Titanic_Classification_BI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "16ikk3UKm2UZ"
   },
   "source": [
    "#  Bharat Intern Internship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XdDDmvpERjMt"
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   "source": [
    "# Task 1 : Titanic Classification\n",
    "By TANISA DE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4si0Y4Vm9d1"
   },
   "source": [
    "# About the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sxm3zZt8m8bu"
   },
   "source": [
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew.\n",
    "\n",
    "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
    "\n",
    "In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).\n",
    "\n",
    "Objective:\n",
    "1. Understand the Dataset & cleanup (if required).\n",
    "2. Build a strong classification model to predict whether the passenger survives or not.\n",
    "3. Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B0cvi7rlS-7u"
   },
   "source": [
    "# link to the Dataset : https://www.kaggle.com/datasets/yasserh/titanic-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AfcwCIF5n2OJ"
   },
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lVQ1Axh7Myhy"
   },
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
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       "     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",
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     "execution_count": 1304,
     "metadata": {},
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   "source": [
    "# Loading the Dataset\n",
    "titanic = pd.read_csv('/content/Titanic Dataset.csv')\n",
    "titanic"
   ]
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       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89a30509-fb1a-490b-bdcd-705f6ff9cbbf')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "    .colab-df-convert:hover {\n",
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       "\n",
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       "\n",
       "      <script>\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-89a30509-fb1a-490b-bdcd-705f6ff9cbbf');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 1305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading first 5 rows\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "UMkbnxGZO6Ze",
    "outputId": "6e2997cc-5903-4874-a84d-6cd2de3a3efa"
   },
   "outputs": [
    {
     "data": {
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
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       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "            + ' to learn more about interactive tables.';\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 1306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading last 5 rows\n",
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1__8TO0kO2qt",
    "outputId": "ce307b92-a0f5-4145-ecfa-7c953c095dc3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 1307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing no. of rows and columns of dataset\n",
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OwHs3txydbhM",
    "outputId": "5a9c1c8c-004a-4c11-ae49-624cfbb22242"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for columns\n",
    "titanic.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhrzcPmeMIz7"
   },
   "source": [
    "Information about Columns\n",
    "\n",
    "1. PassengerId: unique id number to each passenger\n",
    "2. Survived: passenger survive(1) or died(0)\n",
    "3. Pclass: passenger class\n",
    "4. Name: name\n",
    "5. Sex: gender of passenger\n",
    "6. Age: age of passenger\n",
    "7. SibSp: number of siblings/spouses\n",
    "8. Parch: number of parents/children\n",
    "9. Ticket: ticket number\n",
    "10. Fare: amount of money spent on ticket\n",
    "11. Cabin: cabin category\n",
    "12. Embarked: port where passenger embarked (C = Cherbourg, Q = Queenstown, S = Southampton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zBjf7nyGoaCn"
   },
   "source": [
    "# Data Preprocessing and Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SC2tEMVdcA99",
    "outputId": "e307f32d-0e8c-4538-937a-138b7443a1f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 1309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for data types\n",
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eMmK51BJcMgN"
   },
   "source": [
    "Handling Duplicated Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B6Vpm7q6PUl7",
    "outputId": "0708da3f-f04b-429a-c0c5-ed79b87dd8e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicated values\n",
    "titanic.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzOJYecwcQtC"
   },
   "source": [
    " Null Values Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SGUXkg3UPaaC",
    "outputId": "389bf52f-b818-4127-e7fa-c03d9ade10dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin       687\n",
       "Age         177\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "nv = titanic.isna().sum().sort_values(ascending=False)\n",
    "nv = nv[nv>0]\n",
    "nv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AA0yNrCKjvgL",
    "outputId": "4bc4b1de-85a1-4702-c350-14433cf74c76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin          77.104377\n",
       "Age            19.865320\n",
       "Embarked        0.224467\n",
       "PassengerId     0.000000\n",
       "Survived        0.000000\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cheecking what percentage column contain missing values\n",
    "titanic.isnull().sum().sort_values(ascending=False)*100/len(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "knzNYvYv3pyk",
    "outputId": "05654eac-7fb4-423f-b2a0-56c523c616c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since Cabin Column has more than 75 % null values .So , we will drop this column\n",
    "titanic.drop(columns = 'Cabin', axis = 1, inplace = True)\n",
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j3dgb3Ln6vAx"
   },
   "outputs": [],
   "source": [
    "# Filling Null Values in Age column with mean values of age column\n",
    "titanic['Age'].fillna(titanic['Age'].mean(),inplace=True)\n",
    "\n",
    "# filling null values in Embarked Column with mode values of embarked column\n",
    "titanic['Embarked'].fillna(titanic['Embarked'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i9o_Dp2E-Eev",
    "outputId": "b93e2662-547c-4609-ce9a-bb23a004859c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "titanic.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c6UotBhWcewu"
   },
   "source": [
    "Checking for unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dw4fhir5cmH-",
    "outputId": "20fe10fd-24ea-49e7-94c7-e799174deb02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived         2\n",
       "Sex              2\n",
       "Pclass           3\n",
       "Embarked         3\n",
       "SibSp            7\n",
       "Parch            7\n",
       "Age             89\n",
       "Fare           248\n",
       "Ticket         681\n",
       "PassengerId    891\n",
       "Name           891\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding no. of unique values in each column of dataset\n",
    "titanic[['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
    "       'Parch', 'Ticket', 'Fare', 'Embarked']].nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z44F63rvrtpK"
   },
   "source": [
    "Showing unique values of different columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BXOXP5ezhVsr",
    "outputId": "c02c1394-4974-42b7-8953-68dbf43c92d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 1317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JZKI0xr1ghTR",
    "outputId": "547fbc0c-d135-4c7f-a91a-71a67fb13879"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 1318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Scg4hsNbghBU",
    "outputId": "265fa7b4-2351-407f-f720-daa17c5b3dda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2])"
      ]
     },
     "execution_count": 1319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Pclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bUZYkKhnghbi",
    "outputId": "93c2e4ec-582c-47f5-f70b-7026786544a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 4, 2, 5, 8])"
      ]
     },
     "execution_count": 1320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['SibSp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3Owsttheghfs",
    "outputId": "924729c9-20e8-4c1a-d54e-95f746905660"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 5, 3, 4, 6])"
      ]
     },
     "execution_count": 1321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Parch'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aPiiwF-TghkJ",
    "outputId": "ff8da613-cd0a-41da-e9f8-249a4319017f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q'], dtype=object)"
      ]
     },
     "execution_count": 1322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XcISB243uKUS"
   },
   "source": [
    "# Dropping Some Unnecessary Columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9iiqo5r2uQ2A"
   },
   "source": [
    "There are 3 columns i.e.. 'PassengerId' , 'Name' , 'Ticket' are unnecessary columns which have no use in data modelling . So, we will drop these 3 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zsROuU5TuvJK",
    "outputId": "e5ed07c8-e2f1-4329-d7b8-1bb89a96f66f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
       "       'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.drop(columns=['PassengerId','Name','Ticket'],axis=1,inplace=True)\n",
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wbIch9wXPD6T",
    "outputId": "828f88b4-7bf3-4fc0-f2f3-2245a1ec5b2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       891 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Fare      891 non-null    float64\n",
      " 7   Embarked  891 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 55.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Showing inforamation about the dataset\n",
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "3iUruNiaPIkL",
    "outputId": "7659a394-329a-43fa-acf5-e34b7e9a9524"
   },
   "outputs": [
    {
