Machine Learning

  Machine Learning MCQs 👉1.  Machine Learning MCQs - UNIT 1 👉2.  Machine Learning MCQs-2 (Performance Metrics, Linear Regression, Naïve Bayes Classifier ) 👉3.  Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree) 👉4.  Machine Learning MCQs - 4 (Clustering, Dimensionality Reduction) 👉5. Machine Learning MCQs - 5 (Ensemble Models)

  Predicting Date Fruit Varieties with Support Vector Machines Context Objective is to leverage advanced machine learning techniques to predict the variety of date fruits, empowering farmers and agricultural stakeholders to improve classification accuracy and streamline post-harvest processes. Your role is to analyze various morphological, colorimetric, and textural attributes of date fruits to build predictive models that distinguish different varieties effectively. Dataset Description You have been provided with a comprehensive dataset containing morphological and colorimetric features of different varieties of date fruits. The dataset includes the following attributes: Morphological Attributes: AREA:  Surface area of the date fruit. PERIMETER:  Perimeter measurement around the fruit. MAJOR_AXIS:  Length of the major axis of the date fruit. MINOR_AXIS:  Length of the minor axis of the date fruit. ECCENTRICITY:  Ratio describing the shape of the fruit