About me
ML Specialist with a Bachelor's degree in AI engineering and Data Science. specializing in transforming data into actionable insights through statistical analysis, machine learning, and data visualization. Passionate about solving real-world problems with data and continuously improving my skills in analytics, predictive modeling, and AI technologies.
I enjoy building intelligent systems, deploy ML models, and contributing to projects that create measurable value through data-informed decision-making.
Currently Focusing On
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Advanced Machine Learning: Deepening my understanding of ML, model optimization, feature engineering, ensemble methods, and modern deep learning techniques.
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MLOps & Machine Learning in Production: Learning how to build, deploy, monitor, and maintain scalable machine learning systems using industry best practices, automation workflows, model versioning, and cloud-based infrastructure.
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Applied Maths: Strengthening my foundations in linear algebra, calculus, probability, statistics, and optimization to better understand machine learning algorithms and improve analytical problem-solving skills.
Projects Done
Food Delivery Intelligence System with Predictive Analytics
Abstract:
In this project, we developed an end-to-end data science pipeline for a food delivery dataset containing over 15,000 records. The workflow includes data cleaning, exploratory data analysis (EDA), and predictive modeling. Regression models were used to predict delivery time, while classification models were applied to determine whether an order is likely to be canceled. In addition, an interactive Streamlit dashboard was built to visualize insights and allow real-time interaction with the predictive models.
Keywords:
Food Delivery Analytics, Machine Learning, Business Intelligence
Links:
View Project Report • GitHub • Live Dashboard
Data-Driven E-commerce Intelligence: Customer Analytics and Revenue Forecasting System
Abstract:
This project presents an end-to-end e-commerce analytics and forecasting platform built using a synthetic Amazon-style dataset of 100,000 transactions (2020–2024). The system integrates sales analysis, customer segmentation, product analytics, and revenue forecasting within an interactive Streamlit dashboard. Among four evaluated models, XGBoost achieved the best performance (RMSE: 61,570, MAPE: 3.71%). The forecasts highlight stable but stagnant revenue trends through 2026, suggesting the need for strategies such as market expansion, discount optimization, and customer retention programs.
Keywords:
E-commerce Analytics, Time Series Forecasting, Business Intelligence
Links:
View Project Report • GitHub • Live Dashboard
Robustness Analysis of Deep Learning Models Under Controlled Image Corruptions
Abstract:
This study evaluates the robustness of deep learning models (MLPs, CNNs, and ResNets) under different image corruption conditions. The models were tested on CIFAR-10 using various real-world corruptions such as noise, blur, and compression at different severity levels. Performance was measured using accuracy, precision, recall, F1-score, and robustness-specific metrics such as accuracy drop and sensitivity. Results show that while deeper models perform better on clean data, they are less stable under corrupted inputs compared to simpler architectures.
Keywords:
Robustness, Image Corruption, CIFAR-10, Deep Learning, CNN
Links:
View Project Report • GitHub
OpenSource-connect
About the platform:
Where Developers Build the Future Together, A platform where developers can post projects and find contributors by list required roles, and manage contributor applications.
Startify
About the platform:
Startify is a collaborative platform designed for innovators and entrepreneurs to post their SaaS, business, and startup ideas, receive valuable feedback, and find collaborators to help bring their products to life. The platform emphasizes community interaction, real-time communication, and comprehensive user and content management to foster productive engagement and innovation.
API-hub
About the platform:
Marketplace platform for sharing and discovering APIs, it enables users to explore, review, and interact with APIs shared by other developers.