Day_18_Titanic_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a59021b",
   "metadata": {},
   "source": [
    "## TItanic dataset from Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee308d6",
   "metadata": {},
   "source": [
    "### Importing Libraries and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a64f28e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from IPython.display import display\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e5984a5",
   "metadata": {},
   "source": [
    "### Importing Datasets and displaying them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "541f8551",
   "metadata": {},
   "outputs": [
    {
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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",
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      ],
      "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  "
      ]
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "  <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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "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  "
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training = \"/home/chinmayee/my_machine_learning_journey/100_Days_of_code_challenge/train.csv\"\n",
    "testing = \"/home/chinmayee/my_machine_learning_journey/100_Days_of_code_challenge/test.csv\"\n",
    "df_train = pd.read_csv(training)\n",
    "df_test = pd.read_csv(testing)\n",
    "display(df_train.head())\n",
    "display(df_test.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0fd56b4",
   "metadata": {},
   "source": [
    "### Plotting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aecee61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "sns.displot(df_train['Age'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6acb48",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "0. remove unnecessary columns\n",
    "1. Null value handle\n",
    "2. string value handle\n",
    "3. standardization / normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b81e711",
   "metadata": {},
   "source": [
    "### Removing unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef8adb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unnecessary columns\n",
    "\n",
    "X = df_train.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'PassengerId'], axis =1)\n",
    "y = df_train['Survived']\n",
    "X_t = df_test.drop(['Name', 'Ticket', 'Cabin', 'PassengerId'], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbac114",
   "metadata": {},
   "source": [
    "### Null value handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10f0351c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset : Pclass        int64\n",
      "Sex          object\n",
      "Age         float64\n",
      "SibSp         int64\n",
      "Parch         int64\n",
      "Fare        float64\n",
      "Embarked     object\n",
      "dtype: object\n",
      "\n",
      "Test dataset : Pclass        int64\n",
      "Sex          object\n",
      "Age         float64\n",
      "SibSp         int64\n",
      "Parch         int64\n",
      "Fare        float64\n",
      "Embarked     object\n",
      "dtype: object\n",
      "\n",
      "Target:  int64\n",
      "\n",
      "\n",
      "Null Values\n",
      "\n",
      "Train dataset : Pclass        0\n",
      "Sex           0\n",
      "Age         177\n",
      "SibSp         0\n",
      "Parch         0\n",
      "Fare          0\n",
      "Embarked      2\n",
      "dtype: int64\n",
      "\n",
      "Test dataset : Pclass       0\n",
      "Sex          0\n",
      "Age         86\n",
      "SibSp        0\n",
      "Parch        0\n",
      "Fare         1\n",
      "Embarked     0\n",
      "dtype: int64\n",
      "\n",
      "Target:  0\n",
      "\n",
      "\n",
      "Null Values\n",
      "\n",
      "Train dataset : Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Fare        0\n",
      "Embarked    0\n",
      "dtype: int64\n",
      "\n",
      "Test dataset : Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Fare        0\n",
      "Embarked    0\n",
      "dtype: int64\n",
      "\n",
      "Target:  0\n"
     ]
    }
   ],
   "source": [
    "# Printing data types of each data set\n",
    "print('Train dataset :', X.dtypes)\n",
    "print('\\nTest dataset :', X_t.dtypes)\n",
    "print('\\nTarget: ', y.dtypes)\n",
    "\n",
    "# Printing the null values in each column\n",
    "print('\\n\\nNull Values')\n",
    "print('\\nTrain dataset :', X.isnull().sum())\n",
    "print('\\nTest dataset :', X_t.isnull().sum())\n",
    "print('\\nTarget: ', y.isnull().sum())\n",
    "\n",
    "# Filling null datas\n",
    "\n",
    "#loc stands for locate.It locates the assigned index\n",
    "def fill_nulldata(df):\n",
    "    for col in df.columns:\n",
    "        \n",
    "        if df[col].dtype == object:\n",
    "            df.loc[df[col].isnull() == True, col] = df[col].mode()[0]# [0] is written for the object type data\n",
    "            \n",
    "        else:\n",
    "            df.loc[df[col].isnull() == True, col] = df[col].mean()\n",
    "    \n",
    "fill_nulldata(X)\n",
    "fill_nulldata(X_t)\n",
    "\n",
    "\n",
    "print('\\n\\nNull Values')\n",
    "print('\\nTrain dataset :', X.isnull().sum())\n",
    "print('\\nTest dataset :', X_t.isnull().sum())\n",
    "print('\\nTarget: ', y.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db4ba60",
   "metadata": {},
   "source": [
    "### String and character value handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02b685b4",
   "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_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</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>3.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass        Age  SibSp  Parch     Fare  Sex_female  Sex_male  \\\n",
       "0       3.0  22.000000    1.0    0.0   7.2500           0         1   \n",
       "1       1.0  38.000000    1.0    0.0  71.2833           1         0   \n",
       "2       3.0  26.000000    0.0    0.0   7.9250           1         0   \n",
       "3       1.0  35.000000    1.0    0.0  53.1000           1         0   \n",
       "4       3.0  35.000000    0.0    0.0   8.0500           0         1   \n",
       "..      ...        ...    ...    ...      ...         ...       ...   \n",
       "886     2.0  27.000000    0.0    0.0  13.0000           0         1   \n",
       "887     1.0  19.000000    0.0    0.0  30.0000           1         0   \n",
       "888     3.0  29.699118    1.0    2.0  23.4500           1         0   \n",
       "889     1.0  26.000000    0.0    0.0  30.0000           0         1   \n",
       "890     3.0  32.000000    0.0    0.0   7.7500           0         1   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S  \n",