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       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   13.002015    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   22.000000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   29.699118    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   35.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
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     "execution_count": 1325,
     "metadata": {},
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   ],
   "source": [
    "# showing info. about numerical columns\n",
    "titanic.describe()"
   ]
  },
  {
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   "execution_count": null,
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    "id": "Td2ERRtIPL1R",
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91666dda-6584-49c8-b27f-41ef01482e27')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-91666dda-6584-49c8-b27f-41ef01482e27 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-91666dda-6584-49c8-b27f-41ef01482e27');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         Sex Embarked\n",
       "count    891      891\n",
       "unique     2        3\n",
       "top     male        S\n",
       "freq     577      646"
      ]
     },
     "execution_count": 1326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing info. about categorical columns\n",
    "titanic.describe(include='O')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bf-5Cyc8sASn"
   },
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6rongLy9BCDw"
   },
   "source": [
    "1. Sex Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yLFy5PdyBFhZ",
    "outputId": "5e7c1c3c-9052-4c18-cf72-018d00873e7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 1327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = titanic['Sex'].value_counts()\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "Wvkke8AkBf_W",
    "outputId": "379d8405-d0ef-4087-fac0-36a64691d881"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Count plot for sex column\n",
    "sns.countplot(x=titanic['Sex'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "1u5kBgpuAks5",
    "outputId": "743e9a40-c70c-4a39-a6d1-eb3bf14457c3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Percantage Distribution of Sex Column\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.pie(d1.values,labels=d1.index,autopct='%.2f%%')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "s1COkvx-AkwM",
    "outputId": "e3fe32ec-0167-4420-af10-a86d6c1dc3b9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Distribution of Sex Column Survived Wise\n",
    "sns.countplot(x=titanic['Sex'],hue=titanic['Survived']) # In Sex (0 represents female and 1 represents male)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69eaKV_nWyBn"
   },
   "source": [
    "This plot clearly shows male died more than females and females survived more than male percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "f34fYIVwAk0Y",
    "outputId": "ef9ab800-437a-46fd-ea00-205a41b95962"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Distribution of Embarked Sex wise\n",
    "sns.countplot(x=titanic['Embarked'],hue=titanic['Sex'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k76f5jzlZqz5"
   },
   "source": [
    "2. Pclass Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "r5eo4ZeZAk4B",
    "outputId": "31b5d2fe-62ea-4e12-ef05-3662a9501285"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting CountPlot for Pclass Column\n",
    "sns.countplot(x=titanic['Pclass'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "e-01FkZKAk7Y",
    "outputId": "3acaa5f2-c789-4aac-f0fb-f31a3980bfa4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Distribution of Pclass Sex wise\n",
    "sns.countplot(x=titanic['Pclass'],hue=titanic['Sex'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "523ocVGBAlBq",
    "outputId": "bfc51c6c-ce3f-4ab5-ebd9-5d96ba16eb0b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Distribution\n",
    "sns.kdeplot(x=titanic['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EChbx7CHezyp"
   },
   "source": [
    "From this plot it came to know that most of the people lie between 20-40 age group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vr-KNnsm_eXH"
   },
   "source": [
    "Analysing Target Variable\n",
    "\n",
    "Survived Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "UcA6qecHsrOr",
    "outputId": "7d98f503-a7bb-4fd0-941e-148b17eeb305"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting CountPlot for Survived Column\n",
    "print(titanic['Survived'].value_counts())\n",
    "sns.countplot(x=titanic['Survived'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_YQ4YOZRb04a"
   },
   "source": [
    "This plot Clearly shows most people are died"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "jCZC1MFmsrSG",
    "outputId": "90799c3d-1413-4d24-d9d1-89c007a990d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Distribution of Parch Survived Wise\n",
    "sns.countplot(x=titanic['Parch'],hue=titanic['Survived'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "VgnDKealsrXV",
    "outputId": "d5bda805-36c6-48df-c220-00d241e614dc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Distribution of SibSp Survived Wise\n",
    "sns.countplot(x=titanic['SibSp'],hue=titanic['Survived'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "OOtcegFpsras",
    "outputId": "b04f04a7-cec0-48a3-e27a-3e4dbd912239"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BACAIWCwWRUREGF2GKXFjEAAAmJqhYWft2rUaNGiQunfvru7du2vYsGHavHmzd/4DDzwgu93u83nmmWd81nH8+HGNHj1a3bp1U2pqqp5//nlVV1df6l0BAABBytDbWC1bttSUKVPUtm1beTwevf/++xo3bpzee+89XXvttZKke++9VxMnTvQuExkZ6f3b5XJpzJgxstlsys3NVVFRkTIyMhQWFqYnn3zyku8PAAAIPoaGnVtuucVn+oknntAbb7yhvLw8b9iJiIhQfHz8eZffunWrCgoKlJOTI5vNpk6dOmnSpElasGCBxo8fr/Dw8FrV43K5/NsRKSADqMyiLscRAICaqun3TdAMUHa5XProo49UXl6ulJQUb/v69ev1wQcfKD4+XjfffLPGjh3rvbqTl5enxMRE2Ww2b/+0tDRlZmaqoKBAnTt3rlUNe/bs8av2yMjIWm/LzPLz81VRUWF0GQAASAqCsJOfn6/77rtPlZWVioqK0iuvvKKEhARJ0l133aXWrVurRYsWys/P14IFC3T48GEtWbJEklRcXOwTdCR5px0OR61rSUpK4gpNANjtdqNLAAA0AC6Xq0YXKgwPO+3atdP777+v06dP6+OPP1ZGRoZWr16thIQEDRs2zNvPbrcrPj5eDz74oAoLC3X11VcHvBar1UrYCQCOIQAgmBj+6Hl4eLjatm2rLl26aPLkyerYsaNWrlx53r7dunWTJO8bJm02m4qLi336nJ2+0DgfAADQsBgedn7K7XZf8A2S+/fvl/R/QSY5OVlfffWVSkpKvH0+++wzRUdHe2+FAQCAhs3Q21gLFy7UTTfdpFatWqmsrEwbNmzQ9u3btXz5chUWFmr9+vVKT09X06ZNlZ+fr3nz5qlHjx7q2LGjpB8HIyckJGjatGmaOnWqHA6HFi1apOHDh9f6SSwAAGBOhoadkpISZWRkqKioSDExMbLb7Vq+fLn69Omjb7/9Vtu2bdPKlStVXl6uVq1a6fbbb9fYsWO9y1utVi1dulSZmZkaNmyYIiMjNXjwYJ/38gAAgIbN0LAzd+7cC85r1aqVVq9efdF1XHnllXrttdcCWRYAADCRoBuzAwAAEEiEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqGhp21a9dq0KBB6t69u7p3765hw4Zp8+bN3vmVlZXKyspSz549lZKSogkTJqi4uNhnHcePH9fo0aPVrVs3paam6vnnn1d1dfWl3hUAABCkDA07LVu21JQpU/Tuu+9q3bp16tWrl8aNG6evv/5akjR37lz9/e9/16JFi7Rq1SoVFRVp/Pjx3uVdLpfGjBkjp9Op3NxcZWdn67333tPixYuN2iUAABBkQo3c+C233OIz/cQTT+iNN95QXl6eWrZsqXXr1mnBggVKTU2V9GP4GTBggPLy8pScnKytW7eqoKBAOTk5stls6tSpkyZNmqQFCxZo/PjxCg8Pr1U9LpfL732xWq1+L2s2dTmOAADUVE2/bwwNO//J5XLpo48+Unl5uVJSUrR37145nU717t3b26dDhw5q3bq1N+zk5eUpMTFRNpvN2yctLU2ZmZkqKChQ586da1XDnj17/Ko9MjKy1tsys/z8fFVUVBhdBgAAkoIg7OTn5+u+++5TZWWloqKi9MorryghIUH79+9XWFiYmjRp4tM/Li5ODodDklRcXOwTdCR5p8/2qY2kpCSu0ASA3W43ugQAQAPgcrlqdKHC8LDTrl07vf/++zp9+rQ+/vhjZWRkaPXq1YbUYrVaCTsBwDEEAAQTw8NOeHi42rZtK0nq0qWL9uzZo5UrV+rOO++U0+lUaWmpz9WdkpISxcfHS/rxKs7u3bt91nf2aa2zfQAAQMMWdO/ZcbvdqqqqUpcuXRQWFqZt27Z55x06dEjHjx9XcnKyJCk5OVlfffWVSkpKvH0+++wzRUdHKyEh4VKXDgAAgpChV3YWLlyom266Sa1atVJZWZk2bNig7du3a/ny5YqJidHQoUOVnZ2t2NhYRUdHa86cOUpJSfGGnbS0NCUkJGjatGmaOnWqHA6HFi1apOHDh9f6SSwAAGBOhoadkpISZWRkqKioSDExMbLb7Vq+fLn69OkjSZoxY4YsFosmTpyoqqoqpaWladasWd7lrVarli5dqszMTA0bNkyRkZEaPHiwJk6caNQuAQCAIBPi8Xg8RhdhNJfL5X2cvS6Da4cv2qAD/3sigJVdXjpe2VxrHr/L6DIAAA1ETb+/g27MDgAAQCARdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkZGnb+8Ic/aOjQoUpJSVFqaqrGjh2rQ4cO+fR54IEHZLfbfT7PPPOMT5/jx49r9OjR6tatm1JTU/X888+rurr6Uu4KAAAIUqFGbnz79u0aPny4kpKS5HK59Lvf/U6jRo3Sn//8Z0VFRXn73XvvvZo4caJ3OjIy0vu3y+XSmDFjZLPZlJubq6KiImVkZCgsLExPPvnkJd0fAAAQfAwNO8uXL/eZzs7OVmpqqvbt26cePXp42yMiIhQfH3/edWzdulUFBQXKycmRzWZTp06dNGnSJC1YsEDjx49XeHh4ve4DAAAIboaGnZ86ffq0JCk2Ntanff369frggw8UHx+vm2++WWPHjvVe3cnLy1NiYqJsNpu3f1pamjIzM1VQUKDOnTvXePsul8vv2q1Wq9/Lmk1djiMAADVV0++boAk7brdbc+fOVffu3ZWYmOhtv+uuu9S6dWu1aNFC+fn5WrBggQ4fPqwlS5ZIkoqLi32CjiTvtMPhqFUNe/bs8av2yMjIWoUqs8vPz1dFRYXRZQAAICmIwk5WVpa+/vprrV271qd92LBh3r/tdrvi4+P14IMPqrCwUFdffXVAa0hKSuIKTQDY7XajSwAANAAul6tGFyqCIuw8++yz+vTTT7V69Wq1bNnyZ/t269ZNknTkyBFdffXVstls2r17t0+f4uJiSbrgOJ8LsVqthJ0A4BgCAIKJoY+eezwePfvss/rkk0+0YsUKtWnT5qLL7N+/X9L/BZnk5GR99dVXKikp8fb57LPPFB0drYSEhPopHAAAXDb8CjsjRoxQaWnpOe0//PCDRowYUeP1ZGVl6YMPPtDChQvVuHFjORwOORwOnTlzRpJUWFioV155RXv37tWxY8f017/+VRkZGerRo4c6duwo6cfByAkJCZo2bZoOHDigLVu2aNGiRRo+fDhPYgEAAP9uY23fvl1Op/Oc9srKSv3jH/+o8XreeOMNST++OPA/zZs3T0OGDFFYWJi2bdumlStXqry8XK1atdLtt9+usWPHevtarVYtXbpUmZmZGjZsmCIjIzV48GCf9/IAAICGq1Zh58CBA96/CwoKfJ52crvd2rJli6644ooary8/P/9n57dq1UqrV6++6HquvPJKvfbaazXeLgAAaDhqFXbuvvtuhYSEKCQkRCNHjjxnfkREhGbOnBmw4gAAAOqqVmHnr3/9qzwej2677Ta9/fbbat68uXdeWFiY4uLieBIHAAAElVqFnSuvvFKS7+0sAACAYOb3e3a++eYbffHFFyopKZHb7faZN378+DoXBgAAEAh+hZ233npLmZmZatasmWw2m0JCQrzzQkJCCDsAACBo+BV2Xn31VT3++OMaPXp0oOsBAAAIKL9eKnjq1Cndeeedga4FAAAg4PwKO3fccYe2bt0a6FoAAAACzq/bWG3bttVLL72kL7/8UomJiQoN9V1NbX4yAgAAoD75FXbefPNNRUVFafv27dq+fbvPvJCQEMIOAAAIGn6Fnb/97W+BrgMAAKBe+DVmBwAA4HLh15Wd3/72tz87f968eX4VAwAAEGh+hZ3S0lKf6erqan399dcqLS1Vr169AlIYAABAIPgVdl555ZVz2txutzIzM9WmTZs6FwUAABAoARuzY7FY9OCDD2rFihWBWiUAAECdBXSA8tGjR1VdXR3IVQIAANSJX7exfjoA2ePxyOFw6NNPP9XgwYMDUhgAAEAg+BV2/vWvf/lMWywWNW/eXNOnT9fQoUMDUhguP3ExEfK4XQqxWI0uxXAcBwAIHn6FnVWrVgW6DphATES4QixWFb87Xc7iQ0aXY5gwW3vZhmQbXQYA4P/zK+ycdeLECR069OOXWvv27dW8efOAFIXLm7P4kJzf7Te6DAAAJPkZdsrLyzV79mz96U9/ktvtliRZrVb98pe/1NNPP63IyMiAFgkAAOAvv57Gys7O1o4dO/Tqq69q586d2rlzp37/+99rx44dys7m8j0AAAgefoWdjz/+WM8995zS09MVHR2t6Ohopaena/bs2fr4448DXSMAAIDf/Ao7Z86ckc1mO6c9Li5OZ86cqXNRAAAAgeJX2ElOTtbixYtVWVnpbTtz5oyWLFmi5OTkQNUGAABQZ34NUJ4xY4YeeeQR3XTTTerYsaMk6cCBAwoPD9frr78e0AIBAADqwq+wY7fb9Ze//EXr16/3Pnp+1113adCgQYqIiAhogQAAAHXhV9j5wx/+oLi4ON17770+7e+8845OnDih0aNHB6Q4AACAuvJrzM6bb76p9u3bn9N+7bXXKjc3t85FAQAABIpfYcfhcCg+Pv6c9ubNm8vhcNR4PX/4wx80dOhQpaSkKDU1VWPHjvXeFjursrJSWVlZ6tmzp1JSUjRhwgQVFxf79Dl+/LhGjx6tbt26KTU1Vc8//zy/vg4AACT5GXZatWqlf/7zn+e0/+Mf/1CLFi1qvJ7t27dr+PDheuutt5STk6Pq6mqNGjVK5eXl3j5z587V3//+dy1atEirVq1SUVGRxo8f753