Assignments-Naive Bayes-salarydata.ipynb

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    "#1) Prepare a classification model using Naive Bayes for salary data \n",
    "\n",
    "#Data Description:\n",
    "\n",
    "#age -- age of a person\n",
    "#workclass\t-- A work class is a grouping of work \n",
    "#education\t-- Education of an individuals\t\n",
    "#maritalstatus -- Marital status of an individulas\t\n",
    "#occupation\t -- occupation of an individuals\n",
    "#relationship -- \t\n",
    "#race --  Race of an Individual\n",
    "#sex --  Gender of an Individual\n",
    "#capitalgain --  profit received from the sale of an investment\t\n",
    "#capitalloss\t-- A decrease in the value of a capital asset\n",
    "#hoursperweek -- number of hours work per week\t\n",
    "#native -- Native of an individual\n",
    "#Salary -- salary of an individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6445921",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "from matplotlib.pyplot import imread\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "deebd5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('SalaryData_Train (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4db147c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('SalaryData_Test (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ac5c7fe",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
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       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
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       "      <th>Salary</th>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
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       "      <td>Cuba</td>\n",
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       "      <td>United-States</td>\n",
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      "text/plain": [
       "       age          workclass    education  educationno        maritalstatus  \\\n",
       "0       39          State-gov    Bachelors           13        Never-married   \n",
       "1       50   Self-emp-not-inc    Bachelors           13   Married-civ-spouse   \n",
       "2       38            Private      HS-grad            9             Divorced   \n",
       "3       53            Private         11th            7   Married-civ-spouse   \n",
       "4       28            Private    Bachelors           13   Married-civ-spouse   \n",
       "...    ...                ...          ...          ...                  ...   \n",
       "30156   27            Private   Assoc-acdm           12   Married-civ-spouse   \n",
       "30157   40            Private      HS-grad            9   Married-civ-spouse   \n",
       "30158   58            Private      HS-grad            9              Widowed   \n",
       "30159   22            Private      HS-grad            9        Never-married   \n",
       "30160   52       Self-emp-inc      HS-grad            9   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship    race      sex  capitalgain  \\\n",
       "0            Adm-clerical   Not-in-family   White     Male         2174   \n",
       "1         Exec-managerial         Husband   White     Male            0   \n",
       "2       Handlers-cleaners   Not-in-family   White     Male            0   \n",
       "3       Handlers-cleaners         Husband   Black     Male            0   \n",
       "4          Prof-specialty            Wife   Black   Female            0   \n",
       "...                   ...             ...     ...      ...          ...   \n",
       "30156        Tech-support            Wife   White   Female            0   \n",
       "30157   Machine-op-inspct         Husband   White     Male            0   \n",
       "30158        Adm-clerical       Unmarried   White   Female            0   \n",
       "30159        Adm-clerical       Own-child   White     Male            0   \n",
       "30160     Exec-managerial            Wife   White   Female        15024   \n",
       "\n",
       "       capitalloss  hoursperweek          native  Salary  \n",
       "0                0            40   United-States   <=50K  \n",
       "1                0            13   United-States   <=50K  \n",
       "2                0            40   United-States   <=50K  \n",
       "3                0            40   United-States   <=50K  \n",
       "4                0            40            Cuba   <=50K  \n",
       "...            ...           ...             ...     ...  \n",
       "30156            0            38   United-States   <=50K  \n",
       "30157            0            40   United-States    >50K  \n",
       "30158            0            40   United-States   <=50K  \n",
       "30159            0            20   United-States   <=50K  \n",
       "30160            0            40   United-States    >50K  \n",
       "\n",
       "[30161 rows x 14 columns]"
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   ]
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  {
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       "      <td>Divorced</td>\n",
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       "      <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>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15060 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      workclass      education  educationno        maritalstatus  \\\n",
       "0       25        Private           11th            7        Never-married   \n",
       "1       38        Private        HS-grad            9   Married-civ-spouse   \n",
       "2       28      Local-gov     Assoc-acdm           12   Married-civ-spouse   \n",
       "3       44        Private   Some-college           10   Married-civ-spouse   \n",
       "4       34        Private           10th            6        Never-married   \n",
       "...    ...            ...            ...          ...                  ...   \n",
       "15055   33        Private      Bachelors           13        Never-married   \n",
       "15056   39        Private      Bachelors           13             Divorced   \n",
       "15057   38        Private      Bachelors           13   Married-civ-spouse   \n",
       "15058   44        Private      Bachelors           13             Divorced   \n",
       "15059   35   Self-emp-inc      Bachelors           13   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship                 race      sex  \\\n",
       "0       Machine-op-inspct       Own-child                Black     Male   \n",
       "1         Farming-fishing         Husband                White     Male   \n",
       "2         Protective-serv         Husband                White     Male   \n",
       "3       Machine-op-inspct         Husband                Black     Male   \n",
       "4           Other-service   Not-in-family                White     Male   \n",
       "...                   ...             ...                  ...      ...   \n",
       "15055      Prof-specialty       Own-child                White     Male   \n",
       "15056      Prof-specialty   Not-in-family                White   Female   \n",
       "15057      Prof-specialty         Husband                White     Male   \n",
       "15058        Adm-clerical       Own-child   Asian-Pac-Islander     Male   \n",
       "15059     Exec-managerial         Husband                White     Male   \n",
       "\n",
       "       capitalgain  capitalloss  hoursperweek          native  Salary  \n",
       "0                0            0            40   United-States   <=50K  \n",
       "1                0            0            50   United-States   <=50K  \n",
       "2                0            0            40   United-States    >50K  \n",
       "3             7688            0            40   United-States    >50K  \n",
       "4                0            0            30   United-States   <=50K  \n",
       "...            ...          ...           ...             ...     ...  \n",
       "15055            0            0            40   United-States   <=50K  \n",
       "15056            0            0            36   United-States   <=50K  \n",
       "15057            0            0            50   United-States   <=50K  \n",
       "15058         5455            0            40   United-States   <=50K  \n",
       "15059            0            0            60   United-States    >50K  \n",
       "\n",
       "[15060 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a26f839",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30161 entries, 0 to 30160\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   age            30161 non-null  int64 \n",
