Projects using Matplotlib
Projects using Matplotlib
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Venkata Anirudh Parakala
Geographical Understanding of Twitter Health Topics using NLP
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7
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Uthara
Allergy Prediction Model Development
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11
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Aryan Kushwah
NASDAQ Momentum Trading Strategy
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7
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Aaron Segiel
CIFAR-10 Image Classification with CNN
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8
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Anastasiya Kotelnikova
Spiking Neural Networks with PyTorch & Norse
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4
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Júlio Silva
Movies Data Scraping and Analysis
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10
3
Ifigeneia Tsiflidou
Revenue Prediction Model Development
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25
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Sharles Maurício Mariano
Personality Type Mapping with Machine Learning
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5
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Ravi Mahawar
Fractal Image Generation with OpenMP and MPI
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5
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Eyad Gad
Image Processing and Analysis of the Scratch Assay
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3
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Persia Cooper
Netflix Data Analysis
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1
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hussein hafez
Fraud Detection on Credit Card Transactions
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4
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Uchenna Ejike
Global Power Plant Database Analysis
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3
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Ismael Charpentier
Hopefield Neural Network
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10
2
Yusuf Adetona
1M+ Row Migration ROI: SQL Server to PostgreSQL I engineered a Python ETL pipeline to migrate legacy SQL Server data to PostgreSQL, prioritizing cost-efficiency and absolute integrity. The Problem: High licensing costs and a need for cloud-scalability without risking the "Source of Truth." The Outcomes: 🔹Savings: Projected 65% reduction in annual licensing fees 🔹Integrity: 1,055,008 rows reconciled with zero violations 🔹Audit: Flagged 24,000+ quality issues (negative prices, invalid emails) during the move The Impact: Shifted from reactive maintenance to proactive cloud-scaling on AWS/Azure with a cleaner, more reliable database. Why Me? As an Accountant & Data Engineer, I bring "ledger-style" reconciliation to architecture. I don’t just move data; I audit it. Open for Migration contracts. Let’s cut your costs.
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Molka B
AI Bias Detection & Mitigation Tool for Hiring Models My role: AI/ML Engineer & Fairness Auditor Project description: AI Bias Detection and Mitigation Framework for Recruitment Systems. Built a complete, production-ready tool that: Detects gender, race, and age bias in hiring ML models Uses industry-standard fairness metrics (Disparate Impact, Demographic Parity, Equal Opportunity) Applies Reweighing mitigation (AIF360) → improves fairness while keeping accuracy loss < 2% Includes interactive Gradio demo for live testing Full business impact section + compliance notes (EEOC 4/5th rule, GDPR, EU AI Act)
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