       "0             0           0           1  \n",
       "1             1           0           0  \n",
       "2             0           0           1  \n",
       "3             0           0           1  \n",
       "4             0           0           1  \n",
       "..          ...         ...         ...  \n",
       "886           0           0           1  \n",
       "887           0           0           1  \n",
       "888           0           0           1  \n",
       "889           1           0           0  \n",
       "890           0           1           0  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr 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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</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>3.0</td>\n",
       "      <td>34.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>3.0</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1.0</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3.0</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3.0</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3.0</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass       Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
       "0       3.0  34.50000    0.0    0.0    7.8292           0         1   \n",
       "1       3.0  47.00000    1.0    0.0    7.0000           1         0   \n",
       "2       2.0  62.00000    0.0    0.0    9.6875           0         1   \n",
       "3       3.0  27.00000    0.0    0.0    8.6625           0         1   \n",
       "4       3.0  22.00000    1.0    1.0   12.2875           1         0   \n",
       "..      ...       ...    ...    ...       ...         ...       ...   \n",
       "413     3.0  30.27259    0.0    0.0    8.0500           0         1   \n",
       "414     1.0  39.00000    0.0    0.0  108.9000           1         0   \n",
       "415     3.0  38.50000    0.0    0.0    7.2500           0         1   \n",
       "416     3.0  30.27259    0.0    0.0    8.0500           0         1   \n",
       "417     3.0  30.27259    1.0    1.0   22.3583           0         1   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S  \n",
       "0             0           1           0  \n",
       "1             0           0           1  \n",
       "2             0           1           0  \n",
       "3             0           0           1  \n",
       "4             0           0           1  \n",
       "..          ...         ...         ...  \n",
       "413           0           0           1  \n",
       "414           1           0           0  \n",
       "415           0           0           1  \n",
       "416           0           0           1  \n",
       "417           1           0           0  \n",
       "\n",
       "[418 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.get_dummies(X)\n",
    "X_t = pd.get_dummies(X_t)\n",
    "display(X, X_t, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0dc88d2",
   "metadata": {},
   "source": [
    "### Feature scaling: Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec82c854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "\n",
    "# call sc.fit_transform for the first variable then the nxet will be fitted accordingly\n",
    "X_scaled = sc.fit_transform(X)\n",
    "X_t_scaled = sc.transform(X_t) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89f40bf7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b866a6b3",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f43ec28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8103295461678489\n",
      "Best Hyperparameters: {'n_estimators': 1000, 'min_samples_split': 2, 'max_leaf_nodes': 90000, 'max_features': 'sqrt', 'max_depth': 90000, 'criterion': 'gini', 'class_weight': 'balanced_subsample', 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_repeats= 10, n_splits= 5, random_state = 1)\n",
    "space = dict()\n",
    "#space['algorithm'] = ['auto', 'ball_tree', 'kd_tree', 'brute']\n",
    "#space['metric'] = ['euclidean', 'manhattan', 'chebyshev', 'minkowski','wminkowski', 'seuclidean', 'mahalanobis']\n",
    "#space['leaf_size'] = (0, np.inf)\n",
    "#space['weights']= ['uniform', 'distance']\n",
    "#space['p'] = (0, np.inf)\n",
    "\n",
    "space['criterion'] = ['gini', 'entropy']\n",
    "space['n_estimators'] = (-100, 1000)\n",
    "space['max_depth'] = (-10, 90000)\n",
    "space['min_samples_split']= (2, 90000)\n",
    "space['max_leaf_nodes'] = (2, 90000)\n",
    "space['max_features'] = ['auto', 'sqrt', 'log2']\n",
    "space['bootstrap'] = [True, False]\n",
    "space['class_weight'] = ['balanced', 'balanced_subsample']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "search = RandomizedSearchCV(model, space, scoring='accuracy', n_jobs=-1, cv=cv)\n",
    "result = search.fit(X_scaled, y)\n",
    "\n",
    "print('Best score: %s' % result.best_score_)\n",
    "print('Best Hyperparameters: %s' % result.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24350be6",
   "metadata": {},
   "source": [
    "### Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "191544f7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomForestClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-18cbb9947536>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_tuned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_split\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_leaf_nodes\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m90000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m'gini'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbootstrap\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel_tuned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_scaled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "model_tuned = RandomForestClassifier(n_estimators =1000, min_samples_split= 2, max_leaf_nodes= 2, max_features= 'auto', max_depth= 90000, criterion= 'gini', class_weight= 'balanced', bootstrap= False)\n",
    "model_tuned.fit(X_scaled, y) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3284f701",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f7cd7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model_tuned.predict(X_t_scaled)\n",
    "Submission = pd.DataFrame({'PassengerId':list(range(892,1310))})\n",
    "Submission['Survived']=predict\n",
    "\n",
    "Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dbda5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv('submission5.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d239bec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "345158d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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