Data Science Enthusiast · Python Developer · ML Learner
Building intelligent systems that make sense of data.
Hello! I'm Malkit Choudhary, a Data Science enthusiast and Python developer who loves turning raw data into meaningful insights.
Currently pursuing BCA while diving deep into Machine Learning, Deep Learning, and building real-world AI-powered applications that solve actual problems.
Object oriented logic & analytics algorithms
Advanced structures and algorithmic efficiency
Highly scalable REST API structures
Asynchronous web structures & dashboards
Semantic page structures and accessible markup
Responsive layouts, animations, and visual systems
Fast responsive UI grids and component styling
Structured table cleanups and data wrangling
Array maths, numerical logic, and vector operations
Charts, trend views, and exploratory visuals
Statistical plots and polished data visualizations
Exploratory analysis and insight discovery
Cleaning, encoding, scaling, and pipeline readiness
Creating stronger predictors from raw data
Predictive models, training workflows, and evaluation
Supervised classification, regression networks
High-level neural network model development
Multi-layer dense and recurrent neural nets
Token cleaning, text pipelines, and language features
Feature extraction and cosine text similarities
Deep learning training and model workflows
Dynamic dashboards & transactional reporting
Data joining, relational tables, schema designs
VLOOKUP, Pivot reports, statistical logic
CI/CD deployment and version controls
Data structures, problem solving, and algorithms
Distributions, metrics, and analytical reasoning
User-item similarity and recommendation logic
ML
Classifies skin conditions by combining image analysis with patient details to support faster, more informed screening.
ML
Recommends relevant research papers using text similarity, helping users discover papers aligned with their interests.
ML
Predicts likely health conditions from patient information through a machine-learning model for preliminary decision support.
ML
ML
ML
DL
Power BI
Built an interactive Power BI dashboard to track revenue, profit margin, regional sales, product performance, and monthly growth trends for fast executive decision-making.
Designed a customer analytics report with churn rate, retention cohorts, high-risk segments, revenue leakage, and drill-through pages to identify customers likely to leave.
Power BI
Created a finance dashboard covering revenue, expenses, variance analysis, YoY growth, budget vs actuals, and forecasting visuals using Power Query and DAX measures.
Certified in data tracking, audience analysis, and performance measurement using Google Analytics, with practical understanding of user behavior, traffic insights, and data-driven decision-making strategies.
Completed a comprehensive Deep Learning and Artificial Intelligence program with hands-on projects, covering neural networks, model building, and real-world problem-solving using modern AI techniques.
Completed a real-world data analytics simulation involving data cleaning, exploratory analysis, experimentation design, and generating actionable business insights using relevant tools and techniques.
Completed a Deloitte data analytics job simulation involving data analysis and forensic investigation. Gained experience deriving insights and solving problems using data-driven approaches.
Successfully completed Claude 101 certification by Anthropic, covering AI workflows, prompt engineering, and real-world generative AI applications with hands-on practical learning experience.
Completed Kaggle’s Intro to Machine Learning course, learning core ML concepts, data handling, model training, and prediction techniques using Python for real-world AI applications.
JUN 2024 — JUN 2026 · CURRENT
Building end-to-end ML and DL pipelines on real-world datasets, from preprocessing and EDA to production deployment.
JUN 2024 — JUN 2027
Coursework across Python, data structures, statistics, databases, OOP and web technologies.
MARCH 2026
Customer behaviour analysis, experimentation and visual storytelling for data-informed decisions.
2026
Data analysis and forensic investigation through a practical virtual internship.
Kaggle Competition
Ranked in the competitive tier of the Kaggle Playground Series S6E7 — Predicting Student Health Risk, achieving a 94.70% accuracy score.
I'm open to internships, collaborations, freelance work, and exciting data science projects. Let's connect!