Crop Data Preparation (Crop_data_prep.ipynb):

In this notebook, we meticulously preprocessed the data to ensure its optimal quality and readiness for subsequent stages.

Final Recommendation Data Creation (Final_recommendationdata_creation.ipynb):

This notebook focuses on the seamless merging of both datasets, streamlining the information for enhanced coherence and relevance.

Crop Recommendation Model Building (Crop_Recommendation_Model.ipynb):

In this comprehensive notebook, we employed a diverse set of techniques, including Decision Trees, Naive Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, and XG Boost, to craft a robust crop recommendation model.