Supervised learning, also known as supervised machine learning, is a type of machine learning that trains the model using labeled datasets to predict outcomes. A Labeled dataset is one that consists of input data (features) along with corresponding output data (targets).
The main objective of supervised learning algorithms is to learn an association between input data samples and corresponding outputs after performing multiple training data instances.
How does Supervised Learning Work?
In supervised machine learning, models are trained using a dataset that consists of input-output pairs.
The supervised learning algorithm analyzes the dataset and learns the relation between the input data (features) and correct output (labels/ targets). In the process of training, the model estimates the algorithm’s parameters by minimizing a loss function. The loss function measures the difference between the model’s predictions and actual target values.
The model iteratively updates its parameters until the loss/ error has been sufficiently minimized.
Once the training is completed, the model parameters have optimal values. The model has learned the optimal mapping/ relation between the inputs and targets. Now, the model can predict values for the new and unseen input data.
Types of Supervised Learning Algorithm
Supervised machine learning is categorized into two types of problems − classification and regression.
1. Classification
The key objective of classification-based tasks is to predict categorical output labels or responses for the given input data such as true-false, male-female, yes-no etc. As we know, the categorical output responses mean unordered and discrete values; hence, each output response will belong to a specific class or category.
Some popular classification algorithms are decision trees, random forests, support vector machines (SVM), logistic regression, etc.
2. Regression
The key objective of regression-based tasks is to predict output labels or responses, which are continuous numeric values, for the given input data. Basically, regression models use the input data features (independent variables) and their corresponding continuous numeric output values (dependent or outcome variables) to learn specific associations between inputs and corresponding outputs.
Some popular regression algorithms are linear regression, polynomial regression, Laso regression, etc.
Algorithms for Supervised Learning
Supervised learning is one of the important models of learning involved in training machines. This chapter talks in detail about the same.
There are several algorithms available for supervised learning. Some of the widely used algorithms of supervised learning are as shown below −
Linear Regression
k-Nearest Neighbors
Decision Trees
Naive Bayes
Logistic Regression
Support Vector Machines
Random Forest
Gradient Boosting
Let’s discuss each of the above mentioned supervised machine learning algorithms in detail.
1. Linear Regression
Linear regression is a type of algorithm that tries to find the linear relation between input features and output values for the prediction of future events. This algorithm is widely used to perform stock analysis, weather forecasting and others.
2. K-Nearest Neighbors
The k-Nearest Neighbors (kNN) is a statistical technique that can be used for solving classification and regression problems. This algorithm classifies or predicts values for new data by mathematically calculating the nearest distance with other points in training data.
Let us discuss the case of classifying an unknown object using kNN. Consider the distribution of objects as shown in the image given below −
The diagram shows three types of objects, marked in red, blue and green colors. When you run the kNN classifier on the above dataset, the boundaries for each type of object will be marked as shown below −
Now, consider a new unknown object you want to classify as red, green or blue. This is depicted in the figure below.
As you see it visually, the unknown data point belongs to a class of blue objects. Mathematically, this can be concluded by measuring the distance of this unknown point with every other point in the data set. When you do so, you will know that most of its neighbors are blue in color. The average distance between red and green objects would definitely be more than the average distance between blue objects. Thus, this unknown object can be classified as belonging to blue class.
The kNN algorithm can also be used for regression problems. The kNN algorithm is available as ready-to-use in most of the ML libraries.
3. Decision Trees
A Decision tree is a tree-like structure used to make decisions and analyze the possible consequences. The algorithm splits the data into subsets based on features, where each parent node represents internal decisions and the leaf node represents final prediction.
A simple decision tree in a flowchart format is shown below −
You would write a code to classify your input data based on this flowchart. The flowchart is self-explanatory and trivial. In this scenario, you are trying to classify an incoming email to decide when to read it.
In reality, the decision trees can be large and complex. There are several algorithms available to create and traverse these trees. As a Machine Learning enthusiast, you need to understand and master these techniques of creating and traversing decision trees.
4. Naive Bayes
Naive Bayes is used for creating classifiers. Suppose you want to sort out (classify) fruits of different kinds from a fruit basket. You may use features such as color, size, and shape of fruit; for example, any fruit that is red in color, round in shape, and about 10 cm in diameter may be considered an Apple. So to train the model, you would use these features and test the probability that a given feature matches the desired constraints. The probabilities of different features are then combined to arrive at the probability that a given fruit is an Apple. Naive Bayes generally requires a small number of training data for classification.
