Nisa Tek
PYTHON DEVELOPER | DATA & AUTOMATION SOLUTIONS
New to Contra
Nisa is ready for their next project!
Global Job Intelligence — Updated Version
I designed and built an automated job-market intelligence platform that collects, cleans, normalizes, deduplicates, and analyzes listings from six global sources through daily data pipelines.
The latest version includes:
• Approximately 20,000 job records
• 93 passing automated tests
• 16 standardized job categories
• Improved parent/child category hierarchy
• More accurate normalization and deduplication
• Expanded filters for company, category, seniority, work mode, salary availability, source, and publication date
• Interactive market intelligence and pipeline-health monitoring
• Downloadable CSV and Excel reports
Built with Python, PostgreSQL, Pandas, Streamlit, Plotly, APIs, Playwright, and GitHub Actions.
This project demonstrates how I transform fragmented web data into reliable, searchable, and decision-ready business intelligence.
Live Dashboard:
https://global-job-intelligence.streamlit.app/
GitHub:
https://github.com/niisa0/global-job-intelligence
Portfolio:
https://niisa0.github.io/
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I’ve expanded my Web Change Monitor project with Playwright support, enabling it to monitor selected content on both static and JavaScript-rendered websites.
The application uses Requests and Beautiful Soup for static pages, while Playwright handles content that appears only after JavaScript runs.
Users can provide a page URL and CSS selector to monitor specific information such as prices, stock availability, product details, or announcements.
Key capabilities:
• URL and CSS selector validation
• Static and JavaScript-rendered page support
• SQLite storage for monitored pages and latest values
• Comparison of current and previous results
• Timestamped change history with old and new values
• Error handling and activity logging
The system can be adapted for price tracking, stock monitoring, competitor research, and other website-monitoring workflows.
Built with Python, Requests, Beautiful Soup, Playwright, SQLite, and Logging.
GitHub: https://github.com/niisa0/web-change-monitor
(https://github.com/niisa0/web-change-monitor)Portfolio: https://niisa0.github.io/
#Python #Playwright #WebAutomation #WebScraping #SQLite
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Built a full Business Analytics & Reporting Platform designed to transform raw sales data into decision-ready insights.
The platform covers the complete analytics workflow — from data cleaning and KPI calculation to interactive filtering, profitability analysis, automated business insights, and downloadable CSV/Excel reporting.
The project is deployed as a live Streamlit web application, allowing users to interact with filters, explore business performance, and generate reports directly in the browser.
Key features include dynamic KPI tracking, revenue and profit trend analysis, product/category/regional performance breakdowns, data quality monitoring, and multi-sheet Excel reports with Executive Summary, KPIs, Product Performance, Regional Analysis, and Filtered Data.
Built with Python, Pandas, Streamlit, Plotly, and OpenPyXL using a modular project structure focused on maintainability and real-world reporting workflows.
Live Demo: https://business-analytics-platform.streamlit.app/
(https://business-analytics-platform.streamlit.app/)GitHub: https://github.com/niisa0/business-analytics-platform
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Customer Retention Intelligence is a deployed interactive analytics application built to turn 7,043 telecom customer records into actionable retention insights.
I designed the project as a modular data product rather than a static dashboard. It combines customer data preparation with dynamic segmentation, a tenure × contract churn risk matrix, churn-driver exploration, priority segment analysis, reported churn reasons, financial exposure metrics, and filtered CSV/Excel exports.
The analysis surfaced a clear contract pattern: observed churn was 45.8% among Month-to-Month customers, compared with 10.7% for One Year and 2.5% for Two Year contracts. These results are presented as observed associations rather than causal claims.
The codebase separates data processing, analytics, visualization, and export logic for maintainability. The application was built with Python, Pandas, Streamlit, Plotly, and openpyxl and deployed as a live Streamlit application.
Live Demo: https://nisa-retention-intelligence.streamlit.app/
Source Code: https://github.com/niisa0/customer-retention-intelligence
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