House Price Prediction and Geospatial Analysis

In this tutorial, we will explore how to build a House Price Prediction model with geospatial analysis using Python, Dash, and Random Forest Regression. This project will guide you step-by-step through building an interactive dashboard that predicts house prices based on user input and visualizes the price trends across different regions on a map.

This blog will provide a thorough yet easy-to-understand explanation of the entire process, suitable for readers with no prior knowledge of machine learning or web application development.

Introduction

House prices are influenced by various factors like the number of rooms, population, and household density in an area. In this project, we’ll build a Random Forest Regression model to predict house prices and display the results on an interactive Dash dashboard. The dashboard will allow users to adjust key parameters like the number of rooms, bedrooms, and households to see how these factors affect house prices.

We’ll also incorporate geospatial data to visualize the house prices across different locations on a map.

Let’s get started!

Project Overview

The objective is to build a machine learning model that predicts house prices and then visualize the…