customer_behaviour_analysis

📊 Data Analytics Project

Overview

This project demonstrates an end-to-end Data Analytics workflow, starting from raw dataset loading and data cleaning to exploratory data analysis, SQL-based analysis, and interactive dashboard creation.

The project uses Python, SQL, PostgreSQL/MySQL/SQL Server, and Power BI to transform raw data into meaningful business insights. A detailed report and presentation were also created to communicate the findings effectively.

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📁 Dataset

The project uses a structured dataset containing relevant business/customer/sales information.

The dataset was:

• Loaded and explored using Python
• Checked for missing and duplicate values
• Cleaned and transformed for analysis
• Used for SQL-based analysis
• Connected to Power BI for dashboard development

Dataset: Add your dataset name or source here.

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🛠️ Tools & Technologies

Tool	Purpose
Python	Data loading, cleaning & EDA
Pandas	Data manipulation and analysis
NumPy	Numerical operations
Matplotlib / Seaborn	Data visualization
PostgreSQL / MySQL / SQL Server	SQL analysis & querying
Power BI	Interactive dashboard & visualization
GitHub	Project documentation & version control

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🔄 Project Steps

1. Data Loading

The dataset was imported into Python using Pandas and initially inspected to understand:

• Dataset structure
• Number of rows and columns
• Data types
• Missing values
• Duplicate records

2. Exploratory Data Analysis (EDA)

EDA was performed to identify patterns, trends, relationships, and potential issues in the data.

Key activities included:

• Descriptive statistics
• Distribution analysis
• Categorical analysis
• Correlation analysis
• Visualization of important variables

3. Data Cleaning

The raw dataset was cleaned and prepared for further analysis.

Major steps included:

• Handling missing values
• Removing duplicate records
• Correcting data types
• Standardizing column values
• Handling inconsistent data
• Preparing the final dataset for SQL and Power BI

4. SQL Analysis

The cleaned data was loaded into PostgreSQL / MySQL / SQL Server.

SQL queries were written to answer business-related questions and extract meaningful insights.

The analysis included:

• Aggregations
• Filtering and sorting
• GROUP BY
• HAVING
• JOIN
• Subqueries
• Common Table Expressions (CTEs)
• Window functions
• Business KPI calculations

5. Power BI Dashboard

An interactive Power BI dashboard was created to present the most important findings.

The dashboard includes:

• Key Performance Indicators (KPIs)
• Charts and graphs
• Category-wise analysis
• Trend analysis
• Filters and slicers
• Interactive visualizations

6. Report Creation

A detailed project report was prepared covering:

• Business objective
• Dataset overview
• Data preparation
• EDA findings
• SQL analysis
• Dashboard insights
• Final conclusions

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📊 Dashboard

The Power BI dashboard provides an interactive view of the analyzed data and allows users to explore different business metrics and trends.

Power BI File: data analysis project.pbix

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📈 Results & Key Insights

The analysis helped identify important trends and patterns from the dataset.

Key outcomes include:

• Identified major trends and performance patterns
• Analyzed important business/customer/sales KPIs
• Compared performance across different categories
• Identified high-performing and low-performing segments
• Used SQL to answer business-driven questions
• Created an interactive dashboard for easier decision-making

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📂 Project Structure

Data-Analytics-Project/
│
├── Dataset/
│   └── dataset.csv
│
├── Python/
│   └── data_analysis.ipynb
│
├── SQL/
│   └── analysis_queries.sql
│
├── PowerBI/
│   └── dashboard.pbix
│
├── Report/
│   └── project_report.pdf
│
└── README.md

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▶️ How to Run

Step 1 — Clone the Repository

git clone https://github.com/Preeti422/customer_behaviour_analysis

Step 2 — Open the Python Notebook

Open the .ipynb file using:

• Jupyter Notebook
• JupyterLab
• VS Code

Install the required Python libraries if needed:

pip install pandas numpy matplotlib seaborn

Step 3 — Run SQL Queries

Import the cleaned dataset into your preferred database:

• PostgreSQL
• MySQL
• SQL Server

Open the SQL file and execute the queries using the appropriate database client.

Step 4 — Open the Power BI Dashboard

Open the .pbix file in Power BI Desktop.

If required, update the data source connection before refreshing the dashboard.

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Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis
• Python for Data Analytics
• Pandas & NumPy
• Data Visualization
• SQL
• PostgreSQL / MySQL / SQL Server
• Business Analysis
• Power BI
• Dashboard Development
• Data Storytelling
• Report Writing
• Presentation Development

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The project highlights the ability to work with data, extract actionable insights, and communicate findings in a clear and business-friendly manner.