Income Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "\n",
    "Problem 1:  Prediction task is to determine whether a person makes over 50K a year. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The description of the dataset is as follows:\n",
    "\n",
    "Data Set Information:\n",
    "Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: \n",
    "####((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))\n",
    "\n",
    "Attribute Information:\n",
    "Listing of attributes:\n",
    "* >50K, <=50K.\n",
    "* age: continuous.\n",
    "* workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov,Without-pay, Never-worked.\n",
    "* fnlwgt: continuous and  represents final weight, which is the number of units in the target population that the responding unit represents.\n",
    "* education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc,\n",
    "9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "education-num: continuous and stands for the number of years of education in total.\n",
    "\n",
    "* marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed,\n",
    "Married-spouse-absent, Married-AF-spouse.\n",
    "\n",
    "* occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-\n",
    "specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing,\n",
    "\n",
    "Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "* relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "* race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "* sex: Female, Male.\n",
    "* capital-gain: continuous.\n",
    "* capital-loss: continuous.\n",
    "* hours-per-week: continuous.\n",
    "* native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany,\n",
    "Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras,\n",
    "Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France,\n",
    "Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala,\n",
    "Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong,\n",
    "Holand-Netherlands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: unrecognized arguments: # to show the figures in the Jupyter Notebook itself\n"
     ]
    }
   ],
   "source": [
    "#code to load required libraries and data:\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from pandas.plotting import scatter_matrix\n",
    "%matplotlib inline # to show the figures in the Jupyter Notebook itself\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load training and test data into corresponding dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading training and test sets\n",
    "\n",
    "train_set = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data',na_values=\"?\")\n",
    "\n",
    "test_set = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test', skiprows = 1,na_values=\"?\")\n",
    "\n",
    "# extracting  the column names\n",
    "col_labels = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status',\n",
    "'occupation','relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week',\n",
    "'native_country', 'wage_class']\n",
    "train_set.columns = col_labels\n",
    "test_set.columns = col_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32560, 15), (16280, 15))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape,test_set.shape # getting the shape of the loaded data to check the number of rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View training and test data sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
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       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
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       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>wage_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14160</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>680390</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27047</th>\n",
       "      <td>45</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>50567</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>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>28867</th>\n",
       "      <td>29</td>\n",
       "      <td>Private</td>\n",
       "      <td>185908</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>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5667</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>306108</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass  fnlwgt   education  education_num  \\\n",
       "14160   38     Private  680390     HS-grad              9   \n",
       "27047   45   State-gov   50567     HS-grad              9   \n",
       "28867   29     Private  185908   Bachelors             13   \n",
       "5667    51     Private  306108   Doctorate             16   \n",
       "\n",
       "            marital_status        occupation    relationship    race      sex  \\\n",
       "14160             Divorced      Adm-clerical       Unmarried   White   Female   \n",
       "27047   Married-civ-spouse   Exec-managerial            Wife   White   Female   \n",
       "28867   Married-civ-spouse   Exec-managerial         Husband   Black     Male   \n",
       "5667         Never-married    Prof-specialty   Not-in-family   White     Male   \n",
       "\n",
       "       capital_gain  capital_loss  hours_per_week  native_country wage_class  \n",
       "14160             0             0              20   United-States      <=50K  \n",
       "27047             0             0              40   United-States      <=50K  \n",
       "28867             0             0              55   United-States       >50K  \n",
       "5667              0             0              40   United-States       >50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.sample(4, random_state = 42) #Returns a random sample of the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>13633</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>82726</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
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       "      <th>1921</th>\n",
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       "      <td>Private</td>\n",
