Data Visualization Projects in Antalya

Data Visualization Projects in AntalyaData Visualization Projects in Antalya

Cover image for Built a full Business Analytics

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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Cover image for Developed an automated e-commerce analytics

Developed an automated e-commerce analytics dashboard using Python and data visualization tools. The system analyzes sales performance, revenue trends, product statistics, and customer behavior to generate interactive dashboards and automated reports for faster business insights.

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Cover image for Customer Retention Intelligence is a

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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