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https://github.com/dhrupad17/cricmind-t20-world-cup-score-predictor
The T20 World Cup Score Prediction project aims to predict the total runs scored by a team in a T20 cricket match using the XGBoost algorithm. XGBoost is a popular machine learning algorithm used for predictive modeling.
https://github.com/dhrupad17/cricmind-t20-world-cup-score-predictor
flask-application jupyter-notebook machine-learning python xgboost-algorithm
Last synced: about 2 months ago
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The T20 World Cup Score Prediction project aims to predict the total runs scored by a team in a T20 cricket match using the XGBoost algorithm. XGBoost is a popular machine learning algorithm used for predictive modeling.
- Host: GitHub
- URL: https://github.com/dhrupad17/cricmind-t20-world-cup-score-predictor
- Owner: dhrupad17
- License: mit
- Created: 2023-05-01T06:18:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-05T16:45:03.000Z (3 months ago)
- Last Synced: 2024-11-15T19:43:02.016Z (about 2 months ago)
- Topics: flask-application, jupyter-notebook, machine-learning, python, xgboost-algorithm
- Language: Jupyter Notebook
- Homepage:
- Size: 6.58 MB
- Stars: 1
- Watchers: 1
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧤 CricMind - T20 World Cup Score Predictor 🏏
---
## Project Description and Workflow
The T20 World Cup Score Prediction project aims to predict the total runs scored by a team in a T20 cricket match using the XGBoost algorithm. XGBoost is a popular machine learning algorithm used for predictive modeling.
#### The workflow for the T20 World Cup Score Prediction project is as follows:
- ```Data Collection:``` Collect data on past T20 cricket matches including the team playing, runs scored, wickets taken, overs bowled, and other relevant information. This data can be sourced from various cricket databases, APIs or websites.
- ```Data Preprocessing:``` Clean and preprocess the data to ensure that it is consistent and accurate. This can involve tasks such as removing missing values, handling categorical variables, and feature engineering.
- ```Feature Selection:``` Identify the most relevant features for the prediction model. This can be done using techniques such as correlation analysis, feature importance ranking, and domain knowledge.
- ```Model Training:``` Train an XGBoost model using the preprocessed data and the selected features. The XGBoost model is a gradient boosting algorithm that uses decision trees as base learners.
- ```Model Evaluation:``` Evaluate the performance of the XGBoost model using metrics such as mean absolute error, mean squared error, and R-squared.
- ```Prediction:``` Use the trained and optimized XGBoost model to predict the total runs scored by a team in a T20 cricket match based on the relevant features.
## Tools Used:
Anaconda
Flask
Git
HTML5
JavaScript
Pycharm
Python
## Website OverView:-
