Churn Prediction - Telco Company

The customer loss forecast of the telecommunications company

Data Source:

Kaggle Telecom customer loss data set

github code library:

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Telco Customer churn -github

Overview of the project

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This project uses machine learning technology to predict the loss of customers in telecommunications companies. The main goal is to identify a model that may cause customers to cancel the service, enabling the company to implement active customer retention strategies before the customer stops service.

The project focuses on the recall rate indicator. Even if the cost is increased by some misunderstandings, it is necessary to capture most of the loss customers as much as possible, because preventive retention measures are more beneficial to the business.

Exploratory Data Analysis (EDA)

In the EDA process, we explored the data concentration model to understand factors related to customer loss. The main discovery includes:

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Monthly contract and long -term contract: The possibility of losing the monthly contract customer is more likely, which indicates that long -term contracts may be more conducive to improving customer loyalty.

value -added service:

The customer loss rate of subscribing value -added services (such as online security or technical support) is often low.

Customer use time and monthly fees:
• Customers with longer use of use show higher loyalty; higher monthly fees are positive related to customer loss. Delete the TotalCharges column:
Since the TotalCharges column is related to the height of the use time, it may affect the stability of the model, so delete it.
Technical selection
• Why choose the SVM algorithm?
• The reasons for selecting the support vector machine (SVM) are as follows:
Handling the efficiency of small data sets:

For data sets of about 7,000 lines, SVM can effectively capture complex modes without any fitting.

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Flexible nuclear function options: By combining linear nuclear and RBF nuclei, SVM can identify linearity and non -linear relationships through GridsearchCV.

Binary classification:

SVM is very suitable for such dual problems, the goal is to predict customer loss (yes or not).

Pre -processing steps:

1. MinmaxScaler: Models like SVM are sensitive to scale differences. Application to radiate numerical variables between 0 and 1.
code (Onehotencoder):
2. Convert the classification variables to virtual variables. This ensures that the category is expressed correctly in the format that can be understood by a model.
Data segmentation and verification:
Data sets are divided into 70% training sets and 30% test sets.

Use 50 % off verification to verify to ensure the stability of the results.

Machine learning process
1. The steps are as follows:
1. Data set division: Separate due to variables (loss) and independent variables to ensure the correct data segmentation of training sets and test sets.
SVM's super -reuse adjustment:
2. Optimized with GridsearchCV for optimization, adjustment: <:> C:
   regularization parameters, control the balance between the margins and errors.
   • Core function: Evaluate linear nucleus and RBF nucleus.
   • Model evaluation index:
   Use the following indicator evaluation model:
accuracy rate:
3. the percentage of the correct prediction. Recall rate:
   The proportion of the loss of customers (real examples).
   • accuracy: percentage of the loss customer who is recognized correctly.
   • F1 score: The conformity of accuracy and recall rate.
   ROC AUC:
   • Modeling indicators of different categories of capabilities.
   • Results
   Results analysis:
Although the accuracy is very high, the main focus is on the recall rate, reaching 56%. This means identifying most customers who may be lost, so as to take positive intervention measures.

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In the future, the direction of improvement

指标	值
准确率	80.81%
召回率	56.09%
精确率	74.35%
F1 分数	63.95%
ROC AUC	85.42%

Integrated external data:

Use customer satisfaction feedback (such as NPS or survey reply) rich dataset.

Including economic or regional indicators to identify specific modes.

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Try other models:

Test XGBOOST or LIGHTGBM and other models that can handle complex interaction well.
1. Implementation of the importance of characteristics to optimize variable selection.
   • Automation:
   Develop a real -time process, and use the data update model with regular update.
Integrate the model into the CRM system to achieve automated customer reservation measures.
2. Customer subdivision:
   • The focus of the customer's reservation work is focused on high -value or high -risk customers' market segment.
   Design personalized customer retaining strategies according to the characteristics of the market.
3. • Project file
   EDA.IPYNB:
Exploring data analysis and main opinion.
4. Pre_processing.py: Data pre -processing and conversion script.
   • ml_application.py:
   Machine learning training, verification and results export.
   config_template.py:
   Environment variable (data set path) template.

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Contact information:

If you have any questions or cooperation opportunities, please contact LinkedIn at any time.

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