- This is the frontend for my ML house project.
- Users interested in finding a house can input a certain number of beds, baths, square feet and price.
- The output will be a house from a dataset that matches (as close as it can get in terms of machine learning) to those requirements.
- This shows the output
- The output consists of the top 3 closely matched houses to the data entered
- It provides with the address of the house which can then be entered into the house searcher
- This is the backend code
- It uses machine learning to predict a closely related house
- Each of the 3 houses goes in order of closeness
- The data is located in a CSV file I downloaded from a redfin API
- This is the frontend code
- It displays the table created to keep it all organized
- The prediction is achieved through a POST request to the backend
- The original data was 2 beds, 2 baths, 2000 square feet, and 10 million
- The house data from the data set is 3 beds, 2 baths, 1550 square feet and roughly 10 million, the model says it’s a 94% match from the dataset.
- This shows how my model works and utilizes ML to have an estimate of the house data entered.