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    Home»Machine Learning»End-to-End Churn Prediction: SQL, Power BI, ML & an Interactive Web App for Churn Probability | by Aditi Talpade | Mar, 2025
    Machine Learning

    End-to-End Churn Prediction: SQL, Power BI, ML & an Interactive Web App for Churn Probability | by Aditi Talpade | Mar, 2025

    Team_AIBS NewsBy Team_AIBS NewsMarch 2, 2025No Comments2 Mins Read
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    Step one concerned creating an SQL database in SQL Server Administration Studio (SSMS) and loading buyer knowledge, together with:

    • Demographics (age, gender, location)
    • Cost Historical past (billing cycles, defaults)
    • Service Utilization (subscription sort, engagement ranges)
    • Churn Standing (Keep vs. Churned)

    The dataset was structured throughout a number of tables, requiring SQL joins for evaluation. A database named db_Churn was created, and the uncooked knowledge was imported right into a staging desk, stg_Churn.

    CREATE DATABASE db_Churn;

    The dataset was imported as a CSV file into stg_Churn, serving because the staging desk containing uncooked knowledge.

    Encountered an error:

    SELECT * FROM stg_Churn;

    SQL Server returned an “Invalid object identify” error regardless of the desk being seen within the database.

    Troubleshooting Steps Taken:

    1. Checked if the desk existed:
    SELECT * FROM sys.tables WHERE identify = 'stg_Churn';

    2. Verified the schema to make sure the desk was underneath dbo.

    3. Confirmed the database context:

    USE db_Churn;

    4. Used a completely certified desk identify:

    SELECT * FROM db_Churn.dbo.stg_Churn;

    5. Refreshed SSMS and reconnected to rule out short-term caching points.

    Lesson Realized:

    Explicitly defining the schema and making certain the right database context prevents question failures.

    After resolving SQL points, important knowledge cleansing steps — resembling dealing with null values and changing lacking knowledge — had been utilized to stg_Churn. A remaining production-ready desk, prod_churn, was created for additional evaluation. Two views requiring SQL joins had been additionally created:

    Views in SQL act as digital tables that present outcomes dynamically when queried. They’re notably helpful for simplifying advanced queries.



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