🏠 California House Price Prediction Model By Mirza Yasir Abdullah Baig 

🧠 Introduction

The real estate market is influenced by multiple complex factors such as location, income, population, and housing conditions. Accurately estimating house prices is a challenging yet valuable problem — especially for buyers, sellers, and investors.

To explore how machine learning can address real-world business use cases, I built an AI-powered web application that predicts median house prices in California using the California Housing Dataset.

This project demonstrates an end-to-end ML workflow — from data preprocessing, feature scaling, model training, and evaluation to interactive deployment using Streamlit — bridging the gap between raw data and actionable real estate insights.

Also Read: https://github.com/mirzayasirabdullahbaig07/House-Price-Prediction-Model

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💡 Motivation & Intuition Behind the Project

1. Understanding Real-World Regression Problems:
   Most practical ML tasks are regression-based — predicting continuous values like price, temperature, or sales. I wanted to implement such a use case using a real dataset.

2. Showcasing End-to-End ML Skills:
   My goal was to demonstrate the complete data science lifecycle — preprocessing, training, scaling, saving, and deploying a model in a web app with user interaction.

3. Practical & Business-Oriented Problem:
   Real estate price prediction is a tangible, understandable domain that connects data science with real-world economics — perfect for interviews and portfolio showcases.

4. Portfolio & Learning Value:
   This project helped me strengthen my knowledge of Linear Regression, feature scaling, and deployment — all core concepts for ML engineering roles.

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📊 Dataset Overview

Dataset Name: California Housing Dataset

Source: Scikit-learn Built-in Datasets

The dataset contains real-world information about California districts collected from the 1990 U.S. Census, making it perfect for housing price prediction tasks.

Why This Dataset?

• It’s clean, reliable, and widely used in regression benchmarks.

• It demonstrates how socioeconomic and geographic factors affect housing prices.

• Ideal for understanding linear and non-linear feature relationships.

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⚙️ Techniques & Workflow

The entire project was built following a professional Machine Learning Pipeline.

1️⃣ Data Preprocessing

• Loaded dataset using sklearn.datasets.fetch_california_housing().

• Checked for missing values and outliers.

• Scaled features using StandardScaler for better regression performance.

• Selected key predictors: MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude.

2️⃣ Feature Scaling

Since the dataset includes features of different scales (income in thousands, rooms in hundreds, etc.), I applied feature scaling using StandardScaler to bring all features to a comparable range.
This step prevents bias in regression weights.

3️⃣ Model Selection

Chosen Model: Linear Regression

I experimented with a few models (Decision Tree, Random Forest), but chose Linear Regression for deployment because:

• It’s simple, interpretable, and efficient for continuous value prediction.

• The relationship between predictors and target is largely linear.

• Excellent baseline model for regression tasks.

4️⃣ Model Training

• Split data into training and test sets (80/20).

• Trained Linear Regression model on scaled features.

• Evaluated using R² score, MAE, and RMSE.

5️⃣ Model Serialization

• Saved the trained model as AIModel_For_House.pkl.

• Saved the fitted scaler as scaler.pkl for consistent input normalization during deployment.

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💻 Web App Development – Streamlit Deployment

I built a Streamlit web app to make the model accessible to anyone, even non-technical users.

🌐 App Features

• Sidebar input form for key housing features.

• Predicts median house price instantly using the trained Linear Regression model.

• Displays predicted value with a clear visual style.

• Includes an About Me section with portfolio links.

🛠️ Tech Stack

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📈 Results & Insights

• The model accurately predicts median house prices within a reasonable range.

• The most influential feature is Median Income, followed by Latitude and House Age — consistent with real-world housing market trends.

• Achieved a strong balance between simplicity and performance.

📊 Key Insights:

• Areas with higher median income → higher predicted prices.

• Newer houses (lower HouseAge) → higher value.

• Population density has a mild negative effect due to congestion.

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🧩 Interview Talking Points

If asked to elaborate this project in an interview, here’s how to respond:

1. What problem does it solve?
   It predicts house prices based on socioeconomic and geographic data — useful for buyers and investors.

2. Why did you choose Linear Regression?
   It provides high interpretability, requires minimal tuning, and fits well with continuous target prediction problems.

3. What preprocessing did you perform?
   Data scaling, feature selection, and train-test splitting to ensure balanced and unbiased learning.

4. How did you evaluate your model?
   Used R², MAE, and RMSE to measure accuracy and reliability.

5. What did you learn?

   • How to design and deploy a full ML system from scratch.

   • The importance of data preprocessing and scaling.

   • Real-world regression interpretation using feature importance.

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🚀 Demo & Access

🎥 Video Demo: House-Prediction.webm
🔗 Live App: https://housepredictionapp07.streamlit.app/
🧠 Model Files:

• AIModel_For_House.pkl

• scaler.pkl

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Mirza Yasir Abdullah Baig

• 💼 LinkedIn
• 🌐 Kaggle
• 💻 GitHub

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• California Housing Dataset
• Streamlit Documentation
• Scikit-learn

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⚠️ Disclaimer

This project is for educational and demonstration purposes only.
It is not intended for commercial real estate use without further professional validation.