Project: Unravealing Real Estate Ads

This project requires Python 3.X and the following Python libraries installed:

To run this project, the Anaconda manager was used, in a Jupyter notebook environment.

train_test_split -> This library is used to separate test and training data.
DecisionTreeRegressor -> This library was used to predict each of the attributes based on all the others, to see which attributes are easier to predict.
PCA -> This library was used to find the main components that direct the data in this dataset.
LabelEncoder -> This library was used to code the categorical columns in a readable format for training models.
ProfileReport -> This library was used to generate a report of the attributes present in the dataset, with the purpose of helping in cleaning the data.

Files

Real Estate Ads Report.pdf -> Report created from the data set, taking into account the attributes present.

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
Cópia de [EN] Technical Test - Data Science - Real Estate.docx		Cópia de [EN] Technical Test - Data Science - Real Estate.docx
Draft - First contact with the data.ipynb		Draft - First contact with the data.ipynb
Draft - Second contact with the data..ipynb		Draft - Second contact with the data..ipynb
README.md		README.md
Unraveling Real Estate Ads.ipynb		Unraveling Real Estate Ads.ipynb
real_estate_ads_report.html		real_estate_ads_report.html
real_estate_ads_report_2.html		real_estate_ads_report_2.html