Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libabry\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv(\"~/Downloads/Data Science/data set/SalaryData_Train.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/Data Science/data set/SalaryData_Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>native</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</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>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</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>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>30156</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30157</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30158</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30159</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30160</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30161 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass    education  educationno        maritalstatus  \\\n",
       "0       39          State-gov    Bachelors           13        Never-married   \n",
       "1       50   Self-emp-not-inc    Bachelors           13   Married-civ-spouse   \n",
       "2       38            Private      HS-grad            9             Divorced   \n",
       "3       53            Private         11th            7   Married-civ-spouse   \n",
       "4       28            Private    Bachelors           13   Married-civ-spouse   \n",
       "...    ...                ...          ...          ...                  ...   \n",
       "30156   27            Private   Assoc-acdm           12   Married-civ-spouse   \n",
       "30157   40            Private      HS-grad            9   Married-civ-spouse   \n",
       "30158   58            Private      HS-grad            9              Widowed   \n",
       "30159   22            Private      HS-grad            9        Never-married   \n",
       "30160   52       Self-emp-inc      HS-grad            9   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship    race      sex  capitalgain  \\\n",
       "0            Adm-clerical   Not-in-family   White     Male         2174   \n",
       "1         Exec-managerial         Husband   White     Male            0   \n",
       "2       Handlers-cleaners   Not-in-family   White     Male            0   \n",
       "3       Handlers-cleaners         Husband   Black     Male            0   \n",
       "4          Prof-specialty            Wife   Black   Female            0   \n",
       "...                   ...             ...     ...      ...          ...   \n",
       "30156        Tech-support            Wife   White   Female            0   \n",
       "30157   Machine-op-inspct         Husband   White     Male            0   \n",
       "30158        Adm-clerical       Unmarried   White   Female            0   \n",
       "30159        Adm-clerical       Own-child   White     Male            0   \n",
       "30160     Exec-managerial            Wife   White   Female        15024   \n",
       "\n",
       "       capitalloss  hoursperweek          native  Salary  \n",
       "0                0            40   United-States   <=50K  \n",
       "1                0            13   United-States   <=50K  \n",
       "2                0            40   United-States   <=50K  \n",
       "3                0            40   United-States   <=50K  \n",
       "4                0            40            Cuba   <=50K  \n",
       "...            ...           ...             ...     ...  \n",
       "30156            0            38   United-States   <=50K  \n",
       "30157            0            40   United-States    >50K  \n",
       "30158            0            40   United-States   <=50K  \n",
       "30159            0            20   United-States   <=50K  \n",
       "30160            0            40   United-States    >50K  \n",
       "\n",
       "[30161 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [col for col in X.columns if X[col].dtype==object]\n",
    "categorical1 = [col for col in test.columns if test[col].dtype==object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       workclass  education  maritalstatus  occupation  relationship  race  \\\n",
      "0              0          0              0           0             0     0   \n",
      "1              1          0              1           1             1     0   \n",
      "2              2          1              2           2             0     0   \n",
      "3              2          2              1           2             1     1   \n",
      "4              2          0              1           3             2     1   \n",
      "...          ...        ...            ...         ...           ...   ...   \n",
      "30156          2          6              1           9             2     0   \n",
      "30157          2          1              1           8             1     0   \n",
      "30158          2          1              6           0             4     0   \n",
      "30159          2          1              0           0             3     0   \n",
      "30160          5          1              1           1             2     0   \n",
      "\n",
      "       sex  native  Salary  \n",
      "0        0       0       0  \n",
      "1        0       0       0  \n",
      "2        0       0       0  \n",
      "3        0       0       0  \n",
      "4        1       1       0  \n",
      "...    ...     ...     ...  \n",
      "30156    1       0       0  \n",
      "30157    0       0       1  \n",
      "30158    1       0       0  \n",
      "30159    0       0       0  \n",
      "30160    1       0       1  \n",
      "\n",
      "[30161 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "for col in categorical:\n",
    "    temp = {}\n",
    "    count = 0\n",
    "    for val in X[col].values:\n",
    "        try:\n",
    "            temp[val]\n",
    "        except:\n",
    "            temp[val] = count\n",
    "            count += 1\n",
    "    for val in test[col].values:\n",
    "        try:\n",
    "            temp[val]\n",
    "        except:\n",
    "            temp[val] = count\n",
    "            count += 1\n",
    "    X[col] = [temp[x] for x in X[col].values]\n",
    "    test[col] = [temp[x] for x in test[col].values]\n",
    "print(X[categorical])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spilit data\n",
    "\n",
    "ytrain = X.Salary\n",
    "X.drop(['Salary'], axis=1, inplace=True)\n",
    "Xtrain = X\n",
    "del X\n",
    "\n",
    "ytest = test.Salary\n",
    "test.drop(['Salary'], axis=1, inplace=True)\n",
    "Xtest = test\n",
    "del test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implement Model\n",
    "ignb = GaussianNB()\n",
    "pred_gnb = ignb.fit(Xtrain,ytrain).predict(Xtest)\n",
    "\n",
    "#multinomial naive bayes\n",
    "\n",
    "imnb = MultinomialNB()\n",
    "pred_mnb = imnb.fit(Xtrain,ytrain).predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.800265604249668"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest,pred_gnb) # GaussianNB model\n",
    "pd.crosstab(ytest.values.flatten(),pred_gnb) # confusion matrix using \n",
    "np.mean(pred_gnb==ytest.values.flatten()) # 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7749667994687915"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix multinomial naive bayes model\n",
    "confusion_matrix(ytest,pred_mnb) # multinomial naive bayes model\n",
    "pd.crosstab(ytest.values.flatten(),pred_mnb) # confusion matrix using \n",
    "np.mean(pred_mnb==ytest.values.flatten()) # 60%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10891,   469],\n",
       "       [ 2920,   780]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest,pred_mnb) # Multinomal model"
   ]
  }
 ],
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