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Machine Learning Practical

W3.CSS

Machine Learning Practical



Topics for Practical
1. Implementing a K-Nearest Neighbor (KNN) algorithm

  • (e.g. to classify handwritten digits)
  • 2. Building a decision tree model using the ID3 algorithm

  • (e.g. to predict whether a customer will churn or not).
  • 3. Developing a Support Vector Machine (SVM) model

  • (e.g. to classify email messages as spam or not spam).
  • 4. Building a Naïve Bayes classifier

  • (e.g. to classify movie reviews as positive or negative sentiment).
  • 5. Implementing linear regression

  • (e.g. to predict housing prices based on features such as size and location).
  • 6. Using logistic regression

  • (e.g. to predict whether a credit card transaction is fraudulent or not).
  • 7. Evaluating a classification model using metrics such as accuracy, precision, recall, and F1 score.

    8. Applying hierarchical clustering

  • (e.g. to group customer segments based on their purchasing behavior).
  • 9. Implementing the K-means clustering algorithm

  • (e.g. to identify distinct clusters in a customer demographic dataset).
  • 10. Utilizing Principal Component Analysis (PCA) for dimensionality reduction to improve the efficiency and interpretability of a model.

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