titanic_recap_answers.ipynb

{
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3 (ipykernel)",
            "language": "python"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2,
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "# import library\n",
                "import pandas as pd\n",
                "import seaborn as sns\n",
                "import matplotlib.pyplot as plt\n",
                "import numpy as np\n",
                "from sklearn.linear_model import LogisticRegression\n",
                "from sklearn.model_selection import train_test_split\n",
                "from sklearn.metrics import accuracy_score, confusion_matrix"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Create DataFrame\n",
                "data = pd.read_csv('train.csv')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\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\u003c/style\u003e\n\u003ctable border=\"1\" class=\"dataframe\"\u003e\n  \u003cthead\u003e\n    \u003ctr style=\"text-align: right;\"\u003e\n      \u003cth\u003e\u003c/th\u003e\n      \u003cth\u003ePassengerId\u003c/th\u003e\n      \u003cth\u003eSurvived\u003c/th\u003e\n      \u003cth\u003ePclass\u003c/th\u003e\n      \u003cth\u003eName\u003c/th\u003e\n      \u003cth\u003eSex\u003c/th\u003e\n      \u003cth\u003eAge\u003c/th\u003e\n      \u003cth\u003eSibSp\u003c/th\u003e\n      \u003cth\u003eParch\u003c/th\u003e\n      \u003cth\u003eTicket\u003c/th\u003e\n      \u003cth\u003eFare\u003c/th\u003e\n      \u003cth\u003eCabin\u003c/th\u003e\n      \u003cth\u003eEmbarked\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003cth\u003e0\u003c/th\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003e3\u003c/td\u003e\n      \u003ctd\u003eBraund, Mr. Owen Harris\u003c/td\u003e\n      \u003ctd\u003emale\u003c/td\u003e\n      \u003ctd\u003e22.0\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003eA/5 21171\u003c/td\u003e\n      \u003ctd\u003e7.2500\u003c/td\u003e\n      \u003ctd\u003eNaN\u003c/td\u003e\n      \u003ctd\u003eS\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e1\u003c/th\u003e\n      \u003ctd\u003e2\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003eCumings, Mrs. John Bradley (Florence Briggs Th...\u003c/td\u003e\n      \u003ctd\u003efemale\u003c/td\u003e\n      \u003ctd\u003e38.0\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003ePC 17599\u003c/td\u003e\n      \u003ctd\u003e71.2833\u003c/td\u003e\n      \u003ctd\u003eC85\u003c/td\u003e\n      \u003ctd\u003eC\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e2\u003c/th\u003e\n      \u003ctd\u003e3\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e3\u003c/td\u003e\n      \u003ctd\u003eHeikkinen, Miss. Laina\u003c/td\u003e\n      \u003ctd\u003efemale\u003c/td\u003e\n      \u003ctd\u003e26.0\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003eSTON/O2. 3101282\u003c/td\u003e\n      \u003ctd\u003e7.9250\u003c/td\u003e\n      \u003ctd\u003eNaN\u003c/td\u003e\n      \u003ctd\u003eS\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e3\u003c/th\u003e\n      \u003ctd\u003e4\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003eFutrelle, Mrs. Jacques Heath (Lily May Peel)\u003c/td\u003e\n      \u003ctd\u003efemale\u003c/td\u003e\n      \u003ctd\u003e35.0\u003c/td\u003e\n      \u003ctd\u003e1\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003e113803\u003c/td\u003e\n      \u003ctd\u003e53.1000\u003c/td\u003e\n      \u003ctd\u003eC123\u003c/td\u003e\n      \u003ctd\u003eS\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e4\u003c/th\u003e\n      \u003ctd\u003e5\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003e3\u003c/td\u003e\n      \u003ctd\u003eAllen, Mr. William Henry\u003c/td\u003e\n      \u003ctd\u003emale\u003c/td\u003e\n      \u003ctd\u003e35.0\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003e0\u003c/td\u003e\n      \u003ctd\u003e373450\u003c/td\u003e\n      \u003ctd\u003e8.0500\u003c/td\u003e\n      \u003ctd\u003eNaN\u003c/td\u003e\n      \u003ctd\u003eS\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e",
                        "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  "
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Display the first 5 rows of the DataFrame\n",
                "data.head()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\u003cclass 'pandas.core.frame.DataFrame'\u003e\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n"
                }
            ],
            "source": [
                "# understand the type of data\n",
                "data.info()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\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\u003c/style\u003e\n\u003ctable border=\"1\" class=\"dataframe\"\u003e\n  \u003cthead\u003e\n    \u003ctr style=\"text-align: right;\"\u003e\n      \u003cth\u003e\u003c/th\u003e\n      \u003cth\u003ePassengerId\u003c/th\u003e\n      \u003cth\u003eSurvived\u003c/th\u003e\n      \u003cth\u003ePclass\u003c/th\u003e\n      \u003cth\u003eAge\u003c/th\u003e\n      \u003cth\u003eSibSp\u003c/th\u003e\n      \u003cth\u003eParch\u003c/th\u003e\n      \u003cth\u003eFare\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003cth\u003ecount\u003c/th\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n      \u003ctd\u003e714.000000\u003c/td\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003emean\u003c/th\u003e\n      \u003ctd\u003e446.000000\u003c/td\u003e\n      \u003ctd\u003e0.383838\u003c/td\u003e\n      \u003ctd\u003e2.308642\u003c/td\u003e\n      \u003ctd\u003e29.699118\u003c/td\u003e\n      \u003ctd\u003e0.523008\u003c/td\u003e\n      \u003ctd\u003e0.381594\u003c/td\u003e\n      \u003ctd\u003e32.204208\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003estd\u003c/th\u003e\n      \u003ctd\u003e257.353842\u003c/td\u003e\n      \u003ctd\u003e0.486592\u003c/td\u003e\n      \u003ctd\u003e0.836071\u003c/td\u003e\n      \u003ctd\u003e14.526497\u003c/td\u003e\n      \u003ctd\u003e1.102743\u003c/td\u003e\n      \u003ctd\u003e0.806057\u003c/td\u003e\n      \u003ctd\u003e49.693429\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003emin\u003c/th\u003e\n      \u003ctd\u003e1.