Top 10 Machine Learning Algorithms You Must Know

Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.

Eric Schmidt (Google Chairman)

We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to the cloud. But what makes it defining is not what has happened but what is coming our way in years to come. What makes this period exciting and enthralling for someone like me is the democratization of the various tools, techniques, and machine learning algorithms that followed the boost in computing. Welcome to the world of Data Science!

Today, as a data scientist, I can build data-crunching machines with complex algorithms for a few dollars per hour. But reaching here wasn’t easy! I had my dark days and nights.

This article will cover some popular machine learning algorithms. We will discuss different types of machine learning algorithms and elucidate the categories, such as supervised and unsupervised learning. You will also learn how to use these machine learning algorithms. By the end of this article, you will have the skill to select the appropriate algorithms for your tasks.

Who Can Benefit the Most From this Guide?

What I am giving out today is probably the most valuable guide I have ever created. The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning (which is part of artificial intelligence) enthusiasts across the world. Through this guide, I will enable you to work on machine-learning problems and gain from experience. I am providing a high-level understanding of various machine learning algorithms along with R & Python codes to run them. These should be sufficient to get your hands dirty. You can also check out our Machine Learning Course.

Essentials of machine learning algorithms with implementation in R and Python. I have deliberately skipped the statistics behind these techniques and artificial neural networks, as you don’t need to understand them initially. So, if you are looking for a statistical understanding of these algorithms, you should look elsewhere. But, if you want to equip yourself to start building a machine learning project, you are in for a treat , especially when it comes to software development for startup environments.

3 Types of Machine Learning Algorithms

Supervised Learning Algorithms

These algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Supervised learning algorithms for classification and regression involve generating a function that maps input data to the desired outputs. Using this set of variables, we generate a function that maps input data to desired outputs. The training process continues until the model achieves the desired level of accuracy on the training data.

Best supervised machine learning algorithms include Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc. Each of these algorithms serves different types of data and problem requirements, making them widely applicable across various fields.

Also Read: Supervised learning vs unsupervised learning

Unsupervised Learning Algorithms

These algorithms work with unlabeled data, where there is no target/outcome variable to predict. Unsupervised learning algorithms for clustering and data mining are designed to identify hidden patterns or structures within the data. Using these patterns, we group data points with similar characteristics, generating a function that maps input data to clusters or groups. This process continues until the model successfully identifies meaningful patterns in the data.

Common unsupervised learning algorithms include K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA). Each of these algorithms serves different types of data and problem requirements, making them widely applicable across various fields such as customer segmentation, anomaly detection, and pattern recognition.

Reinforcement Learning Algorithms

How it works: Using this algorithm, the machine is trained to make specific decisions. The machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process

Also Read: Everything You Need to Know about Machine Learning

List of Top 10 Common Machine Learning Algorithms

Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. SVM
5. Naive Bayes
6. kNN
7. K-Means
8. Random Forest
9. Dimensionality Reduction Algorithms
10. Gradient Boosting algorithms
    • GBM
    • XGBoost
    • LightGBM
    • CatBoost

1. Linear Regression

It is used to estimate real values (cost of houses, number of calls, total sales, etc.) based on a continuous variable(s). Here, we establish the relationship between independent and dependent variables by fitting the best line.

This best-fit line is known as the regression line and is represented by a linear equation Y= a*X + b.

Example 1

The best way to understand linear regression is to relive this experience of childhood. Let us say you ask a child in fifth grade to arrange people in his class by increasing the order of weight without asking them their weights! What do you think the child will do? They would likely look (visually analyze) at the height and build of people and arrange them using a combination of these visible parameters. This is linear regression in real life! The child has actually figured out that height and build would be correlated to weight by a relationship, which looks like the equation above.

In this equation:

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

These coefficients a and b are derived based on minimizing the sum of the squared difference of distance between data points and the regression line.

Example 2

Look at the below example. Here we have identified the best-fit line having linear equation y=0.2811x+13.9. Now using this equation, we can find the weight, knowing the height of a person.

Linear Regression is mainly of two types: Simple Linear Regression and Multiple Linear Regression. Simple Linear Regression is characterized by one independent variable. And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding the best-fit line, you can fit a polynomial or curvilinear regression. And these are known as polynomial or curvilinear regression.

