🏠 House Price Prediction

A Machine Learning project that predicts house prices based on size, bedrooms and location — built with Python, Scikit-Learn and Matplotlib.

---

📌 About this project

This is my first end-to-end Machine Learning project. I built a model that can predict the selling price of a house when given three inputs — the size of the house, the number of bedrooms, and the location quality score.

I built this project to apply what I learned from the Machine Learning Specialization taught by Andrew Ng on Coursera.

---

🎯 Problem Statement

Given:
  → Size of house (sqft)
  → Number of bedrooms
  → Location score (1 to 10)

Predict:
  → Estimated price of the house (USD)

---

🗂️ Dataset

I created a realistic dataset of 20 houses. No external dataset was used.

Feature	Type	Description
size_sqft	int	Size of house in square feet
bedrooms	int	Number of bedrooms
location_score	int	Location quality from 1 (poor) to 10 (excellent)
price_usd	float	House price in USD — this is what we predict

---

🔄 Project Workflow

1. Create Dataset
       ↓
2. Explore and Understand Data
       ↓
3. Visualize with Charts
       ↓
4. Split into Train and Test sets (80/20)
       ↓
5. Train Linear Regression Model
       ↓
6. Evaluate Performance (R² Score, MSE)
       ↓
7. Predict New House Prices

---

📊 Model Results

Metric	Result
Algorithm	Linear Regression
R² Score	~0.99
Train set	80% of data
Test set	20% of data

R² Score of 0.99 means the model explains 99% of the price variation — very high accuracy for this dataset.

---

🖼️ Visualizations included

• 📉 House Size vs Price — scatter plot
• 🛏️ Bedrooms vs Price — scatter plot
• 🎯 Actual Price vs Predicted Price — comparison chart

---

💡 Sample prediction

predict_price(size=1500, bedrooms=3, location_score=7)

# ╔══════════════════════════════╗
# ║  House Price Prediction      ║
# ║  Size          : 1500 sqft   ║
# ║  Bedrooms      : 3           ║
# ║  Location Score: 7/10        ║
# ║  Predicted Price: $96,500    ║
# ╚══════════════════════════════╝

---

🛠️ Tech stack

Tool	Version	Purpose
Python	3.x	Programming language
NumPy	Latest	Numerical computation
Pandas	Latest	Data handling
Matplotlib	Latest	Charts and visualization
Scikit-Learn	Latest	ML model
Jupyter Notebook	Latest	Development environment

---

🚀 How to run

On Google Colab — no installation needed

1. Open the .ipynb file in this repository
2. Click Open in Colab
3. Click Runtime → Run all

On your local machine

pip install numpy pandas matplotlib scikit-learn jupyter
jupyter notebook House_Price_Prediction.ipynb

---

📁 Repository structure

House-Price-Prediction/
├── House_Price_Prediction.ipynb   # main notebook
└── README.md                      # project documentation

---

🔮 What I plan to add next

• Use a real dataset from Kaggle with 1000+ houses
• Add more features — house age, garage, garden, floors
• Compare with Random Forest and XGBoost models
• Build a simple web interface using Flask
• Deploy the model online using Streamlit

---

🧠 What I learned

Before this project, I only knew the theory of Linear Regression. Building it end-to-end taught me how to prepare real data, train a model, measure its accuracy, and make actual predictions. It also taught me how important data visualization is — the charts made it easy to understand what the model was learning.

This project gave me the confidence to take on more complex ML problems.

---

🔗 Related

• 📂 ML-Practice Repository — all my ML learning and practice notebooks
• 📂 Python-Documentation — my complete 30-class Python training course

---

🎓 Rayhan Uddin · Computer Science Student · Bangladesh

"Every expert was once a beginner. This project is one step in my journey."