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e1.000000\u003c/td\u003e\n      \u003ctd\u003e0.420000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e25%\u003c/th\u003e\n      \u003ctd\u003e223.500000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e2.000000\u003c/td\u003e\n      \u003ctd\u003e20.125000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e7.910400\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e50%\u003c/th\u003e\n      \u003ctd\u003e446.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e3.000000\u003c/td\u003e\n      \u003ctd\u003e28.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e14.454200\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e75%\u003c/th\u003e\n      \u003ctd\u003e668.500000\u003c/td\u003e\n      \u003ctd\u003e1.000000\u003c/td\u003e\n      \u003ctd\u003e3.000000\u003c/td\u003e\n      \u003ctd\u003e38.000000\u003c/td\u003e\n      \u003ctd\u003e1.000000\u003c/td\u003e\n      \u003ctd\u003e0.000000\u003c/td\u003e\n      \u003ctd\u003e31.000000\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003emax\u003c/th\u003e\n      \u003ctd\u003e891.000000\u003c/td\u003e\n      \u003ctd\u003e1.000000\u003c/td\u003e\n      \u003ctd\u003e3.000000\u003c/td\u003e\n      \u003ctd\u003e80.000000\u003c/td\u003e\n      \u003ctd\u003e8.000000\u003c/td\u003e\n      \u003ctd\u003e6.000000\u003c/td\u003e\n      \u003ctd\u003e512.329200\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e",
                        "text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  "
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# understand the data\n",
                "data.describe()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# look for missing values\n",
                "data.isnull().sum()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "\u003cFigure size 2000x2000 with 0 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": "\u003cFigure size 640x480 with 1 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Create a pivot table of survival rate by class\n",
                "survival_pivot = data.pivot_table(index='Pclass',\n",
                "                    columns='Survived',\n",
                "                    values='PassengerId',\n",
                "                    aggfunc='count')\n",
                "\n",
                "# Plot the pivot table\n",
                "plt.figure(figsize=(20, 20))\n",
                "survival_pivot.plot(kind='bar',\n",
                "                    stacked=True, # what happens if you make this False?\n",
                "                    color=['lightgreen', 'lightblue'])\n",
                "\n",
                "plt.title('Survival Count by Class', fontsize=20)\n",
                "plt.xlabel('Pclass', fontsize=15)\n",
                "plt.ylabel('Count', fontsize=15)\n",
                "plt.xticks(fontsize=12)\n",
                "plt.yticks(fontsize=12)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "\u003cFigure size 2000x2000 with 1 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Plot a bar plot showing the average fare paid by passengers in each class, differentiated by survival status\n",
                "plt.figure(figsize=(20, 20))\n",
                "sns.barplot(x='Pclass',\n",
                "            y='Fare',\n",
                "            hue='Survived',\n",
                "            data=data,\n",
                "            palette='Set2')\n",
                "\n",
                "plt.title('Average Fare by Class and Survival Status', fontsize=20)\n",
                "plt.xlabel('Class', fontsize=15)\n",
                "plt.ylabel('Average Fare', fontsize=15)\n",
                "plt.xticks(fontsize=12)\n",
                "plt.yticks(fontsize=12)\n",
                "plt.legend(title='Survived', fontsize=12, title_fontsize=15)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "\u003cAxes: xlabel='Age', ylabel='Count'\u003e"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": "\u003cFigure size 640x480 with 1 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Plot a histogram of passenger ages\n",
                "sns.histplot(data['Age'].dropna(), # what is the dropna doing?\n",
                "             kde=True, # adds a kernel density estimate to the plot\n",
                "             color='skyblue')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": [
                "# creating a backup copy of the data \n",
                "data_original = data.copy()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Populating null Age values with the average age by sex, Pclass, and Survived\n",
                "data['Age'] = data.groupby(['Sex', 'Pclass'],group_keys=False)['Age'].apply(lambda x: x.fillna(x.mean()))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": "\u003cFigure size 600x800 with 2 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Plot before and after imputation\n",
                "fig, axes = plt.subplots(1, 2, figsize=(6, 8), sharey=True)\n",
                "sns.histplot(data_original['Age'], ax=axes[0], kde=True, color='red').set_title('Before Imputation')\n",
                "sns.histplot(data['Age'], kde=True, ax=axes[1], color='green').set_title('After Imputation')\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Convert categorical variables into dummy/indicator variables\n",
                "data = pd.get_dummies(data, columns=['Sex', 'Embarked'], drop_first=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Define features we will use for the model\n",
                "X = data[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_male', 'Embarked_Q', 'Embarked_S']]\n",
                "\n",
                "# define the target variable \n",
                "y = data['Survived']"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Split the data into training and testing sets\n",