5. Logistic Regression
Logistic regression is a type of statistical algorithm that estimates the probability of occurrence of an event.
Look at the following diagram. It shows the distribution of data points in the XY plane.
From the diagram, we can visually inspect the separation of red and green dots. You may draw a boundary line to separate out these dots. Now, to classify a new data point, you will just need to determine on which side of the line the point lies.
6. Support Vector Machines
Support Vector Machines (SVM) algorithm can be typically used for both classification and regression. For classification tasks, the algorithm creates a hyperplane to separate data into classes. While for regression, the algorithm tries to fit a regression line with minimal error.
Look at the following distribution of data. Here the three classes of data cannot be linearly separated. The boundary curves are non-linear. In such a case, finding the curve’s equation becomes a complex job.
Source: http://uc-r.github.io/svm
The Support Vector Machines (SVM) come in handy in determining the separation boundaries in such situations.
7. Random Forest
Random forest is also a supervised learning algorithm that is flexible for classification and regression. This algorithm is a combination of multiple decision trees which are merged to improve the accuracy of prediction .
The following diagram illustrates how the Random Forest Algorithm works −
8. Gradient Boosting
Gradient boosting combines weak learners(decision trees), to create a strong model. It builds new models that correct errors of the previous ones. The goal of this algorithm is to minimize the loss function. It can be efficiently used for classification and regression tasks.
Advantages of Supervised Learning
Supervised learning algorithms are one of the most popular among the machine learning models. Some benefits are:-
The goal in supervised learning is well-defined, which improves the prediction accuracy.
Models trained using supervised learning are effective at predicting and classification since they use labeled datasets.
It can be highly versatile, i.e., applied to various problems, like spam detection, stock prices, etc.
Disadvantages of Supervised Learning
Though supervised learning is the most used, it comes with certain challenges too. Some of them are:
Supervised learning requires a large amount of labeled data for the model to train effectively. It is practically very difficult to collect such huge data; it is expensive and time-consuming.
Supervised learning cannot predict accurately if the test data is different from the training data.
Accurately labeling the data is complex and requires expertise and effort.
Applications of Supervised learning
Supervised learning models are widely used in many applications in various sectors, including the following-
Image recognition − A model is trained on a labeled dataset of images, where each image is associated with a label. The model is fed with data, which allows it to learn patterns and features. Once trained, the model can now be tested using new, unseen data. This is widely used in applications like facial recognition and object detection.
Predictive analytics − Supervised learning algorithms are used to train labeled historical data, allowing the model to learn patterns and relations between input features and output to identify trends and make accurate predictions. Businesses use this method to make data-driven decisions and enhance strategic planning.
There are various Machine Learning algorithms, techniques and methods that can be used to build models for solving real-life problems by using data. In this chapter, we are going to discuss such different kinds of methods.
There are four main types of machine learning methods classified based on human supervision −
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
In the next four chapters, we will discuss each of these machine learning models in detail. Here, let’s have a brief overview of these methods:
Supervised Learning
Supervised learning algorithms or methods are the most commonly used ML algorithms. This method or learning algorithm takes the data sample i.e. the training data and its associated output i.e. labels or responses with each data sample during the training process.
The main objective of supervised learning algorithms is to learn an association between input data samples and corresponding outputs after performing multiple training data instances.
For example, we have
x: Input variables and
Y: Output variable
Now, apply an algorithm to learn the mapping function from the input to output as follows −
Y=f(x)
Now, the main objective would be to approximate the mapping function so well that even when we have new input data (x), we can easily predict the output variable (Y) for that new input data.
It is called supervised because the whole process of learning can be thought as it is being supervised by a teacher or supervisor. Examples of supervised machine learning algorithms includes Decision tree, Random Forest, KNN, Logistic Regression etc.
Based on the ML tasks, supervised learning algorithms can be divided into the following two broad classes −
Classification
Regression
Classification
The key objective of classification-based tasks is to predict categorial output labels or responses for the given input data. The output will be based on what the model has learned in the training phase. As we know the categorial output responses means unordered and discrete values, hence each output response will belong to a specific class or category. We will discuss Classification and associated algorithms in detail in the upcoming chapters also.
