IRIS_Dataset

Lab1: Introduction to Machine Learning

This lab introduces some basic concepts of machine learning with Python. In this lab you will use the K-Nearest Neighbor (KNN) algorithm to classify the species of iris flowers, given measurements of flower characteristics.

By the completion of this lab, you will:

1. Follow and understand a complete end-to-end machine learning process including data exploration, data preparation, modeling, and model evaluation.
2. Develop a basic understanding of the principles of machine learning and associated terminology.
3. Understand the basic process for evaluating machine learning models.

Lab Steps

1. Make sure that you have completed the setup requirements as described in requirement.txt.
2. Now, run jupyter notebook and open the “VisualizingDataForClassification.ipynb” notebook under this project.
3. Examine the notebook and answer the questions along the way.

Question1: From the plot, which species are more separated than the others?
Question2: What is the accuracy printed?
Question3: How many cases are mis-classified?

Lab2: Bagging

Lab Steps

1. Make sure that you have completed the setup requirements as described in requirement.txt.
2. Now, run jupyter notebook and open the “Bagging.ipynb” notebook under this project.
3. Examine the notebook and answer the questions along the way.

Question1: What is the accuracy of the model with 40 trees? Question2: What is the accuracy of the model with reduced feature sets?

Lab3: Boosting

Lab Steps

1. Make sure that you have completed the setup requirements as described in requirement.txt.
2. Now, run jupyter notebook and open the “Boosting.ipynb” notebook under this project.
3. Examine the notebook and answer the questions along the way.

Question1: What is the accuracy of the model with reduced feature sets?

Lab4: Neural Networks

Lab Steps

1. Make sure that you have completed the setup requirements as described in requirement.txt.
2. Now, run jupyter notebook and open the “NeuralNetworks.ipynb” notebook under this project.
3. Examine the notebook and answer the questions along the way.

Question1: What is the accuracy of the model with (100,100) hidden_layer_size?

Lab5: SVM

Lab Steps

1. Make sure that you have completed the setup requirements as described in requirement.txt.
2. Now, run jupyter notebook and open the “SupportedVectorMachines.ipynb” notebook under this project.
3. Examine the notebook and answer the questions along the way.

Question1: What is the accuracy of the model with nonlinear SVM?

Lab6: Naive Bayes

Lab Steps

1. Make sure that you have completed the setup requirements as described in requirement.txt.
2. Now, run jupyter notebook and open the “NaiveBayes.ipynb” notebook under this project.
3. Examine the notebook and answer the questions along the way.

Question1: What is the accuracy of the model with Gaussian Naive Bayes?