https://github.com/animesh-algorithm/customer-churn-prediction-2020-competition---96-percent-accuracy

This competition was about predicting whether a customer will change telecommunications provider, something known as "churning". My Machine Learning model was 96.3% accurate in predicting the customer churn. I have followed the complete lifecycle of data science project - Data Collection(from kaggle), Exploratory Data Analysis, Feature Engineering, Model Building, Hyperparameter Tuning and Model Evaluation.
https://github.com/animesh-algorithm/customer-churn-prediction-2020-competition---96-percent-accuracy

churn-prediction classification data-science kaggle machine-learning

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This competition was about predicting whether a customer will change telecommunications provider, something known as "churning". My Machine Learning model was 96.3% accurate in predicting the customer churn. I have followed the complete lifecycle of data science project - Data Collection(from kaggle), Exploratory Data Analysis, Feature Engineering, Model Building, Hyperparameter Tuning and Model Evaluation.

• Host: GitHub
• URL: https://github.com/animesh-algorithm/customer-churn-prediction-2020-competition---96-percent-accuracy
• Owner: animesh-algorithm
• Created: 2021-01-20T12:58:37.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-20T13:09:22.000Z (over 4 years ago)
• Last Synced: 2024-12-14T02:38:29.931Z (5 months ago)
• Topics: churn-prediction, classification, data-science, kaggle, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Churn Prediction 2020 Competition (96% Accuracy)

This competition was about predicting whether a customer will change telecommunications provider, something known as "churning". My Machine Learning model was 96.3% accurate in predicting the customer churn. I have followed the complete lifecycle of data science project - Data Collection(from kaggle), Exploratory Data Analysis, Feature Engineering, Model Building, Hyperparameter Tuning and Model Evaluation.

Notebooks - 


Churn Prediction using Machine Learning

The training dataset contains 4250 samples. Each sample contains 19 features and 1 boolean variable "churn" which indicates the class of the sample. The 19 input features and 1 target variable are:

1) "state", string. 2-letter code of the US state of customer residence 


2) "account_length", numerical. Number of months the customer has been with the current telco provider 


3) "area_code", string="area_code_AAA" where AAA = 3 digit area code.


4) "international_plan", (yes/no). The customer has international plan.


5) "voice_mail_plan", (yes/no). The customer has voice mail plan.


6) "number_vmail_messages", numerical. Number of voice-mail messages.


7) "total_day_minutes", numerical. Total minutes of day calls.


8) "total_day_calls", numerical. Total minutes of day calls.


9) "total_day_charge", numerical. Total charge of day calls.


10) "total_eve_minutes", numerical. Total minutes of evening calls.


11) "total_eve_calls", numerical. Total number of evening calls.


12) "total_eve_charge", numerical. Total charge of evening calls.


13) "total_night_minutes", numerical. Total minutes of night calls.


14) "total_night_calls", numerical. Total number of night calls.


15) "total_night_charge", numerical. Total charge of night calls.


16) "total_intl_minutes", numerical. Total minutes of international calls.


17) "total_intl_calls", numerical. Total number of international calls.


18) "total_intl_charge", numerical. Total charge of international calls


19) "number_customer_service_calls", numerical. Number of calls to customer service


20) "churn", (yes/no). Customer churn - target variable.