Projects using Python in India
Projects using Python in India
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Aman Mollah
Web Scraper & Automation
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25
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Himanshu Kumar
Workflow Management System for Cascade Water Services
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Subhradip Roy
Clearbook : AI powered Application to Automate Data Analytics
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Parth Desai
YouTube Data Extractor | Simplify Your Video Data Collection
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Ritik Kansal
Pro
Gymed - A Comprehensive Fitness App
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Aishwary Dhare
Pro
Migration of ETL Pipelines to Apache Airflow
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Chhavi Verma
Hi Contra community! Just wrapped a rewarding project for Nauvyashree—delivered deep-dive EDA, custom visualizations, and rich survey sentiment analysis across four datasets. Each milestone included well-designed PDFs packed with clear charts and approachable explanations—making insights accessible for all audiences. I went the extra mile with collaborative Google Meet sessions to walk the client through every analysis, helping connect the findings to big-picture goals. Feedback was fantastic, and milestone-based payments made the process smooth and transparent. This project really strengthened my ability to turn complex results into stories that empower client decision-making. If you want visually engaging, client-focused analytics—or need easy-to-follow reports and hands-on walkthroughs for your next project—let’s connect!
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HEMANTH KUMAR J
:I am a QA Automation Engineer with 3 years of experience in web application testing. Skilled in Robot Framework. I help businesses automate their testing process efficiently.
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25
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Rachit Bedi
ChatGPT Prompt Engineer
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Rishabh Bhandari
Key Phrase Extractor
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Hindol Banerjee
Dropout Analysis
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Hemant Manglani
Recent completed the e-commerce fashion brand project using Django and Reactjs hosted on AWS.
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Eswar T
RAG Based Chatbots
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Karan Singh
Scientific Image Forgery Detection — Kaggle Competition Participated in the ongoing Kaggle competition on Copy-Move Forgery Detection in Scientific Images, aimed at identifying manipulated biomedical figures that can compromise research integrity. For this challenge, I developed a ResNet50 + U-Net hybrid segmentation model using PyTorch, designed to detect and segment forged regions at the pixel level. My approach combines Dice and Focal losses for balanced training, WeightedRandomSampling to oversample forged images, and Test-Time Augmentation (TTA) to improve prediction robustness. Achieved an initial score of 0.303 on the public leaderboard. I’m continuing to experiment with architecture tuning, learning rate schedules, and other loss functions to further enhance performance and generalization.
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Utkarsh Trivedi
FileLingo
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Rohit P.
Hand Gesture Video Control | Computer Vision
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38
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