Forecast any mathematical number sequence with Regression (Machine Learning)
Prodramp
This hands-on exercise is for those who wanted to generate number series using any mathematical function i.e. Quadratic equations. exponentials, logarithmic, trigonometric (Sin, Cos, Tan) in Python and then forecast newer sequence using machine learning regression methods.
Combining pandas and numpy you are going to first general a mathematical number series and then using scikit-learn regression model you will generate or forecast the future numbers based on source number series... You will also use matplotlib to visualize the data and validate the new number series belong to the same source number series...
GitHub Source code used in this Video Tutorial: https://github.com/prodramp/python-projects/tree/main/PredictNumberSeries
ā¬ā¬ā¬ā¬ā¬ā¬ ā° TUTORIAL TIME STAMPS ā° ā¬ā¬ā¬ā¬ā¬ā¬
- (00:00) Tutorial Starts
- (02:13) Content Introduction
- (05:03) First Exercise (1/2) Starts
- (08:06) Adding noise to number sequence
- (11:04) Number generator and plot Function
- (12:06) Uniform vs Exponential sequence
- (13:49) Side by Side sequence Plotting
- (14:13) y = x*x function series sequence generation
- (14:47) y = x*x + x + 1 function series sequence generation
- (15:01) y = -x*x -x -1 function series sequence generation
- (16:15) y = -x*x + const function series sequence generation
- (16:28) y = exp(x) function series sequence generation
- (17:43) y = log(x) function series sequence generation
- (18:17) y = sin(x) function series sequence generation
- (18:47) y = cos(x) function series sequence generation
- (19:04) y = arctan(x) function series sequence generation
- (19:20) Second Exercise (2/2) Starts
- (19:32) Generating Sequence
- (22:38) Selecting X and y for ML
- (23:35) Defining Target Series for forecasting
- (24:39) Linear Regression with Scikit-learn
- (25:58) Regression Coefficient
- (28:57) Generating new number using regression model
- (30:22) Number Series Validation
- (32:05) Sequence forecasting for Sin(x)
- (33:27) Sequence forecasting for Cos(x)
- (34:16) Saving notebooks to GitHub
- (34:39) Recap
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Content Creator: Avkash Chauhan (@avkashchauhan)
Tags: #python #regression #ml #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #gnn ... https://www.youtube.com/watch?v=Oa8iWFJn1oE
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