diff --git a/Practice_for_Beginners/Titanic_Survival.ipynb b/Practice_for_Beginners/Titanic_Survival.ipynb
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@@ -0,0 +1,3332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#                            Exploring the Titanic Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DrjCN4JpVPdG"
+   },
+   "source": [
+    "We'll start off by loading the required base libraries for the project. These are the only ones we need to manipulate and visualize our data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "id": "g2zpYSJZ4UNs",
+    "outputId": "ff220118-7be1-43d5-9713-ad6cbe9b1b06"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['figure.figsize']=(12,9)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2hD7tkqVct2"
+   },
+   "source": [
+    "We'll also be needing several libraries that contain various classifier models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "ONo8YusGVWph"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC, LinearSVC\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.linear_model import Perceptron\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZMV_QsACVj_o"
+   },
+   "source": [
+    "## Loading and Viewing the Training and Test Data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "id": "d3HiWAPTVp_6",
+    "outputId": "adc13917-5884-4283-da28-14b13207051b"
+   },
+   "outputs": [],
+   "source": [
+    "data=pd.read_csv(\"train.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "imPS3TvULr72"
+   },
+   "outputs": [],
+   "source": [
+    "titanic_df=pd.DataFrame(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
+    },
+    "id": "rpDJdxABhZ5q",
+    "outputId": "693b0dd3-57a7-4d2f-b8b6-7e7e6e9f7adb"
+   },
+   "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",
+       "    <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>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 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",
+       "886          887         0       2   \n",
+       "887          888         1       1   \n",
+       "888          889         0       3   \n",
+       "889          890         1       1   \n",
+       "890          891         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",
+       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
+       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
+       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
+       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
+       "890                                Dooley, Mr. Patrick    male  32.0      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",
+       "886      0            211536  13.0000   NaN        S  \n",
+       "887      0            112053  30.0000   B42        S  \n",
+       "888      2        W./C. 6607  23.4500   NaN        S  \n",
+       "889      0            111369  30.0000  C148        C  \n",
+       "890      0            370376   7.7500   NaN        Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gLfyB5RrV3RO"
+   },
+   "source": [
+    "As we can see, we've the following 12 columns: \n",
+    "* PassengerID : the unique id of the individual passengers\n",
+    "*   Survived: survival information, 0 indicate failure   (death) while 1 indicates success(survival)\n",
+    "*   Pclass: The class in which the passengers were travelling.\n",
+    "*   Name: Name of the passengers.\n",
+    "*   Sex: Passengers' gender.\n",
+    "*   Age: Passengers' Age.\n",
+    "*   Parch: Number of parents/gaurdians abord the titanic for each passenger.\n",
+    "*   SibSB: Number of siblings aboard the titanic for each passenger.\n",
+    "*   Ticket: ticket number of each passenger.\n",
+    "*   Fare: Fare that each passenger paid for the voyage.\n",
+    "*   Cabin: Cabin number where each passenger stayed.\n",
+    "*   Embarked: The city from which each passenger embarked upon the titanic.\n",
+    "\n",
+    "The Survived Column is obviously our label i.e. this is what we have to predict, and it is thus missing from the test set.\n",
+    "\n",
+    "As for the other columns, categorizing them would help us better understand and decide upon the features.\n",
+    "\n",
+    "The following are the categories and the labels that fit in those categories:\n",
+    "\n",
+    "   * Categorical: Sex and Embarked.\n",
+    "   * Ordinal: Pclass\n",
+    "   * Numerical:\n",
+    "      1. Continuous: Age and Fare\n",
+    "      2. Discreet: SibSp and Parch\n",
+    "   * Alphanumerical: Ticket\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ha4QWv8zXhu8"
+   },
+   "source": [
+    "#                      More Details about the Dataset  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "a5t_iLQzZQFK"
+   },
+   "source": [
+    "Checking for null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "H41_ZuVyOoLF",
+    "outputId": "9249526c-197e-47cb-d997-18e3f6a8afb7"
+   },
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 269
+    },
+    "id": "ho4oN5c1JI4D",
+    "outputId": "509baedb-b70b-401b-acb3-7fd95e0238c2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "CWqXuMVsh3Pj",
+    "outputId": "1efe6b85-0779-41ca-aff4-3ae503111ce0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2368e7b6970>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1750x1750 with 56 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(titanic_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ejJExcrWZXr9",
+    "outputId": "08677e6e-d089-4574-a3b6-7b81ac3fd1aa"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "Qh7vZqGtiL7p",
+    "outputId": "8aa07071-99c9-497a-fe39-232d4d046681"
+   },
+   "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>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wI6UwxkNZ7KE"
+   },
+   "source": [
+    "In the above table, we can observe:\n",
+    "\n",
+    "* There are a total of 891 values in each column of the dataset.\n",
+    "* The survival rate among these 891 passengers is around 38%, in comparison to the factual 32% survival rate of the incident reports.\n",
+    "* Nearly 30% of the passengers had siblings aboard.\n",
+    "Fare variation is low, with the exception of some person paying a relatively large amount of 512 dollars. There are some  outliers.\n",
