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Day 12: Linear Regression Basics—Predict Numbers with AI Magic! | #DailyAIWizard
Welcome to Day 12 of DailyAIWizard, 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! 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! Subscribe for Daily Lessons: Don’t miss Day 13, where we’ll explore Logistic Regression Basics. Hit the bell to stay updated! #AIForBeginners #LinearRegression #MachineLearning #WisdomAcademyAI #PythonDemo #ScikitLearnDemoGenerate house_prices.csv:import pandas as pdimport numpy as np#Set a random seed for reproducibilitynp.random.seed(42)#Generate data for 100 housesnum_rows = 100#Size: 800-3000 square feetsize = np.random.randint(800, 3001, size=num_rows)#Price: Linear relationship with size (price = 200 * size + 50000 + noise)noise = np.random.normal(0, 20000, size=num_rows)price = 200 * size + 50000 + noise#Create DataFramedf = pd.DataFrame({ 'size': size, 'price': price})#Save to CSVdf.to_csv("house_prices.csv", index=False)print("Generated house_prices.csv with 100 rows!")Linear Regression Script:import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import r2_score#Step 1: Load the datasetdf = pd.read_csv("house_prices.csv")print("Original Dataset:")print(df.head())#Step 2: Prepare the dataX = df[['size']]y = df['price']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)#Step 3: Train Linear Regressionmodel = LinearRegression()model.fit(X_train, y_train)#Step 4: Predict and evaluatey_pred = model.predict(X_test)r2 = r2_score(y_test, y_pred)print("\nR-squared:", r2)
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By OliverWelcome to Day 12 of DailyAIWizard, 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! 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! Subscribe for Daily Lessons: Don’t miss Day 13, where we’ll explore Logistic Regression Basics. Hit the bell to stay updated! #AIForBeginners #LinearRegression #MachineLearning #WisdomAcademyAI #PythonDemo #ScikitLearnDemoGenerate house_prices.csv:import pandas as pdimport numpy as np#Set a random seed for reproducibilitynp.random.seed(42)#Generate data for 100 housesnum_rows = 100#Size: 800-3000 square feetsize = np.random.randint(800, 3001, size=num_rows)#Price: Linear relationship with size (price = 200 * size + 50000 + noise)noise = np.random.normal(0, 20000, size=num_rows)price = 200 * size + 50000 + noise#Create DataFramedf = pd.DataFrame({ 'size': size, 'price': price})#Save to CSVdf.to_csv("house_prices.csv", index=False)print("Generated house_prices.csv with 100 rows!")Linear Regression Script:import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import r2_score#Step 1: Load the datasetdf = pd.read_csv("house_prices.csv")print("Original Dataset:")print(df.head())#Step 2: Prepare the dataX = df[['size']]y = df['price']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)#Step 3: Train Linear Regressionmodel = LinearRegression()model.fit(X_train, y_train)#Step 4: Predict and evaluatey_pred = model.predict(X_test)r2 = r2_score(y_test, y_pred)print("\nR-squared:", r2)