Startup Success Prediction with Ensemble Modeling.

Introduction

This project aims to predict the success or failure of startups based on a dataset with various features. We will use ensemble modeling techniques, MLflow for tracking experiments, Docker for containerization, and create a Streamlit dashboard for interactive visualization.

This readme file is still under more development as the project goes on..████▒▒▒▒ 50%

Table of Contents

• Dataset
• Requirements
• Ensemble Modeling
• MLflow Integration
• Dockerization
• Streamlit Dashboard

Dataset

The dataset is from kaggle : Link

Requirements

• Python 3.9
• Scikit-Learn
• MLflow
• Docker
• Streamlit
• Pandas
• NumPy

I will be uploading the requierements file soon so that installations can be made with a simple command : pip install -r requierements.txt

Ensemble Modeling

We will experiment with various machine learning algorithms to predict startup success.

MLflow Integration

We will use MLflow for experiment tracking. All experiments are logged in the mlflow_runs directory.

to access the MLflow UI at http://localhost:5000 to view and compare experiments.

Dockerization

We will include a Dockerfile to containerize the project.

Streamlit Dashboard

we will use a Streamlit dashboard that can be found in app.py. to start the dashboard :

streamlit run app.py

The dashboard will be available at http://localhost:8501 in the web browser.