Logistic Regression Model Building

Building a reliable Logistic Regression model involves more than just calling .fit() - it's a sequence of deliberate steps that shape how well the model performs on new data.

The Model Building Workflow

1. Collect and clean the data
2. Select relevant features
3. Split data into train and test sets
4. Scale/standardize features (if needed)
5. Fit the Logistic Regression model
6. Generate predictions on the test set
7. Evaluate performance

Implementing in Python

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

df = pd.DataFrame({
    "Income": [20000, 25000, 30000, 40000, 50000, 60000, 70000, 80000],
    "Age": [22, 25, 28, 32, 35, 40, 45, 50],
    "Buys": [0, 0, 0, 1, 1, 1, 1, 1]
})

X = df[["Income", "Age"]]
y = df["Buys"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

model = LogisticRegression()
model.fit(X_train_scaled, y_train)

print(model.predict(X_test_scaled))

Why Scaling Matters

Logistic Regression relies on gradient-based optimization, so features on very different scales (like income in thousands versus age in years) can slow convergence or bias the model - scaling puts every feature on comparable footing.

Coming Up Next

Next, you'll learn how to properly evaluate the performance of a Logistic Regression model.