Building a Stock Prediction Dashboard with Taipy, Machine Learning, and Data Visualization

Learn to build a sophisticated Data Science Dashboard that predicts stock market trends using multiple algorithms in this comprehensive Python tutorial. Master the creation of a dynamic GUI with Taipy while working with S&P 500 stock exchange data dating back to 2010. Develop skills in plotting historic stock values and implementing parallel predictions using Linear Regression, K Nearest Neighbors, and Recurrent Neural Network algorithms through Taipy's Scenario Management Backend. Gain hands-on experience in arranging time series data, creating interactive plots with plotly, and implementing machine learning models using Scikit-learn and Tensorflow. Follow along with detailed environment setup instructions, including WSL installation and GitHub repository cloning, while exploring essential concepts in full-stack application development, from frontend design to backend implementation. Perfect for developers interested in combining data science with practical application development, this hands-on guide covers everything from basic GUI design to advanced machine learning model implementation for stock market prediction.

Syllabus

- intro
- environment setup and wireframe
- images and text
- vertical group of elements part block
- date range selector
- horizontal group of elements layout
- dropdown selector
- download dataset from Kaggle
- install cuDF Pandas via GPU [optional]
- fill GUI placeholders with dataset values
- on change function
- add icons for dropdown elements
- basic Taipy scenarios logic presentation
- configure input and output data nodes
- configure task
- configure scenario
- initialize scenario orchestrator
- define function for scenario task
- write inputs, submit scenario and read outputs
- display graph with plotly
- display multiple functions in one graph
- on init function
- split timeseries data into features and targets
- Linear Regression, KNN, RNN