How to Apply Machine Learning That will bolster your Business Operations

Machine learning (ML) refers to a set of tools that can be used to facilitate making predictions and decisions based on existing data. ML tasks are based on available data that is observed through instructions or experiences.

DataCamp Data Scientist Dr Hugo Bowne-Anderson defines ML as “the science and art of giving computers the ability to learn and make decisions from data without being explicitly programmed.”

ML can be used in real business to forecast customer attrition. Often businesses are required to analyze market trends and map a better way to predict customer churn. In the era of big data and machine learning, predicting consumer abrasion is not a walk in the park.

Businesses are increasingly becoming data-driven making it essential for the adoption of data visualization to make it easier to analyze. Machine learning entails collecting data, cleaning it, training the algorithm and then applying it.

To make work easier it’s advisable for businesses to apply four machine language methods and opt for the best based on performance. The four approaches are: decision tree, logistic regression, gradient boosting and random forest classifier.

According to a report published by Towards Data Science, the assessment can be based on training time and predictive power. AUC is the metric that can be applied in the measurement of predictive power because of its efficiency to distinguish between customers that churn and those that don’t.

Then the models are trained on a data set of clients based on gender, date a customer joined, country identifier and contact proclivity.

The first step of machine learning emerges during data preparation. At this stage data is analyzed then merged and cleaned. 

After data preparation, hot encoding is applied as machine language techniques interpret numbers and not words. At this stage variables are prepared to qualify machine learning models to work on them.

Modeling is then applied, where the four machine learning methods will be trained following a standard modeling conduit. Then after modeling, model evaluation will be done to ascertain the predictive power.

As had been stated earlier, the best model will be applied based on training speed and performance. Other than predicting customer churn, machine language can also be used to determine email spam and credit card fraud.

Phases of Machine Language in Summary

Problem analysis, data gathering, data preparation, choosing the right model, training the model, evaluating the results, searching for biases, tuning it, deploying the model, monitoring it and finally retraining it.