Title: Netflix Stock Price Prediction
Description: This project aims to predict the stock price of Netflix, Inc. (NFLX) using historical data and machine learning algorithms. The project was completed as an assignment for a Data Mining class and involves various stages of the data mining process, such as data cleaning, exploratory data analysis, feature engineering, and model selection and evaluation.
The project's main objective is to build a predictive model that can forecast the future stock prices of Netflix based on past performance. To achieve this goal, we used a dataset of daily stock prices and volume traded from February 2018 to February 2022. We then performed data cleaning and preprocessing to handle missing values, outliers, and other anomalies in the data.
Next, we conducted exploratory data analysis to gain insights into the data and identify potential features that could help predict stock prices. We then used various machine learning algorithms, including linear regression, decision trees, Support Vector Machines, and K-Nearest Neighbors, to train and evaluate predictive models.
The project's results indicate that machine learning algorithms can be used to predict Netflix stock prices with reasonable accuracy. The best-performing model was a linear regression model, which achieved a mean absolute error of around $0 and an R-squared value of 1.00 on the test set.
The code and the results of the project are available in this repository, along with the necessary data and documentation. The project's insights and findings can be useful for investors and financial analysts who are interested in predicting Netflix's future stock prices.