Machine Learning Programs
👉Data Preprocessing in Machine Learning
👉Data Preprocessing in Machine learning (Handling Missing values )
👉Linear Regression - ML Program - Weight Prediction
👉Naïve Bayes Classifier - ML Program
👉LOGISTIC REGRESSION - PROGRAM
👉KNN Machine Learning Program
👉Support Vector Machine (SVM) - ML Program
👉Decision Tree Classifier on Iris Dataset
👉Classification of Iris flowers using Random Forest
👉DBSCAN
👉 Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file
👉For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
👉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.
👉Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
👉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.
👉Write a program to implement k-Nearest Neighbors algorithm to classify the iris data set. Print both correct and wrong predictions.
👉Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.
👉Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions.
👉Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.
👉 Write a program using scikit-learn to implement K-means Clustering
👉Program to calculate the entropy and the information gain
👉Program to implement perceptron.
👉Data Preprocessing in Machine learning (Handling Missing values )
👉Linear Regression - ML Program - Weight Prediction
👉Naïve Bayes Classifier - ML Program
👉LOGISTIC REGRESSION - PROGRAM
👉KNN Machine Learning Program
👉Support Vector Machine (SVM) - ML Program
👉Decision Tree Classifier on Iris Dataset
👉Classification of Iris flowers using Random Forest
👉DBSCAN
👉 Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file
👉For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
👉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.
👉Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
👉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.
👉Write a program to implement k-Nearest Neighbors algorithm to classify the iris data set. Print both correct and wrong predictions.
👉Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.
👉Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions.
👉Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.
👉 Write a program using scikit-learn to implement K-means Clustering
👉Program to calculate the entropy and the information gain
👉Program to implement perceptron.