                "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "\u003cstyle\u003e#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 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-1 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-1 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-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.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-1 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-1 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-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 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\nclickable 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-1 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-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 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-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 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,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na: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 */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.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\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.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 `\u003ca\u003e` HTML tag */\n\n#sk-container-id-1 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-1 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-1 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-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n\u003c/style\u003e\u003cdiv id=\"sk-container-id-1\" class=\"sk-top-container\"\u003e\u003cdiv class=\"sk-text-repr-fallback\"\u003e\u003cpre\u003eLogisticRegression(max_iter=200)\u003c/pre\u003e\u003cb\u003eIn a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \u003cbr /\u003eOn GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"sk-container\" hidden\u003e\u003cdiv class=\"sk-item\"\u003e\u003cdiv class=\"sk-estimator fitted sk-toggleable\"\u003e\u003cinput class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked\u003e\u003clabel for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"\u003e\u0026nbsp;\u0026nbsp;LogisticRegression\u003ca class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\"\u003e?\u003cspan\u003eDocumentation for LogisticRegression\u003c/span\u003e\u003c/a\u003e\u003cspan class=\"sk-estimator-doc-link fitted\"\u003ei\u003cspan\u003eFitted\u003c/span\u003e\u003c/span\u003e\u003c/label\u003e\u003cdiv class=\"sk-toggleable__content fitted\"\u003e\u003cpre\u003eLogisticRegression(max_iter=200)\u003c/pre\u003e\u003c/div\u003e \u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e",
                        "text/plain": "LogisticRegression(max_iter=200)"
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Train the logistic regression model\n",
                "model = LogisticRegression(max_iter=200)\n",
                "model.fit(X_train, y_train)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Predict on the test set\n",
                "y_pred = model.predict(X_test)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Accuracy: 0.8555555555555555\nConfusion Matrix:\n[[46  8]\n [ 5 31]]\n"
                }
            ],
            "source": [
                "# Evaluate the model using accuracy and confusion matrix\n",
                "accuracy = accuracy_score(y_test, y_pred)\n",
                "conf_matrix = confusion_matrix(y_test, y_pred)\n",
                "\n",
                "print(f'Accuracy: {accuracy}')\n",
                "print(f'Confusion Matrix:\\n{conf_matrix}')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": "\u003cFigure size 600x400 with 1 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Calculate feature importance\n",
                "feature_importance = model.coef_[0]\n",
                "\n",
                "# Create a DataFrame to display feature importance\n",
                "importance_df = pd.DataFrame({\n",
                "    'Feature': X.columns,\n",
                "    'Importance': feature_importance\n",
                "}).sort_values(by='Importance', ascending=False)\n",
                "\n",
                "# Plot feature importance\n",
                "plt.figure(figsize=(6, 4))\n",
                "sns.barplot(x='Importance', y='Feature', data=importance_df, color = 'lightgreen')\n",
                "plt.title('Feature Importance')\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Import new test data\n",
                "test_data = pd.read_csv('test.csv')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Populating null Age values with the average age by sex, Pclass, and Survived\n",
                "test_data['Age'] = test_data.groupby(['Sex', 'Pclass'],group_keys=False)['Age'].apply(lambda x: x.fillna(x.mean()))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "PassengerId      0\nPclass           0\nName             0\nSex              0\nAge              0\nSibSp            0\nParch            0\nTicket           0\nFare             1\nCabin          327\nEmbarked         0\ndtype: int64"
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# check for null values \n",
                "test_data.isnull().sum()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [],
            "source": [
                "# using an average of sex and PClass for the missing fare value\n",
                "test_data['Fare'] = test_data.groupby(['Sex', 'Pclass'],group_keys=False)['Fare'].apply(lambda x: x.fillna(x.mean()))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Preprocess the test data in the same way as the training data\n",
                "test_data = pd.get_dummies(test_data, columns=['Sex', 'Embarked'], drop_first=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Ensure the test data has the same columns as the training data\n",
                "test_data = test_data.reindex(columns=X.columns, fill_value=0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Predict on the new test data\n",
                "test_predictions = model.predict(test_data)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [],
            "source": [
                "# adding the survived field back to the test data\n",
                "test_data['Survived_predicated'] = test_predictions"
            ]
        }
    ]
}