      " 1   workclass      30161 non-null  object\n",
      " 2   education      30161 non-null  object\n",
      " 3   educationno    30161 non-null  int64 \n",
      " 4   maritalstatus  30161 non-null  object\n",
      " 5   occupation     30161 non-null  object\n",
      " 6   relationship   30161 non-null  object\n",
      " 7   race           30161 non-null  object\n",
      " 8   sex            30161 non-null  object\n",
      " 9   capitalgain    30161 non-null  int64 \n",
      " 10  capitalloss    30161 non-null  int64 \n",
      " 11  hoursperweek   30161 non-null  int64 \n",
      " 12  native         30161 non-null  object\n",
      " 13  Salary         30161 non-null  object\n",
      "dtypes: int64(5), object(9)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31b1c9ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educationno</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30161.000000</td>\n",
       "      <td>30161.000000</td>\n",
       "      <td>30161.000000</td>\n",
       "      <td>30161.000000</td>\n",
       "      <td>30161.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.438115</td>\n",
       "      <td>10.121316</td>\n",
       "      <td>1092.044064</td>\n",
       "      <td>88.302311</td>\n",
       "      <td>40.931269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.134830</td>\n",
       "      <td>2.550037</td>\n",
       "      <td>7406.466611</td>\n",
       "      <td>404.121321</td>\n",
       "      <td>11.980182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</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>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>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>47.000000</td>\n",
       "      <td>13.000000</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>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": [
       "                age   educationno   capitalgain   capitalloss  hoursperweek\n",
       "count  30161.000000  30161.000000  30161.000000  30161.000000  30161.000000\n",
       "mean      38.438115     10.121316   1092.044064     88.302311     40.931269\n",
       "std       13.134830      2.550037   7406.466611    404.121321     11.980182\n",
       "min       17.000000      1.000000      0.000000      0.000000      1.000000\n",
       "25%       28.000000      9.000000      0.000000      0.000000     40.000000\n",
       "50%       37.000000     10.000000      0.000000      0.000000     40.000000\n",
       "75%       47.000000     13.000000      0.000000      0.000000     45.000000\n",
       "max       90.000000     16.000000  99999.000000   4356.000000     99.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0c2c56b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15060 entries, 0 to 15059\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   age            15060 non-null  int64 \n",
      " 1   workclass      15060 non-null  object\n",
      " 2   education      15060 non-null  object\n",
      " 3   educationno    15060 non-null  int64 \n",
      " 4   maritalstatus  15060 non-null  object\n",
      " 5   occupation     15060 non-null  object\n",
      " 6   relationship   15060 non-null  object\n",
      " 7   race           15060 non-null  object\n",
      " 8   sex            15060 non-null  object\n",
      " 9   capitalgain    15060 non-null  int64 \n",
      " 10  capitalloss    15060 non-null  int64 \n",
      " 11  hoursperweek   15060 non-null  int64 \n",
      " 12  native         15060 non-null  object\n",
      " 13  Salary         15060 non-null  object\n",
      "dtypes: int64(5), object(9)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6013b9cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educationno</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15060.000000</td>\n",
       "      <td>15060.000000</td>\n",
       "      <td>15060.000000</td>\n",
       "      <td>15060.000000</td>\n",
       "      <td>15060.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.768327</td>\n",
       "      <td>10.112749</td>\n",
       "      <td>1120.301594</td>\n",
       "      <td>89.041899</td>\n",
       "      <td>40.951594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.380676</td>\n",
       "      <td>2.558727</td>\n",
       "      <td>7703.181842</td>\n",
       "      <td>406.283245</td>\n",
       "      <td>12.062831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</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>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>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>13.000000</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>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>3770.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age   educationno   capitalgain   capitalloss  hoursperweek\n",
       "count  15060.000000  15060.000000  15060.000000  15060.000000  15060.000000\n",
       "mean      38.768327     10.112749   1120.301594     89.041899     40.951594\n",
       "std       13.380676      2.558727   7703.181842    406.283245     12.062831\n",
       "min       17.000000      1.000000      0.000000      0.000000      1.000000\n",
       "25%       28.000000      9.000000      0.000000      0.000000     40.000000\n",
       "50%       37.000000     10.000000      0.000000      0.000000     40.000000\n",
       "75%       48.000000     13.000000      0.000000      0.000000     45.000000\n",
       "max       90.000000     16.000000  99999.000000   3770.000000     99.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77072801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Married-civ-spouse       6990\n",
       " Never-married            4872\n",
       " Divorced                 2083\n",
       " Separated                 472\n",
       " Widowed                   450\n",
       " Married-spouse-absent     182\n",
       " Married-AF-spouse          11\n",
       "Name: maritalstatus, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['maritalstatus'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75935ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3258, 14)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22ccd9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
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       "      <th>Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</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>525</th>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;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",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30110</th>\n",
       "      <td>47</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30118</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30132</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</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>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30157</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</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>30159</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3258 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass      education  educationno        maritalstatus  \\\n",
       "326     33   Private      Bachelors           13   Married-civ-spouse   \n",
       "364     27   Private      Bachelors           13        Never-married   \n",
       "518     24   Private        HS-grad            9        Never-married   \n",
       "525     24   Private        HS-grad            9        Never-married   \n",
       "619     33   Private      Bachelors           13   Married-civ-spouse   \n",
       "...    ...       ...            ...          ...                  ...   \n",
       "30110   47   Private        HS-grad            9   Married-civ-spouse   \n",
       "30118   20   Private        HS-grad            9        Never-married   \n",
       "30132   22   Private   Some-college           10        Never-married   \n",
       "30157   40   Private        HS-grad            9   Married-civ-spouse   \n",
       "30159   22   Private        HS-grad            9        Never-married   \n",
       "\n",
       "               occupation    relationship    race      sex  capitalgain  \\\n",
       "326       Exec-managerial         Husband   White     Male            0   \n",
       "364          Craft-repair   Not-in-family   White     Male            0   \n",
       "518     Handlers-cleaners       Unmarried   Black   Female            0   \n",
       "525          Craft-repair       Own-child   White     Male            0   \n",
       "619       Exec-managerial         Husband   White     Male            0   \n",
