🤖 Machine Learning Projects Repository

This repository contains a collection of machine learning projects that I am working on.
Each project focuses on real-world applications and uses Python-based ML frameworks to implement models, visualize results, and evaluate performance.

The projects cover a variety of domains such as sports predictions, financial forecasting, computer vision, and data analysis, and are continuously updated as I explore new techniques and libraries.

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📂 Projects Overview

1. 🏏 T20 Tournament Prediction using XGBoost

• Predicts winners of group stage, Super 4, and final matches in T20 tournaments (Asia Cup-style).
• Uses historical T20I match data from Cricsheet.
• Features include team statistics extraction, XGBoost training, tournament simulation, and visualizations.
• Folder: Asian_Cup_2025_Predictions/

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2. 💹 NVIDIA Stock Analysis & Next-Day Prediction (NVDA)

• Builds a multi-model ensemble AI system to forecast NVIDIA (NVDA) next-day stock prices.
• Combines statistical, machine learning, and deep learning models for robust forecasting.
• Key models used: ARIMA-GARCH, XGBoost, LSTM-GRU Attention, and Monte Carlo Simulation.
• Produces confidence intervals, volatility forecasts, risk metrics, and trading recommendations.
• Folder: Stock_Analysis_NVDA/

Example Output:

Metric	Value
Current Price	$183.16
Predicted Next-Day Price	$186.58 (+1.87%)
Confidence Interval (95%)	[$182.07, $184.05]
Recommendation	🟢 BUY

⚡ Each project has its own folder, scripts, datasets, trained models, and dedicated README.md describing its workflow, architecture, and results.

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🛠️ Libraries & Tools

Programming & ML Libraries

Data Visualization

Tools & IDEs

Other Utilities

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🚀 Workflow

1. Collect or load datasets (CSV, JSON, or API-based financial data).
2. Preprocess and clean the data, handle missing values, and engineer new features.
3. Train models using supervised, unsupervised, or hybrid AI approaches.
4. Evaluate models with metrics like Accuracy, RMSE, Sharpe Ratio, or F1-score.
5. Visualize predictions and analyze feature importance and performance metrics.
6. Export results, charts, and reports for future analysis or deployment.

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📊 Goals of this Repository

• Build a strong, well-documented portfolio of machine learning projects.
• Explore a diverse range of ML algorithms, econometrics, and deep learning models.
• Apply AI/ML techniques to real-world domains — sports, finance, and predictive analytics.
• Strengthen expertise in data visualization, feature engineering, and model deployment.

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🧠 Featured Technologies

Category	Libraries / Tools
Data Processing	Pandas, NumPy, Excel, JSON
Modeling	Scikit-learn, XGBoost, TensorFlow, Keras, ARCH
Visualization	Matplotlib, Seaborn, Plotly
Development	VS Code, Jupyter, Git, GitHub
Deployment Ready	Saved model files (.h5, .pkl, .json)

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🏁 Conclusion

This repository serves as a central hub for my machine learning journey — a portfolio of projects combining sports analytics, financial forecasting, and data-driven AI modeling.
Each folder is a self-contained research and implementation unit that demonstrates my growing skillset and practical application of ML.

💡 Ongoing work: Expanding NVDA financial predictor to live data streams + deploying an interactive dashboard.

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📅 Last Updated

Date: October 2025
Maintainer: Manimaran K