ml_pipeline_titanic

Project: Titanic Survival Prediction Pipeline

This project builds a complete machine learning pipeline to predict the survival of Titanic passengers.

Prerequisites

• Python 3.7+
• Required Libraries: pandas, scikit-learn, joblib, fastapi, uvicorn, gradio, ngrok

Steps to Run

Part 1: Data Cleaning

1. Navigate to the Part1_Data_Cleaning folder.

2. Run the script:

   python data_cleaning.py

   This will clean the data and save it as cleaned_titanic.csv.

Part 2: Model Building

1. Navigate to the Part2_Model_Building folder.

2. Run the script:

   python model_building.py

   This will train the model and display evaluation metrics.

Part 3: Model Saving and Loading

1. Navigate to the Part3_Model_IO folder.

2. Run the script:

   python model_io.py

   This will save and load the model using joblib.

Part 4: FastAPI Endpoint for the Model

1. Navigate to the Part4_API folder.

2. Run the FastAPI server:

   uvicorn api:app --reload

   The server will start at http://127.0.0.1:8000.

Part 5: Deployment with Gradio

1. Navigate to the Part5_Deployment folder.

2. Run the Gradio app:

   python app.py

3. Follow instructions to deploy the UI on Hugging Face Spaces.

Deployment

• Deploy the FastAPI backend on Hugging Face Spaces.

• Deploy the Gradio UI on Hugging Face Spaces.

• data_cleaning.py: Script to clean and preprocess the Titanic dataset.

• model_building.py: Script to build, train, and evaluate the machine learning model.

• model_io.py: Script to save and load the trained model using joblib.

• api.py: FastAPI application for serving the trained model.

• app.py: Gradio-based user interface for interacting with the deployed model.

Author Bilal Akhtar Feel free to reach out if you have any questions or feedback!