#wahajfaisal ,#pakistanengineeringcouncil #ASPIREPakistanOfficial To build a robust Streamlit dashboard for Pakistani industries, you can leverage datasets from the Pakistan Bureau of Statistics (PBS), State Bank of Pakistan (SBP), Pakistan Telecommunication Authority (PTA), and other relevant government and industry sources. Additionally, Kaggle and World Bank Open Data can provide supplementary datasets that can be adapted for Pakistani industries. These datasets will enable you to develop AI-driven insights and predictions for predictive maintenance, sales forecasting, and customer segmentation, tailored to the specific needs of Pakistani industries. To build a robust Streamlit dashboard for Pakistani industries, the following organized approach will be taken to gather and utilize the necessary data: Primary Data Sources: Pakistan Bureau of Statistics (PBS): Access the data portal for industrial production, sales, and employment data in CSV and Excel formats. Utilize this data for sales forecasting and customer segmentation. State Bank of Pakistan (SBP): Download economic indicators, including industrial production indices, from the economic data section in Excel format. Use this data to support sales forecasting models. Pakistan Telecommunication Authority (PTA): Extract data on telecom subscribers and usage patterns. Apply this data for customer segmentation in the telecom industry. Supplementary Data Sources: Kaggle: Search for Pakistani datasets or similar datasets that can be adapted for the specific industries. World Bank Open Data: Use broader economic data on Pakistan for additional context and validation. Industry-Specific Associations: Explore data from associations like the All Pakistan Textile Mills Association (APTMA) and Pakistan Automotive Manufacturers Association (PAMA) for industry-specific insights. Data Collection Steps: Download and Extract: Obtain CSV and Excel files from PBS and SBP data portals. Extract relevant data from PTA publications or reports. Explore and Adapt: Search Kaggle and World Bank for supplementary datasets Consider web scraping if necessary data is only available on websites. Use synthetic data for any missing components to ensure comprehensive coverage. Data Preprocessing: Cleaning: Handle missing values, inconsistencies, and outliers in the data. Normalize and standardize data as needed for consistency. Integration: Merge datasets from different sources to create a unified data framework. Ethical and Legal Considerations: Compliance: Ensure all data usage complies with copyright and privacy laws. Obtain necessary permissions for proprietary or sensitive data. Implementation Plan: Prioritize Accessible Data: Focus on readily available data from PBS and SBP to build initial models. Simulate Missing Data: Use general sensor data or simulate machinery failure patterns for predictive maintenance if specific data is unavailable. Adapt Global Datasets: Adjust international datasets to fit the Pakistani cont
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