Photo by Jorge Franganillo on Unsplash

As discussed in the previous article via this link, we want to make a supervised learning classification to predict the churn rate from the telecommunication dataset. Before we go any further, the source codes and dataset can be downloaded from this Github.

Supervised learning is grouped into regression and classification. Regression is an analytical technique to identify a relationship between two or more variables that aim to find a function by modeling data to minimize the error or difference between the predicted value and the actual value. This technique is used to predict continuous values.

In contrast to regression, classification…


Photo by Egor Myznik on Unsplash

After finishing the data cleaning step in this link, we will try to make something from the clean dataset. This project will lead to Machine Learning Classification modeling for churn prediction of telecommunication dataset that will conduct in the next article. The source codes and dataset can be downloaded from this Github.

First and foremost, we will make data encoding first before we choose some variables using the Feature Selection Method. For proceeding with the data using this method, all data types should be numerical in which needs those data to be transformed (encode). Before that, we have to be…


Photo by Priscilla Du Preez on Unsplash

Cleaning the dataset is an essential task to do before using it for further proceedings, such as applying machine learning. This work is part of the data preparation after collecting some data needed. Optimizing the time consuming for data cleaning, which has to be needed most of our time during the project, we can make a list of several activities to clean the data. Before we go any further, the source codes and dataset can be downloaded from this Github.

Now let’s begin! First, I would like to load the dataset. …

Azizha Zeinita

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