Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables

Introduction

In this tutorial, we will learn how to predict home prices using multiple variable linear regression in Python. This process involves using three independent variables: area, bedrooms, and age of the home. We will utilize libraries like Pandas and Scikit-learn to handle data processing and model training. By the end, you will be equipped with the knowledge to implement linear regression for your own datasets.

Step 1: Prepare the Dataset

1. Obtain the Dataset

   • Download the dataset that contains information on home prices along with the variables: area, number of bedrooms, and age.

2. Load Data into Pandas DataFrame

   • Use the following code to load your dataset:

   import pandas as pd
   
   # Load the dataset
   df = pd.read_csv('path_to_your_dataset.csv')
   

Step 2: Data Preprocessing

1. Handle Missing Values

   • Check for any missing values in your dataset:

   print(df.isnull().sum())
   

   • Fill missing values with appropriate methods, such as the mean or median:

   df.fillna(df.mean(), inplace=True)
   

2. Feature Selection

   • Select the features (independent variables) and the target variable (dependent variable):

   X = df[['area', 'bedrooms', 'age']]  # Independent variables
   y = df['price']  # Dependent variable
   

Step 3: Train the Linear Regression Model

1. Import the Linear Regression Module

   • Import the linear regression model from Scikit-learn:

   from sklearn.linear_model import LinearRegression
   

2. Create and Train the Model

   • Initialize the model and fit it with the data:

   model = LinearRegression()
   model.fit(X, y)
   

Step 4: Make Predictions

1. Predict Home Prices

   • Use the trained model to make predictions:

   predicted_prices = model.predict(X)
   

2. Evaluate the Model

   • You can evaluate the model's performance using metrics such as Mean Absolute Error (MAE) or R² score:

   from sklearn.metrics import mean_absolute_error, r2_score
   
   print("Mean Absolute Error:", mean_absolute_error(y, predicted_prices))
   print("R² Score:", r2_score(y, predicted_prices))
   

Step 5: Exercise

• To solidify your understanding, work on the provided exercise to predict the salary of hired candidates based on specific parameters. You can find the exercise and its solution in the linked Jupyter Notebook on GitHub.

Conclusion

You have now learned how to implement multiple variable linear regression in Python to predict home prices. By preparing your dataset, handling missing values, training a model, and making predictions, you can apply this knowledge to various real-world applications. For further learning, consider exploring more complex models or diving into other machine learning techniques.