Classification Models
Followings are some common classification models −
Logistic Regression
Decision Trees
Random Forest
K-nearest Neighbor
Support Vector Machine
Naive Bayes
Linear Discriminant Analysis
Neural Networks
Regression
The key objective of regression-based tasks is to predict output labels or responses, which are continuous numeric values, for the given input data. The output will be based on what the model has learned in its training phase. Basically, regression models use the input data features (independent variables) and their corresponding continuous numeric output values (dependent or outcome variables) to learn specific associations between inputs and corresponding outputs. We will discuss regression and associated algorithms in detail in further chapters.
Regression Models
Followings are some common regression models −
Linear Regression
Ridge regression
Decision Trees
Random Forest
K-nearest Neighbor
Neural Network Regression
Unsupervised Learning
As the name suggests, unsupervised learning is opposite to supervised ML methods or algorithms in which we do not have any supervisor to provide any sort of guidance. Unsupervised learning algorithms are handy in the scenario in which we do not have the liberty, like in supervised learning algorithms, of having pre-labeled training data and we want to extract useful pattern from input data.
For example, it can be understood as follows −
Suppose we have −
x: Input variables, then there would be no corresponding output variable and the algorithms need to discover the interesting pattern in data for learning.
Examples of unsupervised machine learning algorithms includes K-means clustering, K-nearest neighbors etc.
Based on the ML tasks, unsupervised learning algorithms can be divided into the following broad classes −
Clustering
Association
Dimensionality Reduction
Clustering
Clustering methods are one of the most useful unsupervised ML methods. These algorithms used to find similarity as well as relationship patterns among data samples and then cluster those samples into groups having similarity based on features. The real-world example of clustering is to group the customers by their purchasing behavior.
Clustering Models
Followings are some common clustering models −
K-Means Clustering
Hierarchical Clustering
Mean-shift Clustering
DBSCAN Clustering
HDBSCAN Clustering
BIRCH Clustering
Affinity Propagation
Agglomerative Clustering
Association
Another useful unsupervised ML method is Association which is used to analyze large dataset to find patterns which further represents the interesting relationships between various items. It is also termed as Association Rule Mining or Market basket analysis which is mainly used to analyze customer shopping patterns.
Association Models
Followings are some common association models −
Apriori Algorithm
Eclat algorithm
FP-growth algorithm
Dimensionality Reduction
This unsupervised ML method is used to reduce the number of feature variables for each data sample by selecting set of principal or representative features. A question arises here is that why we need to reduce the dimensionality? The reason behind is the problem of feature space complexity which arises when we start analyzing and extracting millions of features from data samples. This problem generally refers to curse of dimensionality. PCA (Principal Component Analysis), K-nearest neighbors and discriminant analysis are some of the popular algorithms for this purpose.
Dimensionality Reduction Models
Followings are some common dimensionality Reduction models −
Principal Component Analysis(PCA)
Autoencoders
Singular value decomposition (SVD)
Anomaly Detection
This unsupervised ML method is used to find out the occurrences of rare events or observations that generally do not occur. By using the learned knowledge, anomaly detection methods would be able to differentiate between anomalous or a normal data point. Some of the unsupervised algorithms like clustering, KNN can detect anomalies based on the data and its features.
Semi-supervised Learning
Semi-supervised learning algorithms or methods are neither fully supervised nor fully unsupervised. They basically fall between the two i.e. supervised and unsupervised learning methods. These kinds of algorithms generally use small supervised learning component i.e. small amount of pre-labeled annotated data and large unsupervised learning component i.e. lots of unlabeled data for training. We can follow any of the following approaches for implementing semi-supervised learning methods −
The first and simple approach is to build the supervised model based on small amount of labeled and annotated data and then build the unsupervised model by applying the same to the large amounts of unlabeled data to get more labeled samples. Now, train the model on them and repeat the process.
The second approach needs some extra efforts. In this approach, we can first use the unsupervised methods to cluster similar data samples, annotate these groups and then use a combination of this information to train the model.
Reinforcement Learning
Reinforcement learning methods are different from previously studied methods and very rarely used also. In this kind of learning algorithms, there would be an agent that we want to train over a period of time so that it can interact with a specific environment. The agent will follow a set of strategies for interacting with the environment and then after observing the environment it will take actions regards the current state of the environment. The following are the main steps of reinforcement learning methods −
Step 1 − First, we need to prepare an agent with some initial set of strategies.
