  The paper focuses on predicting the Nifty 50 Index by using 8 Supervised
Machine Learning Models. The techniques used for empirical study are Adaptive
Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial
Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD),
Support Vector Machine (SVM) and Decision Trees (DT). Experiments are based on
historical data of Nifty 50 Index of Indian Stock Market from 22nd April, 1996
to 16th April, 2021, which is time series data of around 25 years. During the
period there were 6220 trading days excluding all the non trading days. The
entire trading dataset was divided into 4 subsets of different size-25% of
entire data, 50% of entire data, 75% of entire data and entire data. Each
subset was further divided into 2 parts-training data and testing data. After
applying 3 tests- Test on Training Data, Test on Testing Data and Cross
Validation Test on each subset, the prediction performance of the used models
were compared and after comparison, very interesting results were found. The
evaluation results indicate that Adaptive Boost, k- Nearest Neighbors, Random
Forest and Decision Trees under performed with increase in the size of data
set. Linear Regression and Artificial Neural Network shown almost similar
prediction results among all the models but Artificial Neural Network took more
time in training and validating the model. Thereafter Support Vector Machine
performed better among rest of the models but with increase in the size of data
set, Stochastic Gradient Descent performed better than Support Vector Machine.
