ML Research

Context: Academic research at IIM Indore spanning multiple domains – finance, pharma, EdTech, and government.

Challenge: Each domain had unique data characteristics and analytical needs, requiring tailored machine learning approaches to support decision‑making.

Solution: Applied a comprehensive ML toolkit across diverse problems:

• Classification – logistic regression, decision trees, random forest, XGBoost, SVM
• Regression – linear, polynomial, and ridge regression
• Clustering – K‑means, hierarchical
• Time‑series forecasting – ARIMA, exponential smoothing
• Natural Language Processing – text analysis, TF‑IDF, topic modeling
• Survival models – Kaplan‑Meier, Cox PH
• Dimensionality reduction – PCA
• Deep learning – CNN, RNN, LSTM

Outcome: Delivered actionable insights and robust predictive models across finance, pharma, EdTech, and government research projects, demonstrating the versatility of ML across sectors.