https://github.com/lilivalgo/ml_time_series
This project uses time series data to predict corn crop yield in Colombia
https://github.com/lilivalgo/ml_time_series
matplotlib numpy pandas scipy seaborn sklearn
Last synced: 19 days ago
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This project uses time series data to predict corn crop yield in Colombia
- Host: GitHub
- URL: https://github.com/lilivalgo/ml_time_series
- Owner: LiliValGo
- License: mit
- Created: 2024-03-17T18:03:02.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-03-17T18:37:21.000Z (11 months ago)
- Last Synced: 2024-12-07T18:06:56.606Z (3 months ago)
- Topics: matplotlib, numpy, pandas, scipy, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 3.26 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Colombia Crop Yield Prediction
This project uses time series data to predict corn crop yield in Colombia.
## Data
The training data was obtained from [Datos Abiertos Colombia](https://www.datos.gov.co/Agricultura-y-Desarrollo-Rural/RENDIMIENTO-DE-CULTIVOS-EN-COLOMBIA-POR-A-O/fgh5-rjkd). All data related to corn crops were selected.
## Methodology
* Data processing and visualization were performed with Pandas and Seaborn.
* Two-time series linear regression models were constructed using two data transformation processes:
- Time-Step Feature
- Lag Feature
## Metrics
The models were evaluated using the following metrics:
* R-squared
* Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
* Mean Absolute Error (MAE)
* Residual Analysis
- A visual analysis of the residuals was carried out using Matplotlib and Seaborn.
- A quantitative analysis of the residuals was conducted using the Shapiro-Wilk normality test.
## Conclusions
It was concluded that using either transformation technique for the model's input features (Time-Step Feature and Lag Feature) does not represent much variation in the model's results except for the p-value of the Shapiro-Wilk normality test which marked a slight difference.