• Arpan G Singh
• Prof. Manish Kumar Singhal

In this paper explores the development and evaluation of a prediction model based on the Iris dataset, leveraging various machine learning techniques. The Iris dataset, a widely used benchmark in classification problems, contains features describing the sepal and petal dimensions of three flower species: Iris-setosa, Iris-versicolor, and Iris-virginica. Machine learning algorithms, including decision trees, support vector machines, k-nearest neighbors, and logistic regression, were employed to classify the species based on their features. he dataset was preprocessed to ensure data quality, and techniques like train-test splitting and cross-validation were applied to optimize model performance and avoid over fitting. The models were assessed using metrics such as accuracy, precision, recall, and F1-score, providing insights into their predictive capabilities. Among the algorithms tested, specific models demonstrated superior performance, highlighting the effectiveness of feature selection and hyperparameter tuning. The results underscore the potential of machine learning in solving classification problems and serve as a foundation for further applications in predictive analytics

Machine Learning, Iris Flower, Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, SVM

"Prediction Model Based on Iris Dataset Using Machine Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.d487-d495, December-2024, Available :http://www.jetir.org/papers/JETIR2412360.pdf

"Prediction Model Based on Iris Dataset Using Machine Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppd487-d495, December-2024, Available at : http://www.jetir.org/papers/JETIR2412360.pdf