Fashion MNIST Classification Project

This project aims to classify fashion products from the Fashion MNIST dataset using convolutional neural networks.

Table of Contents

1. Introduction
2. Project Steps
3. Usage
4. Requirements
5. Additional Notes

Introduction

This project is a comprehensive Jupyter notebook that guides the user through the process of machine learning model development, from data loading to model evaluation and prediction. The steps outlined here represent a common workflow in machine learning and deep learning projects.

Project Steps

• Importing Libraries: This step involves including all the necessary Python libraries that are required to process data, build machine learning models, and visualize results. Typical libraries include NumPy for numerical operations, Pandas for data manipulation, TensorFlow for building neural networks, and Matplotlib for plotting.
• Loading the Dataset: In this phase, we load the dataset from a file, a database, or an online source. For the Traffic Signs project, this would involve loading images of traffic signs and their labels. For the Fashion MNIST project, this means loading the dataset of fashion product images and their associated category labels.
• Visualizing the Data: Data visualization is a crucial step to understand the characteristics of the data. This might include displaying images of traffic signs or fashion items, plotting the distribution of classes, and other exploratory data analysis techniques.
• Preprocessing the Data: Before feeding the data into a machine learning model, it often needs to be preprocessed. This could include resizing images, normalizing pixel values, encoding labels, augmenting the dataset, or splitting the dataset into training, validation, and test sets.
• Building the CNN Model: Constructing the Convolutional Neural Network (CNN) architecture suitable for image classification. This involves defining the layers, neurons, activation functions, and connecting them to form the network.
• Compiling the Model: Once the model is built, it needs to be compiled. This step includes selecting the loss function suitable for the classification task, choosing an optimizer, and defining the metrics that will be used to evaluate the model's performance.
• Training the Model: This step involves fitting the model to the data. The model learns to classify images by adjusting its weights through backpropagation, using the training data over a number of epochs.
• Evaluating the Model: After training, the model's performance is evaluated on a test set. Metrics such as accuracy, precision, recall, and F1 score are calculated to determine how well the model performs on unseen data.
• Making Predictions: Using the trained model to predict the classes of new, unseen images. For the Fashion MNIST project, it involves classifying the type of clothing item.
• Visualizing Incorrect Predictions: It's helpful to visualize the cases where the model made incorrect predictions to understand its limitations and to get insights into potential improvements. This involves comparing the predicted labels with the true labels and displaying the images where they don't match.

Usage

To run this project, follow these steps:

1. Download the Jupyter notebook.
2. Open it in Jupyter Lab or Jupyter Notebook.
3. Execute the cells in sequential order.

Requirements

The following Python libraries are required to run the notebook:

• NumPy
• Pandas
• Matplotlib
• TensorFlow

Make sure you have the above libraries installed before attempting to run the notebook.

Additional Notes

The Fashion MNIST dataset includes 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).