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A gym can have plenty of members and still have a retention problem.
So I took this gym customer dataset and looked at it from a business perspective.
Instead of asking only, "How many customers churned?”
I asked:
What kind of customers are more likely to leave?
What behaviours are linked to retention?
And what can the business identify early enough to act on?
I built this customer retention and churn analysis dashboard to answer these questions.
One thing the analysis made clear is that customer behaviour can reveal warning signs long before a customer is simply counted as “churned.”
That is where I find data analysis more useful, it doesn't just show what happened, but also helps explain why it happened.
If you run a business, what customer behaviour would you want to understand better?
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I analyzed 3M+ job postings across 161 countries and 93K companies to identify hiring trends, in-demand roles, work-mode patterns, and salary insights.
I used Python for data cleaning and preprocessing, SQL for analysis, and Power BI to build an interactive dashboard.
Key areas analyzed:
• Global hiring demand
• Top job roles by demand
• Remote vs. onsite/hybrid work patterns
• Average salary trends
• Monthly hiring trends
This project helped me turn a large dataset into clear, actionable insights for understanding the global job market.
AI-Assisted Sales Intelligence Dashboard
Project Overview
This project is an end-to-end Sales Analytics solution built using Python, SQL, and Power BI.
The workflow includes:
Data Cleaning using Python (Pandas)
Data Storage using SQLite
Business Analysis using SQL Queries
Interactive Dashboard using Power BI
Automated Business Report Generation using Python
The goal of this project is to analyze sales performance, identify profitable products, monitor regional performance, and generate business recommendations.
Dataset
Dataset: Sample Superstore Dataset
Records: 9,994
Features:
Order Details
Customer Information
Product Information
Sales
Profit
Discount
Region
Category
Sub-Category
Tools & Technologies
Python
Pandas
SQLite
SQL
Power BI
Data Visualization
Business Intelligence
Project Workflow
CSV Dataset
↓
Data Cleaning (Python)
↓
SQLite Database
↓
SQL Analysis
↓
Power BI Dashboard
↓
AI Business Report
Dashboard Pages
Executive Overview Total Sales Total Profit Total Orders Profit Margin Regional Sales Analysis Category Performance Analysis
Product Performance Analysis Top Profitable Products Loss-Making Products Profit by Category Sales by Sub-Category
Sales Trends Analysis Sales Trends Over Time Profit Trends Seasonal Performance Insights Key Business Insights West Region generated the highest sales. Technology is the most profitable category. Several products generate significant losses and require pricing review. Sales performance varies significantly across regions. Automated AI Report The project includes a Python-based reporting system that automatically generates:
Total Sales
Total Profit
Business Insights
Strategic Recommendations
Author
Gourav Jangid
Aspiring Data Analyst
Open to Data Analytics Opportunities
PROFESSIONAL SUMMARY
Data-driven professional with strong expertise in Data Analysis, Excel, and SQL. Proficient in building data dashboards, performing complex data cleaning, and deriving actionable business insights. Demonstrated ability to work with large datasets (1400+ records) and create comprehensive analytics solutions. Seeking part-time opportunities to leverage analytical skills while expanding expertise in data-driven decision-making.
TECHNICAL SKILLS
Data Analysis & Visualization: Excel (Advanced), SQL (Intermediate), Data Cleaning, Data Modeling
Excel Expertise: Advanced formulas, VLOOKUP, Pivot Tables, Dashboards, Data Validation, Conditional Formatting
SQL Skills: Query writing, Data extraction, Database analysis
Programming: Python (Basic).
Project Links :