       "...                   ...             ...     ...      ...          ...   \n",
       "30110        Craft-repair         Husband   White     Male            0   \n",
       "30118   Machine-op-inspct       Own-child   White     Male            0   \n",
       "30132        Adm-clerical       Own-child   White     Male            0   \n",
       "30157   Machine-op-inspct         Husband   White     Male            0   \n",
       "30159        Adm-clerical       Own-child   White     Male            0   \n",
       "\n",
       "       capitalloss  hoursperweek          native  Salary  \n",
       "326              0            40   United-States    >50K  \n",
       "364              0            50   United-States   <=50K  \n",
       "518              0            40   United-States   <=50K  \n",
       "525              0            40   United-States   <=50K  \n",
       "619              0            40   United-States    >50K  \n",
       "...            ...           ...             ...     ...  \n",
       "30110            0            40   United-States   <=50K  \n",
       "30118            0            40   United-States   <=50K  \n",
       "30132            0            40   United-States   <=50K  \n",
       "30157            0            40   United-States    >50K  \n",
       "30159            0            20   United-States   <=50K  \n",
       "\n",
       "[3258 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b720e8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "Train =train.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2eea4afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
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       "      <th>2</th>\n",
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       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>30154</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
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       "      <td>&gt;50K</td>\n",
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       "    <tr>\n",
       "      <th>30155</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30156</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
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       "      <td>Married-civ-spouse</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30158</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
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       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>30160</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</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>26903 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass      education  educationno  \\\n",
       "0       39          State-gov      Bachelors           13   \n",
       "1       50   Self-emp-not-inc      Bachelors           13   \n",
       "2       38            Private        HS-grad            9   \n",
       "3       53            Private           11th            7   \n",
       "4       28            Private      Bachelors           13   \n",
       "...    ...                ...            ...          ...   \n",
       "30154   53            Private        Masters           14   \n",
       "30155   22            Private   Some-college           10   \n",
       "30156   27            Private     Assoc-acdm           12   \n",
       "30158   58            Private        HS-grad            9   \n",
       "30160   52       Self-emp-inc        HS-grad            9   \n",
       "\n",
       "             maritalstatus          occupation    relationship    race  \\\n",
       "0            Never-married        Adm-clerical   Not-in-family   White   \n",
       "1       Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                 Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3       Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4       Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "...                    ...                 ...             ...     ...   \n",
       "30154   Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "30155        Never-married     Protective-serv   Not-in-family   White   \n",
       "30156   Married-civ-spouse        Tech-support            Wife   White   \n",
       "30158              Widowed        Adm-clerical       Unmarried   White   \n",
       "30160   Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "\n",
       "           sex  capitalgain  capitalloss  hoursperweek          native  Salary  \n",
       "0         Male         2174            0            40   United-States   <=50K  \n",
       "1         Male            0            0            13   United-States   <=50K  \n",
       "2         Male            0            0            40   United-States   <=50K  \n",
       "3         Male            0            0            40   United-States   <=50K  \n",
       "4       Female            0            0            40            Cuba   <=50K  \n",
       "...        ...          ...          ...           ...             ...     ...  \n",
       "30154     Male            0            0            40   United-States    >50K  \n",
       "30155     Male            0            0            40   United-States   <=50K  \n",
       "30156   Female            0            0            38   United-States   <=50K  \n",
       "30158   Female            0            0            40   United-States   <=50K  \n",
       "30160   Female        15024            0            40   United-States    >50K  \n",
       "\n",
       "[26903 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9e8d0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.isnull().sum().sum()\n",
    "## there is no nan values in the Train Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6d94f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(930, 14)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc47df0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>551</th>\n",
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       "      <td>Craft-repair</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>99999</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15043</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15044</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>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>15049</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15052</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15053</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass      education  educationno        maritalstatus  \\\n",
       "551     59   Private        HS-grad            9             Divorced   \n",
       "601     32   Private        HS-grad            9   Married-civ-spouse   \n",
       "681     40   Private        HS-grad            9   Married-civ-spouse   \n",
       "799     24   Private      Bachelors           13        Never-married   \n",
       "1044    44   Private    Prof-school           15   Married-civ-spouse   \n",
       "...    ...       ...            ...          ...                  ...   \n",
       "15043   51   Private        HS-grad            9   Married-civ-spouse   \n",
       "15044   22   Private   Some-college           10        Never-married   \n",
       "15049   32   Private        HS-grad            9   Married-civ-spouse   \n",
       "15052   31   Private        HS-grad            9   Married-civ-spouse   \n",
       "15053   25   Private        HS-grad            9        Never-married   \n",
       "\n",
       "               occupation    relationship    race      sex  capitalgain  \\\n",
       "551         Other-service   Not-in-family   White   Female            0   \n",
       "601                 Sales         Husband   White     Male            0   \n",
       "681          Craft-repair         Husband   White     Male            0   \n",
       "799        Prof-specialty   Not-in-family   White     Male            0   \n",
       "1044       Prof-specialty         Husband   White     Male        99999   \n",
       "...                   ...             ...     ...      ...          ...   \n",
       "15043        Craft-repair         Husband   White     Male            0   \n",
       "15044        Craft-repair       Own-child   White     Male            0   \n",
       "15049   Handlers-cleaners         Husband   White     Male            0   \n",
       "15052        Craft-repair         Husband   White     Male            0   \n",
       "15053       Other-service       Own-child   White   Female            0   \n",
       "\n",
       "       capitalloss  hoursperweek          native  Salary  \n",
