Jitendra Upadhyay's Projects
A tutorial on merging and joining data frames using Python Pandas. See accommpanying blog post at www.shanelynn.ie
Description Classification using XGBOOST
Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.
Given 5 datasets, each containing hourly sensor data (00h to 23h) from a manufacturing tool (A, B, C, D, E) infer from unseen signals about the labels
Gilbert-Shannon-Reeds model for shuffling
GA
Twitter Data Analytics
Topic Modelling
This Repo comprises of the code and descriptive for the Marketing mix modelling dashboard which can be use for campaign planning along with the descriptive analysis utility
This Repo comprises of the code and the methodology for calculating the price elasticity of provided sku