Linear Models For Stock Price Prediction

This comprehensive article provides an in-depth exploration of predicting stock prices using Linear Models in Python.

The article provides a detailed guide on preparing the environment for data analysis and machine learning using various Python libraries and modules such as pandas, numpy, datetime, seaborn, matplotlib, sklearn, and tensorflow.

The process is broken down into distinct steps: setting up the code, preparing and analyzing the data, forecasting using machine learning, and visualizing the results. This includes steps for calculating the dot product, importing necessary Python libraries for data analysis and visualization, creating and using functions for time series prediction models, preprocessing stock market data, and visualizing different segments of stock market data.

The article further guides you through the process of normalizing data for machine learning, training a simple linear forecasting model, and analyzing the loss of the model against different learning rates. Additionally, the article demonstrates how to use Keras to set up and train a simple machine learning model for time series forecasting.

Moreover, you’ll also learn to generate a forecast of future values using the trained model, reverse the normalization process applied to forecasts, and visualize forecasted values from the linear model in comparison to the actual values. Overall, this comprehensive guide walks you through each step involved in predicting stock prices using Linear Models in Python.

Find the link to download the source in the pinned comment.