       "551              0            40   United-States   <=50K  \n",
       "601              0            40   United-States   <=50K  \n",
       "681              0            40   United-States   <=50K  \n",
       "799              0            35   United-States   <=50K  \n",
       "1044             0            60   United-States    >50K  \n",
       "...            ...           ...             ...     ...  \n",
       "15043            0            40   United-States   <=50K  \n",
       "15044            0            40   United-States   <=50K  \n",
       "15049            0            40   United-States   <=50K  \n",
       "15052            0            40   United-States   <=50K  \n",
       "15053            0            40   United-States   <=50K  \n",
       "\n",
       "[930 rows x 14 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a766c642",
   "metadata": {},
   "outputs": [],
   "source": [
    "Test=test.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dc185587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
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       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
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       "      <th>1</th>\n",
       "      <td>38</td>\n",
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       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15055</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15056</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15057</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15058</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</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>15059</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</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>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14130 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      workclass      education  educationno        maritalstatus  \\\n",
       "0       25        Private           11th            7        Never-married   \n",
       "1       38        Private        HS-grad            9   Married-civ-spouse   \n",
       "2       28      Local-gov     Assoc-acdm           12   Married-civ-spouse   \n",
       "3       44        Private   Some-college           10   Married-civ-spouse   \n",
       "4       34        Private           10th            6        Never-married   \n",
       "...    ...            ...            ...          ...                  ...   \n",
       "15055   33        Private      Bachelors           13        Never-married   \n",
       "15056   39        Private      Bachelors           13             Divorced   \n",
       "15057   38        Private      Bachelors           13   Married-civ-spouse   \n",
       "15058   44        Private      Bachelors           13             Divorced   \n",
       "15059   35   Self-emp-inc      Bachelors           13   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship                 race      sex  \\\n",
       "0       Machine-op-inspct       Own-child                Black     Male   \n",
       "1         Farming-fishing         Husband                White     Male   \n",
       "2         Protective-serv         Husband                White     Male   \n",
       "3       Machine-op-inspct         Husband                Black     Male   \n",
       "4           Other-service   Not-in-family                White     Male   \n",
       "...                   ...             ...                  ...      ...   \n",
       "15055      Prof-specialty       Own-child                White     Male   \n",
       "15056      Prof-specialty   Not-in-family                White   Female   \n",
       "15057      Prof-specialty         Husband                White     Male   \n",
       "15058        Adm-clerical       Own-child   Asian-Pac-Islander     Male   \n",
       "15059     Exec-managerial         Husband                White     Male   \n",
       "\n",
       "       capitalgain  capitalloss  hoursperweek          native  Salary  \n",
       "0                0            0            40   United-States   <=50K  \n",
       "1                0            0            50   United-States   <=50K  \n",
       "2                0            0            40   United-States    >50K  \n",
       "3             7688            0            40   United-States    >50K  \n",
       "4                0            0            30   United-States   <=50K  \n",
       "...            ...          ...           ...             ...     ...  \n",
       "15055            0            0            40   United-States   <=50K  \n",
       "15056            0            0            36   United-States   <=50K  \n",
       "15057            0            0            50   United-States   <=50K  \n",
       "15058         5455            0            40   United-States   <=50K  \n",
       "15059            0            0            60   United-States    >50K  \n",
       "\n",
       "[14130 rows x 14 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b81c2665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test.isnull().sum().sum()\n",
    "## there is no nan values in the Train Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e6a6fa33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    20023\n",
       " >50K      6880\n",
       "Name: Salary, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train['Salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ce950872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    10620\n",
       " >50K      3510\n",
       "Name: Salary, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test['Salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2a663ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Salary</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>2816</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed-Forces</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craft-repair</th>\n",
       "      <td>2472</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>1903</td>\n",
       "      <td>1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Farming-fishing</th>\n",
       "      <td>843</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handlers-cleaners</th>\n",
       "      <td>1081</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine-op-inspct</th>\n",
       "      <td>1442</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-service</th>\n",
       "      <td>2782</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Priv-house-serv</th>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>2085</td>\n",
       "      <td>1698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protective-serv</th>\n",
       "      <td>424</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>2315</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech-support</th>\n",
       "      <td>594</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport-moving</th>\n",
       "      <td>1118</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Salary               <=50K   >50K\n",
       "occupation                       \n",
       " Adm-clerical         2816    475\n",
       " Armed-Forces            8      1\n",
       " Craft-repair         2472    757\n",
       " Exec-managerial      1903   1743\n",
       " Farming-fishing       843    114\n",
       " Handlers-cleaners    1081     78\n",
       " Machine-op-inspct    1442    223\n",
       " Other-service        2782    132\n",
       " Priv-house-serv       140      1\n",
       " Prof-specialty       2085   1698\n",
       " Protective-serv       424    202\n",
       " Sales                2315    890\n",
       " Tech-support          594    264\n",
       " Transport-moving     1118    302"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Train['occupation'],Train['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc3a89a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Salary</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "  <tbody>\n",
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       "      <th>Federal-gov</th>\n",
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       "      <td>356</td>\n",
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       "    <tr>\n",
       "      <th>Local-gov</th>\n",
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       "      <td>593</td>\n",
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       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>14902</td>\n",
       "      <td>4311</td>\n",
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       "      <th>Self-emp-inc</th>\n",
