Predicting Survivors on Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Survivors on Titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In This notebook, I'll build a model to predict the survivors on titanic, given various features...using Kaggle titanic dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It shows that Age contains many missing values, and \n",
    "Very less instances have non-null Cabin attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that about 75% of people did not survive...and 75% of people on titanic were less than 38 years)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4739703400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f473942fb70>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f473945e518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4739406e48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f47393b17b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f47393df160>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "train[['Age','Fare','Parch','Pclass','SibSp','Survived']].hist(bins = 50, figsize = (12,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Plots we Can observe that:\n",
    "\n",
    "1.) Age and Fare are not bell shaped and are tail heavy, so we need to normalize these for better performance\n",
    "\n",
    "2.) Age of majority of people on titanic is around 20-30 years\n",
    "\n",
    "3.) Large number of people were travelling without family members\n",
    "\n",
    "4.) Majority of people on ship were third class ticket category people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now look at the effect of various features on the survival of person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "survived_byclass = train.groupby('Pclass').Survived.mean()\n",
    "survived_bygender = train.groupby('Sex').Survived.mean()\n",
    "survived_byage = train.groupby('Age').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4738d61c50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_byclass.plot(kind = 'bar', fontsize  = 15)\n",
    "plt.title('Survived People in each Ticket Class')\n",
    "plt.xlabel(' Ticket class', fontsize = 15)\n",
    "plt.ylabel('Fraction of Survivers of each class', fontsize = 15)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4738b2f748>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_bygender.plot(kind = 'bar', fontsize = 15)\n",
    "plt.title('Survived People by Gender')\n",
    "plt.xlabel('Sex', fontsize = 15)\n",
    "plt.ylabel('Fraction of Survivers', fontsize = 15)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fraction of Survivers')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_byage.plot(kind = 'line', figsize = (15,7))\n",
    "plt.title('Age vs Fraction of Survivers', fontsize = 17)\n",
    "plt.xlabel('Age', fontsize = 15)\n",
    "plt.ylabel('Fraction of Survivers', fontsize = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inferences::\n",
    "\n",
    "1.) From 1st figure we see that Higher class people were more influential so they survived and poor died\n",
    "\n",
    "2.) From 2nd figure we see that females had better chances of survival than males\n",
    "\n",
    "3.) The older and children people survived, whereas the adult people died...This was perhaps because the adult people\n",
    "    were busy saving everyone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Age, Sex, Ticket class are pretty important attributes for predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation and Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the label from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train.drop('Survived', axis = 1)\n",
    "train_y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class dropaddfeatures(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self,X,y=None):\n",
    "        X = X.drop('Ticket',axis = 1)\n",
    "        X['Name'] = X['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
    "        X['Cabin'] = X['Cabin'].apply(lambda x: str(x)[0] if not pd.isnull(x) else 'Unknown')\n",
    "        X['Family'] = X['SibSp'] + X['Parch']\n",
    "        X = X.rename(columns = {'Name' : 'Title', 'Cabin' : 'Deck'})\n",
    "        X['Sex'] = X['Sex'].apply(lambda x: 1 if x == 'male' else 0)\n",
    "        X['Family-Fare'] = (X['Family']+1) * X['Fare']\n",
    "        X['Embarked'] = X['Embarked'].apply(lambda x: x if not pd.isnull(x) else 'Unknown')\n",
    "        return X\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "      <th>Family-Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>14.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>142.5666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>106.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass Title  Sex   Age  SibSp  Parch     Fare     Deck  \\\n",
       "0            1       3    Mr    1  22.0      1      0   7.2500  Unknown   \n",
       "1            2       1   Mrs    0  38.0      1      0  71.2833        C   \n",
       "2            3       3  Miss    0  26.0      0      0   7.9250  Unknown   \n",
       "3            4       1   Mrs    0  35.0      1      0  53.1000        C   \n",
       "4            5       3    Mr    1  35.0      0      0   8.0500  Unknown   \n",
       "\n",
       "  Embarked  Family  Family-Fare  \n",
       "0        S       1      14.5000  \n",
       "1        C       1     142.5666  \n",
