TITANIC.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNr4O7GTVOT1rwb3HcE6k9V",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lovnishverma/Python-Getting-Started/blob/main/TITANIC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "o0qW3N2g3s90",
        "outputId": "b8073b83-d51e-4c7a-d38c-68bd19c0a8dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\n",
            "👥 Total Passengers: 891\n",
            "✅ Total Survivors: 342\n",
            "📊 Survival Rate: 38.4%\n",
            "\n",
            "👨 Male Stats:\n",
            " - Total Males: 577\n",
            " - Survived: 109\n",
            " - Survival Rate: 18.9%\n",
            "\n",
            "👩 Female Stats:\n",
            " - Total Females: 314\n",
            " - Survived: 233\n",
            " - Survival Rate: 74.2%\n",
            "\n",
            "📊 Visual Survival Bars:\n",
            "Overall:  🟩🟩🟩🟩⬜⬜⬜⬜⬜⬜ 38.4%\n",
            "Males:    🟩🟩⬜⬜⬜⬜⬜⬜⬜⬜ 18.9%\n",
            "Females:  🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜ 74.2%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "👶 Age Group Survival Rates:\n",
            " - Children: 🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜ 58.0%\n",
            " - Teens: 🟩🟩🟩🟩⬜⬜⬜⬜⬜⬜ 41.1%\n",
            " - Adults: 🟩🟩🟩🟩⬜⬜⬜⬜⬜⬜ 36.5%\n",
            " - Seniors: 🟩🟩🟩⬜⬜⬜⬜⬜⬜⬜ 26.9%\n",
            "\n",
            "Passenger Class Survival Rates:\n",
            " - Class 1: 🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜ 63.0%\n",
            " - Class 2: 🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 47.3%\n",
            " - Class 3: 🟩🟩⬜⬜⬜⬜⬜⬜⬜⬜ 24.2%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 🛳️ Titanic Survival Analysis\n",
        "\n",
        "# 📦 Step 1: Import Libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# 🌐 Step 2: Load Titanic Dataset\n",
        "url = \"https://raw.githubusercontent.com/lovnishverma/datasets/refs/heads/main/titanic.csv\"\n",
        "df = pd.read_csv(url)\n",
        "# print(df.head())\n",
        "# print(df.info())\n",
        "# print(df.describe())\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# 🧼 Clean Data (Fix missing age)\n",
        "df[\"Age\"] = df[\"Age\"].fillna(df[\"Age\"].median())\n",
        "\n",
        "# 👥 Step 3: Total Passengers\n",
        "total_passengers = len(df)\n",
        "print(f\"👥 Total Passengers: {total_passengers}\")\n",
        "\n",
        "# ✅ Step 4: Total Survivors\n",
        "total_survivors = df[df[\"Survived\"] == 1].shape[0]\n",
        "print(f\"✅ Total Survivors: {total_survivors}\")\n",
        "\n",
        "# 📊 Step 5: Overall Survival Rate\n",
        "survival_rate = round((total_survivors / total_passengers) * 100, 1)\n",
        "print(f\"📊 Survival Rate: {survival_rate}%\")\n",
        "\n",
        "# 👨 Step 6: Male Stats\n",
        "males = df[df[\"Sex\"] == \"male\"]\n",
        "total_males = len(males)\n",
        "survived_males = males[males[\"Survived\"] == 1].shape[0]\n",
        "male_survival_rate = round((survived_males / total_males) * 100, 1)\n",
        "\n",
        "print(\"\\n👨 Male Stats:\")\n",
        "print(f\" - Total Males: {total_males}\")\n",
        "print(f\" - Survived: {survived_males}\")\n",
        "print(f\" - Survival Rate: {male_survival_rate}%\")\n",
        "\n",
        "# 👩 Step 7: Female Stats\n",
        "females = df[df[\"Sex\"] == \"female\"]\n",
        "total_females = len(females)\n",
        "survived_females = females[females[\"Survived\"] == 1].shape[0]\n",
        "female_survival_rate = round((survived_females / total_females) * 100, 1)\n",
        "\n",
        "print(\"\\n👩 Female Stats:\")\n",
        "print(f\" - Total Females: {total_females}\")\n",
        "print(f\" - Survived: {survived_females}\")\n",
        "print(f\" - Survival Rate: {female_survival_rate}%\")\n",
        "\n",
        "# 🎨 Step 8: Emoji Bar Function\n",
        "def emoji_bar(rate, emoji=\"🟩\", total=10):\n",
        "    filled = round(rate / 10)\n",
        "    return emoji * filled + \"⬜\" * (total - filled)\n",
        "\n",
        "# 📊 Step 9: Visual Bar Summary\n",
        "print(\"\\n📊 Visual Survival Bars:\")\n",
        "print(f\"Overall:  {emoji_bar(survival_rate)} {survival_rate}%\")\n",
        "print(f\"Males:    {emoji_bar(male_survival_rate)} {male_survival_rate}%\")\n",
        "print(f\"Females:  {emoji_bar(female_survival_rate)} {female_survival_rate}%\")\n",
        "\n",
        "# 📈 Step 10: Bar Chart\n",
        "labels = [\"Overall\", \"Males\", \"Females\"]\n",
