Project objective
End-to-end exploratory data analysis and predictive modeling for customer churn prediction. The EDA pipeline could be repurposed for other datasets with minor tweaks.
Implementations
Will existing customers churn?
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Data cleansing and preprocessing.
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Data visualization and Exploratory Data Analysis
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Statistical analysis of the data.
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Model generation for the prediction of customer churn behavior.
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Application of Logistic Regression, SVM-Linear, SVM-RBF, and Random Forest algorithms on data and performance comparison.
Input data Data source - Kaggle & IBM sample dataset community. Dataset - Prediction of user behavior to retain customers. The dependent variable have binary value, 1 - churned and 0 - not or true/false. The data set includes information about:
Project description
-> Customers who left within the last month – the column is called Churn -> Services that each customer has signed up for: phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies -> Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges -> Demographic info about customers – gender, age range, and whether they have partners and dependents.
Getting Started
There are no additional dependencies to install. The package includes all files, and the code makes all necessary imports.
Simply fork the repository.
To execute the file, run the following in your editor terminal
python3 churn_rate.py
**NOTE - Please see the Jupyter notebook(.ipynb file) for complete explanation and in-depth Exploratory Data Analysis **
**Data was in a csv file format. For other formats, use another read function of pandas. **Update the file path to the local directory before running the file.
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