vcrk0ZswYOZ1O5ebmKjs7W++9954WL17sz64BAACT8WvMzj333KO5c+equrpavXr1kiRt27ZNL7zwgh5++OEar2f58uU+09nZ2UpNTdW+ffvUo0cPnT59WuvWrdOCBQuUmpoq6cfwM2DAAOXl5Sk5OVlbt25VQUGBcnJyZLPZ1KlTJ02aNEkLFizQ+PHjFR4e7s8uAgAAk/Ar7DzyyCM6efKksrKy5HQ6JUmNGjXSI488ojFjxvhdzOnTpyVJsbGxkqS9e/fK6XSqd+/e3j4dOnRQ69atvWEnLy9PiYmJPi85TEtLU2ZmpgoKCtS5c+cab9/lcvldu9Vq9XtZmFNdzicAwMXV9N9Zv8JOSEiIpk6dqrFjx+rgwYOKiIjQNddcU6erKG63W3PnzlX37t2VmJgoSSouLlZYWJiaNGni0zcuLs47Nqi4uPictzmfna7N+CFJ2rNnj1+1R0ZG1ipUoWHIz89XRUWF0WUAQIPnV9g5q3HjxuratWtACsnKytLXX3+ttWvXBmR9/khKSuIKDQLGbrcbXQIAmJrL5arRhYo6hZ1AefbZZ/Xpp59q9erVatmypbfdZrPJ6XSqtLTU5+pOSUmJ92kwm82m3bt3+6zv7NNa53ti7OdYrVbCDgKGcwkAgoNfT2MFisfj0bPPPqtPPvlEK1asUJs2bXzmd+nSRWFhYdq2bZu37dChQzp+/Lj3N7iSk5P11VdfqaSkxNvns88+U3R0tBISEi7JfgAAgOBl6JWdrKwsbdiwQb///e/VuHFj7xibmJgYRUREKCYmRkOHDlV2drZiY2MVHR2tOXPmKCUlxRt20tLSlJCQoGnTpmnq1KlyOBxatGiRhg8fzpNYAADA2LDzxhtvSJIeeOABn/Z58+ZpyJAhkn780VGLxaKJEyeqqqpKaWlpmjVrlrev1WrV0qVLlZmZqWHDhikyMlKDBw/WxIkTL92OAACAoGVo2MnPz79on0aNGmnWrFk+AeenrrzySr322muBLA0AAJiEoWN2AAAA6hthBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmJqhYWfHjh167LHHlJaWJrvdrk2bNvnMnz59uux2u89n1KhRPn1OnjypyZMnq3v37rrhhhs0Y8YMlZWVXcrdAAAAQSzUyI2Xl5fLbrdr6NChGj9+/Hn79O3bV/PmzfNOh4eH+8yfMmWKHA6HcnJy5HQ6NWPGDD3zzDNauHBhvdYOAAAuD4aGnfT0dKWnp/9sn/DwcMXHx5933sGDB7Vlyxa98847SkpKkiTNnDlTo0eP1rRp03TFFVcEvGYAAHB5MTTs1MT27duVmpqqJk2aqFevXnr88cfVrFkzSdKuXbvUpEkTb9CRpN69e8tisWj37t3q169frbblcrn8rtNqtfq9LMypLucTAODiavrvbFCHnb59+6pfv3666qqrdPToUf3ud7/To48+qjfffFNWq1XFxcVq3ry5zzKhoaGKjY2Vw+Go9fb27NnjV52RkZHq3LmzX8vCvPLz81VRUWF0GQDQ4AV12Bk4cKD377MDlG+77Tbv1Z5AS0pK4goNAsZutxtdAgCYmsvlqtGFiqAOOz/Vpk0bNWvWTEeOHFFqaqpsNptOnDjh06e6ulqnTp264Difn2O1Wgk7CBjOJQAIDpfVe3a+++47nTx50htkUlJSVFpaqr1793r7fP7553K73eratatRZQIAgCBi6JWdsrIyFRYWeqePHTum/fv3KzY2VrGxsVqyZIn69+8vm82mo0eP6oUXXlDbtm3Vt29fSVKHDh3Ut29fPf3008rKypLT6dTs2bM1cOBAnsQCAACSDA47e/fu1YgRI7zTZ9+nM3jwYGVmZuqrr77S+++/r9OnT6tFixbq06ePJk2a5POunQULFmj27NkaOXKkLBaLbr/9ds2cOfOS7wsAAAhOhoadnj17Kj8//4Lzly9fftF1NG3alBcIAgCAC7qsxuwAAADUFmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYmqFhZ8eOHXrssceUlpYmu92uTZs2+cz3eDx66aWXlJaWpq5du+rBBx/UN99849Pn5MmTmjx5srp3764bbrhBM2bMUFlZ2SXcCwAAEMwMDTvl5eWy2+2aNWvWeee/9tprWrVqlTIzM/XWW28pMjJSo0aNUmVlpbfPlClTVFBQoJycHC1dulQ7d+7UM888c6l2AQAABLlQIzeenp6u9PT0887zeDxauXKlfvOb3+i2226TJM2fP1+9e/fWpk2bNHDgQB08eFBbtmzRO++8o6SkJEnSzJkzNXr0aE2bNk1XXHFFrepxuVx+74vVavV7WZhTXc4nAMDF1fTfWUPDzs85duyYHA6Hevfu7W2LiYlRt27dtGvXLg0cOFC7du1SkyZNvEFHknr37i2LxaLdu3erX79+tdrmnj17/Ko1MjJSnTt39mtZmFd+fr4qKiqMLgMAGrygDTsOh0OSFBcX59MeFxen4uJiSVJxcbGaN2/uMz80NFSxsbHe5WsjKSmJKzQIGLvdbnQJAGBqLperRhcqgjbsGMFqtRJ2EDCcSwAQHIL20fP4+HhJUklJiU97SUmJbDabJMlms+nEiRM+86urq3Xq1Cnv8gAAoGEL2rBz1VVXKT4+Xtu2bfO2/fDDD/ryyy+VkpIiSUpJSVFpaan27t3r7fP555/L7Xara9eul7xmAAAQfAy9jVVWVqbCwkLv9LFjx7R//37FxsaqdevWGjFihF599VW1bdtWV111lV566SW1aNHC+3RWhw4d1LdvXz399NPKysqS0+nU7NmzNXDgwFo/iQUAAMzJ0LCzd+9ejRgxwjs9b948SdLgwYOVnZ2tRx99VBUVFXrmmWdUWlqq66+/XsuWLVOjRo28yyxYsECzZ8/WyJEjZbFYdPvtt2vmzJmXfF8ABC+P26UQC2OoOA5oqAwNOz179lR+fv4F54eEhGjSpEmaNGnSBfs0bdpUCxcurI/yAJhEiMWq4neny1l8yOhSDBNmay/bkGyjywAMwdNYABoEZ/EhOb/bb3QZAAwQtAOUAQAAAoGwAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wA5iYy+02ugQAMBy/eg6YmNVi0cy1W3S46JTRpRimt721xt3Z3egyABiIsAOY3OGiUzrwvyeMLsMw18Q3MboEAAbjNhYAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1oA47L7/8sux2u8/njjvu8M6vrKxUVlaWevbsqZSUFE2YMEHFxcUGVgwAAIJNqNEFXMy1116rnJwc77TVavX+PXfuXG3evFmLFi1STEyMZs+erfHjxys3N9eIUgEAQBAK+rBjtVoVHx9/Tvvp06e1bt06LViwQKmpqZJ+DD8DBgxQXl6ekpOTL3GlAAAgGAV92Dly5IjS0tLUqFEjJScna/LkyWrdurX27t0rp9Op3r17e/t26NBBrVu39jvsuFwuv+v8zytOgFS38ylQOC/xU8FwXgKBUtPzOajDTteuXTVv3jy1a9dODodDr7zyioYPH67169eruLhYYWFhatKkic8ycXFxcjgcfm1vz549fi0XGRmpzp07+7UszCs/P18VFRWGbZ/zEudj9HkJGCGow056err3744dO6pbt266+eab9eGHHyoiIiLg20tKSuK/hBEwdrvd6BKAc3BewkxcLleNLlQEddj5qSZNmuiaa65RYWGhevfuLafTqdLSUp+rOyUlJecd41MTVquVsIOA4VxCMDL6vHS53bJagvpB4EuC43BpXVZhp6ysTEePHlV8fLy6dOmisLAwbdu2Tf3795ckHTp0SMePH2dwMgAEKavFoplrt+hw0SmjSzFMuxaxmvPrvkaX0aAEddh5/vnndfPNN6t169YqKirSyy+/LIvForvuuksxMTEaOnSosrOzFRsbq+joaM2ZM0cpKSmEHQAIYoeLTunA/54wugw0IEEddr777js9+eSTOnnypJo3b67rr79eb731lpo3by5JmjFjhiwWiyZOnKiqqiqlpaVp1qxZBlcNAACCSVCHnRdffPFn5zdq1EizZs0i4AAAgAtidBQAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AAJdQXEyEPG6X0WUEhUt1HIL65yIAADCbmIhwhVisKn53upzFh4wuxzBhtvayDcm+JNsi7AAAYABn8SE5v9tvdBkNArexAACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqZkm7KxZs0a33HKLkpKSdM8992j37t1GlwQAAIKAKcLOxo0bNW/ePI0bN07vvfeeOnbsqFGjRqmkpMTo0gAAgMFMEXZycnJ07733aujQoUpISFBWVpYiIiK0bt06o0sDAAAGCzW6gLqqqqrSvn37NGbMGG+bxWJR7969tWvXrhqtw+PxeNdltVr9qsNqteralrEKt4b4tbwZtIlrLJfLJWt8otyWcKPLMYw17hq5XC65XC6jS+G8FOflWcFyXnJOck6eFYhz8uyyZ7/HLyTEc7EeQe7777/XTTfdpNzcXKWkpHjb58+frx07dujtt9++6Dqqqqq0Z8+e+iwTAADUk6SkJIWHXzg4XvZXdgIhNDRUSUlJslgsCglpuP+1AQDA5cTj8cjtdis09OfjzGUfdpo1ayar1XrOYOSSkhLZbLYarcNisfxsIgQAAJevy36Acnh4uK677jpt27bN2+Z2u7Vt2zaf21oAAKBhuuyv7EjSQw89pIyMDHXp0kVdu3bVihUrVFFRoSFDhhhdGgAAMJgpws6AAQN04sQJLV68WA6HQ506ddKyZctqfBsLAACY12X/NBYAAMDPuezH7AAAAPwcwg4AADA1wg4AADA1wg4AADA1UzyNBeOdOHFCL730kjZv3qzi4mLFxsaqY8eOGjt2rK6//nqjy0MD5XA4tHTpUn366af6/vvvFRcXp06dOmnkyJFKTU01ujwAlwhhBwExYcIEOZ1OZWdnq02bNiopKdG2bdt08uRJo0tDA3Xs2DH96le/UpMmTTRt2jQlJiaqurpaW7duVVZWlj766COjS0QD8+2332rx4sXasmWLTp48qfj4eN16660aN26cmjVrZnR5psaj56iz0tJS9ejRQ6tWrdKNN95odDmAJOnRRx9Vfn6+PvroI0VFRfnMKy0tVZMmTQyqDA3R0aNHNWzYMF1zzTV6/PHHddVVV+nrr7/WCy+8IKfTqTfffFNNmzY1ukzTYswO6iwqKkpRUVHatGmTqqqqjC4H0MmTJ7VlyxYNHz78nKAjiaCDSy4rK0thYWF6/fXXdeONN6p169ZKT09XTk6Ovv/+e7344otGl2hqhB3UWWhoqLKzs/X+++/rhhtu0H333aff/e53OnDggNGloYEqLCyUx+NR+/btjS4F0MmTJ7V161b9+te/VkREhM+8+Ph4DRo0SB9++KG40VJ/CDsIiP79+2vLli169dVX1bdvX23fvl1DhgzRu+++a3RpaID40kAwOXLkiDwejzp06HDe+R06dNCpU6d04sSJS1xZw0HYQcA0atRIffr00bhx45Sbm6vBgwfr5ZdfNrosNEBt27ZVSEiIDh06ZHQpgNfFQnhYWNglqqThIeyg3iQkJKi8vNzoMtAANW3aVGlpaVqzZs15z8HS0lIDqkJDdfXVVyskJEQHDx487/yDBw+qefPmjCWrR4Qd1Nm///1vjRgxQn/605904MABHT16VB9++KGWLVumW2+91ejy0EDNmjVLbrdb99xzjz7++GN98803OnjwoFauXKlhw4YZXR4akGbNmqlPnz5au3atzpw54zPP4XBo/fr1Gjx4sEHVNQw8eo46q6qq0ssvv6z/+Z//UWFhoaqrq9WyZUvdcccdeuyxx84ZkAdcKkVFRd6XChYVFal58+a67rrr9OCDD6pnz55Gl4cG5JtvvtF9992nDh06nPPoudVq1Zo1a9S4cWOjyzQtwg4AAJfAsWPHtGTJEm3ZskUlJSXyeDy6/fbbNX/+fEVGRhpdnqkRdgAAMMDixYuVk5OjnJwcJScnG12OqRF2AAAwyLp163T69GmNGDFCFgvDaOsLYQcAAJgaMRIAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQfAZeXll1/WL3/5y3pZ9xdffCG73R7Q3846duyY7Ha79u/fH7B1AqidUKMLAGBe06dP13vvvXdOe1pampYvX25ARQAaIsIOgHrVt29fzZs3z6ctPDzcoGouzOl0Gl0CgHrCbSwA9So8PFzx8fE+n9jYWEmS3W5Xbm6uxowZo27duunOO+/Url27dOTIET3wwANKTk7Wfffdp8LCwnPWm5ubq/T0dHXr1k2TJk3S6dOnvfN2796thx56SD179tT111+v+++/X/v27fNZ3m63a+3atXrssceUnJyspUuXnrONiooKPfLII7rvvvu8t7befvtt3XnnnUpKStIdd9yhNWvW+Cyze/du3X333UpKStKQIUO4fQUEAcIOAEP9/ve/1y9/+Uu9//77at++vSZPnqxnnnlGo0eP1rp16+TxePTss8/6LFNYWKgPP/xQS5cu1bJly7R//35lZmZ655eVlenuu+/W2rVr9dZbb6lt27YaPXq0fvjhB5/1LFmyRP369dP69es1dOhQn3mlpaV66KGH5PF4lJOToyZNmuiDDz7QSy+9pCeeeEIbN27Uk08+qcWLF3tv1ZWVlWnMmDHq0KGD3n33XU2YMEHPP/98/Rw4ADXnAYB6kpGR4enUqZMnOTnZ5/Pqq696PB6PJzEx0fPiiy96++/atcuTmJjoefvtt71tGzZs8CQlJXmnFy9e7OnUqZPnu+++87Zt3rzZ07FjR09RUdF563C5XJ6UlBTP3/72N29bYmKi57nnnvPp9/nnn3sSExM9BQUFnkGDBnkmTJjgqays9M6/7bbbPOvXr/dZ5pVXXvEMGzbM4/F4PLm5uZ4bb7zRc+bMGe/8tWvXehITEz3/+te/Lnq8ANQPxuwAqFc9e/b0ueoiyXsbS/rxdtJZcXFxkqTExESftsrKSv3www+Kjo6WJLVq1UpXXHGFt09KSorcbrcOHz6s+Ph4FRcXa9GiRdq+fbtKSkrkdrtVUVGh48eP+9TRpUuX89b88MMPq2vXrnrxxRdltVolSeXl5SosLNRTTz2lp59+2tu3urpaMTExkqSDBw/KbrerUaNGPrUBMBZhB0C9ioyMVNu2bS84PywszPt3SEjIBdvcbneNt5mRkaGTJ0/qqaeeUuvWrRUeHq5hw4adMwg5KirqvMunp6frL3/5iwoKCrxhrLy8XJI0e/ZsdevWzac/v1YNBDf+HwrgsvPtt9/q+++/907n5eXJYrGoXbt2kqR//vOfeuCBB5Senq5rr71W4eHh+ve//13j9U+ZMkWDBw/Wgw8+qIKCAkmSzWZTixYtdPToUbVt29bn06ZNG0lShw4dlJ+fr8rKSp/aABiLsAOgXlVVVcnhcPh8Tpw4Uad1NmrUSNOnT9eBAwe0c+dOzZkzR3feeafi4+MlSddcc40++OADHTx4UF9++aWmTJmiiIiIWm0jIyNDgwYN0siRI3Xw4EFJ0sSJE/XHP/5RK1eu1OHDh5Wfn69169YpJydHknTXXXcpJCREM2fOVEFBgTZv3qzXX3+9TvsKoO64jQWgXm3ZskVpaWk+be3atdNHH33k9zqvvvpq9evXT48++qhOnTqlX/ziF5o1a5Z3/nPPPaenn35agwcPVqtWrfTEE09o/vz5td7OjBkz5Ha7NXLkSK1atUr33HOPIiIitHz5cs2fP19RUVFKTEzUyJEjJUmNGzfW0qVLNWvWLN19991KSEjQlClTNGHCBL/3FUDdhXg8Ho/RRQAAANQXbmMBAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABT+38n/j4gAklz3wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Distribution of Embarked Survived wise\n",
    "sns.countplot(x=titanic['Embarked'],hue=titanic['Survived'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "hr7TKliksreB",
    "outputId": "43e50f07-c2b4-4b2f-84d2-ebee1307a789"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showinf Distribution of Age Survived Wise\n",
    "sns.kdeplot(x=titanic['Age'],hue=titanic['Survived'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i2u8RjFZgcEr"
   },
   "source": [
    "This Plot showing most people of age group of 20-40 are died"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 853
    },
    "id": "lK43to0wSmNK",
    "outputId": "26b42add-9d33-4f2b-be66-f221998644f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Histplot for Dataset\n",
    "titanic.hist(figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "G2HE3bkaspGI",
