Problem

A client in the financial industry approached us with a problem: they needed to predict customer churn rates in order to improve their retention efforts. They had a large dataset but didn't have the expertise to build and implement machine learning models.

We knew that this was a complex problem that required a range of different models, including Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, and Logistic Regression. We also knew that we needed to perform extensive data preprocessing in order to ensure that our models were accurate.

Learn more about our approach here.

Solution

We started by performing extensive data cleaning and preprocessing. This involved removing duplicates, handling missing values, and scaling the data appropriately. We then split the data into training and testing sets.

Next, we built several different machine learning models using Python. We used Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, and Logistic Regression in order to find the best fit for our dataset. We evaluated each model using various metrics such as mean squared error (MSE) and R-squared value.

Impact

Reduced customer churn rate by X%
Increased customer retention by X%

"This project had an incredible impact on our business - we were able to retain more customers than ever before thanks to the insights provided by these machine learning models."

Team

Team Member #1 - Data Scientist
- Built and evaluated machine learning models
- Performed extensive data preprocessing
Team Member #2 - Project Manager
- Oversaw the project from start to finish
- Liaised with the client to ensure their needs were met

Predicting Customer Churn with Machine Learning