Customer_behavior_analysis

Data analytics project showcasing customer behavior analysis using python, sql and power bi.

Data Analytics Project

Overview

This project demonstrates an end-to-end data analytics workflow, covering data loading, exploration, cleaning, querying, visualization, and reporting. The goal is to extract meaningful insights from raw data and present them through an interactive dashboard and structured reports.

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Dataset

• Source: Public dataset (used for educational purpose)
• Format: CSV
• Description: This dataset includes customer behavior and transactional data, such as customer demographics, purchase history, and engagement metrics. It is used to perform exploratory data analysis, build SQL queries, and create visual insights through dashboards.

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Tools & Technologies

• Python – Data loading, cleaning, and exploratory data analysis (EDA)
• Pandas, NumPy – Data manipulation and visualization
• PostgreSQL – Data storage and SQL-based analysis
• Power BI – Dashboard creation and data visualization
• Gamma – Presentation (PPT) creation
• Jupyter Notebook – Development environment

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Project Steps

1. Data Loading

• Imported dataset using Python (Pandas)
• Performed initial inspection of structure and data types

2. Exploratory Data Analysis (EDA)

• Conducted initial data exploration using Pandas (.info(), .describe())
• Reviewed data types and summary statistics
• Identified missing values and basic data inconsistencies

3. Data Cleaning

• Handled missing values
• Removed duplicates
• Standardized formats (dates, categories, etc.)
• Prepared data for analysis and database storage

4. SQL Analysis (PostgreSQL)

• Loaded cleaned data into PostgreSQL

• Wrote SQL queries to:

  • Aggregate key metrics
  • Filter and segment data
  • Identify trends and patterns

5. Dashboard Development (Power BI)

• Connected Power BI to PostgreSQL / processed dataset

• Built interactive dashboard with:

  • KPIs
  • Charts and filters
  • Drill-down capabilities

6. Reporting

• Summarized key findings in a structured report
• Highlighted insights and business implications

7. Presentation (Gamma)

• Created a professional presentation
• Included visuals, insights, and recommendations
• Designed for stakeholder communication

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Dashboard

The Power BI dashboard provides:

• Key performance indicators (KPIs)
• Trend analysis over time
• Category-level insights
• Interactive filters for deeper exploration

[Add dashboard screenshots or link here]

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Results & Insights

• Identified key trends and patterns in the dataset
• Highlighted top-performing segments and areas of concern
• Provided actionable insights to support decision-making

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How to Run

1. Clone the repository

   git clone https://github.com/Revanth-Insights/customer_behavior_analysis.git
   cd customer_behavior_analysis

2. Install dependencies

   pip install -r requirements.txt

3. Run Python scripts / notebooks

   • Open Jupyter Notebook
   • Execute EDA and data cleaning files

4. Set up PostgreSQL

   • Create a database
   • Import cleaned dataset
   • Run SQL scripts

5. Open Power BI Dashboard

   • Load .pbix file
   • Refresh data connection if needed

6. View Reports & Presentation

   • Open report document
   • Access Gamma presentation

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Author

[Revanth Prasad] [https://www.linkedin.com/in/revanth-prasad-34331a27a/] [https://github.com/Revanth-Insights]