02_03_housing_csv_dataset.py

# Python code to illustrate
# regression using data set
import matplotlib.pyplot as plt
from sklearn import linear_model
import pandas as pd
import numpy as np

# Load CSV and columns
df = pd.read_csv("csv/Housing.csv")

Y = df['price']
X = df['lotsize']

X = X.values.reshape(len(X), 1)
Y = Y.values.reshape(len(Y), 1)

# Split the data into training/testing sets
X_train = X[:-250]
X_test = X[-250:]

# Split the targets into training/testing sets
Y_train = Y[:-250]
Y_test = Y[-250:]

# Plot outputs
plt.scatter(X_test, Y_test, color='black')
plt.title('Test Data')
plt.xlabel('Size')
plt.ylabel('Price')
plt.xticks(())
plt.yticks(())

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(X_train, Y_train)

# Plot outputs
plt.plot(X_test, regr.predict(X_test), color='red', linewidth=3)
plt.show()


new_house_lotsize = np.array([5000]).reshape(1, 1)  # Reshape for prediction

predicted_price = regr.predict(new_house_lotsize)

predicted_price_value = predicted_price[0][0]  # Extract actual price
print("Predicted Price for New House:", predicted_price_value)