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We provide abstract of svm machine learning in this paper.

Introduction: Crop yield prediction is crucial for agricultural planning and management. Traditional methods often lack accuracy and efficiency. Machine learning algorithms, such as Support Vector Machines (SVM), offer promising solutions to improve prediction accuracy.

Methodology: This study employs SVM, a supervised learning algorithm, to predict crop yields. SVM works by finding the optimal hyperplane that best separates different classes in the feature space.

Data Collection and Preprocessing: The study gathers relevant data including weather patterns, soil characteristics, crop types, and historical yield records. Preprocessing involves cleaning the data, handling missing values, and normalization to ensure consistency and reliability.

Feature Selection: Feature selection is conducted to identify the most influential variables impacting crop yields. This step helps improve model performance by focusing on relevant factors.

Evaluation Metrics: Performance evaluation is crucial to assess the effectiveness of the SVM model. Metrics such as accuracy, precision, recall, and F1-score are employed to measure prediction quality and model reliability.

Results and Discussion: The study presents the results of crop yield prediction using SVM. Comparative analysis with traditional methods demonstrates the superiority of SVM in terms of accuracy and efficiency.

Conclusion: Crop yield prediction using SVM proves to be a valuable approach for optimizing agricultural practices. The study underscores the importance of leveraging advanced technologies like machine learning for enhanced decision-making in agriculture. Further research can explore additional algorithms and datasets to improve prediction accuracy and address emerging challenges in food security and sustainability.

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