bakkeshks / HomeScopeCA

A web application for predicting house price in California using MindsDB AI & Node.js.

HomeScopeCA

HomeScopeCA is a open source web app that uses MindsDB AI & Node.js to predict or forecast house price in California. The prediction is based on the trained machine-learning model developed using historical data on house prices in California.

HomeScopeCA is a powerful web application that can provide valuable insights to anyone who is interested in buying & selling property in California.

License

MIT

Demo Url

[Demo Live Site]

Demo Video

Screenshot

Tech Stack

• HTML, Bootstrap 3, Chart.js , EJS template engine, MindsDB JavaScript SDK (Frontend).
• Express, Node.js (Backend).
• MindsDB (Machine Learning, AI Tables)
• Linode Cloud (For Hosting Mindsdb docker image, Node.js on linode VM)

System requirements

• 4 core cpu Intel or Amd.
• 6 gb ram.
• 30 gb hard disk.
• Installed latest Docker Engine.
• Installed latest Node.js & Npm.
• Ubuntu (Recommend) or Mac os or Windows.
• 4 gb data to download mindsdb docker image.

Project Flow

• A user who want to predict the house price in CA can visit the site home page.
• In the form the user have to enter the values required to predict the house price in CA.
• After submiting form the request is sent to mindsdb server by node.js server to predict the house price for the given input.
• The node.js server get the response from the mindsdb server with the data.
• In the result page the value of the house price, Ocean proximity along with the California median values by Ocean Proximity is displayed.
• The user can view the average no of rooms, median price in CA over time in the Bar chart & Scatter Plot by clicking the dashboard page on the upper right of the home page.

Installation

Steps to run app on localhost

Step 1] Clone the project from github:

  git clone https://github.com/bakkeshks/HomeScopeCA.git

Step 2] Install the dependencies:

  npm install

Step 3] Install the latest version of MindsDB Docker Image (Docker Engine must installed on your local machine):

  docker pull mindsdb/mindsdb
  sudo systemctl start docker

Step 4] Download the dataset from kaggle:

https://www.kaggle.com/datasets/camnugent/california-housing-prices

Step 5] Run this command to start MindsDB in Docker.

docker run -p 47334:47334 -p 47335:47335 mindsdb/mindsdb

Step 6] Go to http://localhost:47334 & select the option to upload the data through files (.csv).

Step 7] Import the housing.csv & give home_table as the name of the table in the datasource name field.

Step 8] After you press save , it will import data to files database and it had created home_table in the files.

Step 9] Once table is created , you have to create & train the model with the data.

Train the Model:

CREATE MODEL
mindsdb.home_model
FROM files
(SELECT * FROM home_table)
PREDICT median_house_value;

Predict the model:

SELECT median_house_value
FROM home_model
WHERE longitude='-122.23' AND
latitude=37.88 AND
housing_median_age=41 AND
total_rooms=880 AND
total_bedrooms=129 AND
population=322 AND
households=126 AND
median_income=8.3252 AND
ocean_proximity='NEAR BAY'

Step 10] Now you can write the query & predit the value in the mindsdb editor.

Step 11] Start the node.js server on your machine:

node app.js 

Read Complete tutorial On My Hashnode Blog

Predicting house price In CA Using MindsDB AI

FAQ

Can I use MindsDB Cloud in this project?

Yes, you can use MindsDB cloud by registering the account on mindsdb by visiting https://cloud.mindsdb.com and by adding api key to project by changing some code.

Which dataset you used in the project?

I had used California Housing Prices dataset from kaggle which contains more than 20000 thousand rows. It was 1990 data set.

How long does it take to train the dataset?

I had used linode cloud hosting virtual machine running on ubuntu 22, 8gb ram, 4 core cpu took 35 - 40 minutes to create & train the model.