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📊 Churn Analysis Project

📌 Overview

This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.
The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.

🎯 Problem Statement

Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:

  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them

This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.

🛠️ Tech Stack

  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting

🔄 Project Workflow

1️⃣ SQL Server (ETL & Data Cleaning)

  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers

2️⃣ Power BI (Transformations & Dashboards)

Transformations (Power Query):

  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity

Key DAX Measures:

Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About

End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).

Topics

business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server

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GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
Skip to content
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📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server
Resources
Readme
Activity
Stars
0 stars
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0 watching
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0 forks
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, 'i');
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GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
Skip to content
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📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server
Resources
Readme
Activity
Stars
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0 watching
Forks
0 forks
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GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
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📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server
Resources
Readme
Activity
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try {
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
Skip to content
BranchesTags
Repository files navigation
📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server
Resources
Readme
Activity
Stars
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0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
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GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
Skip to content
BranchesTags
Repository files navigation
📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
Skip to content
BranchesTags
Repository files navigation
📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
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GitHub - Ahadxcode/Churn-Analysis-Project: End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML). · GitHub
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📊 Churn Analysis Project
📌 Overview
This is an end-to-end churn analysis pipeline built using SQL Server Management Studio 21 (SSMS 21), Power BI, and Python.

The project helps businesses understand churn patterns, build predictive insights, and design proactive strategies to reduce customer churn.
🎯 Problem Statement
Customer churn — when customers stop using a service — leads to significant revenue loss. Businesses need to:
  • Identify customers at risk of leaving
  • Understand key churn drivers
  • Take proactive actions to retain them
This project provides a data-driven churn analysis solution by combining SQL, Power BI, and Python.
🛠️ Tech Stack
  • SQL Server Management Studio 21 (SSMS 21): ETL, data cleaning, preprocessing, and view creation
  • Power BI: Data transformation, modeling, and interactive dashboards
  • Python (Pandas, NumPy, Matplotlib, Scikit-learn): Exploratory Data Analysis (EDA)
  • Machine Learning (Random Forest Classifier): Predictive modeling for churn analysis
  • GitHub: Version control and project hosting
🔄 Project Workflow
1️⃣ SQL Server (ETL & Data Cleaning)
  • Imported raw customer dataset into SSMS 21
  • Cleaned missing values, duplicates, and invalid entries
  • Created staging & production tables for analysis
  • Built views to separate churned vs. retained customers
2️⃣ Power BI (Transformations & Dashboards)
Transformations (Power Query):
  • prod_Churn → Added Churn Status flag, Monthly Charge ranges
  • mapping_AgeGrp → Categorized age into groups (<20, 20–35, 36–50, >50)
  • mapping_TenureGrp → Grouped tenure into <6M, 6–12M, 12–18M, 18–24M, >=24M
  • prod_Services → Unpivoted services, renamed fields for clarity
Key DAX Measures:
Total Customers = COUNT(prod_Churn[Customer_ID])
New Joiners = CALCULATE(
COUNT(prod_Churn[Customer_ID]),
prod_Churn[Customer_Status] = "Joined"
)
Total Churn = SUM(prod_Churn[Churn Status])
Churn Rate = [Total Churn] / [Total Customers]

About
End-to-end churn analysis project using SQL Server (ETL, Data Cleaning), Power BI (Dashboard), and Python (EDA & ML).
Topics
business-analyticsdata-analysisdata-scienceetl-pipelinegitpushjupyter-notebookmachine-learningpowerbipowerbi-reportpowerbi-visualspowerbidesktoppythonrandom-forestsqlsql-server
Resources
Readme
Activity
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