Data Science - Regression Analysis (DIY for newbies)
Data Science - Regression Analysis (DIY for newbies)
Includes 100 solved problems
About the Book
The book contains stepwise solutions to regression problems for beginners. In this book, regression concepts are broken down into simple steps. Each problem addresses a concept in regression. The problems are solved using both Python and R programming language.
About the Author
Table of Contents
-
Supervised Learning Algorithms:
- Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression:
- Random Forest Regression:
-
100 Solved Questions
- Q1 (Python - Prediction with Linear Regression)
- Q2 (Python - Linear Regression, PCA, MSE)
- Q3 (R - Prediction with Linear model)
- Q4 (Python - Jaccod Index)
- Q5 (R - Stochastic Gradient Boosting)
- Q6 (Python - Boxplot)
- Q7 (Python - Sort using attrgetter)
- Q8 (Python - Heatmap)
- Q9 (Python - Euclidean Distance)
- Q10 (Python - Manhattan Distance)
- Q11 (R - Bar Plot)
- Q12 (R - Formatting Date)
- Q13 (Python - Forward selection)
- Q14 - (Linear Model)
- Q15 (Python - Correlation)
- Q16 (Python - Manhattan Distance)
- Q17 (Python - Word Split)
- Q18 (Python - PCA)
- Q19 (Python - Eigenvector)
- Q20 (Python - Eigenvalues)
- Q21 (R - Linear Regression)
- Q22 (Python - OLS)
- Q23 (Python - OLS)
- Q24 (Python - Decision Tree)
- Q25 (R - Random Forest)
- Q26 (Python - Text)
- Q27 (Python - Sort)
- Q28 (Python - Indexing)
- Q29 (Python - OLS )
- Q30 (Python - OLS)
- Q31 (Python - Chi-Square)
- Q32 (Python - PCA)
- Q33 (Python - Variance Inflation Factor)
- Q34 (Python - Slope and P-Value)
- Q35 (Python - Recusive Feature)
- Q36 (R - Random Forest)
- Q37 (R - Support Vector Regression)
- Q38 (R - Polynomial Regression)
- Q39 (R - Linear Regression)
- Q40 (Python - Chi value)
- Q41 (R - Attribute Importance)
- Q42 (R - Baggging model)
- Q43 (R - Stacking Algorithm)
- Q44 (R - PCA)
- Q45 (Python)
- Q46 (Python - Random Forest)
- Q47 (R - Forward selection)
- Q48 (R - Support Vector Regressor)
- Q49 (R - Polynomial Regression)
- Q50 (R - Mean, Median, Mode)
- Q51 (Python - Correlation coefficients)
- Q52 (R - Linear Regression)
- Q53 (R - Linear Regression)
- Q54 (Python - Text)
- Q55 (R - Polynomial Regression)
- Q56 (R - Support Vector Regression)
- Q57 (R - Decision Tree)
- Q58 (R - Random Forest)
- Q59 (Python - Outliers)
- Q60 (Python - PCA)
- Q61 (Python - Ridge Regression)
- Q62 (Python - Lasso)
- Q63 (Python - Impute)
- Q64 (Python - Impute)
- Q65 (Python - Support Vector Regression)
- Q66 (Python - Decision Tree)
- Q67 (Python - Random Forest)
- Q68 (Python- Mean, Median, Mode,IQR)
- Q69 (R - Outliers)
- Q70 (Python - Outlier)
- Q71 (Python - Decision Tree)
- Q72 (R - Decision Tree)
- Q73 (R - Ridge Regression)
- Q74 (R - Ridge Regression )
- Q75 (R - Ridge Regression)
- Q76 (Python - Ridge Regression)
- Q77 (Python - Ridge Regression)
- Q78 (Python - Ridge Regression)
- Q79 (R - Lasso)
- Q80 (R - Lasso)
- Q81 (R - Lasso)
- Q82 (Python - Lasso)
- Q83 (Python - Lasso)
- Q84 (Python - Lasso)
- Q85 (R - IQR)
- Q86 (Python - IQR)
- Q87 (R - Linear)
- Q88 (Python - Linear Regression)
- Q89 (R - Random Forest)
- Q90 (R - Random Forest)
- Q91 (Python - Random Forest)
- Q92 (Python - Random Forest)
- Q93 (Python - Polynomial Regression)
- Q94 (Python - Impute)
- Q95 (R - Impute)
- Q96 (R - Impute)
- Q97 (Python - Linear)
- Q98 (Python - Adaboost)
- Q99 (Calculation - Slope and Intercept)
- Q100 (Python - Backward Elimination)
- Appendix:
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