+    "* The mean age of the passengers is 29 years with a standard deviation of 14. Nearly 75% of the passengers are under 38 years of age. A few outliers exist(above 80 years).\n",
+    "* Most of the passengers(nearly 75%) did not travel with parents/gaurdians.\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u2mhxmPtdIYT"
+   },
+   "source": [
+    "# Preprocessing\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OzEPNNNlc2tm"
+   },
+   "source": [
+    "Using the above observations, we can do the following from the get-go:\n",
+    "\n",
+    "*  Drop the Name since they're not features(we could do \n",
+    "something with them but lets keep this one simple).\n",
+    "*  Drop Cabin because of its high null value count.\n",
+    "*  Drop Ticket because of its low unique value count.\n",
+    "*  Additionally, drop PassengerId from the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "R77G63Tal6m8"
+   },
+   "outputs": [],
+   "source": [
+    "titanic_df.drop(columns=['PassengerId','Name','Ticket','Cabin'],inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxYRyn5edo0Y"
+   },
+   "source": [
+    "## Combining the SibSp and Parch Features\n",
+    "Since we observed earlier that there was no correlation among these 2 features, we can combine them to create a stronger feature. Let's call it familysize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "knx-KEHqi7Ek"
+   },
+   "outputs": [],
+   "source": [
+    "titanic_df[\"familysize\"]=titanic_df[\"SibSp\"]+titanic_df[\"Parch\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 332
+    },
+    "id": "_AY6Oo7feBXx",
+    "outputId": "7f7da886-cb56-4d2f-ba04-3977a14cbc77"
+   },
+   "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>familysize</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.724138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.578431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.552795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.303538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.136364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   familysize  Survived\n",
+       "3           3  0.724138\n",
+       "2           2  0.578431\n",
+       "1           1  0.552795\n",
+       "6           6  0.333333\n",
+       "0           0  0.303538\n",
+       "4           4  0.200000\n",
+       "5           5  0.136364\n",
+       "7           7  0.000000\n",
+       "8          10  0.000000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df[['familysize', 'Survived']].groupby(['familysize'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qzhGOEAleb6m"
+   },
+   "source": [
+    "Let's combine these features into a categorical one called IsAlone. If that has better correlation with survival, we'll drop all three FamilySize SibSp, and Parch in favor of IsAlone. Otherwise we would keep FamilySize and drop the other two.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 112
+    },
+    "id": "FYzUsN-WensK",
+    "outputId": "dce2866f-c8f0-48ab-c929-74239863cf4c"
+   },
+   "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>IsAlone</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.552795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.346575</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   IsAlone  Survived\n",
+       "1        1  0.552795\n",
+       "0        0  0.346575"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df['IsAlone'] = 0\n",
+    "titanic_df.loc[titanic_df['familysize'] == 1, 'IsAlone'] = 1\n",
+    "titanic_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PnnGLknze3Pz"
+   },
+   "source": [
+    "Thus, we drop the previous three columns and obtain a better feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "tQOTB0EwfIEx"
+   },
+   "outputs": [],
+   "source": [
+    "titanic_df.drop(['SibSp', 'Parch', 'familysize'], axis = 1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
+    },
+    "id": "WEEtNTfhfmAW",
+    "outputId": "81878b39-4900-43b9-9f02-4bf1a0536839"
+   },
+   "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>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>IsAlone</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>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</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>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</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>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</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>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</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>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Survived  Pclass     Sex   Age     Fare Embarked  IsAlone\n",
+       "0           0       3    male  22.0   7.2500        S        1\n",
+       "1           1       1  female  38.0  71.2833        C        1\n",
+       "2           1       3  female  26.0   7.9250        S        0\n",
+       "3           1       1  female  35.0  53.1000        S        1\n",
+       "4           0       3    male  35.0   8.0500        S        0\n",
+       "..        ...     ...     ...   ...      ...      ...      ...\n",
+       "886         0       2    male  27.0  13.0000        S        0\n",
+       "887         1       1  female  19.0  30.0000        S        0\n",
+       "888         0       3  female   NaN  23.4500        S        0\n",
+       "889         1       1    male  26.0  30.0000        C        0\n",
+       "890         0       3    male  32.0   7.7500        Q        0\n",
+       "\n",
+       "[891 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-m6wjVEUid2i"
+   },
+   "source": [
+    "Now,filling the missing values in Age column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 554
+    },
+    "id": "ESxcm61TmiYo",
+    "outputId": "bbbe241c-be04-482f-e4d8-a47a286f0f1f"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(titanic_df['Age'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "ONZtoLTFfmDb"
+   },
+   "outputs": [],
+   "source": [
+    "def filling_missing():\n",
+    "    global titanic_df\n",
+    "    for pclass in [1,2,3]:\n",
+    "      for sex in ['male','female']:\n",
+    "        for survived in [1,0]:\n",
+    "            k=titanic_df[\"Age\"][(titanic_df.Pclass==pclass) & (titanic_df.Age.isnull() ) & (titanic_df.Sex==sex) & (titanic_df.Survived==survived)].index.tolist()\n",
