IPL Score Prediction

This repository contains a Jupyter Notebook for predicting the score in an IPL (Indian Premier League) cricket match using a neural network model. The notebook processes the data, trains a model, and provides a widget-based interface for making predictions based on user input.

Dataset

The dataset used in this project is assumed to be ipl_data.csv, which contains various features related to IPL matches. The relevant columns used in the notebook include:

• venue
• bat_team
• bowl_team
• batsman
• bowler
• total (target variable)

Installation

To run this notebook, you need to have the following Python libraries installed:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• keras
• tensorflow
• ipywidgets

You can install these packages using pip:

pip install pandas numpy matplotlib seaborn scikit-learn keras tensorflow ipywidgets

Usage

Load Data: The dataset ipl_data.csv is loaded into a pandas DataFrame. Preprocess Data: Unnecessary columns are dropped. Categorical features are label encoded. Split Data: The data is split into training and testing sets. Scale Data: Features are scaled using MinMaxScaler. Build Model: A neural network model is defined using Keras. Train Model: The model is trained on the training data. Evaluate Model: Model performance is evaluated using mean absolute error. Prediction Interface: An interactive widget-based interface is provided for making predictions.

Interactive Prediction

The notebook includes an interactive widget-based interface where users can select:

Venue Batting Team Bowling Team Striker Bowler Upon clicking the "Predict Score" button, the model predicts the score based on the selected inputs.