House price prediction is a practical application. Regression models are well-suited for this task. We aim to predict a continuous value. That value is the house price. This guide explains the process in Python. We will use common machine learning tools.
First, data loading is necessary. We use Pandas to load house price data. Assume the data is in a CSV file. Explore the data to understand features. Features could include size and location. Target variable is the house price itself.
import pandas as pd
data = pd.read_csv('house_prices.csv')
print(data.head())
Data preprocessing is a crucial step. Handle missing values appropriately. Impute missing values or remove rows. Feature scaling improves model performance. Standardize or normalize numerical features. Scikit-learn provides scaling tools.
from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler imputer = SimpleImputer(strategy='mean') numerical_features = data.select_dtypes(include=['number']).columns data[numerical_features] = imputer.fit_transform(data[numerical_features]) scaler = StandardScaler() data[numerical_features] = scaler.fit_transform(data[numerical_features])
Feature engineering can improve accuracy. Create new features from existing ones. For example, create area per room. Combine location features if needed. This step depends on data understanding.
Model selection is important next. Linear Regression is a simple option. Random Forest Regressor is more complex. It often performs better. Choose a model based on data complexity. Scikit-learn offers various regressors.
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
X = data.drop('price', axis=1) # Features
y = data['price'] # Target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestRegressor()
model.fit(X_train, y_train)
Evaluate the model’s performance. Use metrics like Mean Squared Error (MSE). R-squared is another useful metric. Lower MSE and higher R-squared are better. Evaluate on the test set for generalization.
from sklearn.metrics import mean_squared_error, r2_score
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")
Finally, use the trained model for predictions. Provide new house features to the model. The model predicts the house price. Regression models are powerful for price prediction. This process helps understand the workflow. Experiment with different models and features. Improve prediction accuracy further.