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Data Science

Implementing Generative AI with HuggingFace, Langchain, and RAG Application

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Machine Learning

Implementation of End-To-End Projects with MlOps, AirFlow, DagsHub, and ETL Pipelines

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Web Development

Using Various Python Frameworks: Flask, Django, Streamlit, and FastAPI, Web Development: HTML, CSS, JavaScript


Customer Segment Analysis

Unleashing the power of precision marketing.

Customer segmentation transforms raw data into actionable insights, enabling businesses to classify their audience into distinct groups based on behaviors, preferences, and demographics. By tailoring strategies to meet the unique needs of each segment, this approach enhances customer satisfaction, drives engagement, and maximizes ROI. It's not just analysis—it's the science of delivering the right message to the right customer at the right time.

Customer Segmentation

Credit Card Fraud Detection

Strong Algorithms to Check Credit Cards

Machine learning model to identify fraudulent credit card transactions accurately. Using algorithms like Logistic Regression and Random Forest, I trained the model to distinguish between genuine and suspicious transactions. I handled class imbalance with techniques like SMOTE to ensure the model detected fraud effectively. Key metrics, such as precision and recall, guided the evaluation, balancing high accuracy with minimal false positives. This project highlights the potential of machine learning to enhance security in financial systems by detecting fraud in real-time.

Fraud Detection

Titanic Survival Prediction

Predicting Passenger Survival

This app predicts the likelihood of survival for passengers aboard the Titanic based on their demographic and travel details, such as age, gender, ticket class, and more. It leverages the AdaBoost algorithm, a powerful ensemble learning technique that combines multiple weak classifiers to form a strong predictive model. By analyzing patterns in the dataset, the app provides an accurate and insightful prediction of survival outcomes, demonstrating the capabilities of advanced machine learning techniques in real-world scenarios.

Fraud Detection

Catalyst : The Expanse Tracker

Revolve the Tracking System with Better Visualization and Summary

Catalyst – The Expense Tracker, is a web-based platform designed to help users manage and monitor their expenses over a three-month period. Built with HTML, CSS, JavaScript, Python, Django, and PostgreSQL, the application features a user-friendly interface that enables clients to log, categorize, and visualize their spending. With integrated graphical representations, users can easily gain insights into their financial habits, making it a valuable tool for informed financial decision-making and budgeting.

Fraud Detection

CertEase : Simplified Certificate Delivery

Change the way of Certificate Deliveration with CertEase

CertEase is an intuitive web application designed to streamline the generation and distribution of certificates for educational institutions, training centers, and event organizers. Built with Python, Django, and SQLite for the backend, and a responsive frontend using HTML, CSS, JavaScript, and Bootstrap, the platform offers a seamless experience for users. CertEase enables bulk certificate generation with personalized details from uploaded CSV files, automated email delivery to recipients, and efficient data management through database integration. With a Bootstrap-based design ensuring usability across devices, CertEase simplifies and automates the certificate distribution process, making it a hassle-free solution for organizations.

Fraud Detection