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Machine Learning · Mini Project

IPL Score Prediction

Predicts the first-innings total from the live state of an IPL match

1-2 weeks build

Overview

Built on public ball-by-ball IPL data from Cricsheet, this kit reconstructs the match state at every over and predicts the final first-innings total from teams, venue, current score, wickets lost and recent run rate. Regressors are compared across overs, and a Streamlit app takes a live scorecard and returns a projected total.

What makes it stand up

  • 01Ball-by-ball data rebuilt into over-by-over match states
  • 02Venue and recent-form features alongside score and wickets
  • 03Error tracked by over, so you can see accuracy improve through an innings
  • 04Linear, random forest and XGBoost regressors compared
  • 05Streamlit app that projects a total from a live scorecard

Module breakdown

  1. 01

    Data parsing

    Converts Cricsheet match files into tabular deliveries.

  2. 02

    Feature building

    Cumulative runs, wickets, run rate and last-five-over form.

  3. 03

    Modelling

    Trains regressors on seasons held out by year.

  4. 04

    Evaluation

    MAE per over and residual plots by venue.

  5. 05

    App

    Input form for teams, venue and current state.

After this, you will be able to

  • Explain why you split by season rather than at random
  • Defend the features that matter most at each stage of an innings
  • Demo a projection updating as overs are entered

Technology stack

  • Python
  • Pandas
  • scikit-learn
  • XGBoost
  • Streamlit

Complete kit

₹5,399₹9,199
-41%
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Delivered in under 48 hours

Level
Mini Project
Domain
Machine Learning
Build time
1-2 weeks
Difficulty

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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