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customer-behaviour-analysis

data analytics project showcasing customer behaviour analysis using power bi ,sql and python Here’s a clean, professional, recruiter-friendly README.md you can directly paste into your project:


📊 Data Analytics End-to-End Project

🔎 Overview

This project demonstrates a complete end-to-end Data Analytics workflow — from raw data processing to business insights and dashboard reporting.

The objective of this project is to:

  • Load and process raw data using Python
  • Perform Exploratory Data Analysis (EDA) and data cleaning
  • Store and query data using SQL (PostgreSQL)
  • Build an interactive Power BI dashboard
  • Generate a structured analytical report
  • Create a presentation using Gamma

This project showcases practical skills in data analysis, SQL querying, business intelligence, and reporting.


📁 Dataset

  • Source: (https://surl.li/aspzsj)

  • Format: CSV

  • Size: (e.g., 10,000+ records)

  • Features include:

    • Numerical variables
    • Categorical variables
    • Date/time columns
    • Business KPIs

The dataset was cleaned and structured before analysis.


🛠 Tools & Technologies Used

Category Tools
Programming Python
Libraries Pandas, NumPy
Database PostgreSQL
SQL Joins, Aggregations, CTEs, Window Functions
Visualization Power BI
Reporting MS Word / PDF
Presentation Gamma

⚙️ Project Workflow / Steps

1️⃣ Data Loading (Python)

  • Imported dataset using Pandas
  • Inspected structure and data types
  • Checked missing values and duplicates

2️⃣ Exploratory Data Analysis (EDA)

  • Statistical summary
  • Distribution analysis
  • Correlation analysis
  • Outlier detection
  • Trend and pattern discovery

3️⃣ Data Cleaning

  • Handled missing values
  • Removed duplicates
  • Standardized formats
  • Feature engineering where required

4️⃣ SQL Analysis

  • Loaded cleaned data into database

  • Wrote optimized SQL queries:

    • Aggregations (SUM, AVG, COUNT)
    • Joins
    • GROUP BY analysis
    • Subqueries
    • Window functions
  • Extracted business insights using SQL

5️⃣ Power BI Dashboard

  • Connected Power BI to SQL database
  • Built interactive dashboards
  • Created KPIs and visual insights
  • Implemented filters and slicers
  • Designed clean, business-ready visuals

6️⃣ Reporting & Presentation

  • Structured business report summarizing:

    • Key insights
    • Trends
    • Performance metrics
  • Created presentation slides using Gamma for stakeholder communication


📊 Dashboard Highlights

The Power BI dashboard includes:

  • KPI summary cards
  • Revenue / performance trends
  • Category-wise breakdown
  • Geographic analysis (if applicable)
  • Interactive filters
  • Drill-down functionality

The dashboard enables decision-makers to quickly interpret data and identify actionable insights.


📈 Key Results & Insights

  • Identified top-performing categories/products
  • Discovered trends and seasonal patterns
  • Highlighted performance gaps
  • Provided data-driven recommendations

This project demonstrates the ability to translate raw data into meaningful business insights.


▶️ How to Run the Project

1. Clone the Repository

git clone <repository-link>
cd <project-folder>

2. Install Dependencies

pip install -r requirements.txt

3. Run Python Scripts

python eda_analysis.py

4. Setup Database

  • Create database in PostgreSQL / MySQL / SQL Server
  • Import cleaned dataset
  • Run SQL queries provided in /sql folder

5. Open Power BI

  • Open .pbix file
  • Update database connection if needed
  • Refresh data

🎯 Skills Demonstrated

  • Data Cleaning & Preprocessing
  • Exploratory Data Analysis
  • SQL Query Optimization
  • Business Intelligence Reporting
  • Dashboard Design
  • Data Storytelling
  • End-to-End Analytics Workflow

📌 Conclusion

This project reflects a production-style data analytics pipeline — integrating Python, SQL, and Power BI to deliver business insights in a clear, structured, and decision-oriented manner.

It demonstrates both technical depth and business understanding, making it suitable for Data Analyst / Business Intelligence roles.


If you want, I can now:

  • ✨ Customize this README for a specific dataset (Sales / HR / Finance / Healthcare)
  • ✨ Make a stronger “resume impact” version
  • ✨ Add GitHub badges and visuals
  • ✨ Convert this into a professional portfolio description

Tell me which one you want.

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