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      ],
      "text/plain": [
       "Salary              <=50K   >50K\n",
       "workclass                       \n",
       " Federal-gov          573    356\n",
       " Local-gov           1421    593\n",
       " Private            14902   4311\n",
       " Self-emp-inc         466    583\n",
       " Self-emp-not-inc    1733    698\n",
       " State-gov            914    339\n",
       " Without-pay           14      0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Train['workclass'],Train['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d76bd4a7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Adm-clerical</th>\n",
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       "      <td>277</td>\n",
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       "      <td>211</td>\n",
       "      <td>28</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>187</td>\n",
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       "      <td>667</td>\n",
       "      <td>291</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>112</td>\n",
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       "      <th>Private</th>\n",
       "      <td>2378</td>\n",
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       "      <td>369</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "occupation          Adm-clerical   Armed-Forces   Craft-repair  \\\n",
       "workclass                                                        \n",
       " Federal-gov                 309              9             62   \n",
       " Local-gov                   277              0            139   \n",
       " Private                    2378              0           2391   \n",
       " Self-emp-inc                 28              0             97   \n",
       " Self-emp-not-inc             49              0            487   \n",
       " State-gov                   247              0             52   \n",
       " Without-pay                   3              0              1   \n",
       "\n",
       "occupation          Exec-managerial   Farming-fishing   Handlers-cleaners  \\\n",
       "workclass                                                                   \n",
       " Federal-gov                    175                 8                  22   \n",
       " Local-gov                      211                28                  46   \n",
       " Private                       2333               430                1064   \n",
       " Self-emp-inc                   369                51                   2   \n",
       " Self-emp-not-inc               375               419                  15   \n",
       " State-gov                      183                15                   9   \n",
       " Without-pay                      0                 6                   1   \n",
       "\n",
       "occupation          Machine-op-inspct   Other-service   Priv-house-serv  \\\n",
       "workclass                                                                 \n",
       " Federal-gov                       14              34                 0   \n",
       " Local-gov                         11             187                 0   \n",
       " Private                         1581            2370               141   \n",
       " Self-emp-inc                      10              27                 0   \n",
       " Self-emp-not-inc                  35             173                 0   \n",
       " State-gov                         13             122                 0   \n",
       " Without-pay                        1               1                 0   \n",
       "\n",
       "occupation          Prof-specialty   Protective-serv   Sales   Tech-support  \\\n",
       "workclass                                                                     \n",
       " Federal-gov                   165                27      14             66   \n",
       " Local-gov                     667               291       7             38   \n",
       " Private                      2042               185    2530            669   \n",
       " Self-emp-inc                  156                 5     275              3   \n",
       " Self-emp-not-inc              360                 6     368             26   \n",
       " State-gov                     393               112      11             56   \n",
       " Without-pay                     0                 0       0              0   \n",
       "\n",
       "occupation          Transport-moving  \n",
       "workclass                             \n",
       " Federal-gov                      24  \n",
       " Local-gov                       112  \n",
       " Private                        1099  \n",
       " Self-emp-inc                     26  \n",
       " Self-emp-not-inc                118  \n",
       " State-gov                        40  \n",
       " Without-pay                       1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Train['workclass'],Train['occupation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27c498d6",
   "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"
    },
    {
     "data": {
      "text/plain": [
       " <=50K    20023\n",
       " >50K      6880\n",
       "Name: Salary, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.countplot(x='Salary',data= Train)\n",
    "plt.xlabel('Salary')\n",
    "plt.ylabel('count')\n",
    "plt.show()\n",
    "Train['Salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8ac5995d",
   "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"
    },
    {
     "data": {
      "text/plain": [
       " <=50K    10620\n",
       " >50K      3510\n",
       "Name: Salary, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.countplot(x='Salary',data= Test)\n",
    "plt.xlabel('Salary')\n",
    "plt.ylabel('count')\n",
    "plt.show()\n",
    "Test['Salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eef0e642",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='occupation', ylabel='workclass'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(Train['occupation'],Train['workclass'],hue=Train['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "883dc296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='education'>"
      ]
     },
     "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": [
    "pd.crosstab(Train['Salary'],Train['education']).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "db8c9bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='occupation'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "pd.crosstab(Train['Salary'],Train['occupation']).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "501987b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='workclass'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9r0pq2oUt44CLJV0u6XBJm9Zd0EhIOgb4HHB0aXoe8L36KhqR7Wz/sW/D9h+AHeorZ8T2HqT+v66vnBF7B7AMmC3pV5I+LemldRc1Ar8GZkqaL+lQSS+sq5AEQmF7lu2/pppX6b+BEyTdUXNZw2b7S7a3AQ4HXgL8QtLPai5rJN5J9Yf9JwDb9wEb1FrR8I2RtGHfhqSN6JEh3cM0VtLafRuS1gXWHuL5PcX2b2x/xfZOwN8A2wF31VzWsNk+zfauwEHAJOBGSWdLenO3a2nSL223vBzYmuo/5tZ6S2nLA8DvgIeATWquZSSesm1JBpD0groLGoH/B1wl6dyyvT9wbI31jNT3gLmSTqeaMubDwKx6SxoZSZOA9wIHAM8An621oBEqMz5vXW4PAjcAn5L0UdvTu1ZHrkOoSDoBeBfwP8Bs4PzWw+heJ+kwqj+G8cC5wL/bbkygSfo0MBnYAziO6k3pB7ZPrrWwYSp9wG+mmpdrbpP+7QEk7Q3sTlX/f9m+uOaShk3SfKouxh9S/d7fWXNJIyLpRKqj47nAd2xf0/LY7bZf2bVaEggVSYcC59pu5CyJko4HzrF9fd21tEvSHsBbqd6ULrZ9Sc0lDVv5hLcpLUfdtu+pr6LRQ9LWtn9ddx3tkvRhqr/dxwZ57IW2H+5aLQmEfpLeAbyhbP7C9o/rrGekJG0P/FXZvNz2DXXWMxKSTrD9uZW19SJJHweOAe6n6q4QYNvb1VrYMEl6F3ACVRej6K9/XK2FDVM5CXsMLX+7wJe7+UbaqXIOajKwTl+b7V92vY4EQkXScVQnlL9fmg4EFtg+esV79Q5Jn6BaK+L80vROYKbtb9ZX1fBJutb2jgPabmzCm6qkRVTrdzxUdy3tKPXvY/u2umtph6TzgJvpP+/xAWB72++qr6rhk/S3wBFUszxfD0wD5tnereu1JBAqkm4EXm37z2V7LHBdE96Q4Nn6d7H9p7L9Aqpfqp6uv5z7+BjwMqrzN302AK60/f5aChsBSZcCe9heVnct7ZB0ZRnl0kiSrrf96pW19SpJNwE7A1fbfrWkrYEv2T6g27VklNHyXgT8vtyvbSxwm0TVXdGnr+ui150NXEh1IvmolvZHbf9+8F16zp3AZZJ+CjzZ12j7xPpKGpEFkv4d+BHL13/+CvfoLY9Ler3tKwAk7Qo8XnNNI/GE7SckIWlt27+W1LUTya0SCP2OA64rn/ZE1R/ZiO6i4nRgvqT/KNv7Ad+pr5zhKf28D1N10SFpE6p+1PUlrd+QE7P3lNvzy61pxgGPUZ3Q72P6ux973WHArHIuQVQf6j5Ya0Ujs7hcKf4j4BJJf6CmBcLSZdRC0mZUh24C5tv+Xc0ljYikHYHXU9X/S9vX1VzSsEnaBziR6qK6B4AtgNvKxXYRKyVpHIDtR+qupV2S3kjVO3GR7ae6/v0TCJXyZjrQw8BvmtA3XK6OHehR2093vZg2SLoB2A34me0dylWaB9qeUXNpKyTpG7aPlPRjBl8D/B01lDVskj5r+yuSvsng9X+ihrJGTNKnBml+GFjYlGHYLR/mTHXu7No66kiXUb9TgB2BG6k+YW9b7r9Y0qG2/6vO4obhWqp1qf9AVf+LgCWSHgA+YnthjbUNx9O2H5I0RtIY25eWiwV72Vnl69dqraJ9faOKFtRaReemllvfMPG3US3Le6ikH9r+Sm2VDYOkL1Bd3d7XRXd6qbvrEwzmCKGQdA7wj31Ld0qaAnwG+Eeqq5ZfXWN5KyXpX4H/6LvCVNJbgb2orro+yfZr66xvZcq8S/tRncvZmKrbaGfbjZqBVtKOdX26WxUk/UUDu0ovBt5t+3/L9vpUV+u/k+ooYUqd9a2MpNuAHWw/UbbXBa61/apu15LJ7fpt3bqOc5l6YIcGXQY/tXW6gXJE8wbbV9OMicr2pTqx+UngIqohqPvUWlF7Tqu7gA79Z90FtOGlQGt/+9PAFrYfp2XUVA+7m5YL0qj+Xv9n8KeuXuky6ne7pFOBc8r2AcB/l1kgm9AP/3tJn2P5+v9Qrqf4c31lDdsM4Ie2F9OwidUGaMJQ36E0sf6zgaslXVC29wF+UK7FacKcUk8Ct0i6hOocwh7AFZJOhu6ey0mXUVEO0z5G/yidK6jOKzwBrNd3ONqrJG1Mdfl+X/2XA1+mOrn2UtuLaixvpcp6CO+lGjJ4DtW8UvfXW9XISdrP9o/qrqNdkj5m+5S66xgpSTvR8rdruzHnRSQdPNTjtrv2ASmBMAhJOzXgJOwKSdrM9pK662iHpO2ojm7eDSy2/ZaaSxqWMh9Q3yiRK2z/x0p26RmSzrL9gZW1NYGkGbZn1l1Hu+o+B5VzCIP7dt0FdOindRfQgcat5yDpFOBQ4CaqOXU+Kulf6q1qRJa71qN0M+5UUy2dOrTuAjpU6zmonEMYXBP7UVs1rv5B1nP4SIPWFHgjsK3L4bakWVTh0NMkHQ18HlhX0iP0/948BTT1U3bjfvcHqLX+BMLgvlR3AR1q4hHOFsCRTbmQaIDbqUa6/KZsT6S6hqWn2T4OOE7ScU2Z1XcYmjgyrVWt7z2j/hzCCq5QflavjylfwRXKz+r1CeIkjbP9yIp+jl6vH0DSL6imPOlb6WpnYB7VMNqev2IZnrMWyGW2f1JnPSMhaVPgn4GX2N67XEO0i+2en8sLQNJc27uvrK0rtSQQdOkQD7uOOclHQtJdVCcyWw81+7Zt+2W1FDZMkn5i++0r+jl6vX54dv6ZFbL9i27V0o41YC2QC6kmd/w/treXtBbV1PV/WXNpQ5K0DrAecCnwJvp/98cBF9ZxYdqoD4SonyQBExsys+kKlcnVWpfQ7PmjG1gj1gL5le2dJV1ne4fS1vPrIUg6AjiSakLH1tlNHwG+bftb3a4p5xBaSNoWmMLyy9idWV9FI6MeWYZvpGy7TNvdyJEtkmZQTXHyONVFgKI62un5o5sWL6K5a4H8SdKLKRP0SZpGdf1NT7N9EnCSpI+7R1Y2TCAU5cKoN1EFwn8Ce1NdnNaIQNAKluGjmkG0Ca6WtLPtX9VdSBs+A2xj+8G6C2lT09cC+RQwB9hK0pVUI9X2r7ekEXlY0kEDG+v4MJouo6IsY7c91aHy9uVE1Wm2GzFqoZeW4WuHpFuBV1LN6/InaM5C9ZIuAt5l+7G6a2lXk9cCKdPLPEP1+yOqUV9jbDdhHiPK9ON91gF2p5rc7j3driVHCP0et/1nSctKX/ADNOuQv2eW4WvT3nUX0IGjgaskzWf5JSgbsZ5AMQZ4kOo94RWSXtGE7sZinu0dgWcnp5R0LdV09j3P9sdbt1Wt/HbWCp6+WiUQ+i1QtYzdt4GFwP/SP4ywCXpmGb6RULVk5ueBl1NdzHWcm7fi1b8BP6eqvwkTCS6nrDtxANUbal/9Bno6ECT9BbA51YV1O7D8KJ31aiusc49RnQvsunQZ8ewolwm27y3bk4Bxtnv+4qLBqOZl+EaidLcspHrzeTuwge0P1lrUCEm6qmnrNrSSdDuwXVO6WPqUSeE+SLU4Tutkdo8CZ9huxJrQWn7FvbHAq4DZto/qei0JhIqkhbYbOcoFnh1ZcYvtR8v2BsAU2/PrrWxoA4cHSrq2HP43hqRjqa5S/jHLdxk1ZdjphcD+vT6j74pIerft8+quo10DrmNZRrVs7+JaakkgVMpkZGc0dJQLkq4DdmyZT2cM1cVFPf3mqmot5TfRf7i/3EU6TXhTLRfVDdSIi+oAJJ1HNaBiLg09ByLpbVST9LUOuf5yfRWNTBnEsnPZvMb2A3XUkXMI/d5MtQbr3TRslEsht6R7OUHehP/fF1J1GbVeodw3XUgjxvLb3rLuGjo0p9waSdXysetR/Q2fBryHBp3/k/Re4KvAZVR/B9+U9Bnb53a9lhwhVCRtMVi77d8M1t5rJJ1P9Qt1amn6GPBm2/vVVdNoIWk9qrHwL7U9Q9Jk4JVNmg+oT93z8bdD0o22t2v5uj7VOuhvrbu24ShHyXv0HRVIGg/8zPb23a4l6yEU5Y1/IrBbuf8Yzfr3ORR4HfDbcnst1bKUsfqdTjVldN+J5cXAP9VXTkeauCb04+XrY5JeQrXkbZOO2sYM6CJ6iJree5rQpdAV5UrlqVQXt5wOPA/4HrBrnXUNV/mFml53HaPUVrYPkHQggO3Hy8i1Jmpi3T8pQ66/StXdaJoVbBdJuhj4Qdk+gGq2hK5LIPR7J7ADpf/a9n1lpE4jSJoAfJMqwEw17cYRdY1WGGWeUrUmd98J/a1oOTnbME1cC+QrZcjseZJ+QnVi+Ymaaxo225+R9G6qv10BM13TEqwJhH5PlUnW+v6oX1B3QSN0OnA2/XO4vL+07VFbRaPHMcBFwERJ36f6w/5grRWNkFrWhJakut6Q2jSPclVyCYYnm3SlMkAZNlv70NkEQr/Zkv4NeJGkjwAfplkrj423fXrL9hmSjqyrmNHE9iXlDWga1Se8I5o00Z2qNaFfTn+XxUclvcX24TWWtVJrypXKJYxPoFpDXPSPcBzX9VoyyqifpD2At1L9h1xs+5KaSxo2ST8DzqD/j/pA4EOuYdWl0UzSF21/se46RkLSLSy/JvQY4Cbb29Rb2dAGXKn8K/oDoWlXKi8C9rF9W+21JBDWDJJeCnwL2IWqL/sq4BNNX3SmaRp6pfX5wCf7hliXIdjH2z6w3sqGZw24UvlK2z0xeGXUB4KkR+mfR+Q56jhsW1Ukfc32p+uuY00laUvbdw1oe3bVrqZQQ9eElrQPcGNLkH0BeDfVNCJHDPy/6TWlqwjgjcBfUE1M2XqleNePcEZ9IPSR9GXgd1TTzgp4H9VEa1+ptbAOSLrH9kvrrmNN1Tf/lVoWRJc0xmUpyqZQQ9eEVrX05zTbj0l6O3AiVVfpDlRzM+1Za4ErIen0IR627Q93rZgigVBImm/7tStraxJJ99qeWHcda6oyf9SPgL8Fvj7wcdsndrumTqhha0JLuqHval5J3wVut31C2W5M152kXW1fubK2bmjSlbir2zOS3idprKQxkt5HtQpTT5O00QpuL6aZFxk1yXSq8e5rARsMcmsESTMk3Q/cSDWN9EKWn066V0nS+uUk+O5Uk/P1WWcF+/SiwdZTrmWN5Qw77fc3wEnlZuDK0tbrFlLVO9ibf0+vhdB0tm8HTihz6FxYdz0daOqa0N+gWj/8EeA22wsAyhDUJfWVNTySdqGa7mS8pE+1PDSOal2ErksgFLbvBvatu46RWgNm2mys1j9iSa8a+HiDuoz+h3ICuUlsf7dM+bAJcEPLQ78DPlRPVSPyfGB9+o8w+zxCNWNr1+UcQiHpFVQzhW5qe1tJ2wHvsN3UScpiNSvzX62Q7UZMA1E+UZ8ONHlN6MaStEWvzKqcQCjK0LvPAP/WN2xQ0s22t623sojVS9I1VHNfLbcmtO1ZtRU1Ckj6hu0jtfwSms+qY7hvuoz6rWf7mgGTVC6rq5hojjXg6HKZ7U+t/Gmxip1Vvn6t1ipaJBD6PVhmqey7fP89NODEVPSEb1OOLgFs3yjpbJqzJsKlkmbQ0DWhG+wDkl4GXGX7t3UXAwmEVocDM4GtJf0WuIvq4rSIlWn60WXfaLqjW9oasXxpwy2imnb/q+V35yqq0Y1XATfUcYHjqA8ESdNsX237TuAtZdrrMbYfrbu2aIxGH11mpFo9bH+Lav4xJG1GNW3664BPUo2c6vq0OaM+EIBTKPOmS5pne5ea64nmafTR5Zq0JnTTlJX1/pIqCHYFplAdOZw11H6rSwJh+Qu6mnR1Y/SINeDo8nSqCxxb14T+IZBAWI0kXUJ1FHA9cDXwz3VPgZ2pK2CMpA3LVA9995+dAqLu4qJ3SdqnTBXd5++BKyTNkdSkbpityiSOT0O1JjSZ9qQb7qTqZpxcbi+XtHGdBeUIAV5I9emo7w/g2pbHcmIthnIs1SpplNk230//bJv/CvT0bJst1qQ1oRvD9kfh2UkFp1EdoR0uaTxws+2Du13TqA8E25PqriEay7b7pnx4F/Ad2wuBhZI+VmNdI9X4NaEb7kmqqUMeL/cnUE1r0XW5UjmiTWU+/tdR/THfBby7ZYK1W21PqbO+kShdpn1rQl/dwInuGkfS16l+fyZTnUe4qu9m+4911DTqjxAiOvANGjzbZivbDwE/LWtC52Ryd9wFfB+4znZPTLWfI4SIDkjanDLbZt+FRGVM+fOauJ51kxaWiVUvRwgRHShTDvx2QFsjjg4GWxOajC4a1TLsNGL0OhdAUutKYzvVVEv0gBwhRIxeY8qaDq8YsNgP0KgFfmIVyRFCxOi1RqwJHatOTipHjHKS9m74mtCxiiQQIkapAQu7P0e6jEafnEOIGL3SLRTLyRFCREQAOakcMepJeoWkuZJuLtvbSfqHuuuK7ksgRMS3qZbP7Jv++kaqEUgxyiQQImI929cMaGvSmtCxiiQQIqLRa0LHqpOTyhGjnKSXUa0J/TrgD5Q1oW3/ptbCousSCBEBQIPXhI5VJF1GEaPUGrQmdKwiCYSI0etYYCkstyb0h4E5VGtCxyiTQIgYvQZdE9r2acD4GuuKmiQQIkYvSVpf0hhgd6B1XYR1aqopapS5jCJGr2+whqwJHatGRhlFjGJr2prQ0ZkEQkREADmHEBERRQIhIiKABEJERBQJhIiIABIIERFRJBAi2iTpbkkbD/O5X5T06dVdU0QnEggRbZA0tu4aIla1BEKMOpI+K+kT5f7XJf283N9d0vckHSjpJkk3SzqhZb//lfRlSfOBXVra15V0kaSPlO2DJN0o6QZJZw3y/T8i6Vfl8fMkrVfa9y/f8wZJvyxt20i6RtL15TUnr9Z/nBjVEggxGv0S+KtyfyqwvqTnAa8H7gBOAHYDXg3sLGm/8twXADfbfq3tK0rb+sCPgbNtf1vSNsD/AXazvT1wxCDf/3zbO5fHbwMOKe1fAPYs7e8obYcCJ9l+dal1cac/fMSKJBBiNFoI7CRpA+BJYB7Vm+1fAX8ELrO91PYy4PvAG8p+zwDnDXitC4DTbZ9ZtncDzrX9IIDt3w/y/beVdLmkm4D3AduU9iuBM8qRRl+X1Dzg85I+B2xh+/EOfu6IISUQYtSx/TRwN/Ah4CrgcuDNwFbAUPP3PGH7mQFtVwJ7S1LZFmVt4iGcAfyd7b8EvkSZWdT2ocA/ABOB6yW92PbZVEcLjwMXS9ptOD9jRDsSCDFa/RL4dPl6OVXXzPXA1cAbJW1cThwfCPxiiNf5AvAQcErZngu8V9KLASRtNMg+GwBLSjfV+/oaJW1le77tLwAPAhPLesd32j6ZauGa7dr8eSNWKoEQo9XlwGbAPNv3A08Al9teAhwNXArcAFxr+4KVvNaRwDqSvmL7FqqVyH4h6QbgxEGe/3+B+cAlwK9b2r/adzKbKqhuAA4AbpZ0PbA1cCYRq0lmO42ICCBHCBERUSQQIiICSCBERESRQIiICCCBEBERRQIhIiKABEJERBQJhIiIAOD/A/lIQjxgCD/kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(Train['Salary'],Train['workclass']).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ffb5d70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "pd.crosstab(Train['Salary'],Train['sex']).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2664d945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='relationship'>"
      ]
     },
     "execution_count": 36,
     "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": [
    "pd.crosstab(Train['Salary'],Train['relationship']).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "567e90c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "string_columns = [\"workclass\",\"education\",\"maritalstatus\",\"occupation\",\"relationship\",\"race\",\"sex\",\"native\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a41c218d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-b761bae27bef>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Train[i]= number.fit_transform(Train[i])\n",
      "<ipython-input-38-b761bae27bef>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Test[i]=number.fit_transform(Test[i])\n"
     ]
    }
   ],
   "source": [
    "##Preprocessing the data. As, there are categorical variables\n",
    "number = LabelEncoder()\n",
    "for i in string_columns:\n",
    "        Train[i]= number.fit_transform(Train[i])\n",
    "        Test[i]=number.fit_transform(Test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16fb5bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>native</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>2174</td>\n",
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       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30154</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
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       "    <tr>\n",
       "      <th>30155</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30156</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30158</th>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30160</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26903 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  education  educationno  maritalstatus  occupation  \\\n",
       "0       39          5          9           13              4           0   \n",
       "1       50          4          9           13              2           3   \n",
       "2       38          2         11            9              0           5   \n",
       "3       53          2          1            7              2           5   \n",
       "4       28          2          9           13              2           9   \n",
       "...    ...        ...        ...          ...            ...         ...   \n",
       "30154   53          2         12           14              2           3   \n",
       "30155   22          2         15           10              4          10   \n",
       "30156   27          2          7           12              2          12   \n",
       "30158   58          2         11            9              6           0   \n",
       "30160   52          3         11            9              2           3   \n",
