Titanic Survival Prediction 🚢

My first machine learning project! Predicting passenger survival on the Titanic using Python.

📊 Project Overview

• Goal: Predict whether a Titanic passenger survived or not
• Dataset: Kaggle Titanic dataset (891 passengers)
• Accuracy Achieved: 81.01%

🛠️ Technologies Used

• Python 3.9
• pandas (data manipulation)
• matplotlib (visualization)
• scikit-learn (machine learning)

📈 Key Findings

• Females had 74.2% survival rate vs males 18.9%
• 1st class passengers had 63% survival vs 3rd class 24.2%
• Age, fare, and family size also influenced survival

🔍 Process

1. Data exploration and visualization
2. Handling missing values (Age, Embarked)
3. Feature engineering (converting text to numbers)
4. Train/test split (80/20)
5. Logistic Regression model training
6. Model evaluation

📁 Files

• titanic.py - Main Python script
• train.csv - Training dataset
• README.md - This file

🚀 How to Run

pip install pandas numpy matplotlib scikit-learn
python3 titanic.py

📚 What I Learned

• Complete machine learning workflow
• Data cleaning and preparation
• Model training and evaluation
• The importance of exploratory data analysis

🔮 Future Improvements

• Try Random Forest or Decision Trees
• Feature engineering (family size, title extraction)
• Hyperparameter tuning
• Ensemble methods

👤 About

This is my first data science project as I learn Python and machine learning!

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Dataset from Kaggle Titanic Competition