IPL Score Prediction

Overview

This project focuses on predicting the scores of Indian Premier League (IPL) cricket matches using machine learning techniques. The goal is to build a model that accurately predicts the total score of a team in an IPL match based on various features such as venue, team performance, batting order, and more.

Installation

Clone the repository:

git clone https://github.com/nky001/ipl.git

Navigate to the project directory:
```
cd ipl
```
Install the required dependencies:
```
pip install -r requirements.txt
```

Dataset

The dataset used for this project. It contains historical data of IPL matches including match statistics, player performance, team rankings, and more.

Model Evaluation

This repository contains a machine learning model for predicting IPL match scores using historical data from previous IPL matches. The model is built using a neural network architecture with multiple dense layers.

Data Preprocessing

Feature Encoding: Categorical features such as venue, batting team, bowling team, batsman, and bowler were encoded using LabelEncoder from scikit-learn.
Feature Scaling: Min-max scaling was applied to normalize the features using MinMaxScaler from scikit-learn.

Training and Testing

The dataset was split into training and testing sets with a ratio of 70% for training and 30% for testing.
The neural network model was trained using the Adam optimizer and Huber loss function with a delta of 1.0.
Training data was scaled using MinMaxScaler to ensure convergence during training.

Model Evaluation Metrics

Loss Function: Huber loss was used as the primary loss function during model training.
Evaluation Metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared (R2) score.

License

This project is licensed under the MIT License - see the LICENSE file for details.

IPL Analysis Project