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https://github.com/sudhan670/acadia
The Data Science for the missing data the dataset given by the company after running it generate the complete as like this,
https://github.com/sudhan670/acadia
Last synced: about 1 month ago
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The Data Science for the missing data the dataset given by the company after running it generate the complete as like this,
- Host: GitHub
- URL: https://github.com/sudhan670/acadia
- Owner: sudhan670
- Created: 2024-11-19T10:13:07.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-11-19T10:25:30.000Z (3 months ago)
- Last Synced: 2024-11-19T11:25:40.074Z (3 months ago)
- Language: HTML
- Size: 4.56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### 1. **Project Structure**
Your project should have the following structure:
```
Acadia/
│
├── data/ # Directory for input datasets
├── output/ # Directory for generated reports and plots
├── src/ # Python scripts for processing
│ ├── analysis.py # Script for data analysis and report generation
├── README.md # Project description and links
└── requirements.txt # Python dependencies
```
---
### 2. **Python Script: Data Analysis and Report Generation**
Here's a script (`src/analysis.py`) to generate reports and save them in the `output/` directory.
### 3. **README.md**
Create a `README.md` file to describe the project and provide a link to the generated report.
```markdown
# Acadia: Data Science for Missing Data
## Overview
Acadia automates data analysis, focusing on detecting and visualizing missing values, categorizing columns by data type, and generating comprehensive reports.
## Features
- Detects missing values and generates a detailed summary.
- Categorizes columns into numeric and categorical data types.
- Creates box plots for numeric columns.
- Outputs a downloadable PDF report with visualizations.
## Usage
1. Place your dataset in the `data/` directory (e.g., `dataset.csv`).
2. Run the analysis script:
```bash
python src/analysis.py
```
3. Find the generated report in the `output/` directory.
## Report
The generated report can be accessed [here](https://github.com/sudhan670/Acadia/blob/main/Data%20Report.pdf).
## Dependencies
Install required libraries:
```bash
pip install pandas matplotlib seaborn fpdf
```
```Juptyer Notebooks or Colab
!pip install pandas matplotlib seaborn fpdf
## License
This project is licensed under the MIT License.
```
---
### 4. **Hosting on GitHub**
- Push your project to a GitHub repository.
- Ensure the `Data_Report.pdf` file is in the `output/` folder and included in your commit.
- Update the README link to point to the correct GitHub path.
---
This setup ensures the project is well-organized, with clear instructions and a functional link to the generated report. Let me know if you need further adjustments or enhancements!