1. titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from pandas) (2.2.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from pandas) (2025.1)\n",
      "Requirement already satisfied: six>=1.5 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 25.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:1: SyntaxWarning: invalid escape sequence '\\T'\n",
      "<>:1: SyntaxWarning: invalid escape sequence '\\T'\n",
      "C:\\Users\\banda\\AppData\\Local\\Temp\\ipykernel_2316\\3538413857.py:1: SyntaxWarning: invalid escape sequence '\\T'\n",
      "  ds = pd.read_csv(\".\\Titanic-Dataset.csv\")\n"
     ]
    }
   ],
   "source": [
    "ds = pd.read_csv(\".\\Titanic-Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['Embarked'] = ds['Embarked'].fillna(ds['Embarked'].mode()[0])    # Filling the missing values in Embarked column with the mode value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the columns 'Cabin' because it has (more than 50%) missing values\n",
    "ds = ds.drop(columns=['Cabin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['Age'] = ds['Age'].fillna(ds['Age'].median())    # Filling the missing values in Age column with the median value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
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       "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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
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      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['FamilySize'] = (ds['SibSp'] + ds['Parch'] + 1).astype(int)   # Adding a new column 'FamilySize' by adding the columns 'SibSp' and 'Parch' and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  FamilySize  \n",
       "0      0         A/5 21171   7.2500        S           2  \n",
       "1      0          PC 17599  71.2833        C           2  \n",
       "2      0  STON/O2. 3101282   7.9250        S           1  \n",
       "3      0            113803  53.1000        S           2  \n",
       "4      0            373450   8.0500        S           1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['IsAlone'] = (ds['FamilySize']==1).astype(int)  # Adding a new column 'IsAlone' by checking if the 'FamilySize' is 1 and converting it to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (1.6.1)\n",
      "Requirement already satisfied: numpy>=1.19.5 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from scikit-learn) (2.2.2)\n",
      "Requirement already satisfied: scipy>=1.6.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from scikit-learn) (1.15.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from scikit-learn) (3.5.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 25.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['Sex'] = LabelEncoder().fit_transform(ds['Sex'])    # Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Embarked  FamilySize  IsAlone  \n",
       "0         A/5 21171   7.2500        S           2        0  \n",
       "1          PC 17599  71.2833        C           2        0  \n",
       "2  STON/O2. 3101282   7.9250        S           1        1  \n",
       "3            113803  53.1000        S           2        0  \n",
       "4            373450   8.0500        S           1        1  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.get_dummies(ds, columns=['Embarked'])    # One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  FamilySize  IsAlone  Embarked_C  Embarked_Q  \\\n",
       "0         A/5 21171   7.2500           2        0       False       False   \n",
       "1          PC 17599  71.2833           2        0        True       False   \n",
       "2  STON/O2. 3101282   7.9250           1        1       False       False   \n",
       "3            113803  53.1000           2        0       False       False   \n",
       "4            373450   8.0500           1        1       False       False   \n",
       "\n",
       "   Embarked_S  \n",
       "0        True  \n",
       "1       False  \n",
       "2        True  \n",
       "3        True  \n",
       "4        True  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds.drop(columns=['Name', 'SibSp', 'Parch', 'Ticket'])    # Dropping the columns 'Name', 'SibSp', 'Parch', 'Ticket'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age     Fare  FamilySize  IsAlone  \\\n",
       "0            1         0       3    1  22.0   7.2500           2        0   \n",
       "1            2         1       1    0  38.0  71.2833           2        0   \n",
       "2            3         1       3    0  26.0   7.9250           1        1   \n",
       "3            4         1       1    0  35.0  53.1000           2        0   \n",
       "4            5         0       3    1  35.0   8.0500           1        1   \n",
       "\n",
       "   Embarked_C  Embarked_Q  Embarked_S  \n",
       "0       False       False        True  \n",
       "1        True       False       False  \n",
       "2       False       False        True  \n",
       "3       False       False        True  \n",
       "4       False       False        True  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardizing the features\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scalar = StandardScaler()\n",
    "\n",
