Customer Churn Prediction for Travel and Tours Agency

Problem Statement

Problem: The travel and tours agency faces the challenge of customer churn, impacting business sustainability and revenue. Identifying customers likely to churn is crucial for implementing proactive retention strategies.

Solution

Prediction Model: Leveraging Python, Pandas, Matplotlib, Seaborn, and Scikit-Learn, we developed a predictive model to anticipate customer churn. This model empowers the agency to take preemptive measures to retain customers at risk of attrition.

Technology Used

1. Python: The core programming language for flexibility and extensive data science libraries.
2. Pandas: Efficient data manipulation for preparing datasets.
3. Matplotlib and Seaborn: Data visualization for insightful analysis.
4. Scikit-Learn: Building and training the predictive model.

Visual Overview of Data

In the notebook, you'll find visualizations using Matplotlib and Seaborn to gain insights into the data. Explore charts and graphs that provide a comprehensive overview of customer behavior and potential churn factors.

Model Performance

• Precision (0.5): The model accurately predicts 50% of customers at risk of churn, minimizing false positives.

• Recall (0.8): Effectively identifies 80% of actual churn instances, reducing the likelihood of overlooking potential churn.

• F1 Score (0.6): Achieves a balanced trade-off between precision and recall, providing a comprehensive evaluation of the model's predictive capabilities.

How to Open the Notebook

To explore how the model works, follow these steps:

1. Install Jupyter Notebook: If not already installed, run pip install notebook in your terminal or command prompt.

2. Download the Notebook: Obtain the predictive model notebook from the designated repository or source.

3. Navigate to the Notebook's Directory: Open your terminal or command prompt, use cd to navigate to the directory where the notebook is located.

4. Launch Jupyter Notebook: Type jupyter notebook in the terminal and press Enter. This will open a new tab in your web browser.

5. Access the Notebook: In the Jupyter Notebook interface, navigate to the directory where the notebook is located and click on the notebook file (with a .ipynb extension).

6. Run the Notebook Cells: Once the notebook is open, run each cell sequentially to observe the model's functionality and visualize the results.

This hands-on approach allows you to interact with the model, understand its inner workings, and explore the insights it provides regarding customer churn for the travel and tours agency.