Hello, I am Harshi Gupta, a passionate AI and Data Science professional focused on creating intelligent, practical, and influential solutions with technology. I am currently pursuing my degree in Artificial Intelligence and Data Science (AI-DS), and I'm building a strong foundation in analytical thinking, machine learning, and problem-solving.
My interest in AI began with my curiosity about how data can be converted into content, so I started looking into data analysis, different machine learning algorithms, and building models. I have experience using Python programming, file handling, NumPy operations, and other data science tools, and I work on building my technical skills each day.
I am interested in using AI to solve real-world problems—through smarter decision-making systems, pattern recognition, or predictive analytics. I want to build accurate, ethical, and influential solutions while learning and growing from the ever-changing and growing landscape of AI.
0 + Projects completed
Enthusiastic and self-motivated Data Science undergraduate with a passion for open-source development and problem-solving. Seeking to contribute to meaningful projects as a GSSoC contributor while enhancing my technical and collaborative skills. Eager to learn, grow, and make valuable contributions to the developer community.
CGPA:9.06
Grade(Class XII): 81.2%
Grade(Class X): 89.7%
Below are the sample Data science projects on Pandas Numpy Matpoltlib & Seaborn .
A neighborhood assistance application prototype enabling locals to connect and support each other.Focused on user flows, accessibility, and rule-based interactions to encourage community engagement.
Processed and explored a Netflix dataset containing 8,800+ movies and TV shows across 12+ attributes, reducing missing values by 20 to 25% through data cleaning, type conversion, and feature engineering using Pandas.Derived content insights using Matplotlib and Seaborn, showing that 70%+ of titles are Movies, the US contributes 30% of total content, and post-2015 releases account for over 55% of the catalog.
Processed the Titanic dataset (891 passengers) using Pandas, handling missing values in Age and Embarked columns and preparing categorical features, improving overall data usability by 15%. Visualized survival patterns with Matplotlib/Seaborn, showing 74% survival for females vs 19% for males and higher survival rates for 1st-class passengers compared to lower classes
Analyzed 200+ order records across 50+ customers and 30+ products, performing data cleaning, feature engineering, and revenue analysis using Python (Pandas, Matplotlib) to compute Customer Lifetime Value (CLV), top-10 customers, and monthly sales trends.Identified the highest revenue-generating product category and analyzed repeat vs one-time customer behavior, supported by category-wise, time-based, and customer-distribution visualizations.
Designed a Python-based concession stand management system to simulate real-world order processing and billing.Implemented rule-based logic using dictionaries and control structures to manage menu items, calculate totals, and generate structured outputs.
Below are the details to reach out to me!
Delhi, India