Linear Regression for House Price Prediction Overview This project applies Linear Regression to predict house prices based on various features. The goal is to create a model that can accurately estimate the price of a house given its attributes, which can be useful for buyers, sellers, and real estate professionals.
Python 3.x Libraries: pandas numpy scikit-learn matplotlib (optional, for visualization) seaborn (optional, for visualization) You can install the required libraries using pip:
pip install pandas numpy scikit-learn matplotlib seaborn
The house price data should be in a CSV file with the following structure: HouseID: Unique identifier for each house Price: The price of the house Size: The size of the house in square feet Bedrooms: Number of bedrooms Bathrooms: Number of bathrooms Location: Location of the house Example:
csv
HouseID,Price,Size,Bedrooms,Bathrooms,Location 1,250000,2000,3,2,Downtown 2,300000,2500,4,3,Suburb 3,150000,1200,2,1,Rural
- Load the Data python Copy code import pandas as pd
data = pd.read_csv('house_prices.csv') 2. Preprocess the Data Handle missing values and convert categorical data:
python
data = data.fillna(data.mean())
data = pd.get_dummies(data, columns=['Location'], drop_first=True) 3. Split the Data Split the data into training and testing sets:
python
from sklearn.model_selection import train_test_split
X = data.drop(['HouseID', 'Price'], axis=1) y = data['Price']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 4. Train the Linear Regression Model python
from sklearn.linear_model import LinearRegression
model = LinearRegression() model.fit(X_train, y_train) 5. Evaluate the Model python
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^2 Score: {r2}') 6. Analyze the Results python
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()
This README provides a simple guide to applying Linear Regression on house price data to predict prices based on various features. The resulting model can help in estimating house prices, facilitating better decision-making in the real estate market.
Feature engineering and selection can significantly improve model performance. Regularization techniques such as Ridge or Lasso regression can be applied to handle multicollinearity and improve model robustness. Evaluate the model using cross-validation for more reliable performance metrics.