Predictive Modeling for Agriculture

This project was a valuable learning experience in understanding how individual features contribute to predicting crop types. By working with the soil dataset, I learned how to perform exploratory data analysis, handle data splitting, and evaluate models using single features one at a time.I realized that not all features are equally informative; in this case, Potassium (K) turned out to be the best predictor, although the accuracy was modest. This taught me the importance of feature selection and how using multiple features or more advanced models can improve predictive performance.Overall, this project strengthened my skills in Python, data handling with pandas, and applying machine learning models with scikit-learn. It also helped me appreciate the importance of clear code structure and interpreting model results thoughtfully.