The process for writing any machine learning algorithm generally involves doing these five steps or answering the associated questions: write your answers in very short sentences
Define your problem: What is it you want to solve?
Prepare the data: In order to solve your problem, what form should your input data have? This may involve looking at the dataset as a table or set of grouped tables or visualizing it using graphs or charts.
Evaluate available algorithms: What type of algorithm are you considering using? Will it be a classification or binning algorithm, a regression to find relationships, or perhaps even a topological analysis of the data space? This step involves reviewing algorithms and determining the most appropriate one(s) to attack your problem. This will also involve adjusting the algorithm as you proceed, training your algorithm with data referred to as training data.
Improve your results: After some training, how does your algorithm work on non-training data? You need to use other data to test your model and see how the non-training data results compare to the testing data results.
Present your results: How will you visualize and share your results? Build a method to see how effectively your data model predicts output for other datasets in a comprehensive and easily understood manner.
Define your problem: Predict customer churn rate in a subscription-based service.
Prepare the data: Gather customer data including demographics, usage behavior, and subscription history.
Evaluate available algorithms: Consider using a classification algorithm to predict churn rate based on customer data.
Improve your results: Test the model using new customer data to see how accurately it predicts churn rate.
Present your results: Create visualizations to show the accuracy and reliability of the churn rate predictions.