    "outputId": "21cf45f9-3cea-49ca-a93b-ed3151148a17"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GAADmNGjQIOXk5KhZs2ZKTU31djluowcFAACLy8rKUk5OjiQpJydHWVlZXq7IfQQUAAAsbtSoUZedtiICCgAAFpaUlCTDMJzmGYahpKQkL1XkGQQUAAAsqqioSBs3biy3bePGjSoqKqrmijyHgAIAgEW99NJLbrWbGQEFAACLmjJlilvtZkZAAQDAogICAtS/f/9y2+655x4FBARUc0WeQ0ABAMDCJk2aJB8fH6d5Pj4+eu6557xUkWcQUAAAsLj58+dfdtqKCCgAAFhcaGio6tWrJ0mqV6+eQkNDvVyR+wgoAABY3KlTp1RYWChJKiws1KlTp7xbkAcQUAAAsLgpU6aotLRUklRaWqrnn3/eyxW5j4ACAICF7d69W5mZmU7z9u7dq927d3upIs8goAAAYFGlpaWKi4srty0uLs7Rq2JFBBQAACwqPT1deXl55bbl5eUpPT29mivyHAIKAAAWFRUVpYYNG5bbFhQUpKioqGquyHMIKAAAWJSvr+8lL/HEx8fL19e6X/PWrRwAAKhTp06y2WxO88LCwtSxY0cvVeQZBBQAACzupZdecvSW+Pr6KiEhwcsVuY+AAgCAxTVq1EgPPPCAfH199cADD6hRo0beLsltdbxdAAAAcN/jjz+uxx9/3NtleAw9KAAAwHQIKAAA1ACjRo1S9+7dNWrUKG+X4hEEFAAALO7IkSOO4e4zMzN15MgRL1fkPgIKAAAWN2LEiMtOWxEBBQAAC0tJSVFBQYHTvIKCAqWkpHipIs8goAAAYFElJSVauHBhuW0LFy5USUlJNVfkOQQUAAAs6s0333Sr3cwIKAAAWNQjjzziVruZEVAAALCoOnXqaMiQIeW2DR06VHXqWHc8VgIKAAAWZRiGsrOzy237+uuvZRhGNVfkOQQUAAAs6vDhw9q1a1e5bbt27dLhw4eruSLPIaAAAGBRrVq1UufOneXn5+c038/PT7fccotatWrlpcrcR0ABAMCifHx89Mwzz1xyvo+Pjxeq8gwCCgAAFtayZUv179/faV7//v11/fXXe6kizyCgAABgcZs3b77stBURUAAAsLCUlBT98ssvTvMY6h4AAHgNQ90DAADTYah7AABgOgx1DwAATKdOnToaOXJkuW1PPfVU7RnqfuHChRo0aJAiIyMVFRWlp556SocOHXJaprCwUPHx8erSpYsiIyM1ZswYnTx50mmZ48ePa8SIEQoPD1dUVJSSkpIsfZ0MAABvGTp0qOrXr+80r379+pf8jR6rcCmgfPbZZxo6dKjefvttLVu2TCUlJRo2bJgKCgocy0yfPl1bt25VcnKyli9frhMnTmj06NGOdrvdrpEjR6q4uFirVq1SYmKi1qxZo3nz5nlurwAAqEUWLVp02WkrcimgLF26VAMHDtTvf/97tWvXTomJiTp+/Lj27dsnSTpz5oxSU1MVGxurqKgohYaGavr06dqzZ48yMjIkSTt27NDBgwc1a9YstW/fXj169NC4ceOUkpKioqIij+8gAAA13Q033CCbzSZJstlsuuGGG7xckfvcujh15swZSVJQUJAkKSsrS8XFxYqOjnYs06ZNG7Vo0UIZGRmKiIhQRkaGgoOD1bRpU8cyMTExiouL08GDB3XTTTdV+PXtdrs75QMAUGNceCXCrN+PrtRV6YBSWlqq6dOnq2PHjgoODpYknTx5Uv7+/mrYsKHTsk2aNFFOTo5jmQvDiSTHdNkyFZWZmVnZ8gEAgIlVOqDEx8frm2++0cqVKz1Zj0tsNttFv+AIAADMyW63V7hzoVIBZdq0adq2bZtWrFih3/zmN475TZs2VXFxsfLy8px6UXJzc9WsWTPHMnv37nXaXtlTPmXLVJSfnx8BBQCAGsilm2QNw9C0adO0efNm/e1vf9Nvf/tbp/bQ0FD5+/srPT3dMe/QoUM6fvy4IiIiJEkRERH6+uuvlZub61hm586dCgwMVNu2bd3YFQAAUFO41IMSHx+vDRs26PXXX9fVV1/tuGekQYMGqlu3rho0aKBBgwYpMTFRQUFBCgwMVEJCgiIjIx0BJSYmRm3bttXEiRM1YcIE5eTkKDk5WUOHDlVAQIDHdxAAAFiPj2EYRkUXDgkJKXf+jBkzNHDgQEnnB2pLTEzUxo0bVVRUpJiYGE2dOtXp8s3333+vuLg4ffbZZ6pXr54GDBig8ePHV3jEO7vd7ngqiEs8AABYgyvf3y4FFLMgoAAAYD2ufH/zWzwAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAANQAS5YsUc+ePbVkyRJvl+IRBBQAACzu1KlTWrFihUpLS7VixQqdOnXK2yW5jYACAIDFTZkyRaWlpZKk0tJSPf/8816uyH0EFAAALGz37t3KzMx0mrd3717t3r3bSxV5BgEFAACLKi0tVVxcXLltcXFxjl4VKyKgAABgUenp6crLyyu3LS8vT+np6dVckecQUAAAsKioqCg1bNiw3LagoCBFRUVVc0WeQ0ABAMCifH19L3mJJz4+Xr6+1v2at27lAABAnTp1ks1mc5oXFhamjh07eqkizyCgAABgcU8//bTT9Lhx47xTiAcRUAAAsLgJEyZcdtqKCCgAAFhYWlqafvrpJ6d5P/30k9LS0rxUkWcQUAAAsCi73a7ExMRy2xITE2W326u5Is8hoAAAYFFr1qyRYRjlthmGoTVr1lRzRZ5DQAEAwKJ+/vlnt9rNjIACAIBFPfroo261mxkBBQAAi/L19VVAQEC5bQEBAQzUBgAAql96erqKiorKbSsqKuK3eAAAQPXjt3gAAIDp8Fs8AADAlPgtHgAAYErDhg1zmn7ssce8VInnEFAAALC48ePHX3baiggoAABY2Ouvv37RkPZ2u12vv/66lyryDAIKAAAWVVxcrFWrVpXbtmrVKhUXF1dzRZ5DQAEAwKKSk5PdajczAgoAABbVpUsXt9rNjIACAIBFxcTEXHKo+6uuukoxMTHVXJHnEFAAALAoX19fJSYmltuWlJTEQG0AAMA7OnXqpLZt2zrNa9u2LQO1AQAA75ozZ85lp62IgAIAgMXl5eVddtqKCCgAAFjciBEjLjttRQQUAAAsLCUlRQUFBU7zCgoKlJKS4qWKPIOAAgCARZWUlGjhwoXlti1cuFAlJSXVXJHnEFAAALCoN9980612MyOgAABgUY888ohb7WZGQAEAwKLq1Kmjnj17ltv2hz/8QXXq1KnegjyIgAIAgEXZ7XZ99NFH5bZt375ddru9mivyHAIKAAAWtX79+kuGELvdrvXr11dzRZ5DQAEAwKL69evnVruZEVAAALCo5cuXu9VuZgQUAAAsKjg42K12MyOgAABgUdHR0W61m5nLAWXXrl164oknFBMTo5CQEG3ZssWp3TAMvfLKK4qJiVFYWJgeeeQRfffdd07LnDp1SuPHj1fHjh3VqVMnTZ48Wfn5+W7tCAAAtc2nn37qVruZuRxQCgoKFBISoqlTp5bbvnjxYi1fvlxxcXF6++23Va9ePQ0bNkyFhYWOZZ577jkdPHhQy5Yt04IFC7R792698MILld8LAABqoaioKDVs2LDctqCgIEVFRVVzRZ7jckDp0aOHnnnmGd1+++0XtRmGobfeektPPvmkevfurXbt2mnmzJk6ceKEo6clOztbH330kRISEhQeHq5OnTrp+eef18aNG/Xjjz+6v0cAANQSvr6+iouLK7ctPj5evr7WvZPDo0PMHTt2TDk5OU7XvBo0aKDw8HDt2bNH/fv31549e9SwYUPZbDbHMtHR0fL19dXevXvLDT6XYuUBaAAA8ITIyEjZbDZlZmY65tlsNoWHh5vue9KVejwaUHJyciRJTZo0cZrfpEkTnTx5UpJ08uRJXXPNNc5F1KmjoKAgx/oVdeH/DAAAaqv+/fs7fSf2799fGRkZ3ivIA6w7SL/OJ0Q/Pz9vlwEAgFeNHTvWaXrjxo2aN2+el6q5NLvdXuHOBY8GlGbNmkmScnNz1bx5c8f83NxctWvXTpLUtGlT/fTTT07rlZSU6PTp0471K8rPz4+AAgCo1Xbv3n3Rl35mZqb27NmjTp06eakq93n07pmWLVuqWbNmSk9Pd8w7e/asvvzyS0VGRko6f60sLy9PWVlZjmU++eQTlZaWKiwszJPlAABQo5WWll7yJtm4uDiVlpZWb0Ee5HJAyc/P1/79+7V//35J52+M3b9/v44fPy4fHx899NBDeuONN/TBBx/oP//5jyZOnKjmzZurd+/ekqQ2bdqoW7du+stf/qK9e/fq888/14svvqj+/fvr2muv9ezeAQBQg6WnpysvL6/ctry8PKcOA6tx+RJPVlaWHnroIcf0jBkzJEkDBgxQYmKihg8frl9++UUvvPCC8vLydPPNN2vJkiW66qqrHOvMnj1bL774oh5++GH5+vqqT58+ev755z2wOwAA1B4333yzW+1m5mMYhuHtIlxlt9uVkZGhiIgI7kEBANRaL7zwgrZt23bJ9p49e2ratGnVV9AVuPL9bd0RXAAAqOVuu+02t9rNjIAC1CIff/yx7r33Xn388cfeLgWAB/BjgQAs79y5c3r55Zf1448/6uWXX9a5c+e8XRIAN23YsMGtdjMjoAC1xIoVK5Sbmyvp/NhEKSkpXq4IgLvuvPNOt9rNjIAC1ALHjh1TSkqKyu6JNwxDKSkpOnbsmJcrA+CO1157za12MyOgADWcYRiaO3fuJedb8EE+AP+nRYsWbrWbGQEFqOEOHz6sXbt2XfQrona7Xbt27dLhw4e9VBkAd2VnZ7vVbmYEFKCGa9WqlTp37iwfHx+n+T4+PrrlllvUqlUrL1UGwF1ffvmlW+1mRkABajgfHx/dd999F13KMQxD991330XBBYB1tGnTxq12MyOgADWcYRj6+9//Xm7bypUruQcFsLCHH37YrXYzI6AANVzZPSjl4R4UwNo2bdrkVruZEVCAGq5ly5ZutQMwr9GjR7vVbmYEFKCGW7dunVvtAMxr7dq1brWbGQEFqOGuvfZat9oBmNe//vUvt9rNrI63CwBQtaKiouTj41PuzbA+Pj6KioryQlUALmQYRqV+H2vmzJkaOHDgZdt/+eWXCm+vbt26pnmyj4AC1HDffffdJZ/UMQxD3333naUfRQSszjAMjRo1SllZWR7f9uXCS3lsNptee+01U4QULvEANdwXX3zhVjuAqmeGQGA29KAANdyJEyfcagdQtXx8fPTaa69V6hKPdP4fGX/+858vmp+UlKSIiAiXtsUlHgDV5ptvvnGrHUDV8/HxUb169Sq1bteuXdWhQwft27fPMS8sLMzy95dxiQeo4X7/+9+71Q7A/KZOner4u6+vrxISErxYjWcQUIAa7uuvv3arHYD5BQUFOf4+ZMgQNWrUyHvFeAiXeACLqOxjiH369NGePXsu227VxxABXMzKv79zIQIKYAFV+RhiUlKSkpKSKry8mR5DBFBzcYkHsAgCAYDahB4UwALcfQwxISFBH3300UXze/bsWe7jiZfDJR4A1YGAAliEO48hvvTSS+revftF86dNm+ZuWQBQJbjEA9QSv77PJDk52TuFAEAFEFCAWuLCESUbNGigjh07eq8YALgCAgpQC61evdrbJQDAZRFQAACA6RBQAACA6RBQAACA6RBQAACA6RBQAACA6TBQG1ANKvtDf5504et7uxaJEWkBXB4BBagG586dU9++fb1dhsMf//hHb5eg999/v9Ij4wKo+QgoAAC4gR7Si3mih5SAAlQz+512733yjP/7r7eurJRIfml+XnpxoGrQQ3oxT/SQElCAamAYhuPvfEGfd+ExAYBfI6AAAOAhPa9vKj8v3fxdFvq9dfO53TC07fuTHtseAQWoBheeMOx3e/ESj7eVSH7vnu9B4gke1ER+Pj6q4+ut97aXP1Olnt1cbT1NAt5TR3zyAOAKOE0C1a3Ei69tgptkAaAiCChANSu7xAEAuDSGugcAAKZDDwpQDerWrav333/fqzWcO3fOMT7CunXrVLduXa/W4+3XB2BuBBSgGvj4+JhqWPe6deuaqh4A+DUCCgAAHmIvrb0DEHp63wkoAOCiu+66S3l5eWrYsKE2bNjg7XLgZReOirztuOcGKrMyT4wUzU2yAOCCL774Qnl5eZKkvLw8ffHFF16uCKiZ6EEBABc8/fTTF03/+9//9k4xNcCgQYOUk5OjZs2aKTU11dvlVMqFoyL3bNFUfl4bSda77KWGowfJEyNF04MCABX0wgsvuDQfl5eVlaWcnBxJUk5OjrKysrxckQd4MZsYhuHdH+H08L7TgwLUQv3799eHH37o7TIspbCwUNu2bSu3bdu2bSosLNRVV11VvUVZ3KhRoy6a3r59u5eq8QxP/lhebUcPClBLbN261fH3kpISbdmyxYvVWM+UKVPcaoezpKSki/61bxiGkpKSvFQRzMbH8GJ/UEpKipYuXaqcnBy1a9dOf/nLXxQWFnbF9ex2uzIyMhQRESE/P4YNByqie/fuF83j3omKO3v2rPr163fJ9k2bNikwMLAaK7KuoqIi9e7d+5LtW7ZsUUBAQDVW5B7DMHTu3Dmv1mDGgRjLuw/Fle9vr13i2bRpk2bMmKH4+HiFh4frb3/7m4YNG6b33ntPTZo08VZZ+BXDMHTq1Cm3t1FYWOjW+pL7N11dddVVbm2jUaNGHrnxq7LcOQmOHj263PnDhg3Ta6+95tK2LnXisZLKHMvExMQrtrvai1ITjmVlvPjii1dsv9IyZsJAjFXDawFl2bJl+t///V8NGjRIkhQfH69t27YpNTVVI0aMcGvbnvhSLS0tdTxK6E0NGzaUr2/lr8S586VqGIZGjRpVM25c8wCbzabXXnvNK18oVfX/4ptvvlHfvn1dWsebx0Fy//NtGIZiY2N14MABzxWl871RrvZItWvXTomJiZU+lu6GZm+dK6/0aPYXX3yhb7/91qVtevNc6Qnu9sJcuK472zFTaPZKQCkqKtK+ffs0cuRIxzxfX19FR0drz549Fd6O3W6/aJ5hGBozZoz27dvnkVqtLjQ0VPPmzavUG86rd4ObkGEYstvtXgsoZuHt41CTPt8HDhzQ//zP/1R6fXc/32Y9lmfOnNHDDz9cra/pzrF0l6f/X5Rd6qmMqj4O5X1vX4pXAsrPP/8su91+0aWcJk2a6NChQxXeTmZm5kXzDMNQQUGB2zXWFPn5+crIyKj0m+2xxx7T2bNnK/36hmFo8eLFOnbsWKW34SktW7bU8OHDK30sAgMD9eWXX3q4qop77LHHVFRU5PJ6L7zwwmXXCwgI0LRp0yq8vYCAAK8dBz7fztz5fHMsnbl7rnSHmf5fePM4/JqlHzO22Wzl3mSzdOlSLvH8H293W0pSt27d3Opy9NQ9KGbquqxO77zzzmX/RfXOO++oYcOG1ViRezzx+a7sfVHDhw9Xfn7+RfOvvvpqLV682OXtefu+KG+dK+fOnVvuPzDL2Gw2PfPMMy5t0+rnyqVLl7p9o60nzpVVfZ602+2X/X9/Ia8ElMaNG8vPz0+5ublO83Nzc9W0adMKb8fPz++SdwG7sp1Lad68udvbwHk83eA9jRs3VtOmTXXy5MXjMzRv3lyNGzf2QlXu8cTnuzLS0tLKfRoqLS3NC9V4hjfOlfPnzy/3OF7YXhtxnnTmlXFQAgIC1KFDB6WnpzvmlZaWKj09XZGRkd4oCajR/vnPf5Y7f/Xq1dVcifU9+uijl51GxVzqhmIefUcZrw3U9uijj+rtt9/WmjVrlJ2drbi4OP3yyy8aOHCgt0oCarSnnnrqstOoGAIKUD28OlDbihUrHAO1tW/fXs8//7zCw8OvuB4DtQGVc2G3Ov9ShRnwnqxdXPn+9mpAqSwCCgAA1uPK9ze/xQMAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEzHK79m7K6ywW/tdruXKwEAABVV9r1dkUHsLRlQSktLJUmZmZlergQAALiq7Hv8ciz5WzylpaUqKSmRr6+vfHx8vF0OAACoAMMwVFpaqjp16sjX9/J3mVgyoAAAgJqNm2QBAIDpEFAAAIDpEFAAAIDpEFAAAIDpEFAAAIDpEFAAAIDpEFAAAIDpEFCqwaeffqqQkBDl5eVV6evExsbqqaeeqtLXqA4PPvigXnrpJW+XgVosJCREW7ZskSQdO3ZMISEh2r9/v5erQnWdS2EOlhzqvrJ++uknvfLKK9q+fbtOnjypoKAgtWvXTk899ZRuvvnmKnvdyMhI7dixQw0aNKiy1zCb2NhYrVmzRpLk7++v6667Tn/84x/1xBNPqE6dWvW2qxZ79uzR/fffr27dumnRokXeLsf0rnQu2LFjh4KCglza5ubNm7V48WJlZ2ertLRULVq0UHR0tKZMmVJFe+F9fM6rzoXH9kL/+te/1KpVKy9UVP1q1TtozJgxKi4uVmJion77298qNzdX6enpOnXqVKW2ZxiG7Hb7FT+IAQEBatasWaVew8q6deumGTNmqKioSNu3b9e0adPk7++vkSNHeru0Gmf16tV64IEHtHr1av3444+69tprvV2SqV3pXODq5zU9PV3PPPOMnn76af3hD3+Qj4+PsrOz9fHHH1dB9ebiqc+53W6Xj4/PFYc/r03Kju2FrrnmGpe2YeXjar2KKykvL0+7d+/Wc889p1tvvVXXX3+9wsLCNHLkSN12223lduPm5eUpJCREn376qaT/3724fft2DRw4UDabTampqQoJCVF2drbT67355pvq3bu303p5eXk6e/aswsLCtH37dqflN2/erMjISP3yyy+SpB9++EHjxo1Tp06ddMstt+jJJ5/UsWPHHMvb7XbNmDFDnTp1UpcuXTRz5swK/TpkdSoLZtdff73uv/9+RUdH68MPP5Qkff7553rwwQcVHh6uzp07a9iwYTp9+nS521m7dq0GDhyoyMhIde3aVePHj1dubq6j/fTp0xo/frxuvfVWhYWFqU+fPkpNTZUkFRUVadq0aYqJiZHNZlOvXr20cOHCqt/5apSfn69NmzbpvvvuU8+ePS/6V9cHH3ygPn36yGaz6cEHH9SaNWsu6ibfvXu37r//foWFhalHjx5KSEhQQUFBde9KtbjSuUByvsRT5tChQxoyZIhsNpvuuusuffbZZ462Dz/8UJGRkXr88cd14403qnXr1urdu7emTp3qWObVV1/VH//4R61atUo9evRQeHi4xo0bpzNnzlTPjleRS33Oly1bprvvvlsRERHq0aOH4uLilJ+f71jvn//8pzp16qQPPvhA/fr1k81m0/Hjx1VUVKRZs2apR48eCg0N1e2336533nnH6TX37dungQMHKjw8XEOGDNGhQ4eqe7erRdmxvfDPW2+9VenjmpSUpG7duikiIkL33nuv47vNrGpNQKlfv77q16+vLVu2qKioyK1tvfzyyxo/frw2bdqkvn37KjQ0VO+++67TMuvXr9ddd9110bqBgYHq2bOnNmzY4DT/3XffVe/evVWvXj0VFxdr2LBhuvrqq5WSkqK///3vql+/vh5//HFH7X/961+1Zs0aTZ8+XStXrtTp06e1efNmt/arql111VUqLi7W/v379cgjj6hNmzb6xz/+oZUrV6pXr16On+H+tZKSEo0bN07r16/X/Pnz9f333ys2NtbR/sorryg7O1uLFy/Wpk2bFBcXp8aNG0uSli9frg8//FDJycl67733NGvWLF1//fXVsr/VJS0tTTfeeKNuvPFG3XPPPUpNTXWE1aNHj2rcuHG67bbbtG7dOg0ZMkRz5851Wv/IkSMaPny4+vTpo/Xr12vu3Ln6/PPP9eKLL3pjd6pcZc8FM2fO1KOPPqq1a9cqIiJCTzzxhH7++WdJ53tcDh48qK+//vqy2zhy5IjS0tK0YMECLVmyRPv371dcXJw7u2M6ZZ9zHx8fTZkyRRs2bFBiYqI++eQTzZo1y2nZc+fOafHixUpISNCGDRvUpEkTTZw4URs3btTzzz+vtLQ0TZs2TVdffbXTenPnzlVsbKxSU1Pl5+enyZMnV+cuelVlj+u0adO0Z88ezZ07V+vXr9cdd9yhxx9/XN999513dqQijFrkvffeMzp37mzYbDZj8ODBxssvv2zs37/fMAzDOHr0qBEcHGx89dVXjuVPnz5tBAcHG5988olhGIbxySefGMHBwcbmzZudtrts2TKjd+/ejulDhw4ZwcHBxsGDB53WO336tGEYhrF582YjIiLCKCgoMAzDMM6cOWPYbDZj+/bthmEYxtq1a42+ffsapaWljm0WFhYaYWFhxkcffWQYhmF07drVWLx4saO9uLjY6N69u/Hkk0965mC5adKkSY5aSktLjY8//tgIDQ01EhMTjWeffdYYMmTIJdd94IEHjISEhEu279271wgODjbOnj1rGIZhjBw50oiNjS132RdffNF46KGHnI5lTTN48GDjzTffNAzj/PugS5cujvfsrFmzjLvuustp+Tlz5ji9HydPnmz85S9/cVpm165dRrt27Yxz585Vwx5Uv8udCwzDcPqcl50bFi5c6Ggv+7wtWrTIMAzDyM/PN4YPH24EBwcbvXr1Mp5++mnjnXfeMQoLCx3rzJs3z2jfvr3x3//+1zFv+/btRrt27YwTJ05U9S5Xict9zn8tLS3NuOWWWxzTqampRnBwsNNxLzt3fvzxx+W+Xtm5dOfOnY5527ZtM4KDg2vce3XSpElG+/btjYiICMefMWPGXLRcRY7r999/f9F7zzAM4+GHHzZefvnlqtsJN9Wqe1D69u2rnj17avfu3crIyNBHH32kJUuWKCEhQbfcckuFt2Oz2Zym+/fvr5kzZyojI0MRERF699131aFDB7Vp06bc9bt37y5/f399+OGH6t+/v95//30FBgYqOjpaknTgwAEdOXJEHTt2dFqvsLBQR44c0ZkzZ5STk6Pw8HBHW506dRQaGmqqyzzbtm1TZGSkiouLZRiG7rrrLo0ZM0Z/+tOfdMcdd1R4O1lZWXrttdd04MABnT592rGPP/zwg9q2bav77rtPY8eO1VdffaWuXbuqd+/ejmM3YMAAPfbYY7rjjjvUrVs39ezZUzExMVWyv95w6NAhZWZmav78+ZLOvw/69eun1atXq0uXLvr2228VGhrqtE5YWJjT9IEDB/Sf//zHqRfQ+L+fRD927Ngl38dWdrlzwcCBA8tdJzIy0vH3ss9b2aWF+vXra9GiRTpy5Ig+/fRTZWRkKCkpSW+99Zb+8Y9/qF69epKk6667zun+oMjISJWWlurbb7+17H1ql/qc79y5UwsXLtShQ4d09uxZ2e12FRYW6pdffnEcD39/f4WEhDi2tX//fvn5+alz586Xfc0L1yk7brm5uWrRokUV7KH3dOnSxamHrV69epU6rl9//bXsdvtF592ioiI1atSoOnalUmpVQJHOdz927dpVXbt21ahRozRlyhS9+uqrSklJkSSnL/iSkpJyt1H2JijTrFkz3XrrrdqwYYMiIiK0YcMG3XfffZesISAgQH379tW7776r/v37a8OGDerXr5/jZtuCggJ16NBBs2fPvmhdV2+Q8qayD5e/v7+aN2/u2L+6detWeBsFBQUaNmyYYmJiNHv2bDVu3Fg//PCDhg0bpuLiYklSjx49tHXrVm3fvl0ff/yxHnnkEQ0dOlSTJk1Shw4d9MEHH+jf//63du7cqaefflrR0dGaN29elexzdVu9erVKSkrUrVs3xzzDMBQQEKAXXnihQtsoKCjQkCFD9OCDD17Udt1113msVrO51LngUgGlIm644QbdcMMNuvfee/XEE0/ojjvu0KZNmzRo0CAPVm4u5X3Ojx07ppEjR+q+++7TM888o6CgIH3++eeaMmWKiouLHefQunXrysfHx7Gtip4bLnwwoWz90tJSD+6VOdSrV8/piZ3KHteCggL5+fk5LoldqH79+tWzM5VQa+5BuZS2bduqoKDA8cWfk5PjaHNl3IO7775bmzZt0p49e3T06FH169fvisvv2LFD33zzjT755BPdfffdjrYOHTro8OHDatKkiVq1auX0p0GDBmrQoIGaNWumL7/80rFOSUmJ9u3bV+F6q0PZh6tFixZOJ5SQkBClp6dXaBuHDh3SqVOn9Nxzz6lTp05q06aN0w2yZa655hoNGDBAs2fP1uTJk/WPf/zD0RYYGKh+/fopISFBc+fO1fvvv1/pJ7fMpKSkROvWrVNsbKzWrl3r+LNu3To1b95cGzZsUOvWrZWVleW0XmZmptP0TTfdpIMHD170XmvVqpUCAgKqc5e8quxccCkZGRmOv5d93m688cZLLt+yZUvVrVvXceO7dL7X78cff3Tapq+vr1q3bu1e8V5U3ud83759MgxDsbGxioiIUOvWrXXixIkrbis4OFilpaXatWtXVZdtSZU9ru3bt5fdbtdPP/100WfczD13tSag/Pzzz3rooYe0bt06HThwQEePHlVaWpqWLFmi2267TXXr1lVERIQWLVqk7OxsffbZZ0pOTq7w9m+//Xbl5+crLi5OXbp0ueJjnp07d1bTpk313HPPqWXLlk6Xa+6++241btxYTz75pHbv3q2jR4/q008/VUJCgv773/9Kkh566CEtXrxYW7ZsUXZ2tuLj4y0zeNGIESOUmZmpuLg4HThwQNnZ2Vq5cqV++umni5Zt0aKF/P39tXz5ch09elQffPCBXn/9dadlXnnlFW3ZskWHDx/WN998o23btjkuSyxbtkwbNmxQdna2vv32W7333ntq1qyZGjZsWC37WpW2bdum06dP609/+pOCg4Od/vTp00erV6/W4MGD9e2332rWrFn69ttvtWnTJsdTPmX/who+fLj27NmjadOmaf/+/fruu++0ZcsWTZs2zZu7V2WudC64lJUrV2rz5s3Kzs7WtGnTdPr0aUfPyKuvvqqZM2fq008/1dGjR/XVV19p8uTJKikpcVy6lc732sTGxurAgQPavXu3EhISdOedd5r6S6IyWrVqpeLiYsfndu3atVq1atUV12vZsqUGDBigyZMna8uWLY5z36ZNm6qhavOr7HFt3bq17r77bk2cOFH/+te/dPToUe3du1cLFy7Utm3bqr7wSqo1l3iuvvpqhYeH629/+5uOHDmikpIS/eY3v3F0xUrS9OnTNWXKFA0cOFCtW7fWhAkT9Nhjj1Vo+4GBgerVq5fS0tI0ffr0Ky7v4+Oj/v37a8mSJRo1apRTW7169bRixQrNnj1bo0ePVn5+vq699lpFRUUpMDBQkvTYY48pJydHkyZNkq+vrwYNGqTbb7/dEo8stm7dWn/96181Z84c3Xvvvapbt67CwsLKferpmmuuUWJioubMmaPly5erQ4cOmjRpkp588knHMv7+/pozZ46+//571a1bVzfffLPmzJkj6fz/9yVLlujw4cPy9fWVzWbTokWLLDkmwK+tXr1a0dHR5Q4A2LdvXy1ZskT5+fl65ZVXHPdDlD19EhcX5+gdadeunZYvX67k5GTdf//9kqTf/va3V+wFtKqKnAvKM378eC1atEj79+9Xq1at9MYbbzh6Xjt37qyVK1dq0qRJjoHf2rdvr6VLlzr1stxwww26/fbbNXz4cJ0+fVo9e/Z0ehS5pmjXrp3+/Oc/a/HixZozZ446deqkZ599VpMmTbriunFxcZozZ47i4uJ06tQptWjRgrGT/o87x3XGjBl64403lJiYqBMnTqhRo0aKiIhQz549q77wSvIxzHRXJYAq98Ybb2jVqlUXjcWDqvXqq69qy5YtWrdunbdLASyh1vSgALVVSkqKbDabGjdurM8//1xLly7V0KFDvV0WAFwWAQWo4Q4fPqw33nhDp0+fVosWLfToo4/SZQ7A9LjEAwAATMf6dwoCAIAah4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABM5/8BvyzBkyyZ3+kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Boxplot for dataset\n",
    "# Checking for outliers\n",
    "sns.boxplot(titanic)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D6XD0lLOg0qL"
   },
   "source": [
    "This Plot showing Outliers in 2 columns i.e.. Age and Fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "id": "QhIwHpARsruZ",
    "outputId": "927b3e76-78f9-41be-ac78-386f902b1519"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-149c29a2-7dd8-44f8-8ce4-e25cb6e8a499\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.069809</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>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331339</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>-0.069809</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.091566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.232625</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>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.179191</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>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.091566</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-149c29a2-7dd8-44f8-8ce4-e25cb6e8a499')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-149c29a2-7dd8-44f8-8ce4-e25cb6e8a499 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-149c29a2-7dd8-44f8-8ce4-e25cb6e8a499');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "Survived  1.000000 -0.338481 -0.069809 -0.035322  0.081629  0.257307\n",
       "Pclass   -0.338481  1.000000 -0.331339  0.083081  0.018443 -0.549500\n",
       "Age      -0.069809 -0.331339  1.000000 -0.232625 -0.179191  0.091566\n",
       "SibSp    -0.035322  0.083081 -0.232625  1.000000  0.414838  0.159651\n",
       "Parch     0.081629  0.018443 -0.179191  0.414838  1.000000  0.216225\n",
       "Fare      0.257307 -0.549500  0.091566  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 1342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing Correlation\n",
    "titanic.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "id": "X_Wa8dvisryZ",
    "outputId": "526097e8-719d-4b52-aee4-e42a30ec098b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing Correlation Plot\n",
    "sns.heatmap(titanic.corr(),annot=True,cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "68hujCXmtJYn"
   },
   "source": [
    "This Plot is clearly showing\n",
    "\n",
    "1. Strong Positive Correlation between SibSp and Parch\n",
    "2. Strong Negative Correlation between Pclass and Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "HJ3PQy_xsN7b",
    "outputId": "e8250a8b-4015-42c9-e973-69b99b87d378"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting pairplot\n",
    "sns.pairplot(titanic)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "thOJS06rIpYM"
   },
   "source": [
    "# Checking the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BQmRKV4aC67a",
    "outputId": "a289deff-15e8-45d7-fbb9-dac5704e0458"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 1345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "SuN4A9HUC7CP",
    "outputId": "4d43fa29-faa6-4436-f19b-12c8df69162f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=titanic['Survived'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YRbu_M5_JHBD"
   },
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "c1hYeAngC7F4",
    "outputId": "2e5aae8a-5643-43fa-8564-62408e856090"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-3159c6e7-b986-4cdb-ae7d-2d4ab8f8a6de\">\n",
       "    <div class=\"colab-df-container\">\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "          document.querySelector('#df-3159c6e7-b986-4cdb-ae7d-2d4ab8f8a6de button.colab-df-convert');\n",
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       "          const element = document.querySelector('#df-3159c6e7-b986-4cdb-ae7d-2d4ab8f8a6de');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "0         0       3    1  22.0      1      0   7.2500         2\n",
       "1         1       1    0  38.0      1      0  71.2833         0\n",
       "2         1       3    0  26.0      0      0   7.9250         2\n",
       "3         1       1    0  35.0      1      0  53.1000         2\n",
       "4         0       3    1  35.0      0      0   8.0500         2"
      ]
     },
     "execution_count": 1347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# Create an instance of LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Apply label encoding to each categorical column\n",
    "for column in ['Sex','Embarked']:\n",
    "    titanic[column] = le.fit_transform(titanic[column])\n",
    "\n",
    "titanic.head()\n",
    "\n",
    "# Sex Column\n",
    "\n",
    "# 0 represents female\n",
    "# 1 represents Male\n",
    "\n",
    "# Embarked Column\n",
    "\n",
    "# 0 represents C\n",
    "# 1 represents Q\n",
    "# 2 represents S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7p9kwvJRbQhI"
   },
   "source": [
    "# Data Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Sw5Xg7HBbTz0"
   },
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3500WE1SJAl2"
   },
   "source": [
    "# Selecting the independent and dependent Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MnysSyF_C7JR",
    "outputId": "d0d060e2-aed7-4ba1-e2ef-d2e5c7dcc7cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 7)\n",
      "(891,)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "cols = ['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']\n",
    "x = titanic[cols]\n",
    "y = titanic['Survived']\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "print(type(x))  # DataFrame\n",
    "print(type(y))  # Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "7V3cmVshC7Mb",
    "outputId": "caff9c32-74b1-4b71-8b4d-ce53ddb78e44"
   },
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
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       "        buttonEl.style.display =\n",
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       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-0417083b-5b03-41e2-95c2-17df92a65e6a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "0       3    1  22.0      1      0   7.2500         2\n",
       "1       1    0  38.0      1      0  71.2833         0\n",
       "2       3    0  26.0      0      0   7.9250         2\n",
       "3       1    0  35.0      1      0  53.1000         2\n",
       "4       3    1  35.0      0      0   8.0500         2"
      ]
     },
     "execution_count": 1350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yfRCVI-gC7Pz",
    "outputId": "b60770a9-19b4-46b3-b1ee-fa0cffb1fb36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 1351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OUS8xM8BdOZ4"
   },
   "source": [
    "# Train_Test_Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hNesqve2dXNR",
    "outputId": "a28e58c6-404c-485f-da83-20479ec76e94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.10000000000001\n"
     ]
    }
   ],
   "source": [
    "print(891*0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oFa86c9KC7TD",
    "outputId": "f5b93c95-3702-4a84-bf15-168cacad73d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(801, 7)\n",
      "(90, 7)\n",
      "(801,)\n",
      "(90,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.10,random_state=1)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UzRdArOxdiXf"
   },
   "source": [
    "# Creating Functions to compute Confusion Matrix, Classification Report and to generate Training and the Testing Score(Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z1GJitEKC7V8"
   },
   "outputs": [],
   "source": [
    "def cls_eval(ytest,ypred):\n",
    "    cm = confusion_matrix(ytest,ypred)\n",
    "    print('Confusion Matrix\\n',cm)\n",
    "    print('Classification Report\\n',classification_report(ytest,ypred))\n",
    "\n",
    "def mscore(model):\n",
    "    print('Training Score',model.score(x_train,y_train))  # Training Accuracy\n",
