International Journal For Multidisciplinary Research

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Comparative Analysis of Financial Forecasting Techniques in Machine Learning for Netflix Stock Price Prediction

Author(s) S. Habeeb Mohamed Sathak Amina, K.Annsheela, A H Sumaiya Jailani
Country India
Abstract Abstract - In the stock market, predicting the price of stocks is still a hot research issue. One of the most challenging tasks in the field of computation is prediction of the stock market. The prediction depends on a number of variables, including physiological vs. Physical elements, rational vs. irrational investor behavior, investor attitude, market rumors, etc. All these factors work together to make stock values unpredictable and very challenging to predict accurately. Supervised Machine Learning (SML) algorithms is used to predict future value by using past stock price data. ML approaches have the ability to reveal trends and knowledge that hadn't previously noticed, and they can be used to predict that are incredibly accurate.
The primary objective of this project is to predict stock prices of Netflix for analyzing profit on day closing. Data was gathered between the years of 2002 and 2022, and separated into two parts: training set and testing set. Only the testing portion is to be used for the final forecast. Four machine learning algorithms such as linear regression, decision tree, MLP, and KNN are used to forecast the close value of the Netflix stock price. This project's output adds to the body of knowledge on predicting stock prices and offers investors some new information for accurate prediction of stock prices.
Keywords Keyword: Netflix, Stock, Prediction, Supervised Machine Learning, Linear Regression
Field Computer Applications
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-08-26
Cite This Comparative Analysis of Financial Forecasting Techniques in Machine Learning for Netflix Stock Price Prediction - S. Habeeb Mohamed Sathak Amina, K.Annsheela, A H Sumaiya Jailani - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.26544
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.26544
Short DOI https://doi.org/gt8gzb
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