https://github.com/anmamun0/data-analysis-home-cleaning-services
This repository contains the analysis and visualization of data from a home cleaning services dataset. The project provides valuable insights into revenue generation, customer trends, and regional performance, helping businesses make data-driven decisions.
https://github.com/anmamun0/data-analysis-home-cleaning-services
matplotlib numpy pandas
Last synced: over 1 year ago
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This repository contains the analysis and visualization of data from a home cleaning services dataset. The project provides valuable insights into revenue generation, customer trends, and regional performance, helping businesses make data-driven decisions.
- Host: GitHub
- URL: https://github.com/anmamun0/data-analysis-home-cleaning-services
- Owner: anmamun0
- Created: 2024-12-07T14:27:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-09T20:48:55.000Z (over 1 year ago)
- Last Synced: 2025-01-09T21:34:24.433Z (over 1 year ago)
- Topics: matplotlib, numpy, pandas
- Language: Jupyter Notebook
- Homepage: https://github.com/anmamun0/data-analysis-home-cleaning-services
- Size: 122 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏠 Exploratory Data Analysis: Home Cleaning Services
This repository contains the analysis and visualization of data from a home cleaning services dataset.
The project provides valuable insights into revenue generation, customer trends, and regional performance,
helping businesses make data-driven decisions.
---
## 📋 Project Overview
The goal of this project is to analyze and visualize key metrics, such as:
- Total revenue generated by the business.
- Customer spending patterns.
- Regional performance based on services provided and revenue.
## ⚙️ Features
- **Data Cleaning**: Handle missing values, remove duplicates, and ensure accurate data types.
- **Data Analysis**: Derive insights using pandas and numpy.
- **Visualization**: Create informative charts and graphs using matplotlib and seaborn.
- **Report Generation**: Output insights to a text file for stakeholders.
## 📊 Key Insights
1. **Total Revenue Generated**: 💰 `$55,509,587.02`
2. **Top Regions**: Identified high-performing regions based on revenue and service counts.
3. **Customer Trends**: Insights into spending behavior of different customer types.
## 📁 Files
- `data_server.csv`: The dataset used for analysis.
- `analysis_output.txt`: Text file containing detailed results.
- Python scripts for:
- Data Cleaning
- Data Analysis
- Visualizations
## 🚀 Getting Started
### Prerequisites
Ensure you have Python installed along with the following libraries:
- pandas
- numpy
- matplotlib
### Setup
1. Clone this repository:
```bash
git clone https://github.com/your-username/home-cleaning-services-eda.git