titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train = pd.read_csv('train.csv')\n",
    "dataset_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dataset_train);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = dataset_train.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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(dataset_train.corr());\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col = ['PassengerId', 'Name', 'Ticket', 'Fare', 'Cabin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train.drop(drop_col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "0         0       3    male  22.0      1      0        S\n",
       "1         1       1  female  38.0      1      0        C\n",
       "2         1       3  female  26.0      0      0        S\n",
       "3         1       1  female  35.0      1      0        S\n",
       "4         0       3    male  35.0      0      0        S"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0\n",
       "Sex          0\n",
       "Age         86\n",
       "SibSp        0\n",
       "Parch        0\n",
       "Embarked     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test = pd.read_csv('test.csv')\n",
    "dataset_test.drop(drop_col, axis=1, inplace=True)\n",
    "dataset_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "61          1       1  female  38.0      0      0      NaN\n",
       "829         1       1  female  62.0      0      0      NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train[dataset_train.Embarked.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.72664459161148"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_male = dataset_train[dataset_train['Sex']== 'male']['Age'].mean()\n",
    "age_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_female = dataset_train[dataset_train['Sex']== 'female']['Age'].mean()\n",
    "age_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Embarked      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.drop([61,829], axis= 0, inplace= True)\n",
    "dataset_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Embarked    0\n",
       "age         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train['age']=np.where(pd.isnull(dataset_train['Age']) & (dataset_train['Sex']=='male'),age_male,dataset_train['Age'])\n",
    "dataset_train['age'].fillna(age_female, inplace=True)\n",
    "dataset_train.drop('Age', axis=1, inplace=True)\n",
    "dataset_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Embarked    0\n",
       "age         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test['age'] = np.where(pd.isnull(dataset_test['Age']) & (dataset_test['Sex']=='male'), age_male, dataset_test['Age'])\n",
    "dataset_test.age.fillna(age_female, inplace = True)\n",
    "dataset_test.drop('Age', axis=1, inplace=True)\n",
    "dataset_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = dataset_train.iloc[:, 1:].values\n",
    "X_test = dataset_test.iloc[:, :].values\n",
    "y_train = dataset_train.iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#categorical variable\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "label_x = LabelEncoder()\n",
    "X_train[:, 1] = label_x.fit_transform(X_train[:, 1])\n",
    "X_train[:, 4] = label_x.fit_transform(X_train[:, 4])\n",
    "onehot = OneHotEncoder(categorical_features=[4])\n",
    "X_train = onehot.fit_transform(X_train).toarray()\n",
    "X_train = X_train[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "label_x_t = LabelEncoder()\n",
    "X_test[:, 1] = label_x_t.fit_transform(X_test[:, 1])\n",
    "X_test[:, 4] = label_x_t.fit_transform(X_test[:, 4])\n",
    "onehot = OneHotEncoder(categorical_features=[4])\n",
    "X_test = onehot.fit_transform(X_test).toarray()\n",
    "X_test = X_test[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_x = StandardScaler()\n",
    "X_train = sc_x.fit_transform(X_train)\n",
    "X_test = sc_x.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_test1 = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
       "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test1.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_test1.drop(['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
    "       'Ticket', 'Fare', 'Cabin', 'Embarked'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_test1['Survived'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_test1.to_csv('submit7.csv',index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bytree=0.9, gamma=0.06, learning_rate=0.1,\n",
       "              max_delta_step=0, max_depth=5, min_child_weight=5, missing=None,\n",
       "              n_estimators=100, n_jobs=-1, nthread=-1,\n",
       "              objective='binary:logistic', random_state=42, reg_alpha=0,\n",
       "              reg_lambda=0.5, scale_pos_weight=1, seed=None, silent=True,\n",
       "              subsample=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xgboost\n",
    "from xgboost import XGBClassifier\n",
    "classifier = XGBClassifier(max_depth=5, learning_rate=0.1, n_jobs=-1, nthread=-1, \n",
    "                    gamma=0.06, min_child_weight=5, \n",
    "                    subsample=1, colsample_bytree=0.9, \n",
    "                    reg_alpha=0, reg_lambda=0.5, \n",
    "                    random_state=42)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Shubham\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:1544: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 8.\n",
      "  \" = {}.\".format(effective_n_jobs(self.n_jobs)))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
       "                   multi_class='warn', n_jobs=-1, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#knn \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(n_jobs= -1, max_iter=500)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=7, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovo', degree=3, gamma=0.08, kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC(kernel='rbf', C= 7, gamma=0.08, decision_function_shape= 'ovo')\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grid search for svc\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = [{'C': [6, 7, 8, 9],\n",
    "               'kernel': ['rbf'],\n",
    "               'decision_function_shape' : ['ovo', 'ovr'],\n",
    "              'gamma': [0.07, 0.08, 0.09]}]\n",
    "grid_search = GridSearchCV(estimator = svc,\n",
    "                           param_grid = parameters,\n",
    "                           scoring = 'accuracy',\n",
    "                           cv = 10,\n",
    "                           n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8357705286839145"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 7, 'decision_function_shape': 'ovo', 'gamma': 0.08, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 0.1"
   ]
  }
 ],
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