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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10
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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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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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kaze nesia
Implementing Dynamic Pricing for an E-commerce Platform Developed a dynamic pricing system to adjust prices in real-time based on market demand, competition, and customer behavior. Conducted market research, built pricing algorithms, and integrated them into the platform. Used data analysis to identify optimal pricing strategies and monitored their impact on key metrics. Findings: Increased Revenue: Experienced a 10% increase in revenue after implementing dynamic pricing. Improved Conversion Rate: Achieved a 5% increase in conversion rate by offering more competitive prices. Enhanced Competitive Advantage: Effectively positioned products and captured a larger market share. Identified Customer Segments: Gain insights into different customer segments and their willingness to pay.
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