⚓ Titanic Survival Prediction App

Let's See --> Who Can Make It

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:

   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:

   pip install -r requirements.txt

3. Run the App: To start the Streamlit app, run the following command:

   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 - pinnamaneniganesh24@gmail.com

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