Linear Regression in Python

Python's Scikit-learn library makes it straightforward to fit a Linear Regression model, make predictions, and evaluate how well it performs - all in just a few lines of code.

Preparing the Data

import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.DataFrame({
    "HoursStudied": [1, 2, 3, 4, 5, 6, 7, 8],
    "Score": [35, 45, 50, 60, 65, 75, 80, 90]
})

X = df[["HoursStudied"]]   # features (must be 2D)
y = df["Score"]            # target

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

Fitting the Model

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

print("Slope:", model.coef_)
print("Intercept:", model.intercept_)

Making Predictions

predictions = model.predict(X_test)
print(predictions)

# Predicting for a new value
new_score = model.predict([[6.5]])
print(new_score)

Evaluating the Model

from sklearn.metrics import mean_squared_error, r2_score

print("MSE:", mean_squared_error(y_test, predictions))
print("R-squared:", r2_score(y_test, predictions))

Coming Up Next

Next, you'll extend this idea to Multiple Linear Regression, where more than one feature is used to predict the target.