+    "            \n",
+    "            y=titanic_df[\"Age\"][(titanic_df.Pclass==pclass) & (~titanic_df.Age.isnull() ) & (titanic_df.Sex==sex) & (titanic_df.Survived==survived)].mean()\n",
+    "    \n",
+    "            for i in k:\n",
+    "                titanic_df.at[i,\"Age\"]=int(y)\n",
+    "    null_index=titanic_df[titanic_df.Embarked.isnull()].index.tolist()\n",
+    "    \n",
+    "    for i in null_index:\n",
+    "      titanic_df.at[i,\"Embarked\"]=\"C\"\n",
+    "  \n",
+    "filling_missing()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "78bDo9SXfmG3",
+    "outputId": "481ebe54-a0b8-422a-ffc0-5ae836dfb6a9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Survived    0\n",
+       "Pclass      0\n",
+       "Sex         0\n",
+       "Age         0\n",
+       "Fare        0\n",
+       "Embarked    0\n",
+       "IsAlone     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 556
+    },
+    "id": "S8ccFSg3fmIu",
+    "outputId": "b1feb76b-f940-46c1-a1d4-9e21e67aa3fd"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(titanic_df['Age'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 623
+    },
+    "id": "nTQ96avifmLW",
+    "outputId": "69464216-0af4-43fa-8682-0be8dce142b3"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Fare'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(titanic_df.Fare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1Q5SQTWE8ExF"
+   },
+   "source": [
+    "As there are some passengers who gave more than others for the ticket .So, converting it into categorical column "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 175
+    },
+    "id": "eXMsWJHR8cCs",
+    "outputId": "a70ed58c-1ae0-47be-b8fc-9c31f025dec7"
+   },
+   "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>FareRange</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(-0.001, 7.91]</td>\n",
+       "      <td>0.197309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(7.91, 14.454]</td>\n",
+       "      <td>0.303571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(14.454, 31.0]</td>\n",
+       "      <td>0.454955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(31.0, 512.329]</td>\n",
+       "      <td>0.581081</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         FareRange  Survived\n",
+       "0   (-0.001, 7.91]  0.197309\n",
+       "1   (7.91, 14.454]  0.303571\n",
+       "2   (14.454, 31.0]  0.454955\n",
+       "3  (31.0, 512.329]  0.581081"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "titanic_df['FareRange'] = pd.qcut(titanic_df['Fare'], 4)\n",
+    "titanic_df[['FareRange', 'Survived']].groupby(['FareRange'], as_index=False).mean().sort_values(by='FareRange', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "CkYgeD1LfmOD"
+   },
+   "outputs": [],
+   "source": [
+    "titanic_df.loc[titanic_df['Fare'] <= 7.91, 'Fare'] = 0.0\n",
+    "titanic_df.loc[(titanic_df['Fare'] > 7.91) & (titanic_df['Fare'] <= 14.454), 'Fare'] = 1.0\n",
+    "titanic_df.loc[(titanic_df['Fare'] > 14.454) & (titanic_df['Fare'] <= 31.0), 'Fare']   = 2.0\n",
+    "titanic_df.loc[titanic_df['Fare'] > 31.0, 'Fare'] = 3.0\n",
+    "titanic_df['Fare'] = titanic_df['Fare'].astype(int)\n",
+    "titanic_df.drop(['FareRange'], axis=1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
+    },
+    "id": "6N6Fo-vLfmPv",
+    "outputId": "c495d06b-57bd-4410-fe4d-76a418ab0c0a"
+   },
+   "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>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>IsAlone</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>0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</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>3</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</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>3</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Survived  Pclass     Sex   Age  Fare Embarked  IsAlone\n",
+       "0           0       3    male  22.0     0        S        1\n",
+       "1           1       1  female  38.0     3        C        1\n",
+       "2           1       3  female  26.0     1        S        0\n",
+       "3           1       1  female  35.0     3        S        1\n",
+       "4           0       3    male  35.0     1        S        0\n",
+       "..        ...     ...     ...   ...   ...      ...      ...\n",
+       "886         0       2    male  27.0     1        S        0\n",
+       "887         1       1  female  19.0     2        S        0\n",
+       "888         0       3  female  23.0     2        S        0\n",
+       "889         1       1    male  26.0     2        C        0\n",
+       "890         0       3    male  32.0     0        Q        0\n",
+       "\n",
+       "[891 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "s54N-Mw29dF2"
+   },
+   "source": [
+    "# Data Visualisation\n",
+    "Now, data is ready for plotting the graphs and visuaisation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 143
+    },
+    "id": "EP0U18v8t7Yn",
+    "outputId": "f94a8df8-3392-4bc7-cb7a-960518e6a28f"
+   },
+   "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>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>IsAlone</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>577</td>\n",
+       "      <td>577</td>\n",
+       "      <td>577</td>\n",
+       "      <td>577</td>\n",
+       "      <td>577</td>\n",
+       "      <td>577</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Survived  Pclass  Age  Fare  Embarked  IsAlone\n",
+       "Sex                                                   \n",
+       "female       314     314  314   314       314      314\n",
+       "male         577     577  577   577       577      577"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.groupby(\"Sex\").count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 675
+    },
+    "id": "e_ID0aTg_mtJ",
+    "outputId": "08c3b20e-057c-4497-e49c-d5d03d84cc2e"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    549\n",
+      "1    342\n",
+      "Name: Survived, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(titanic_df[\"Survived\"].value_counts())\n",
+    "sns.countplot(titanic_df[\"Survived\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 476
+    },
