FORECASTING THE FUTURE: ANALYZING NETFLIX STOCK PRICES
Author(s):
Ms. D. Ravalika, Abdul Sumiya Parveen, Chandolu Naga Venkata Abhiram, Yerramsetti Siva Surya, Bodi Devi Veera Sai Sravani
Keywords:
Supervised Learning Algorithm are Linear Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Trees, Stock Market, Predictions, Forecasting.
Abstract
This project aims to predict Netflix stock prices using machine learning algorithms such as Supervised Learning, Linear Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Trees. The data set used for this analysis comprises historical Netflix stock prices along with relevant financial indicators. To ensure robustness and accuracy, k-fold cross validation is employed as the validation method. This methodology involves Preprocessing and Cleaning the data set, Feature Engineering to enhance Model Performance, Data Standardization to standardize the data with a fixed range to ensure the machine learning models are trained well and accurately, Cross Validation is used to detect problems like over fitting and selection bias since it tests the ability of the machine learning model on predicting new data and the implementation of linear regression algorithm. In supervised learning, the model is trained on a labelled data set, where the algorithm learns the relationship between input features and corresponding output labels. By training the model on historical data and validating its accuracy, it aims to provide aiding in decision-making process related to stock investments, stocks buying or selling strategies and risk assessment. This research contributes to the field of financial forecasting by providing insights into the effectiveness of various machine learning techniques in predicting stock prices.
Article Details
Unique Paper ID: 164197

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 472 - 477
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