Dr.Muttipati

 def find_s_algorithm(data, target):     """     Implements the Find-S algorithm for concept learning.     Parameters:     - data: List of examples (list of lists).     - target: List of target values (list of strings, e.g., 'Yes' or 'No').     Returns:     - The most specific hypothesis.     """     # Step 1: Initialize the most specific hypothesis     hypothesis = ["Φ"] * len(data[0])     # Step 2: Iterate over the dataset     for i, instance in enumerate(data):         if target[i] == "Yes":  # Process only positive examples             for j in range(len(instance)):                 if hypothesis[j] == "Φ":  # Initialize to the first positive example                     hypothesis[j] = instance[j]     ...

from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score, classification_report # Step 1: Load a sample dataset (e.g., 20 Newsgroups) categories = ['sci.space', 'comp.graphics', 'rec.sport.baseball'] newsgroups = fetch_20newsgroups(subset='all', categories=categories) # Print information about the newsgroups dataset print("=== Newsgroups Dataset Information ===") print(f"Number of documents: {len(newsgroups.data)}") print(f"Number of categories: {len(newsgroups.target_names)}") print("Categories:", newsgroups.target_names) print("First document sample:\n", newsgroups.data[0][:500])  # Print first 500 characters of the first document print("\n") # Step 2: Preprocess the text data vectorizer = TfidfVectorize...

The Naive Bayes Classifier is a probabilistic machine learning model widely used for classification tasks, including document classification. Based on Bayes' Theorem, it assumes that the features (in this case, words or terms in the documents) are conditionally independent given the class label. Despite this "naive" assumption, it often performs well in practice, especially for text classification. Steps to Perform Document Classification Using Naive Bayes 1.  Prepare the Dataset Documents : Assume you have a set of documents, each labeled with a category (e.g., "Sports", "Politics", "Technology"). Preprocessing : Tokenize the text into words. Remove stop words (e.g., "the", "is", "and"). Perform stemming or lemmatization to reduce words to their base forms. Convert text into a numerical representation, such as a  bag-of-words  or  TF-IDF  vector. 2.  Split the Dataset Divide the dataset into a  training set  and...

def find_s_algorithm ( data , target ):     # Step 1: Initialize the most specific hypothesis     hypothesis = [ "Φ" ] * len (data[ 0 ])     # Step 2: Iterate over the dataset     for i, instance in enumerate (data):         if target[i] == "Yes" :   # Process only positive examples             for j in range ( len (instance)):                 if hypothesis[j] == "Φ" :   # Initialize to the first positive example                     hypothesis[j] = instance[j]                 elif hypothesis[j] != instance[j]:   # Generalize if there's a mismatch                     hypothesis[j] = "?"         return hypothesis # Example Dataset attributes = [     [ "Sunny" , "Warm" , ...

1. Using matplotlib and seaborn to perform data visualization on the standard dataset a. Perform the preprocessing b. Print the no of rows and columns c. Plot box plot d. Heat map e. Scatter plot f. Bubble chart g. Area chart 2. Build a Linear Regression model using Gradient Descent methods in Python for a wine data set 3. Build a Linear Regression model using an ordinary least-squared model in Python for a wine data set  4. Implement quadratic Regression for the wine dataset 5. Implement Logistic Regression for the wine data set 6. Implement classification using SVM for Iris Dataset 7. Implement Decision-tree learning for the Tip Dataset 8. Implement Bagging using Random Forests  9.  Implement K-means Clustering    10.  Implement DBSCAN clustering  11.  Implement the Gaussian Mixture Model  12. Solve the curse of Dimensionality by implementing the PCA algorithm on a high-dimensional 13. Comparison of Classification algorithms  14. Compa...

Machine Learning Quiz Machine Learning Quiz 1. What is supervised learning? A. Learning without labeled data B. Learning with labeled data C. A method to clean data D. None of the above 2. Which algorithm is used for classification problems? A. Linear Regression B. Logistic Regression C. K-Means Clustering D. Gradient Descent 3. What is overfitting in a machine learning model? A. The model performs well on training data but poorly on test data B. The model performs well on both training and test data C. The model does not learn anything D. None of the above...