Notes

Certainly! Here's an extended list of tasks spread over 15 days for your data scraping project:

**Day 1: PDF to Image Conversion**
- Set up your environment for PDF to image conversion.
- Install and configure any necessary tools.
- Convert a sample PDF to images to ensure the process is working correctly.

**Day 2: PDF to Image Conversion (continued)**
- Fine-tune the PDF to image conversion process.
- Handle edge cases and exceptions in PDFs.
- Document the conversion process.

**Day 3: Table Detection**
- Research and select a suitable table detection library or algorithm.
- Begin implementing the table detection process.
- Test the chosen method on your PDF images to detect tables accurately.

**Day 4: Table Detection (continued)**
- Refine the table detection algorithm.
- Handle cases where tables are complex or have irregular structures.
- Document the table detection process.

**Day 5: Data Scraping from Tables**
- Develop a script or code to scrape data from the detected tables.
- Create a system for organizing and storing the scraped data.
- Ensure that the data extraction process works reliably on your sample data.

**Day 6: Data Scraping from Tables (continued)**
- Implement error handling and data validation during the scraping process.
- Optimize data scraping for speed and accuracy.
- Document the data scraping process.

**Day 7: CSV Conversion**
- Implement functionality to convert the scraped data into CSV format.
- Test the CSV conversion process and validate the output against your expectations.
- Handle cases where special characters or formatting issues may affect CSV generation.

**Day 8: CSV Conversion (continued)**
- Optimize the CSV conversion process for performance.
- Create a system for organizing and storing the generated CSV files.
- Document the CSV conversion process.

**Day 9: CRM Data Entry**
- Identify the CRM system you'll be using for data entry.
- Develop code to take data from the CSV and input it into the CRM.
- Test the CRM data entry process to ensure data is correctly populated.

**Day 10: CRM Data Entry (continued)**
- Handle errors and duplicates during CRM data entry.
- Create a logging system for tracking data entry progress.
- Document the CRM data entry process.

**Day 11: Integration and Optimization**
- Integrate all the components of your data scraping pipeline.
- Run end-to-end tests on a larger dataset to ensure everything is working smoothly.
- Optimize your code for efficiency and reliability.

**Day 12: Integration and Optimization (continued)**
- Fine-tune the integration to handle real-world scenarios.
- Implement a scheduler for automated data scraping and CRM entry.
- Document the complete data scraping pipeline.

**Day 13: Testing and Validation**
- Conduct comprehensive testing with various PDFs and data sources.
- Validate the accuracy and completeness of the scraped and entered data.
- Address any issues or anomalies that arise during testing.

**Day 14: Documentation and Review**
- Document your entire workflow, including code and processes.
- Review the project, making notes of any improvements or future enhancements.
- Prepare for deployment or further use of your data scraping model.

**Day 15: Deployment and Training**
- Deploy your data scraping model in a production environment.
- Train relevant team members on how to use and maintain the system.
- Monitor the system's performance and address any issues that arise in production.

This extended timeline allows for more thorough development, testing, and documentation of your data scraping project over a 15-day period. Adjust the schedule as needed based on project complexity and resources available. Good luck with your project!