Feesout
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c_cpp
5 months ago
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#####Linear regression # Import necessary libraries import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression # Prepare the data data = {'Size': [1000, 1500, 1200, 2000, 1800, 1300, 1600, 1400, 1700, 1100], 'Price': [150000, 200000, 180000, 250000, 220000, 190000, 210000, 200000, 225000, 170000]} df = pd.DataFrame(data) # Separate features (X) and target variable (y) X = df[['Size']] # Independent variable (size) y = df['Price'] # Dependent variable (price) # Create a linear regression model model = LinearRegression() # Train the model on the data model.fit(X, y) # Make a prediction for a new house size new_size = 1850 predicted_price = model.predict([[new_size]]) print("Predicted price for a house of 1850 sq ft:", predicted_price[0]) # Visualize the results plt.scatter(X, y, color='blue', label='Actual Data') plt.plot(X, model.predict(X), color='red', label='Regression Line') plt.xlabel("Size (sq ft)") plt.ylabel("Price (USD)") plt.title("House Price Prediction") plt.legend() plt.show() ###### Ploolynomial Regression import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression # Read the data from a CSV file df = pd.read_csv('house_prices.csv') # Separate features (X) and target variable (y) X = df[['Size']] # Independent variable (size) y = df['Price'] # Dependent variable (price) # Create polynomial features (e.g., degree 2) poly_features = PolynomialFeatures(degree=2) X_poly = poly_features.fit_transform(X) # Create a linear regression model model = LinearRegression() # Train the model on the polynomial features model.fit(X_poly, y) # Make a prediction for a new house size new_size = 1850 new_size_poly = poly_features.transform([[new_size]]) predicted_price = model.predict(new_size_poly) print("Predicted price for a house of 1850 sq ft:", predicted_price[0]) # Visualize the results plt.scatter(X, y, color='blue', label='Actual Data') plt.plot(X, model.predict(poly_features.fit_transform(X)), color='red', label='Polynomial Regression') plt.xlabel("Size (sq ft)") plt.ylabel("Price (USD)") plt.title("House Price Prediction") plt.legend() plt.show()
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