Data Science, time series forecasting, machine learning, and deep learning
Stock price prediction is one of the most challenging problems in financial markets due to the high volatility and unpredictability of stocks. This project aims to develop a model that predicts future stock prices using historical stock data, technical indicators, and external market factors. Machine learning techniques like LSTMs, XGBoost, and ARIMA can be used to improve accuracy. The project can also include sentiment analysis from news and social media for better forecasting.
Programming Languages: Python, R Libraries & Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, PyTorch, XGBoost, Statsmodels, Prophet Databases: PostgreSQL, MySQL (for storing financial data) APIs & Data Sources: Yahoo Finance API, Alpha Vantage, Quandl, Google Finance API Tools & Platforms: Jupyter Notebook, Google Colab, AWS/Azure for cloud-based analysis Before Commencing the project the following links have to be examined.
https://finance.yahoo.com/
https://www.kaggle.com/
https://data.nasdaq.com/institutional-investors
https://www.alphavantage.co/
https://www.datacamp.com/datalab/datasets/