Here’s a coding window to try out your hand and build your own linear Regression Model:

Python

# importing required libraries
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# read the train and test dataset
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

print(train_data.head())

# shape of the dataset
print('\nShape of training data :',train_data.shape)
print('\nShape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Item_Outlet_Sales

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Item_Outlet_Sales'],axis=1)
train_y = train_data['Item_Outlet_Sales']

# seperate the independent and target variable on training data
test_x = test_data.drop(columns=['Item_Outlet_Sales'],axis=1)
test_y = test_data['Item_Outlet_Sales']

'''
Create the object of the Linear Regression model
You can also add other parameters and test your code here
Some parameters are : fit_intercept and normalize
Documentation of sklearn LinearRegression: 

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

 '''
model = LinearRegression()

# fit the model with the training data
model.fit(train_x,train_y)

# coefficeints of the trained model
print('\nCoefficient of model :', model.coef_)

# intercept of the model
print('\nIntercept of model',model.intercept_)

# predict the target on the test dataset
predict_train = model.predict(train_x)
print('\nItem_Outlet_Sales on training data',predict_train) 

# Root Mean Squared Error on training dataset
rmse_train = mean_squared_error(train_y,predict_train)**(0.5)
print('\nRMSE on train dataset : ', rmse_train)

# predict the target on the testing dataset
predict_test = model.predict(test_x)
print('\nItem_Outlet_Sales on test data',predict_test) 

# Root Mean Squared Error on testing dataset
rmse_test = mean_squared_error(test_y,predict_test)**(0.5)
print('\nRMSE on test dataset : ', rmse_test)

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)

2. Logistic Regression

Don’t get confused by its name! It is a classification algorithm, not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on a given set of independent variable(s). In simple words, it predicts the probability of the occurrence of an event by fitting data to a logistic function. Hence, it is also known as logit regression. Since it predicts the probability, its output values lie between 0 and 1 (as expected).

Again, let us try and understand this through a simple example.

Let’s say your friend gives you a puzzle to solve. There are only 2 outcome scenarios – either you solve it, or you don’t. Now imagine that you are being given a wide range of puzzles/quizzes in an attempt to understand which subjects you are good at. The outcome of this study would be something like this – if you are given a trigonometry-based tenth-grade problem, you are 70% likely to solve it. On the other hand, if it is a grade fifth history question, the probability of getting an answer is only 30%. This is what Logistic Regression provides you.

Coming to the math, the log odds of the outcome are modeled as a linear combination of the predictor variables.

odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence
ln(odds) = ln(p/(1-p))
logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk

Above, p is the probability of the presence of the characteristic of interest. It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression).

Now, you may ask, why take a log? For the sake of simplicity, let’s just say that this is one of the best mathematical ways to replicate a step function. I can go into more details, but that will beat the purpose of this article.

Build your own logistic regression model in Python here and check the accuracy:

# importing required libraries
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')


print(train_data.head())

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the Logistic Regression model
You can also add other parameters and test your code here
Some parameters are : fit_intercept and penalty
Documentation of sklearn LogisticRegression: 

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

 '''
model = LogisticRegression()

# fit the model with the training data
model.fit(train_x,train_y)

# coefficeints of the trained model
print('Coefficient of model :', model.coef_)

# intercept of the model
print('Intercept of model',model.intercept_)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

R Code:

x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
logistic <- glm(y_train ~ ., data = x,family='binomial')
summary(logistic)
#Predict Output
predicted= predict(logistic,x_test)

Furthermore…

There are many different steps that could be tried in order to improve the model:

• including interaction terms
• removing features
• regularization techniques
• using a non-linear model

3. Decision Tree

This is one of my favorite algorithms, and I use it quite frequently. It is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets. This is done based on the most significant attributes/ independent variables to make as distinct groups as possible. For more details, you can read Decision Tree Simplified.

In the image above, you can see that population is classified into four different groups based on multiple attributes to identify ‘if they will play or not’. To split the population into different heterogeneous groups, it uses various techniques like Gini, Information Gain, Chi-square, and entropy.

The best way to understand how the decision tree works, is to play Jezzball – a classic game from Microsoft (image below). Essentially, you have a room with moving walls and you need to create walls such that the maximum area gets cleared off without the balls.

So, every time you split the room with a wall, you are trying to create 2 different populations within the same room. Decision trees work in a very similar fashion by dividing a population into as different groups as possible.

Read More: Simplified Version of Decision Tree Algorithms

Let’s get our hands dirty and code our own decision tree in Python!

