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Data-pageup/README.md

Amirtha Ganesh R

Data Scientist · ML Engineer

M.Sc Data Science @ VIT AP · B.Sc Data Science & Analytics @ Sharda University

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About

I build machine learning systems that solve real-world problems — from statistical modelling and deep learning to computer vision. Published researcher (IEEE CICN 2025), focused on turning research and data into production-ready systems.

How I Work

Collect → Clean → Explore → Engineer → Model → Evaluate → Deploy → Monitor

Data collection and cleaning, exploratory analysis, feature engineering, model training and evaluation, deployment, and ongoing monitoring — the full lifecycle, end to end.

Let's Connect

Open to full-time roles, research collaborations, and interesting problems to work on.

Email LinkedIn GitHub


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  1. EcoWaste-AI-Smart-Waste-Classification-Carbon-Footprint-Estimator EcoWaste-AI-Smart-Waste-Classification-Carbon-Footprint-Estimator Public

    EcoWaste AI uses MobileNetV2 to classify waste as organic or recyclable and a RandomForest model to estimate CO₂ savings based on item weight. It helps users make better disposal choices by providi…

    Jupyter Notebook 2

  2. YOLOv8_garbage_detection YOLOv8_garbage_detection Public

    A study comparing YOLOv8 Nano at 416×416 and 608×608 for garbage detection. It measures mAP, precision, recall, and speed, showing that higher resolution boosts accuracy while lower resolution runs…

  3. SmartFit_AI SmartFit_AI Public

    SmartFit AI uses machine learning to analyze fitness and health data, predict calorie burn, BMI, and body fat, and group users into fitness archetypes. It combines PCA, K-Means, and neural networks…

    Python

  4. RippleWorks RippleWorks Public

    A machine learning system that predicts Water Quality Index (WQI) and Water Quality Classification (WQC) using engineered water-quality features. The project includes full preprocessing with missin…

    Jupyter Notebook

  5. Hybrid_Generative_Adversarial_Neural_Network_and_Copula_DataSynthesizer Hybrid_Generative_Adversarial_Neural_Network_and_Copula_DataSynthesizer Public

    A Streamlit app that preprocesses your dataset and generates high-quality synthetic data using Gaussian Copula and CTGAN models, with built-in evaluation and easy CSV export.

    Python

  6. FIFA-2026-Outcome-Prediction-Using-Poisson-Regression-and-Monte-Carlo-Simulation FIFA-2026-Outcome-Prediction-Using-Poisson-Regression-and-Monte-Carlo-Simulation Public

    historical match data trains two Poisson regression models (one for home goals, one for away goals), those models power a match simulator, and that simulator runs the entire 48-team World Cup 1,000…

    Jupyter Notebook