titanic_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dfa57371-fbb1-48a8-ae32-e5afbae8cbc2",
   "metadata": {},
   "source": [
    "# PREDICTING SURVIVAL ON TITANIC "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8125cc-10a3-4c69-967f-770e675c4fd8",
   "metadata": {},
   "source": [
    "##### The RMS Titanic was a British passenger liner that sank in the North Atlantic Ocean in the early morning hours of 15 April 1912, after it collided with an iceberg during its maiden voyage from Southampton to New York City. There were an estimated 2,224 passengers and crew aboard the ship, and more than 1,500 died.\n",
    "\n",
    "Dataset: https://www.kaggle.com/c/titanic/data\n",
    "\n",
    "1. Survival - Survival (0 = No; 1 = Yes). Not included in test.csv file.\n",
    "2. Pclass - Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "3. Name - Name\n",
    "4. Sex - Sex\n",
    "5. Age - Age\n",
    "6. Sibsp - Number of Siblings/Spouses Aboard\n",
    "7. Parch - Number of Parents/Children Aboard\n",
    "8. Ticket - Ticket Number\n",
    "9. Fare - Passenger Fare\n",
    "10. Cabin - Cabin\n",
    "11. Embarked - Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e30f6d5-a37b-4689-8850-0e4a3c1374db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91429491-12b1-4eb7-916a-afa726c883f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db325f7f-81b5-44c4-8dda-157b1785f8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdac7090-af4f-4bb7-8b86-6d932bb70e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>886 rows × 12 columns</p>\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",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         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",
       "881                                 Markun, Mr. Johann    male  33.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "883                      Banfield, Mr. Frederick James    male  28.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "881      0            349257   7.8958   NaN        S  \n",
       "882      0              7552  10.5167   NaN        S  \n",
       "883      0  C.A./SOTON 34068  10.5000   NaN        S  \n",
       "884      0   SOTON/OQ 392076   7.0500   NaN        S  \n",
       "885      5            382652  29.1250   NaN        Q  \n",
       "\n",
       "[886 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ac42def-f479-40a1-afce-02737ad52b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68ee087f-d6ed-4c42-8580-b98d07d5bc09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "683dd134-62b6-408a-a34c-e8e2679b188e",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_columns = [ 'Survived','Age', 'Pclass', 'Sex', 'Fare', 'SibSp', 'Parch','Embarked']\n",
    "data_selected = train_data[selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f1e5e20-018e-49bf-8540-180dbd68f406",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</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>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.543351</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.168240</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>-0.155660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.077221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>-0.093254</td>\n",
       "      <td>0.093254</td>\n",
       "      <td>0.036261</td>\n",
