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### Data Science - Regression Analysis (DIY for newbies)

###### Includes 100 solved problems

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.

• ### Categories

• Machine Learning
• Python
• R
• ### Feedback

Email the Author(s)

• 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)
• Q99 (Calculation - Slope and Intercept)
• Q100 (Python - Backward Elimination)
• Appendix:

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