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https://github.com/ganesh2409/titanic_survival_prediction

A Streamlit-based web application that predicts the survival of Titanic passengers based on various input features. The app allows users to explore the Titanic dataset and make predictions using a trained machine learning model. Includes data cleaning, feature engineering, and model training using Logistic Regression.
https://github.com/ganesh2409/titanic_survival_prediction

data-processing feature-engineering machine-learning streamlit titanic-survival-prediction

Last synced: about 1 month ago
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Host: GitHub
URL: https://github.com/ganesh2409/titanic_survival_prediction
Owner: Ganesh2409
Created: 2023-10-06T18:48:04.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-10-18T09:06:54.000Z (4 months ago)
Last Synced: 2024-11-12T05:12:35.116Z (3 months ago)
Topics: data-processing, feature-engineering, machine-learning, streamlit, titanic-survival-prediction
Language: Jupyter Notebook
Homepage: https://ganesh2409-titanic-survival-prediction.streamlit.app/
Size: 1.09 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# ⚓ Titanic Survival Prediction App
### Let's See --> [Who Can Make It ](https://ganesh2409-titanic-survival-prediction.streamlit.app/) ![Predict](https://img.shields.io/badge/Streamlit-indigo)

This project is a Streamlit-based web application that predicts whether a passenger would survive the Titanic disaster based on various input features. The app allows users to explore the Titanic dataset and make predictions using a trained machine learning model.

## 📁 Project Structure

- **`app.py`**: The main file that runs the Streamlit app. It includes options to either explore the dataset or make survival predictions.
- **`deploy.py`**: Handles the prediction functionality. Loads the pre-trained machine learning model and processes user inputs to predict the survival of a Titanic passenger.
- **`explore.py`**: Contains the code to visualize various aspects of the Titanic dataset, providing insights into factors that influenced survival rates.
- **`Titanic_Survival.ipynb`**: A Jupyter Notebook containing the steps involved in data cleaning, exploration, feature engineering, and model training.
- **`models/`**: A directory where the pre-trained models and transformers are stored for making predictions.
- `column_transformer.pkl`: The ColumnTransformer used for processing the dataset.
- `standard_scaler.pkl`: The StandardScaler used to standardize the features.
- `classifier.pkl`: The Logistic Regression model trained to predict survival.

## 🛠️ How to Run the App

1. **Clone the Repository**:
```bash
git clone https://github.com/Ganesh2409/Titanic_Survival_Prediction.git
cd Titanic_Survival_Prediction
```

2. **Install Dependencies**:
Make sure you have Python installed, then install the required packages:
```bash
pip install -r requirements.txt
```

3. **Run the App**:
To start the Streamlit app, run the following command:
```bash
streamlit run app.py
```

4. **Interact with the App**:
- Navigate to the "Explore" page to visualize the dataset.
- Use the "Predict" page to input passenger details and predict their survival.

## 📄 Dataset

The dataset used in this project is the famous Titanic dataset, which contains information about the passengers aboard the Titanic. Key features include:

- **Pclass**: Passenger class (1st, 2nd, 3rd)
- **Sex**: Gender of the passenger
- **Age**: Age of the passenger
- **SibSp**: Number of siblings/spouses aboard the Titanic
- **Parch**: Number of parents/children aboard the Titanic
- **Fare**: Fare paid for the ticket
- **Embarked**: Port of Embarkation (C=Cherbourg; Q=Queenstown; S=Southampton)

## 🚀 Model

The model used in this project is a Logistic Regression model trained on the processed Titanic dataset. The pipeline involves:

1. **Data Cleaning**: Handling missing values, dropping unnecessary columns.
2. **Feature Engineering**: Applying transformations to categorical and numerical features.
3. **Model Training**: Training a Logistic Regression model using Scikit-learn.

## Results

The model achieves a reasonable accuracy on the test set, and the app provides users with real-time predictions based on their input.

## 🖥️ Future Improvements

- Enhance the model's accuracy by experimenting with other algorithms like RandomForest or XGBoost.
- Incorporate additional data preprocessing steps to handle outliers and imbalanced data.
- Deploy the app on a cloud platform for wider accessibility.

## 🤝 Contact
For any questions or feedback, please co ntact:
- **Name** - [Ganesh Chowdhary P]()
- **Email** - [[email protected] ](mailto:[email protected])

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