+    "id": "EogqCrWyzEJW",
+    "outputId": "74d2bde1-6183-487e-bdaa-fa4a145b0efd"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n",
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x2368a0d4430>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 598x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.factorplot('Pclass','Survived',hue='Sex',data=titanic_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "EmOlpgibsiOw",
+    "outputId": "0a93bff8-7165-4c68-b1f9-43a51d435964"
+   },
+   "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>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>91</td>\n",
+       "      <td>70</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
+       "      <th>0</th>\n",
+       "      <td>77</td>\n",
+       "      <td>91</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>45</td>\n",
+       "      <td>17</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Pclass            1   2    3\n",
+       "Sex    Survived             \n",
+       "female 0          3   6   72\n",
+       "       1         91  70   72\n",
+       "male   0         77  91  300\n",
+       "       1         45  17   47"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab([titanic_df.Sex,titanic_df.Survived],titanic_df.Pclass)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 710
+    },
+    "id": "GIOehq79K9Ry",
+    "outputId": "aaf0c583-e40f-49a7-86fe-af3eb29eb57d"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Sex     Survived\n",
+       "female  1           233\n",
+       "        0            81\n",
+       "male    0           468\n",
+       "        1           109\n",
+       "Name: Survived, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(titanic_df.Survived,hue=titanic_df.Sex)\n",
+    "titanic_df.groupby(\"Sex\")[\"Survived\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 537
+    },
+    "id": "qwPlKeFO2bKd",
+    "outputId": "1d93c330-1c85-45c1-f783-a806c142aca1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Sex'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "titanic_df['Sex'].value_counts().plot(kind='pie',autopct='%.2f')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 269
+    },
+    "id": "vR9FX7GV277h",
+    "outputId": "f2373073-34f4-4787-c9ef-e4d95c9f8453"
+   },
+   "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>Sex</th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>91</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>70</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>72</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>72</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Sex              female  male\n",
+       "Pclass Survived              \n",
+       "1      0              3    77\n",
+       "       1             91    45\n",
+       "2      0              6    91\n",
+       "       1             70    17\n",
+       "3      0             72   300\n",
+       "       1             72    47"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab([titanic_df.Pclass,titanic_df.Survived],titanic_df.Sex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 645
+    },
+    "id": "-U9TmsPr-6wv",
+    "outputId": "0cd696f0-be3a-49c0-c2c1-a88e16859625"
+   },
+   "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></th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">C</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>44</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">Q</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>24</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">S</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>55</td>\n",
+       "      <td>231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>46</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>61</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>33</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Sex                       female  male\n",
+       "Embarked Survived Pclass              \n",
+       "C        0        1            1    25\n",
+       "                  2            0     8\n",
+       "                  3            8    33\n",
+       "         1        1           44    17\n",
+       "                  2            7     2\n",
+       "                  3           15    10\n",
+       "Q        0        1            0     1\n",
+       "                  2            0     1\n",
+       "                  3            9    36\n",
+       "         1        1            1     0\n",
+       "                  2            2     0\n",
+       "                  3           24     3\n",
+       "S        0        1            2    51\n",
+       "                  2            6    82\n",
+       "                  3           55   231\n",
+       "         1        1           46    28\n",
+       "                  2           61    15\n",
+       "                  3           33    34"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab([titanic_df.Embarked,titanic_df.Survived,titanic_df.Pclass],titanic_df.Sex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 269
+    },
+    "id": "QnN4QOWU4A7y",
+    "outputId": "ecb664cc-66ff-43d3-8209-517362097b01"
+   },
+   "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>Sex</th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>25.666667</td>\n",
+       "      <td>44.461039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34.846154</td>\n",
+       "      <td>36.220444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>33.340659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>28.078571</td>\n",
+       "      <td>16.019412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>23.625000</td>\n",
+       "      <td>27.183333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19.215278</td>\n",
+       "      <td>22.221702</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Sex                 female       male\n",
+       "Pclass Survived                      \n",
+       "1      0         25.666667  44.461039\n",
+       "       1         34.846154  36.220444\n",
+       "2      0         36.000000  33.340659\n",
+       "       1         28.078571  16.019412\n",
+       "3      0         23.625000  27.183333\n",
