Indian Premier League(IPL) Score Predictor

[Implementation of an End-to-End Machine Learning Project using Docker, GitHub Actions, and a CI/CD Pipeline, with Deployment on the Heroku Platform]

• The IPL Score Predictor project is about predicting the batting team's score.
• The team names for both the batting and bowling dropdown options in the web application are the up-to-date names of the teams.

Overview

• This dataset is from Year 2008 to 2022

• I performed exploratory data analysis (EDA) on the dataset.

• I trained the dataset using 7 different machine learning models:

    - Linear Regression
    - Ridge
    - Lasso
    - Decision Tree
    - Random Forest
    - Ada Boost
    - Gradient Boost
  

• I also performed hyperparameter tuning on these models to optimize their performance.

• After evaluating all the models, I chose the best one.

• To make the score predictions more accessible, I developed an application using Flask, Javascript, Bootstrap, CSS, and HTML.

• I then deployed this application on the Heroku platform using a Dockerfile, which is a text document containing instructions to build a Docker image.

• To specify the command to start the Flask application when the container is launched, I used a Procfile.

• Additionally, I used a main.yaml file to configure the application's deployment on the Heroku platform. This file contains information about the resources the application needs, such as memory and CPU requirements, as well as any environment variables or other configurations necessary to run the application on Heroku.

• Finally, I created a dashboard in Power BI using this dataset to visualize the IPL scores over the years.

App Demo

• App Link : Click Here

Dashboard

• Dashboard Link: Click Here

Technologies Used

License :

Apache License

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