This project, ‘Analyzing and Predicting AAPL and TSLA Stock Prices’, explores the application of AI and Data Science techniques in predicting stock prices. The project focuses on two globally influential companies, Apple Inc. (AAPL) and Tesla Inc. (TSLA), and leverages seven years of historical stock price data collected from finance.yahoo.com. The data was preprocessed and analyzed using tools like Numpy, Pandas, Matplotlib, and Seaborn. Linear regression models were built and trained using Scikit-Learn, achieving an accuracy of 99.25% and 98.18% in predicting AAPL and TSLA stock prices respectively. The project also delves into the impact of significant events, such as the Covid-19 pandemic, inclusion in S&P500, etc. on stock prices. The results demonstrate the potential of AI and Data Science in financial forecasting and provide valuable insights for investors and stakeholders. However, the inherent uncertainty of stock markets is acknowledged, and the predictions are intended to serve as a guide rather than a definitive forecast.
The financial market is a sophisticated system in which a wide range of variables interact in complex ways to affect stock values. Accurately predicting these values is difficult because of their volatility and the wide range of affecting factors. By projecting the stock values of two highly significant firms in the world, Apple Inc. (AAPL) and Tesla Inc. (TSLA), this research seeks to answer this difficulty. "Can we correctly analyze the stock prices and carry out diagnostic analysis alongside developing a model that accurately predicts the future stock prices of AAPL and TSLA based on historical data?" is the specific problem this project aims to solve. We will use a variety of AI and Data Science tools like Python libraries like Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn, etc. and approaches like Exploratory Data Analysis and Regression to evaluate past stock price data and create a predictive model in order to respond to this issue. We will perform majority of this project in Jupyter notebooks. The project's success will be determined by how well our model predicts the future. A good conclusion would offer important insights into the elements influencing these prices in addition to proving that stock prices can be predicted using AI and data science techniques.
- Data Collection
- Data Preprocessing
- Exploratory Data Analysis
- Model Building
- Model Evaluation
- Prediction
These steps are well explained in the notebook.
This project, ‘Analyzing and Predicting AAPL and TSLA Stock Prices’, has demonstrated the potential of AI and Data Science in predicting stock prices with high accuracy. The linear regression models built and trained on seven years of historical data were able to predict the stock prices of AAPL and TSLA with an accuracy of 99.25% and 98.18% respectively. The exploratory data analysis provided valuable insights into the factors influencing stock prices, such as the significant impact of the Covid-19 pandemic in early 2020. These insights could be invaluable for investors and stakeholders in making informed decisions. However, it’s important to note that while the model showed high accuracy, stock market predictions are inherently uncertain and influenced by numerous unpredictable factors. Therefore, the predictions made by the model should be used as a guide rather than a definitive forecast. The success of this project opens up several avenues for future work. More sophisticated models could be explored to improve prediction accuracy. Additionally, other factors such as news sentiment and macroeconomic indicators could be incorporated into the model to capture a wider range of influences on stock prices. In conclusion, this project has shown that with the right tools and methodologies, AI and Data Science can provide valuable insights and predictions in the complex world of stock market prices.