Predicting Runs made by Virat Kohli using nueral networks with three layers two running on linear regression and last one on ReLU and predicts run based on opponent using dataset
Virat Kohli Runs Predictor This project is a machine learning web application that predicts the runs Virat Kohli might score in a cricket match given various match conditions and his recent performance. It uses:
PyTorch for building and training a simple neural network. Streamlit for creating a user-friendly web interface. Matplotlib for visualizing the training loss curve. scikit-learn for label-encoding categorical features. Table of Contents Features Tech Stack Project Structure Installation Usage Model Details Screenshot Contributing License Features User Inputs: Enter match details such as Opponent, Match Format, Venue, Pitch Type, Weather, Toss Decision, Bowler Type Faced Most, and Kohli’s last 5 scores. Real-Time Predictions: Click a button to see how many runs Kohli might score based on these conditions. Training Loss Visualization: Displays a chart of the model’s training loss over epochs. Dark-Themed UI: The app uses a dark theme for a modern, sleek look. Tech Stack Python 3.7+ PyTorch for the model (torch, torch.nn, torch.optim) Streamlit for the web interface scikit-learn for label encoding Matplotlib for plotting the training loss Project Structure arduino Copy . ├── app.py ├── .streamlit │ └── config.toml ├── README.md └── requirements.txt (optional) app.py: Main Streamlit application, including data preprocessing, model definition, training, and UI code. .streamlit/config.toml: Configuration file for Streamlit’s dark theme. README.md: Project documentation (this file). requirements.txt: List of Python dependencies (optional). Installation Clone or Download this repository.
Create a Virtual Environment (recommended but optional):
bash Copy python -m venv venv
venv\Scripts\activate
source venv/bin/activate Install Dependencies:
bash Copy pip install streamlit torch torchvision torchaudio scikit-learn matplotlib Or, if you prefer conda:
bash Copy conda install pytorch scikit-learn matplotlib -c pytorch pip install streamlit Set up Dark Theme:
Make sure you have a .streamlit folder in the same directory as app.py. Inside .streamlit, create a config.toml with the following content: toml Copy [theme] base="dark" primaryColor="#F63366" backgroundColor="#0E1117" secondaryBackgroundColor="#262730" textColor="#FFFFFF" This ensures Streamlit uses the dark theme. Usage Open a Terminal in the project folder (where app.py is located). Run the Streamlit App: bash Copy streamlit run app.py or bash Copy python -m streamlit run app.py Open the Local URL (usually http://localhost:8501) in your browser to access the app. Interacting with the App Match Conditions: Use the top row of dropdowns/radio buttons to select Opponent, Match Format, Venue, Pitch Type, Weather, Toss Decision, and Bowler Type. Kohli’s Last 5 Scores: Enter the last 5 scores in the provided numeric fields. Predict: Click the Predict Runs button to see the model’s prediction. Training Loss Curve: Scroll down to see how the training loss evolved over 300 epochs. Model Details Model Architecture: A simple feed-forward neural network: Input Layer: Size = number of features (7 categorical + 5 numeric scores = 12). Hidden Layer: 16 units, ReLU activation. Output Layer: 1 unit (predicted runs). Loss Function: Mean Squared Error (MSE). Optimizer: Adam, learning rate = 0.01. Training Data: A small mock dataset with 10 rows for demonstration. Each row includes the categorical columns, last 5 scores, and a Predicted_Runs label for supervised training. Screenshot Below is an example screenshot of the dark-themed UI with horizontally arranged inputs at the top and a training loss curve at the bottom.
(Replace the URL with an actual screenshot if you want to show the real UI.)
Contributing Contributions are welcome! If you’d like to:
Fix a bug Add new features Refactor code Please open a pull request or raise an issue.
License This project is open-source. You can include a license of your choice (e.g., MIT, Apache, etc.). For example:
sql Copy MIT License
Copyright (c) 2025 ...
Permission is hereby granted, free of charge, to any person obtaining a copy ... That’s it! Feel free to update this README with more details about your project, references, or screenshots as you see fit.