Step 2 − Then observe the environment and its current state.
Step 3 − Next, select the optimal policy regards the current state of the environment and perform important action.
Step 4 − Now, the agent can get corresponding reward or penalty as per accordance with the action taken by it in previous step.
Step 5 − Now, we can update the strategies if it is required so.
Step 6 − At last, repeat steps 2-5 until the agent got to learn and adopt the optimal policies.
Reinforcement Learning Models
Following are some common reinforcement learning algorithms −
Q-learning
Markov Decision Process (MDP)
SARSA
DQN
DDPG
We will discuss each of the above machine learning models in detail in upcoming chapters.
Data preparation is a critical step in the machine learning process, and can have a significant impact on the accuracy and effectiveness of the final model. It requires careful attention to detail and a thorough understanding of the data and the problem at hand.
Let’s discuss how data should be prepared in order to fit right with the model for better accuracy and outcome.
What is Data Preparation?
Data preparation is the process of dealing with raw data i.e, cleaning, organizing and transforming it to align with the machine learning algorithms. Data preparation is a continuous process, and has a huge impact on the performance of machine learning model. Clean and structured data would result in better outcomes.
Importance of Data Preparation
In Machine learning, the model learns from the data that is fed. So, the algorithm can learn efficiently only if the data is organized and perfect. The quality of the data you use for your model can have a significant impact on the performance of the model.
Few aspects that define the importance of data preparation in machine learning are −
Improves model accuracy − Machine learning algorithms reply completely on data. When you provide clean and structured data to models, the outcomes are accurate.
Facilitates Feature Engineering − Data preparation often includes the process of selecting or creating new features to train the model. Hence, data preparation would make feature engineering easy.
Data Quality − Collected data most often would contain inconsistencies, errors and irrelevant information. Hence when tasks like data cleaning, transformation are applied, the data is formatted and neat. This can be used for gaining insights and patterns.
Enables rate of prediction − Prepared data makes it easier to analyze results and would yield accurate outcomes.
Data Preparation Process Steps
Data preparation process involves a sequence of steps that is required to make data suitable for analysis and modeling. The goal of data preparation is to make sure that the data is accurate, complete, and relevant for the analysis.
The following are some of the key steps involved in data preparation −
Data Collection
Data Cleaning
Data Transformation
Data Reduction
Data Splitting
The process shown is not always sequential. You might, for example, split your data before you transform it. You might need to collect more data.
Let’s understand each of the above steps in detail −
Data Collection
Data collection is the first step in the process of machine learning, where data from different sources is gathered to make decisions, answer research questions and statistical planning. Different sources such as databases, text files, pictures, sound files, or web scraping may be used for data collection. Once the data is selected, the data has to be preprocessed in order to gain insights. This process is carried out to put the data in an appropriate format that would be useful for problem solving. Some time data collection follows the data integration step.
Data integration involves combining data from multiple sources into a single dataset for analysis. This may involve matching or linking records across different datasets, or merging datasets based on common variables.
After selecting the raw data, the most important task is data preprocessing. In broad sense, data preprocessing will convert the selected data into a form we can work with or can feed to ML algorithms. We always need to preprocess our data so that it can be as per the expectation of machine learning algorithm. The data preprocessing includes data cleaning, transformation and reduction. Let’s discuss each of these three in detail.
Data Cleaning
Data cleaning is the process of identifying and correcting errors, missing values, duplicate values and outliers, etc. in the data. This step is crucial in the process of machine learning as it ensures that the data is accurate, relevant and error free.
Common techniques used for data cleaning include imputation, outlier detection and removal, etc. The following is a sequence of steps for data cleaning −
1. Handling duplicate values
Duplicates in the dataset means that there is repeated data, which might occur due to data entry errors or issues while collecting data. The technique used to remove duplicates is first they are identified and then deleted using drop_duplicates function in Pandas.
2. Fixing syntax errors
In this step, structural errors like inconsistencies in data format or naming conventions should be addressed. Standardizing formats and fixing errors would ensure data consistence and accurate analysis.
3. Dealing outliers
Outliers are values that are unusual and differ greatly with the data. The techniques used to detect outliers include statistical methods like z-score or IQR method and machine learning methods like clustering and SVM’s.
