Gourav Jangid
Data Analyst | Power BI, Excel, SQL & Python
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:
Author
Gourav Jangid
Aspiring Data Analyst
Open to Data Analytics Opportunities
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This project is an interactive Power BI dashboard developed to analyze sales performance, profitability, customer segments, and regional trends.
Key Metrics
Total Sales: $2.30M
Total Profit: $286.40K
Total Quantity Sold: 38K+
Key Insights
West region generated the highest sales and profit.
Consumer segment contributed the highest revenue and profit.
Technology category showed strong business performance.
Tools Used
Power BI
Data Visualization
Business Analytics
KPI Reporting
Skills Demonstrated
Dashboard Design
Data Storytelling
Business Intelligence
KPI Analysis
Data Analytics
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📌 Project Overview
This project analyzes unemployment data in India using Python to understand unemployment trends, regional differences, rural vs urban patterns, and the impact of COVID-19.
🎯 Objectives
Analyze unemployment rate trends over time
Compare unemployment between Rural and Urban areas
Identify regions with high and low unemployment
Investigate the impact of COVID-19 on unemployment
Identify monthly patterns
Generate useful insights from the data
🛠️ Tools & Technologies
Python
Pandas
NumPy
Matplotlib
Google Colab
📊 Key Findings
Average unemployment rate: 11.79%
Rural and Urban unemployment rates were compared
Tripura had the highest average unemployment rate at 28.35%
Meghalaya had the lowest average unemployment rate at 4.80%
Pre-COVID average unemployment: 9.51%
COVID-period average unemployment: 17.77%
Unemployment increased by 8.26 percentage points during the COVID period
May 2020 recorded the highest monthly average unemployment rate: 24.88%
📈 Analysis Performed
Data cleaning
Exploratory Data Analysis (EDA)
Rural vs Urban comparison
Regional analysis
Time-series trend analysis
COVID-19 impact analysis
Monthly pattern analysis
Correlation analysis
📁 Files
unemployment_analysis.ipynb - Complete analysis notebook
Unemployment in India.csv - Original dataset
cleaned_unemployment_data.csv - Cleaned dataset
🎓 Internship
This project was completed as part of the Data Science Internship.
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Project Overview
This project analyzes Netflix's content catalog using Python and exploratory data analysis (EDA) techniques.
The objective is to uncover trends in content types, countries, ratings, genres, and content growth over time.
Tools Used
Python
Pandas
NumPy
Matplotlib
Google Colab
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