House Price Predictor Project

This project is a web application for predicting house prices in California using machine learning (I use a sklearn dataset fetch_california_housing). Users can input various characteristics of a house, and based on this data, the model predicts the price of the house.

Project Structure

house_price_predictor/
│
├── app/
│ ├── init.py
│ ├── routes.py
│ ├── forms.py
│ └── templates/
│ ├── base.html
│ ├── index.html
├── models/
│ ├── train_model.py
│ └── model.pkl
├── run.py
└── requirements.txt

• app/: Directory containing the code for the web application.
  • routes.py: Application routes.
  • forms.py: Form definition for data input.
  • templates/: HTML templates for displaying the user interface.
• models/: Directory containing files for model training and the saved trained model.
  • model.pkl: Trained model
  • train_model.py: Model training file
• run.py: File to run the web application.
• requirements.txt: Project dependencies file.

Input Data

Users can input the following house characteristics for price prediction:

1. Median Income (Median Income): Median household income in the area (in tens of thousands of dollars).
2. House Age (House Age): Average house age in the area (in years).
3. Average Number of Rooms (Average Rooms): Average number of rooms in the house.
4. Average Number of Bedrooms (Average Bedrooms): Average number of bedrooms in the house.
5. Population (Population): Population of the area.
6. Average Occupancy (Average Occupancy): Average house occupancy.
7. Latitude (Latitude): Geographic latitude of the area.
8. Longitude (Longitude): Geographic longitude of the area.

Output Data

1. Predicted House Price: Predicted house price based on the entered data
2. Rounded Prediction: Rounded house price

Usage example

If you want to predict the value of a house with the following characteristics:

• Median Income: 60 000$
• House Age: 25 years
• Average Rooms: 7
• Average Bedrooms: 3
• Population: 2500 people
• Average Occupancy: 3.5 people
• Latitude: 34.05
• Longitude: -118.25

Then the form will be filled out as follows:

• Median Income: 6.0
• House Age: 25.0
• Average Number of Rooms: 7.0
• Average Number of Bedrooms: 3.0
• Population: 2500.0
• Average Occupancy: 3.5
• Latitude: 34.05
• Longitude: -118.25

Running the Application

1. Install the repository

git clone https://github.com/Bebrowskiy/house-price-predictor.git

2. Install dependencies

pip install -r requirements.txt

3. Train the model

python models/train_model.py

4. Run the web application

python run.py

Then, open a web browser and go to http://127.0.0.1:5000/ to start using the application.