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       "      <td>HS-grad</td>\n",
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       "      <td>Own-child</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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       "    <tr>\n",
       "      <th>12140</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>111635</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9933</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>121789</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</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>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       age workclass  fnlwgt      education  education_num  \\\n",
       "13633   39   Private   82726   Some-college             10   \n",
       "1921    20   Private  223811        HS-grad              9   \n",
       "12140   35   Private  111635        HS-grad              9   \n",
       "9933    25   Private  121789   Some-college             10   \n",
       "\n",
       "            marital_status      occupation    relationship    race      sex  \\\n",
       "13633   Married-civ-spouse           Sales            Wife   White   Female   \n",
       "1921         Never-married   Other-service       Own-child   White     Male   \n",
       "12140   Married-civ-spouse   Other-service         Husband   White     Male   \n",
       "9933         Never-married           Sales   Not-in-family   White     Male   \n",
       "\n",
       "       capital_gain  capital_loss  hours_per_week  native_country wage_class  \n",
       "13633             0             0              40   United-States     <=50K.  \n",
       "1921              0             0              23   United-States     <=50K.  \n",
       "12140             0             0              40   United-States     <=50K.  \n",
       "9933              0             0              38   United-States     <=50K.  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.sample(4, random_state = 42) #Returns a random sample of the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check for null values if any  in training and test data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "wage_class        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "wage_class        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>object</td>\n",
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       "      <td>object</td>\n",
       "      <td>object</td>\n",
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       "      <th>capital_gain</th>\n",
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       "      <td>int64</td>\n",
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       "    <tr>\n",
       "      <th>capital_loss</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
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       "    <tr>\n",
       "      <th>hours_per_week</th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
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       "    <tr>\n",
       "      <th>native_country</th>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wage_class</th>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               train_set test_set\n",
       "age                int64    int64\n",
       "workclass         object   object\n",
       "fnlwgt             int64    int64\n",
       "education         object   object\n",
       "education_num      int64    int64\n",
       "marital_status    object   object\n",
       "occupation        object   object\n",
       "relationship      object   object\n",
       "race              object   object\n",
       "sex               object   object\n",
       "capital_gain       int64    int64\n",
       "capital_loss       int64    int64\n",
       "hours_per_week     int64    int64\n",
       "native_country    object   object\n",
       "wage_class        object   object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([train_set.dtypes, test_set.dtypes], index = ['train_set','test_set']).T # checking the datatypes of all the columns in the training and test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the columns having data types as object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workclass\n",
      "education\n",
      "marital_status\n",
      "occupation\n",
      "relationship\n",
      "race\n",
      "sex\n",
      "native_country\n",
      "wage_class\n"
     ]
    }
   ],
   "source": [
    "for i in train_set.columns: \n",
    "    if train_set[i].dtypes == 'object':\n",
    "        print(i)\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the frequency of occurrence of different categorical values in the columns which have a datatype object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Private             22696\n",
       " Self-emp-not-inc     2541\n",
       " Local-gov            2093\n",
       " ?                    1836\n",
       " State-gov            1297\n",
       " Self-emp-inc         1116\n",
       " Federal-gov           960\n",
       " Without-pay            14\n",
       " Never-worked            7\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.workclass.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " United-States                 29169\n",
       " Mexico                          643\n",
       " ?                               583\n",
       " Philippines                     198\n",
       " Germany                         137\n",
       " Canada                          121\n",
       " Puerto-Rico                     114\n",
       " El-Salvador                     106\n",
       " India                           100\n",
       " Cuba                             95\n",
       " England                          90\n",
       " Jamaica                          81\n",
       " South                            80\n",
       " China                            75\n",
       " Italy                            73\n",
       " Dominican-Republic               70\n",
       " Vietnam                          67\n",
       " Guatemala                        64\n",
       " Japan                            62\n",
