Assume we defined: De-activation+15 days as Target Variable (Churn)In this case, the row in the model base shouldn’t just represent the latest customer data, but customer data, with respect to time (month here).Say, I churned in Jan 2019, returned in Mar 2019, again churned in May 2019, returned in Aug 2019. Make learning your daily ritual. For a banking domain problem of loan churn (balance transfer), even if we got the high-risk customers using Machine Learning, since all the loan terms are fixed, there is nothing you can offer your customer to retain him/her. Are the expected gains more than the cost of implementation?Let’s take an example of a churn model for bank loans. ‘Exploratory data analysis’ is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.” Introduction. Say for eg. Kaggle is the market leader when it comes to data science hackathons.
All the datasets are available. In the churn prediction problem for the streaming service, we could create variables like:We must be creative here. We are predicting the probability that a customer will churn at a particular The Target Variable definition should be exactly the same as it is defined in the Problem Statement from part 1). etc. Can the data for those factors be availed?If the model is ready, is it possible to implement? Avoid Deep Learning.Project is over. Do hyper-parameter tuning. Here we’ll add some filters to restrict the data to just datasets with many downloads but only a few kernels.In the top-left of the pivot widget, click the Plus (You can see the filters immediately applied and the data reduced from 7,666 unique combinations to just the 612 unique combinations matching our filters.icon in the top right of the pivot widget and selectbox to the right of the drop down selection box as shown below.Click the lower drop down selection box and change “Click the leftmost arrow twice so that it is pointing up to sort the data.We can immediately see a variety of very popular datasets that have been downloaded thousands of times yet have very few or no kernels developed. (May not be always).What is the cost involved in the implementation? A customer having a low probability of churn but a heavy loss if he churns, he must be contacted.Few days before the EMI payment date? Probability of a Customer A to churn in Jan 2020 could be different than Oct 2019. Lower interest rate, or any other benefit.Are we only considering the churn probability to determine the population to target? We must get data from multiple sources, transform it into a usable form and create new features out of it to be included in the model base. If the ROI is less, there is no point in doing this project.Since we are predicting the probability of churn, this is a predictive analytics projectLet’s directly talk about predictive analytics, since we are dealing with churn modelling.Doing a bivariate visualization of each variable with the target variable churn, we could know the impact of each variable on Churn. Is it possible to implement?Can we think of a few factors including customer demographics, customer-product characteristics, external variables that may be leading to churn? The implementation part may seem more challenging than the predictive modelling part. How much of population to contact? Check the distribution. It is all-inclusive of the cost of execution of the project, implementation of the project, etc. In highly dynamic situations, models would frequently demand refurbishment.Hope you got a feel of how complex things are in the real world.Note: This article is not intended to be critical of the academic courses or the Kaggle platform.Note: I would be more than happy to help the data science beginner students in the US who are looking for internships/full-time job in data science. (40 mins read for beginners. They won’t be convinced with how much ever visualizations you show them. are used in the industry for EDA.Try many algorithms. Explore the columns to get an understanding of data.
Finding a few of those high-risk profiles and only targeting them, could save us from investing time and resources behind Machine Learning. Likewise, there would be historical data for all the customers. Generally, tools like Excel, Tableau, PowerBI etc.
The fact is that despite the concerns Kaggle was never intended to copy machine learning and data science in the real world. For this project, I wanted to delve deep into Kaggle AirBnB datasets for Seattle and Boston. Churn could be a permanent churn or a temporary churn (wherein customer returns after a certain time) depending on the domain.Streaming service providers (like Netflix) have temporary churn phenomenon. No points for presentation, focus on getting models more fine-tuned.The management wants a dashboard with slicers, showing all the variable relationships. Datasets, high-quality & relevant data. This is more of an art than science. Do we have the availability of the required data and resources? There’s no spoon-feeding of the features. How to contact? What is the probability I will churn in April 2020 if I deactivate (Don’t recharge on April 1st)?This is how the rows of my data would look like in a model base. Use the link below to go to the dataset on Kaggle.
This is not as simple as it seems. These words are believed to belong to a prominent American mathematician — John Tukey. Target variable column is already available. Etc. Whereas in permanent churn problems, the customer never returns. Would they want to call the active customers too?