https://github.com/rajull-agrawal/customer_analysis

This project is for data analysis on customer-related datasets using PySpark and tablaeu.
https://github.com/rajull-agrawal/customer_analysis

csv jupyter-notebook pyspark python tableau

Last synced: 4 months ago
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This project is for data analysis on customer-related datasets using PySpark and tablaeu.

• Host: GitHub
• URL: https://github.com/rajull-agrawal/customer_analysis
• Owner: Rajull-Agrawal
• Created: 2024-09-08T08:20:27.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-08T09:54:10.000Z (over 1 year ago)
• Last Synced: 2025-04-05T20:43:14.700Z (about 1 year ago)
• Topics: csv, jupyter-notebook, pyspark, python, tableau
• Language: Jupyter Notebook
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# Customer Data Analysis

## Author: Rajul Agrawal

### Overview

This Jupyter notebook performs data analysis on customer-related datasets using PySpark. The analysis involves loading various datasets, cleaning data, transforming date formats, and displaying distinct demographic information.

### Dataset

The notebook processes the following CSV datasets:

- **Customer Product**: Contains product details held by customers.

- **Customer Channel Activity**: Captures customer interactions through various channels.

- **Customer Demographics**: Includes demographic details of customers.

- **Customer Transaction History**: Tracks customers' transaction details.

- **Product Lookup**: Provides details about different products.

### Key Steps

1. **Data Loading**: The datasets are loaded into PySpark DataFrames.

2. **Data Transformation**: 

   - A UDF is applied to standardize various date formats across the datasets.

   - The schema of each dataset is printed to understand the structure.

   - Distinct values of demographic fields like `Marital_Status` are analyzed.

3. **Data Export**: Commented-out sections allow exporting the DataFrames to CSV for further analysis.

### Dependencies

- PySpark

- Pandas

- Datetime

- Pgeocode (for geographic data processing)

### Tableau Dashboard

This analysis is complemented by a Tableau dashboard that visualizes key insights from the data. Screenshots of the dashboard can be added here.

![Dashboard 1](https://github.com/user-attachments/assets/dae892a5-e2c4-4f1a-bfcc-dacb66f3fab7)

![Dashboard 2](https://github.com/user-attachments/assets/3e13cad5-7777-4c96-bb5e-7158c6139d10)

![Dashboard 3](https://github.com/user-attachments/assets/ba2ec6fb-0181-46c1-825d-3b3e01bffe44)