This project aims to predict house prices in Brazil using data scraped from the Zap real estate website. The project is divided into the following steps:
- Web Scraping: Data is scraped from the Zap real estate website using the
requestsandBeautifulSouplibraries. - Data Exploration: The dataset is explored and visualizations are created to gain a better understanding of the data.
- Data Preprocessing: The data is preprocessed and prepared for training.
- Modeling: Regression and classification models are trained and validated using the prepared data.
The project folder is structured as follows:
- /data: Contains the raw_data and processed_data folders, which store the data to be used.
- /images: Holds the images used in the notebooks.
- /notebooks: Contains the Jupyter notebooks for various applications, including:
- 00_web_scraping.ipynb: Notebook used for scraping data from the Zap real estate website.
- 01_data_understand.ipynb: Notebook for exploring and visualizing the dataset.
- 02_prepare_data.ipynb: Notebook used to process data for training.
- 03_modeling_regression.ipynb: Notebook for training and validating regression models.
- 04_modeling_classification.ipynb: Notebook for training and validating classification models.
- /scripts: Contains additional scripts used in the project.
- columns.txt: A text file listing all the columns in the dataset.
- columns_described.txt: A text file describing the columns in the dataset.
- requiriments.txt: A file listing the project dependencies.
In the /notebooks directory, you will find Jupyter notebooks that serve different purposes within the project. Here's a brief overview:
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00_web_scraping.ipynb: This notebook is used to scrape data from the Zap real estate website.
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01_data_understand.ipynb: In this notebook, the dataset is explored and visualizations are created to gain a better understanding of the data.
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02_prepare_data.ipynb: This notebook focuses on data preprocessing and preparing the data for training.
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03_modeling_regression.ipynb: Here, regression models are trained and validated using the prepared data.
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04_modeling_classification.ipynb: This notebook involves training and validating classification models.
The /scripts directory contains additional scripts that are used in the project. You can find more details about each script within their respective files.
The project has dependencies listed in the requiriments.txt file. Make sure to install these dependencies before running the code. You can install them by running the following command:
pip install -r requiriments.txt