We can use regression models in Python to predict housing prices using various techniques. Here is an example using linear regression:
import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split
df = pd.read_csv('housing.csv')
X = df.drop(['Price'], axis=1) y = df['Price']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression() regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.metrics import mean_squared_error, r2_score mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print("Mean squared error (MSE):", mse) print("Coefficient of determination (R²):", r2)
import matplotlib.pyplot as plt plt.scatter(y_test, y_pred) plt.xlabel("Actual Prices") plt.ylabel("Predicted Prices") plt.title("Actual vs Predicted Prices") plt.show()
Note that there are various other regression models and techniques that can be used for housing price prediction, including decision trees, random forests, and support vector regression (SVR).
Please start posting anonymously - your entry will be published after you log in or create a new account. This space is reserved only for answers. If you would like to engage in a discussion, please instead post a comment under the question or an answer that you would like to discuss
Asked: 2023-05-01 15:37:51 +0000
Seen: 19 times
Last updated: May 01 '23
How can I use oversampling to address a problem?
What is the relationship between ESP8266 and Javascript AES?
What is the process of using Debye's equation in either Matlab or Python to model experimental data?
How to eliminate results from find_all?
How can the conditional user interface expression be expressed in the Maximo system?
What are the components that explain the state of ECMAScript execution context specification?