    "ds[['Age','Fare']] = Scalar.fit_transform(ds[['Age','Fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex       Age      Fare  FamilySize  \\\n",
       "0            1         0       3    1 -0.565736 -0.502445           2   \n",
       "1            2         1       1    0  0.663861  0.786845           2   \n",
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       "4            5         0       3    1  0.433312 -0.486337           1   \n",
       "\n",
       "   IsAlone  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0        0       False       False        True  \n",
       "1        0        True       False       False  \n",
       "2        1       False       False        True  \n",
       "3        0       False       False        True  \n",
       "4        1       False       False        True  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['Embarked_C'] = LabelEncoder().fit_transform(ds['Embarked_C'])\n",
    "ds['Embarked_Q'] = LabelEncoder().fit_transform(ds['Embarked_Q'])\n",
    "ds['Embarked_S'] = LabelEncoder().fit_transform(ds['Embarked_S'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex       Age      Fare  FamilySize  \\\n",
       "0            1         0       3    1 -0.565736 -0.502445           2   \n",
       "1            2         1       1    0  0.663861  0.786845           2   \n",
       "2            3         1       3    0 -0.258337 -0.488854           1   \n",
       "3            4         1       1    0  0.433312  0.420730           2   \n",
       "4            5         0       3    1  0.433312 -0.486337           1   \n",
       "\n",
       "   IsAlone  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0        0           0           0           1  \n",
       "1        0           1           0           0  \n",
       "2        1           0           0           1  \n",
       "3        0           0           0           1  \n",
       "4        1           0           0           1  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ds[['PassengerId','Pclass',\t'Sex'\t,'Age',\t'Fare',\t'FamilySize'\t,'IsAlone'\t,'Embarked_C',\t'Embarked_Q',\t'Embarked_S']]  # Example features\n",
    "y = ds['Survived']  # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "Scalar = StandardScaler()\n",
    "\n",
    "X_train = Scalar.fit_transform(X_train)\n",
    "X_test = Scalar.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.45306593, -1.61413602,  0.7243102 , ..., -0.46146201,\n",
       "        -0.30335547,  0.59248936],\n",
       "       [ 1.11387354, -0.40055118,  0.7243102 , ..., -0.46146201,\n",
       "        -0.30335547,  0.59248936],\n",
       "       [-0.25427511,  0.81303367,  0.7243102 , ..., -0.46146201,\n",
       "        -0.30335547,  0.59248936],\n",
       "       ...,\n",
       "       [ 1.60890168,  0.81303367,  0.7243102 , ..., -0.46146201,\n",
       "        -0.30335547,  0.59248936],\n",
       "       [-0.04768856, -1.61413602, -1.38062393, ..., -0.46146201,\n",
       "        -0.30335547,  0.59248936],\n",
       "       [-1.34567573, -1.61413602,  0.7243102 , ..., -0.46146201,\n",
       "        -0.30335547,  0.59248936]], shape=(712, 10))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "confusion = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 25.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from seaborn) (2.2.2)\n",
      "Requirement already satisfied: pandas>=1.2 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from seaborn) (3.10.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.55.8)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.1)\n",
      "Requirement already satisfied: six>=1.5 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n",
      "Requirement already satisfied: matplotlib in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (3.10.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (4.55.8)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (1.4.8)\n",
      "Requirement already satisfied: numpy>=1.23 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (2.2.2)\n",
      "Requirement already satisfied: packaging>=20.0 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (11.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in e:\\github\\ml - cost function\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 25.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install seaborn\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw the matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.heatmap(confusion, annot=True, cmap='Blues', xticklabels=['Predicted No', 'Predicted Yes'], yticklabels=['Actual No', 'Actual Yes'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8044692737430168\n",
      "Precision: 0.782608695652174\n",
      "Recall: 0.7297297297297297\n",
      "F1 Score: 0.7552447552447552\n",
      "Confusion Matrix: [[90 15]\n",
      " [20 54]]\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:', accuracy)\n",
    "print('Precision:', precision)\n",
    "print('Recall:', recall)\n",
    "print('F1 Score:', f1)\n",
    "print('Confusion Matrix:', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Draw the ROC curve\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "y_pred_proba = model.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}