Housing Price Prediction Tutorial

This tutorial will guide you through the process of building a housing price prediction model using machine learning. We'll cover the basics, data preprocessing, model selection, and evaluation.

Prerequisites

Basic understanding of Python
Familiarity with machine learning concepts
Access to a dataset (e.g., Boston Housing Dataset)

Step 1: Data Preparation

First, we need to load and preprocess the data. The Boston Housing Dataset is a commonly used dataset for this purpose.

from sklearn.datasets import load_boston
import pandas as pd

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)
df['MEDV'] = boston.target

Step 2: Exploratory Data Analysis

It's important to understand the data before building a model. Let's visualize some basic statistics.

import matplotlib.pyplot as plt

df.describe()
plt.figure(figsize=(10, 6))
df.hist(figsize=(10, 6))
plt.show()

Step 3: Splitting the Data

We need to split the data into training and testing sets.

from sklearn.model_selection import train_test_split

X = df.drop('MEDV', axis=1)
y = df['MEDV']

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

Step 4: Model Selection

For this tutorial, we'll use a simple linear regression model.

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

Step 5: Model Evaluation

Let's evaluate the model using the test set.

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}")

Step 6: Further Reading

If you're interested in learning more about machine learning and housing price prediction, check out our Machine Learning Basics tutorial.