# Regression models in R and RStudio for Machine Learning and Data Sciense cheat sheets.

The R programming language and its libraries combined together form a powerful tool for solving Regression analysis tasks.

Regression study is a predictive modelling method that analyzes the relation between the target or dependent variable and features or independent variables in a dataset. The different types of regression analysis methods are used when the target and independent features described by a linear or non-linear relationships between each other, and the target variable contains continuous values. The regression technique gets used mainly to determine the predictor strength, forecast trends, time series, and sometimes in case of cause & effect relation.

Regression analysis is the basic technique to solve the regression problems in machine learning ML using data models. It consists of determining the best fit line, which is a line that passes through all the data points in such a way that distance of the line from each data point is optimal/minimized.

## Data Preprocessing Template in RStudio

library(caTools)

### Splitting the dataset into the Training set and Test set

set.seed(123)
split = sample.split(dataset\$DependentVariable, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

### Taking care of missing data

dataset\$feature = ifelse(is.na(dataset\$feature), ave(dataset\$feature, FUN = function(x) mean(x, na.rm = TRUE)), dataset\$feature)
dataset\$target = ifelse(is.na(dataset\$target), ave(dataset\$target, FUN = function(x) mean(x, na.rm = TRUE)), dataset\$target)

### Encoding categorical data

dataset\$feature2 = factor(dataset\$feature2, levels = c('Option1, 'Option2', 'Option3), labels = c(1, 2, 3))
dataset\$feature3 = factor(dataset\$feature3, levels = c('No', 'Yes'), labels = c(0, 1))

### Feature Scaling

training_set[,2:3]=scale(training_set[,2:3])
test_set[,2:3]=scale(test_set[,2:3])

### Fitting Simple Linear Regression to the Training set in R

regressor = lm(formula = Salary ~ YearsExperience, data = training_set)

### Getting information about our model in R

summary(regressor)

### Predicting the Test set results in R

y_pred = predict(regressor, newdata = test_set)

### Installing visualization library in RStudio

install.packages('ggplot2')

### Visualising the Training set results in RStudio

library(ggplot2)
ggplot() + geom_point(aes(x = training_set\$feature, y = training_set\$target), colour = 'red') + geom_line(aes(x = training_set\$feature, y = predict(regressor, newdata = training_set)), colour = 'blue') + ggtitle('Target vs feature (Training set)') + xlab('feature') + ylab('target')

### Visualising the Test set results in RStudio

ibrary(ggplot2)
ggplot() + geom_point(aes(x = test_set\$feature, y = test_set\$target), colour = 'red') + geom_line(aes(x = training_set\$feature, y = predict(regressor, newdata = training_set)), colour = 'blue') + ggtitle('Target vs feature (Test set)') + xlab('feature') + ylab('target')

### Fitting Multiple Linear Regression to the Training set

regressor = lm(formula = Target ~ ., data = training_set)

### Features filtering using Backward Elimination technique

regressor = lm(formula = Target ~ Feature1 + Feature2 + Feature3 + Feature4, data = dataset)
summary(regressor)
# Then remove the feature with highest P-value if it is higher than significance level e.g 5% and fit regressor again.

### Training the Polynomial Regression model on the whole dataset in RStudio

# first creating new polynomial features
dataset\$feature2 = dataset\$feature^2
dataset\$feature3 = dataset\$feature^3
dataset\$feature4 = dataset\$feature^4

# training the model and displaying results
poly_reg = lm(formula = Target ~ ., data = dataset)
summary(poly_reg)

### Predicting a new single result with Linear Regression- single feature

lin_reg = lm(formula = Target ~ ., data = dataset)
predict(lin_reg, data.frame(feature = put_here_any_value))

### Predicting a new single result with Polynomial Regression- single feature

predict(poly_reg, data.frame(feature = put_here_any_value, feature2 = put_here_any_value, feature3 = put_here_any_value^3, feature4 = put_here_any_value^4))

## Support Vector Regression (SVR) in R

#Importing the dataset
dataset = read.csv('my_dataset.csv') dataset = dataset[2:3]

# Fitting SVR to the dataset
#install.packages('e1071')
library(e1071)
regressor = svm(formula = Target ~ ., data = dataset, type = 'eps-regression', kernel = 'radial')

# Predicting a new result with SVR - single feature
y_pred = predict(regressor, data.frame(Feature = put_here_any_value))

# Visualising the SVR results
# install.packages('ggplot2')
library(ggplot2)
ggplot() + geom_point(aes(x = dataset\$Feature, y = dataset\$Target), colour = 'red') + geom_line(aes(x = dataset\$Feature, y = predict(regressor, newdata = dataset)), colour = 'blue') + ggtitle('Target vs Feature (SVR)') + xlab('Feature') + ylab('Target')

## Decision Tree Regression in R

#Importing the dataset
dataset = read.csv('my_dataset.csv') dataset = dataset[2:3]

# Fitting Decision Tree Regression to the dataset
#install.packages('rpart')
library(rpart)
regressor = rpart(formula = Target ~ ., data = dataset, control = rpart.control(minsplit = 1))

# Predicting a new result with Decision Tree Regression- single feature
y_pred = predict(regressor, data.frame(Feature = put_here_any_value))

# Visualising the Decision Tree Regression results with high resolution
# install.packages('ggplot2')
library(ggplot2)
x_grid = seq(min(dataset\$Feature), max(dataset\$Feature), 0.01)
ggplot() + geom_point(aes(x = dataset\$Feature, y = dataset\$Target), colour = 'red') + geom_line(aes(x = x_grid, y = predict(regressor, newdata = data.frame(Feature = x_grid))), colour = 'blue') + ggtitle('Target vs Feature (Decision Tree Regression)') + xlab('Feature') + ylab('Target')
# Plotting the tree

plot(regressor)
text(regressor)

## Random Forest Regression model in R

# #The only difference in code from Decision Tree Regression above is Training the Random Forest Regression model, on the whole datase (without split) in this example
install.packages('randomForest')
library(randomForest)
set.seed(1234)
regressor = randomForest(x = dataset, y = dataset\$Target, ntree = 500)

## Regression models tips and features.

Model: Linear Regression.
Pros: Works on any size of dataset, gives informations about relevance of features.
Cons: Linear Regression Assumptions.

Model: Polynomial Regression.
Pros: Works on any size of dataset, works very well on non linear problems.
Cons: Needed to choose the right polynomial degree for a good bias/variance tradeoff.

Model: SVR.
Pros: Easily adaptable, works very well on non linear problems, not biased by outliers.
Cons: Compulsory to apply feature scaling, difficult interpretations.

Model: Decision Tree Regression.
Pros: Interpretability, no need for feature scaling, works on both linear / nonlinear problems.
Cons: Poor results on too small datasets, overfitting can easily occur.

Model: Random Forest Regression.
Pros: Powerful and accurate, good performance on many problems, including non linear.
Cons: No interpretability, overfitting can easily occur, needed to choose the number of trees.