Titanic EDA & Machine Learning 🚢

This project analyzes the Titanic dataset to explore survival patterns and build predictive models.
It involves Exploratory Data Analysis (EDA), data cleaning, visualization, and applying machine learning algorithms.

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📌 Project Objectives

• Perform EDA to understand passenger demographics and survival rates.
• Clean and preprocess data (handle missing values, encode categorical variables).
• Visualize key insights (age distribution, gender survival, class-based survival).
• Train machine learning models to predict passenger survival.

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📂 Project Structure

• Titanic.ipynb → Main Jupyter Notebook with full analysis and models.
• requirements.txt → Python dependencies.
• data/ → Dataset (or download link provided below).
• images/ → Saved plots for documentation.

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📊 Key Insights

• Women and children had higher survival rates.
• First-class passengers had significantly better survival chances.
• Age and fare also influenced survival probability.

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🤖 Machine Learning

• Logistic Regression
• Decision Trees / Random Forests
• Model evaluation using accuracy, precision, recall

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📦 Installation & Usage

git clone https://github.com/yourusername/Titanic-EDA-ML.git
cd Titanic-EDA-ML
pip install -r requirements.txt