+       "       1         19.215278  22.221702"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.pivot_table(index=['Pclass','Survived'],columns='Sex')[\"Age\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RjDX-3khFbkj",
+    "outputId": "3de999c8-ee8c-4a82-a4bc-bbea4d320a36"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex     Pclass\n",
+       "female  1         96.808511\n",
+       "        2         92.105263\n",
+       "        3         50.000000\n",
+       "male    1         36.885246\n",
+       "        2         15.740741\n",
+       "        3         13.544669\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(titanic_df.groupby(['Sex','Pclass']).mean()['Survived']*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 537
+    },
+    "id": "ffUQi33YEdAW",
+    "outputId": "ae7cdd32-db5f-4ea7-8cf8-dfba81218b95"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Survived'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(titanic_df.groupby(['Sex','Pclass']).mean()['Survived']*100).plot(kind='pie',autopct='%.2f')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "FbnoLH6P6HWG",
+    "outputId": "ee1ce69c-73de-4acc-db4d-0aa14b0453d0"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\AppData\\Local\\Temp\\ipykernel_23460\\556173813.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
+      "  titanic_df[(titanic_df.Sex=='female') & (titanic_df.Pclass==1)].mean()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Survived     0.968085\n",
+       "Pclass       1.000000\n",
+       "Age         34.553191\n",
+       "Fare         2.914894\n",
+       "IsAlone      0.414894\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df[(titanic_df.Sex=='female') & (titanic_df.Pclass==1)].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 568
+    },
+    "id": "fxvyAt_5F3tJ",
+    "outputId": "ad0f7ccb-beec-4d05-e690-dbecb72cc590"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(titanic_df.Age,bins=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 568
+    },
+    "id": "wNtbaUWQGOAe",
+    "outputId": "e5318935-fc78-4b66-f1af-e7498434fc9f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Pclass', ylabel='Embarked'>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(pd.crosstab(titanic_df.Embarked,titanic_df.Pclass))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 623
+    },
+    "id": "1aJqJnGaG3SU",
+    "outputId": "c38960cc-7528-45b0-d64d-cd79f71b0993"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(titanic_df['Pclass'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 551
+    },
+    "id": "03TapL5w2Jk-",
+    "outputId": "a248b059-8244-4e7c-952a-75448692e258"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='IsAlone', hue='Pclass', data=titanic_df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "3mazkORDhmiO"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 551
+    },
+    "id": "sX70P5ctkIIh",
+    "outputId": "361c8821-3dae-4b4d-f2b9-645a6c86ec25"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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evXtn+PDh7fb17Nkz/fr1q+2fPHlyLrzwwgwbNizDhg3LhRdemB49euT4449PkjQ2Nubkk0/OmWeemX79+qVv374566yzMmLEiI2+2A4AAAD+1h//+MeMHj06e+65Z773ve9l6NCheeihh/LFL34xP/3pT3Pvvfemb9++dZ2xrl9E9/ecffbZWbt2bU499dSsWLEi++23X2677bb07t27tmbGjBnp3LlzJk6cmLVr1+aQQw7JrFmz0qlTpzpODgAAQOlOO+20dO3aNbfddlu6d++eJNl9993znve8J29/+9tz7rnn5oorrqjrjEVF+/z589s9rlQqmTZtWqZNm7bZ53Tr1i2XXXZZLrvssq07HAAAAG8azz33XH72s5/lggsuqAX7y5qbm3PCCSfk5ptvzuWXX/53vwx9a6r732kHAACAbe3RRx9NtVrNXnvttcnje+21V1asWJFnnnlmG0/WnmgHAACAV6hWq0mSrl271nUO0Q4AAMAOZ4899kilUsnDDz+8yeO//e1vs+uuu2bnnXfetoO9gmgHAABgh9OvX7+MHTs2l19+edauXdvuWEtLS2688cZMmjSpPsP9DdEOAADADmnmzJlpa2vLYYcdljvvvDNLly7N3LlzM3bs2Oy55545//zz6z1iWd8eDwAAwJvDL0//Zb1H+LuGDRuWhQsXZtq0aZk4cWKWL1+earWaCRMm5Prrr0+PHj3qPaI77QAAAOy43vrWt2bWrFlpaWnJSy+9lPPPPz+33XZb/uM//qPeoyVxpx0AAABqvvKVr+Stb31r/s//+T/Zb7/9stNO9b3XLdoBAADgb3zqU5+q9wg13h4PAAAAhRLtAAAAUCjRDgAAwCZVq9V6j7Bd64ifn2gHAACgnS5duiRJXnjhhTpPsn17+ef38s9zS/giOgAAANrp1KlTdt555yxfvjxJ0qNHj1QqlTpPtf2oVqt54YUXsnz58uy8887p1KnTFp9LtAMAALCR5ubmJKmFO6/fzjvvXPs5binRDgAAwEYqlUoGDhyYAQMGZP369fUeZ7vTpUuXN3SH/WWiHQAAgM3q1KlTh8QnW8YX0QEAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUqq7RfsUVV2SfffZJnz590qdPn4wePTo//elPa8cnTZqUSqXSbtt///3bnaOtrS2nn356+vfvn549e+boo4/Ok08+ua0vBQAAADpcXaN9t912y9e//vXcd999ue+++zJmzJgcc8wxeeihh2prDj/88Cxbtqy2/eQnP2l3jsmTJ2fOnDmZPXt27r777qxZsyZHHnlkNmzYsK0vBwAAADpU53q++FFHHdXu8QUXXJArrrgi9957b971rnclSRoaGtLc3LzJ569atSpXXXVVrr/++hx66KFJkhtuuCGDBw/O7bffnsMOO2zrXgAAAABsRcV8pn3Dhg2ZPXt2nn/++YwePbq2f/78+RkwYED23HPPnHLKKVm+fHnt2KJFi7J+/fqMGzeutm/QoEEZPnx47rnnns2+VltbW1pbW9ttAAAAUJq6R/sDDzyQXr16paGhIZ/97GczZ86c7L333kmS8ePH58Ybb8wdd9yRiy++OAsXLsyYMWPS1taWJGlpaUnXrl2zyy67tDtnU1NTWlpaNvua06dPT2NjY20bPHjw1rtAAAAA2EJ1fXt8krzjHe/I4sWLs3Llynz/+9/PiSeemAULFmTvvffOcccdV1s3fPjwjBo1KkOGDMmtt96aCRMmbPac1Wo1lUpls8enTp2aKVOm1B63trYKdwAAAIpT92jv2rVr9thjjyTJqFGjsnDhwvzLv/xLrrzyyo3WDhw4MEOGDMmjjz6aJGlubs66deuyYsWKdnfbly9fngMOOGCzr9nQ0JCGhoYOvhIAAADoWHV/e/wrVavV2tvfX+nZZ5/N0qVLM3DgwCTJyJEj06VLl8ybN6+2ZtmyZXnwwQdfNdoBAABge1DXO+3nnHNOxo8fn8GDB2f16tWZPXt25s+fn7lz52bNmjWZNm1ajj322AwcODCPP/54zjnnnPTv3z8f/ehHkySNjY05+eSTc+aZZ6Zfv37p27dvzjrrrIwYMaL2bfIAAACwvaprtD/99NP5xCc+kWXLlqWxsTH77LNP5s6dm7Fjx2bt2rV54IEHct1112XlypUZOHBgDj744Nx8883p3bt37RwzZsxI586dM3HixKxduzaHHHJIZs2alU6dOtXxygAAAOCNq1Sr1Wq9h6i31tbWNDY2ZtWqVenTp0+9x3ldRn7xunqPwDa06JufrPcIAABAB3itHVrcZ9oBAACAvxLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQqLpG+xVXXJF99tknffr0SZ8+fTJ69Oj89Kc/rR2vVquZNm1aBg0alO7du+eggw7KQw891O4cbW1tOf3009O/f//07NkzRx99dJ588sltfSkAAADQ4eoa7bvttlu+/vWv57777st9992XMWPG5JhjjqmF+UUXXZRLLrkkM2fOzMKFC9Pc3JyxY8dm9erVtXNMnjw5c+bMyezZs3P33XdnzZo1OfLII7Nhw4Z6XRYAAAB0iEq1Wq3We4i/1bdv33zzm9/MSSedlEGDBmXy5Mn50pe+lOSvd9WbmpryjW98I5/5zGeyatWq7Lrrrrn++utz3HHHJUmeeuqpDB48OD/5yU9y2GGHvabXbG1tTWNjY1atWpU+ffpstWvbGkZ+8bp6j8A2tOibn6z3CAAAQAd4rR1azGfaN2zYkNmzZ+f555/P6NGjs2TJkrS0tGTcuHG1NQ0NDTnwwANzzz33JEkWLVqU9evXt1szaNCgDB8+vLZmU9ra2tLa2tpuAwAAgNLUPdofeOCB9OrVKw0NDfnsZz+bOXPmZO+9905LS0uSpKmpqd36pqam2rGWlpZ07do1u+yyy2bXbMr06dPT2NhY2wYPHtzBVwUAAABvXN2j/R3veEcWL16ce++9N5/73Ody4okn5uGHH64dr1Qq7dZXq9WN9r3S31szderUrFq1qrYtXbr0jV0EAAAAbAV1j/auXbtmjz32yKhRozJ9+vTsu++++Zd/+Zc0NzcnyUZ3zJcvX167+97c3Jx169ZlxYoVm12zKQ0NDbVvrH95AwAAgNLUPdpfqVqtpq2tLUOHDk1zc3PmzZtXO7Zu3bosWLAgBxxwQJJk5MiR6dKlS7s1y5Yty4MPPlhbAwAAANurzvV88XPOOSfjx4/P4MGDs3r16syePTvz58/P3LlzU6lUMnny5Fx44YUZNmxYhg0blgsvvDA9evTI8ccfnyRpbGzMySefnDPPPDP9+vVL3759c9ZZZ2XEiBE59NBD63lpAAAA8IbVNdqffvrpfOITn8iyZcvS2NiYffbZJ3Pnzs3YsWOTJGeffXbWrl2bU089NStWrMh+++2X2267Lb17966dY8aMGencuXMmTpyYtWvX5pBDDsmsWbPSqVOnel0WAAAAdIji/k57Pfg77Wwv/J12AAB4c9ju/k47AAAA0J5oBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQtU12qdPn573ve996d27dwYMGJCPfOQj+d3vftduzaRJk1KpVNpt+++/f7s1bW1tOf3009O/f//07NkzRx99dJ588slteSkAAADQ4eoa7QsWLMhpp52We++9N/PmzcuLL76YcePG5fnnn2+37vDDD8+yZctq209+8pN2xydPnpw5c+Zk9uzZufvuu7NmzZoceeSR2bBhw7a8HAAAAOhQnev54nPnzm33+JprrsmAAQOyaNGifOhDH6rtb2hoSHNz8ybPsWrVqlx11VW5/vrrc+ihhyZJbrjhhgwePDi33357DjvssK13AQAAALAVFfWZ9lWrViVJ+vbt227//PnzM2DAgOy555455ZRTsnz58tqxRYsWZf369Rk3blxt36BBgzJ8+PDcc889m3ydtra2tLa2ttsAAACgNMVEe7VazZQpU/LBD34ww4cPr+0fP358brzxxtxxxx25+OKLs3DhwowZMyZtbW1JkpaWlnTt2jW77LJLu/M1NTWlpaVlk681ffr0NDY21rbBgwdvvQsDAACALVTXt8f/rc9//vO5//77c/fdd7fbf9xxx9X+PXz48IwaNSpDhgzJrbfemgkTJmz2fNVqNZVKZZPHpk6dmilTptQet7a2CncAAACKU8Sd9tNPPz0/+tGP8otf/CK77bbbq64dOHBghgwZkkcffTRJ0tzcnHXr1mXFihXt1i1fvjxNTU2bPEdDQ0P69OnTbgMAAIDS1DXaq9VqPv/5z+cHP/hB7rjjjgwdOvTvPufZZ5/N0qVLM3DgwCTJyJEj06VLl8ybN6+2ZtmyZXnwwQdzwAEHbLXZAQAAYGur69vjTzvttNx000354Q9/mN69e9c+g97Y2Jju3btnzZo1mTZtWo499tgMHDgwjz/+eM4555z0798/H/3oR2trTz755Jx55pnp169f+vbtm7POOisjRoyofZs8AAAAbI/qGu1XXHFFkuSggw5qt/+aa67JpEmT0qlTpzzwwAO57rrrsnLlygwcODAHH3xwbr755vTu3bu2fsaMGencuXMmTpyYtWvX5pBDDsmsWbPSqVOnbXk5AAAA0KEq1Wq1Wu8h6q21tTWNjY1ZtWrVdvf59pFfvK7eI7ANLfrmJ+s9AgAA0AFea4cW8UV0AAAAwMZEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUaouifcyYMVm5cuVG+1tbWzNmzJg3OhMAAACQLYz2+fPnZ926dRvt/8tf/pK77rrrDQ8FAAAAJJ1fz+L777+/9u+HH344LS0ttccbNmzI3Llz85a3vKXjpgMAAIAd2OuK9ne/+92pVCqpVCqbfBt89+7dc9lll3XYcAAAALAje13RvmTJklSr1bztbW/Lr3/96+y66661Y127ds2AAQPSqVOnDh8SAAAAdkSvK9qHDBmSJHnppZe2yjAAAADA//e6ov1v/f73v8/8+fOzfPnyjSL+/PPPf8ODAQAAwI5ui6L9u9/9bj73uc+lf//+aW5uTqVSqR2rVCqiHQAAADrAFkX71772tVxwwQX50pe+1NHzAAAAAP/PFv2d9hUrVuRjH/tYR88CAAAA/I0tivaPfexjue222zp6FgAAAOBvbNHb4/fYY4+cd955uffeezNixIh06dKl3fEvfOELHTIcAAAA7Mi2KNq/853vpFevXlmwYEEWLFjQ7lilUhHtAAAA0AG2KNqXLFnS0XMAAAAAr7BFn2kHAAAAtr4tutN+0kknverxq6++eouGAQAAAP6/LYr2FStWtHu8fv36PPjgg1m5cmXGjBnTIYMBAADAjm6Lon3OnDkb7XvppZdy6qmn5m1ve9sbHgoAAADowM+077TTTvmv//W/ZsaMGR11SgAAANihdegX0f3hD3/Iiy++2JGnBAAAgB3WFr09fsqUKe0eV6vVLFu2LLfeemtOPPHEDhkMAAAAdnRbFO2/+c1v2j3eaaedsuuuu+biiy/+u98sDwAAALw2WxTtv/jFLzp6DgAAAOAVtijaX/bMM8/kd7/7XSqVSvbcc8/suuuuHTUXAAAA7PC26Ivonn/++Zx00kkZOHBgPvShD+Uf/uEfMmjQoJx88sl54YUXOnpGAAAA2CFtUbRPmTIlCxYsyI9//OOsXLkyK1euzA9/+MMsWLAgZ555ZkfPCAAAADukLXp7/Pe///38r//1v3LQQQfV9n34wx9O9+7dM3HixFxxxRUdNR8AAADssLboTvsLL7yQpqamjfYPGDDA2+MBAACgg2zRnfbRo0fny1/+cq677rp069YtSbJ27dp85StfyejRozt0QACArWHkF6+r9whsQ4u++cl6jwCwRbYo2i+99NKMHz8+u+22W/bdd99UKpUsXrw4DQ0Nue222zp6RgAAANghbVG0jxgxIo8++mhuuOGG/Pa3v021Ws3HP/7xnHDCCenevXtHzwgAAAA7pC2K9unTp6epqSmnnHJKu/1XX311nnnmmXzpS1/qkOEAAABgR7ZFX0R35ZVX5p3vfOdG+9/1rnfl29/+9hseCgAAANjCaG9pacnAgQM32r/rrrtm2bJlb3goAAAAYAujffDgwfnlL3+50f5f/vKXGTRo0BseCgAAANjCz7R/+tOfzuTJk7N+/fqMGTMmSfLzn/88Z599ds4888wOHRAAAAB2VFsU7WeffXaee+65nHrqqVm3bl2SpFu3bvnSl76UqVOnduiAAAAAsKPaomivVCr5xje+kfPOOy+PPPJIunfvnmHDhqWhoaGj5wMAAIAd1hZF+8t69eqV973vfR01CwAAAPA3tuiL6AAAAICtT7QDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABSqrtE+ffr0vO9970vv3r0zYMCAfOQjH8nvfve7dmuq1WqmTZuWQYMGpXv37jnooIPy0EMPtVvT1taW008/Pf3790/Pnj1z9NFH58knn9yWlwIAAAAdrq7RvmDBgpx22mm59957M2/evLz44osZN25cnn/++dqaiy66KJdccklmzpyZhQsXprm5OWPHjs3q1atrayZPnpw5c+Zk9uzZufvuu7NmzZoceeSR2bBhQz0uCwAAADpE53q++Ny5c9s9vuaaazJgwIAsWrQoH/rQh1KtVnPppZfm3HPPzYQJE5Ik1157bZqamnLTTTflM5/5TFatWpWrrroq119/fQ499NAkyQ033JDBgwfn9ttvz2GHHbbNrwsAAAA6QlGfaV+1alWSpG/fvkmSJUuWpKWlJePGjautaWhoyIEHHph77rknSbJo0aKsX7++3ZpBgwZl+PDhtTUAAACwParrnfa/Va1WM2XKlHzwgx/M8OHDkyQtLS1JkqampnZrm5qa8qc//am2pmvXrtlll102WvPy81+pra0tbW1ttcetra0ddh0AAADQUYq50/75z38+999/f773ve9tdKxSqbR7XK1WN9r3Sq+2Zvr06WlsbKxtgwcP3vLBAQAAYCspItpPP/30/OhHP8ovfvGL7LbbbrX9zc3NSbLRHfPly5fX7r43Nzdn3bp1WbFixWbXvNLUqVOzatWq2rZ06dKOvBwAAADoEHWN9mq1ms9//vP5wQ9+kDvuuCNDhw5td3zo0KFpbm7OvHnzavvWrVuXBQsW5IADDkiSjBw5Ml26dGm3ZtmyZXnwwQdra16poaEhffr0abcBAABAaer6mfbTTjstN910U374wx+md+/etTvqjY2N6d69eyqVSiZPnpwLL7www4YNy7Bhw3LhhRemR48eOf7442trTz755Jx55pnp169f+vbtm7POOisjRoyofZs8AAAAbI/qGu1XXHFFkuSggw5qt/+aa67JpEmTkiRnn3121q5dm1NPPTUrVqzIfvvtl9tuuy29e/eurZ8xY0Y6d+6ciRMnZu3atTnkkEMya9asdOrUaVtdCgAAAHS4SrVardZ7iHprbW1NY2NjVq1atd29VX7kF6+r9whsQ4u++cl6jwDwpuF36I7F71CgNK+1Q4v4IjoAAABgY6IdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUHWN9jvvvDNHHXVUBg0alEqlkltuuaXd8UmTJqVSqbTb9t9//3Zr2tracvrpp6d///7p2bNnjj766Dz55JPb8CoAAABg66hrtD///PPZd999M3PmzM2uOfzww7Ns2bLa9pOf/KTd8cmTJ2fOnDmZPXt27r777qxZsyZHHnlkNmzYsLXHBwAAgK2qcz1ffPz48Rk/fvyrrmloaEhzc/Mmj61atSpXXXVVrr/++hx66KFJkhtuuCGDBw/O7bffnsMOO6zDZwYAAIBtpfjPtM+fPz8DBgzInnvumVNOOSXLly+vHVu0aFHWr1+fcePG1fYNGjQow4cPzz333LPZc7a1taW1tbXdBgAAAKUpOtrHjx+fG2+8MXfccUcuvvjiLFy4MGPGjElbW1uSpKWlJV27ds0uu+zS7nlNTU1paWnZ7HmnT5+exsbG2jZ48OCteh0AAACwJer69vi/57jjjqv9e/jw4Rk1alSGDBmSW2+9NRMmTNjs86rVaiqVymaPT506NVOmTKk9bm1tFe4AAAAUp+g77a80cODADBkyJI8++miSpLm5OevWrcuKFSvarVu+fHmampo2e56Ghob06dOn3QYAAACl2a6i/dlnn83SpUszcODAJMnIkSPTpUuXzJs3r7Zm2bJlefDBB3PAAQfUa0wAAADoEHV9e/yaNWvy2GOP1R4vWbIkixcvTt++fdO3b99MmzYtxx57bAYOHJjHH38855xzTvr375+PfvSjSZLGxsacfPLJOfPMM9OvX7/07ds3Z511VkaMGFH7NnkAAADYXtU12u+7774cfPDBtccvf878xBNPzBVXXJEHHngg1113XVauXJmBAwfm4IMPzs0335zevXvXnjNjxox07tw5EydOzNq1a3PIIYdk1qxZ6dSp0za/HgAAAOhIdY32gw46KNVqdbPHf/azn/3dc3Tr1i2XXXZZLrvsso4cDQAAAOpuu/pMOwAAAOxIRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABSqrtF+55135qijjsqgQYNSqVRyyy23tDterVYzbdq0DBo0KN27d89BBx2Uhx56qN2atra2nH766enfv3969uyZo48+Ok8++eQ2vAoAAADYOuoa7c8//3z23XffzJw5c5PHL7roolxyySWZOXNmFi5cmObm5owdOzarV6+urZk8eXLmzJmT2bNn5+67786aNWty5JFHZsOGDdvqMgAAAGCr6FzPFx8/fnzGjx+/yWPVajWXXnppzj333EyYMCFJcu2116apqSk33XRTPvOZz2TVqlW56qqrcv311+fQQw9Nktxwww0ZPHhwbr/99hx22GHb7FoAAACgoxX7mfYlS5akpaUl48aNq+1raGjIgQcemHvuuSdJsmjRoqxfv77dmkGDBmX48OG1NZvS1taW1tbWdhsAAACUpthob2lpSZI0NTW129/U1FQ71tLSkq5du2aXXXbZ7JpNmT59ehobG2vb4MGDO3h6AAAAeOOKjfaXVSqVdo+r1epG+17p762ZOnVqVq1aVduWLl3aIbMCAABARyo22pubm5Nkozvmy5cvr919b25uzrp167JixYrNrtmUhoaG9OnTp90GAAAApSk22ocOHZrm5ubMmzevtm/dunVZsGBBDjjggCTJyJEj06VLl3Zrli1blgcffLC2BgAAALZXdf32+DVr1uSxxx6rPV6yZEkWL16cvn37Zvfdd8/kyZNz4YUXZtiwYRk2bFguvPDC9OjRI8cff3ySpLGxMSeffHLOPPPM9OvXL3379s1ZZ52VESNG1L5NHgAAALZXdY32++67LwcffHDt8ZQpU5IkJ554YmbNmpWzzz47a9euzamnnpoVK1Zkv/32y2233ZbevXvXnjNjxox07tw5EydOzNq1a3PIIYdk1qxZ6dSp0za/HgAAAOhIlWq1Wq33EPXW2tqaxsbGrFq1arv7fPvIL15X7xHYhhZ985P1HgHgTcPv0B2L36FAaV5rhxb7mXYAAADY0dX17fEAALAtPPHVEfUegW1o9/MfqPcI0GHcaQcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAACiUaAcAAIBCiXYAAAAolGgHAACAQol2AAAAKJRoBwAAgEKJdgAAAChU53oPALx2T3x1RL1HYBva/fwH6j0CAAB15k47AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABRKtAMAAEChRDsAAAAUSrQDAABAoUQ7AAAAFEq0AwAAQKFEOwAAABSq6GifNm1aKpVKu625ubl2vFqtZtq0aRk0aFC6d++egw46KA899FAdJwYAAICOU3S0J8m73vWuLFu2rLY98MADtWMXXXRRLrnkksycOTMLFy5Mc3Nzxo4dm9WrV9dxYgAAAOgYxUd7586d09zcXNt23XXXJH+9y37ppZfm3HPPzYQJEzJ8+PBce+21eeGFF3LTTTfVeWoAAAB444qP9kcffTSDBg3K0KFD8/GPfzx//OMfkyRLlixJS0tLxo0bV1vb0NCQAw88MPfcc8+rnrOtrS2tra3tNgAAAChN0dG+33775brrrsvPfvazfPe7301LS0sOOOCAPPvss2lpaUmSNDU1tXtOU1NT7djmTJ8+PY2NjbVt8ODBW+0aAAAAYEsVHe3jx4/PsccemxEjRuTQQw/NrbfemiS59tpra2sqlUq751Sr1Y32vdLUqVOzatWq2rZ06dKOHx4AAADeoKKj/ZV69uyZESNG5NFHH619i/wr76ovX758o7vvr9TQ0JA+ffq02wAAAKA021W0t7W15ZFHHsnAgQMzdOjQNDc3Z968ebXj69aty4IFC3LAAQfUcUoAAADoGJ3rPcCrOeuss3LUUUdl9913z/Lly/O1r30tra2tOfHEE1OpVDJ58uRceOGFGTZsWIYNG5YLL7wwPXr0yPHHH1/v0QEAAOANKzran3zyyfzTP/1T/vznP2fXXXfN/vvvn3vvvTdDhgxJkpx99tlZu3ZtTj311KxYsSL77bdfbrvttvTu3bvOkwO8cR+47AP1HoFt6Jen/7LeIwAABSo62mfPnv2qxyuVSqZNm5Zp06Ztm4EAAABgG9quPtMOAAAAOxLRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhepc7wEAAAA60gcu+0C9R2Ab+uXpv6z3CFuVO+0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFAo0Q4AAACFEu0AAABQKNEOAAAAhRLtAAAAUCjRDgAAAIUS7QAAAFCoN020X3755Rk6dGi6deuWkSNH5q677qr3SAAAAPCGvCmi/eabb87kyZNz7rnn5je/+U3+4R/+IePHj88TTzxR79EAAABgi70pov2SSy7JySefnE9/+tPZa6+9cumll2bw4MG54oor6j0aAAAAbLHtPtrXrVuXRYsWZdy4ce32jxs3Lvfcc0+dpgIAAIA3rnO9B3ij/vznP2fDhg1pampqt7+pqSktLS2bfE5bW1va2tpqj1etWpUkaW1t3XqDbiUb2tbWewS2odVdNtR7BLahF9e+WO8R2Ia2x99B2zu/Q3csfofuWPwO3bFsr79DX567Wq2+6rrtPtpfVqlU2j2uVqsb7XvZ9OnT85WvfGWj/YMHD94qs0FHGV7vAYCtpvFLjfUeAd7U/A6FN6/t/Xfo6tWr09i4+WvY7qO9f//+6dSp00Z31ZcvX77R3feXTZ06NVOmTKk9fumll/Lcc8+lX79+mw19oD5aW1szePDgLF26NH369Kn3OACw3fA7FMpWrVazevXqDBo06FXXbffR3rVr14wcOTLz5s3LRz/60dr+efPm5ZhjjtnkcxoaGtLQ0NBu384777w1xwTeoD59+vgfHACwBfwOhXK92h32l2330Z4kU6ZMySc+8YmMGjUqo0ePzne+85088cQT+exnP1vv0QAAAGCLvSmi/bjjjsuzzz6br371q1m2bFmGDx+en/zkJxkyZEi9RwMAAIAt9qaI9iQ59dRTc+qpp9Z7DKCDNTQ05Mtf/vJGH2kBAF6d36Hw5lCp/r3vlwcAAADqYqd6DwAAAABsmmgHAACAQol2AAAAKJRoBwAAgEKJdqBol19+eYYOHZpu3bpl5MiRueuuu+o9EgAU784778xRRx2VQYMGpVKp5JZbbqn3SMAWEu1AsW6++eZMnjw55557bn7zm9/kH/7hHzJ+/Pg88cQT9R4NAIr2/PPPZ999983MmTPrPQrwBvmTb0Cx9ttvv7z3ve/NFVdcUdu311575SMf+UimT59ex8kAYPtRqVQyZ86cfOQjH6n3KMAWcKcdKNK6deuyaNGijBs3rt3+cePG5Z577qnTVAAAsG2JdqBIf/7zn7Nhw4Y0NTW129/U1JSWlpY6TQUAANuWaAeKVqlU2j2uVqsb7QMAgDcr0Q4UqX///unUqdNGd9WXL1++0d13AAB4sxLtQJG6du2akSNHZt68ee32z5s3LwcccECdpgIAgG2rc70HANicKVOm5BOf+ERGjRqV0aNH5zvf+U6eeOKJfPazn633aABQtDVr1uSxxx6rPV6yZEkWL16cvn37Zvfdd6/jZMDr5U++AUW7/PLLc9FFF2XZsmUZPnx4ZsyYkQ996EP1HgsAijZ//vwcfPDBG+0/8cQTM2vWrG0/ELDFRDsAAAAUymfaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQB4w+bPn59KpZKVK1du1deZNGlSPvKRj2zV1wCAkoh2AHgTWb58eT7zmc9k9913T0NDQ5qbm3PYYYflV7/61VZ93QMOOCDLli1LY2PjVn0dANjRdK73AABAxzn22GOzfv36XHvttXnb296Wp59+Oj//+c/z3HPPbdH5qtVqNmzYkM6dX/1/MnTt2jXNzc1b9BoAwOa50w4AbxIrV67M3XffnW984xs5+OCDM2TIkLz//e/P1KlTc8QRR+Txxx9PpVLJ4sWL2z2nUqlk/vz5Sf7/29x/9rOfZdSoUWloaMhVV12VSqWS3/72t+1e75JLLslb3/rWVKvVdm+PX7VqVbp37565c+e2W/+DH/wgPXv2zJo1a5Ik//mf/5njjjsuu+yyS/r165djjjkmjz/+eG39hg0bMmXKlOy8887p169fzj777FSr1a3yswOAUol2AHiT6NWrV3r16pVbbrklbW1tb+hcZ599dqZPn55HHnkk//iP/5iRI0fmxhtvbLfmpptuyvHHH59KpdJuf2NjY4444ohNrj/mmGPSq1evvPDCCzn44IPTq1ev3Hnnnbn77rvTq1evHH744Vm3bl2S5OKLL87VV1+dq666KnfffXeee+65zJkz5w1dFwBsb0Q7ALxJdO7cObNmzcq1116bnXfeOR/4wAdyzjnn5P7773/d5/rqV7+asWPH5u1vf3v69euXE044ITfddFPt+O9///ssWrQo/+W//JdNPv+EE07ILbfckhdeeCFJ0tramltvvbW2fvbs2dlpp53yr//6rxkxYkT22muvXHPNNXniiSdqd/0vvfTSTJ06Nccee2z22muvfPvb3/aZeQB2OKIdAN5Ejj322Dz11FP50Y9+lMMOOyzz58/Pe9/73syaNet1nWfUqFHtHn/84x/Pn/70p9x7771JkhtvvDHvfve7s/fee2/y+UcccUQ6d+6cH/3oR0mS73//++ndu3fGjRuXJFm0aFEee+yx9O7du/YOgb59++Yvf/lL/vCHP2TVqlVZtmxZRo8eXTtn586dN5oLAN7sRDsAvMl069YtY8eOzfnnn5977rknkyZNype//OXstNNff+3/7efC169fv8lz9OzZs93jgQMH5uCDD67dbf/e97632bvsyV+/mO4f//Efa+tvuummHHfccbUvtHvppZcycuTILF68uN32+9//Pscff/yWXzwAvMmIdgB4k9t7773z/PPPZ9ddd02SLFu2rHbsb7+U7u854YQTcvPNN+dXv/pV/vCHP+TjH//4310/d+7cPPTQQ/nFL36RE044oXbsve99bx599NEMGDAge+yxR7utsbExjY2NGThwYO3OfpK8+OKLWbRo0WueFwDeDEQ7ALxJPPvssxkzZkxuuOGG3H///VmyZEn+5//8n7noootyzDHHpHv37tl///3z9a9/PQ8//HDuvPPO/PM///NrPv+ECRPS2tqaz33uczn44IPzlre85VXXH3jggWlqasoJJ5yQt771rdl///1rx0444YT0798/xxxzTO66664sWbIkCxYsyBlnnJEnn3wySXLGGWfk61//eubMmZPf/va3OfXUU7Ny5cot+tkAwPZKtAPAm0SvXr2y3377ZcaMGfnQhz6U4cOH57zzzsspp5ySmTNnJkmuvvrqrF+/PqNGjcoZZ5yRr33ta6/5/H369MlRRx2V//iP/2h313xzKpVK/umf/mmT63v06JE777wzu+++eyZMmJC99torJ510UtauXZs+ffokSc4888x88pOfzKRJkzJ69Oj07t07H/3oR1/HTwQAtn+Vqj94CgAAAEVypx0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQoh0AAAAKJdoBAACgUKIdAAAACiXaAQAAoFCiHQAAAAol2gEAAKBQ/xcKA3fDhiPB5wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='Survived', hue='Embarked', data=titanic_df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
+    },
+    "id": "o9BylGvDIk0R",
+    "outputId": "16b25e44-4959-477b-c9c1-ca28527d2d30"
+   },
+   "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>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>IsAlone</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>0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</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>3</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</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>3</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Survived  Pclass     Sex   Age  Fare Embarked  IsAlone\n",
+       "0           0       3    male  22.0     0        S        1\n",
+       "1           1       1  female  38.0     3        C        1\n",
+       "2           1       3  female  26.0     1        S        0\n",
+       "3           1       1  female  35.0     3        S        1\n",
+       "4           0       3    male  35.0     1        S        0\n",
+       "..        ...     ...     ...   ...   ...      ...      ...\n",
+       "886         0       2    male  27.0     1        S        0\n",
+       "887         1       1  female  19.0     2        S        0\n",
+       "888         0       3  female  23.0     2        S        0\n",
+       "889         1       1    male  26.0     2        C        0\n",
+       "890         0       3    male  32.0     0        Q        0\n",
+       "\n",
