Practice project 1( Kaggle Titanic dataset).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Titanic ML competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Downloading the dataset (https://github.com/shubh2002/DataSets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview\n",
    "The data has been split into two groups:\n",
    "\n",
    "training set (Titanic_train_dataset.csv)\n",
    "The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.\n",
    "\n",
    "test set (Titanic_test_dataset.csv)\n",
    "The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The dataset has the following features:\n",
    "\n",
    "PassengerId: Unique Id of a passenger\n",
    "survival: Whether a passenger survived or not; 1 if survived and 0 if not.\n",
    "pclass: Ticket class\n",
    "Sex: Gender\n",
    "Age: Age in years\n",
    "sibsp: # of siblings / spouses aboard the Titanic\n",
    "parch: # of parents / children aboard the Titanic\n",
    "ticket: Ticket number\n",
    "fare: Passenger fare\n",
    "cabin: Cabin number\n",
    "embarked: Port of Embarkation"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "pclass: A proxy for socio-economic status (SES)\n",
    "1st = Upper\n",
    "2nd = Middle\n",
    "3rd = Lower\n",
    "\n",
    "age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "sibsp: The dataset defines family relations in this way...\n",
    "Sibling = brother, sister, stepbrother, stepsister\n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "parch: The dataset defines family relations in this way...\n",
    "Parent = mother, father\n",
    "Child = daughter, son, stepdaughter, stepson\n",
    "Some children travelled only with a nanny, therefore parch=0 for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Importing Libraries\"\"\"\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Reading training data\"\"\"\n",
    "\n",
    "df_train=pd.read_csv('Titanic_train_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Features conatin null values\"\"\"\n",
    "df_train.columns[df_train.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cabin       77.10\n",
      "Age         19.87\n",
      "Embarked     0.22\n",
      "Fare         0.00\n",
      "Ticket       0.00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Percentage of null value in features\"\"\"\n",
    "print(round((df_train.isnull().sum()*100)/len(df_train),2).sort_values(ascending=False).head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Saving the Survived value fro training data\"\"\"\n",
    "\n",
    "y_train=df_train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a3a382390>"
      ]
     },
     "execution_count": 67,
     "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.countplot(x='Pclass',hue='Survived',data=df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People in first class have more chance of