GOOGLE STOCK PRICE: TIME-SERIES ANALYSIS, VISUALIZATION, FORECASTING, AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

Alphabet, Inc. is a holding company, which engages in the business of acquisition and operation of different companies. It operates through the Google and Other Bets segments. The Google segment includes its main Internet products such as ads, Android, Chrome, hardware, Google Cloud, Google Maps, Google Play, Search, and YouTube. The Other Bets segment consists of businesses such as Access, Calico, CapitalG, GV, Verily, Waymo, and X. The company was founded by Lawrence E. Page and Sergey Mikhaylovich Brin on October 2, 2015 and is headquartered in Mountain View, CA. The data starts from 19-Aug-2004 and is updated till 11-Oct-2021. It contains 4317 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 Google 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 Google 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.