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- W3106739516 abstract "Machine Learning is an important aspect that has had a drastic impact on our daily lives. From healthcare to social media, machine learning has had a game-changing impact across several industries. With the help of ML, Amazon and Netflix decides what specific products or shows to recommend for the particular user. One very important feature of ML is its ability to perform prediction and forecasting and this is extensively used in the Finance sector. The Stock Market is one of the most unpredictable aspects of our daily lives. Financial analysts and Investment Bankers have the task of performing predictions in such an ever-changing environment. Therefore, the use of ML in this domain is needed to allow these professionals to make better decisions with regards to people and companies portfolios. Stock market prediction has attracted people regardless of their affiliation with the finance sector. This is due to fact that decision making of business leaders heavily depends on the behaviour of this metric. Furthermore, due to its uncertain patters, predicting it to the highest accuracy possible is extremely vital. In this paper, we will conduct stock market prediction using various forms of regression and neural networks. The regression techniques are Random Forest Regression, XGBoost, Linear Regression, Support Vector Regressor (SVR) and Artificial Neural Networks(ANN). We will use 3 metrics to compare accuracies of the algorithms: RMSE (Root Mean Square Error), R2 score (R squared score) and MAE (Mean Absolute Error). In conclusion, we will be comparing the algorithms used and figure out the one giving best results for this dataset. We will also be comparing the algorithms with the benchmark algorithm i.e. SVM and verify the best performing one." @default.
- W3106739516 created "2020-12-07" @default.
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- W3106739516 date "2020-01-01" @default.
- W3106739516 modified "2023-09-28" @default.
- W3106739516 title "MACHINE LEARNING ALGORITHMS USING STOCK MARKET DATASETA COMPARATIVE STUDY" @default.
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