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https://github.com/sejalmankar1012/customer_churn

The goal of this project was to develop a machine learning model that predicts customer churn based on historical customer data.
https://github.com/sejalmankar1012/customer_churn

dataset deep-learning deployment machine-learning model-building modeling numpy optimization pandas seaborn streamlit tensorflow

Last synced: 21 days ago
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The goal of this project was to develop a machine learning model that predicts customer churn based on historical customer data.

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README 

        
# Customer Churn Prediction Project



Welcome to the Customer Churn Prediction project! In this project, we aim to develop a machine learning model that predicts customer churn based on historical customer data. This README file provides an overview of the project's structure, instructions, and key components.



## Objective



The primary objective of this project is to build a machine learning model that predicts customer churn using historical customer data. The project follows a typical machine learning project pipeline, encompassing data preprocessing, feature engineering, model building, model optimization, and model deployment.



## Dataset



We are provided with a dataset in CSV format containing historical customer information, including customer attributes, interactions, and whether they churned or not. The dataset will be used to train, validate, and test our machine learning model.



## Project Structure



The project is structured as follows:



1. **Data Preprocessing:** Load the dataset, handle missing data and outliers, encode categorical variables, and split the data into training and testing sets.

2. **Feature Engineering:** Create relevant features from the dataset to enhance model prediction accuracy. Apply feature scaling or normalization if required.

3. **Model Building:** Choose suitable machine learning algorithms, train and validate the model, and evaluate its performance using appropriate metrics.

4. **Model Optimization:** Fine-tune model parameters to improve predictive performance. Employ techniques like cross-validation and hyperparameter tuning.

5. **Model Deployment:** Deploy the trained model in a simulated production environment. Showcase its ability to make predictions on new customer data.



## Deliverables



The following deliverables are expected from this project:



1. **Code Implementation:** A Jupyter Notebook or Python script containing the entire code for the project, organized by the project's structure.

2. **Project Report:** A detailed report summarizing your approach, including preprocessing steps, feature engineering decisions, model selection rationale, and key findings.

3. **Performance Metrics and Visualizations:** Include relevant performance metrics (accuracy, precision, recall, F1-score) and visualizations depicting model performance.



## Usage Instructions



To run the code and reproduce the results:



1. Install the required libraries by running: `pip install -r requirements.txt`

2. Execute the Jupyter Notebook or Python script, ensuring the dataset file path is correctly provided.

3. Follow the step-by-step instructions in the code to preprocess the data, engineer features, build, optimize, and deploy the model.



## Additional Information



- Python and popular machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) are utilized in this project.

- The project emphasizes proficiency in data preprocessing, feature engineering, model development, and deployment.

- Comprehensive documentation is essential to ensure transparency and share insights gained from the project.



For any questions or inquiries, feel free to contact [Sejal Mankar] at [[email protected]].



Happy coding and exploring customer churn prediction!