This repository contains a machine-learning model for predicting customer churn in the telecom industry.
The project aims to predict customer churn using a machine learning model. The model is trained on a dataset containing various customer attributes and their past behavior. It can help businesses identify customers who are likely to stop using their services, enabling proactive strategies to retain them.
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Data Preprocessing: The script handles missing values, outliers, and categorical variables. It also performs necessary data cleaning tasks.
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Feature Engineering: New features are created based on the existing ones to improve the model's predictive power. This includes creating interaction terms, binning numerical variables, and more.
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Model Training: The script trains a Random Forest Classifier on the preprocessed data. The model is chosen for its ability to handle a large number of features and its robustness to overfitting.
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Model Evaluation: The performance of the model is evaluated using accuracy, precision, recall, and F1 score. Cross-validation is used to get a more reliable estimate of the model's performance.
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Prediction on New Data: The trained model can be used to make predictions on new data. The script provides a function to load new data, preprocess it in the same way as the training data, and make predictions using the trained model.
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Clone the repository :
git clone https://github.com/ashroyalc/Machine-Learning.git -
Install the required packages :
pip install pandas
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
MIT