Cat vs Non-Cat Image Classification Using a Logistic regression along with Neural Networks

INTRODUCTION

• Here I have used logistic regression along with neural networks, forward and backward propagation, to build an image recognition system i.e a cat classifier in this case. This cat classifier takes an image as an input and then it predicts whether the image contains a cat or not.

LOGISTIC REGRESSION

• Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).
• Like all regression analyses, the logistic regression is used for predictive analysis.

DATASET

• The dataset is saved here:https://github.com/aditimukerjee/Cat-and-Non-cat-for-logistic-regression

CONCLUSION

• The accuracy of the training set is 99.99% and the accuracy of the testing set is 72%. Out of the six images used for prediction, one image was predicted incorrectly since the accuracy is still low and it can be further improved.