Project $1$ Iris Dataset Context: This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. The data set contains $3$ classes of $50$ instances each, where each class refers to a type of iris plant. One class is linearly separable from the other $2$; the latter are NOT linearly separable from each other. Dataset Source: click here Task: Predict the class of iris plant. Questions to Answer: Perform EDA and Explore the features using histograms Encode the target variable ie convert it to numeric type Experiment using two different ratios of training, validation and test data ie $60-20-20,80-10-10.$ On the two different split ratios do the following o Implement KFold Cross Validation o Implement Grid Search to find optimal hyperparameters for any $3$ algorithms $($out of LR$,$ SVM$,$ MLP$,$ RF$,$ Boosting$)$ o Analyze the results on Validation set and test set and mention which model performed the best and why? o Compare the performance of models$($using precision, recall, accuracy, latency$).$ What was the best proportion or split ratio of data from the set of experiments you conducted?