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This repository contains the solution to the attached problem statement. The ipynb file contains an implementation of Gradient Boosting Classifier from scratch.The implementation is performed using the MNIST dataset.
About Gradient Boosting:
Gradient Boosting is a technique of producing an additive predictive model by combining various weak predictors, typically Decision Trees. Gradient Boosting Trees can be used for both regression and classification. Here, we have used them for regression scenario as per the problem statement.
You can read more about the Gradient Boosting Classifier here
About MNIST Dataset:
The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
You can read more about the dataset as well as download the dataset from this link.