Did you know that the range of minimum and maximum prices of Google shares reaches half of their value? With so much volatility, there is a high probability of buying a stock and waiting for it to rise for several years.
That’s why it’s important to analyze all of its parameters before buying a stock. And it’s not just about closing prices. Because stock price fluctuations within a day can be as much as 5 percent or more.
Stock market analysis is a frequent situation in the Data Science industry. There are a huge number of methods and libraries for both analyzing and visualizing data, as well as building predictions using machine learning methods. In today’s post, I will show you how I analyzed Google stock prices over the past 12 months. Using the Python programming language and its many libraries, the following work was done:
- Exploratory data analysis;
- Visualizing the history of quotes;
- Construction of distribution curves of opening, closing, high, low prices;
- Analysis of anomalous values presence;
- Attempting to predict future stock prices using ML models and Logistic Regression, Support Vector Machine, XGBClassifier.
- Google Collaboratory;
- Python and its libraries (Pandas, Numpy, Datetime, Yfinance, Plotly, Matplotlib, Seaborn, Sklearn, XGBoost).