In this k-NN classification algorithm is used,which means k nearest neighbours .K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
In the proposed study on "Crop Prediction Based on Environmental Factors using Machine Learning," several key steps will be undertaken to achieve accurate and reliable crop yield predictions. The outline of the research process includes:
Data Collection and Preprocessing:
Gathering a comprehensive dataset that includes historical crop yield data and various environmental parameters such as temperature, humidity, precipitation, soil attributes, and more.
Cleaning and preprocessing the dataset to handle missing values, outliers, and inconsistencies that might affect the quality of predictions.
Feature Selection and Engineering:
Identifying the most relevant environmental factors that have a significant impact on crop yields through statistical analysis and domain knowledge.
Performing feature engineering to transform, normalize, or combine features, enhancing the models' ability to capture complex relationships.
Interpreting Model Insights:
Analyzing feature importance scores from models like decision trees and random forests to understand the contribution of each environmental factor to crop yield variations.
Identifying patterns, trends, and interactions among different factors that impact crop yields.
Forecasting and Prediction:
Utilizing the selected machine learning model to make predictions for future crop yields based on upcoming environmental conditions.
Validating the predictions against actual yield outcomes to assess the model's accuracy in real-world scenarios.