DataScience 2023 Portfolio Project Idea

Kickstart your 2023 with this portfolio project Idea!

Here is the complete guide + code snippets to help you out with this project which was personally handcrafted by me!

Churn analysis is the process of studying customer behavior to identify factors that contribute to a customer's decision to stop doing business with a company.

It is an important part of customer relationship management, as it can help a company identify and address issues that may be causing customers to leave.

1/ Data

In order to perform a churn analysis, you will need customers' interaction data with the company.

This may include data on their purchases, customer service interactions, and other types of interactions.

You will also need data on whether or not the customer has churned, or stopped doing business with your company.

https://www.kaggle.com/datasets/blastchar/telco-customer-churn

https://www.kaggle.com/datasets/santoshd3/bank-customers

2/ Clean and prepare the data:

Before you can analyze the data, you will need to clean and prepare it.

This may involve removing any missing or incomplete data, as well as formatting the data in a way that is suitable for analysis.

3/ Explore the data:

Once you have cleaned and prepared the data, you can begin exploring it to get a better understanding of your customers and their behavior.

This may involve creating visualizations, such as histograms or scatter plots, to identify patterns and trends.

4/ Build a churn prediction model:

Once you have a good understanding of the data, you can build a churn prediction model using machine learning techniques.

This may involve using techniques such as logistic regression or decision trees to predict whether or not a customer is likely to churn based on their behavior.

5/ Validate and fine-tune the model:

Once you have built a churn prediction model, you will need to validate it to ensure that it is accurate and reliable.

This may involve testing the model on a separate dataset or using techniques such as cross-validation to ensure that it is not overfitting the data.

You may also need to fine-tune the model by adjusting its parameters or trying different machine-learning algorithms.

6/ Implement the model and take action:

Once you have a validated and fine-tuned churn prediction model, you can implement it in your business and use it to identify at-risk customers.

You can then take action to try and prevent those customers from churning, such as by offering incentives or addressing any issues they may be having with the company.

Hope this guide gives you the motivation to get started or improve your project portfolio in 2023!