Data Analytics Project – End-to-End Pipeline (Python, SQL, Power BI)

This project demonstrates a complete, industry standard, end-to-end data analytics workflow—from loading and exploring a dataset in Python, to cleaning and transforming the data, running SQL queries using PostgreSQL, and building an interactive Power BI dashboard. It also includes a final business insights report and presentation.

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🔍 1. Project Overview

The goal of this project is to simulate a corporate-grade end-to-end data analytics workflow, demonstrating the ability to translate raw data into strategic business intelligence through:

• Exploratory Data Analysis (EDA) in Python
• Data cleaning and preprocessing
• Data Analysis (SQL) to simulate business transactions, and run queries to extract insights on customer segments, loyalty, and purchase drivers.
• Data visualization and storytelling in Power BI to highlight key patterns and trends
• Final business recommendations presented through a detailed report and slide deck

This project simulates a practical analytics workflow used by data analysts and data engineers.

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📁 2. Dataset

• Source: Sample dataset provided internally
• Format: CSV

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🛠️ 3. Tools & Technologies

Category	Tools
Programming	Python (Pandas, NumPy, Matplotlib, Seaborn)
Database	PostgreSQL + SQL
Visualization	Power BI
Documentation	Gamma App
Environment	Jupyter Notebook

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🚀 4. Project Steps

Step 1 — Load & Explore Data (Python)

• Loaded dataset using Pandas
• Checked datatypes, missing values, duplicates
• Performed descriptive statistics
• Visualized distributions, correlations, and outliers

Step 2 — Data Cleaning & Preprocessing

• Handled missing values
• Standardized and formatted columns
• Removed inconsistencies and duplicates
• Created engineered features for analysis

Step 3 — SQL Analysis using PostgreSQL

• Created database and imported cleaned dataset
• Wrote SQL queries for:
  • Aggregations
  • Segmentation
  • Time-series performance
  • KPI analysis
• Validated results against Python

Step 4 — Power BI Dashboard

• Connected Power BI to PostgreSQL
• Designed analytics-ready data model
• Built an interactive dashboard with:
  • KPIs
  • Trend charts
  • Category breakdowns
  • Filters and slicers

**Step 5 — Business Insights Report **

• Summarized findings
• Highlighted key KPIs and trends
• Explained data-driven recommendations
• Created a visual slide deck

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📊 5. Dashboard & Results

The Power BI dashboard highlights:

• Top-performing segments
• Sales trends
• User or customer behavior patterns
• Revenue drivers
• Areas for business optimization

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▶️ 6. How to Run Locally

Prerequisites

• Python 3.9+
• PostgreSQL installed
• Power BI Desktop (or Power BI Web + Gateway)
• Jupyter Notebook

🛠️ How to Use This Project

1. Clone the repository

   git clone https://github.com/msdokania/customer_behavior_analysis.git

2. Open Customer_Shopping_Behavior_Analysis.ipynb notebook

   This file contains:

   • Data Import

   • Data exploration

   • Data cleaning

   • Connection to SQL Database

3. Load the data from Python notebook into MySQL/PostgreSQL/MS SQL Server

   • Create a database in SQL

   • Run Python code to load data into SQL database

   • Open customer_behavior_sql_queries.sql

   • Answer Business Questions using SQL Queries

4. Connect the SQL Database to Power BI

   • Open customer_behavior_dashboard.pbix

   • Create interactive dashboard in Power BI

5. Create Project Report and Presentation

   • Create project report

   • Build presentation deck using Gamma AI