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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Stock Market Analysis and Prediction Applying Machine Learning |
| Authors Name: | Mr K.Pavan Kumar , K. Varalakshmi , P.Prashanth , B.Mounika , V.Pavan Kumar, K.Yugandhar |
| Unique Id: | IJSDR2005075 |
| Published In: | Volume 5 Issue 5, May-2020 |
| Abstract: | The Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange and explains the prediction of a stock applying Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market applying machine learning is Python. Here, we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. We will use machine learning techniques called Support Vector Machine (SVM) and Naive Bayes to predict stock prices for the large and small capitalizations, employing prices with both daily and up-to-the-minute frequencies. The support vector machine (SVM) is a data classification technique that has been recently proven to perform better than other machine learning techniques especially in stock market prediction. SVM try to build a model using a set of training examples given to it. Each training data instance is marked as belonging to one of two categories. The SVM will attempt to classify the data instances into those two categories. The trained SVM model can then be tested with new data instances to predict which category they belong to base on the training performance. Bayesian networks are also used for prediction. The Naïve Bayes algorithm is a Bayesian Network technique used for the Bayesian Network construction using the historical data. The algorithm is called Naïve because it assumes that the features in a class are unrelated to the other features and all of them independently contribute to the probability calculation. The final prediction result is used to decide the algorithm which gives the best prediction of stocks based on the data. |
| Keywords: | Stock Market, Support Vector Machine, Naive Bayes, Prediction. |
| Cite Article: | "Stock Market Analysis and Prediction Applying Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 5, page no.476 - 481, May-2020, Available :http://www.ijsdr.org/papers/IJSDR2005075.pdf |
| Downloads: | 000337067 |
| Publication Details: | Published Paper ID: IJSDR2005075 Registration ID:191843 Published In: Volume 5 Issue 5, May-2020 DOI (Digital Object Identifier): Page No: 476 - 481 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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