This repo consists of the Fashion Class Classification Case Study from Super Data Science's course, Machine Learning Practical: 6 Real-World Applications.
Need to classify a dataset of 10,000 images, each of which is a 28x28 Grayscale Image, into one of the associated 10 Classes. For training of the model, a dataset of 60,000 images is also provided.
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
The case study utilises a Convolutional Neural Network (CNN) Model, with a total of 32 Filters / Feature Selectors, each having a size of 3x3, then a Max Pooling Layer of size 2x2, then the Flatten Layer, and then a Dense Layer with 64 Neurons, and the final Dense (Output) Layer, classifying the Input into 1 of the 10 Associated Classes.
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Scikit Learn
- Keras
The following plot (Matplotlib Subplots) shows 225 Random Images from the Training Dataset, consisting of around 60,000 Images
The following plot (Matplotlib Subplots) shows 25 samples with the True Class & Predicted Class of each, from the Test Set, consisting of around 10,000 Images
The following plot (Seaborn Heatmap) shows the Confusion Matrix for the Test Set Results & the Predicted Results
