\n",
      "f1 Score 0.22720897615708277 0.93\n",
      "f1 Score 0.18304107614700935 0.9400000000000001\n",
      "f1 Score 0.14401572868026544 0.9500000000000001\n",
      "f1 Score 0.09696969696969698 0.96\n",
      "f1 Score 0.06431695394906098 0.97\n",
      "f1 Score 0.026246719160104987 0.98\n",
      "f1 Score 0.0021259633271326073 0.99\n"
     ]
    }
   ],
   "source": [
    "t = np.arange(start=0,stop=1,step=0.01)\n",
    "for i in t:\n",
    "    pred = model.predict_proba(X_test)\n",
    "    pred = (pred[:,1]>=i).astype(\"int\")\n",
    "    score = print(\"f1 Score\",f1_score(Y_test,pred),i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\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, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision is defined as the accuracy of judgment.\n",
    "Recall is the ability to identify the number of samples that would really count positive for a specific attribute.\n",
    "An F1-score means a statistical measure of the accuracy of a test or an individual. It is composed of two primary attributes, viz. precision and recall, both calculated as percentages and combined as harmonic mean to assign a single number, easy for comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train data f1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 Score 0.8074542089476036\n",
      "0.7979777688496049\n",
      "[[11182  3777]\n",
      " [ 2257 12652]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.75      0.79     14959\n",
      "           1       0.77      0.85      0.81     14909\n",
      "\n",
      "    accuracy                           0.80     29868\n",
      "   macro avg       0.80      0.80      0.80     29868\n",
      "weighted avg       0.80      0.80      0.80     29868\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model1.predict(X_train)\n",
    "print(\"f1 Score\",f1_score(Y_train,y_pred))\n",
    "print(accuracy_score(Y_train,y_pred))\n",
    "print(confusion_matrix(Y_train,y_pred))\n",
    "print(classification_report(Y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train data f1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 Score 0.8041682551785487\n",
      "0.7936529191215854\n",
      "[[2763  946]\n",
      " [ 595 3164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.74      0.78      3709\n",
      "           1       0.77      0.84      0.80      3759\n",
      "\n",
      "    accuracy                           0.79      7468\n",
      "   macro avg       0.80      0.79      0.79      7468\n",
      "weighted avg       0.80      0.79      0.79      7468\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model1.predict(X_test)\n",
    "print(\"f1 Score\",f1_score(Y_test,y_pred))\n",
    "print(accuracy_score(Y_test,y_pred))\n",
    "print(confusion_matrix(Y_test,y_pred))\n",
    "print(classification_report(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.applying knn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier()\n",
    "knn.fit(X_train,Y_train)\n",
    "KNeighborsClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.6101815700531\n"
     ]
    }
   ],
   "source": [
    "#to measure time\n",
    "import time\n",
    "start=time.time()\n",
    "y_pred=knn.predict(X_test)\n",
    "print(time.time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8012854847348688"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.now applying pca,for this we have to first standardise my data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29868, 80)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA \n",
    "pca=PCA(n_components=None)#none will give no of principle components equal to no of columns\n",
    "#in this step we found eigen values and eigen vectors and we shifted our data to new coordinate axis,and\n",
    "#now my data is in new transform coordinate\n",
    "X_train=pca.fit_transform(X_train) \n",
    "X_test=pca.transform(X_test)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29868, 10)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reducing the principle components to 10\n",
    "\n",
    "pca=PCA(n_components=10)\n",
    "X_train_new=pca.fit_transform(X_train)\n",
    "X_test_new=pca.transform(X_test)\n",
    "X_train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier()\n",
    "knn.fit(X_train_new,Y_train)\n",
    "KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3064148426055908\n"
     ]
    }
   ],
   "source": [
    "#to measure time\n",
    "import time\n",
    "start=time.time()\n",
    "y_pred=knn.predict(X_test_new)\n",
    "print(time.time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7967327262988751"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7206748794858061\n",
      "0.7936529191215854\n",
      "0.7992769148366363\n",
      "0.7994108194965185\n",
      "0.7984734868773433\n",
      "0.7995447241564007\n",
      "0.8028923406534547\n",
      "0.791644349223353\n",
      "0.787627209426888\n",
      "0.7976700589180503\n",
      "0.788430637386181\n",
      "0.8002142474558115\n",
      "0.7995447241564007\n",
      "0.7960632029994644\n",
      "0.7951258703802893\n",
      "0.7991430101767542\n",
      "0.7994108194965185\n",
      "0.8020889126941617\n",
      "0.800615961435458\n",
      "0.802356722013926\n",
      "0.8010176754151045\n",
      "0.803160149973219\n",
      "0.7980717728976968\n",
      "0.8003481521156937\n",
      "0.8011515800749867\n",
      "0.8030262453133369\n",
      "0.8004820567755758\n",
      "0.7986073915372255\n",
      "0.800615961435458\n",
      "0.7987412961971077\n",
      "0.8030262453133369\n",
      "0.7991430101767542\n",
      "0.8018211033743974\n",
      "0.7991430101767542\n",
      "0.7995447241564007\n",
      "0.7999464381360472\n",
      "0.8053026245313337\n",
      "0.801419389394751\n",
      "0.8003481521156937\n",
      "0.8018211033743974\n",
      "0.7996786288162828\n",
      "0.799812533476165\n",
      "0.7994108194965185\n",
      "0.801419389394751\n",
      "0.8003481521156937\n",
      "0.7994108194965185\n",
      "0.8035618639528656\n",
      "0.8002142474558115\n",
      "0.8018211033743974\n",
      "0.8002142474558115\n",
      "0.7995447241564007\n",
      "0.799009105516872\n",
      "0.8004820567755758\n",
      "0.8010176754151045\n",
      "0.8022228173540439\n",
      "0.7987412961971077\n",
      "0.8000803427959293\n",
      "0.8024906266738082\n",
      "0.7992769148366363\n",
      "0.8022228173540439\n",
      "0.7972683449384038\n",
      "0.7980717728976968\n",
      "0.7987412961971077\n",
      "0.7988752008569898\n",
      "0.8008837707552223\n",
      "0.8004820567755758\n",
      "0.7988752008569898\n",
      "0.7986073915372255\n",
      "0.799009105516872\n",
      "0.7971344402785216\n",
      "0.8000803427959293\n",
      "0.8007498660953402\n",
      "0.8000803427959293\n",
      "0.8012854847348688\n",
      "0.8011515800749867\n",
      "0.8010176754151045\n",
      "0.8012854847348688\n",
      "0.8012854847348688\n",
      "0.8012854847348688\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,80):\n",
    "    pca=PCA(n_components=i)\n",
    "    X_train_new=pca.fit_transform(X_train)\n",
    "    X_test_new=pca.transform(X_test)\n",
    "    knn.fit(X_train_new,Y_train)\n",
    "    y_pred=knn.predict(X_test_new)\n",
    "    print(accuracy_score(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.applying svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_classifier=SVC(kernel=\"rbf\")\n",
    "rbf_classifier.fit(X_train,Y_train)\n",
    "y_pred=rbf_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.785083020889127"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_classifier=SVC(kernel=\"poly\")\n",
    "rbf_classifier.fit(X_train,Y_train)\n",
    "y_pred=rbf_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7810658810926621"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_classifier=SVC(kernel=\"linear\")\n",
    "rbf_classifier.fit(X_train,Y_train)\n",
    "y_pred=rbf_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7628548473486877"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_classifier=SVC(kernel=\"sigmoid\")\n",
    "rbf_classifier.fit(X_train,Y_train)\n",
    "y_pred=rbf_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7400910551687199"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#play with the code"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}