1. Forecasting Demand Profiles of New Products This research explores machine learning-based approaches to forecast demand profiles for new products. The study utilizes K-Means Clustering, Random Forest, and Quantile Regression Forest (QRF) to classify demand patterns and predict sales. The dataset includes historical demand data from various companies, allowing for effective segmentation and prediction of new product demand. Random Forest emerged as the most accurate model, while QRF provided valuable uncertainty estimates. Results indicate that machine learning-based demand forecasting significantly improves inventory planning and production efficiency. Dataset: Companies demand data (Wallmart) 2. Failure Prediction of Indian Banks This research explores machine learning-based predictive modeling for bank failure in India. The study utilizes SMOTE (Synthetic Minority Oversampling Technique) to address the issue of imbalanced data, Lasso Regression for feature selection, and ensemble learning techniques (Random Forest and AdaBoost) to improve prediction accuracy. The dataset includes financial records of 58 public and private sector banks from 2000 to 2017, categorized as failed or survived. Among the models tested, AdaBoost performed best, offering the highest accuracy(98.8%) with the lowest Type-II(1.73%) error. The study provides a robust framework for early warning systems to detect potential bank failures and financial stress. Dataset: Financial records of Indian banks (2000–2017) Ciatation 1. Shrivastava, Santosh, P. Mary Jeyanthi, and Sarbjit Singh. "Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting." Cogent Economics & Finance 8, no. 1 (2020): 1729569. 2. Van Steenbergen, R. M., and Martijn RK Mes. "Forecasting demand profiles of new products." Decision support systems 139 (2020): 113401.