This machine learning project’s objective is to find the best suitable crop for agricultural land by learning from the past yielded crops. Various factors are considered for determining the best yielding crop, such as the climate, which includes rainfall, temperature, and the soil contents such as the pH level, nutrient content such as N, P, K of the soil, and more. During this project, several machine learning algorithms are applied, and performance comparison is made between them.
The features for our model are:
- Rainfall in mm
- Temperature in Celsius
- Humidity
- pH level of soil
- Nitrogen (N) level of soil.
- Phosphorous (P) level of soil.
- Potassium (K) level of soil
- Iron (Fe) level in soil
- Calcium (Ca) level of soil
- Magnesium (Mg) level of soil
- Sulphur (S) level of soil
- Manganese (Mn) level of soil
jupyter notebook ML_final_code.ipynb