The summary of the text is:
- The speaker presents a project on stock market prediction using numerical and textual analysis.
- The speaker uses Python to perform data analysis, data visualization, data preprocessing, and model building on historical stock prices and news headlines.
- The speaker uses LSTM and Dense layers to create a hybrid model for predicting stock prices based on past 90 days data.
- The speaker evaluates the model performance using MAE, MSE, RMSE, and R-squared metrics and plots the training, testing, and predicted data.
- The speaker also performs sentiment analysis on the news headlines using TextBlob library and plots the correlation matrix between sentiment and stock prices.
- The speaker concludes that the stock prices are somewhat negatively correlated with the sentiment of the news headlines.
Here are the key facts extracted from the text:
- The text is a transcript of a project presentation on stock market prediction using numerical and textual analysis.
- The project uses Python, pandas, numpy, matplotlib, seaborn, tensorflow and TextBlob libraries for data processing, analysis and visualization.
- The project collects historical stock prices from Yahoo Finance and textual news data from Google Drive.
- The project performs sentiment analysis on the news headlines using polarity and subjectivity scores.
- The project builds a hybrid model using LSTM and dense layers for stock price performance prediction.
- The project evaluates the model performance using MAE, MSE, RMSE and R-squared metrics.
- The project plots the training, testing and predicted data, as well as the correlation matrix and the heatmap.
- The project concludes that the stock prices are somewhat negatively correlated with the sentiment of the news headlines.