Stock market analysis and prediction with machine learning

Stock market analysis and prediction with machine learning

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).


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