/Tesla-Stock-Price-Analysis-and-Prediction-using-Machine-Learning-and-Python

Using the Logistic Regression Model

Primary LanguageJupyter Notebook

Tesla-Stock-Price-Analysis-and-Prediction-using-Machine-Learning-and-Python

June 26, 2023

The aims:

  • Apply EDA
  • Apply Features Selection and Engineering
  • Build Forecasting Model

Conclusion

We found:

  • The actual price and the predictions were closely matching until 2020.
  • All models that applied indicate that the statistical models based non pure empirical data is not sufficient to explain the sudden rise of the stock price. This indicates anomaly, and we have to dig deep to know more on the reason of the rapid rise (can be Covid, or Stock Bubble)
  • We conclud that the Time series forecasting models are much more robust that simple linear regression (LSTM shows better result than ARIMA)