Machine Learning for Marketing Basics

Basic Data Exploration

# Print the data types of telco_raw dataset
print(telco_raw.**dtypes**) ## Similar to .info()

# Print the header of telco_raw dataset
print(telco_raw.head())

# Print the number of unique values in each telco_raw column
print(telco_raw.**nunique()**)

Separate Categorical and Numerical Columns

Separate the identifier and target variable names as lists

custid = ['customerID']
target = ['Churn']

Separate categorical and numeric column names as lists

categorical = telco_raw.nunique()[telcom.nunique()<10].keys().tolist()
categorical.remove(target[0])
numerical = [col for col in telco_raw.columns if col not in custid + target + categorical]

One-Hot Encoding

Transforming categorical variables into a various boolean variables.

# Perform one-hot encoding to categorical variables 
telco_raw = pd.get_dummies(data = telco_raw, columns = categorical, drop_first=True)

Scaling Numerical Features

# Import StandardSclaer library
from sklearn.preprocessing import StandardScaler

# Initialize StandardScaler instance
scaler = StandardScaler()

# Fit the scaler to numerical columns
scaled_numerical  = scaler.fit_trasform(telco_raw[numerical])

Bringing it All Together

# Drop non-scaled numerical columns 
telco_raw = telco_raw.drop(columns=numerical, axis=1)

# Merge the non-numerical with the scaled numerical data
telco = telco_raw.merge(right=scaled_numerical, 
												how='left',
												left_index=True,
												right_index=True)

Supervised Learning Steps

Split data to training and testing
Initialize the model