# importing required libraries
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the Decision Tree model
You can also add other parameters and test your code here
Some parameters are : max_depth and max_features
Documentation of sklearn DecisionTreeClassifier: 

https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

 '''
model = DecisionTreeClassifier()

# fit the model with the training data
model.fit(train_x,train_y)

# depth of the decision tree
print('Depth of the Decision Tree :', model.get_depth())

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

R Code:

library(rpart)
x <- cbind(x_train,y_train)
# grow tree 
fit <- rpart(y_train ~ ., data = x,method="class")
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

4. SVM (Support Vector Machine)

It is a classification method. In SVM algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have), with the value of each feature being the value of a particular coordinate.

For example, if we only had two features like the Height and Hair length of an individual, we’d first plot these two variables in two-dimensional space where each point has two coordinates (these co-ordinates are known as Support Vectors)

Now, we will find some lines that split the data between the two differently classified groups of data. This will be the line such that the distances from the closest point in each of the two groups will be the farthest away. If there are more variables, a hyperplane is used to separate the classes.

In the example shown above, the line which splits the data into two differently classified groups is the black line since the two closest points are the farthest apart from the line. This line is our classifier. Then, depending on where the testing data lands on either side of the line, that’s what class we can classify the new data as.

Think of this algorithm as playing JezzBall in n-dimensional space. The tweaks in the game are:

• You can draw lines/planes at any angle (rather than just horizontal or vertical as in the classic game)
• The objective of the game is to segregate balls of different colors in different rooms.
• And the balls are not moving.

Try your hand and design an SVM model in Python through this coding window:

# importing required libraries
import pandas as pd
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the Support Vector Classifier model
You can also add other parameters and test your code here
Some parameters are : kernal and degree
Documentation of sklearn Support Vector Classifier: 

https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

 '''
model = SVC()

# fit the model with the training data
model.fit(train_x,train_y)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

R Code:

library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-svm(y_train ~ ., data = x)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

5. Naive Bayes

Naive Bayes is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other, a Naive Bayes algorithm for classification would treat each property as independently contributing to the probability that this fruit is an apple.

The Naive Bayesian model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes classification is known to outperform even highly sophisticated methods for tasks such as text classification, spam detection, and sentiment analysis.

Naive Bayes Equation

Bayes theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x), and P(x|c). Look at the equation below:

Here,

• P(c|x) is the posterior probability of class (target) given predictor (attribute).
• P(c) is the prior probability of the class.
• P(x|c) is the likelihood which is the probability of the predictor given the class.
• P(x) is the prior probability of the predictor.

Example

Let’s understand it using an example. Below is a training data set of weather and the corresponding target variable, ‘Play.’ Now, we need to classify whether players will play or not based on weather conditions. Let’s follow the below steps to perform it.

Problem: Players will pay if the weather is sunny. Is this statement correct?

We can solve it using above discussed method, so P(Yes | Sunny) = P( Sunny | Yes) * P(Yes) / P (Sunny)

Here we have P (Sunny | Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P(Yes)= 9/14 = 0.64

Now, P (Yes | Sunny) = 0.33 * 0.64 / 0.36 = 0.60, which has a higher probability.

Naive Bayes uses a similar method to predict the probability of different classes based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

Code for a Naive Bayes classification model in Python:

# importing required libraries
import pandas as pd
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the Naive Bayes model
You can also add other parameters and test your code here
Some parameters are : var_smoothing
Documentation of sklearn GaussianNB: 

https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html

 '''
model = GaussianNB()

# fit the model with the training data
model.fit(train_x,train_y)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('Target on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

R Code:

library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-naiveBayes(y_train ~ ., data = x)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

6. kNN (k- Nearest Neighbors)

KNN can be used for both classification and regression problems. However, it is more widely employed as a classification algorithm in machine learning. K-Nearest Neighbors is a simple, intuitive algorithm that stores all available cases and classifies new cases by a majority vote of its k nearest neighbors. The class assigned to a new case is the one most common among its K nearest neighbors as measured by a distance function.

The distance functions used in KNN can be Euclidean, Manhattan, Minkowski, or Hamming distances. The first three are typically used for continuous variables, while Hamming distance is applied for categorical variables. If K = 1, the case is simply assigned to the class of its nearest neighbor. However, choosing the right value of K can be challenging and often depends on the dataset being used in KNN modeling.

In real-life scenarios, KNN can be likened to discovering more about a person based on their close friends or social circles. If you know nothing about someone, their neighbors’ characteristics can offer insights.