       "\n",
       "       relationship  race  sex  capitalgain  capitalloss  hoursperweek  \\\n",
       "0                 1     4    1         2174            0            40   \n",
       "1                 0     4    1            0            0            13   \n",
       "2                 1     4    1            0            0            40   \n",
       "3                 0     2    1            0            0            40   \n",
       "4                 5     2    0            0            0            40   \n",
       "...             ...   ...  ...          ...          ...           ...   \n",
       "30154             0     4    1            0            0            40   \n",
       "30155             1     4    1            0            0            40   \n",
       "30156             5     4    0            0            0            38   \n",
       "30158             4     4    0            0            0            40   \n",
       "30160             5     4    0        15024            0            40   \n",
       "\n",
       "       native  Salary  \n",
       "0          37   <=50K  \n",
       "1          37   <=50K  \n",
       "2          37   <=50K  \n",
       "3          37   <=50K  \n",
       "4           4   <=50K  \n",
       "...       ...     ...  \n",
       "30154      37    >50K  \n",
       "30155      37   <=50K  \n",
       "30156      37   <=50K  \n",
       "30158      37   <=50K  \n",
       "30160      37    >50K  \n",
       "\n",
       "[26903 rows x 14 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad6812e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
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       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <th>...</th>\n",
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       "      <td>&lt;=50K</td>\n",
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       "      <th>15056</th>\n",
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       "      <td>0</td>\n",
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       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <td>&lt;=50K</td>\n",
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       "<p>14130 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  education  educationno  maritalstatus  occupation  \\\n",
       "0       25          2          1            7              4           6   \n",
       "1       38          2         11            9              2           4   \n",
       "2       28          1          7           12              2          10   \n",
       "3       44          2         15           10              2           6   \n",
       "4       34          2          0            6              4           7   \n",
       "...    ...        ...        ...          ...            ...         ...   \n",
       "15055   33          2          9           13              4           9   \n",
       "15056   39          2          9           13              0           9   \n",
       "15057   38          2          9           13              2           9   \n",
       "15058   44          2          9           13              0           0   \n",
       "15059   35          3          9           13              2           3   \n",
       "\n",
       "       relationship  race  sex  capitalgain  capitalloss  hoursperweek  \\\n",
       "0                 3     2    1            0            0            40   \n",
       "1                 0     4    1            0            0            50   \n",
       "2                 0     4    1            0            0            40   \n",
       "3                 0     2    1         7688            0            40   \n",
       "4                 1     4    1            0            0            30   \n",
       "...             ...   ...  ...          ...          ...           ...   \n",
       "15055             3     4    1            0            0            40   \n",
       "15056             1     4    0            0            0            36   \n",
       "15057             0     4    1            0            0            50   \n",
       "15058             3     1    1         5455            0            40   \n",
       "15059             0     4    1            0            0            60   \n",
       "\n",
       "       native  Salary  \n",
       "0          37   <=50K  \n",
       "1          37   <=50K  \n",
       "2          37    >50K  \n",
       "3          37    >50K  \n",
       "4          37   <=50K  \n",
       "...       ...     ...  \n",
       "15055      37   <=50K  \n",
       "15056      37   <=50K  \n",
       "15057      37   <=50K  \n",
       "15058      37   <=50K  \n",
       "15059      37    >50K  \n",
       "\n",
       "[14130 rows x 14 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4021990e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'education', 'educationno', 'maritalstatus',\n",
       "       'occupation', 'relationship', 'race', 'sex', 'capitalgain',\n",
       "       'capitalloss', 'hoursperweek', 'native', 'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Capturing the column names which can help in futher process\n",
    "colnames = Train.columns\n",
    "colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "23040e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6294d1b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>&lt;=50K</td>\n",
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       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>30155</th>\n",
       "      <td>22</td>\n",
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       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <th>30156</th>\n",
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       "    <tr>\n",
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       "      <td>58</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30160</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26903 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  education  educationno  maritalstatus  occupation  \\\n",
       "0       39          5          9           13              4           0   \n",
       "1       50          4          9           13              2           3   \n",
       "2       38          2         11            9              0           5   \n",
       "3       53          2          1            7              2           5   \n",
       "4       28          2          9           13              2           9   \n",
       "...    ...        ...        ...          ...            ...         ...   \n",
       "30154   53          2         12           14              2           3   \n",
       "30155   22          2         15           10              4          10   \n",
       "30156   27          2          7           12              2          12   \n",
       "30158   58          2         11            9              6           0   \n",
       "30160   52          3         11            9              2           3   \n",
       "\n",
       "       relationship  race  sex  capitalgain  capitalloss  hoursperweek  \\\n",
       "0                 1     4    1         2174            0            40   \n",
       "1                 0     4    1            0            0            13   \n",
       "2                 1     4    1            0            0            40   \n",
       "3                 0     2    1            0            0            40   \n",
       "4                 5     2    0            0            0            40   \n",
       "...             ...   ...  ...          ...          ...           ...   \n",
       "30154             0     4    1            0            0            40   \n",
       "30155             1     4    1            0            0            40   \n",
       "30156             5     4    0            0            0            38   \n",
       "30158             4     4    0            0            0            40   \n",
       "30160             5     4    0        15024            0            40   \n",
       "\n",
       "       native  Salary  \n",
       "0          37   <=50K  \n",
       "1          37   <=50K  \n",
       "2          37   <=50K  \n",
       "3          37   <=50K  \n",
       "4           4   <=50K  \n",
       "...       ...     ...  \n",
       "30154      37    >50K  \n",
       "30155      37   <=50K  \n",
       "30156      37   <=50K  \n",
       "30158      37   <=50K  \n",
       "30160      37    >50K  \n",
       "\n",