       " Poland                           60\n",
       " Columbia                         59\n",
       " Taiwan                           51\n",
       " Haiti                            44\n",
       " Iran                             43\n",
       " Portugal                         37\n",
       " Nicaragua                        34\n",
       " Peru                             31\n",
       " France                           29\n",
       " Greece                           29\n",
       " Ecuador                          28\n",
       " Ireland                          24\n",
       " Hong                             20\n",
       " Trinadad&Tobago                  19\n",
       " Cambodia                         19\n",
       " Thailand                         18\n",
       " Laos                             18\n",
       " Yugoslavia                       16\n",
       " Outlying-US(Guam-USVI-etc)       14\n",
       " Hungary                          13\n",
       " Honduras                         13\n",
       " Scotland                         12\n",
       " Holand-Netherlands                1\n",
       "Name: native_country, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.native_country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Husband           13193\n",
       " Not-in-family      8304\n",
       " Own-child          5068\n",
       " Unmarried          3446\n",
       " Wife               1568\n",
       " Other-relative      981\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.relationship.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unique counts for the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 16, 7, 42)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.workclass.nunique(),train_set.education.nunique(),train_set.marital_status.nunique(),train_set.native_country.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenate training datasets and test datasets into a common dataframe Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_set.copy()\n",
    "X_test = test_set.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
       "       'wage_class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
       "       'wage_class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sample = X_train.append(X_test)\n",
    "Sample.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education_num</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48840.000000</td>\n",
       "      <td>4.884000e+04</td>\n",
       "      <td>48840.000000</td>\n",
       "      <td>48840.000000</td>\n",
       "      <td>48840.000000</td>\n",
       "      <td>48840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643857</td>\n",
       "      <td>1.896657e+05</td>\n",
       "      <td>10.078092</td>\n",
       "      <td>1079.067301</td>\n",
       "      <td>87.505897</td>\n",
       "      <td>40.422400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710652</td>\n",
       "      <td>1.056048e+05</td>\n",
       "      <td>2.570954</td>\n",
       "      <td>7452.168393</td>\n",
       "      <td>403.012415</td>\n",
       "      <td>12.391697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.175535e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.781445e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.376472e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education_num  capital_gain  capital_loss  \\\n",
       "count  48840.000000  4.884000e+04   48840.000000  48840.000000  48840.000000   \n",
       "mean      38.643857  1.896657e+05      10.078092   1079.067301     87.505897   \n",
       "std       13.710652  1.056048e+05       2.570954   7452.168393    403.012415   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.175535e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.781445e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.376472e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.490400e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours_per_week  \n",
       "count    48840.000000  \n",
       "mean        40.422400  \n",
       "std         12.391697  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sample.describe() #Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset's distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary Statistics of Categorical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native_country</th>\n",
       "      <th>wage_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "      <td>48840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</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>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>33905</td>\n",
       "      <td>15784</td>\n",
       "      <td>22379</td>\n",
       "      <td>6172</td>\n",
       "      <td>19716</td>\n",
       "      <td>41761</td>\n",
       "      <td>32648</td>\n",
       "      <td>43830</td>\n",
       "      <td>24719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       workclass education       marital_status       occupation relationship  \\\n",
       "count      48840     48840                48840            48840        48840   \n",
       "unique         9        16                    7               15            6   \n",
       "top      Private   HS-grad   Married-civ-spouse   Prof-specialty      Husband   \n",
       "freq       33905     15784                22379             6172        19716   \n",
       "\n",
       "          race    sex  native_country wage_class  \n",
       "count    48840  48840           48840      48840  \n",
       "unique       5      2              42          4  \n",
       "top      White   Male   United-States      <=50K  \n",
       "freq     41761  32648           43830      24719  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sample.describe(include=['O']) #Generate descriptive statistics only for columns with object datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA VISUALIZATION - CATEGORICAL FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_distribution(dataset, cols=5, width=20, height=30, hspace=0.2, wspace=0.5):\n",
