https://github.com/adilshamim8/titanic-machine-learning-from-disaster
Predict Titanic survivors using machine learning with efficient EDA, feature engineering, and model training
https://github.com/adilshamim8/titanic-machine-learning-from-disaster
kaggle-competition kaggle-dataset kaggle-solution kaggle-titanic machine-learning machine-learning-algorithms
Last synced: about 1 month ago
JSON representation
Predict Titanic survivors using machine learning with efficient EDA, feature engineering, and model training
- Host: GitHub
- URL: https://github.com/adilshamim8/titanic-machine-learning-from-disaster
- Owner: AdilShamim8
- License: other
- Created: 2025-04-12T15:16:34.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-04-12T15:30:06.000Z (about 1 month ago)
- Last Synced: 2025-04-12T16:30:05.689Z (about 1 month ago)
- Topics: kaggle-competition, kaggle-dataset, kaggle-solution, kaggle-titanic, machine-learning, machine-learning-algorithms
- Language: Jupyter Notebook
- Homepage: http://adilshamim.me/
- Size: 243 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Titanic - Machine Learning from Disaster
A concise project for predicting Titanic survivors using machine learning. This repository demonstrates data cleaning, exploratory analysis, feature engineering, and model training with Python and Jupyter Notebook.
## Repository Structure
```
Titanic-Machine-Learning-from-Disaster/
├── Datasets/ # Contains train.csv, test.csv, and other data files
├── Model/ # Jupyter notebooks for EDA, data preprocessing, and model building
└── README.md # Project overview and instructions
```
## Project Overview
- **Objective:** Predict survival of Titanic passengers.
- **Data Source:** Kaggle’s Titanic dataset.
- **Approach:**
- **Data Preprocessing:** Clean missing values & encode features
- **Exploratory Data Analysis:** Visualize data distributions and relationships
- **Modeling:** Train and validate models (e.g., Logistic Regression, Decision Trees)
- **Submission:** Generate predictions in a submission file for Kaggle
## Setup & Usage
1. **Clone the Repository:**
```bash
git clone https://github.com/AdilShamim8/Titanic-Machine-Learning-from-Disaster.git
cd Titanic-Machine-Learning-from-Disaster
```
2. **(Optional) Set Up a Virtual Environment:**
```bash
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
```
3. **Install Dependencies:**
```bash
pip install numpy pandas scikit-learn matplotlib seaborn jupyter
```
4. **Run Jupyter Notebook:**
```bash
jupyter notebook
```
Open the notebooks in the `Model/` folder to explore the analysis and model development.
## Future Enhancements
- Experiment with advanced models (e.g., ensemble methods)
- Further feature engineering and hyperparameter optimization
- Deploy the model via a web app or API
## License
Distributed under the MIT License. See `LICENSE` for details.