       "      <td>-0.022405</td>\n",
       "      <td>-0.032523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.131900</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>-0.243292</td>\n",
       "      <td>0.221009</td>\n",
       "      <td>0.081720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.182333</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>0.269335</td>\n",
       "      <td>-0.117216</td>\n",
       "      <td>-0.166603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>-0.059528</td>\n",
       "      <td>-0.026354</td>\n",
       "      <td>0.070941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245489</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-0.011069</td>\n",
       "      <td>-0.081228</td>\n",
       "      <td>0.063036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>0.543351</td>\n",
       "      <td>-0.093254</td>\n",
       "      <td>-0.131900</td>\n",
       "      <td>0.182333</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>0.245489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>0.074115</td>\n",
       "      <td>-0.125722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.093254</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.082853</td>\n",
       "      <td>-0.074115</td>\n",
       "      <td>0.125722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>0.168240</td>\n",
       "      <td>0.036261</td>\n",
       "      <td>-0.243292</td>\n",
       "      <td>0.269335</td>\n",
       "      <td>-0.059528</td>\n",
       "      <td>-0.011069</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>-0.082853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148258</td>\n",
       "      <td>-0.778359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.003650</td>\n",
       "      <td>-0.022405</td>\n",
       "      <td>0.221009</td>\n",
       "      <td>-0.117216</td>\n",
       "      <td>-0.026354</td>\n",
       "      <td>-0.081228</td>\n",
       "      <td>0.074115</td>\n",
       "      <td>-0.074115</td>\n",
       "      <td>-0.148258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.496624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_S</th>\n",
       "      <td>-0.155660</td>\n",
       "      <td>-0.032523</td>\n",
       "      <td>0.081720</td>\n",
       "      <td>-0.166603</td>\n",
       "      <td>0.070941</td>\n",
       "      <td>0.063036</td>\n",
       "      <td>-0.125722</td>\n",
       "      <td>0.125722</td>\n",
       "      <td>-0.778359</td>\n",
       "      <td>-0.496624</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Survived       Age    Pclass      Fare     SibSp     Parch  \\\n",
       "Survived    1.000000 -0.077221 -0.338481  0.257307 -0.035322  0.081629   \n",
       "Age        -0.077221  1.000000 -0.369226  0.096067 -0.308247 -0.189119   \n",
       "Pclass     -0.338481 -0.369226  1.000000 -0.549500  0.083081  0.018443   \n",
       "Fare        0.257307  0.096067 -0.549500  1.000000  0.159651  0.216225   \n",
       "SibSp      -0.035322 -0.308247  0.083081  0.159651  1.000000  0.414838   \n",
       "Parch       0.081629 -0.189119  0.018443  0.216225  0.414838  1.000000   \n",
       "Sex_female  0.543351 -0.093254 -0.131900  0.182333  0.114631  0.245489   \n",
       "Sex_male   -0.543351  0.093254  0.131900 -0.182333 -0.114631 -0.245489   \n",
