EstateIQ is a Machine Learning-based web application that predicts house prices based on various property features.
The system uses historical housing data and a Linear Regression model to estimate the price of a house based on factors such as location, number of rooms, population, income level, and ocean proximity.
The project demonstrates a complete Machine Learning workflow including data preprocessing, feature engineering, model training, evaluation, and deployment using Streamlit.
- 🏠 House price prediction
- 🤖 Machine Learning based prediction
- 📊 Data preprocessing and analysis
- 🔢 Feature engineering using One-Hot Encoding
- 📈 Model evaluation using MAE, RMSE and R² Score
- 🌐 Interactive Streamlit web application
- 🎨 Custom background and user interface
- Python
- Scikit-learn
- Linear Regression
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Streamlit
- Joblib
EstateIQ/
│
├── app.py
├── train.py
├── test_model.py
├── requirements.txt
├── README.md │ ├── data/
│ └── housing.csv │ ├── models/
│ └── house_price_model.pkl │ ├── images/
│ └── background.jpg | └── notebooks/
└── learning.ipynb
git clone <repository-link>
---
## Step 2: Install Required Libraries
Install all required Python libraries using:
```bash
pip install -r requirements.txt
## step 3 : Run the application
Start the Streamlit application using:
#Step 4: Add Machine Learning Workflow
---
# 🧠 Machine Learning Workflow
The project follows a complete Machine Learning pipeline:
Dataset
↓
Data Cleaning
↓
Missing Value Handling
↓
Feature Engineering
↓
Train-Test Split
↓
Linear Regression Model
↓
Prediction
↓
Model Evaluation
↓
Deployment
# Step 5 : Add Model Evaluation Section
---
# 📊 Model Evaluation
The trained model is evaluated using the following performance metrics:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R² Score
The evaluation helps measure how accurately the model predicts house prices.
#Step 6: Add Dataset Information
---
# 📚 Dataset Information
Dataset Used:
**California Housing Dataset**
The dataset contains information about different housing areas including:
- Longitude
- Latitude
- Housing Median Age
- Total Rooms
- Total Bedrooms
- Population
- Households
- Median Income
- Ocean Proximity
The target variable used for prediction is:
- Median House Value