surviving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a39cb2cf8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAUWklEQVR4nO3df7RV5X3n8fc3gJKISoRrolziJdW0ShBSwB91dFHTpcZadDIi2DRiJSUTNaEr086YdiWaH05tmpnUGCcNq6ZiYgQS24KsxqyMiU6jico1+APQBQkducJUQCUxLn+A3/njbJ7cwEEOcPc9l8v7tdZdd+9nP2ef71ls+LCfvfdzIjORJAngTe0uQJI0cBgKkqTCUJAkFYaCJKkwFCRJxdB2F7A/Ro8enV1dXe0uQ5IOKN3d3Zszs6PZtgM6FLq6uli+fHm7y5CkA0pE/N/dbXP4SJJUGAqSpMJQkCQVB/Q1BUnqa6+99ho9PT28/PLL7S5lvw0fPpzOzk6GDRvW8msMBUnqpaenh8MPP5yuri4iot3l7LPMZMuWLfT09DBu3LiWX+fwkST18vLLLzNq1KgDOhAAIoJRo0bt9RmPoSBJOznQA2GHffkchoIkqTAUJKkF119/PePHj+fkk09m0qRJPPjgg/u9z6VLl3LDDTf0QXUwYsSIPtnPQX+hefKf39buEgaM7r+5rN0lSAPSj370I5YtW8YjjzzCoYceyubNm3n11Vdbeu22bdsYOrT5P7XTp09n+vTpfVnqfvNMQZL2YOPGjYwePZpDDz0UgNGjR3PsscfS1dXF5s2bAVi+fDnTpk0D4LrrrmPu3Lmcc845XHbZZZx66qmsXLmy7G/atGl0d3dz6623cvXVV7N161a6urp4/fXXAXjppZcYO3Ysr732Gj/96U8577zzmDx5MmeeeSZPPvkkAOvWreP0009n6tSpfPKTn+yzz2ooSNIenHPOOaxfv553vetdXHnlldx33317fE13dzdLlizhm9/8JrNmzWLx4sVAI2A2bNjA5MmTS98jjzySiRMnlv3eddddnHvuuQwbNoy5c+dy00030d3dzRe+8AWuvPJKAObNm8dHPvIRHn74Yd7+9rf32Wc1FCRpD0aMGEF3dzfz58+no6ODmTNncuutt77ha6ZPn86b3/xmAC655BK+9a1vAbB48WJmzJixS/+ZM2eyaNEiABYuXMjMmTN58cUXeeCBB5gxYwaTJk3iwx/+MBs3bgTg/vvv59JLLwXggx/8YF99VK8pSFIrhgwZwrRp05g2bRoTJkxgwYIFDB06tAz57Pw8wGGHHVaWx4wZw6hRo3jsscdYtGgRX/3qV3fZ//Tp0/nEJz7Bc889R3d3N2effTa//OUvGTlyJCtWrGhaUx23znqmIEl78NRTT7FmzZqyvmLFCo477ji6urro7u4G4M4773zDfcyaNYvPf/7zbN26lQkTJuyyfcSIEZxyyinMmzePCy64gCFDhnDEEUcwbty4cpaRmTz66KMAnHHGGSxcuBCA22+/vU8+JxgKkrRHL774IrNnz+akk07i5JNPZtWqVVx33XVce+21zJs3jzPPPJMhQ4a84T4uvvhiFi5cyCWXXLLbPjNnzuQb3/gGM2fOLG233347t9xyCxMnTmT8+PEsWbIEgBtvvJGbb76ZqVOnsnXr1r75oEBkZp/trL9NmTIl9/dLdrwl9Ve8JVWC1atXc+KJJ7a7jD7T7PNERHdmTmnW3zMFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSp8IlmSdpLfX0reyu3g999993MmzeP7du386EPfYhrrrmmT2vYwTMFSRrgtm/fzlVXXcV3vvMdVq1axR133MGqVatqeS9DQZIGuIceeojjjz+ed77znRxyyCHMmjWrPNnc1wwFSRrgnnnmGcaOHVvWOzs7eeaZZ2p5L0NBkga4ZtMR1TFDKhgKkjTgdXZ2sn79+rLe09PDscceW8t7GQqSNMBNnTqVNWvWsG7dOl599VUWLlxY23c7e0uqJO2l/p5ReOjQoXz5y1/m3HPPZfv27VxxxRWMHz++nveqZa+SpD51/vnnc/7559f+Pg4fSZIKQ0GSVBgKkqTCUJAkFbWHQkQMiYifRMSyan1cRDwYEWsiYlFEHFK1H1qtr622d9VdmyTp1/XHmcI8YHWv9b8GvpiZJwDPA3Oq9jnA85l5PPDFqp8kqR/VektqRHQCvw9cD3w8Gs9lnw38YdVlAXAd8BXgwmoZ4NvAlyMistnz3ZLURk9/ZkKf7u8dn3p8j32uuOIKli1bxtFHH80TTzzRp+/fW91nCn8L/Ffg9Wp9FPBCZm6r1nuAMdXyGGA9QLV9a9X/10TE3IhYHhHLN23aVGftkjRgXH755dx99921v09toRARFwDPZmZ37+YmXbOFbb9qyJyfmVMyc0pHR0cfVCpJA99ZZ53FUUcdVfv71Dl8dAYwPSLOB4YDR9A4cxgZEUOrs4FOYEPVvwcYC/RExFDgSOC5GuuTJO2ktjOFzPxEZnZmZhcwC/h+Zn4A+AFwcdVtNrDjmyKWVutU27/v9QRJ6l/teE7hv9G46LyWxjWDW6r2W4BRVfvHgXq+gFSStFv9MiFeZt4L3Fst/ww4pUmfl4EZ/VGPJKk5Z0mVpL3Uyi2kfe3SSy/l3nvvZfPmzXR2dvLpT3+aOXPm7PmFe8lQkKQDwB133NEv7+PcR5KkwlCQJBWGgiTtZLDcDb8vn8NQkKRehg8fzpYtWw74YMhMtmzZwvDhw/fqdV5olqReOjs76enpYTDMrTZ8+HA6Ozv36jWGgiT1MmzYMMaNG9fuMtrG4SNJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVNQWChExPCIeiohHI2JlRHy6ah8XEQ9GxJqIWBQRh1Tth1bra6vtXXXVJklqrs4zhVeAszNzIjAJOC8iTgP+GvhiZp4APA/MqfrPAZ7PzOOBL1b9JEn9qLZQyIYXq9Vh1U8CZwPfrtoXABdVyxdW61Tb3xsRUVd9kqRd1XpNISKGRMQK4Fnge8BPgRcyc1vVpQcYUy2PAdYDVNu3AqOa7HNuRCyPiOWbNm2qs3xJOujUGgqZuT0zJwGdwCnAic26Vb+bnRXkLg2Z8zNzSmZO6ejo6LtiJUn9c/dRZr4A3AucBoyMiKHVpk5gQ7XcA4wFqLYfCTzXH/VJkhrqvPuoIyJGVstvBn4PWA38ALi46jYbWFItL63WqbZ/PzN3OVOQJNVn6J677LNjgAURMYRG+CzOzGURsQpYGBGfA34C3FL1vwX4ekSspXGGMKvG2iRJTdQWCpn5GPCeJu0/o3F9Yef2l4EZddUjSdozn2iWJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVLQUChFxTyttkqQD2xs+vBYRw4G3AKMj4q38atK6I4Bja65NktTP9vRE84