Implement a machine learning model using Python and scikit-learn to predict house prices based on features such as square footage, number of bedrooms, and location. Use linear regression for the prediction and split the dataset into training and testing sets to evaluate the model's performance.

To implement a machine learning model to predict house prices using Python and scikit-learn, follow these steps:

1. Import necessary libraries
2. Load the dataset
3. Preprocess the data (if needed)
4. Split the dataset into training and testing sets
5. Train the linear regression model
6. Evaluate the model's performance

Here is an example implementation:

```python
# Step 1: Import necessary libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Step 2: Load the dataset
data = pd.read_csv('house_prices.csv') # Assuming you have a CSV file named 'house_prices.csv'

# Step 3: Preprocess the data (if needed)
# For this example, we assume that there are no missing values and all features are numerical.
# If not, preprocess the data accordingly.

# Step 4: Split the dataset into training and testing sets
X = data.drop('price', axis=1) # Features (square footage, number of bedrooms, location, etc.)
y = data['price'] # Target variable (house price)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 5: Train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Step 6: Evaluate the model's performance
y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("Root Mean Squared Error:", rmse)
print("R-squared Score:", r2)
```

Replace `'house_prices.csv'` with the path to your dataset file. Make sure that the dataset contains features such as square footage, number of bedrooms, and location, as well as the target variable (house price). If the data needs preprocessing, modify Step 3 accordingly.

This example uses linear regression for prediction and splits the dataset into training and testing sets to evaluate the model's performance. The evaluation metrics used are mean squared error (MSE), root mean squared error (RMSE), and R-squared score.