       "Embarked_C  0.168240  0.036261 -0.243292  0.269335 -0.059528 -0.011069   \n",
       "Embarked_Q  0.003650 -0.022405  0.221009 -0.117216 -0.026354 -0.081228   \n",
       "Embarked_S -0.155660 -0.032523  0.081720 -0.166603  0.070941  0.063036   \n",
       "\n",
       "            Sex_female  Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "Survived      0.543351 -0.543351    0.168240    0.003650   -0.155660  \n",
       "Age          -0.093254  0.093254    0.036261   -0.022405   -0.032523  \n",
       "Pclass       -0.131900  0.131900   -0.243292    0.221009    0.081720  \n",
       "Fare          0.182333 -0.182333    0.269335   -0.117216   -0.166603  \n",
       "SibSp         0.114631 -0.114631   -0.059528   -0.026354    0.070941  \n",
       "Parch         0.245489 -0.245489   -0.011069   -0.081228    0.063036  \n",
       "Sex_female    1.000000 -1.000000    0.082853    0.074115   -0.125722  \n",
       "Sex_male     -1.000000  1.000000   -0.082853   -0.074115    0.125722  \n",
       "Embarked_C    0.082853 -0.082853    1.000000   -0.148258   -0.778359  \n",
       "Embarked_Q    0.074115 -0.074115   -0.148258    1.000000   -0.496624  \n",
       "Embarked_S   -0.125722  0.125722   -0.778359   -0.496624    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numeric = pd.get_dummies(train_data[selected_columns])\n",
    "\n",
    "corrmat = data_numeric.corr()\n",
    "corrmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17fa79fb-48d4-4038-9a6c-240d1ce4173a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [6, 3]\n",
    "plt.rcParams['figure.dpi'] = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adbabd13-93c6-4d99-a557-e53992b72e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [\"#d73027\", \"#ffffbf\", \"#1a9850\"]  # Red, Yellow, Green\n",
    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dbd6705e-f5bf-45ba-94ca-28ff1e06d394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x240 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corrmat, cmap=cmap)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "569f059b-5aa0-4711-b0cc-250172cad13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Survived'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99e8ea6e-ff80-4a37-aaad-3c407d942892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26303ab9be0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgDrpij7EPX/+fN1xxx12zgMAwFXzKW7nzp3TF198QdgAGK1b+9q/0smNwY3UqHED7fjoE1u2vyd/S61v0wQ+HVASEBCgiooKVVVV2T0PAABXzee3JePj43X//fdr9OjR3rOWSOcvYgoAQG1KTU3V6tWrVVJSosGDB6t58+bKy8vz+fE+x23Pnj2SpL///e/e+xwOB3EDAAP5+nk0u/znhU1rwue4ZWVlXdUTAQBwrfgct40bN170/qv9LAIAALXN57hNnjzZ+3NlZaUOHDigrl27ateuXbYMBgBATfkctx07dlS7vX37di1cuLC25wEA4Kr5fG7J/9arVy9t3bq1NmcBAKBWXPHRktL5D3Vv27ZNZ8+etWUoAACuhs9xGz58uPfnhg0bKjIyUv/85z9tGQoAgKvhc9wOHTokScrPz1dmZqYiIyPVs2dP2wYDAPjPosfdtmx3zMsun9ZVVlZq5MiR+uyzz9SkSRO1adNGr732miIjI316/GX/5zZo0CDt3r1bknT06FHFx8frww8/1JQpU/TCCy/49CTXo7lz58rlcmnu3Ln+HgUA6qUJEybowIEDysnJ0fDhwzVu3DifH3vZuB05csR7BdTFixdrwIABeu+995Sdna0333yzxkObLj8/Xzk5OcrPz/f3KABQ7zRu3Fj33XefHA6HJKlPnz4qKCjw+fGXjVuTJk28P2dnZ+u+++6TJN10000KDLzia50CAHDFXnnllWrHflzOZePWoEEDFRcXq7y8XBs2bNCAAQO8v6uoqKjZlAAA+GjGjBnKy8vTzJkzfX7MZXe9pk2bph49eigwMFCJiYmKioqSdH4vLiIiosbDAgBwOX/5y1+0YsUKrV27Vk2bNvX5cZeN2wMPPKCEhAQdP35c3bp1894fERGh+fPn12xaAAAuw+12a8mSJVq7dq1atmx5RY/16Z9mwcHBCg4OrnbfrbfeekVPBACAr4qLizV58mS1a9dOiYmJkqQbbrhBH330kU+P54gQAMAFfP08ml2cTqcsy6rx42t8bkkAAOoq4gYAMA5xAwAYh7gBAIxD3AAAxiFuAADj8FEAAECddM8996ikpEQNGjRQ8+bNNWfOHPXo0cOnxxI3AMAFurW/05bt7snf4vPat956y3tmknfeeUcpKSnKycnx6bHEDQBs5DlTVe07fPefp9w6deqU9/I3vrA9brm5uRo7dqy+/PJL3XjjjVq4cKGio6MvutayLN11113atWuXvv76a7tHAwDbfXvS4+8R6rUxY8YoKytLkrRmzRqfH2f7ASWpqamaMGGCDh48qKlTpyolJeWSa2fPnq327dvbPRIAoJ5YtGiRioqK9Kc//UlTp071+XG2xq20tFQ7d+7U6NGjJUnJyckqKipSXl7eBWv37dunlStX6o9//KOdIwEA6qGxY8cqKytLX331lU/rbY1bUVGRQkJCvFfsdjgcCgsLU2FhYbV1Z8+e1fjx45WWlqaAgAA7RwIA1ANff/21jh496r29cuVK3XLLLbr55pt9enydOKDkueee0wMPPKDOnTuroKDgR9e63W653W7v7fLycpunAwBca6dOndKDDz6o7777Tg0aNFDr1q317rvv+nxQia1xCw0N1bFjx+TxeBQYGCjLslRYWKiwsLBq6zZs2KDCwkK9+uqr8ng8On36tCIiIrRjxw61bt262lqXyyWX6/8vxeB0Ou18CQBwXbqSQ/btEB4eru3bt9f48ba+LdmmTRvFxsYqIyNDkrR8+XI5nU5FRkZWW7dp0yYdPnxYBQUF2rx5s1q0aKGCgoILwgYAgC9sP1oyLS1NaWlpioqK0qxZs5Seni5JGjdunDIzM+1+egDAdcj2/7l17NhRW7duveD+BQsWXHR9REQEn3EDAFwVTpwMADAOcQMAGIe4AQCMQ9wAAMYhbgAA49SJM5T4W+k+364PdCXOfFvu/W7H9ttEd6/1bQKAKdhzAwAYh7gBAIxD3AAAxiFuAADjEDcAgHGIGwDAOMQNAGAc4gYAMA5xAwAYh7gBAIxD3AAAxiFuAADjEDcAgHGIGwDAOMQNAGAc4gYAMA5xAwAYh7gBAIxD3AAAxiFuAADjEDcAgHGIGwDAOMQNAGAc4gYAMA5xAwAYh7gBAIxD3AAAxgn09wCmCm97a7XvAIBrh7jZZGzyz/09AgBct3hbEgBgHOIGADAOcQMAGIe4AQCMQ9wAAMYhbgAA4xA3AIBxiBsAwDjEDQBgHOIGADAOcQMAGIe4AQCMQ9wAAMYhbgAA4xA3AIBxiBsAwDjEDQBgHK7EDa+5c+cqPz9f7du316RJk/w9DgDUGHGDV35+vnJycvw9BgBcNd6WBAAYh7gBAIxje9xyc3OVkJCgqKgoxcfHa9++fResWbdunXr16qUuXbooOjpaU6ZMUVVVld2jAQAMZXvcUlNTNWHCBB08eFBTp05VSkrKBWtuuukm/etf/9Jnn32mjz/+WNnZ2Vq0aJHdowEADGVr3EpLS7Vz506NHj1akpScnKyioiLl5eVVW9ejRw+1a9dOktS4cWPFxMSooKDAztEAAAazNW5FRUUKCQlRYOD5gzIdDofCwsJUWFh4yceUlJRo2bJluv/+++0cDQBgsDp1QMnp06c1bNgwTZkyRXFxcRdd43a75XQ6vV/l5eXXeEoAQF1na9xCQ0N17NgxeTweSZJlWSosLFRYWNgFa7/55hsNGTJEw4cPl8vluuQ2XS6XiouLvV9BQUG2zQ8AqJ9sjVubNm0UGxurjIwMSdLy5cvldDoVGRlZbV15ebmGDBmiIUOG6KmnnrJzJADAdcD2tyXT0tKUlpamqKgozZo1S+np6ZKkcePGKTMzU5L0yiuvaPv27VqxYoViYmIUExOj6dOn2z0aAMBQtp9+q2PHjtq6desF9y9YsMD785NPPqknn3zS7lEAANeJOnVACQAAtYG4AQCMQ9wAAMbhkjeo17gGHYCLIW6o17gGHYCL4W1JAIBxiBsAwDjEDQBgHOIGADAOB5TUU4sed9f6Nku+KDr/Pa/Ilu2PefnSJ8QGgNrEnhsAwDjEDQBgHOIGADAOcQMAGIe4AQCMQ9wAAMbhowC4Zrq1v7PWt3ljcCM1atxAOz76xJbt78nfUuvbBGA/9twAAMYhbgAA4xA3AIBxiBsAwDjEDQBgHOIGADAOcQMAGIe4AQCMQ9wAAMYhbgAA4xA3AIBxiBsAwDjEDQBgHK4KgHrNc6aq2ncAkIgb/sPNjVtU+14ffHvS4+8RANRBxA1evW6N9vcIAFAr+J8bAMA4xA0AYBziBgAwDnEDABiHuAEAjEPcAADGIW4AAOMQNwCAcYgbAMA4xA0AYBziBgAwDnEDABiHuAEAjEPcAADGIW4AAOMQNwCAcYgbAMA4xA0AYBziBgAwDnEDABiHuAEAjEPcAADGIW4AAOMQNwCAcWyPW25urhISEhQVFaX4+Hjt27fvoutef/11dejQQe3bt9f48eN19uxZu0cDABjK9rilpqZqwoQJOnjwoKZOnaqUlJQL1hw6dEhPP/20Nm3apLy8PB0/flzz58+3ezQAgKFsjVtpaal27typ0aNHS5KSk5NVVFSkvLy8auuWLVumpKQkBQcHy+FwaOLEiVqyZImdowEADGZr3IqKihQSEqLAwEBJksPhUFhYmAoLC6utKywsVHh4uPd2RETEBWsAAPBVoL8HuFJut1tut9t7u6SkRE6n048TmaW8vFxBQUH+HqPO4G+r7uFvtLqr/RsNCgrS/v37a2mausPWuIWGhurYsWPyeDwKDAyUZVkqLCxUWFhYtXVhYWHKz8/33i4oKLhgzQ9cLpdcLpedY1/XnE6niouL/T0GcEn8jcIXtr4t2aZNG8XGxiojI0OStHz5cjmdTkVGRlZbl5ycrMzMTJWUlMiyLM2bN08jR460czQAgMFsP1oyLS1NaWlpioqK0qxZs5Seni5JGjdunDIzMyVJ7dq103PPPac777xTkZGRat26tVJTU+0eDQBgKIdlWZa/h0Dd4Xa7edsXdRp/o/AFcQMAGIfTbwEAjEPcAADGIW74UevXr1dMTIy/x4ABVq1apc6dOysmJkZ79+619bkWLlyon/3sZ7Y+B+q2evchbgD107x58/TMM89o1KhR/h4F1wH23AzmcDg0ffp09e7dWxEREVq5cqVmzpypuLg4dejQQevXr5ckeTweDR48WHFxcYqOjtYjjzyib7/99qLb/OCDD9S3b1/17NlTvXr1UlZW1jV8RaivHnvsMW3atEnTpk1TQkKCduzYoYEDByouLk49evTQ22+/Len8CRxatmypp59+WrGxserQoYO2bNmiP/zhD4qJiVHXrl316aefSjp/dqLExET17NlT0dHR+t3vfqeqqqqLPv8bb7yh3r17KzY2Vv3791dOTs41e+3wEwvGkmS9/PLLlmVZ1tq1a61mzZpZ6enplmVZ1ltvvWXFxcVZlmVZVVVV1pdffun9eeLEidbMmTMty7KsrKwsq3v37pZlWVZ+fr7Vp08f69SpU5ZlWVZubq4VHBxsVVZWXsNXhfpqwIAB1jvvvGOVlZVZMTEx1tGjRy3LsqwTJ05YoaGhVnFxsXXo0CFLkvXOO+9YlmVZCxYssJo1a2atW7fOsizLevHFF60RI0ZYlmVZ3333nfXNN99YlmVZHo/HGjp0qLVkyRLLsiwrPT3dGj58uGVZlrV582br3nvv9f6dbty40erSpcu1etnwE96WNNzDDz8sSYqLi9O3337rPfNLr169lJubK0myLEuzZ8/W6tWr5fF4dOrUKSUkJFywrffff195eXnq37+/974GDRqosLBQHTp0uAavBibIzs7WF198oXvvvbfa/QcOHFC7du3UuHFj7//L4uLiFBQUpMTEREnn/27ffPNNSVJVVZWmTp2qzZs3y7IslZaWqmvXrhec3WjVqlXKyclR7969vfedPHlS3333nZo0aWLjK4U/ETfDNW7cWJIUEBBwwW2PxyNJWrx4sdatW6cNGzaoRYsWmjNnjtatW3fBtizL0t13363Fixdfo+lhIsuyFB0drezs7At+V1BQoBtuuMF7OyAgwPs3+8PtH/5u3W63SktL9dFHH6lx48ZyuVyqrKy86PONHTtWM2bMsOHVoK7if25QWVmZWrVqpRYtWuibb77RwoULL7pu8ODBWrt2rfbs2eO9b/v27ddoSpgiISFBhw4d0tq1a7337d69W2fOnLmi7ZSVlSk4OFiNGzdWSUmJ9/92/y0pKUkZGRney2hVVVVp586dNX8BqBfYc4PGjBmjVatWqWPHjmrdurX69eunw4cPX7AuMjJSixcvVmpqqioqKnTmzBn16NGDPTlckZtuukmrV6/WE088ocmTJ+vs2bMKCwvTypUrr2g7v//97zVixAhFR0fr1ltv1aBBgy66rl+/fnrxxRf185//XB6PR2fOnNHQoUMVFxdXC68GdRWn3wIAGIe3JQEAxiFuAADjEDcAgHGIGwDAOMQNAGAc4gZchRUrVqhnz56KiYlRp06dNHDgwEue3xDAtcNHAYAaOnbsmG6//XZ9/PHHCg8PlyTt2rVLPXr0kMPh8PN0wPWNPTegho4fP66AgADdfPPN3vtiY2PlcDiUm5uroUOHKj4+Xt26ddOrr74qSXrzzTcVFxen77//XpZladiwYZo+fbq/XgJgLPbcgBqqqqrSgw8+qKysLA0YMEAJCQl65JFHFBwcrN69eysjI0OdOnVSRUWF+vTpo9dff13x8fFKTU1Vw4YNFR4ernXr1mnNmjXs6QG1jLgBV2n//v3asGGD3nvvPa1fv14bN25U79691bFjR++aU6dO6dlnn9XYsWNVWVmp3r17q6ysTLt27VKrVq38OD1gJuIG1KIhQ4aoX79++tvf/qYjR45cdE1hYaH69u2rqqoqrV+/XpGRkdd4SsB8/M8NqKEjR45oy5Yt3ttlZWU6dOiQunbtqhYtWig9Pd37u7y8PJ08eVIej0cjR47U888/L7fbrYceekjff/+9P8YHjMaeG1BDhw8f1oQJE3To0CE1bdpUHo9HjzzyiKZNm6b8/Hw9/vjjOnz4sM6dO6dWrVpp8eLFmj17tr766itv+CZNmqSqqiq99tprfn41gFmIGwDAOLwtCQAwDnEDABiHuAEAjEPcAADGIW4AAOMQNwCAcYgbAMA4xA0AYBziBgAwzv8Bd0YW60f2qvQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 447.125x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Sex', y = 'Survived', kind = 'bar', data = train_data, hue = 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b9f9c5e-a268-49b0-8c0c-13158e8abff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "836fa133-cb80-42ab-b10b-3c65e9547247",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Age'] = train_data['Age'].fillna(train_data['Age'].median())\n",
    "test_data['Age'] = test_data['Age'].fillna(test_data['Age'].median())\n",
    "train_data['Embarked'] = train_data['Embarked'].fillna(train_data['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "39341c5b-03db-4d9b-90f4-3e0e56d221d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['FamilySize'] = train_data['SibSp'] + train_data['Parch'] + 1\n",
    "test_data['FamilySize'] = test_data['SibSp'] + test_data['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b56ad63-eb50-44b6-ba32-8d90f01cc9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Title'] = train_data['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "test_data['Title'] = test_data['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "# Simplify titles\n",
    "title_mapping = {\n",
    "    \"Mr\": \"Mr\", \"Miss\": \"Miss\", \"Mrs\": \"Mrs\", \"Master\": \"Master\", \"Dr\": \"Rare\", \n",
    "    \"Rev\": \"Rare\", \"Col\": \"Rare\", \"Major\": \"Rare\", \"Mlle\": \"Miss\", \"Mme\": \"Mrs\", \n",
    "    \"Countess\": \"Rare\", \"Ms\": \"Miss\", \"Lady\": \"Rare\", \"Jonkheer\": \"Rare\", \n",