       "[26903 rows x 14 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fb9e8a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>native</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>37</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15055</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15056</th>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15057</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15058</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15059</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14130 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  education  educationno  maritalstatus  occupation  \\\n",
       "0       25          2          1            7              4           6   \n",
       "1       38          2         11            9              2           4   \n",
       "2       28          1          7           12              2          10   \n",
       "3       44          2         15           10              2           6   \n",
       "4       34          2          0            6              4           7   \n",
       "...    ...        ...        ...          ...            ...         ...   \n",
       "15055   33          2          9           13              4           9   \n",
       "15056   39          2          9           13              0           9   \n",
       "15057   38          2          9           13              2           9   \n",
       "15058   44          2          9           13              0           0   \n",
       "15059   35          3          9           13              2           3   \n",
       "\n",
       "       relationship  race  sex  capitalgain  capitalloss  hoursperweek  \\\n",
       "0                 3     2    1            0            0            40   \n",
       "1                 0     4    1            0            0            50   \n",
       "2                 0     4    1            0            0            40   \n",
       "3                 0     2    1         7688            0            40   \n",
       "4                 1     4    1            0            0            30   \n",
       "...             ...   ...  ...          ...          ...           ...   \n",
       "15055             3     4    1            0            0            40   \n",
       "15056             1     4    0            0            0            36   \n",
       "15057             0     4    1            0            0            50   \n",
       "15058             3     1    1         5455            0            40   \n",
       "15059             0     4    1            0            0            60   \n",
       "\n",
       "       native  Salary  \n",
       "0          37   <=50K  \n",
       "1          37   <=50K  \n",
       "2          37    >50K  \n",
       "3          37    >50K  \n",
       "4          37   <=50K  \n",
       "...       ...     ...  \n",
       "15055      37   <=50K  \n",
       "15056      37   <=50K  \n",
       "15057      37   <=50K  \n",
       "15058      37   <=50K  \n",
       "15059      37    >50K  \n",
       "\n",
       "[14130 rows x 14 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "945292a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    6490\n",
       "4    4501\n",
       "0    2026\n",
       "5     470\n",
       "6     450\n",
       "3     182\n",
       "1      11\n",
       "Name: maritalstatus, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test['maritalstatus'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "91ac6e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the values in x_train,y_train,x_test & y_test for spliting the data in train and test for analysis\n",
    "x_train = Train[colnames[0:13]].values\n",
    "y_train = Train[colnames[13]].values\n",
    "x_test = Test[colnames[0:13]].values\n",
    "y_test = Test[colnames[13]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4789ce19",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Normalmization\n",
    "def norm_func(i):\n",
    "    x = (i-i.min())/(i.max()-i.min())\n",
    "    return (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0202e282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39,  5,  9, ...,  0, 40, 37],\n",
       "       [50,  4,  9, ...,  0, 13, 37],\n",
       "       [38,  2, 11, ...,  0, 40, 37],\n",
       "       ...,\n",
       "       [27,  2,  7, ...,  0, 38, 37],\n",
       "       [58,  2, 11, ...,  0, 40, 37],\n",
       "       [52,  3, 11, ...,  0, 40, 37]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9d22ed42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25,  2,  1, ...,  0, 40, 37],\n",
       "       [38,  2, 11, ...,  0, 50, 37],\n",
       "       [28,  1,  7, ...,  0, 40, 37],\n",
       "       ...,\n",
       "       [38,  2,  9, ...,  0, 50, 37],\n",
       "       [44,  2,  9, ...,  0, 40, 37],\n",
       "       [35,  3,  9, ...,  0, 60, 37]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "97ba4ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' <=50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eb5b7639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' <=50K', ' >50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bcdb2225",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = norm_func(x_train)\n",
    "x_test =  norm_func(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b14666",
   "metadata": {},
   "source": [
    "# Applying naive bayes for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "12454089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing a naive bayes model on training data set \n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB as MB\n",
    "\n",
    "M_model=MB()\n",
    "train_pred_multi=M_model.fit(x_train,y_train).predict(x_train)\n",
    "test_pred_multi=M_model.fit(x_train,y_train).predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "412d9bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7442664386871353"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_acc_multi=np.mean(train_pred_multi==y_train)\n",
    "train_acc_multi ## train accuracy 74.42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9fa42373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7515923566878981"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc_multi=np.mean(test_pred_multi==y_test)\n",
    "test_acc_multi ## test acuracy 75.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4015424d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, test_pred_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6ab87be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10620,     0],\n",
       "       [ 3510,     0]], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the matrix\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cdd1b54b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7515923566878981\n"
     ]
    }
   ],
   "source": [
    "#calculating the accuracy of this model w.r.t. this dataset\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test,test_pred_multi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "13278f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "## GaussianNB for numerical data\n",
    "from sklearn.naive_bayes import GaussianNB as GB\n",
    "G_model=GB()\n",
    "train_pred_gau=G_model.fit(x_train,y_train).predict(x_train)\n",
    "test_pred_gau=G_model.fit(x_train,y_train).predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "26df7f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7933687692822362"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_acc_gau=np.mean(train_pred_gau==y_train)\n",
    "train_acc_gau ## train accuracy 79.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e427b82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7951875442321302"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc_gau=np.mean(test_pred_gau==y_test)\n",
    "test_acc_gau ## test acuracy 79.57"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d349c615",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, test_pred_gau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "642933cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10056,   564],\n",
       "       [ 2330,  1180]], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the matrix\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e48e0a0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7951875442321302\n"
     ]
    }
   ],
   "source": [
    "#calculating the accuracy of this model w.r.t. this dataset\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test,test_pred_gau))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56801dfb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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