Key Considerations for KNN

• KNN is computationally expensive as it stores all available data.
• Feature scaling is essential—variables should be normalized, as higher range variables can bias the results.
• Pre-processing steps such as outlier removal and noise reduction significantly impact the performance of KNN models.

Python Code:

# importing required libraries
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the K-Nearest Neighbor model
You can also add other parameters and test your code here
Some parameters are : n_neighbors, leaf_size
Documentation of sklearn K-Neighbors Classifier: 

https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

 '''
model = KNeighborsClassifier()  

# fit the model with the training data
model.fit(train_x,train_y)

# Number of Neighbors used to predict the target
print('\nThe number of neighbors used to predict the target : ',model.n_neighbors)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('accuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('Target on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('accuracy_score on test dataset : ', accuracy_test)

R Code:

library(knn)
x <- cbind(x_train,y_train)
# Fitting model
fit <-knn(y_train ~ ., data = x,k=5)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

7. K-Means

It is a type of unsupervised algorithm which solves the clustering problem. Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). Data points inside a cluster are homogeneous and heterogeneous to peer groups.

Remember figuring out shapes from ink blots? k means is somewhat similar to this activity. You look at the shape and spread to decipher how many different clusters/populations are present!

How K-means forms cluster:

1. K-means picks k number of points for each cluster known as centroids.
2. Each data point forms a cluster with the closest centroids, i.e., k clusters.
3. Finds the centroid of each cluster based on existing cluster members. Here we have new centroids.
4. As we have new centroids, repeat steps 2 and 3. Find the closest distance for each data point from new centroids and get associated with new k-clusters. Repeat this process until convergence occurs, i.e., centroids do not change.

How to determine the value of K:

In K-means, we have clusters, and each cluster has its own centroid. The sum of the square of the difference between the centroid and the data points within a cluster constitutes the sum of the square value for that cluster. Also, when the sum of square values for all the clusters is added, it becomes a total within the sum of the square value for the cluster solution.

We know that as the number of clusters increases, this value keeps on decreasing, but if you plot the result, you may see that the sum of squared distance decreases sharply up to some value of k and then much more slowly after that. Here, we can find the optimum number of clusters.

Python Code:

# importing required libraries
import pandas as pd
from sklearn.cluster import KMeans

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to divide the training data into differernt clusters
# and predict in which cluster a particular data point belongs.  

'''
Create the object of the K-Means model
You can also add other parameters and test your code here
Some parameters are : n_clusters and max_iter
Documentation of sklearn KMeans: 

https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
 '''

model = KMeans()  

# fit the model with the training data
model.fit(train_data)

# Number of Clusters
print('\nDefault number of Clusters : ',model.n_clusters)

# predict the clusters on the train dataset
predict_train = model.predict(train_data)
print('\nCLusters on train data',predict_train) 

# predict the target on the test dataset
predict_test = model.predict(test_data)
print('Clusters on test data',predict_test) 

# Now, we will train a model with n_cluster = 3
model_n3 = KMeans(n_clusters=3)

# fit the model with the training data
model_n3.fit(train_data)

# Number of Clusters
print('\nNumber of Clusters : ',model_n3.n_clusters)

# predict the clusters on the train dataset
predict_train_3 = model_n3.predict(train_data)
print('\nCLusters on train data',predict_train_3) 

# predict the target on the test dataset
predict_test_3 = model_n3.predict(test_data)
print('Clusters on test data',predict_test_3) 

R Code:

library(cluster)
fit <- kmeans(X, 3) # 5 cluster solution

8. Random Forest

Random Forest is a trademarked term for an ensemble learning of decision trees. In Random Forest, we’ve got a collection of decision trees (also known as “Forest”). To classify a new object based on attributes, each tree gives a classification, and we say the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Each tree is planted & grown as follows:

1. If the number of cases in the training set is N, then a sample of N cases is taken at random but with replacement. This sample will be the training set for growing the tree.
2. If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during the forest growth.
3. Each tree is grown to the largest extent possible. There is no pruning.

