Titanic - Logistic Regression by Radha Jayaraman.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('train.csv')\n",
    "test_data=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRAINING SET \n",
    ">In training set we have missing values in Age,Cabin,Embarked with Age having the maximum number of missing values out of all. We will replace missing values in Age by median age grouped by PClass instead of median of the whole dataset.A reason for finding median grouped by PClass is that it is most related to Age which can be seen from the heat map below.And as age is right skewed, it will be better to replace with median than mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1891b8812c8>"
      ]
     },
     "execution_count": 4,
     "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(train_data.corr(),annot=True)\n",
    "#Age is correlated to PCLASS the most with corr value of -0.37."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age\n",
       "0       1  37.0\n",
       "1       2  29.0\n",
       "2       3  24.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=train_data.groupby('Pclass',as_index=False)['Age'].median()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Replacing missing vales in training set\n",
    "#AGE\n",
    "for i,row in train_data.iterrows():\n",
    "    if(np.isnan(train_data['Age'][i])==True):\n",
    "        if(train_data['Pclass'][i]==1):\n",
    "            train_data['Age'][i]=38.0\n",
    "        elif(train_data['Pclass'][i]==2):\n",
    "            train_data['Age'][i]=30.0\n",
    "        else:\n",
    "            train_data['Age'][i]=25.0\n",
    "#CABIN (It has maximum number of missing values and hence it will NOT be worth a feature to work on. Still we will replace\n",
    "#the missing values with its mode)\n",
    "train_data['Cabin']=train_data['Cabin'].fillna(train_data['Cabin'].mode()[0])\n",
    "#EMBARKED\n",
    "#For this we have two missing values each having equivalent Fare of $80 and same PClass,same ticketnumber this\n",
    "#means that they had to board from the same station.And on google search, I got to know that Amelie Icard boarded from SouthHampton.\n",
    "#This means that both of them boarded from SouthHampton.\n",
    "train_data['Embarked']=train_data['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        891 non-null    object \n",
      " 11  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()\n",
    "#no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TEST SET\n",
    ">In test set we have missing values in Age,Cabin,Fare with Age having the maximum number of missing values out of all. We will do the similar process in test data also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age\n",
