Machine Learning Enthusiast with strong programming skills in Python. Area of Interest includes Artificial Intelligence, Machine Learning, Deep Learning
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AI-Job-Copilot
May 3, 2026 – Present
Land your dream job with AI-powered resume analysis, tailoring, interview prep, and application tracking.
View ProjectAI-Travel-Agent
June 17, 2024 – July 25, 2024
AI Travel Agent with MariaDB, FastAPI, Next.js, & MindsDB
View ProjectRecommender
October 16, 2022 – June 9, 2024
Build a recommendation engine using Django & a Machine Learning technique called Collaborative Filtering.
View ProjectDeploy-Django-into-Production-with-Kubernetes-Docker-Github-Actions
March 2, 2022 – June 18, 2022
Deploy-Django-into-Production-with-Kubernetes-Docker-Github-Actions — GitHub repository
View ProjectVideo-Membership-Webapp
December 24, 2021 – February 24, 2022
Building a membership application using FastAPI and a managed Cassandra database called AstraDB
View ProjectHome-Credit-Default-Risk
December 8, 2018 – December 9, 2018
In this notebook, we will take an initial look at the Home Credit default risk machine learning competition currently hosted on Kaggle. The objective of this competition is to use historical loan application data to predict whether or not an applicant will be able to repay a loan.
View ProjectClassification-of-Iris-flowers
October 13, 2018 – October 13, 2018
My First Machine Learning Project . Based on Multi Class Classification of iris flowers
View ProjectMachineLearningPythonAndR
August 24, 2018 – October 12, 2018
Implementation of different Machine Learning Algorithms or Models in Python and R
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic in nature, focusing on foundational machine learning concepts. While demonstrating initiative, there is no information regarding collaborative work, team environments, or contributions to open-source projects, making it difficult to assess cultural fit for a senior role. The project diversity is good within the ML domain, but lacks depth in real-world, production-grade data science applications.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are brief, and there are no completed psychometric or English tests.