Ai-powered Real Estate Analyzer
Eduardo Takemura
Real Estate Analyzer
The Real Estate Analyzer is a data-driven application designed to fully extract, analyze, and model real estate data. It consists of an ETL pipeline module, for web scraping, data preprocessing, and database management, along with a front-end and back-end system that allows users to filter, analyze, and predict real estate prices based on current market.
Overview
The Real Estate Analyzer automates the process of gathering and analyzing real estate data. It scrapes property listings from real estate websites, processes the data, and stores it in a SQL database. The application provides an API for querying the data and includes a machine learning model to predict rental and selling prices based on user-defined filters. The app has a React front-end for user interaction, a Flask back-end to serve the data, and integrated tools for analysis and modeling.
Project Architecture
ETL (Extract, Transform, Load) Pipeline
Scrapper Module:
This module scrapes real estate data from web pages using Selenium.
It collects data such as property prices, locations, descriptions, and other relevant details from real estate listings.
Preprocessor Module:
After the data is scrapped, this module cleans, formats, and transforms the raw data into a structured format, ensuring it is suitable for storage and later analysis.
Common tasks include handling missing values, normalizing data, and removing duplicates.
Loader Module:
The preprocessed data is then loaded into a SQL database.
This database acts as the centralized storage for all real estate data, making it easy to query and retrieve specific subsets of information based on user requirements.
API
API:
The DB is accessible through an API built with Flask.
The API allows for filtering and querying the data based on various criteria such as location, price range, and property size.
It serves as the bridge between the DB and the application.
Application Components
React Frontend:
The front-end of the application is built with React. Initially, the user is presented with a simple form to input filters such as location, price, and property type.
Upon submitting the form, the React Frontend fetch data from the Flask Backend, which query the API server to retrieve the corresponding real estate data.
Data Analysis (Analyzer Module):
Once the data is fetched from the API, it is passed to the Analyzer Module.
This module performs various data analysis tasks on the filtered real estate data, generating visual reports in the form of charts and tables.
It helps users gain insights from the data by summarizing trends and patterns in the real estate market.
Price Prediction (Price Model Module):
The Modeller Module is a machine learning model that is trained on historical real estate data.
Its primary purpose is to predict renting or selling prices.
On the app, the model is loaded and used for inference after the data is analyzed, offering users a forecast of property values.
In the project there's also a ModelProcessor module, which goal is to prepare the data for the model training.
Flask Backend:
The Flask backend serves the React frontend, handling requests and communicating with the API, Analyzer and Price Model Modules.
It ensures the seamless flow of data (connection) between the Frontend and Backend, allowing users to interact with the real estate data in real-time.
Libraries
The project relies most on the following libraries:
Flask: Backend serving.
React.js: Frontend user interface.
Selenium: Web scrapping data tool.
Pandas/Numpy: Data preprocessing and manipulation.
SQLAlchemy: For handling the database.
PostgreSQL: Data Storage.
Scikit-learn: Machine learning tools.
Matplotlib/Seaborn: For visualizing data and charts in reports.
Kneed: Clustering Analysis tools.
Folium: Interactive maps.
TensorFlow/Keras: Deep Learning Model Outlining, Compiling, Saving and Loading.
Features
Web Scraping: Automatically scrapes real estate listings from multiple websites.
Data Preprocessing: Cleans and processes the scraped data for analysis.
SQL Database: Stores preprocessed real estate data for fast querying.
Filters: Allows users to filter real estate data by price, location, size, etc.
Data Analysis: Performs data analysis on queried data, displaying charts and tables.
Price Prediction: Predicts renting and selling prices using pre-trained machine learning models.
API Integration: The backend provides API endpoints for querying the database and returning results.
Interactive Frontend: A React-based frontend where users can input filters and receive analysis reports.
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Posted Nov 1, 2024
Data-driven application designed to fully extract, analyze, and model real estate data, allowing users to predict prices and assess insights on current market.
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