ML Task

Given the Wine Dataset, perform the following tasks with either Python or R (numpy, pandas etc. can be used):
1. Perform dimesnionality reduction and narrow down the relevant features to the top 8 (construct a heat map as necessary to show the results
   of the dimensionality reduction). You can use any one (must be implemented from scratch):
   (a) Principal Component Analysis
   (b) Singular Value Decomp.
   (c) Fisher's Linear Discriminant
   
2. Use the most relevant features to classify the dataset. Either one can be used (from scratch implementation of the classifier model is not
   required):
   (a) Support Vector Machine
   (b) K means clustering
   (c) Naive Bayes
   (d) Logistic Regression
   
3. Compare predicted class labels with the actual class labels (under 'name') and give accuracy score in the output

Some helpful links:
-> dimensionality reduction
https://www.kaggle.com/arthurtok/interactive-intro-to-dimensionality-reduction
-> classification
https://www.kaggle.com/ryanholbrook/clustering-with-k-means
https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_support_vector_machine.htm
https://www.tutorialspoint.com/machine_learning_with_python/classification_algorithms_naive_bayes.htm
https://www.kdnuggets.com/2020/03/linear-logistic-regression-explained.html

Wine Dataset
https://archive.ics.uci.edu/ml/datasets/wine