+       "[891 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "efZKiak1EnMK"
+   },
+   "source": [
+    "# Modelling\n",
+    "Now we're completely ready to train different machine learning models and test their scores but before that I want to do One Hot Encoding on 'Sex' and 'Embarked' columns.\n",
+    "One thing to note here is that while training different models, we may observe that some models perform better than others. This, however, is never universal, rather always varies depending on the dataset, what data wrangling we've done, among other things.\n",
+    "\n",
+    "Below is the list of classifiers we'll be feeding our training data to :\n",
+    "\n",
+    "But first, let's split our data into training and test data.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "id": "QOJ-csIDFFgO"
+   },
+   "outputs": [],
+   "source": [
+    "titanic_df=pd.get_dummies(titanic_df,columns=['Embarked','Sex'],drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "id": "LX3UWIJ6JCJk"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "id": "NQ7_XHPLKQ6j"
+   },
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(titanic_df.drop([\"Survived\"],axis=1),\n",
+    "                                               titanic_df[\"Survived\"],test_size=0.40,\n",
+    "                                               random_state=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ps-j-XzgHseO",
+    "outputId": "93607635-22e2-4ae3-ae6d-37f7912203e0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(534, 7)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "gNHNk7iqHxdl",
+    "outputId": "65bb9d15-bdaa-40c3-f58e-721f27f110a6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(357, 7)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fc0vClvFtgf"
+   },
+   "source": [
+    "# Pipelining the training process.\n",
+    "Although it is usually sufficient to import the neccessary model training and preprocessing libraries and then test each one independently, we can make our tasks much easier by pipelining the steps for our final model prediction.\n",
+    "\n",
+    "Thus, we create a scaler object from sklearns preprocessing module as well as a classifier object for each of the imported machine learning classifiers, namely:\n",
+    "\n",
+    "* Stochastic Gradient Descent: The most basic perceptron with a custom learning rule and a cost func.\n",
+    "* Logistic Regression: An improvement upon the SGD in terms of regularization ability.\n",
+    "* The Support Vector Machine: We're the using the rbf kernel with a pretty high inverse sigma for better results in this case.\n",
+    "* Linear SVC: A linear version of the Support Vector Machine.\n",
+    "* K-nearest Neighbors: with k = 3.\n",
+    "* And a default decision tree and Random Forest Object.\n",
+    "\n",
+    "We then put each of them in an iterable list.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "id": "FViFZmFSHxhi"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "sgd = SGDClassifier()\n",
+    "log_reg = LogisticRegression(solver = 'liblinear')\n",
+    "svc = SVC(kernel = 'rbf', C = 10.0)\n",
+    "linear_svc = LinearSVC()\n",
+    "knn_3 = KNeighborsClassifier(n_neighbors = 3)\n",
+    "decision_tree = DecisionTreeClassifier()\n",
+    "random_forest = RandomForestClassifier(n_estimators=100)\n",
+    "\n",
+    "clf_list = [sgd, log_reg, svc, linear_svc, knn_3, decision_tree, random_forest]\n",
+    "\n",
+    "#clf_names = [\"Stochastic Gradient Descent\", \"Logistic Regression\", \"SVM\", \"Linear SVC\", \"K-Nearest-Neighbours(n = 3)\", \"Decision Tree\", \"Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "SXdMAZySHxll",
+    "outputId": "c1bf35b3-d468-4120-aca0-5ad3094550ac"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[SGDClassifier(),\n",
+       " LogisticRegression(solver='liblinear'),\n",
+       " SVC(C=10.0),\n",
+       " LinearSVC(),\n",
+       " KNeighborsClassifier(n_neighbors=3),\n",
+       " DecisionTreeClassifier(),\n",
+       " RandomForestClassifier()]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UgBiFZciGQJO"
+   },
+   "source": [
+    "# Accuracy Testing with the training set.\n",
+    "For each classifer in clf_list, we build a pipeline that does the following:\n",
+    "\n",
+    "1. Scales the training data.\n",
+    "2. Fits the scaled training data to the particular classifier model for wieght training.\n",
+    "3. Predicts and prints the average accuracy score as compared to the known target values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uKL6kGJSHx4e",
+    "outputId": "b5dc7ee2-22d9-4e6f-eb7a-a977092af0b7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accurary Scores tested against the training data's target values:\n",
+      "SGDClassifier : 78.83895131086143\n",
+      "LogisticRegression : 80.71161048689139\n",
+      "SVC : 86.70411985018727\n",
+      "LinearSVC : 79.40074906367042\n",
+      "KNeighborsClassifier : 87.82771535580525\n",
+      "DecisionTreeClassifier : 97.00374531835206\n",
+      "RandomForestClassifier : 97.00374531835206\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gauta\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
+      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "print(\"Accurary Scores tested against the training data's target values:\")\n",
+    "for classifier in clf_list:\n",
+    "    pipeline = Pipeline([('sclaer', scaler), ('clf', classifier)])\n",
+    "    pipeline.fit(x_train, y_train)\n",
+    "    y_predict = pipeline.predict(x_train)\n",
+    "    print(\"{} : {}\".format(type(classifier).__name__, ((y_predict == y_train)).sum() / y_predict.shape[0] * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WM--CoM7JBez"
+   },
+   "source": [
+    "###  Although we used only 'train.csv' and did train test split and divided the 'train.csv' dataset into training and testing, What you can do is , you can use 'test.csv' for testing and 'train.csv' for training and process will remain same ,might the accuracy result will change."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Practice_for_Beginners/train.csv b/Practice_for_Beginners/train.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/Practice_for_Beginners/train.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
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