Titanic Prediction Pipeline Project

This project is built over the most infamous titanic dataset. This project is a complete end-to-end machine learning project.

The development of the machine learning model is done in 5 different stages. This project is deployed using Streamlit.

🛣️ Development Stages

1. Data Ingestion Stage Ingesting the dataset from the souce URL provided.

2. Data Validation Stage Checking the structure of the ingested dataset, and validating it.Based on the validation, a validation file is generated saving the reference of the result of validation.

3. Data Transformation Stage Transforming the dataset to be feed into the model.

4. Model Training Stage Modeling various machine learning algorithms and then passing the dataset thorugh the model for training the model.

5. Model Evaluation Stage After the model has been trained, evaluating the model based on the unknown dataset and then evaluating the accuracy of the model.

⚒️ Workflows

1. Update config.yaml
2. Update parmas.yaml
3. Update entity
4. Update the configuration manager in the src config
5. Update the components
6. Update the pipeline
7. Update the main.py
8. Update the app.py

⚙️ Tech Stack

Programming Language: Python

Package used: Scikit-Learn, DVC (Data-Version-Control), Pandas, Numpy, Matplotlib, Seaborn, Streamlit, Scipy, Flask, pyYAML, catBoost, ensure, python_box

💻 How to run?

STEPS,-

Step 1: Clone this repository

https://github.com/krishanwalia30/Titanic_Pipeline_Project

Step 2: Create a conda environment inside the repository

conda create -n titanic python=3.9 -y

conda activate titanic

Step 3: Install the requirements

pip install -r requirements.txt

# Finally run the following command
python streamlit_app.py

Step 4: See the project running

open the link in the terminal [usually at port 8501]

📏 Outcome

• Learned to create an end-to-end machine learning pipeline.
• Created different modules and components for different stages in pipeline development
• Learned about Data Version Control (DVC)
• Integrated DVC with the project to make it more efficient.
• Learned to create a Flask webapp for the model.
• Discovered the functionalities of Streamlit.
• Deployed the webapp successfully on Streamlit.
• Developed Continuous Integration and Development pipeline.

⭐ Features

• Machine learning model with an accurcy of 93.475
• Live Prediction, in minimum time.
• Train Endpoint integration on the webapp

🚀 Deployment

The project is deployed as a webapp using Streamlit Link: https://titanicpipelineproject123.streamlit.app/

📑 Appendix

Running the DVC:

Run the following command in the main project command prompt,-

dvc init
dvc repro