This dataset is randomly collected from an Iranian telecom companyâs database over a period of 12 months. A total of 3150 rows of data, each representing a customer, bear information for 13 columns. The attributes that are in this datasetare call failures, frequency of SMS, number of complaints, number of distinct calls, subscription length, age group, the charge amount, type of service, seconds of use, status, frequency of use, and Customer Value.
All of the attributes except for attribute churn is the aggregated data of the first 9 months. The churn labels are the state of the customers at the end of 12 months. The three months is the designated planning gap.
Contents of this notebook:
- EDA for Iranian Churn Dataset
- Grid Search for hyperparameter tuning with cross-val&hold-out methods
- Decision Tree
- Naive Bayes
- SVM
- Neural Networks
- Bagging ensemble method
- Boosting ensemble method
I hope you can enjoy while learning :)