AMAZON STOCK PRICE: VISUALIZATION, FORECASTING, AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

Amazon.com, Inc. engages in the provision of online retail shopping services. It operates through the following business segments: North America, International, and Amazon Web Services (AWS). The North America segment includes retail sales of consumer products and subscriptions through North America-focused websites such as www.amazon.com and www.amazon.ca. The International segment offers retail sales of consumer products and subscriptions through internationally-focused websites. The Amazon Web Services segment involves in the global sales of compute, storage, database, and AWS service offerings for start-ups, enterprises, government agencies, and academic institutions. The company was founded by Jeffrey P. Bezos in July 1994 and is headquartered in Seattle, WA. The data starts from 14-May-1997 and is updated till 27-Oct-2021. It contains 6155 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. To perform forecasting based on regression on Adj Close price of Amazon stock price, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression. The machine learning models used predict Amazon stock daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.