https://github.com/sabsar42/bangladesh-medicine-data-analysis

https://github.com/sabsar42/bangladesh-medicine-data-analysis

Last synced: 6 days ago
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• Host: GitHub
• URL: https://github.com/sabsar42/bangladesh-medicine-data-analysis
• Owner: sabsar42
• Created: 2024-01-28T21:18:46.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-03-01T15:21:16.000Z (9 months ago)
• Last Synced: 2024-03-01T16:43:09.426Z (9 months ago)
• Language: Jupyter Notebook
• Size: 36.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bangladesh Medicine Dataset Analysis  

This repository contains an analysis of a medicine dataset obtained through web scraping. The analysis explores various aspects of the dataset, providing insights into pharmaceuticals, dosage descriptions, generic names, pharmaceutical companies, and retail prices.

## Dataset Overview

The dataset used in this analysis comprises information related to medicines, including brand names, dosages, generic names, pharmaceutical companies, and retail prices.

## Analysis Highlights

1. **Pharmaceutical Companies:**

   - Identification of top pharmaceutical companies based on the number of unique medicines produced.

   - Visual representation of the distribution of medicines among the top pharmaceutical companies.

2. **Dosage Descriptions:**

   - Identification of medicines with multiple dosage descriptions.

   - Exploration of the top medicines with multiple dosage descriptions.

3. **Generic Names:**

   - Analysis of the most common generic names in the dataset.

   - Identification of pharmaceutical companies producing the most unique generic names.

4. **Retail Prices:**

   - Overview of the distribution of retail prices for allopathic medicines.

   - Identification of medicines with specific dosage descriptions and their corresponding retail prices.

## Methodology

The analysis was conducted using Python and popular data analysis libraries such as Pandas, Matplotlib, and Seaborn. The dataset was cleaned, filtered, and visualized to derive meaningful insights.

## Web Scraping

The dataset was obtained through web scraping techniques. The process involved extracting relevant information from online sources to compile a comprehensive dataset for analysis.

## Repository Structure

- **datasets/:** 

  - `.json`: Contains the raw and processed medicine datasets in JSON format.

  

- **notebooks/:**

  - `dgda-medicine-dataset-analysis.ipynb`: Jupyter notebook used for data analysis.

  - `web-scrapping-medicines-dgda.ipynb`: Python scripts for web scraping and data preprocessing.

  - `medicine-dataset-analysis-report.ipynb`: Visualizations and report generated during the analysis.