eBP6URAN38KhR+DtxcY12SpDZ4w1DIzBuBGyPio5l5Uz/VJElqk5bmPsrMmyLid4Cu3q/JzNtqqkuS1AYthUJEfB34DWAFsL1qTsBQkKRBpNVZUqcAJ/n9BpI0uLX6nMITwNvrLESS1H6tnimMBlZFxEPAKzsaM3N6LVVJktqi1VC4rs4iJEkDQ6t3H91XdyGSpPZr9e6jX9C42wjgEGAY8MvMPKKuwiRJ/a/VM4XDe69HxEU0+Z5lSdKBbZ9mSc3MfwbO7uNaJElt1urw0ft7rb6JxnMLPrMgSYNMq3cf/UGv5W3AvwEX9nk1kqS2avWawh/XXYgkqf1aHT7qBG4CzqAxbPRDYF5m9tRYmyQB8PRnJrS7hAHjHZ96vNb9t3qh+R+ApTS+V2EMcFfVJkkaRFoNhY7M/IfM3Fb93Ap01FiXJKkNWg2FzRHxRxExpPr5I2BLnYVJkvpfq6FwBXAJ8P+AjcDFgBefJWmQafWW1M8CszPzeYCIOAr4Ao2wkCQNEq2eKZy8IxAAMvM54D31lCRJapdWQ+FNEfHWHSvVmUKrZxmSpANEq/+w/w/ggYj4No3nFC4Brq+tKklSW7T6RPNtEbGcxiR4Abw/M1fVWpkkqd+1PARUhYBBIEmD2D5Nnd2KiBgbET+IiNURsTIi5lXtR0XE9yJiTfX7rVV7RMSXImJtRDwWEb9dV22SpOZqCwUas6n+l8w8ETgNuCoiTgKuAe7JzBOAe6p1gPcBJ1Q/c4Gv1FibJKmJ2kIhMzdm5iPV8i+A1TTmTboQWFB1WwBcVC1fCNyWDT8GRkbEMXXVJ0naVZ1nCkVEdNF4ruFB4G2ZuREawQEcXXUbA6zv9bKeqm3nfc2NiOURsXzTpk11li1JB53aQyEiRgB3An+amT9/o65N2nb5drfMnJ+ZUzJzSkeHc/JJUl+qNRQiYhiNQLg9M/+xav73HcNC1e9nq/YeYGyvl3cCG+qsT5L06+q8+yiAW4DVmfk/e21aCsyulmcDS3q1X1bdhXQasHXHMJMkqX/UOVXFGcAHgccjYkXV9hfADcDiiJgDPA3MqLb9C3A+sBZ4CWdhlaR+V1soZOYPaX6dAOC9TfoncFVd9UiS9qxf7j6SJB0YDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkoqhde04Ir4GXAA8m5nvrtqOAhYBXcC/AZdk5vMREcCNwPnAS8DlmflIXbWpuac/M6HdJQwY7/jU4+0uQWqLOs8UbgXO26ntGuCezDwBuKdaB3gfcEL1Mxf4So11SZJ2o7ZQyMz/Azy3U/OFwIJqeQFwUa/227Lhx8DIiDimrtokSc319zWFt2XmRoDq99FV+xhgfa9+PVWbJKkfDZQLzdGkLZt2jJgbEcsjYvmmTZtqLkuSDi79HQr/vmNYqPr9bNXeA4zt1a8T2NBsB5k5PzOnZOaUjo6OWouVpINNf4fCUmB2tTwbWNKr/bJoOA3YumOYSZLUf+q8JfUOYBowOiJ6gGuBG4DFETEHeBqYUXX/Fxq3o66lcUvqH9dVlyRp92oLhcy8dDeb3tukbwJX1VWLJKk1A+VCsyRpADAUJEmFoSBJKmq7piBp/0z+89vaXcKA8U+Ht7uCg4dnCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVAyoUIiI8yLiqYhYGxHXtLseSTrYDJhQiIghwM3A+4CTgEsj4qT2ViVJB5cBEwrAKcDazPxZZr4KLAQubHNNknRQGdruAnoZA6zvtd4DnLpzp4iYC8ytVl+MiKf6obaDwnEwGtjc7joGhGuj3RWoF4/NXvrm2DxudxsGUig0+6S5S0PmfGB+/eUcfCJieWZOaXcd0s48NvvPQBo+6gHG9lrvBDa0qRZJOigNpFB4GDghIsZFxCHALGBpm2uSpIPKgBk+ysxtEXE18F1gCPC1zFzZ5rIONg7LaaDy2OwnkbnLsL0k6SA1kIaPJEltZihIkgpDQU1FxLSIWNbuOjQ4RMTHImJ1RNxe0/6vi4g/q2PfB5sBc6FZ0qB2JfC+zFzX7kL0xjxTGMQioisinoyIv4+IJyLi9oj4vYi4PyLWRMQp1c8DEfGT6vdvNtnPYRHxtYh4uOrn9CNqWUT8HfBOYGlE/GWzYykiLo+If46IuyJiXURcHREfr/r8OCKOqvr9SfXaRyPizoh4S5P3+42IuDsiuiPiXyPit/r3Ex/YDIXB73jgRuBk4LeAPwT+A/BnwF8ATwJnZeZ7gE8B/73JPv4S+H5mTgV+F/ibiDisH2rXIJCZ/5nGg6i/CxzG7o+ld9M4Pk8Brgdeqo7LHwGXVX3+MTOnZuZEYDUwp8lbzgc+mpmTaRzn/6ueTzY4OXw0+K3LzMcBImIlcE9mZkQ8DnQBRwILIuIEGtOKDGuyj3OA6b3GbIcD76Dxl1LaG7s7lgB+kJm/AH4REVuBu6r2x2n8pwbg3RHxOWAkMILGc01FRIwAfgf4VkSZOefQOj7IYGUoDH6v9Fp+vdf66zT+/D9L4y/jf4yILuDeJvsI4D9lppMPan81PZYi4lT2fKwC3ApclJmPRsTlwLSd9v8m4IXMnNS3ZR88HD7SkcAz1fLlu+nzXeCjUf3XKyLe0w91aXDa32PpcGBjRAwDPrDzxsz8ObAuImZU+4+ImLifNR9UDAV9HviriLifxvQizXyWxrDSYxHxRLUu7Yv9PZY+CTwIfI/G9bBmPgDMiYhHgZX4vSx7xWkuJEmFZwqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFaR9V8/isjIjHImJF9QCWdEDziWZpH0TE6cAFwG9n5isRMRo4pM1lSfvNMwVp3xwDbM7MVwAyc3NmboiIyRFxXzVD53cj4piIGFrN7DkNICL+KiKub2fx0u748Jq0D6qJ134IvAX438Ai4AHgPuDCzNwUETOBczPziogYD3wb+BiNp8hPzcxX21O9tHsOH0n7IDNfjIjJwJk0poBeBHyOxvTP36um9hkCbKz6r4yIr9OY+fN0A0EDlaEg7aPM3E5jVtl7q6nIrwJWZubpu3nJBOAF4G39U6G097ymIO2DiPjN6jsodphE4/slOqqL0ETEsGrYiIh4PzAKOAv4UkSM7O+apVZ4TUHaB9XQ0U00vuxlG7AWmAt0Al+iMSX5UOBvgX+icb3hvZm5PiI+BkzOzNntqF16I4aCJKlw+EiSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlS8f8Bbbkw6cZ8/W8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Sex',hue='Survived',data=df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Female survived more than male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a29c46b70>"
