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ML Engineer with less than a year in Data Analytics & Machine Learning.
Analytical and detail-oriented Machine Learning Developer with hands-on experience in Python, SQL, and Machine Learning. Proficient in extracting insights from large datasets, building predictive models, and creating interactive dashboards to drive business decisions. Developed end-to-end analytics projects including recommendation systems, pollution forecasting, and user behavior analysis. Strong command over data cleaning, feature engineering, and visualization using tools like Pandas and Matplotlib. Adept at communicating insights with clarity and solving real-world problems using data.
PES Institute Of Technology & Management
B.Tech · Computer Science and Engineering
December 1, 2021 – June 1, 2025
Coding Club India
Data Analytics & Web Developer Intern
July 1, 2025 – Present
India
Advanced Ecommerce Recommendation System
June 1, 2025 – Present
• Engineered a content-based recommendation system with results within 100 milliseconds. • Supercharged product recommendations on e-commerce platforms for 1 million products. • Attained a remarkable 98% accuracy rate with NLP Models, including Bag of Words and TF-IDF. • Seamlessly integrated the Amazon product advertising API for enhanced functionality.
Handwritten Digit Recognition
June 1, 2025 – Present
• Achieved 95% classification accuracy on MNIST dataset using optimized K-Nearest Neighbors algorithm. • Reduced prediction latency by 25% through efficient vectorized operations and NumPy optimizations. • Enhanced model performance via GridSearchCV and K-fold cross-validation for hyperparameter tuning. • Built a scalable machine learning pipeline with 30% faster processing on large handwritten image datasets.
Air Quality Prediction
June 1, 2025 – Present
• Built a regression-based pollution forecasting model; enabled early alerts and regional AQI trend analysis. • Boosted performance to 98% using NLP techniques like Bag of Words, TF-IDF, and Word2Vec. • Conducted in-depth data analysis and cleaning, ensuring 98% completeness and consistency. • Validated model inputs with 90% feature analysis for reliable and high-quality predictions.
Image Classification using KNN for Real-Time Face Detection
June 1, 2025 – Present
• Implemented KNN classifier for face identification with consistent 97% accuracy on test data. • Integrated OpenCV and HaarCascades for high-speed frontal face detection under 700 ms. • Achieved under 3% error rate using feature engineering on 1,000+ facial images.
Machine Learning Specialization
Coding Club India
June 12, 2026 – Present
Python for Data Science
Coding Club India
June 12, 2026 – Present
Cultural Fit Analysis
The candidate's personal projects demonstrate a strong initiative and passion for machine learning, which aligns well with an innovative culture. The diversity of projects (e-commerce, digit recognition, air quality, face detection) indicates a broad interest and willingness to tackle different problem domains. The internship experience also shows exposure to web development and data analytics, suggesting adaptability. However, the candidate is still pursuing their bachelor's degree, which means their professional experience is limited to an internship, potentially impacting their readiness for a senior role's cultural demands.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, attention to detail (e.g., 98% accuracy, 98% completeness), and an ability to work with diverse datasets. The internship experience also shows an understanding of user experience and A/B testing, which are valuable for operationalizing ML solutions. However, without direct interview data, assessing collaboration, stress handling, and communication clarity in a team setting is limited.