This sniper bot on the Solana network specializes in swiftly buying 'shitcoins' while spotting tokens poised for growth.

9-Qubits Shor's Code for Quantum Error Correction (QEC)

This project aim to classify real time facial emotion recoginition to either of one of the universal seven emotions using Deep convolutional neural network(CNN) learning and openCv

End-to-end project to predict and segment customers. (clustering problem)

The objective is to design a machine learning model capable of accurately counting repetitions and classifying weightlifting exercises based on data collected from accelerometer and gyroscope sensors. The project began with meticulous data collection and preprocessing to ensure suitability.

dimensionality Reduction with python- bioinformatic

Working on small dataset which has 26k records.

Jupiter-Java-examples

Enhanced pandas DataFrame display with resizable columns and sticky headers for Jupyter notebooks

DeepLeaf: CNN-based potato disease classification. Utilizes TensorFlow, Keras, and FastAPI. Frontend designed with HTML/CSS/JS. Android app (PotatoPathoGuard) developed in Android Studio. Enables precision agriculture through on-device disease detection.

Data Analysis Website

Analysis of Denver crime records from January 2018- October 2023

Exploratory Data Analysis (EDA) and Statistical Investigation of customer behavior using Python, Pandas, NumPy, Matplotlib, Seaborn, and SciPy to uncover spending patterns and demographic insights.

The goal of this course was to familiarize with applying different modelling techniques using Python. Learned how to apply math equations to real-world problems.

PROJECT 1` FOR GUVI HCL

The IPL 2023 Auction Analysis explores player bidding trends, team acquisitions, and pricing insights using Python. It involves data cleaning, exploratory data analysis (EDA), and visualization with Pandas, Matplotlib, and Seaborn.

A time series analysis project that explores unemployment trends across industries using trend analysis, seasonality decomposition, correlation analysis, and data visualization to uncover employment patterns and support data-driven economic insights.

A Data Mining project that applies the **Apriori Algorithm** to supermarket transaction data to discover frequent itemsets and generate association rules. The project identifies products that are frequently purchased together, enabling retailers to optimize product placement, cross-selling, promotional campaigns, and inventory management.

This project implements Multiple Linear Regression in Python to predict product sales from TV, Radio, and Newspaper advertising budgets, using the Normal Equation without external ML libraries.