    "    plt.style.use('fivethirtyeight') #Use matplotlib style settings from a style specification.\n",
    "    fig = plt.figure(figsize=(width,height)) #Create a new figure\n",
    "    fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=wspace, hspace=hspace) # customizing the subplots\n",
    "    rows = math.ceil(float(dataset.shape[1]) / cols)\n",
    "    #print(rows)\n",
    "    # iterating over the columns and then showing the data distribution in various columns\n",
    "    for i, column in enumerate(dataset.columns):\n",
    "        # print('befor',i,cols)\n",
    "        ax = fig.add_subplot(rows, cols, i + 1)\n",
    "        # print('after',i,cols)\n",
    "        # print(column)\n",
    "        ax.set_title(column)\n",
    "        if dataset.dtypes[column] == np.object: # plot counts of different values if the column has a datatype object\n",
    "            g = sns.countplot(y=column, data=dataset)\n",
    "            substrings = [s.get_text()[:18] for s in g.get_yticklabels()]\n",
    "            #print(substrings)\n",
    "            g.set(yticklabels=substrings) #Set the y-tick labels with list of strings labels.\n",
    "            plt.xticks(rotation=25) #Get or set the current tick locations and labels of the x-axis.\n",
    "            #plt.show()\n",
    "        else:\n",
    "            g = sns.distplot(dataset[column]) # Plotting if the datatype of the column is not object\n",
    "            plt.xticks(rotation=25) #Get or set the current tick locations and labels of the x-axis.\n",
    "            #plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x2520 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  creating  columnwise histograms\n",
    "plot_distribution(Sample, cols=2, width=20, height=35, hspace=0.8, wspace=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure above, the plot for Native country column is not clear. So, we'll plot it separately again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid') #Use matplotlib style settings from a style specification.\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(20,8)) #Create a new figure\n",
    "g = sns.countplot(y='native_country',data=Sample) # plotting the counts for native country\n",
    "\n",
    "g.set_yticklabels(g.get_yticklabels(), rotation = 0, fontsize = 12) # Set the y-tick labels with list of strings labels.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: \n",
    "\n",
    "The data pertaining to native country  has few unknow values represented by ?\n",
    "Same is the case with workclass and occupation feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eliminating irrelevant data vale ? from the traiining and test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the '?' in the dataset with NaN\n",
    "train_set = train_set.apply(lambda x : x.replace(' ?',np.nan))\n",
    "test_set = test_set.apply(lambda x : x.replace(' ?',np.nan))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1836\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education_num        0\n",
       "marital_status       0\n",
       "occupation        1843\n",
       "relationship         0\n",
       "race                 0\n",
       "sex                  0\n",
       "capital_gain         0\n",
       "capital_loss         0\n",
       "hours_per_week       0\n",
       "native_country     583\n",
       "wage_class           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the frequency of NaN in different columns in the training set\n",
    "train_set.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 0\n",
       "workclass         963\n",
       "fnlwgt              0\n",
       "education           0\n",
       "education_num       0\n",
       "marital_status      0\n",
       "occupation        966\n",
       "relationship        0\n",
       "race                0\n",
       "sex                 0\n",
       "capital_gain        0\n",
       "capital_loss        0\n",
       "hours_per_week      0\n",
       "native_country    274\n",
       "wage_class          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the frequency of NaN in different columns in the test set\n",
    "test_set.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the NaN values from training and test sets\n",
    "test_set.dropna(inplace=True)\n",
    "train_set.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(age               0\n",
       " workclass         0\n",
       " fnlwgt            0\n",
       " education         0\n",
       " education_num     0\n",
       " marital_status    0\n",
       " occupation        0\n",
       " relationship      0\n",
       " race              0\n",
       " sex               0\n",
       " capital_gain      0\n",
       " capital_loss      0\n",
       " hours_per_week    0\n",
       " native_country    0\n",
       " wage_class        0\n",
       " dtype: int64, age               0\n",
       " workclass         0\n",
       " fnlwgt            0\n",
       " education         0\n",
       " education_num     0\n",
       " marital_status    0\n",
       " occupation        0\n",
       " relationship      0\n",
       " race              0\n",
       " sex               0\n",
       " capital_gain      0\n",
       " capital_loss      0\n",
       " hours_per_week    0\n",
       " native_country    0\n",
       " wage_class        0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the training and test datsets after dropping the NaN values\n",
    "test_set.isnull().sum(),train_set.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we have no NaN values in our dataset and we are good to proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Categorical Values to Numerical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' Male': 0, ' Female': 1}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Sex\n",
    "dict_sex = {}\n",
    "count = 0\n",
    "for i in X_train.sex.unique():\n",
    "    dict_sex[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' Self-emp-not-inc': 0,\n",
       " ' Private': 1,\n",
       " ' State-gov': 2,\n",
       " ' Federal-gov': 3,\n",
       " ' Local-gov': 4,\n",
       " ' ?': 5,\n",
