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.