What to read after AMAZON STOCK PRICE: VISUALIZATION, FORECASTING, AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI?

Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it offers a wide range of products, including books, electronics, household items, and more. The company has expanded its operations to various countries and is one of the largest online retailers globally.

In addition to its e-commerce business, Amazon has ventured into other areas. It provides cloud computing services through Amazon Web Services (AWS), which offers on-demand computing power, storage, and other services to individuals, businesses, and governments. AWS has become a significant revenue source for Amazon. Amazon has also made a significant impact on the entertainment industry. It operates Amazon Prime Video, a streaming platform that offers a wide selection of movies, TV shows, and original content.

As for Amazon's stock price, it has experienced substantial growth since the company went public in 1997. The stock has been highly valued by investors due to Amazon's consistent revenue growth, market dominance, and innovation. The stock price has seen both ups and downs over the years, reflecting market trends and investor sentiment.

The dataset used in this project 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.