    "    print('Testing Score',model.score(x_test,y_test))     # Testing Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XoyV75HzdseW"
   },
   "source": [
    "1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "XDqeTHMHC7ZK",
    "outputId": "0a69d0f5-1d10-48cd-89d0-c9ff231cd74f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-168 {color: black;background-color: white;}#sk-container-id-168 pre{padding: 0;}#sk-container-id-168 div.sk-toggleable {background-color: white;}#sk-container-id-168 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-168 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-168 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-168 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-168 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-168 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-168 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-168 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-168 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-168 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-168 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-168 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-168 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-168 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-168 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-168 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-168 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-168 div.sk-item {position: relative;z-index: 1;}#sk-container-id-168 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-168 div.sk-item::before, #sk-container-id-168 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-168 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-168 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-168 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-168 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-168 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-168 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-168 div.sk-label-container {text-align: center;}#sk-container-id-168 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-168 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-168\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-168\" type=\"checkbox\" checked><label for=\"sk-estimator-id-168\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, solver='liblinear')"
      ]
     },
     "execution_count": 1355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the logistic Regression Model\n",
    "lr = LogisticRegression(max_iter=1000,solver='liblinear')\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QKS8zfrTfo28",
    "outputId": "a89081ff-d49c-4e64-d81b-17b35d4dec96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.8052434456928839\n",
      "Testing Score 0.7666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Computing Training and Testing score\n",
    "mscore(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HJzIMs75fuJm",
    "outputId": "25492837-5a8f-4d36-fa69-ef56d0bfee49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0\n",
      " 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
      " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "# Generating Prediction\n",
    "ypred_lr = lr.predict(x_test)\n",
    "print(ypred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wOI2lMKhf1wg",
    "outputId": "5946423f-1152-4f6e-917e-1fe585e2d4f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[46  7]\n",
      " [14 23]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.87      0.81        53\n",
      "           1       0.77      0.62      0.69        37\n",
      "\n",
      "    accuracy                           0.77        90\n",
      "   macro avg       0.77      0.74      0.75        90\n",
      "weighted avg       0.77      0.77      0.76        90\n",
      "\n",
      "Accuracy Score 0.7666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
    "cls_eval(y_test,ypred_lr)\n",
    "acc_lr = accuracy_score(y_test,ypred_lr)\n",
    "print('Accuracy Score',acc_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SDz8T21iPhiR"
   },
   "source": [
    "2. knn Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "QZGxO7DhPl0m",
    "outputId": "a25fc99c-c712-48b6-b43f-31942974ef37"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-169 {color: black;background-color: white;}#sk-container-id-169 pre{padding: 0;}#sk-container-id-169 div.sk-toggleable {background-color: white;}#sk-container-id-169 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-169 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-169 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-169 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-169 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-169 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-169 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-169 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-169 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-169 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-169 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-169 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-169 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-169 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-169 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-169 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-169 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-169 div.sk-item {position: relative;z-index: 1;}#sk-container-id-169 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-169 div.sk-item::before, #sk-container-id-169 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-169 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-169 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-169 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-169 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-169 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-169 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-169 div.sk-label-container {text-align: center;}#sk-container-id-169 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-169 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-169\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=8)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-169\" type=\"checkbox\" checked><label for=\"sk-estimator-id-169\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=8)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 1359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the knnClassifier Model\n",
    "knn=KNeighborsClassifier(n_neighbors=8)\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-1JTcQlMP4NK",
    "outputId": "fa848960-39f1-41cf-c59a-435513b8ecf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.7752808988764045\n",
      "Testing Score 0.6777777777777778\n"
     ]
    }
   ],
   "source": [
    "# Computing Training and Testing score\n",
    "mscore(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R8QACrf8P_dt",
    "outputId": "e6aa8b2b-a30f-42bd-98b2-e2fab7a3d907"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1\n",
      " 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0\n",
      " 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Generating Prediction\n",
    "ypred_knn = knn.predict(x_test)\n",
    "print(ypred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PE_enmoKQFjF",
    "outputId": "369cf6be-8f5d-4b23-da0e-ec244091cd67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[47  6]\n",
      " [23 14]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.89      0.76        53\n",
      "           1       0.70      0.38      0.49        37\n",
      "\n",
      "    accuracy                           0.68        90\n",
      "   macro avg       0.69      0.63      0.63        90\n",
      "weighted avg       0.68      0.68      0.65        90\n",
      "\n",
      "Accuracy Score 0.6777777777777778\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
    "cls_eval(y_test,ypred_knn)\n",
    "acc_knn = accuracy_score(y_test,ypred_knn)\n",
    "print('Accuracy Score',acc_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i6xI6pzNQd03"
   },
   "source": [
    "3. SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "5BJwb1mzQmXs",
    "outputId": "1c7f77c8-bded-4ca3-f897-7636f58f81ee"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-170 {color: black;background-color: white;}#sk-container-id-170 pre{padding: 0;}#sk-container-id-170 div.sk-toggleable {background-color: white;}#sk-container-id-170 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-170 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-170 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-170 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-170 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-170 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-170 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-170 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-170 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-170 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-170 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-170 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-170 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-170 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-170 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-170 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-170 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-170 div.sk-item {position: relative;z-index: 1;}#sk-container-id-170 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-170 div.sk-item::before, #sk-container-id-170 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-170 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-170 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-170 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-170 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-170 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-170 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-170 div.sk-label-container {text-align: center;}#sk-container-id-170 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-170 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-170\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-170\" type=\"checkbox\" checked><label for=\"sk-estimator-id-170\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 1363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building Support Vector Classifier Model\n",
