MNIST Fashion Recognizer

In this project, I built a Fashion Recognizer model with CNN on the MNIST Fashion dataset.

The link to the dataset: https://www.kaggle.com/zalando-research/fashionmnist

Steps followed:

1. Importing Libraries: Importing the required dependencies for the task
2. Data Loading: Loading the data from local directory and storing it as a Pandas.DataFrame object
3. Preprocessing: Converting and reshaping data for it to be used in model fitting
4. Model building: Building a sequential model for CNN by using Conv2D, MaxPool2D and Flatted and finally making a fully connected neural network using Dense with relu as the activation function
5. Model compiling and fitting: Compiling the model with adam optimizer, sparse_categorical_crossentropy as the loss and accuracy as the metric and then fitting the model with the training data and testing data for 10 epochs
6. Model Evaluation: Evaluating the loss and accuracy graphs for training and testing data
7. Model Performance: Checking the performance of our model by plotting various Images and printing their predicted labels
8. Metrics: Plotting confusion matrix to check if the model is well-tuned for Fashion Image classification.