https://github.com/tupizz/FIAP_Oil_prices_Predictor
https://fiap-oil-price-prediction.streamlit.app/
data/brent_crude_oil_prices.csv- This is the main dataset that was used for the analysis.- To collect it I used the following code:
import pandas as pd df = pd.read_html('http://www.ipeadata.gov.br/ExibeSerie.aspx?module=m&serid=1650971490&oper=view', encoding='iso-8859-1', thousands='.', decimal=',')[2] # Use slicing to skip the first row df = df.iloc[1:].reset_index(drop=True) # Rename columns df.columns = ['Date', 'Price'] # Convert 'Date' to datetime df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y') # Convert 'Price' to float or int as necessary df['Price'] = df['Price'].astype(float) # Sort by 'Date' in descending order df = df.sort_values('Date', ascending=False) # save to csv df.to_csv('../data/brent_crude_oil_prices.csv', index=False)
notebook/analysis.ipynb- This is where the main data analysis was done, and the LSTM model was created.app.py- This is the Streamlit app that was created to visualize the data, and show the model being used.