      ]
     },
     "execution_count": 69,
     "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.countplot(x='Embarked',hue='Survived',data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cabin          77.10\n",
      "Age            19.87\n",
      "Embarked        0.22\n",
      "Fare            0.00\n",
      "Ticket          0.00\n",
      "Parch           0.00\n",
      "SibSp           0.00\n",
      "Sex             0.00\n",
      "Name            0.00\n",
      "Pclass          0.00\n",
      "Survived        0.00\n",
      "PassengerId     0.00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(round((df_train.isnull().sum()*100)/len(df_train),2).sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a3a438198>"
      ]
     },
     "execution_count": 71,
     "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.countplot(x='Pclass',hue='Survived',data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a39fb7e80>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G042fD5DkJOBpVbVlpmNVPQR8DZg570uAz1TVDxn8QfrfrKrTgDcBH+j6vAe4tKqeB3zzcX9CaUSGgha8qnqYwZf8WmAauDLJa/Zy2Maq+p9ufQNwXrd+PvCxIf2vBF7RrV/QvcfRwAuAjyW5AfhrBqMWgDOBj3brH9mnDyQ9DovGXYA0H1TVI8BmYHOSm4DVwA4e/R+no3Y65Luzjv3vJN9K8iwGX/yvG/IWG4E/SXI8gwC6FngS8GBVnbq7svbz40j7zZGCFrwkz0iyclbTqcA3gDsZfIED/OpeTnMF8GbgmKq6aeed3WjkKwwuC32yqh6pqm8D/5XkvK6OJHl2d8i/MBhRALxq3z+VtH8MBQmOBtYnuTXJjQz+VvVbgbcB70nyBeCRvZzjKgZf4hv20OdK4NXdcsargDVJvgbcwqN/CvX1wMVJrgeO2bePI+0/f5IqSWocKUiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElq/h8LPMi7HD8ZAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a39a050f0>"
      ]
     },
     "execution_count": 73,
     "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.violinplot(df_train['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    233\n",
       "male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.groupby(['Sex']).Survived.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C     93\n",
       "Q     30\n",
       "S    217\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.groupby(['Embarked']).Survived.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    136\n",
       "2     87\n",
       "3    119\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.groupby(['Pclass']).Survived.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x25a3a64ab00>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1311.88x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df_train,hue='Survived',kind='scatter',diag_kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a3dea5198>"
      ]
     },
     "execution_count": 79,
     "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.boxplot(y='Age',data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a3c6972e8>"
      ]
     },
     "execution_count": 80,
