A README file is included in the supplementary materials to guide users on how to set up the environment, run the code, and interpret the results.
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# SmartSurveil CRM Model

## Description
This repository contains the implementation of the SmartSurveil CRM model for predicting customer churn. The model uses ensemble learning methods including Random Forest, Gradient Boosting, and Support Vector Machine (SVM).

## Requirements
- Python 3.8
- Jupyter Notebook
- Libraries: pandas, scikit-learn, openpyxl

## Setup
1. Clone the repository
2. Install the required libraries
3. Run the Jupyter Notebook `SmartSurveil_CRM.ipynb`

## Data
The dataset `CRM_Data_Sample.csv` contains customer data with features such as age, gender, annual income, spending score, product views, purchase frequency, customer satisfaction, website visits, social media engagement, days since last purchase, complaint count, and churn.

## Running the Code
1. Open the Jupyter Notebook `SmartSurveil_CRM.ipynb`
2. Follow the steps in the notebook to preprocess the data, train the models, and evaluate their performance.

## Results
The results include accuracy, precision, recall, F1-score, and ROC-AUC for each model. The notebook also contains visualizations of the evaluation metrics.