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Introduction: Why Predicting House Prices Matters

In today’s data-driven real estate industry, accurately predicting house prices is crucial for buyers, sellers, and investors. With the rise of data science, machine learning (ML) models have become a go-to tool for predicting housing prices based on historical data and various property features.

This comprehensive guide walks you through an end-to-end ML project: Predicting House Prices Using Machine Learning, complete with:

• Real housing dataset
• Feature engineering
• Model training
• Evaluation and visualization
• Streamlit deployment for real-time prediction

Whether you’re a machine learning student, data science learner, or aspiring AI analyst, this project will boost your portfolio.

Dataset Overview and Download

For this project, we use a structured housing dataset that contains 545 observations across 13 features.

🔗 Download the Dataset

📋 Dataset Columns and Explanation:

• price: Target variable, the house price in INR.
• area: Size of the house in square feet.
• bedrooms: Number of bedrooms.
• bathrooms: Number of bathrooms.
• stories: Number of stories (floors).
• mainroad: Whether the house is on a main road (yes/no).
• guestroom: Availability of a guestroom (yes/no).
• basement: Whether the house has a basement (yes/no).
• hotwaterheating: Availability of hot water heating (yes/no).
• airconditioning: Whether the house has air conditioning (yes/no).
• parking: Number of parking spaces.
• prefarea: Preferred locality (yes/no).
• furnishingstatus: Furnishing status (furnished, semi-furnished, unfurnished).

These features represent a combination of structural and amenity-based characteristics, ideal for training a regression model.

Step 1: Understanding the Dataset

We use a dataset containing 545 records and 13 features, including:

• Numerical Features: area, bedrooms, bathrooms, stories, parking
• Categorical Features: main road, basement, guest room, air conditioning, hot water heating, preferred location, and condition of the furnishings
• Target Variable: price

This rich combination allows us to build a predictive model that reflects real-world housing dynamics.

Step 2: Data Preprocessing

Python Code: Importing Libraries and Data

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import streamlit as st

Explanation:

• pandas, numpy for data handling
• seaborn, matplotlib for visualizations
• sklearn for modeling
• streamlit for deployment

Load the Dataset

df = pd.read_csv('Housing2.csv')
df.head()

Handling Categorical Variables

df = pd.get_dummies(df, drop_first=True)

• Converts categorical features to numeric using one-hot encoding.

Splitting Features and Labels

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

Step 3: Model Building – Linear Regression

Train-Test Split

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

Model Training

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

Evaluation

y_pred = model.predict(X_test)
print("R2 Score:", r2_score(y_test, y_pred))

The model’s ability to explain variance in home prices is indicated by the R-squared score.

Step 4: Streamlit App – Deploy the Model

app.py Sample Code:

import streamlit as st
import numpy as np
import pickle

# Load model
model = pickle.load(open('model.pkl', 'rb'))

st.title("🏠 House Price Predictor")

# Input fields
area = st.number_input('Area (sq ft)', min_value=500)
bedrooms = st.selectbox('Bedrooms', [1, 2, 3, 4, 5])
bathrooms = st.selectbox('Bathrooms', [1, 2, 3, 4])
stories = st.selectbox('Stories', [1, 2, 3, 4])
parking = st.selectbox('Parking Space', [0, 1, 2, 3])
mainroad = st.selectbox('Mainroad Access', ['yes', 'no'])

# Convert input
input_data = np.array([[area, bedrooms, bathrooms, stories, parking, 1 if mainroad=='yes' else 0]])

# Predict
if st.button('Predict Price'):
    price = model.predict(input_data)
    st.success(f"Estimated House Price: INR {price[0]:,.2f}")

Step 5: Saving and Exporting the Model

import pickle
pickle.dump(model, open('model.pkl', 'wb'))

Visualization: Understanding Feature Importance

coeffs = pd.DataFrame(model.coef_, X.columns, columns=['Coefficient'])
coeffs.sort_values(by='Coefficient', ascending=False).plot(kind='bar', figsize=(12, 6))
plt.title('Feature Importance')
plt.show()

Conclusion

Predicting house prices using machine learning is not only a useful real-world application but also an excellent beginner-friendly project to sharpen your skills in data preprocessing, modeling, and deployment.

• Load and clean housing data
• Engineer features
• Train a regression model
• Evaluate its performance
• Deploy it using Streamlit

The concept of House price prediction using ML provides actionable insights for both technical learners and real estate professionals. As you experiment with new features or improve the model, you’ll gain a deeper understanding of how machine learning can be applied in real-world domains.

This hands-on example of House price prediction using ML serves as a cornerstone for aspiring data scientists.

👉 Want to build more projects like this? Join the BiStartX ML Internship Program and become job-ready in just 90 days!

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