       " ' Self-emp-inc': 6,\n",
       " ' Without-pay': 7,\n",
       " ' Never-worked': 8}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Workclass\n",
    "dict_workclass ={}\n",
    "count = 0\n",
    "for i in X_train.workclass.unique():\n",
    "    dict_workclass[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_workclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' Bachelors': 0,\n",
       " ' HS-grad': 1,\n",
       " ' 11th': 2,\n",
       " ' Masters': 3,\n",
       " ' 9th': 4,\n",
       " ' Some-college': 5,\n",
       " ' Assoc-acdm': 6,\n",
       " ' Assoc-voc': 7,\n",
       " ' 7th-8th': 8,\n",
       " ' Doctorate': 9,\n",
       " ' Prof-school': 10,\n",
       " ' 5th-6th': 11,\n",
       " ' 10th': 12,\n",
       " ' 1st-4th': 13,\n",
       " ' Preschool': 14,\n",
       " ' 12th': 15}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Education\n",
    "dict_education = {}\n",
    "count = 0\n",
    "for i in X_train.education.unique():\n",
    "    dict_education[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' Married-civ-spouse': 0,\n",
       " ' Divorced': 1,\n",
       " ' Married-spouse-absent': 2,\n",
       " ' Never-married': 3,\n",
       " ' Separated': 4,\n",
       " ' Married-AF-spouse': 5,\n",
       " ' Widowed': 6}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# function to encode Marital Status\n",
    "dict_marital_status = {}\n",
    "count = 0\n",
    "for i in X_train.marital_status.unique():\n",
    "    dict_marital_status[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_marital_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' Exec-managerial': 0,\n",
       " ' Handlers-cleaners': 1,\n",
       " ' Prof-specialty': 2,\n",
       " ' Other-service': 3,\n",
       " ' Adm-clerical': 4,\n",
       " ' Sales': 5,\n",
       " ' Craft-repair': 6,\n",
       " ' Transport-moving': 7,\n",
       " ' Farming-fishing': 8,\n",
       " ' Machine-op-inspct': 9,\n",
       " ' Tech-support': 10,\n",
       " ' ?': 11,\n",
       " ' Protective-serv': 12,\n",
       " ' Armed-Forces': 13,\n",
       " ' Priv-house-serv': 14}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Occupation\n",
    "dict_occupation = {}\n",
    "count = 0\n",
    "for i in X_train.occupation.unique():\n",
    "    dict_occupation[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' Husband': 0,\n",
       " ' Not-in-family': 1,\n",
       " ' Wife': 2,\n",
       " ' Own-child': 3,\n",
       " ' Unmarried': 4,\n",
       " ' Other-relative': 5}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "   \n",
    "# function to encode Relationship\n",
    "dict_relationship = {}\n",
    "count = 0\n",
    "for i in X_train.relationship.unique():\n",
    "    dict_relationship[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' White': 0,\n",
       " ' Black': 1,\n",
       " ' Asian-Pac-Islander': 2,\n",
       " ' Amer-Indian-Eskimo': 3,\n",
       " ' Other': 4}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Race\n",
    "dict_race = {}\n",
    "count = 0\n",
    "for i in X_train.race.unique():\n",
    "    dict_race[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' United-States': 0,\n",
       " ' Cuba': 1,\n",
       " ' Jamaica': 2,\n",
       " ' India': 3,\n",
       " ' ?': 4,\n",
       " ' Mexico': 5,\n",
       " ' South': 6,\n",
       " ' Puerto-Rico': 7,\n",
       " ' Honduras': 8,\n",
       " ' England': 9,\n",
       " ' Canada': 10,\n",
       " ' Germany': 11,\n",
       " ' Iran': 12,\n",
       " ' Philippines': 13,\n",
       " ' Italy': 14,\n",
       " ' Poland': 15,\n",
       " ' Columbia': 16,\n",
       " ' Cambodia': 17,\n",
       " ' Thailand': 18,\n",
       " ' Ecuador': 19,\n",
       " ' Laos': 20,\n",
       " ' Taiwan': 21,\n",
       " ' Haiti': 22,\n",
       " ' Portugal': 23,\n",
       " ' Dominican-Republic': 24,\n",
       " ' El-Salvador': 25,\n",
       " ' France': 26,\n",
       " ' Guatemala': 27,\n",
       " ' China': 28,\n",
       " ' Japan': 29,\n",
       " ' Yugoslavia': 30,\n",
       " ' Peru': 31,\n",
       " ' Outlying-US(Guam-USVI-etc)': 32,\n",
       " ' Scotland': 33,\n",
       " ' Trinadad&Tobago': 34,\n",
       " ' Greece': 35,\n",
       " ' Nicaragua': 36,\n",
       " ' Vietnam': 37,\n",
       " ' Hong': 38,\n",
       " ' Ireland': 39,\n",
       " ' Hungary': 40,\n",
       " ' Holand-Netherlands': 41}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Native Country\n",
    "dict_native_country ={}\n",
    "count = 0\n",
    "for i in X_train.native_country.unique():\n",
    "    dict_native_country[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_native_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' <=50K': 0, ' >50K': 1}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to encode Wage Class\n",
    "dict_wage_class = {}\n",
    "count = 0\n",
    "for i in X_train.wage_class.unique():\n",
    "    dict_wage_class[i] = count\n",
    "    count +=1\n",
    "    \n",
    "dict_wage_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>wage_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "1   38            Private  215646     HS-grad              9   \n",
       "2   53            Private  234721        11th              7   \n",
       "3   28            Private  338409   Bachelors             13   \n",
       "4   37            Private  284582     Masters             14   \n",
       "\n",
       "        marital_status          occupation    relationship    race      sex  \\\n",
       "0   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "1             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "2   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "3   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "4   Married-civ-spouse     Exec-managerial            Wife   White   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country wage_class  \n",
       "0             0             0              13   United-States      <=50K  \n",
       "1             0             0              40   United-States      <=50K  \n",
       "2             0             0              40   United-States      <=50K  \n",
