naive-bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Title</th>\n",
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       "      <td>1</td>\n",
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       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "  PassengerId  Survived Pclass  Sex  Age  SibSp  Parch      Fare  Embarked_Q  \\\n",
       "0           1     False      3    0    1      1      0  0.014151           0   \n",
       "1           2      True      1    1    4      1      0  0.139136           0   \n",
       "2           3      True      3    1    2      0      0  0.015469           0   \n",
       "3           4      True      1    1    3      1      0  0.103644           0   \n",
       "4           5     False      3    0    3      0      0  0.015713           0   \n",
       "\n",
       "   Embarked_S  Title  \n",
       "0           1      6  \n",
       "1           0      1  \n",
       "2           1      2  \n",
       "3           1      1  \n",
       "4           1      6  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "# Environment settings\n",
    "data_path = 'Data/'\n",
    "\n",
    "# Deserialize previously saved data from \"preprocessing\"\n",
    "with open(data_path+'train_pp.obj', 'rb') as train_pp, \\\n",
    "open(data_path+'test_pp.obj', 'rb') as test_pp:\n",
    "    df_train = pickle.load(train_pp)\n",
    "    df_test = pickle.load(test_pp)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = ['PassengerId','Survived','Parch', 'Embarked_Q', 'Embarked_S']\n",
    "train_feature = df_train.drop(feature, axis=1)\n",
    "train_target = df_train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare taining set\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_feature ,train_target, random_state=1, test_size=0.20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "y_pred= gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#accuracy\n",
    "accuracy_score (y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with cross-validation (split size = 10): 0.8 (+/- 0.05)\n"
     ]
    }
   ],
   "source": [
    "# Cross-validation score\n",
    "cv_iterations = 10\n",
    "cv_score = cross_val_score(gnb, train_feature, train_target, cv=cv_iterations)\n",
    "print('Accuracy with cross-validation (split size = {}): {} (+/- {})'\n",
    "      .format(cv_iterations, round(cv_score.mean(),2), round(cv_score.std() * 2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction on test set\n",
    "dv_test_X = df_test.drop(['PassengerId','Parch', 'Embarked_Q', 'Embarked_S'], axis=1).values\n",
    "\n",
    "test_prediction_results = pd.DataFrame(\n",
    "    data={'PassengerId': df_test['PassengerId'].values,\n",
    "          'Survived': gnb.predict(dv_test_X).astype(int)})\n",
    "\n",
    "# Write results to a csv file\n",
    "test_prediction_results.to_csv(data_path+'outputs/naive-bayes.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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