       "2        S       0       7.9250  \n",
       "3        S       1     106.2000  \n",
       "4        S       0       8.0500  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = dropaddfeatures()\n",
    "temp = transformer.transform(train_x)\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new dataset looks somewhat better. But we still need to convert the categorical variables to numbers for Machine Learning Algorithm to work..\n",
    "\n",
    "First Let us Analyse the Title Categories.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr              517\n",
       "Miss            182\n",
       "Mrs             125\n",
       "Master           40\n",
       "Dr                7\n",
       "Rev               6\n",
       "Mlle              2\n",
       "Major             2\n",
       "Col               2\n",
       "Jonkheer          1\n",
       "Sir               1\n",
       "the Countess      1\n",
       "Capt              1\n",
       "Lady              1\n",
       "Don               1\n",
       "Mme               1\n",
       "Ms                1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.Title.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since The Major Categories are Mr, Miss, Mrs and Master, so let us convert other titles into those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def converttitles(row):\n",
    "    if row.Title == 'Mr' or row.Title == 'Mrs' or row.Title == 'Master' or row.Title == 'Miss':\n",
    "        row.Title = row.Title\n",
    "    elif row.Sex == 1 and row.Age < 18:\n",
    "        row.Title = 'Master'\n",
    "    elif row.Sex == 1 and row.Age >= 18:\n",
    "        row.Title = 'Mr'\n",
    "    elif row.Sex == 0 and row.Age > 26:\n",
    "        row.Title = 'Mrs'\n",
    "    else:\n",
    "        row.Title = 'Miss'\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class newtitles(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X,y=None):\n",
    "        X = X.apply(converttitles, axis = 1)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "      <th>Family-Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>14.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>142.5666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>106.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass Title  Sex   Age  SibSp  Parch     Fare     Deck  \\\n",
       "0            1       3    Mr    1  22.0      1      0   7.2500  Unknown   \n",
       "1            2       1   Mrs    0  38.0      1      0  71.2833        C   \n",
       "2            3       3  Miss    0  26.0      0      0   7.9250  Unknown   \n",
       "3            4       1   Mrs    0  35.0      1      0  53.1000        C   \n",
       "4            5       3    Mr    1  35.0      0      0   8.0500  Unknown   \n",
       "\n",
       "  Embarked  Family  Family-Fare  \n",
       "0        S       1      14.5000  \n",
       "1        C       1     142.5666  \n",
       "2        S       0       7.9250  \n",
       "3        S       1     106.2000  \n",
       "4        S       0       8.0500  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_changer = newtitles()\n",
    "temp = t_changer.fit_transform(temp)\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        536\n",
       "Miss      186\n",
       "Mrs       129\n",
       "Master     40\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.Title.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to convert Categorical variables Title, Deck, Embarked to numbers and Normalize the values.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class encode(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self,X,y=None):\n",
    "        self.enc1 = LabelEncoder()\n",
    "        self.enc2 = LabelEncoder()\n",
    "        self.enc3 = LabelEncoder()\n",
    "        self.enc1.fit(X['Title'])\n",
    "        self.enc2.fit(X['Deck'])\n",
    "        self.enc3.fit(X['Embarked'])\n",
    "    def transform(self,X,y=None):\n",
    "        X['Title'] = self.enc1.transform(X['Title'])\n",
    "        X['Deck'] = self.enc2.transform(X['Deck'])\n",
    "        X['Embarked'] = self.enc3.transform(X['Embarked'])\n",
    "        return X.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now build a pipeline for this transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean = Pipeline([\n",
    "    ('dropadd',dropaddfeatures()),\n",
    "    ('dec_titles',newtitles()),\n",
    "    ('label_encode',encode()),\n",
    "])\n",
    "data_prepare = Pipeline([\n",
    "    ('imputer',SimpleImputer()),\n",
    "    ('scaler',StandardScaler())\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.73010796,  0.82737724,  0.21541946, ...,  0.58111394,\n",
       "         0.05915988, -0.38704001],\n",
       "       [-1.72622007, -1.56610693,  1.61643213, ..., -1.93846038,\n",
       "         0.05915988,  0.38471232],\n",
       "       [-1.72233219,  0.82737724, -1.18559321, ...,  0.58111394,\n",
       "        -0.56097483, -0.42666214],\n",
       "       ...,\n",
       "       [ 1.72233219,  0.82737724, -1.18559321, ...,  0.58111394,\n",
       "         1.29942929,  0.09083603],\n",
       "       [ 1.72622007, -1.56610693,  0.21541946, ..., -1.93846038,\n",
       "        -0.56097483, -0.29363422],\n",
       "       [ 1.73010796,  0.82737724,  0.21541946, ..., -0.67867322,\n",
