Otto Dataset Analysis

Published on April 21, 2020

As a first project, I decided to pick a random dataset from Kaggle to analyse. In this project, I analysed project distributions from the Otto Group in an attempt to classify them using a 4-linear layer model. Below, I have shared the my confusion matrix, showing the class predictions vs actual results, and a basic code of my linear model.

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.l1 = nn.Linear(93, 64)
        self.l2 = nn.Linear(64, 32)
        self.l3 = nn.Linear(32, 16)
        self.l4 = nn.Linear(16, 10)
    
    def forward(self, x):
        x = x.view(-1, 92) 
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        return self.l4(x)
    
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

Confusion Matrix

To read more, please visit my repository for this project

This work is licensed under a Creative Commons Attribuition-ShareAlike 4.0 International License .