This project is designed for investing in the S&P 500 index by leveraging machine learning techniques to predict stock prices. The project workflow includes Exploratory Data Analysis (EDA), hypothesis testing on sector volatility, machine learning model development, and deployment of a Streamlit app for financial resources.
Investing in the S&P 500 index can be enhanced by predicting stock prices using advanced machine learning models. This project aims to provide insights through data analysis and leverage algorithms like XGBoost and Keras TensorFlow for precise predictions. Additionally, it offers a Streamlit app as a resource hub for finance news and the latest investing tips.
Exploratory Data Analysis (EDA): Initial analysis to understand data characteristics and relationships.
Hypothesis Testing: Assessing the volatility of different S&P 500 sectors to guide investment strategies.
XGBoost: Gradient Boosting algorithm implemented for regression tasks.
Keras (TensorFlow): Deep learning models designed for predicting stock prices.
The Streamlit app serves as an interface providing access to recent financial news and data as well as the latest stock price predictions.
Here you can find a comprehensive Presentation about this project.