        "rates = [survival_rate, male_survival_rate, female_survival_rate]\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "bars = plt.bar(labels, rates, color=[\"skyblue\", \"lightcoral\", \"lightgreen\"])\n",
        "plt.ylim(0, 100)\n",
        "plt.title(\"Titanic Survival Rates\", fontsize=16)\n",
        "plt.ylabel(\"Survival Rate (%)\")\n",
        "\n",
        "# Add text labels\n",
        "for bar in bars:\n",
        "    yval = bar.get_height()\n",
        "    plt.text(bar.get_x() + bar.get_width()/2, yval + 1, f'{yval}%', ha='center', fontsize=12)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# 👶 Age Group Analysis\n",
        "def get_age_group(age):\n",
        "    if age < 13:\n",
        "        return \"Children\"\n",
        "    elif age < 20:\n",
        "        return \"Teens\"\n",
        "    elif age < 60:\n",
        "        return \"Adults\"\n",
        "    else:\n",
        "        return \"Seniors\"\n",
        "\n",
        "df[\"AgeGroup\"] = df[\"Age\"].apply(get_age_group)\n",
        "age_group_rates = df.groupby(\"AgeGroup\")[\"Survived\"].mean().sort_values(ascending=False) * 100\n",
        "\n",
        "print(\"\\n👶 Age Group Survival Rates:\")\n",
        "for group, rate in age_group_rates.items():\n",
        "    print(f\" - {group}: {emoji_bar(rate)} {round(rate, 1)}%\")\n",
        "\n",
        "# 💺 Class Survival Rates\n",
        "class_rates = df.groupby(\"Pclass\")[\"Survived\"].mean() * 100\n",
        "\n",
        "print(\"\\nPassenger Class Survival Rates:\")\n",
        "for pclass, rate in class_rates.items():\n",
        "    print(f\" - Class {pclass}: {emoji_bar(rate)} {round(rate, 1)}%\")\n",
        "\n",
        "# 🖼️ Bonus Plot - Class vs Survival by Sex\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.barplot(data=df, x='Pclass', y='Survived', hue='Sex', errorbar=None, palette='pastel')\n",
        "plt.title(\"Survival Rate by Class and Gender\")\n",
        "plt.ylabel(\"Survival Rate\")\n",
        "plt.xlabel(\"Passenger Class\")\n",
        "plt.ylim(0, 1)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 🔁 Continue from previous analysis...\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "# 🧹 Step 1: Data Preprocessing\n",
        "\n",
        "# Encode 'Sex' and 'Embarked' columns\n",
        "df['Sex'] = df['Sex'].map({'male': 0, 'female': 1})\n",
        "df['Embarked'] = df['Embarked'].fillna('S')\n",
        "\n",
        "df = pd.get_dummies(df, columns=['Embarked'], drop_first=True)\n",
        "\n",
        "# Optional: Drop non-numeric or irrelevant columns\n",
        "df.drop(['Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)\n",
        "\n",
        "# ✨ Step 2: Define Features & Target\n",
        "features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_Q', 'Embarked_S']\n",
        "X = df[features]\n",
        "y = df['Survived']\n",
        "\n",
        "# 🧪 Step 3: Train/Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# 🤖 Step 4: Train Model\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# 📈 Step 5: Evaluate Model\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"\\n🎯 Accuracy: {round(accuracy * 100, 2)}%\")\n",
        "\n",
        "print(\"\\n🧾 Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"🧮 Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "# 🔍 Step 6: Predict on Sample Input\n",
        "sample_passenger = pd.DataFrame({\n",
        "    'Pclass': [3],\n",
        "    'Sex': [1],  # 0 = male, 1 = female\n",
        "    'Age': [45],\n",
        "    'SibSp': [0],\n",
        "    'Parch': [0],\n",
        "    'Fare': [7.25],\n",
        "    'Embarked_Q': [0],\n",
        "    'Embarked_S': [1]\n",
        "})\n",
        "\n",
        "prediction = model.predict(sample_passenger)[0]\n",
        "print(\"\\n🔮 Sample Prediction:\")\n",
        "print(\" - Survived\" if prediction == 1 else \" - Did Not Survive\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3y5b0Yt8N9N",
        "outputId": "cbf2ac8c-f408-4aac-8e98-128538e6f9ba"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🎯 Accuracy: 81.01%\n",
            "\n",
            "🧾 Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.86      0.84       105\n",
            "           1       0.79      0.74      0.76        74\n",
            "\n",
            "    accuracy                           0.81       179\n",
            "   macro avg       0.81      0.80      0.80       179\n",
            "weighted avg       0.81      0.81      0.81       179\n",
            "\n",
            "🧮 Confusion Matrix:\n",
            "[[90 15]\n",
            " [19 55]]\n",
            "\n",
            "🔮 Sample Prediction:\n",
            " - Did Not Survive\n"
          ]
        }
      ]
    }
  ]
}