DATASET LINK :https://www.kaggle.com/datasets/aryarishabh/hand-gesture-recognition-dataset

Build and compare different models to classify the hand shapes using the following dataset The dataset consists of 20 hand shapes, the task requires only working on 2 classes (1 \& 2) Download and import the training instances of both classes and do the following: 1-Preprocessing - Display 1 image of each class - Reshape all the images into a 1 dimensional vector for training - Apply PCA transformation on the training samples with 2 components - Plot a scatter plot of the points after applying PCA transformation with 2 components 2-Classification - Import the test set, reshape it to 1 dimensional vector then apply the same PCA transformation - Build different kNN models with different number of neighbors (3,5,7) - Build different SVM models with linear kernel but different C values (0.001,0.01,1, 2) - Build a table for the accuracy of each model against test data set 3-Clustering Work on the train samples only and ignore the labels: - Apply K means on the data. - Apply DBSCAN on the data. - Build a table for the accuracy of each clustering technique (compare output of each technique with actual labels)