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.
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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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