Placement Preparation

Python Program to Implement the Locally Weighted Regression Algorithm Exp. No. 10.   Implement the non-parametric Locally Weighted Regression algorithm in Python in order to fit data points. Select the appropriate data set for your experiment and draw graphs. Locally Weighted Regression Algorithm Regression: Regression is a technique from statistics that are used to predict values

Python Program to Implement the k-Nearest Neighbour Algorithm Exp. No. 9.   Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem. K-Nearest Neighbor Algorithm Training algorithm: For each training example (x, f (x)), add the

Python Program to Implement the K-Means and Estimation & MAximization Algorithm Exp. No. 8.   Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using the k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python

Python Program to Implement the Bayesian network using pgmpy Exp. No. 7.  Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using a standard Heart Disease Data Set. You can use Java/Python ML library classes/API. Theory A Bayesian network is a directed acyclic

Python Program to Implement the Naïve Bayesian Classifier using API for document classification Exp. No. 6.  Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data

Python Program to Implement the Naïve Bayesian Classifier for Pima Indians Diabetes problem Exp. No. 5.  Write a program to implement the Naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets. Bayes’ Theorem is stated as: Where, P(h|D) is

Python Program to Implement the Backpropagation Algorithm Artificial Neural Network Exp. No. 4.  Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.  Python Program to Implement and Demonstrate Backpropagation Algorithm Machine Learning import numpy as np X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float) y

Python Program to Implement Decision Tree ID3 Algorithm Exp. No. 3. Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.  Decision Tree ID3 Algorithm Machine Learning ID3(Examples, Target_attribute, Attributes) Examples are