For more details on this algorithm, compared with the decision tree and tuning model parameters, I would suggest you read these articles:

1. Introduction to Random forest – Simplified
2. Comparing a CART model to Random Forest (Part 1)
3. Comparing a Random Forest to a CART model (Part 2)
4. Tuning the parameters of your Random Forest model

Python Code:

# importing required libraries
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# view the top 3 rows of the dataset
print(train_data.head(3))

# shape of the dataset
print('\nShape of training data :',train_data.shape)
print('\nShape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''

Create the object of the Random Forest model
You can also add other parameters and test your code here
Some parameters are : n_estimators and max_depth
Documentation of sklearn RandomForestClassifier: 

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

'''
model = RandomForestClassifier()

# fit the model with the training data
model.fit(train_x,train_y)

# number of trees used
print('Number of Trees used : ', model.n_estimators)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

R Code:

library(randomForest)
x <- cbind(x_train,y_train)
# Fitting model
fit <- randomForest(Species ~ ., x,ntree=500)
summary(fit)
#Predict Output 
predicted= predict(fit,x_test)

9. Dimensionality Reduction Algorithms

In the last 4-5 years, there has been an exponential increase in data capturing at every possible stage. Corporates/ Government Agencies/ Research organizations are not only coming up with new sources, but also they are capturing data in great detail.

For example, E-commerce companies are capturing more details about customers like their demographics, web crawling history, what they like or dislike, purchase history, feedback, and many others to give them personalized attention more than your nearest grocery shopkeeper.

As data scientists, the data we are offered also consists of many features, this sounds good for building a good robust model, but there is a challenge. How’d you identify highly significant variable(s) out of 1000 or 2000? In such cases, the dimensionality reduction algorithm helps us, along with various other algorithms like Decision Tree, Random Forest, PCA (principal component analysis), Factor Analysis, Identity-based on the correlation matrix, missing value ratio, and others.

To know more about these algorithms, you can read “Beginners Guide To Learn Dimension Reduction Techniques“.

Python Code:

# importing required libraries
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error  

# read the train and test dataset
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# view the top 3 rows of the dataset
print(train_data.head(3))

# shape of the dataset
print('\nShape of training data :',train_data.shape)
print('\nShape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
# target variable - Item_Outlet_Sales
train_x = train_data.drop(columns=['Item_Outlet_Sales'],axis=1)
train_y = train_data['Item_Outlet_Sales']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Item_Outlet_Sales'],axis=1)
test_y = test_data['Item_Outlet_Sales']

print('\nTraining model with {} dimensions.'.format(train_x.shape[1]))

# create object of model
model = LinearRegression()

# fit the model with the training data
model.fit(train_x,train_y)

# predict the target on the train dataset
predict_train = model.predict(train_x)

# Accuray Score on train dataset
rmse_train = mean_squared_error(train_y,predict_train)**(0.5)
print('\nRMSE on train dataset : ', rmse_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)

# Accuracy Score on test dataset
rmse_test = mean_squared_error(test_y,predict_test)**(0.5)
print('\nRMSE on test dataset : ', rmse_test)

# create the object of the PCA (Principal Component Analysis) model
# reduce the dimensions of the data to 12
'''
You can also add other parameters and test your code here
Some parameters are : svd_solver, iterated_power
Documentation of sklearn PCA:

https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
'''
model_pca = PCA(n_components=12)

new_train = model_pca.fit_transform(train_x)
new_test  = model_pca.fit_transform(test_x)

print('\nTraining model with {} dimensions.'.format(new_train.shape[1]))

# create object of model
model_new = LinearRegression()

# fit the model with the training data
model_new.fit(new_train,train_y)

# predict the target on the new train dataset
predict_train_pca = model_new.predict(new_train)

# Accuray Score on train dataset
rmse_train_pca = mean_squared_error(train_y,predict_train_pca)**(0.5)
print('\nRMSE on new train dataset : ', rmse_train_pca)

# predict the target on the new test dataset
predict_test_pca = model_new.predict(new_test)

# Accuracy Score on test dataset
rmse_test_pca = mean_squared_error(test_y,predict_test_pca)**(0.5)
print('\nRMSE on new test dataset : ', rmse_test_pca)

R Code:

library(stats)
pca <- princomp(train, cor = TRUE)
train_reduced  <- predict(pca,train)
test_reduced  <- predict(pca,test)

10. Gradient Boosting Algorithms

Now, let’s look at the 4 most commonly used gradient boosting algorithms.

GBM

GBM is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. Boosting is actually an ensemble of learning algorithms that combines the prediction of several base estimators in order to improve robustness over a single estimator. It combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, and CrowdAnalytix.