    "    \"Don\": \"Rare\", \"Dona\": \"Rare\", \"Sir\": \"Rare\", \"Capt\": \"Rare\"\n",
    "}\n",
    "train_data['Title'] = train_data['Title'].map(title_mapping)\n",
    "test_data['Title'] = test_data['Title'].map(title_mapping)\n",
    "\n",
    "train_data['Title'] = train_data['Title'].fillna('Rare')\n",
    "test_data['Title'] = test_data['Title'].fillna('Rare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e46ec40d-fdaa-4b1a-ab74-8de7055fb465",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['CabinSection'] = train_data['Cabin'].str[0]\n",
    "test_data['CabinSection'] = test_data['Cabin'].str[0]\n",
    "\n",
    "train_data['CabinSection'] = train_data['CabinSection'].fillna('Unknown')\n",
    "test_data['CabinSection'] = test_data['CabinSection'].fillna('Unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b49716ed-2335-49c7-947e-4fead16792e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"Pclass\", \"Sex\", \"Age\", \"Embarked\", \"FamilySize\", \"Title\", \"CabinSection\"]\n",
    "X = pd.get_dummies(train_data[features])\n",
    "X_test = pd.get_dummies(test_data[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "784111d0-acb7-4ec4-b791-09b10a946459",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, X_test = X.align(X_test, join='left', axis=1, fill_value=0)\n",
    "\n",
    "y = train_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "75b2db68-bedc-452b-8218-010a0b2afde0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa44e79c-d9d1-4ba9-bdfa-8dc6e77b5058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, n_estimators=500, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5, n_estimators=500, random_state=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5, n_estimators=500, random_state=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=500, max_depth=5, random_state=1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8f5e205b-c741-451b-a75a-415dccd67505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.8212290502793296\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86       106\n",
      "           1       0.84      0.70      0.76        73\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.82      0.80      0.81       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_val)\n",
    "print(\"Validation Accuracy:\", accuracy_score(y_val, y_pred))\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3cb1e28b-0d04-4aab-8bab-dc032c32fdae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.84916201 0.82022472 0.8258427  0.79775281 0.85955056]\n",
      "Average CV score: 0.8305065595380077\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(model, X, y, cv=5)\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "print(\"Average CV score:\", cv_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e9edbc22-1803-4580-a81b-4eab777c970f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
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       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0876266b-17fd-4996-991a-f416853dda69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('titanic_model.pkl', 'wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aa9175a-629e-42d2-80ef-ea5dc77d6380",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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