The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Built an end-to-end E-Commerce Sales & Customer Analytics project to turn raw sales data into actionable business insights. Cleaned and transformed data using Python, Pandas, and NumPy, analyzed sales and customer performance with SQL Server, and developed an interactive Power BI dashboard.
The project analyzes revenue, profit, customers, products, categories, regions, sales trends, and key performance indicators (KPIs) to identify business trends and performance opportunities.
Tools: Python, Pandas, NumPy, SQL Server, Excel, Power BI
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Challenges
View all
Related posts
Global Superstore Sales Performance Analysis — 51,290 Transactions Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Power BI, DAX • Cleaned and validated a 51,290-row transactional dataset; performed exploratory data analysis in Python to evaluate sales, profit, discount, product, and regional performance. • Built a 3-page interactive Power BI dashboard with 13 DAX measures covering KPI tracking, regional performance, sales trends, and profitability analysis. • Identified underperforming categories and discount-driven profit erosion, translating findings into actionable business insights.
Finding the right job often requires manually comparing a resume against dozens of job descriptions.
This project automates that process by:
Analyzing a resume against multiple real job postings
Generating AI-powered match scores
Identifying missing skills
Providing resume improvement suggestions
Storing results in a SQL database
Visualizing insights in Power BI
Offering a live Streamlit web application
🛠️ Tech Stack
Python
Pandas
SQLite
SQL
Google Gemini AI API
Streamlit
Power BI
VS Code
📊 Key Features
AI Resume Analysis
Resume-to-job match scoring (0–100%)
Missing skill identification
AI-generated recruiter feedback
Resume improvement recommendations
Database & SQL
Stores analyzed jobs in SQLite
Performs analytical queries
Identifies skill gaps
Finds best-fit companies and locations
Power BI Dashboard
Average Match Score
Strong Fit Jobs
Top Match Score
Company Match Analysis
Skill Gap Analysis
Streamlit Application
Paste any job description and instantly receive:
Match Score
Missing Skills
AI Feedback
Resume Improvement Tips
📈 Dashboard Insights
The dashboard provides:
Overall resume strength across jobs
Best matching companies
Skill gap analysis
Job fit distribution
Career improvement opportunities
🎯 Business Value
This project demonstrates:
Data Cleaning
Data Analysis
SQL Querying
AI Integration
Dashboard Development
Data Visualization
End-to-End Analytics Workflow
🔮 Future Improvements
Analyze 100+ job postings
Automated LinkedIn job ingestion
Multiple resume comparison
Industry-specific recommendations
Advanced skill trend tracking
👨💻 Author
Gourav Jangid
Aspiring Data Analyst passionate about transforming data into actionable insights using Analytics, SQL, Python, Power BI, and AI.
Awesome use case! Automating resume-job matching saves so much time.
Built an interactive Power BI dashboard to analyze Netflix content, genres, countries, ratings, and release trends. Used data cleaning, data modeling, DAX, and interactive visualizations to transform raw data into meaningful insights.
