Naive Bayes Project 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age     Fare\n",
       "0           0       3    male  22.0   7.2500\n",
       "1           1       1  female  38.0  71.2833\n",
       "2           1       3  female  26.0   7.9250\n",
       "3           1       1  female  35.0  53.1000\n",
       "4           0       3    male  35.0   8.0500\n",
       "..        ...     ...     ...   ...      ...\n",
       "886         0       2    male  27.0  13.0000\n",
       "887         1       1  female  19.0  30.0000\n",
       "888         0       3  female   NaN  23.4500\n",
       "889         1       1    male  26.0  30.0000\n",
       "890         0       3    male  32.0   7.7500\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies=pd.get_dummies(df.Sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "886       0     1\n",
       "887       1     0\n",
       "888       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Sex'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Age=df.Age.fillna(df.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age     Fare  female  male\n",
       "0         0       3  22.0   7.2500       0     1\n",
       "1         1       1  38.0  71.2833       1     0\n",
       "2         1       3  26.0   7.9250       1     0\n",
       "3         1       1  35.0  53.1000       1     0\n",
       "4         0       3  35.0   8.0500       0     1"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['Survived'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "712"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "709    1\n",
       "439    0\n",
       "840    0\n",
       "720    1\n",
       "39     1\n",
       "      ..\n",
       "433    0\n",
       "773    0\n",
       "25     1\n",
       "84     1\n",
       "10     1\n",
       "Name: Survived, Length: 179, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>3</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>3</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>3</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7.1250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>3</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass        Age     Fare  female  male\n",
       "709       3  29.699118  15.2458       0     1\n",
       "439       2  31.000000  10.5000       0     1\n",
       "840       3  20.000000   7.9250       0     1\n",
       "720       2   6.000000  33.0000       1     0\n",
       "39        3  14.000000  11.2417       1     0\n",
       "..      ...        ...      ...     ...   ...\n",
       "433       3  17.000000   7.1250       0     1\n",
       "773       3  29.699118   7.2250       0     1\n",
       "25        3  38.000000  31.3875       1     0\n",
       "84        2  17.000000  10.5000       1     0\n",
       "10        3   4.000000  16.7000       1     0\n",
       "\n",
       "[179 rows x 5 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.6536312849162"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)*100"
   ]
  },
  {
   "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
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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