       "        -0.56097483, -0.42771672]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_clean.fit(train_x).transform(train_x)\n",
    "X = data_prepare.fit(X).transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we have made the pipelines for data_cleaning and preparation and have prepared the data for training the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection and Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score,confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.8170594837261503 \n",
      "Precision =  0.6988304093567251 \n",
      "Recall =  0.7993311036789298 \n",
      "f1_score =  0.7457098283931357\n"
     ]
    }
   ],
   "source": [
    "ranfor_clf = RandomForestClassifier(n_estimators = 50)\n",
    "prediction = cross_val_predict(ranfor_clf,X,train_y,cv = 5)\n",
    "acc = accuracy_score(prediction,train_y)\n",
    "pre = precision_score(prediction,train_y)\n",
    "recall = recall_score(prediction,train_y)\n",
    "f1 = f1_score(prediction,train_y)\n",
    "print('Accuracy = ',acc,'\\nPrecision = ',pre,'\\nRecall = ',recall,'\\nf1_score = ',f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.8080808080808081 \n",
      "Precision =  0.6783625730994152 \n",
      "Recall =  0.7918088737201365 \n",
      "f1_score =  0.7307086614173227\n"
     ]
    }
   ],
   "source": [
    "mlp_clf = MLPClassifier()\n",
    "prediction = cross_val_predict(mlp_clf,X,train_y,cv = 5)\n",
    "acc = accuracy_score(prediction,train_y)\n",
    "pre = precision_score(prediction,train_y)\n",
    "recall = recall_score(prediction,train_y)\n",
    "f1 = f1_score(prediction,train_y)\n",
    "print('Accuracy = ',acc,'\\nPrecision = ',pre,'\\nRecall = ',recall,'\\nf1_score = ',f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.7351290684624018 \n",
      "Precision =  0.6871345029239766 \n",
      "Recall =  0.6456043956043956 \n",
      "f1_score =  0.6657223796033995\n"
     ]
    }
   ],
   "source": [
    "sgd_clf = SGDClassifier()\n",
    "prediction = cross_val_predict(sgd_clf,X,train_y,cv = 5)\n",
    "acc = accuracy_score(prediction,train_y)\n",
    "pre = precision_score(prediction,train_y)\n",
    "recall = recall_score(prediction,train_y)\n",
    "f1 = f1_score(prediction,train_y)\n",
    "print('Accuracy = ',acc,'\\nPrecision = ',pre,'\\nRecall = ',recall,'\\nf1_score = ',f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.813692480359147 \n",
      "Precision =  0.716374269005848 \n",
      "Recall =  0.7802547770700637 \n",
      "f1_score =  0.7469512195121951\n"
     ]
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier(n_neighbors = 5)\n",
    "prediction = cross_val_predict(knn_clf,X,train_y,cv = 5)\n",
    "acc = accuracy_score(prediction,train_y)\n",
    "pre = precision_score(prediction,train_y)\n",
    "recall = recall_score(prediction,train_y)\n",
    "f1 = f1_score(prediction,train_y)\n",
    "print('Accuracy = ',acc,'\\nPrecision = ',pre,'\\nRecall = ',recall,'\\nf1_score = ',f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HyperParameter Tuning and Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above metrics, we can see that RandomForest works best..Now let's Tweak its hyperparameters\n",
    "to  improve accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 100, num = 10)]\n",
    "max_features = ['auto','sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(start = 10, stop = 30, num = 3)]\n",
    "min_samples_split = [2,5,7,10]\n",
    "min_samples_leaf = [1,2,4,7]\n",
    "bootstrap = [True,False]\n",
    "\n",
    "random_grid = {\n",
    "    'n_estimators' : n_estimators,\n",
    "    'max_features' : max_features,\n",
    "    'max_depth' : max_depth,\n",
    "    'min_samples_split' : min_samples_split,\n",
    "    'min_samples_leaf' : min_samples_leaf,\n",
    "    'bootstrap' : bootstrap\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranfor_clf = RandomForestClassifier()\n",
    "ran_cv = RandomizedSearchCV(estimator = ranfor_clf, param_distributions = random_grid,n_iter = 100,\n",
    "                           cv = 6,random_state = 42, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=6, error_score='raise-deprecating',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30], 'min_samples_split': [2, 5, 7, 10], 'min_samples_leaf': [1, 2, 4, 7], 'bootstrap': [True, False]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran_cv.fit(X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8451178451178452"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = ran_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation on Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x = data_clean.transform(test)\n",
    "test_x = data_prepare.transform(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = final_model.predict(test_x)\n",
    "submit_file = pd.DataFrame({'PassengerId' : test.PassengerId.values, 'Survived' : prediction},\n",
    "                          index = np.arange(len(prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_file.set_index(submit_file['PassengerId'])\n",
    "submit_file.to_csv('predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_values = pd.read_csv('gender_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test Set =  0.8636363636363636\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(actual_values.Survived.values,prediction)\n",
    "print('Accuracy on Test Set = ',acc)"
   ]
  },
  {
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