       "0       1  42.0\n",
       "1       2  26.5\n",
       "2       3  24.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=test_data.groupby('Pclass',as_index=False)['Age'].median()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Replacing Missing Values in Test Set\n",
    "#AGE\n",
    "for i,row in test_data.iterrows():\n",
    "    if(np.isnan(test_data['Age'][i])==True):\n",
    "        if(test_data['Pclass'][i]==1):\n",
    "            test_data['Age'][i]=41.0\n",
    "        elif(test_data['Pclass'][i]==2):\n",
    "            test_data['Age'][i]=28.0\n",
    "        else:\n",
    "            test_data['Age'][i]=24.0\n",
    "#CABIN\n",
    "test_data['Cabin']=test_data['Cabin'].fillna(test_data['Cabin'].mode()[0])\n",
    "#FARE\n",
    "#We can see that Fare is most correlated to Pclass and hence it should be computed by median of fare grouped by pclass.\n",
    "#As the person belonged to class 3, we will take median of class 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Fare\n",
       "0       1  60.0000\n",
       "1       2  15.7500\n",
       "2       3   7.8958"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=test_data.groupby('Pclass',as_index=False)['Fare'].median()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['Fare']=test_data['Fare'].fillna(7.89)\n",
    "test_data.isna().sum()\n",
    "#NO MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1891d993908>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=train_data,x='Age',y='Survived',hue='Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Age doesnt seem to vary much with Survival and I think it should NOT be taken as a feature to build our model upon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PREPARING DATA FOR TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Sex</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</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>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex  Parch Embarked\n",
       "0         0       3    male      0        S\n",
       "1         1       1  female      0        C\n",
       "2         1       3  female      0        S\n",
       "3         1       1  female      0        S\n",
       "4         0       3    male      0        S"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata=train_data.drop(['Name','Ticket','Cabin','PassengerId','Fare','SibSp','Age'],axis=1)\n",
    "testdata=test_data.drop(['Name','Ticket','Cabin','PassengerId','Fare','SibSp','Age'],axis=1)\n",
    "traindata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****Getting dummies for our categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=traindata.iloc[:,1:]\n",
