Welcome to Day 12 of WisdomAcademyAI, where we’re predicting numbers with the magic of Linear Regression! I’m Anastasia, your super thrilled AI guide, and today we’ll explore the basics of Linear Regression—a powerful ML technique to forecast numerical values like house prices. Sophia joins me with a magical demo using Python and scikit-learn to predict house prices based on size—it’s spellbinding! Whether you’re new to AI or following along from Days 1–11, this 27-minute lesson will ignite your curiosity. Let’s make AI magic together! br br Task of the Day: Build a Linear Regression model using Python (like in the demo) and share your R-squared in the comments! Let’s see your magical results! br br Subscribe for Daily Lessons: Don’t miss Day 13, where we’ll explore Logistic Regression Basics. Hit the bell to stay updated! br br #AIForBeginners #LinearRegression #MachineLearning #WisdomAcademyAI #PythonDemo #ScikitLearnDemobr br Generate houseprices.csv:br import pandas as pdbr import numpy as npbr br #Set a random seed for reproducibilitybr np.random.seed(42)br br #Generate data for 100 housesbr numrows = 100br br #Size: 800-3000 square feetbr size = np.random.randint(800, 3001, size=numrows)br br #Price: Linear relationship with size (price = 200 * size + 50000 + noise)br noise = np.random.normal(0, 20000, size=numrows)br price = 200 * size + 50000 + noisebr br #Create DataFramebr df = pd.DataFrame({br 'size': size,br 'price': pricebr })br br #Save to CSVbr df.tocsv("houseprices.csv", index=False)br print("Generated houseprices.csv with 100 rows!")br br Linear Regression Script:br import pandas as pdbr from sklearn.modelselection import traintestsplitbr from sklearn.linearmodel import LinearRegressionbr from sklearn.metrics import r2scorebr br #Step 1: Load the datasetbr df = pd.readcsv("houseprices.csv")br print("Original Dataset:")br print(df.head())br br #Step 2: Prepare the databr X = df[['size']]br y = df['price']br Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=0.3, randomstate=42)br br #Step 3: Train Linear Regressionbr model = LinearRegression()br model.fit(Xtrain, ytrain)br br #Step 4: Predict and evaluatebr ypred = model.