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?