       "3             0             0              40            Cuba      <=50K  \n",
       "4             0             0              40   United-States      <=50K  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the data before converting categorical columns to numerical ones\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping the categorical values against the corresponding numeric values for the respective columns for training dataset \n",
    "\n",
    "X_train['sex'] = X_train['sex'].map(dict_sex)\n",
    "X_train['education'] = X_train['education'].map(dict_education)\n",
    "X_train['wage_class'] = X_train['wage_class'].map(dict_wage_class)\n",
    "X_train['native_country'] = X_train['native_country'].map(dict_native_country)\n",
    "X_train['race'] = X_train['race'].map(dict_race)\n",
    "X_train['occupation']=X_train['occupation'].map(dict_occupation)\n",
    "X_train['marital_status'] = X_train['marital_status'].map(dict_marital_status)\n",
    "X_train['workclass'] = X_train['workclass'].map(dict_workclass)\n",
    "X_train['relationship'] = X_train['relationship'].map(dict_relationship)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>wage_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>83311</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>215646</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>234721</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>338409</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>284582</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt  education  education_num  marital_status  \\\n",
       "0   50          0   83311          0             13               0   \n",
       "1   38          1  215646          1              9               1   \n",
       "2   53          1  234721          2              7               0   \n",
       "3   28          1  338409          0             13               0   \n",
       "4   37          1  284582          3             14               0   \n",
       "\n",
       "   occupation  relationship  race  sex  capital_gain  capital_loss  \\\n",
       "0           0             0     0    0             0             0   \n",
       "1           1             1     0    0             0             0   \n",
       "2           1             0     1    0             0             0   \n",
       "3           2             2     1    1             0             0   \n",
       "4           0             2     0    1             0             0   \n",
       "\n",
       "   hours_per_week  native_country  wage_class  \n",
       "0              13               0           0  \n",
       "1              40               0           0  \n",
       "2              40               0           0  \n",
       "3              40               1           0  \n",
       "4              40               0           0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the data after converting categorical columns to numerical ones\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "wage_class        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the training set for null values\n",
    "X_train.isnull().sum()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>wage_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>3.256000e+04</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "      <td>32560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581634</td>\n",
       "      <td>1.614619</td>\n",
       "      <td>1.897818e+05</td>\n",
       "      <td>3.424570</td>\n",
       "      <td>10.080590</td>\n",
       "      <td>1.458784</td>\n",
       "      <td>4.734275</td>\n",
       "      <td>1.392291</td>\n",
       "      <td>0.221714</td>\n",
       "      <td>0.330805</td>\n",
       "      <td>1077.615172</td>\n",
       "      <td>87.306511</td>\n",
       "      <td>40.437469</td>\n",
       "      <td>1.290356</td>\n",
       "      <td>0.240817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640642</td>\n",
       "      <td>1.490557</td>\n",
       "      <td>1.055498e+05</td>\n",
       "      <td>3.453582</td>\n",
       "      <td>2.572709</td>\n",
       "      <td>1.622263</td>\n",
       "      <td>3.329518</td>\n",
       "      <td>1.535159</td>\n",
       "      <td>0.627357</td>\n",
       "      <td>0.470510</td>\n",
       "      <td>7385.402999</td>\n",
       "      <td>402.966116</td>\n",
       "      <td>12.347618</td>\n",
       "      <td>5.045446</td>\n",
       "      <td>0.427586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.178315e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.783630e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.370545e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age     workclass        fnlwgt     education  education_num  \\\n",
       "count  32560.000000  32560.000000  3.256000e+04  32560.000000   32560.000000   \n",
       "mean      38.581634      1.614619  1.897818e+05      3.424570      10.080590   \n",
       "std       13.640642      1.490557  1.055498e+05      3.453582       2.572709   \n",
       "min       17.000000      0.000000  1.228500e+04      0.000000       1.000000   \n",
       "25%       28.000000      1.000000  1.178315e+05      1.000000       9.000000   \n",
       "50%       37.000000      1.000000  1.783630e+05      2.000000      10.000000   \n",
       "75%       48.000000      1.000000  2.370545e+05      5.000000      12.000000   \n",
       "max       90.000000      8.000000  1.484705e+06     15.000000      16.000000   \n",
       "\n",
       "       marital_status    occupation  relationship          race           sex  \\\n",
       "count    32560.000000  32560.000000  32560.000000  32560.000000  32560.000000   \n",
       "mean         1.458784      4.734275      1.392291      0.221714      0.330805   \n",
       "std          1.622263      3.329518      1.535159      0.627357      0.470510   \n",
       "min          0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%          0.000000      2.000000      0.000000      0.000000      0.000000   \n",
       "50%          1.000000      4.000000      1.000000      0.000000      0.000000   \n",
