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.
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  ChiSquare)
 Q32 (Python  PCA)
 Q33 (Python  Variance Inflation Factor)
 Q34 (Python  Slope and PValue)
 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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