• 1-M   • 2-D

Q1) Implement Label Encoding and One Hot Encoding and display the encoded data.

Q2) Create a sample dataset of two features and 10 records. Scale both the features using both MinMaxScalar and StandardScalar

Q3) Dimensionality reduction using PCA Algorithm (Manual Implementation fromScratch

Q4) Dimensionality reduction using PCA Algorithm (sklearn library function PCA)

Q5) Dimensionality reduction using LDA Algorithm (using sklearn Library function:LinearDiscriminantAnalysis)

Q6) Implement Simple Linear Regression algorithm using the Gradient Descent Algorithm  (Do not make use of ML libraries like sklearn) 

Q7) Implement Linear Regression using sklearn library. Use an appropriate data set and calculate the accuracy of your model. 

Q8) Write program to calculate the Gini Index attribute selection measure used in the construction of a decision tree. 

Q9) Using the sklearn library build a decision tree-based classifier (train the classifier usingID3 algorithm). Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample

Q10) Using the sklearn library build a classifier using the k-Nearest Neighbor algorithm to classify the iris data set. Print both correct and wrong predictions. Use the Python ML library classes can be used for this problem.. 

Q11) Using the sklearn library build a Naïve Bayesian classifier for a sample training dataset stored as a .CSV file. Compute the accuracy of the classifier, considering few test datasets.

Q12) Using the sklearn library build a logistic regression classifier for the Iris data set stored as a .CSV file. Display the performance of the model in terms of accuracy, precision, recall, F1 Score, AUC and also display the confusion matrix.

Q13) Implement K-Means Clustering on iris dataset using the scikit-learn (sklearn) library

Q14) Write a python code for Agglomerative clustering, compute the ward linkage using Euclidean distance, and visualize it using a dendrogram