This README provides an introduction to using linear regression for predicting housing prices. Linear regression is a fundamental statistical method for modeling the relationship between dependent variables (such as housing prices) and one or more independent variables (features).
Ensure you have a dataset containing historical data on housing prices. Typical features include:
- Size of the house: Total square footage.
- Number of bedrooms and bathrooms: Quantitative descriptors.
- Location: Geographical coordinates or categorical region data.
- Age of the house: Years since construction.
- Neighborhood demographics: Average income, crime rates, school ratings, etc.
- Special features: Pool, garden, garage size, etc.
Each row in the dataset represents a single house, with columns representing features that may influence its price.
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Data Preprocessing:
- Handle missing values: Either remove rows or impute missing data.
- Encode categorical variables: Convert categorical data (like location) into numerical form, if necessary.
- Feature scaling: Normalize numerical features to a standard range (e.g., using Min-Max scaling or standardization).
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Split Data:
- Divide the dataset into training and testing sets (typically 70-30 or 80-20 split).
- Ensure the training and testing sets are representative of the overall dataset.
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Train the Model:
- Use the training set to fit the linear regression model.
- The model will learn the coefficients (weights) for each feature, aiming to minimize the difference between predicted and actual housing prices.
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Evaluate the Model:
- Use the testing set to evaluate the model's performance.
- Common evaluation metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (coefficient of determination).
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Make Predictions:
- Apply the trained model to make predictions on new data (e.g., future housing listings).
- Interpret the predictions in the context of the problem domain (housing prices).
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Load dataset (replace with your dataset)
data = pd.read_csv('housing_data.csv')
# Define features and target variable
X = data[['Size', 'Bedrooms', 'Age', 'Location']]
y = data['Price']
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# Initialize Linear Regression model
model = LinearRegression()
# Train the model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')Linear regression provides a simple yet powerful approach to predicting housing prices based on historical data. It assumes a linear relationship between features and the target variable. For more complex relationships, consider advanced techniques like polynomial regression or machine learning algorithms such as Random Forests or Gradient Boosting.
For practical applications, ensure to continuously validate and update your model with new data to maintain accuracy and relevance in predicting housing prices.