Sentimental analysis for stock market predictions📈📉

Project aimed to predict stock market and cryptocurrency trends by analyzing public sentiment and price history data. They utilize Twitter data for sentiment analysis, despite limitations like API constraints and data scarcity. The project employs various machine learning algorithms, including Support Vector Machine (SVM), to process sentiment data and predict market movements. Challenges faced include data limitations and the need for more sophisticated algorithms. Future plans involve investing in more comprehensive data and testing additional algorithms for improved prediction accuracy. The ultimate goal is to understand the relationship between public sentiment and market trends to inform investment strategies.

Project presentation. Repository.

python libarires: alpha_vantage, gdax, tzlocal, csv, json, sklearn, pandas, os, numpy, matplotlib.