Python Code:

# importing required libraries
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the GradientBoosting Classifier model
You can also add other parameters and test your code here
Some parameters are : learning_rate, n_estimators
Documentation of sklearn GradientBoosting Classifier: 

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
'''
model = GradientBoostingClassifier(n_estimators=100,max_depth=5)

# fit the model with the training data
model.fit(train_x,train_y)

# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

R Code:

library(caret)
x <- cbind(x_train,y_train)
# Fitting model
fitControl <- trainControl( method = "repeatedcv", number = 4, repeats = 4)
fit <- train(y ~ ., data = x, method = "gbm", trControl = fitControl,verbose = FALSE)
predicted= predict(fit,x_test,type= "prob")[,2]

GradientBoostingClassifier and Random Forest are two different boosting tree classifiers, and often people ask about the difference between these two algorithms.

XGBoost

Another classic gradient-boosting algorithm that’s known to be the decisive choice between winning and losing in some Kaggle competitions is the XGBoost. It has an immensely high predictive power, making it the best choice for accuracy in events. It possesses both a linear model and the tree learning algorithm, making the algorithm almost 10x faster than existing gradient booster techniques.

One of the most interesting things about the XGBoost is that it is also called a regularized boosting technique. This helps to reduce overfit modeling and has massive support for a range of languages such as Scala, Java, R, Python, Julia, and C++.

The support includes various objective functions, including regression, classification, and ranking. Supports distributed and widespread training on many machines that encompass GCE, AWS, Azure, and Yarn clusters. XGBoost can also be integrated with Spark, Flink, and other cloud dataflow systems with built-in cross-validation at each iteration of the boosting process.

Read this guide to learn more about XGBoost and parameter tuning.

Python Code:

# importing required libraries
import pandas as pd
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the XGBoost model
You can also add other parameters and test your code here
Some parameters are : max_depth and n_estimators
Documentation of xgboost:

https://xgboost.readthedocs.io/en/latest/
'''
model = XGBClassifier()

# fit the model with the training data
model.fit(train_x,train_y)


# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

R Code:

require(caret)

x <- cbind(x_train,y_train)

# Fitting model

TrainControl <- trainControl( method = "repeatedcv", number = 10, repeats = 4)

model<- train(y ~ ., data = x, method = "xgbLinear", trControl = TrainControl,verbose = FALSE)

OR 

model<- train(y ~ ., data = x, method = "xgbTree", trControl = TrainControl,verbose = FALSE)

predicted <- predict(model, x_test)

LightGBM

LightGBM is a gradient-boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient with the following advantages:

• Faster training speed and higher efficiency
• Lower memory usage
• Better accuracy
• Parallel and GPU learning supported
• Capable of handling large-scale data

The framework is a fast and high-performance gradient-boosting one based on decision tree algorithms used for ranking, classification, and many other machine-learning tasks. It was developed under the Distributed Machine Learning Toolkit Project of Microsoft.

Since the LightGBM is based on decision tree algorithms, it splits the tree leaf-wise with the best fit, whereas other boosting algorithms split the tree depth-wise or level-wise rather than leaf-wise. So when growing on the same leaf node in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm, resulting in much better accuracy, which any existing boosting algorithms can rarely achieve.

Also, it is surprisingly very fast, hence the word ‘Light.’

Python Code:

data = np.random.rand(500, 10) # 500 entities, each contains 10 features
label = np.random.randint(2, size=500) # binary target

train_data = lgb.Dataset(data, label=label)
test_data = train_data.create_valid('test.svm')

param = {'num_leaves':31, 'num_trees':100, 'objective':'binary'}
param['metric'] = 'auc'

num_round = 10
bst = lgb.train(param, train_data, num_round, valid_sets=[test_data])

bst.save_model('model.txt')

# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
ypred = bst.predict(data)

R Code:

library(RLightGBM)
data(example.binary)
#Parameters

num_iterations <- 100
config <- list(objective = "binary",  metric="binary_logloss,auc", learning_rate = 0.1, num_leaves = 63, tree_learner = "serial", feature_fraction = 0.8, bagging_freq = 5, bagging_fraction = 0.8, min_data_in_leaf = 50, min_sum_hessian_in_leaf = 5.0)

#Create data handle and booster
handle.data <- lgbm.data.create(x)

lgbm.data.setField(handle.data, "label", y)

handle.booster <- lgbm.booster.create(handle.data, lapply(config, as.character))

#Train for num_iterations iterations and eval every 5 steps

lgbm.booster.train(handle.booster, num_iterations, 5)

