Reinforcement Learning for Supplier Evaluation, Inventory Control, and Dynamic Pricing• Utilised RL methods, like Deep Q-Learning with Neural Networks, to determine optimal suppliers based on products' quality adherence, quantity, price, delay, etc. • Implemented Inventory Control Policies ((s, S), (T, S), (R, Q)) using optimization methods • Generated dynamic demand distributions using RL techniques, eliminating the need for pre-assumed distributions• Secured a Pre-Placement Offer (PPO)

Supervised Learning and Unsupervised Machine Learning for Exploratory Analysis of Geolocational Data. Used K Means algorithm for cluster formation, JSON module for formatting, Folium with Geopy for data visualization