https://github.com/das-amlan/customer-churn-prediction
Predicting customer churn using machine learning algorithms
https://github.com/das-amlan/customer-churn-prediction
customer-churn-prediction imbalanced-data keras-tensorflow machine-learning pandas prediction-model python scikit-learn seaborn tensorflow
Last synced: 3 months ago
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Predicting customer churn using machine learning algorithms
- Host: GitHub
- URL: https://github.com/das-amlan/customer-churn-prediction
- Owner: das-amlan
- Created: 2022-06-16T16:39:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-16T01:43:27.000Z (over 2 years ago)
- Last Synced: 2025-03-04T14:46:01.583Z (8 months ago)
- Topics: customer-churn-prediction, imbalanced-data, keras-tensorflow, machine-learning, pandas, prediction-model, python, scikit-learn, seaborn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 241 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer-Churn-Prediction
This project is aimed at predicting customer churn using machine learning algorithms. The analysis uses several models, including Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Machine (SVM), and Artificial Neural Network.
## Dataset
The analysis is based on a publicly available dataset from [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn). The dataset contains information about customers of a telecommunications company, including demographic information, services used, and whether they churned or not.
## Libraries Used
- pandas
- NumPy
- Matplotlib
- seaborn
- scikit-learn
- TensorFlow
- Keras
## Steps
The project is divided into the following steps:
- Importing required libraries
- Loading the dataset
- Exploratory Data Analysis
- Processing Data for Modeling
- Handling missing values
- Removing irrelevant data
- One-hot encoding for columns with categorical data
- Scaling data
- Handling imbalanced data using the `SMOTE` method
- Test Train Split
- Modeling and Evaluation
Each step is explained in detail in the notebook.
## Outcome
The Random Forest Classifier achieved an impressive accuracy of 84% in our customer churn prediction model. In addition, our precision, recall, and f1 score metrics also showed better performance with the Random Forest Classifier. These results indicate that our model is effective at predicting which customers are most likely to churn, allowing us to take proactive steps to retain valuable customers and improve the overall performance of our business.