Aniket-Thopte/Demand-Forecasting-Public-Bike-Rental-Predictive-Modeling-
Developed multiple predictive models with 90% accuracy for forecasting the daily-hourly bike rental count using Python & Machine Learning techniques like Regression, Clustering, Ensemble, Neural Network to achieve maximum accuracy
Implements comparative modeling across regression (Linear, Ridge, Lasso), K-means clustering, gradient boosting ensembles, and LSTM neural networks with hyperparameter tuning to identify optimal architectures. Incorporates temporal feature engineering and seasonality analysis via Tableau-driven EDA to capture weather, holiday, and time-based demand patterns. Provides model performance visualization and evaluation metrics to support production deployment decisions in bike-sharing demand forecasting workflows.
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May 07, 2021
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