In this project, we analyze the well-known Iris dataset using machine learning techniques to classify different species of Iris flowers. This dataset is a classic in the field of data science and machine learning, often used as an introductory example for classification algorithms. The dataset was first introduced by Sir Ronald Fisher in 1936 and remains a benchmark for evaluating classification models. We employ Support Vector Machines (SVM) to classify the Iris species based on flower measurements. Additionally, we use Grid Search to fine-tune the model’s hyper-parameters for better performance. By comparing the performance of a baseline model with a tuned model, we demonstrate the effectiveness of hyper-parameter optimization.

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Dear professor Shestopaloff, Please find this link for my reports, solutions and results for assignment 2. Thank you Mohammad

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Shamecca Marshall, Angel Gallardo

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