     "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.boxplot(y='Fare',data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df_train.drop(['Survived'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>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>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>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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.read_csv('Titanic_test_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Embarked</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
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       "      <td>330911</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Combining both dataset\"\"\"\n",
    "data=pd.concat([df_train,df_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 11)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>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>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>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>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>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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Droping the column which are not useful for our prediction model\"\"\"\n",
    "data=data.drop(['Name','PassengerId','Ticket'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a3e57b5c0>"
      ]
     },
     "execution_count": 89,
     "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(data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Droping the column with more thn 50% null values\"\"\"\n",
    "data=data.drop(['Cabin'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Fare', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Checking remainig columns with null values\"\"\"\n",
    "data.columns[data.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Fillinng the null values\"\"\"\n",
    "\n",
    "data['Age']=data['Age'].fillna(data['Age'].median())\n",
    "data['Embarked']=data['Embarked'].fillna(data['Embarked'].mode()[0])\n",
    "data['Fare']=data['Fare'].fillna(data['Fare'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sex', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Name of the features with categorical values\"\"\"\n",
    "obj_dtype_col=data.columns[data.dtypes == 'object']\n",
    "obj_dtype_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Changing the Categorical value into numerical value\"\"\"\n",
    "data=pd.get_dummies(data,columns=['Sex','Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  Sex_male  Embarked_Q  Embarked_S\n",
       "0       3  22.0      1      0   7.2500         1           0           1\n",
       "1       1  38.0      1      0  71.2833         0           0           0\n",
       "2       3  26.0      0      0   7.9250         0           0           1\n",
       "3       1  35.0      1      0  53.1000         0           0           1\n",
       "4       3  35.0      0      0   8.0500         1           0           1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 8)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 8)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train=data.iloc[:891,:]\n",
