https://github.com/tupizz/fiap_oil_prices_predictor
FIAP_Oil_prices_Predictor
https://github.com/tupizz/fiap_oil_prices_predictor
Last synced: 2 months ago
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FIAP_Oil_prices_Predictor
- Host: GitHub
- URL: https://github.com/tupizz/fiap_oil_prices_predictor
- Owner: tupizz
- Created: 2024-05-07T23:19:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-11T22:40:14.000Z (about 1 year ago)
- Last Synced: 2025-02-09T14:47:13.627Z (4 months ago)
- Language: Jupyter Notebook
- Size: 5.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### Github
https://github.com/tupizz/FIAP_Oil_prices_Predictor
### Streamlit
https://fiap-oil-price-prediction.streamlit.app/
### Main Files to analyze
- `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:
```python
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.