This project is based on Gene expression dataset from Kaggle. Here Molecular Classification of Cancer by Gene Expression monitoring Dataset is done. This dataset comes from a proof-of-concept study published in 1999 by Golub et al. It showed how new cases of cancer could be classified by gene expression monitoring (via DNA microarray) and thereby provided a general approach for identifying new cancer classes and assigning tumors to known classes. These data were used to classify patients with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL).

One of the worst epidemics in the history of mankind is the deadly disease known as cancer. There are several types of cancer and the one that is more commonly heard of these days is leukemia. There are two types of leukemia – acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) – and the purpose of this study is to take into account the gene expression data of several people and predict what type of leukemia they have by using three machine learning algorithms, XGBoost, Random Forest Classification and Artificial Neural Networks. The dataset’s dimensionality was reduced using principal component analysis (PCA) before using the algorithms on them.

This is a Bio Informatics project for the classification of types of Leukemia Cancer i.e., ALL & AML based on gene expression data. An accuracy of 0.94 has been achieved by using Support Vector Machine(SVM). The dataset has been collected from 'Kaggle' where gene descriptions are given as the features.