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 8)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test=data.iloc[891:,:]\n",
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=data_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model1=DecisionTreeClassifier()\n",
    "model1.fit(x_train,y_train)\n",
    "y_pred=model1.predict(data_test)\n",
    "df_test['Survived']=y_pred\n",
    "df_test[['PassengerId','Survived']].to_csv('submission1.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will give you score 71.77 according to Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "model2=XGBClassifier()\n",
    "model2.fit(x_train,y_train)\n",
    "y_pred=model1.predict(data_test)\n",
    "df_test['Survived']=y_pred\n",
    "df_test[['PassengerId','Survived']].to_csv('submission2.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will give you score of 72.77 according to Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model3=GradientBoostingClassifier()\n",
    "model3.fit(x_train,y_train)\n",
    "y_pred=model3.predict(data_test)\n",
    "df_test['Survived']=y_pred\n",
    "df_test[['PassengerId','Survived']].to_csv('submission3.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model4=RandomForestClassifier()\n",
    "model4.fit(x_train,y_train)\n",
    "y_pred=model4.predict(data_test)\n",
    "df_test['Survived']=y_pred\n",
    "df_test[['PassengerId','Survived']].to_csv('submission4.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By tuning Hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8080808080808081\n",
      "\n",
      "\n",
      "{'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters=[{'criterion':['gini','entropy'],'max_depth':[1,2,3,4,5,6],'max_features':['auto','sqrt','log2']}]\n",
    "GSdtc=GridSearchCV(estimator=model1,param_grid=parameters,scoring='accuracy',cv=10,n_jobs=-1)\n",
    "GSdtc=GSdtc.fit(x_train,y_train)\n",
    "score=GSdtc.best_score_\n",
    "print(score)\n",
    "print('\\n')\n",
    "param=GSdtc.best_params_\n",
    "print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"HyperParameter tuning for random search\"\"\"\n",
    "parameters=[{'max_features':['auto','sqrt','log2'],'n_estimators':[50,100,150,200,500],'criterion':['gini','entropy'],\n",
    "            'max_depth':[1,2,3,4,5,6,7,8,9,10],'n_jobs':[1,-1],'random_state':[0,10,15,42,48,51]}]\n",
    "GSrfc=GridSearchCV(estimator=model4,param_grid=parameters,scoring='accuracy',cv=10,n_jobs=-1)\n",
    "GSrfc=GSrfc.fit(x_train,y_train)\n",
    "score2=GSrfc.best_score_\n",
    "print(score2)\n",
    "print('\\n')\n",
    "param2=GSrfc.best_params_\n",
    "print(param2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these parameters for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model5=RandomForestClassifier(criterion='entropy',max_depth=8, max_features='log2',n_estimators=100,n_jobs=1,random_state=51)\n",
    "model5.fit(x_train,y_train)\n",
    "y_pred=model5.predict(data_test)\n",
    "df_test['Survived']=y_pred\n",
    "df_test[['PassengerId','Survived']].to_csv('submission5.csv',index=False)"
   ]
  }
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