    "svc = SVC(C=1.0)\n",
    "svc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FtJm_shIQmck",
    "outputId": "379edf29-5f63-4ff0-fc3f-8540647e3525"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.6891385767790262\n",
      "Testing Score 0.6333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Computing Training and Testing score\n",
    "mscore(svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eHEzbOtvQmhT",
    "outputId": "380e5c81-0547-4bca-a1a8-9d195cb8205a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Generating Prediction\n",
    "ypred_svc = svc.predict(x_test)\n",
    "print(ypred_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nt8wz67jQmk3",
    "outputId": "5d49db3b-070b-46b1-8c51-c6b258d6fa28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[48  5]\n",
      " [28  9]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.91      0.74        53\n",
      "           1       0.64      0.24      0.35        37\n",
      "\n",
      "    accuracy                           0.63        90\n",
      "   macro avg       0.64      0.57      0.55        90\n",
      "weighted avg       0.64      0.63      0.58        90\n",
      "\n",
      "Accuracy Score 0.6333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
    "cls_eval(y_test,ypred_svc)\n",
    "acc_svc = accuracy_score(y_test,ypred_svc)\n",
    "print('Accuracy Score',acc_svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XsJG6oMGji8w"
   },
   "source": [
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 92
    },
    "id": "traml-2Ngzw2",
    "outputId": "ab7e9297-2ee4-4187-aeb4-afe91f38f602"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-171 {color: black;background-color: white;}#sk-container-id-171 pre{padding: 0;}#sk-container-id-171 div.sk-toggleable {background-color: white;}#sk-container-id-171 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-171 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-171 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-171 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-171 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-171 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-171 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-171 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-171 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-171 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-171 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-171 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-171 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-171 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-171 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-171 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-171 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-171 div.sk-item {position: relative;z-index: 1;}#sk-container-id-171 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-171 div.sk-item::before, #sk-container-id-171 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-171 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-171 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-171 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-171 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-171 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-171 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-171 div.sk-label-container {text-align: center;}#sk-container-id-171 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-171 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-171\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, min_samples_split=5,\n",
       "                       n_estimators=80)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-171\" type=\"checkbox\" checked><label for=\"sk-estimator-id-171\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, min_samples_split=5,\n",
       "                       n_estimators=80)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=10, min_samples_split=5,\n",
       "                       n_estimators=80)"
      ]
     },
     "execution_count": 1367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the RandomForest Classifier Model\n",
    "rfc=RandomForestClassifier(n_estimators=80,criterion='entropy',min_samples_split=5,max_depth=10)\n",
    "rfc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BUoq9-RDjqH8",
    "outputId": "b488fd57-3322-4099-afa6-722f009ae7b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.9113607990012484\n",
      "Testing Score 0.7444444444444445\n"
     ]
    }
   ],
   "source": [
    "# Computing Training and Testing score\n",
    "mscore(rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vzul-iOVjqNK",
    "outputId": "a1f417eb-ba6a-4810-bc38-2a607e72e010"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 1\n",
      " 0 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0\n",
      " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "# Generating Prediction\n",
    "ypred_rfc = rfc.predict(x_test)\n",
    "print(ypred_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aEIWlb1PHhN-",
    "outputId": "f7d1c730-ce7e-4e7e-ef9c-bf6a332991e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[46  7]\n",
      " [16 21]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.87      0.80        53\n",
      "           1       0.75      0.57      0.65        37\n",
      "\n",
      "    accuracy                           0.74        90\n",
      "   macro avg       0.75      0.72      0.72        90\n",
      "weighted avg       0.75      0.74      0.74        90\n",
      "\n",
      "Accuracy Score 0.7444444444444445\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
    "cls_eval(y_test,ypred_rfc)\n",
    "acc_rfc = accuracy_score(y_test,ypred_rfc)\n",
    "print('Accuracy Score',acc_rfc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2A8ufBNHks3U"
   },
   "source": [
    "3. DecisionTree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "a0V7mHzJkvyd",
    "outputId": "83c64ef6-7372-4ab0-ba56-8fc39096aaf1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-172 {color: black;background-color: white;}#sk-container-id-172 pre{padding: 0;}#sk-container-id-172 div.sk-toggleable {background-color: white;}#sk-container-id-172 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-172 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-172 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-172 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-172 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-172 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-172 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-172 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-172 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-172 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-172 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-172 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-172 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-172 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-172 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-172 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-172 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-172 div.sk-item {position: relative;z-index: 1;}#sk-container-id-172 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-172 div.sk-item::before, #sk-container-id-172 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-172 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-172 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-172 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-172 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-172 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-172 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-172 div.sk-label-container {text-align: center;}#sk-container-id-172 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-172 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-172\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, min_samples_split=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-172\" type=\"checkbox\" checked><label for=\"sk-estimator-id-172\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, min_samples_split=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_split=10)"
      ]
     },
     "execution_count": 1371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the DecisionTree Classifier Model\n",
    "dt = DecisionTreeClassifier(max_depth=5,criterion='entropy',min_samples_split=10)\n",
    "dt.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KU4QltvglrOE",
    "outputId": "0f904694-b37e-433c-97d6-db25036be7fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.8526841448189763\n",
      "Testing Score 0.7777777777777778\n"
     ]
    }
   ],
   "source": [
    "# Computing Training and Testing score\n",
    "mscore(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nsq9HNBPlxtU",
    "outputId": "95666f24-3383-40e2-f7dd-fc02f501fd60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1\n",
      " 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
      " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "# Generating Prediction\n",
    "ypred_dt = dt.predict(x_test)\n",
    "print(ypred_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Af6Kt6fDl1lE",
    "outputId": "e7fc3efc-8f9f-4ad5-c6ec-b3a7a610cb58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[46  7]\n",
      " [13 24]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.87      0.82        53\n",
      "           1       0.77      0.65      0.71        37\n",
      "\n",
      "    accuracy                           0.78        90\n",
      "   macro avg       0.78      0.76      0.76        90\n",
      "weighted avg       0.78      0.78      0.77        90\n",
      "\n",
      "Accuracy Score 0.7777777777777778\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model - confusion matrix, classification Report, Accuracy score\n",
