Abstract
The Stock Market Prediction (SMP) has been a fascinating and challenging problem. The involvement of noisy, non-linear, and sparse features and poor feature selection degrades the prediction accuracies. The improved feature quality with an enhanced feature selection mechanism can increase the accuracy of prediction. This research article focuses on the Exploratory Data Analysis (EDA) of the Nifty50 index data of the National Stock Exchange (NSE). It proposes an enhanced hybrid feature engineering mechanism to extract the most relevant features that significantly impact SMP accuracy. This work employs Ensemble Regression Models viz. Random Forest Regression (RFR) and Extreme Gradient Boost Regression (XGBR) to predict the market trend. From the results we conclude that the proposed models achieve an improved R- squared values of 0.97 using RFR and 0.98 using XGBR, respectively.
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Rouf, N., Malik, M.B., Arif, T. (2021). Predicting the Stock Market Trend: An Ensemble Approach Using Impactful Exploratory Data Analysis. In: Bhattacharya, M., Kharb, L., Chahal, D. (eds) Information, Communication and Computing Technology. ICICCT 2021. Communications in Computer and Information Science, vol 1417. Springer, Cham. https://doi.org/10.1007/978-3-030-88378-2_18
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