Interactive Website, Data Analysis, AI/Machine Learning Model
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
• Engineered and deployed a 5-page interactive website using HTML5, JavaScript, and Bootstrap, configured hosting infrastructure through GoDaddy and Cloudflare.
• Researched and compiled data on 100+ U.S. medical practitioners to build a structured prospect dataset.
• Cleansed and validated clinical data using MS Excel and Google Sheets, improving data accuracy and usability to 95%.
• Monitored and automated website performance, DNS, and SSL/TLS infrastructure using Cloudflare and cPanel,saving 10+ hours per week. Analyzed 500K+ financial transactions to build an AI-powered tax fraud detection and risk monitoring system.
• Led a team and engineered 27 data features and optimized machine learning classification models to enhance
anomalous behavior detection.
• Achieved 88% accuracy in identifying high-risk taxpayers through advanced predictive modeling.
• Developed a scalable analytics pipeline using PySpark for distributed data processing and an interactive
Streamlit dashboard with 7 analytical views with KPIs, Tables and Graphs for real-time compliance monitoring. Built an end-to-end ETL data analytics pipeline with 500K+ transaction records for customer purchasing insights.
• Preprocessed the dataset, performed an 80/20 train-test split, and conducted EDA and feature engineering.
• Designed customer segmentation ML models using RFM analysis to classify users into 3 behavioral groups.
• Improved decision-making and strategic planning by delivering classification Machine Learning models with a 92% prediction accuracy
• Researched and compiled data on 100+ U.S. medical practitioners to build a structured prospect dataset.
• Cleansed and validated clinical data using MS Excel and Google Sheets, improving data accuracy and usability to 95%.
• Monitored and automated website performance, DNS, and SSL/TLS infrastructure using Cloudflare and cPanel,saving 10+ hours per week. Analyzed 500K+ financial transactions to build an AI-powered tax fraud detection and risk monitoring system.
• Led a team and engineered 27 data features and optimized machine learning classification models to enhance
anomalous behavior detection.
• Achieved 88% accuracy in identifying high-risk taxpayers through advanced predictive modeling.
• Developed a scalable analytics pipeline using PySpark for distributed data processing and an interactive
Streamlit dashboard with 7 analytical views with KPIs, Tables and Graphs for real-time compliance monitoring. Built an end-to-end ETL data analytics pipeline with 500K+ transaction records for customer purchasing insights.
• Preprocessed the dataset, performed an 80/20 train-test split, and conducted EDA and feature engineering.
• Designed customer segmentation ML models using RFM analysis to classify users into 3 behavioral groups.
• Improved decision-making and strategic planning by delivering classification Machine Learning models with a 92% prediction accuracy
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