A Machine Learning project based on the famous Titanic dataset from Kaggle.
This project predicts whether a passenger survived or not using different classification algorithms and compares their performance.

The Titanic dataset is one of the most popular beginner datasets in Machine Learning.
In this project:

• Data preprocessing and cleaning were performed
• Multiple ML models were trained
• Model accuracy and precision were compared
• Best-performing models were identified

Dataset used:

• Titanic Dataset from Kaggle

https://www.kaggle.com/competitions/titanic

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

✅ BaggingClassifier
✅ Decision Tree
✅ AdaBoost

These models achieved the highest accuracy and precision on the dataset.

• Data Cleaning
• Missing Value Handling
• Exploratory Data Analysis (EDA)
• Feature Encoding
• Model Training
• Model Evaluation
• Accuracy & Precision Comparison

Clone the repository:

git clone https://github.com/eddiebrock911/Titanic-Machine-Learning.git

Move into the project folder:

cd Titanic-Machine-Learning

Install dependencies:

pip install -r requirements.txt

Run the project:

python app.py

• Hyperparameter Tuning
• Cross Validation
• Feature Engineering
• XGBoost Integration
• Model Deployment

Contributions are welcome.
Feel free to fork this repository and submit pull requests.

This project is licensed under the MIT License.

Ankit Kumar

GitHub: https://github.com/eddiebrock911