Many businesses make a concerted effort to create work cultures that increase job happiness, encourage people to find meaning and satisfaction in their work, reward and recognize employees for their actions, and foster both personal and professional growth.
While many tactics are adopted for the company’s benefit and to considerably increase retention, leaders cannot rely only on them. They must face the uncomfortable reality that they will eventually lose significant talent if they do not keep an open mind and a realistic outlook on the future. Influential leaders act daily to safeguard themselves, their teams, and their companies from the risk of attrition because wishful retention thinking is not a viable business strategy.
Is it possible to foresee attrition so that it only impacts the business a little less, given that it is impossible to stop employees from leaving? Well, with the help of technology, it is possible. The churn model, among others, can help in this situation. Are you wondering how? Through an employee churn prediction model, we can make it happen.
After understanding which employees are on the verge of leaving using the churn model, it is possible to reach out to them and understand their grievances.
It’s a predictive model that calculates the likelihood (or vulnerability) of each employee leaving. It tells us how likely we will lose employees or a specific employee in the future at any given time. It classifies employees into two groups (classes): those who quit and those who don’t. It will typically tell us the probability of the employee belonging to which of the groups in addition to placing them in one of the two groups. Thus, a churn model can be used to estimate the chances of resignation.
Modern churn models frequently draw their foundation from machine learning, more specifically from binary classification methods. There are several of these algorithms; therefore, it’s important to test which one works best in each circumstance. Here we have made use of four machine learning models:
Supervised machine learning algorithms like random forest are frequently employed in classification and regression issues. On various samples, it constructs decision trees and uses their average for classification and majority vote for regression.
The Random Forest Algorithm’s ability to handle data sets with both continuous variables, as in regression, and categorical variables, as in classification, is one of its most crucial qualities. In terms of classification issues, it delivers superior outcomes.
One of the simplest machine learning algorithms, based on the supervised learning method, is K-Nearest Neighbour. The K-NN algorithm assumes that the new case and the existing cases are comparable, and it places the latest instance in the category that is most like the existing categories.
A new data point is classified using the K-NN algorithm based on the similarities after storing all the existing data. This means new data can be quickly and accurately sorted into a suitable category using the K-NN method. Although the K-NN approach is most frequently employed for classification problems, it can also be used for regression.
The supervised learning algorithms family includes the decision tree algorithm. The decision tree technique, in contrast to other supervised learning methods, can handle classification and regression issues.
By learning straightforward decision rules derived from previous data, a Decision Tree is used to build a training model that may be used to predict the class or value of the target variable (training data).
One of the most well-liked supervised learning algorithms, Support Vector Machine, or SVM, is used to solve Classification and Regression problems. However, it is employed mainly in Machine Learning Classification issues.
The SVM algorithm’s objective is to establish the best line or decision boundary that can divide n-dimensional space into classes, allowing us to quickly classify new data points in the future. A hyperplane is a name given to this optimal decision boundary.
SVM selects the extreme vectors and points that aid in creating the hyperplane. Support vectors representing these extreme instances form the basis for the SVM method.
Step 1: Loading data to databricks
In the initial stage, CSV data collected are loaded to the churn model. Any type of data sets can be employed here depending on the situation.
Step 2: Transformation: converting to the requisite format
While uploading, objective data sets are transformed into integers.
Step 3: Feature selection
There are four feature selection algorithms from which we take the best one to filter out undesired features. The selection of filtering features differs for each type of data based on the algorithm.
Step 4: Splitting the data
After the feature selection, the next step is to split the data for training and testing—then divide the data into a 7:3 ratio. In the training set, we train our model with data to understand the attrition patterns and later test it with data in the testing set.
Step 5: Standardisation
In this step, data is converted into a standard format that allows for large-scale analytics.
Step 6: Model selection
During model selection, datasets are provided to the machine algorithms like Random Forest, KNN, Decision tree and Support vector machine. Each algorithm produces its own sets of accuracy values; from that, the most accurate predictions are selected. Using the same procedure, we can categorize the employees into groups, for example, those who are planning to resign and those who are not.
Step 7: Result generation
The result is built on how each machine learning model performs with the dataset. The accuracy value depends on the performance of each model—the higher the accuracy, the higher the probability of accurately predicting the outcomes for each employee.