       "75%          3.000000      7.000000      3.000000      0.000000      1.000000   \n",
       "max          6.000000     14.000000      5.000000      4.000000      1.000000   \n",
       "\n",
       "       capital_gain  capital_loss  hours_per_week  native_country  \\\n",
       "count  32560.000000  32560.000000    32560.000000    32560.000000   \n",
       "mean    1077.615172     87.306511       40.437469        1.290356   \n",
       "std     7385.402999    402.966116       12.347618        5.045446   \n",
       "min        0.000000      0.000000        1.000000        0.000000   \n",
       "25%        0.000000      0.000000       40.000000        0.000000   \n",
       "50%        0.000000      0.000000       40.000000        0.000000   \n",
       "75%        0.000000      0.000000       45.000000        0.000000   \n",
       "max    99999.000000   4356.000000       99.000000       41.000000   \n",
       "\n",
       "         wage_class  \n",
       "count  32560.000000  \n",
       "mean       0.240817  \n",
       "std        0.427586  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe() #Generate descriptive statistics that summarize the central tendency,dispersion and shape of a dataset's distribution, excluding ``NaN`` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    24719\n",
      "1     7841\n",
      "Name: wage_class, dtype: int64\n",
      " <=50K.    12434\n",
      " >50K.      3846\n",
      "Name: wage_class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Printing the counts of the individual values in the 'wage_class' column\n",
    "print(X_train.wage_class.value_counts())\n",
    "print(X_test.wage_class.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note\n",
    "Negative class is around 76% of the whole data samples.\n",
    "Positive class is around 24 % of the whole data samples.\n",
    "The dataset seems to be almost  a balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>wage_class</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
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       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt      education  education_num       marital_status  \\\n",
       "0   38     Private   89814        HS-grad              9   Married-civ-spouse   \n",
       "1   28   Local-gov  336951     Assoc-acdm             12   Married-civ-spouse   \n",
       "2   44     Private  160323   Some-college             10   Married-civ-spouse   \n",
       "3   18           ?  103497   Some-college             10        Never-married   \n",
       "4   34     Private  198693           10th              6        Never-married   \n",
       "\n",
       "           occupation    relationship    race      sex  capital_gain  \\\n",
       "0     Farming-fishing         Husband   White     Male             0   \n",
       "1     Protective-serv         Husband   White     Male             0   \n",
       "2   Machine-op-inspct         Husband   Black     Male          7688   \n",
       "3                   ?       Own-child   White   Female             0   \n",
       "4       Other-service   Not-in-family   White     Male             0   \n",
       "\n",
       "   capital_loss  hours_per_week  native_country wage_class  \n",
       "0             0              50   United-States     <=50K.  \n",
       "1             0              40   United-States      >50K.  \n",
       "2             0              40   United-States      >50K.  \n",
       "3             0              30   United-States     <=50K.  \n",
       "4             0              30   United-States     <=50K.  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the test data before converting categorical columns to numerical ones\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping the categorical values against the corresponding numeric values for the respective columns for test dataset\n",
    "X_test['sex'] = X_test['sex'].map(dict_sex)\n",
    "X_test['education'] = X_test['education'].map(dict_education)\n",
    "X_test['wage_class'] = X_test['wage_class'].map(dict_wage_class)\n",
    "X_test['native_country'] = X_test['native_country'].map(dict_native_country)\n",
    "X_test['race'] = X_test['race'].map(dict_race)\n",
    "X_test['occupation']=X_test['occupation'].map(dict_occupation)\n",
    "X_test['marital_status'] = X_test['marital_status'].map(dict_marital_status)\n",
    "X_test['workclass'] = X_test['workclass'].map(dict_workclass)\n",
    "X_test['relationship'] = X_test['relationship'].map(dict_relationship)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>103497</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>198693</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt  education  education_num  marital_status  \\\n",
       "0   38          1   89814          1              9               0   \n",
       "1   28          4  336951          6             12               0   \n",
       "2   44          1  160323          5             10               0   \n",
       "3   18          5  103497          5             10               3   \n",
       "4   34          1  198693         12              6               3   \n",
       "\n",
       "   occupation  relationship  race  sex  capital_gain  capital_loss  \\\n",
       "0           8             0     0    0             0             0   \n",
       "1          12             0     0    0             0             0   \n",
       "2           9             0     1    0          7688             0   \n",
       "3          11             3     0    1             0             0   \n",
       "4           3             1     0    0             0             0   \n",
       "\n",
       "   hours_per_week  native_country  wage_class  \n",
       "0              50               0           0  \n",
       "1              40               0           1  \n",
       "2              40               0           1  \n",
       "3              30               0           0  \n",
