Description
Section 1: Parametric MethodsChapter 1: An Introduction to Simple Linear RegressionChapter goal: Introduces the reader to parametric and understand the underlying assumptions of regression. Subtopics- Regression assumptions.- Detecting missing values.- Descriptive analysis.- Understand correlation.o Plot Pearson correlation matrix. - Determine covariance.o Plot covariance matrix.- Create and reshape arrays.- Split data into training and test data.- Normalize data. - Find best hyper-parameters for a model.- Build your own model.- Review model performance.o Mean Absolute Error.o Mean Squared Error.o Root Mean Squared Error.o R-squared.o Plotting Actual Values vs. Predicted Values.- Residual diagnosis.o Normal Q-Q Plot.o Cook's D Influence Plot.o Plotting predicted values vs. residual values.o Plotting Fitted Values vs. Residual Values.o Plotting Leverage Values vs. Residual Values.o Plotting Fitted Values vs. Studentized Residual Values.o Plotting Leverage Values vs. Studentized Residual Values.
Chapter 2: Advanced Parametric MethodsChapter goal: Highlights methods of dealing with the problem of under-fitting and over-fitting. Subtopics- Issue of multi-collinearity. - Explore methods of dealing with the problem under-fitting and over-fitting.- Understand Ridge, RidgeCV and Lasso regression models.- Find best hyper-parameters for a model.- Build regularized models.- Compare performance of different regression methods. o Mean Absolute Error.o Mean Squared Error.o Root Mean Squared Error.o R-squared.o Plotting actual values vs. predicted values.
Chapter 3: Time Series AnalysisChapter goal: Covers a model for identifying trends and patterns in sequential data and how to forecast a series. - What is time series analysis?- Underlying assumptions of time series analysis. - Different types of time series analysis models. - The ARIMA model. - Test of stationary.o Conduct an ADF Fuller Test.- Test of white noise.- Test of correlation. o Plot Lag Plot.o Plot Lag vs Autocorrelation Plot.o Plot ACF.o Plot PACF.- Understand trends, seasonality and trends.o Plot seasonal components.- Smoothen a time series using Moving Average, Standard Deviation and Exponential techniques. o Plot smoothened time series. - Determine rate of return and rolling rate of return. - Determine parameters of ARIMA model.- Build ARIMA model. - Forecast ARIMA.o Plot forecast. - Residual diagnosis
Chapter 4: High Quality Time SeriesChapter goal: Explores Prophet for better series forecast. - Difference between statsmodel and Prophet. - Understand components in Prophet. - Data preprocessing. - Develop a model using Prophet.- Forecast a series.o Plot forecasted.o Plot seasonal components. - Evaluate model performance using Prophet. Chapter 4: Logistic RegressionChapter goal: Introduces reader to logistic regression - a powerful classification model.Subtopics- Find missing values
Author: Tshepo Chris Nokeri
Publisher: Apress
Published: 03/24/2021
Pages: 252
Binding Type: Paperback
Weight: 1.06lbs
Size: 10.00h x 7.00w x 0.58d
ISBN13: 9781484268698
ISBN10: 1484268695
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Python
Chapter 2: Advanced Parametric MethodsChapter goal: Highlights methods of dealing with the problem of under-fitting and over-fitting. Subtopics- Issue of multi-collinearity. - Explore methods of dealing with the problem under-fitting and over-fitting.- Understand Ridge, RidgeCV and Lasso regression models.- Find best hyper-parameters for a model.- Build regularized models.- Compare performance of different regression methods. o Mean Absolute Error.o Mean Squared Error.o Root Mean Squared Error.o R-squared.o Plotting actual values vs. predicted values.
Chapter 3: Time Series AnalysisChapter goal: Covers a model for identifying trends and patterns in sequential data and how to forecast a series. - What is time series analysis?- Underlying assumptions of time series analysis. - Different types of time series analysis models. - The ARIMA model. - Test of stationary.o Conduct an ADF Fuller Test.- Test of white noise.- Test of correlation. o Plot Lag Plot.o Plot Lag vs Autocorrelation Plot.o Plot ACF.o Plot PACF.- Understand trends, seasonality and trends.o Plot seasonal components.- Smoothen a time series using Moving Average, Standard Deviation and Exponential techniques. o Plot smoothened time series. - Determine rate of return and rolling rate of return. - Determine parameters of ARIMA model.- Build ARIMA model. - Forecast ARIMA.o Plot forecast. - Residual diagnosis
Chapter 4: High Quality Time SeriesChapter goal: Explores Prophet for better series forecast. - Difference between statsmodel and Prophet. - Understand components in Prophet. - Data preprocessing. - Develop a model using Prophet.- Forecast a series.o Plot forecasted.o Plot seasonal components. - Evaluate model performance using Prophet. Chapter 4: Logistic RegressionChapter goal: Introduces reader to logistic regression - a powerful classification model.Subtopics- Find missing values
Author: Tshepo Chris Nokeri
Publisher: Apress
Published: 03/24/2021
Pages: 252
Binding Type: Paperback
Weight: 1.06lbs
Size: 10.00h x 7.00w x 0.58d
ISBN13: 9781484268698
ISBN10: 1484268695
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Python
About the Author
Tsheop Chris Nokeri harnesses advanced analytics and artificial intelligence to foster innovation and optimize business performance. He has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He completed a bachelor's degree in information management and graduated with an honors degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. He also was awarded the Oxford University Press Prize.