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Data Science - Regression Analysis (DIY for newbies)

Includes 100 solved problems

Geetha Subramanian

This book contains detailed stepwise solutions to Regression problems for beginners. It is a collection of solutions in Python and R programming language.

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About

About

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.

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Machine LearningPythonR

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Author

About the Author

Geetha Subramanian

Contents

Table of Contents

Supervised Learning Algorithms:

  1. Regression
  2. Polynomial Regression
  3. Support Vector Regression
  4. Decision Tree Regression:
  5. Random Forest Regression:

100 Solved Questions

  1. Q1 (Python - Prediction with Linear Regression)
  2. Q2 (Python - Linear Regression, PCA, MSE)
  3. Q3 (R - Prediction with Linear model)
  4. Q4 (Python - Jaccod Index)
  5. Q5 (R - Stochastic Gradient Boosting)
  6. Q6 (Python - Boxplot)
  7. Q7 (Python - Sort using attrgetter)
  8. Q8 (Python - Heatmap)
  9. Q9 (Python - Euclidean Distance)
  10. Q10 (Python - Manhattan Distance)
  11. Q11 (R - Bar Plot)
  12. Q12 (R - Formatting Date)
  13. Q13 (Python - Forward selection)
  14. Q14 - (Linear Model)
  15. Q15 (Python - Correlation)
  16. Q16 (Python - Manhattan Distance)
  17. Q17 (Python - Word Split)
  18. Q18 (Python - PCA)
  19. Q19 (Python - Eigenvector)
  20. Q20 (Python - Eigenvalues)
  21. Q21 (R - Linear Regression)
  22. Q22 (Python - OLS)
  23. Q23 (Python - OLS)
  24. Q24 (Python - Decision Tree)
  25. Q25 (R - Random Forest)
  26. Q26 (Python - Text)
  27. Q27 (Python - Sort)
  28. Q28 (Python - Indexing)
  29. Q29 (Python - OLS )
  30. Q30 (Python - OLS)
  31. Q31 (Python - Chi-Square)
  32. Q32 (Python - PCA)
  33. Q33 (Python - Variance Inflation Factor)
  34. Q34 (Python - Slope and P-Value)
  35. Q35 (Python - Recusive Feature)
  36. Q36 (R - Random Forest)
  37. Q37 (R - Support Vector Regression)
  38. Q38 (R - Polynomial Regression)
  39. Q39 (R - Linear Regression)
  40. Q40 (Python - Chi value)
  41. Q41 (R - Attribute Importance)
  42. Q42 (R - Baggging model)
  43. Q43 (R - Stacking Algorithm)
  44. Q44 (R - PCA)
  45. Q45 (Python)
  46. Q46 (Python - Random Forest)
  47. Q47 (R - Forward selection)
  48. Q48 (R - Support Vector Regressor)
  49. Q49 (R - Polynomial Regression)
  50. Q50 (R - Mean, Median, Mode)
  51. Q51 (Python - Correlation coefficients)
  52. Q52 (R - Linear Regression)
  53. Q53 (R - Linear Regression)
  54. Q54 (Python - Text)
  55. Q55 (R - Polynomial Regression)
  56. Q56 (R - Support Vector Regression)
  57. Q57 (R - Decision Tree)
  58. Q58 (R - Random Forest)
  59. Q59 (Python - Outliers)
  60. Q60 (Python - PCA)
  61. Q61 (Python - Ridge Regression)
  62. Q62 (Python - Lasso)
  63. Q63 (Python - Impute)
  64. Q64 (Python - Impute)
  65. Q65 (Python - Support Vector Regression)
  66. Q66 (Python - Decision Tree)
  67. Q67 (Python - Random Forest)
  68. Q68 (Python- Mean, Median, Mode,IQR)
  69. Q69 (R - Outliers)
  70. Q70 (Python - Outlier)
  71. Q71 (Python - Decision Tree)
  72. Q72 (R - Decision Tree)
  73. Q73 (R - Ridge Regression)
  74. Q74 (R - Ridge Regression )
  75. Q75 (R - Ridge Regression)
  76. Q76 (Python - Ridge Regression)
  77. Q77 (Python - Ridge Regression)
  78. Q78 (Python - Ridge Regression)
  79. Q79 (R - Lasso)
  80. Q80 (R - Lasso)
  81. Q81 (R - Lasso)
  82. Q82 (Python - Lasso)
  83. Q83 (Python - Lasso)
  84. Q84 (Python - Lasso)
  85. Q85 (R - IQR)
  86. Q86 (Python - IQR)
  87. Q87 (R - Linear)
  88. Q88 (Python - Linear Regression)
  89. Q89 (R - Random Forest)
  90. Q90 (R - Random Forest)
  91. Q91 (Python - Random Forest)
  92. Q92 (Python - Random Forest)
  93. Q93 (Python - Polynomial Regression)
  94. Q94 (Python - Impute)
  95. Q95 (R - Impute)
  96. Q96 (R - Impute)
  97. Q97 (Python - Linear)
  98. Q98 (Python - Adaboost)
  99. Q99 (Calculation - Slope and Intercept)
  100. Q100 (Python - Backward Elimination)

Appendix:

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