       "4              30               0           0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the test data after converting categorical columns to numerical ones\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' <=50K.': 0, ' >50K.': 1}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_wage_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>wage_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.00000</td>\n",
       "      <td>1.628000e+04</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.00000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.00000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "      <td>16280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.768305</td>\n",
       "      <td>1.62930</td>\n",
       "      <td>1.894334e+05</td>\n",
       "      <td>3.387039</td>\n",
       "      <td>10.073096</td>\n",
       "      <td>1.483354</td>\n",
       "      <td>4.791216</td>\n",
       "      <td>1.393182</td>\n",
       "      <td>0.21726</td>\n",
       "      <td>0.332985</td>\n",
       "      <td>1081.97156</td>\n",
       "      <td>87.904668</td>\n",
       "      <td>40.392260</td>\n",
       "      <td>1.219226</td>\n",
       "      <td>0.236241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.849192</td>\n",
       "      <td>1.50992</td>\n",
       "      <td>1.057177e+05</td>\n",
       "      <td>3.440813</td>\n",
       "      <td>2.567511</td>\n",
       "      <td>1.633460</td>\n",
       "      <td>3.375522</td>\n",
       "      <td>1.534425</td>\n",
       "      <td>0.62222</td>\n",
       "      <td>0.471296</td>\n",
       "      <td>7584.16416</td>\n",
       "      <td>403.117078</td>\n",
       "      <td>12.479715</td>\n",
       "      <td>4.963845</td>\n",
       "      <td>0.424785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.349200e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.167162e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.778295e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.383840e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99999.00000</td>\n",
       "      <td>3770.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age    workclass        fnlwgt     education  education_num  \\\n",
       "count  16280.000000  16280.00000  1.628000e+04  16280.000000   16280.000000   \n",
       "mean      38.768305      1.62930  1.894334e+05      3.387039      10.073096   \n",
       "std       13.849192      1.50992  1.057177e+05      3.440813       2.567511   \n",
       "min       17.000000      0.00000  1.349200e+04      0.000000       1.000000   \n",
       "25%       28.000000      1.00000  1.167162e+05      1.000000       9.000000   \n",
       "50%       37.000000      1.00000  1.778295e+05      2.000000      10.000000   \n",
       "75%       48.000000      1.00000  2.383840e+05      5.000000      12.000000   \n",
       "max       90.000000      8.00000  1.490400e+06     15.000000      16.000000   \n",
       "\n",
       "       marital_status    occupation  relationship         race           sex  \\\n",
       "count    16280.000000  16280.000000  16280.000000  16280.00000  16280.000000   \n",
       "mean         1.483354      4.791216      1.393182      0.21726      0.332985   \n",
       "std          1.633460      3.375522      1.534425      0.62222      0.471296   \n",
       "min          0.000000      0.000000      0.000000      0.00000      0.000000   \n",
       "25%          0.000000      2.000000      0.000000      0.00000      0.000000   \n",
       "50%          1.000000      4.000000      1.000000      0.00000      0.000000   \n",
       "75%          3.000000      7.000000      3.000000      0.00000      1.000000   \n",
       "max          6.000000     14.000000      5.000000      4.00000      1.000000   \n",
       "\n",
       "       capital_gain  capital_loss  hours_per_week  native_country  \\\n",
       "count   16280.00000  16280.000000    16280.000000    16280.000000   \n",
       "mean     1081.97156     87.904668       40.392260        1.219226   \n",
       "std      7584.16416    403.117078       12.479715        4.963845   \n",
       "min         0.00000      0.000000        1.000000        0.000000   \n",
       "25%         0.00000      0.000000       40.000000        0.000000   \n",
       "50%         0.00000      0.000000       40.000000        0.000000   \n",
       "75%         0.00000      0.000000       45.000000        0.000000   \n",
       "max     99999.00000   3770.000000       99.000000       40.000000   \n",
       "\n",
       "         wage_class  \n",
       "count  16280.000000  \n",
       "mean       0.236241  \n",
       "std        0.424785  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.describe()  #Generate descriptive statistics that summarize the central tendency,dispersion and shape of a dataset's distribution, excluding ``NaN`` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae5e643da0>"
      ]
     },
     "execution_count": 55,
     "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": [
    "# Annual Income Data Analysis using Visualization\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(40,20)) #Create a new figure\n",
    "sns.set_style('whitegrid') #Use matplotlib style settings from a style specification.\n",
    "%matplotlib inline\n",
    "sns.countplot('sex',data=train_set,hue='wage_class') # a countplot based on the Sex column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot('workclass',data=train_set,hue='wage_class') # a countplot based on the workclass column\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation = 90, fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot('education',data=train_set,hue='wage_class') # a countplot based on the Education column\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation = 90, fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot('wage_class',data=train_set) # a countplot based on the wage class column\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation = 45, fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame.hist(train_set,figsize = [15,15])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have our train and test data ready to be used in training a machine learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}