Four end-to-end ML projects from hackathons and personal work (2019–2021). Each project is a self-contained Jupyter Notebook with dataset, exploratory analysis, model development, and evaluation.
Stack: Python · Pandas · NumPy · scikit-learn · PyCaret · TensorFlow / Keras · LightGBM · XGBoost · Matplotlib · Streamlit
Predict sales for products across Big Mart outlets given product attributes (weight, fat content, visibility, MRP) and store attributes (size, location type, year established).
Two modeling approaches:
- Manual Gradient Boosting Regression — hand-tuned feature engineering and hyperparameters
- PyCaret + LightGBM — automated pipeline comparison and tuned LightGBM model
Source: Analytics Vidhya practice problem.
Corporate_Crisis_Risk_Management/
Predict financial distress / corporate crisis risk from firm-level financial indicators. Two notebooks:
- Exploratory Data Analysis — distributions, correlations, missing-data handling
- Regression Model — feature engineering, model development, and evaluation
Predict the number of loan applications L&T Financial Services receives each day so they can staff appropriately. Time-series problem with auxiliary data (holidays, inflation rates).
Approach:
- MLP Regressor for segment 1
- XGBoost Regressor for segment 2
- Hyperparameter optimization via GridSearchCV
- Final submission in
LTFS_Solution.csv
Source: Analytics Vidhya — LTFS Data Science FinHack.
Predict the interest rate banks should charge customers based on loan application data. Customer attributes include income, debt-to-income ratio, employment history, credit profile.
Source: Analytics Vidhya — Janata Hackathon.
pip install pandas numpy scikit-learn pycaret tensorflow lightgbm xgboost jupyter
jupyter notebook
Then open any project's .ipynb and run cells top-to-bottom. Train/test CSVs are committed alongside each notebook.
---
These are 2019–2021 hackathon and personal projects. For current production work, see AIAssist-JobSearch (https://github.com/ravitejadureddy/AIAssist-JobSearch).