Harshit Agarwal

I'm a Post Graduate student at IIIT Bengaluru .Through my coursework and projects, I have gained practical experience in cleaning and analyzing datasets, applying statistical and analytical techniques to uncover patterns, and presenting insights in a clear and effective way. Previously, I was responsible for managing and running my family business, where I handled client negotiations, built strong relationships, and ensured smooth day-to-day operations to manage and grow the business.

Email  /  CV  /  Github  /  Leetcode  /  Kaggle  

IIIT Bangalore
PGD in DPDM
Jun. 2025 - Jul. 26

MinkWhite
Ruby on Rails Developer
Aug. 23 - Jan. 24

BMSIT Bengaluru
B.E. in Information Science
Aug. 2019 - Jun. 2023

This is workbook of Tableau, visualizing the dataset of Electric Vehicle Population Data. It was designed by keeping various KPI Requirements such as Average Electric Range, Total BEV Vechicles Relative to Total Vehicles, Total PHEV Vehicles Relative to Total vehicles etc. Charts like Line/Area chart, Map Chart, Pie/Donut Chart, Bar Chart etc were used in Visualization and finally a Dashboard was made for Data Discovery.

Built a XGBoost Regression Model to that helps establish the rates of premium by predicting the charges or payouts done by the firm. Achieved a total of 15-20% improvement in RMSE over baseline models such as Linear Regression. The approach for the project was
1.) Exploratory Data Analysis(EDA)
2.) Build and Evaluate baseline linear model
3.) Improve on the baseline linear model with Data Preprocessing.
4.) Improve the model training process using Sklearn's Pipeline and compare the results of final model using RMSE Error Values.
Other processes involed in the stage of developing this project was Understanding Correlation between the Categorical and Target Variables and various methods used in Correlation Analysis. Implementing BayesSearchCV for XGBoost Hyperparameter Optimization

This project analyse the Titanic - Machine Learning from Disaster dataset provided in Kaggle.

This project analyse the Uber dataset provided in Kaggle.

Implementing Quantum Ciruits

Problem Description - In e-commerce, determining the optimal price point for products is crucial for marketplace success and customer satisfaction. Develop an ML solution that analyzes product details and predict the price of the product.
(The relationship between product attributes and pricing is complex - with factors like brand, specifications, product quantity directly influence pricing.)

We used TF-IDF Vectorization for text data and LightGBM Regression on numerical features extracted from the catalog content.

Source code from Jon Barron