•Implemented an intent-based data visualization chatbot, which uses Natural Language generation using PyTorch. The data visualizations are created using matplotlib and seaborn. •The front-end GUI is built using Tkinter and this app can import CSV datasets and show visualizations charts of different types based on user query in plain text language. The chatbot can also reply to general users queries in plain English.•Data from imported CSVs are stored in the localhost PostgreSQL database.

•Supervised Machine learning to predict a student’s percentage based on the number of hours studied per day, creating a Linear Regression Model with an R-squared score of 0.95.•Unsupervised Machine learning to perform clustering on Iris dataset to cluster the flower species. Exploratory Data Analysis (EDA) on various datasets like SampleSuperstore, Terrorism, etc and gathering useful insights about the data along with some Visualizations of the data.