Stock Market Analysis and Prediction is a project that uses Apple stocks data for technical analysis, visualization, and prediction. By examining data from the stock market, notably from some of the world's largest technological companies and others. We will use pandas to obtain stock data, visualized several elements of it, and then looked at a few different approaches to analyze a stock's risk based on its past performance history. The main goal of this project will be to compare the performance of prediction algorithms on stock market data and gain a broad understanding of this data through visualization in order to forecast future stock behavior and risk value for each stock. We are mainly trying to use NumPy, Pandas, and Data Visualization Libraries in this project. We answer the following end user based questions with the help of our organization:
- What is the change in stock's price over time?
- What is the average daily return on the stock?
- What is the change in the volume traded over time?
- How much of our capital is at risk when we invest in a certain stock?
- How can we forecast future stock performance?
Overview:
- Orchestrated ETL of Apple stock data using pandas and Dataprep, ensuring accurate data and seamless integration with BigQuery.
- Employed SQL queries and Python libraries, achieving a 99% prediction accuracy with key metrics such as RMS (0.05), R- squared (0.98), and MAE (0.02)through BigQuery.
- Executed Linear Regression modeling, validating the model's robustness with an Explained Variance Score of 0.95.
- Leveraged Google Data Studio to craft insightful visualizations, showcasing expertise in stock behavior, daily returns, and volume trends.