Python Tutorial: Preparation for modeling

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-for-marketing-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- You've done a great job! Now, we will explore the data preparation techniques for supervised learning models. The first thing you want to do is to explore the data sample. Ahead method called on a pandas dataframe will print the first five rows. Here we will use the telecom dataset. Next, it's always a good practice to review data types. As you can see most of the columns are strings marked as objects. This means they have text data that we'll have to transform so our model can use it. Before we start our data preparation steps, we need to separate the identifier such as customer id, and the target variable which in this case is the churn flag. We store them as separate lists that we will use later. Then, we separate categorical column names using a rule which defines a column as categorical if it has less than 10 unique values. This number is arbitrary, and it is a good practice to explore your data to see if there are variables with more unique values. We can explore them by running telco_raw.nunique() command on a dataframe and exploring the output. We will analyze this in the exercises for this lesson. The next step is to remove the target variable called Churn from this list so we don't do any transformations on it. Finally, we store the remaining column names into a list called numerical. We use list comprehensions here which are like loops only fit one line of code. It commands to extract all columns that are in the telco_raw but excludes the ones that are not in lists we have defined earlier with column names for customer id, target, and categorical variables. Now, we will convert these variables into binary columns with ones and zeros. This is called one-hot encoding. It transforms a categorical variable with string value