House Price Prediction Tutorial

This tutorial will guide you through the process of predicting house prices using machine learning. We'll cover the basics of data preprocessing, feature selection, and model training.

Prerequisites

Basic knowledge of Python
Familiarity with machine learning concepts

Getting Started

First, you'll need to install the necessary libraries. You can do this using pip:

pip install numpy pandas scikit-learn matplotlib

Data Preprocessing

Before you can train a model, you'll need to preprocess the data. This involves cleaning the data, handling missing values, and encoding categorical variables.

import pandas as pd

# Load the dataset
data = pd.read_csv('/path/to/house_prices.csv')

# Handle missing values
data = data.dropna()

# Encode categorical variables
data['bedrooms'] = data['bedrooms'].astype('category').cat.codes

Feature Selection

Next, you'll need to select the features that will be used to predict house prices. This can be done using various techniques, such as correlation analysis or feature importance.

from sklearn.feature_selection import SelectKBest, f_regression

# Select the top 5 features
selector = SelectKBest(score_func=f_regression, k=5)
selected_features = selector.fit_transform(data, data['price'])

# Get the selected feature names
feature_names = data.columns[selector.get_support()]

Model Training

Now that you have selected the features, you can train a model to predict house prices. We'll use a simple linear regression model for this tutorial.

from sklearn.linear_model import LinearRegression

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(selected_features, data['price'], test_size=0.2, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

Evaluate the Model

After training the model, you should evaluate its performance using metrics such as mean squared error (MSE) or R-squared.

from sklearn.metrics import mean_squared_error, r2_score

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate the MSE and R-squared
mse = mean_squared_error(y_test, y_pred)
r_squared = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse}')
print(f'R-squared: {r_squared}')