https://github.com/cuongdaoo/analyze-and-predict-customer-churn-rate-with-power-bi-and-random-forest
https://github.com/cuongdaoo/analyze-and-predict-customer-churn-rate-with-power-bi-and-random-forest
customer-churn-analysis customer-churn-prediction customer-churn-prediction-with-machine-learning
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/cuongdaoo/analyze-and-predict-customer-churn-rate-with-power-bi-and-random-forest
- Owner: cuongdaoo
- Created: 2024-10-24T18:10:51.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-02T14:12:23.000Z (6 months ago)
- Last Synced: 2024-12-31T03:16:08.694Z (5 months ago)
- Topics: customer-churn-analysis, customer-churn-prediction, customer-churn-prediction-with-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 3.91 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Analyze-and-predict-customer-churn-rate-with-Power-BI-and-Random-Forest
# I. Goal
Objective:
- Maximize Revenue
- Cost of Acquiring New Customers
# II. Company overview
Telecommunications companies provide services such as telephone and internet.
# III. Data ovewview
Telecom customer churn data overview
# IV. Process
## STEP 1 – ETL Process in SQL Server
**Remove null and insert the new data into Prod table**
```
SELECT
Customer_ID,
Gender,
Age,
Married,
State,
Number_of_Referrals,
Tenure_in_Months,
ISNULL(Value_Deal, 'None') AS Value_Deal,
Phone_Service,
ISNULL(Multiple_Lines, 'No') As Multiple_Lines,
Internet_Service,
ISNULL(Internet_Type, 'None') AS Internet_Type,
ISNULL(Online_Security, 'No') AS Online_Security,
ISNULL(Online_Backup, 'No') AS Online_Backup,
ISNULL(Device_Protection_Plan, 'No') AS Device_Protection_Plan,
ISNULL(Premium_Support, 'No') AS Premium_Support,
ISNULL(Streaming_TV, 'No') AS Streaming_TV,
ISNULL(Streaming_Movies, 'No') AS Streaming_Movies,
ISNULL(Streaming_Music, 'No') AS Streaming_Music,
ISNULL(Unlimited_Data, 'No') AS Unlimited_Data,
Contract,
Paperless_Billing,
Payment_Method,
Monthly_Charge,
Total_Charges,
Total_Refunds,
Total_Extra_Data_Charges,
Total_Long_Distance_Charges,
Total_Revenue,
Customer_Status,
ISNULL(Churn_Category, 'Others') AS Churn_Category,
ISNULL(Churn_Reason , 'Others') AS Churn_Reason
INTO [db_Churn].[dbo].[prod_Churn]
FROM [db_Churn].[dbo].[stg_Churn];
```
**Create View for Power BI for prediction**
```
Create View vw_ChurnData as
select * from prod_Churn where Customer_Status In ('Churned', 'Stayed')
```
```
Create View vw_JoinData as
select * from prod_Churn where Customer_Status = 'Joined'
```
## STEP 2 & 3 – Power BI Transform
**Transform**
- Churn status: convert from text to number (Stayed-0, churned-1)
- Create value range for monthly charge: <20, 20-50, 50-100, >100
**Measure**
- Total customer
- New joiners
- Total Churn
- Churn rate
**Create reference table for Age**
- Age group: <20, 20-35, 36-50, >50
**Create reference table for Tenure**
- Tenure group: <6 months, 6- 12 months, 12-18 months, 18-24 months, >=24 months
**Create reference table for Service**
- Unpivot columns related to service
- Set status
## STEP 4 – Power BI Visualization
## STEP 5 – Predict Customer Churn
A random forest is a machine learning algorithm that consists of multiple decision trees. Each decision tree is trained on a random subset of the data and features. The final prediction is made by averaging the predictions (in regression tasks) or taking the majority vote (in classification tasks) from all the trees in the forest. This ensemble approach improves the accuracy and robustness of the model by reducing the risk of overfitting compared to using a single decision tree.
- Import both vw_ChurnData & vw_JoinData from database
- With vw_ChurnData for training model and forecast values of vw_JoinData
[Source code](https://github.com/cuongdaoo/Analyze-and-predict-customer-churn-rate-with-Power-BI-and-Random-Forest/blob/main/Churn_Analyst.ipynb)
Visual prediction data into Power BI
