Data analysis and prediction of Netflix stock

Abstract

In the current financial landscape, precise analysis and forecasting of stock information hold significant importance. Every investor desires the capacity to evaluate the fluctuations of the stock market. The integration of data science and finance has become increasingly intertwined as advancements in technology reshape the investing landscape. The use of Python, a robust programming language for data manipulation, empowers investors to leverage data-driven insights that enhance their decision-making abilities. This article explores the integration of data science and finance, utilizing Python as a versatile platform for conducting data analysis. The study utilized three established analytical methodologies, namely linear regression, random forest regression, and Long short-term Memory (LSTM), to examine collected datasets pertaining to Netflix stocks. The primary objective of this study was to provide investors with a detailed analysis of the trends in Netflix stock prices and its potential to forecast daily returns for a period of 200 days. The objective of this study is to assess the efficacy of various analysis methodologies in order to enable investors to make informed investment decisions and effectively navigate the dynamic financial environment.