    "cls_eval(y_test,ypred_dt)\n",
    "acc_dt = accuracy_score(y_test,ypred_dt)\n",
    "print('Accuracy Score',acc_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aMCISfBrFjPi"
   },
   "source": [
    "4. Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "740MsJfsFaUc",
    "outputId": "90749893-41b3-4dc6-9978-0a4fadd67aab"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-173 {color: black;background-color: white;}#sk-container-id-173 pre{padding: 0;}#sk-container-id-173 div.sk-toggleable {background-color: white;}#sk-container-id-173 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-173 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-173 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-173 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-173 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-173 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-173 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-173 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-173 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-173 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-173 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-173 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-173 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-173 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-173 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-173 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-173 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-173 div.sk-item {position: relative;z-index: 1;}#sk-container-id-173 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-173 div.sk-item::before, #sk-container-id-173 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-173 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-173 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-173 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-173 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-173 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-173 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-173 div.sk-label-container {text-align: center;}#sk-container-id-173 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-173 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-173\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(n_estimators=80)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-173\" type=\"checkbox\" checked><label for=\"sk-estimator-id-173\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=80)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(n_estimators=80)"
      ]
     },
     "execution_count": 1375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Builing the Adaboost model\n",
    "ada_boost  = AdaBoostClassifier(n_estimators=80)\n",
    "ada_boost.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-sgv2-xEGTG3",
    "outputId": "70c51323-4051-4db9-8acc-19616ca80722"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.8564294631710362\n",
      "Testing Score 0.7666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Computing the Training and Testing Score\n",
    "mscore(ada_boost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DfZ6W5vSGYQg"
   },
   "outputs": [],
   "source": [
    "# Generating the predictions\n",
    "ypred_ada_boost = ada_boost.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EqWNhi19GYUV",
    "outputId": "9b2ee646-3d0e-4b1b-9c5d-74507eedb815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[45  8]\n",
      " [13 24]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.85      0.81        53\n",
      "           1       0.75      0.65      0.70        37\n",
      "\n",
      "    accuracy                           0.77        90\n",
      "   macro avg       0.76      0.75      0.75        90\n",
      "weighted avg       0.77      0.77      0.76        90\n",
      "\n",
      "Accuracy Score 0.7666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model - confusion matrix, classification Report, Accuracy Score\n",
    "cls_eval(y_test,ypred_ada_boost)\n",
    "acc_adab = accuracy_score(y_test,ypred_ada_boost)\n",
    "print('Accuracy Score',acc_adab)"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "MpfJYIxhMWEa",
    "outputId": "315b3463-e571-4d5c-f8fc-9c8a4c0aee32"
   },
   "outputs": [
    {
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       "                      Model     Score\n",
       "4  Decision Tree Classifier  0.777778\n",
       "0       Logistic Regression  0.766667\n",
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    "    'Score': [acc_lr,acc_knn,acc_svc,acc_rfc,acc_dt,acc_adab]})\n",
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bRkBAgPHrr78ahmEY58+fN9q2bWusX7/ePo2/v7/D7wtUHJGRkYa3t7cREBBg+Pn5GR4eHoaXl5exdevWK843fPhwY+7cufbX4eHhxoABAxym6dOnj/Hyyy8bhmEYO3fuNHx8fIyffvrJPn7Hjh0OxwD/93//Z9xxxx3Gb7/9Zp9m+/bthpeXl5GZmWmvt3PnzobNZrNP061bN2PgwIH21xcuXDACAgKMf//738Xa7oKfMWPGGIZhfuxWsL0PPfSQwzIXL15sPPHEEw7DTpw4YXh4eBjp6enGf//7X8PDw8M4duzYZWu63PFkgW+++cbw8PAwtm3bdsXpDMNweG+Lsnz5cuPhhx+2vw4MDDTeeeedIqd94IEHjEWLFhU57osvvjA8PDyMX375xTAMw/jll18MDw8P44svvrBP8+fjULNjxoL6Z8+efYUtvPqu9H8yduxY47777jMMwzC++uorIygoyPjjjz8cpunatauxbt06wzDMj71Le6xfHseuf96XOnfubDz11FMO00yaNMmYOnWqw7CvvvrK8PLyMn7//Xfj+PHjhre3t8NngGFc3Bfmz59f5HqXLVtmeHh4GGfPnr1ifX/eB4ty6WfWlXrxzJkzhoeHh8Mx36ViYmKMXr162V9/+OGHhoeHh8M0l+43f/zxh9G2bVtj3759DtNMmjTJ+Mc//uFQ/5X69VrSv39/+3e5vLw8o3379g6fB/379zemT5/uME9YWJjD+/pnNpvNCAwMND755JPLThMTE2N4eXnZP9M9PT2NoKAgY8eOHfZpitMv/fv3N6ZMmeKw7GeeecYYNmyYYRil+y7+Z+zbFQtnolUwaWlpCgwMdEh4b7/9dmVnZ+unn37SuXPnlJeX5/CX3Dp16lzxRoLdu3fX6tWr1bVrV3Xo0EEdO3ZU586dS3Rt8XfffWf/y1lx1apVSxs3btSFCxf06aefavPmzQ5/vTpw4ID27dvncLq9zWbTH3/8oZycHB0+fFhNmzZ1uG/Gpdt9KT8/P/u/s7OzlZGRocmTJ9v/SilJFy5csF/b/vDDD+uJJ55Q9+7d1aFDB3Xq1EkhISGSSvZ+paWlqWnTpmrWrJl9WJs2bVS3bl2lp6fb623evLnDX0EaN26srKys4r2RuKZ88MEHOn36tN58881C+2ubNm0c/jLeqFEjHTx40GGaS0/jLngoB/sKriXdunVTp06dtGfPHn399dfauXOnli9frlmzZql379564IEHFB0drS1btqhv377asmWLXFxc1KNHD/syjOvwJrTXk/bt22v69OnKycnRqlWrZLFY1K1bN/t4m82mJUuWaOvWrTp58qTy8vKUm5tb6B5Xf75spVGjRvbPu4Lfr5feFycwMNBh+rS0NHl6ejqc4RQUFKT8/HwdPnxYDRs2lHTxs/fSG7o3bNhQt912m/21xWJRvXr1TD9rC7a7QMGN2s2O3QrO0vP19XVY3oEDB7R79+5C2yVdPEsnJCREd911l3r27KmQkBCFhISoW7duRZ5xdjl/pZcSEhK0Zs0aHT16VNnZ2bpw4YLDscyQIUM0ZcoUvfvuuwoODlb37t118803S5Iee+wxTZ8+XZ999pmCg4N17733XvaKieIwO2Ys+L+49HiwojEMw76PpKamKjs7W+3bt3eY5vfff7ffV6gkx97XwrHrn/9vDhw4oNTUVPttWKSL71F+fr6OHTumo0ePymazqXv37g7z5ebmql69ekWuo7T7u9lnlpeX12V7sV69eurdu7eGDh2qu+++W3fddZfuu+8+NW7cuFS1/PDDD8rJydETTzzhMDwvL6/QvUCtVmup1lGRpKena//+/Vq8eLGkiw/k6NGjhzZs2GDvj7S0ND3yyCMO8wUEBDhc2nvq1CktWLBAX375pbKyspSfn6+cnBz9+OOPV1z/rbfeqn/+85+S/ndZ6bPPPqs1a9bIarUWq1/S09PVv39/h+UGBQVpzZo1ksrmuzj7dsVCiFYJNGvWTFu3btWuXbu0a9cuRUVFacWKFYqLiyv2JTGlubmrq6ur/b41rVu3VkZGhqZPn66XX35Z0sWwa8yYMbr33nsLzVvSJ3xdegBdcA33zJkz1bZt20I1SRcPZD/++GN9+umn2rVrl8aOHavg4GDFxMSUyfv1Z0V9SPIl8frk4+OjlJQUxcfHy2q1Onyp+vN+4OLiUmg/KM40QEVXrVo13X333br77rs1evRoTZ48WYsWLVLv3r1Vu3ZtdevWTe+884769u2r+Ph43XfffQ43p73lllsc7keCiqVGjRr23+9z5szRgw8+qLffftv+hX/FihVas2aNJk2aJE9PT9WoUUNz5sxxuJ+WVH6fd0Wtp6hh+fn5V1zOpdtdGn9+OmJ2drY6d+6sCRMmFJq24H5rsbGx2rdvnz7//HPFxcXp1Vdf1fr16x0uY7qSli1bysXFRenp6SWqNSkpSRMmTLDfxLpOnTp6//337Tfgli4+ROSBBx7Qjh079OmnnyomJkavvvqq/v73vyssLEwhISHavn27Pv/8cy1btkyRkZEaNGhQieooUNxjxqIuGa0o0tLSdNNNN0m6+GW9UaNGDpdPFij4g29Jjr2vhWPXovb/Rx55pMh9olmzZkpNTZXFYlF8fHyhS3Mv9/9ccFJBenp6keH05Zh9Zpn1YnR0tAYNGqSdO3dqy5YtWrBggWJjYxUQEFDsGgoUfI9ZunRpoZvr//kWCVfziavlZcOGDbpw4YL9IRTSxf3Mzc1N06ZNK/bN/SMjI3X27FlNnjxZN954o9zc3NS/f/9Cv3f+rGrVqg6f6z4+Pvroo4+0evVqzZs3r3Qb9Sdl0Z/s2xULDxaoYFq3bq2kpCSHX1J79+5VrVq11LRpU910002qWrWq/catknT+/HkdOXLkisutXr26QkNDNWXKFK1Zs0ZJSUn2M2CqVq1qeuDo4eGhxMTE0m+YpOHDh2vLli32mzz6+Pjo8OHDatmyZaEfV1dX3Xrrrfrpp58cbtx46XZfTsOGDdW4cWMdPXq00HIvPeisXbu2evTooVmzZunVV1/VBx98oLNnz0q68vt1qdatW+unn37SiRMn7MMOHTqkc+fO2e/tg8qlRYsWWrNmjT7++GPNnDnT2eUAFUKbNm0cbhbdt29f7d27V//5z3+UlJRkf6BAgQceeEC7du3St99+W2hZeXl5DsuCc7m6umrEiBFauHChfv/9d0nSvn371KVLFz344IPy8vJSixYtTI9T/qzg9+vPP/9sH/b1118XmqbgjJ4C+/btsx9DlBezY7fL8fX11ffff6/mzZsXOl4pCAlcXFx0++2365lnntGmTZtUtWpV+8OVinP8Vq9ePYWEhGjt2rVF9s25c+eKnK/gPnejRo2S1WrVLbfcUuQZHbfeeqsGDx6slStX6t5773V4QEOzZs00YMAAvfbaa6YPMTBjdsxY0SUmJurgwYP2ENDX11enTp2SxWIptD0FT9Is6bH3tXbs6uPjo0OHDhX5f+rm5iZvb2/ZbDadPn260PhLr1K51N1336369etr+fLlRY6/3P5enM+sK/ViwfaMGDFC69atk4eHR6GbtxdX69at5ebmph9//LHQdl96NtT14MKFC3r33Xc1ceJEbdq0yf7z7rvvqnHjxvb3sHXr1vrmm28c5v3z63379mnQoEHq2LGjbrvtNrm5uenMmTOlqstisdgfXFCcfmnVqpX27dtXqJ42bdrYX//V7+Ls2xVLxf+tc506f/68vvvuO4efEydOaODAgfrpp580c+ZMpaWl6aOPPtKiRYs0ZMgQubq6qnbt2nrooYf00ksv6YsvvtD333+vyZMny8XFpcib/EkXb9j89ttv6+DBgzp69Kjee+89Va9e3X55QfPmzfXVV1/p5MmTDk+EutTTTz+t999/XzExMUpLS1NqaqqWLVtWom0ueIpXTEyMJGn06NF699139dprr+n7779XWlqa3n//fb366quSLn5YtGjRQpGRkTpw4ID27t1b7Ef3PvPMM1q2bJnWrFmjw4cPKzU1VfHx8fa/oMbGxurf//630tLSdPjwYW3dulWNGjVS3bp1Td+vSwUHB8vDw0MTJkxQSkqKkpOTFRERoTvvvLPCn4aKq+fWW2/VmjVrtG3bNoen5gLXuzNnzuixxx7Tu+++qwMHDujo0aPasmWLli9fri5dutinu+OOO9SyZUtFRkaqVatW9qfzFRg8eLCCgoI0ePBgrV271r6shIQE9e/fXz/88EN5bxquoHv37nJ1dbU/Bbtly5batWuX9u3bp7S0NE2bNq3Qk8zMBAcH65ZbbtHEiRN14MAB7dmzx358UKBnz55yc3PTxIkTdfDgQX3xxReaOXOmHnzwQfulnOXB7NjtSvP98ssv+sc//qHk5GRlZGRo586dev7552Wz2fTNN99oyZIl2r9/v3788Udt27ZNp0+ftt/wunnz5kpNTVV6erpOnz592TMuXnjhBeXn5yssLEwffPCBjhw5orS0NK1Zs6bQJUgFWrZsqRMnTuj9999XRkaG1qxZ4/Cl6vfff9eMGTO0e/duHT9+XHv37tX+/fvtXypnz56tnTt36ujRo0pJSdHu3bv/UkBjdsxYkeTm5iozM1MnT55USkqKlixZoqeeekqdO3fWQw89JOni/h0QEKDRo0frs88+07Fjx7Rv3z69+uqr9j8Yl+TY+1o8dh02bJiSkpI0Y8YMfffddzpy5Ig++ugj+1MGb731VvXs2VMRERHatm2bjh49quTkZC1dulTbt28vcpk1a9bUrFmztGPHDo0cOVK7du3SsWPHtH//fr300kt64YUXipzP7DPrSr149OhRzZ8/X0lJSTp+/Lg+++wzHTlypNQPhKpdu7aeeOIJRUdHa+PGjcrIyFBKSori4uKKfGrltWz79u365Zdf1LdvX3l4eDj83HvvvdqwYYOki5eHx8fHKz4+XocPH1ZMTIy+//57h2Xdcssteu+995SWlqZvvvlGEyZMKNb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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"blue\", \"green\", \"red\", \"yellow\",\"orange\",\"purple\"]\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.ylabel(\"Accuracy %\")\n",
    "plt.xlabel(\"Algorithms\")\n",
    "sns.barplot(x=models['Model'],y=models['Score'], palette=colors )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-sQ-hILGjN0k"
   },
   "source": [
    "DecisionTree Classifier Model got the Highest Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZZOlV9WJUnG3"
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}