Mohd Zaheeruddin

Daily LeetCode problem solutions focused on data structures, algorithms, and optimized approaches with clean, well-documented code.

This repository documents my hands-on learning and implementation of Pandas, the powerful Python library used for data manipulation and analysis.

A structured Data Structures & Algorithms repository organized by problem-solving patterns instead of random topics. Includes curated problems for each pattern, optimized approaches, code templates, and notes to improve recognition speed and interview readiness.

An end-to-end ML app simulating loan evaluation using a two-stage model: approve/reject first, then predict optimal loan amount for approved users. Covers notebooks to modular code, YAML configs, and Streamlit Cloud deployment.

This project focuses on developing a machine learning system to predict customer churn in the telecommunications industry. It covers the entire data science lifecycle, from exploratory data analysis to model deployment, enabling proactive intervention and customer retention

🚀 End-to-End Industry-Ready Projects

Mosquito Detection System is an end-to-end computer vision application that detects mosquitoes in images and live camera feeds using the YOLOv5 object detection model. It helps support public health efforts by enabling early monitoring of mosquito presence to reduce risks of diseases like Dengue, Malaria, Zika, and Chikungunya.

PCAP StoryTeller simplifies network forensics by converting complex packet data into human-readable stories. The application automatically links network events, provides heuristic risk scoring, and presents data through an intuitive visual dashboard, making it accessible for cybersecurity students and newcomers.

This is my personal portfolio website showcasing my projects, skills, and achievements. It is built using HTML, CSS, and JavaScript with a clean and responsive design. The site is deployed using GitHub Pages and automated CI/CD workflows. I’ve focused on creating a minimal user interface with smooth navigation and engaging visuals.

Daily GeeksforGeeks problem-solving practice focused on core DSA concepts and optimized solutions.

This project builds an automated elephant species classifier using deep learning (CNNs + transfer learning). It identifies elephants from photos to support wildlife conservation through better monitoring and data-driven analysis.

Poultry diseases seriously impact bird health and farm productivity. Early detection through fecal analysis is crucial for timely treatment and prevention. It supports better flock welfare, reduces losses, and improves overall production efficiency, creating a healthier and more sustainable poultry farm ecosystem.

This project focuses on building and deploying image classification models using various architectures. Students will gain hands-on experience with model training, hyperparameter optimization, and deployment on AWS EC2, culminating in a functional image classification service.

The AI Powered Job Analyzer is a cloud-native application that leverages GPT-4 to automatically screen resumes against job descriptions for hiring accuracy. Deployed on a Kubernetes cluster, it integrates a full ELK stack (Filebeat, Logstash, Elasticsearch, Kibana) to provide robust, real-time logging and system observability.

An end-to-end AI application that automates YouTube SEO analysis using GPT-4 and a professional GitOps pipeline. This project demonstrates how to build a web app and integrate OpenAI, and deploy it onto a Kubernetes cluster using Jenkins, Docker, and Argo CD.

Develop an AI-powered chatbot tailored for Air India, leveraging AWS Bedrock APIs for natural language processing. This chatbot will provide real-time support by answering queries about flight schedules, bookings, and various airline services, utilizing a knowledge base of Air India-related documents

Develop an intelligent chatbot that translates natural language queries into SQL statements, leveraging Generative AI and Retrieval-Augmented Generation (RAG). This chatbot empowers non-technical users to interact with SQL databases, streamlining data access and reducing query response times.

A Neural-Semantic Matching Protocol enables real-time job matching by using AI to understand meaning and context, not just keywords. Using LLMs and Model Context Protocol (MCP), it improves job market interoperability by linking candidates to roles faster and more accurately.

Resume Genie is an AI-powered suite of services designed to enhance job applications, offering features like a resume checker, cover letter generator, resume scorer, and an AI career coach. Built with Streamlit for a user-friendly interface and deployed on AWS EC2, it provides actionable feedback and personalized career guidance.

Develop an automated system for analyzing and processing global mobility applications, streamlining the immigration and relocation process. This project focuses on building a complete ML pipeline to automate key aspects of the application review, leveraging feature engineering, model training, and deployment strategies for efficiency and accuracy.

Develop an intelligent system to analyze audience sentiment from social video comments. The project focuses on collecting, preprocessing, and classifying user comments to understand overall sentiment trends and provide actionable insights for content creators.

The End-to-End Recommender System is a machine learning-based application designed to recommend books to users based on collaborative filtering. The project encompasses a complete MLOps pipeline, including data ingestion, validation, transformation, model training, and a web-based user interface for interaction.

This project aims to build a robust, end-to-end Machine Learning pipeline for predicting the quality of Drinks based on physicochemical tests. It demonstrates a complete ML workflow, emphasizing modularity, reproducibility, and automation.

Develop a robust thunderstorm forecasting system leveraging machine learning models and MLflow for tracking experiments. This project integrates data preparation, model training, hyperparameter tuning, and deployment to predict thunderstorm occurrences, enhancing weather prediction accuracy and enabling proactive safety measures.

An end-to-end ML app that classifies chest diseases from CT scans using CNNs. It covers the full MLOps lifecycle with experiment tracking, pipeline orchestration, model versioning, CI/CD deployment, and a web interface for users.

An end-to-end ML system to detect and classify waste in images/live video using a state-of-the-art object detection model for real-time accuracy. Built with modular design, automated pipelines, and CI/CD for cloud deployment.

This project predicts student academic outcomes using demographic, academic, and behavioral data. It applies ML classification and probabilistic modeling with preprocessing, feature importance analysis, and evaluation to identify at-risk students and support data-driven decisions.

This project develops a machine learning model to predict cancer risk levels (High, Medium, Low) based on demographic, behavioral, and health data. It addresses class imbalance using techniques like SMOTE and optimizes model performance with hyperparameter tuning, providing crucial insights for early detection and intervention.

This project implements an end-to-end object detection workflow using Faster R-CNN, leveraging DVC for reproducible data versioning and automated pipeline orchestration. Training progress and model metrics are visualized through TensorBoard to ensure optimal performance, while the final model is deployed via FastAPI for high-performance inference.

This project simulates a senior ML engineer role by building a scalable network threat detection system. It includes a structured dev setup, ETL pipelines, and full MLOps with MLflow + Dagshub for reproducible experiments, plus MongoDB Atlas for data management and automated ingestion/transformation.