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Telco Customer Churn Analysis — SQL & Python
Analyzed customer data to identify the key factors associated with churn and understand which customer groups were most likely to leave. Used SQL Server for data extraction and Python for data cleaning, exploratory analysis, segmentation, and visualization, translating the findings into actionable customer-retention insights.
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Built an end-to-end e-commerce BI solution using SQL and Power BI. Cleaned and modeled website session, pageview, order and refund data, developed business KPIs and interactive dashboards to analyze sales, customer behavior, conversion, and marketing performance.
great work💯
Trading Analytics & Self-Learning System
A data-driven analytics and self-learning module built for an automated Telegram trading system.
The system continuously records trading signals and completed trades, analyzes historical performance, and provides real-time statistics directly through Telegram.
It tracks win rate, total and average PnL, best and worst trades, signal distribution, confidence scores, and trading performance over time.
Key features:
Automated trade history tracking
Real-time PnL calculation
Win rate monitoring
BUY / SELL / HOLD signal statistics
Confidence score analysis
Historical performance evaluation
PostgreSQL data storage
Telegram reporting interface
Automated signal logging
This module was developed as part of a larger Python-based automated market analysis and demo trading platform.
Superstore Sales Performance Dashboard | Python + Streamlit
I recently worked on a complete data analysis and visualization project using the Superstore dataset.
The goal was to transform raw data into a clear and interactive dashboard that makes business performance easier to understand and explore.
Data Cleaning & Preparation
Cleaned and prepared the dataset using Python in Jupyter Notebook
Handled data inconsistencies and prepared the data for analysis
Created the necessary transformations and metrics
Data Analysis
Sales and revenue analysis
Order and customer analysis
Customer segmentation
Product and category performance
Temporal and seasonal analysis
Identification of top-performing and loss-making products
Interactive Dashboard
I developed the dashboard using Python + Streamlit, with interactive filters for:
Date
Category
Sub-category
Segment
Region
The dashboard includes KPIs such as revenue, number of orders, average basket value, and number of customers, along with interactive charts and detailed breakdowns.
Tools & Technologies:
Python • Pandas • NumPy • Jupyter Notebook • Streamlit • Plotly
This project was a great exercise in turning raw business data into actionable insights through an interactive data product.
#DataAnalysis #Python #Streamlit #Pandas #DataScience #Dashboard #DataVisualization #JupyterNotebook #Analytics