    "y_train=traindata.iloc[:,0]\n",
    "X_train=pd.get_dummies(X_train,['Sex','Embarked'])\n",
    "X_test=testdata.iloc[:,:]\n",
    "X_test=pd.get_dummies(X_test,['Sex','Embarked'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier=LogisticRegression(random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict=classifier.predict(X_train)\n",
    "y_test=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for training set is:  0.7856341189674523\n",
      "AUC SCORE for training set:  0.7725769341386253\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,classification_report\n",
    "print('Accuracy for training set is: ',accuracy_score(y_train,y_train_predict))\n",
    "print('AUC SCORE for training set: ',roc_auc_score(y_train,y_train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1891ddf3108>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD5CAYAAAAZf+9zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAaN0lEQVR4nO3de3hU1bnH8e9rVECrhAAiJFhoRRGOFRUxalEL9hHwEmy9gFatjQ1VUKnWa0+1qBxrvdZaqSiKohU5XgpF0FKQo1YFERHkdkhRJIFyUQkqCGTynj9mwxlgMjOBSXZm8/v0WU/2rL32Wmv62NfVd6+9x9wdERFpeHuFPQERkT2VArCISEgUgEVEQqIALCISEgVgEZGQKACLiIRk7/oeYMvapdrnJjtp1q5n2FOQRqh6c6Xtbh91iTn7tPpO2vHMLA+YBVS6+5lmNho4BagKmvzU3eeYmQF/APoBG4L62an6rvcALCKS464BFgIHJtRd7+4v7NCuL9ApKMcDI4K/tVIKQkSipSaWeUnDzIqAM4DHMxi5BHja494F8s2sbaoLFIBFJFpi1RkXMyszs1kJpWyH3h4EbgBqdqgfbmZzzewBM2sS1BUCyxPaVAR1tVIAFpFIca+pQ/GR7t49oYzc2o+ZnQmsdvf3dxjiZqAzcBxQANy49ZJk00k1V+WARSRaanZcrO6yk4Czzawf0BQ40MyecfefBOc3mdmTwK+CzxVA+4Tri4AVqQbQClhEosVrMi+punG/2d2L3L0DMACY5u4/2ZrXDXY99Ac+Ci6ZAFxiccVAlbuvTDWGVsAiEi0Z3FzbTc+aWWviKYc5wC+C+knEt6CVE9+Gdlm6jhSARSRa0qxsd6lL9+nA9OC4Vy1tHBhcl34VgEUkUjxWHfYUMqYALCLRkr2bcPVOAVhEoqUeUhD1RQFYRKKl/m/CZY0CsIhEi1bAIiIh0U04EZGQ6CaciEg43JUDFhEJh3LAIiIhUQpCRCQkWgGLiIQktiXsGWRMAVhEokUpCBGRkCgFISISEq2ARURCogAsIhIO1004EZGQKAcsIhISpSBEREKiFbCISEhyaAW8V9gTEBHJKq/JvGTAzPLM7AMzmxh87mhmM8xsiZk9b2b7BvVNgs/lwfkO6fpWABaRaKmuzrxk5hpgYcLnu4EH3L0T8AVQGtSXAl+4+6HAA0G7lBSARSRasrgCNrMi4Azg8eCzAb2AF4ImTwH9g+OS4DPB+d5B+1opAItItNTUZFzMrMzMZiWUsh16exC4AdgarVsC69x96/K5AigMjguB5QDB+aqgfa10E05EoqUOuyDcfSQwMtk5MzsTWO3u75vZqVurk3WTwbmkFIBFJFqytwviJOBsM+sHNAUOJL4izjezvYNVbhGwImhfAbQHKsxsb6A58HmqAZSCEJFoyVIO2N1vdvcid+8ADACmuftFwOvAuUGzS4HxwfGE4DPB+WnurhWwiOxBMt/dsKtuBMaa2Z3AB8CooH4UMMbMyomvfAek60gBWESiJfWicxe79OnA9OB4KdAjSZtvgPPq0q8CsIhESw49CacALCLRogAsIhISvYxHRCQksVjYM8iYArCIRItSECIiIVEAFhEJiXLAIiLh8Jrs7wOuLwrAIhItSkGIiIREuyBEREKiFbCISEhyKADrdZRpxGIxzv3pYK68/rak51+d+gZnX1RGyUWDuOG3aX8CKq2q9V9y+TW30O+CUi6/5haq1n8JwMTXpnHOJVdwziVXcNGga1m0ZOlujyUN76ohpcz5YCofzpnG1Vddvt25a385iOrNlbRs2SKk2UWEe+YlZArAaTzz3+P5TodDkp5btrySx8c8z5gR9zH+2Ue58ZpBGfc7c/Zcfn3nfTvVPz5mHMXduzHp+VEUd+/GqGfGAVDY7mBGP/x7Xn56BL/46UCG/f6hXftCEpquXQ+ntPRCTjjxDI459oec0e80Dj20IwBFRe04rffJLFtWEfIsI6AOP0kUtrQB2Mw6m9mNZvaQmf0hOD6iISYXtn+vXsMbb8/kx2ednvT8CxNeZcCPzqL5gQcA0LJF/rZzTzz7AheUXs05l1zBw4+PyXjM1998h5K+pwFQ0vc0pr3xDgBHH9ll2zjf69qZVavX7tJ3kvB07tyJGTNms3HjN8RiMd548136l/QB4L57f8tNtwwnzfu7JRM1nnkJWcoAbGY3AmOJ/9bRTOC94Pg5M7up/qcXrrv/8CjXXlmKWfL/mpYtr2TZ8kp+8ovruPDnQ3nr3VkA/HPG+3xaUcnYx//Ai6P/xILF5cyaMy+jMT/7Yh2tWxUA0LpVAZ+vq9qpzUsTX+P7xd138VtJWObPX0TPnsUUFLSgWbOm9O3Ti6Kidpx55g+prFzJ3LkLwp5iNMRimZeQpbsJVwp0dfctiZVmdj8wH/hdsouCXxYtA3jkvju5/JKBWZhqw5r+zxkUtMina+dOzJw9N2mb6liMZRWVPPnw3axavZZLr/wVL4/5M2+/N5u3Z87m3J8OAWDDxo0sW76C7t2OZODPh7J58xY2bNxI1fov+fGlgwG49sqfcdLxx6ad18z3P+SliX9nzIh7s/dlpUEsWlTOPff8iVcnP8fXX33Nh3MXEKuOcctNV9On34VhTy8yvBGkFjKVLgDXAO2AZTvUt+X/f6Z5J4m/NLpl7dLw1/m74IO5C5j+1ru8+c57bNq8ha+/3sCNw37P3bfdsK1Nm9atOKprZ/bZe2+K2h1Mh0OKWFZRCQ6XX3wB5/fvt1O/zz32IBDPAY+fNIXh/3nddudbtshnzdrPad2qgDVrP6cgv/m2c4vLP+bW3z3In++7g/zmB9bTN5f69OTosTw5eiwAd95xE6tWrWHgwHOYPWsKAEVFbXlvxmuccNIZrFq1Jsyp5q5GkFrIVLoc8FBgqplNNrORQXkVmApcU//TC88vr7iMqX99hr+/+BT3DLuJHscetV3wBeh98gnMnP0hAF+sq+KT5ZW0b9eWE3scw8uv/J0NGzYCsGrNWj77Yl1G4576/WLGT/4HAOMn/4Mf9DwBgJX/Xs3QW+7grluvp8MhRdn6mtLAWrduCUD79u3o378vY555gXZFR3HoYcUcelgxFRUrOe740xV8d0eWfpSzIaRcAbv7q2Z2GPHfPyoknv+tAN5z9/ATKCF4+LGn6dr5MH7Qs5iTjj+Wt2fO5uyLysjbK4/rBpeS3/xATjr+WJYuW85Fg64FYL9mTbnr1uu3u0lXm8svPp/rfvNfvDTxNdq2ac39d/4agBFP/oWq9V9y571/AiAvL49xT2gnRK757+cfo6BlC7Zsqebqq3/NuiQ5ftlNObQCtvq+65qrKQipX83a9Qx7CtIIVW+utN3t4+tbB2Qcc/a/fexuj7c7tA9YRKIlSykIM2tqZjPN7EMzm29mw4L60Wb2sZnNCUq3oN6C7brlZjbXzI5JN1U9iiwi0ZK9FMQmoJe7f2Vm+wBvmdnk4Nz17v7CDu37Ap2CcjwwIvhbKwVgEYmUbG1D83h+9qvg4z5BSRXdS4Cng+veNbN8M2vr7itru0ApCBGJliw+CWdmeWY2B1gNTHH3GcGp4UGa4QEzaxLUFQLLEy6vCOpqpQAsItFShwBsZmVmNiuhlCV25e4xd+8GFAE9zOw/gJuBzsBxQAFwY9A82Q29lFFeKQgRiZY6PGKc+NBYmnbrzGw60Mfdtz6GusnMngR+FXyuANonXFYErEjVr1bAIhIpXuMZl1TMrLWZ5QfHzYDTgEVm1jaoM6A/8FFwyQTgkmA3RDFQlSr/C1oBi0jUZG8XRFvgKTPLI75YHefuE81smpm1Jp5ymAP8Img/CegHlAMbgMvSDaAALCLRkr1dEHOBo5PU96qlvQOD6zKGArCIREsOPYqsACwi0aIALCISDo+F/5azTCkAi0i0aAUsIhKOdNvLGhMFYBGJFgVgEZGQ5E4KWAFYRKLFq3MnAisAi0i05E78VQAWkWjRTTgRkbBoBSwiEg6tgEVEwqIVsIhIOLw67BlkTgFYRCIlza/NNyoKwCISLQrAIiLh0ApYRCQkCsAiIiHxWLJfh2+cFIBFJFK0AhYRCYnXaAUsIhKKXFoB7xX2BEREssndMi6pmFlTM5tpZh+a2XwzGxbUdzSzGWa2xMyeN7N9g/omwefy4HyHdHNVABaRSPGazEsam4Be7n4U0A3oY2bFwN3AA+7eCfgCKA3alwJfuPuhwANBu5QUgEUkUmpilnFJxeO+Cj7uExQHegEvBPVPAf2D45LgM8H53maWchAFYBGJFK+xjIuZlZnZrIRSltiXmeWZ2RxgNTAF+Bewzn3bGycqgMLguBBYDhCcrwJappqrbsKJSKTUZReEu48ERqY4HwO6mVk+8DJwRLJmwd9kA6d8N6ZWwCISKe6Zl8z79HXAdKAYyDezrYvXImBFcFwBtAcIzjcHPk/VrwKwiERKXVIQqZhZ62Dli5k1A04DFgKvA+cGzS4FxgfHE4LPBOenuacO80pBiEikpNteVgdtgafMLI/4YnWcu080swXAWDO7E/gAGBW0HwWMMbNy4ivfAekGUAAWkUiJZeldEO4+Fzg6Sf1SoEeS+m+A8+oyhgKwiERKFlfA9U4BWEQiRe+CEBEJSV12N4RNAVhEIkUrYBGRkMRqcmd3rQKwiESKUhAiIiGp0S4IEZFwaBuaiEhIlIJIsH/hyfU9hOSg51qeGvYUJKKUghARCYl2QYiIhCSHMhAKwCISLUpBiIiERLsgRERCkv7HjhsPBWARiRRP+tNsjZMCsIhESrVSECIi4dAKWEQkJMoBi4iERCtgEZGQ5NIKOHee2RMRyUAMy7ikYmbtzex1M1toZvPN7Jqg/rdmVmlmc4LSL+Gam82s3MwWm9np6eaqFbCIREoWf5GoGrjO3Web2QHA+2Y2JTj3gLvfm9jYzLoAA4CuQDvgH2Z2mLvHahtAK2ARiZQaLOOSiruvdPfZwfGXwEKgMMUlJcBYd9/k7h8D5UCPVGMoAItIpHgdipmVmdmshFKWrE8z6wAcDcwIqoaY2Vwze8LMWgR1hcDyhMsqSB2wFYBFJFpq6lDcfaS7d08oI3fsz8y+BbwIDHX39cAI4LtAN2AlcN/Wpkmmk/LlbMoBi0ik1Fj2ksBmtg/x4Pusu78E4O6rEs4/BkwMPlYA7RMuLwJWpOpfK2ARiZRYHUoqZmbAKGChu9+fUN82odk5wEfB8QRggJk1MbOOQCdgZqoxtAIWkUjJ4i6Ik4CLgXlmNieouwUYaGbdiKcXPgEGAbj7fDMbBywgvoNicKodEKAALCIRk253Q6bc/S2S53UnpbhmODA80zEUgEUkUvSTRCIiIcliCqLeKQCLSKTk0rsgFIBFJFJiWgGLiIRDK2ARkZAoAIuIhCSHfhJOAVhEokUrYBGRkKR7xLgxUQAWkUjRPmARkZAoBSEiEhIFYBGRkOhdECIiIVEOWEQkJNoFISISkpocSkIoAItIpOgmnIhISHJn/asALCIRoxWwiEhIqi131sAKwCISKbkTfmGvsCcgIpJNNXUoqZhZezN73cwWmtl8M7smqC8wsylmtiT42yKoNzN7yMzKzWyumR2Tbq4KwCISKTV4xiWNauA6dz8CKAYGm1kX4CZgqrt3AqYGnwH6Ap2CUgaMSDeAArCIRIrXoaTsx32lu88Ojr8EFgKFQAnwVNDsKaB/cFwCPO1x7wL5ZtY21RgKwCISKXVJQZhZmZnNSihlyfo0sw7A0cAMoI27r4R4kAYOCpoVAssTLqsI6mqlm3AiEimxOtyGc/eRwMhUbczsW8CLwFB3X29W68smkp1IORmtgEUkUrJ1Ew7AzPYhHnyfdfeXgupVW1MLwd/VQX0F0D7h8iJgRar+FYBFJFK8Dv9JxeJL3VHAQne/P+HUBODS4PhSYHxC/SXBbohioGprqqI2SkGISKRk8Um4k4CLgXlmNieouwX4HTDOzEqBT4HzgnOTgH5AObABuCzdAArA9WTIkFJKfzYQM2PUE3/hj38cxbPPPMJhh30XgObND6Sqaj3H9Tg95JlKXTRrV0CPh66g6UHN8Rpn6TPTKH/8taRtWxz1HXq/Mox3Bv2Ryldm7ta4++Tvzwl/vor92rdmw/I1vDPoIbZUbeCQH53I4YPPAqD662+YfdOTVC34dLfGynXZehuau79F8rwuQO8k7R0YXJcxFIDrQdcuh1P6s4GceNKZbN68hYkTn2Hy5Glc9JMrt7W5++7fsL7qyxBnKbvCq2v4cNizrJv3CXvv35TTXruTVW98xJf/W7l9w72M7/3nAP49fW6d+m99whF0uOBk3hv66Hb1nYeczaq35rP44b9x+JCz6DzkbOYNH8vXn65h+o/uYEvVBg7udRTH3lPKtDNu292vmdP0JNwernPnQ5kx4wM2bvyGWCzGm2+8S0lJn+3anPvjs3h+3PhaepDG6pvV61g37xMgvuJcv2QFzQ5usVO7TqWnU/HKe2xau367+sOuOIPek2/nh1PvosuvfpzxuIWnH8OycW8CsGzcmxT2ORaAz2YtYUvVhvjx+0vYr23BrnytSKnGMy5hUwCuB/MXLKZnz+MpKMinWbOm9OnTi6KidtvOf//7x7N69RrKyz8OcZayu/YrakWLI7/N57P/tV1904NbUNi3O/96+h/b1bc55UgO+M7BTO17K1NOu4UW3+tIq+LOGY3VpHVzvlm9Doj/S6BJq+Y7tek48FRWTvtwF79NdGTrJlxD2OUUhJld5u5P1nKujPijeOTl5bNX3v67OkxOWrSonHvufYTJk57jq6++Zu68BVRXV287f8EFJVr95ri8/Zpw4qihzLl1DNVfbdzuXLfbL2bunWOhZvv/gbc55UjanHIkP5zyXwDsvX8TvtWxDWvfXUSvV4aRt+8+7L1/E/bN/9a2NnOHP8eq6fPSzqf1iV3oeOGpvF5ye5a+Ye7aU15HOQxIGoATNzfv26Qo/H/NhGD06LGMHj0WgDtuv5GKyvhulLy8PPqX9KX4hH5hTk92g+2dx4mjhrLspX9SOWnWTucLjupI8Z+HANCk4AAO7n0UHouBGYv+OIGlY6btdM3WvG1tOeBNa6poelA+36xeR9OD8tm0tmrbueZHtKf7fZfz5kW/Z/MXX2Xzq+akxrCyzVTKAGxmtd1BMKBN9qcTHa1bt2TNms9o374d/fv3pefJJQD07t2TxYv/RWVlyu2B0oh1v//nrF9SyZJHJyc9P+n4X247Pu7BQayY8gErXn2f2MbNdL3hXJa9+E9iGzbR9OAW+JYYmz5bn7SfRCv+Pptvn9+TxQ//jW+f35PK12YD0KywJSeOGsrMq0bw1dJ/Z+cL5rgorYDbAKcDX+xQb8Db9TKjiHh+7EhatmzBli3VXH3Nr1m3Lr5iOf+8s3l+3F9Dnp3sqpY9DqPDeT1Zt+DTbWmCeXc9z35FrQBY+vTUWq9d9T/zOKBTO3pPHAbEb+LNGPJIRgF40cN/o/jRq+g48FQ2VK7lnbKHAOjyy3PYt8UBHHNXfMtpTSzG1D6/2a3vmOtinjsrYPMUkzWzUcCTwX64Hc/9xd0vTDfAnpqCkNSeLTgl7ClII3TeymdrfdFCpi789jkZx5y/LHt5t8fbHSlXwO5emuJc2uArItLQIpMDFhHJNVHKAYuI5JRsPYrcEBSARSRSlIIQEQlJLu2CUAAWkUhRCkJEJCS6CSciEhLlgEVEQqIUhIhISFI93dvYKACLSKTU5Wfpw6YALCKRohSEiEhIcikFoZ8kEpFIqcEzLumY2RNmttrMPkqo+62ZVZrZnKD0Szh3s5mVm9liM0v7k+cKwCISKVn+TbjRQJ8k9Q+4e7egTAIwsy7AAKBrcM0jZpaXqnMFYBGJlJh7xiUdd38D+DzDoUuAse6+yd0/BsqBHqkuUAAWkUipSwrCzMrMbFZCKctwmCFmNjdIUbQI6gqB5QltKoK6WikAi0ik1CUAu/tId++eUEZmMMQI4LtAN2AlcF9Qn+zXNVIus7ULQkQipb53Qbj7qq3HZvYYMDH4WAG0T2haBKxI1ZdWwCISKdncBZGMmbVN+HgOsHWHxARggJk1MbOOQCdgZqq+tAIWkUjJ5st4zOw54FSglZlVALcBp5pZN+LphU+AQQDuPt/MxgELgGpgsLvHUvWvACwikRLz7L2Q0t0HJqkelaL9cGB4pv0rAItIpOTSk3AKwCISKXoXhIhISPRCdhGRkNQoBSEiEg6tgEVEQpLNXRD1TQFYRCJFKQgRkZAoBSEiEhKtgEVEQqIVsIhISGKpX7/QqCgAi0ik6FFkEZGQ6FFkEZGQaAUsIhIS7YIQEQmJdkGIiIREjyKLiIREOWARkZAoBywiEhKtgEVEQpJL+4D3CnsCIiLZ5O4Zl3TM7AkzW21mHyXUFZjZFDNbEvxtEdSbmT1kZuVmNtfMjknXvwKwiERKzGsyLhkYDfTZoe4mYKq7dwKmBp8B+gKdglIGjEjXuQKwiERKjXvGJR13fwP4fIfqEuCp4PgpoH9C/dMe9y6Qb2ZtU/WvACwikVKXFISZlZnZrIRSlsEQbdx9ZTDWSuCgoL4QWJ7QriKoq5VuwolIpNTlSTh3HwmMzNLQlnQ6KSgAi0ikNMA2tFVm1tbdVwYphtVBfQXQPqFdEbAiVUdKQYhIpGQzB1yLCcClwfGlwPiE+kuC3RDFQNXWVEVtLJc2Lec6MysL/i+PyDb656LxMrPngFOBVsAq4Dbgr8A44BDgU+A8d//czAx4mPiuiQ3AZe4+K2X/CsANx8xmuXv3sOchjYv+udhzKQUhIhISBWARkZAoADcs5fkkGf1zsYdSDlhEJCRaAYuIhEQBWEQkJArADcTM+pjZ4uBVdTelv0KiLtmrDmXPogDcAMwsD/gT8dfVdQEGmlmXcGcljcBodn7VoexBFIAbRg+g3N2XuvtmYCzxV9fJHqyWVx3KHkQBuGHU+TV1IhJ9CsANo86vqROR6FMAbhh1fk2diESfAnDDeA/oZGYdzWxfYADxV9eJyB5MAbgBuHs1MAR4DVgIjHP3+eHOSsIWvOrwHeBwM6sws9Kw5yQNS48ii4iERCtgEZGQKACLiIREAVhEJCQKwCIiIVEAFhEJiQKwiEhIFIBFRELyf/ZxUw2XDSJWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_train,y_train_predict)\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.83      0.83       549\n",
      "           1       0.72      0.72      0.72       342\n",
      "\n",
      "    accuracy                           0.79       891\n",
      "   macro avg       0.77      0.77      0.77       891\n",
      "weighted avg       0.79      0.79      0.79       891\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,y_train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    }
   ],
   "source": [
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': y_test})\n",
    "output.to_csv('log-titanic.csv', index=False)\n",
    "print(\"Your submission was successfully saved!\")"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}