Algorithms

Machine learning algorithms are described as learning a target function (f) that best maps input variables (X) to an output variable (Y): Y = f(X)

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Overview

Linear Regression

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Algorithm

Y= a *X + b

• Y – Dependent Variable
• a – Slope
• X – Independent variable
• b – Intercept

Type

Supervised learning – regression.

Example

Predict weight based on height.

When To Use

Predict a value based on training data.

To estimate a value based on a continuous variable (numeric).

Strength is speed rather than accuracy.

Limitations

• Only applies to linear relationships e.g. the relationship between income and age is curved
• Sensitive to outliers

Business Use Cases

• Predicting sales
• Analysing influence of marketing, pricing and promotions on sales
• Assessing risk

R Code

#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train <- input_variables_values_training_datasets
y_train <- target_variables_values_training_datasets
x_test <- input_variables_values_test_datasets
x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary(linear)
#Predict Output
predicted= predict(linear,x_test) 

Code Source

Python Code

#Import Library
#Import other necessary libraries like pandas, numpy...
from sklearn import linear_model
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train=input_variables_values_training_datasets
y_train=target_variables_values_training_datasets
x_test=input_variables_values_test_datasets
# Create linear regression object
linear = linear_model.LinearRegression()
# Train the model using the training sets and check score
linear.fit(x_train, y_train)
linear.score(x_train, y_train)
#Equation coefficient and Intercept
print('Coefficient: \n', linear.coef_)
print('Intercept: \n', linear.intercept_)
#Predict Output
predicted= linear.predict(x_test)

Code Source

Further Reading

• Linear Regression and Correlation: A Beginner’s Guide – Scott Hartshorn
• Machine Learning For Hackers – Conway
• Data Jujitsu – The Art of Turning Data Into Product – Patil
• Numsense! Data Science For The Layman. No Math Added – Annalyn Ng

 

Notes on other algorithms coming soon…

• k-Nearest Neighbour (kNN)
• k-Means Clustering
• Logistic Regression
• Naive Bayes
• Decision Trees
• Random Forest
• Neural Network
• Support Vector Machine
• Principal Component Analysis

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Thanks to this article on the Analytics Vidhya site for getting me started on understanding these algorithms, and to the following to helping my build on that knowledge:

• 10 Machine Learning Algorithms Every Data Scientist Should Know
• A Tour of the Top 10 Algorithms for Machine Learning Newbies
• A Beginner’s Guide to Neural Networks – Part 1
• Comparing supervised learning algorithms
• Limitations of Linear Regression
• Popular Applications of Linear Regression
• 5 Applications of Regression Analysis in Business
• Machine Learning Algorithms and Business Use Cases
• Quick Guide to Boosting Algorithms
• Principal Component Analysis