23 projects found
Created a music recommendation system based using Big Data for musics's metadata, wav recordings, waveforms, lyrics, sentiments scores, etc for precise recommendations.
Built a full-stack application that scans barcodes or nutritional labels of packaged food items and evaluates whether the food is good for you, personalizing recommendations with your diet plan. Allows users to create and follow personalized diet plans with intelligent nutritional analysis and recommendations.
Built a social media platform for civic issues for transparency and accountability. Developed a robust and dynamic AI workflow engine to automate the process of issue resolution and administration.
Created a music genre classifier using CNN and RNN models using waveform and .wav files. Hybridized with Linguistical analysis for better accuracy.
Built a lightweight database system with CRUD operations. Efficient search with B+Trees, Sharding and Replication.
Developed a full-stack medical record management system. Added common format of medical document strategy with a smart NER (Named Entity Recognition) + CV (Computer Vision) parser.
Analyzed air quality data for Delhi-NCR using statistical models. Ground CPCB data, Sentinel-5P Google Earth Engine data, hyperlocal context data from OSMX and weatherdata from IMD for analysis and making a dashboard for better visualization.
Compared multiple ML modeling techniques like LightGBM with robust feature engineering methodologies like ReliefF for getting an accuracy of 67.6%. Kaggle competition winner achieved 69.55% accuracy.chine
Created interactive Tableau dashboards for YouTube data. Performed comprehensive data visualization and exploratory data analysis on video metrics, channel performance, engagement rates, and trending patterns to derive actionable insights.
Built a system to anonymize personally identifiable information (PII). Implemented NER (Named Entity Recognition) models using deep learning techniques to detect and mask sensitive data like names, addresses, phone numbers, and social security numbers while preserving data utility.
Developed an algorithm from scratch for efficient timetable scheduling. Designed a constraint-based optimization system to automatically generate conflict-free schedules for multiple courses, classrooms, and time slots while maximizing resource utilization.
Applied NLP techniques to identify topics in large text corpora. Utilized Latent Dirichlet Allocation (LDA) and BERT-based embeddings for unsupervised topic discovery, enabling automated content categorization and trend analysis across extensive document collections.
Built a deep learning model to generate image captions. Implemented an encoder-decoder architecture combining CNN for image feature extraction and LSTM/Transformer networks for natural language generation, achieving contextual and accurate image descriptions.
Implemented a YOLO-based drowsiness detection system. Developed a real-time computer vision application using YOLO object detection and facial landmark recognition to monitor eye closure patterns and head pose, triggering alerts for driver fatigue prevention.
Developed a web app for sentiment analysis of text data. Integrated web scraping capabilities to extract blog content and applied NLP models including BERT and VADER sentiment analyzers to classify text as positive, negative, or neutral with confidence scores.
Created a predictive model for estimating apartment rental prices. Trained multiple regression models including Random Forest, XGBoost, and Linear Regression on features like location, size, amenities, and neighborhood data to accurately predict rental prices.
Built an ML model to detect spam comments on YouTube videos. Implemented text classification using Naive Bayes, SVM, and neural networks with TF-IDF and word embeddings to identify and filter spam comments with high precision and recall.
Developed a full-stack cafe management system. Built from scratch with core programming principles, implementing inventory management, order processing, staff scheduling, and sales reporting features for efficient cafe operations.
Designed a system to recommend jobs based on Glassdoor profiles. Utilized collaborative filtering and content-based recommendation algorithms with NLP for job description analysis and similarity matching to provide personalized job recommendations to users.
Built a machine learning model to predict car prices based on features. Applied feature engineering and trained regression models including Gradient Boosting and Random Forest on vehicle attributes like brand, model, year, mileage, and condition to estimate market prices accurately.
Developed insights into iNeuron's product sales and customer behavior. Conducted comprehensive data analysis using pandas and visualization libraries to identify sales trends, customer segmentation patterns, product performance metrics, and revenue optimization opportunities.
Performed exploratory data analysis on sales and market trends. Analyzed historical sales data, seasonal patterns, and regional performance using statistical methods and data visualization to identify growth opportunities, optimize inventory, and understand market dynamics.
Analyzed student spending patterns using statistical methods. Performed descriptive and inferential statistics to identify spending categories, budget allocation trends, and financial behavior patterns, providing insights for financial planning and resource allocation strategies.