AI-Driven Customer Data Analytics Pipeline
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
I need an automated AI-driven customer data analytics pipeline that can process data from CSV files and a live PostgreSQL database.
The solution should:
- Automatically clean, validate, and standardize raw customer data.
- Combine CRM, support ticket, and purchase-history data.
- Use Python, SQL, pandas, and scikit-learn for statistical analysis and machine learning.
- Identify customer trends, cohorts, segmentation, value indicators, and potential risks.
- Generate actionable business insights automatically.
- Create clear dashboards/reports using Power BI or Metabase.
- Automatically generate a PDF or written report for non-technical stakeholders.
- Be reusable whenever new data is added, with minimal manual work.
Deliverables:
1. Complete working Python/SQL workflow.
2. Automated analysis using the provided sample dataset.
3. At least one automatically generated dashboard/report.
4. Well-commented and maintainable code.
5. README with setup and usage instructions.
6. Requirements/dependency file so the project can be set up on a fresh machine.
Please recommend a cost-efficient architecture and open-source libraries/cloud services where appropriate. The solution should be robust, scalable, and easy to extend with new models or data sources.
The solution should:
- Automatically clean, validate, and standardize raw customer data.
- Combine CRM, support ticket, and purchase-history data.
- Use Python, SQL, pandas, and scikit-learn for statistical analysis and machine learning.
- Identify customer trends, cohorts, segmentation, value indicators, and potential risks.
- Generate actionable business insights automatically.
- Create clear dashboards/reports using Power BI or Metabase.
- Automatically generate a PDF or written report for non-technical stakeholders.
- Be reusable whenever new data is added, with minimal manual work.
Deliverables:
1. Complete working Python/SQL workflow.
2. Automated analysis using the provided sample dataset.
3. At least one automatically generated dashboard/report.
4. Well-commented and maintainable code.
5. README with setup and usage instructions.
6. Requirements/dependency file so the project can be set up on a fresh machine.
Please recommend a cost-efficient architecture and open-source libraries/cloud services where appropriate. The solution should be robust, scalable, and easy to extend with new models or data sources.
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