This project is focused on predicting customer churn for a tour and travels company. Customer churn refers to when a customer stops using a company's services. In this case, we want to predict when a customer might stop booking tours with our company.
Background
Tour and travels companies depend on customer loyalty for repeat business. If a customer stops using the company's services, it can be costly to acquire a new customer to replace them. By predicting customer churn, a company can take proactive steps to retain customers and prevent them from leaving.
Data
The data used for this project comes from the company's booking system. It includes information about customers such as their age, gender, location, tour package, booking dates, payment method, and booking value. The dataset also includes a binary variable indicating whether the customer has churned or not.
Approach
We will use a machine learning algorithm to predict customer churn based on the features in the dataset. The approach will involve the following steps:
- Exploratory data analysis (EDA) to understand the data and identify any patterns or relationships.
- Data preprocessing to clean and prepare the data for modeling.
- Feature engineering to create new features or transform existing ones to improve model performance.
- Model selection to choose the best algorithm for predicting churn.
The project was developed using the following technologies:
- Python 3.8 above
- Jupyter Notebook
- NumPy
- Pandas
- Scikit-learn
Installation
To run this project, you will need to install the required libraries. You can do this using pip:
pip install numpy pandas scikit-learn
Usage
To use this project, you can run the Jupyter Notebook Tour&Travels Customer-- Churn_Prediction.ipynb. The notebook contains all the code used for the project, including EDA, data preprocessing, feature engineering, and model selection and evaluation.
Contributors This project was created by Tarak Ram . If you'd like to contribute to this project, please fork the repository and create a pull request with your changes.