#Predict
pred <- lgbm.booster.predict(handle.booster, x.test)

#Test accuracy
sum(y.test == (y.pred > 0.5)) / length(y.test)

#Save model (can be loaded again via lgbm.booster.load(filename))
lgbm.booster.save(handle.booster, filename = "/tmp/model.txt")

If you’re familiar with the Caret package in R, this is another way of implementing the LightGBM.

require(caret)
require(RLightGBM)
data(iris)

model <-caretModel.LGBM()

fit <- train(Species ~ ., data = iris, method=model, verbosity = 0)
print(fit)
y.pred <- predict(fit, iris[,1:4])

library(Matrix)
model.sparse <- caretModel.LGBM.sparse()

#Generate a sparse matrix
mat <- Matrix(as.matrix(iris[,1:4]), sparse = T)
fit <- train(data.frame(idx = 1:nrow(iris)), iris$Species, method = model.sparse, matrix = mat, verbosity = 0)
print(fit)

Catboost

CatBoost is one of open-sourced machine learning algorithms from Yandex. It can easily integrate with deep learning frameworks like Google’s TensorFlow and Apple’s Core ML. The best part about CatBoost is that it does not require extensive data training like other ML models and can work on a variety of data formats, not undermining how robust it can be.

Catboost can automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors. Make sure you handle missing data well before you proceed with the implementation.

Python Code:

import pandas as pd
import numpy as np

from catboost import CatBoostRegressor

#Read training and testing files
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")

#Imputing missing values for both train and test
train.fillna(-999, inplace=True)
test.fillna(-999,inplace=True)

#Creating a training set for modeling and validation set to check model performance
X = train.drop(['Item_Outlet_Sales'], axis=1)
y = train.Item_Outlet_Sales

from sklearn.model_selection import train_test_split

X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.7, random_state=1234)
categorical_features_indices = np.where(X.dtypes != np.float)[0]

#importing library and building model
from catboost import CatBoostRegressormodel=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE')

model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)

submission = pd.DataFrame()

submission['Item_Identifier'] = test['Item_Identifier']
submission['Outlet_Identifier'] = test['Outlet_Identifier']
submission['Item_Outlet_Sales'] = model.predict(test)

R Code:

set.seed(1)

require(titanic)

require(caret)

require(catboost)

tt <- titanic::titanic_train[complete.cases(titanic::titanic_train),]

data <- as.data.frame(as.matrix(tt), stringsAsFactors = TRUE)

drop_columns = c("PassengerId", "Survived", "Name", "Ticket", "Cabin")

x <- data[,!(names(data) %in% drop_columns)]y <- data[,c("Survived")]

fit_control <- trainControl(method = "cv", number = 4,classProbs = TRUE)

grid <- expand.grid(depth = c(4, 6, 8),learning_rate = 0.1,iterations = 100, l2_leaf_reg = 1e-3,            rsm = 0.95, border_count = 64)

report <- train(x, as.factor(make.names(y)),method = catboost.caret,verbose = TRUE, preProc = NULL,tuneGrid = grid, trControl = fit_control)

print(report)

importance <- varImp(report, scale = FALSE)

print(importance)

Practice Problems

Now, it’s time to take the plunge and actually play with some other real-world datasets. So are you ready to take on the challenge? Accelerate your data science journey with the following practice problems:

	Practice Problem: Food Demand Forecasting Challenge	Predict the demand for meals for a meal delivery company
	Practice Problem: HR Analytics Challenge	Identify the employees most likely to get promoted
	Practice Problem: Predict Number of Upvotes	Predict the number of upvotes on a query asked at an online question & answer platform

Conclusion

By now, I am sure you would have an idea of commonly used machine learning algorithms. My sole intention behind writing this article and providing the codes in R and Python is to get you started right away. If you are keen to master machine learning algorithms, start right away. Take up problems, develop a physical understanding of the process, apply these codes, and watch the fun!

Hope you like the article and get full understanding about the data science algoeitrhms, machine learning models and with these you will get full understanding about machine learning algorithms.

 If you found this article helpful and want to master your machine learning skills, Enroll in our AI/ML Blackbelt Plus Program today!

Key Takeaways

• We are now familiar with some of the most common ML algorithms used in the industry.
• We’ve covered the advantages and disadvantages of various ML algorithms.
• We’ve also learned the basic implementation details in R and Python languages.

Frequently Asked Questions