4. Handling Missing Values
Missing values are the values or data that is not stored for some values in the dataset. There are several ways to handle missing data like:
Imputation − In this process the missing values are substituted with different value, which can be a central tendency measure like mean, median or mode for numeric values and most frequency category for categorical data. Some other methods in imputation include regression imputation and multiple imputation.
Deletion − In this process the entire instances with missing values are removed. Well, this is not a reliable method since there is loss of data.
5. Validating the data
Data Validation is another stage that makes sure that the data aligns perfectly with the requirements so that the predicted outcome is accurate. Some common data validation procedures it the correctness of data before storing them in databases are:
Data type check
Code Check
Format check
Range check
Data Transformation
Data transformation is the process of converting the data from its original format into a format that is suitable for analysis and modeling. This could include defining the structure, aligning the data, extracting data from source, and then storing it in an appropriate form.
There are many techniques available to transorm data into a sutable format. Some commonly used data transformation techniques are as follows −
Scaling
Normalization − L1 & L2 Normalizations
Standardization
Binarization
Encoding
Log Transformation
Lets discuss each of the above data transformation techniques in detail −
1. Scaling
In most cases, the data we collected consists of attributes with varying scale, but we cannot provide such data to ML algorithm hence it requires rescaling. Data scaling makes sure that attributes are at the same scale i.e, usually range of 0 to 1.
We can rescale the data with the help of MinMaxScaler class of scikit-learn Python library.
Example
In this example we will rescale the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded (as done in the previous chapters) and then with the help of MinMaxScaler class, it will be rescaled in the range of 0 and 1.
The first few lines of the following script are same as we have written in previous chapters while loading CSV data.
from pandas import read_csv
from numpy import set_printoptions
from sklearn import preprocessing
path =r'C:\pima-indians-diabetes.csv'
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
dataframe = read_csv(path, names=names)
array = dataframe.values
Now, we can use MinMaxScaler class to rescale the data in the range of 0 and 1.
From the above output, all the data got rescaled into the range of 0 and 1.
2. Normalization
Normalization is used to rescale the data with a distribution value between 0 and 1. For every feature, the minimum value is set to 0 and the maximum value is set to 1.
This is used to rescale each row of data to have a length of 1. It is mainly useful in Sparse dataset where we have lots of zeros. We can rescale the data with the help of Normalizer class of scikit-learn Python library.
In machine learning, there are two types of normalization preprocessing techniques as follows −
L1 Normalization
It may be defined as the normalization technique that modifies the dataset values in a way that in each row the sum of the absolute values will always be up to 1. It is also called Least Absolute Deviations.
Example
In this example, we use L1 Normalize technique to normalize the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded and then with the help of Normalizer class it will be normalized.
The first few lines of following script are same as we have written in previous chapters while loading CSV data.
from pandas import read_csv
from numpy import set_printoptions
from sklearn.preprocessing import Normalizer
path =r'C:\pima-indians-diabetes.csv'
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
dataframe = read_csv (path, names=names)
array = dataframe.values
Now, we can use Normalizer class with L1 to normalize the data.
It may be defined as the normalization technique that modifies the dataset values in a way that in each row the sum of the squares will always be up to 1. It is also called least squares.
Example
In this example, we use L2 Normalization technique to normalize the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded (as done in previous chapters) and then with the help of Normalizer class it will be normalized.
The first few lines of following script are same as we have written in previous chapters while loading CSV data.
from pandas import read_csv
from numpy import set_printoptions
from sklearn.preprocessing import Normalizer
path =r'C:\pima-indians-diabetes.csv'
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
dataframe = read_csv (path, names=names)
array = dataframe.values
Now, we can use Normalizer class with L1 to normalize the data.
Standardization is used to transform data attributes to a standard Gaussian distribution with a mean of 0 and a standard deviation of 1. This technique is useful in ML algorithms like linear regression, logistic regression that assumes a Gaussian distribution in input dataset and produce better results with rescaled data.
We can standardize the data (mean = 0 and SD =1) with the help of StandardScaler class of scikit-learn Python library.
Example
In this example, we will rescale the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded and then with the help of StandardScaler class it will be converted into Gaussian Distribution with mean = 0 and SD = 1.
The first few lines of following script are same as we have written in previous chapters while loading CSV data.
from sklearn.preprocessing import StandardScaler
from pandas import read_csv
from numpy import set_printoptions
path =r'C:\pima-indians-diabetes.csv'
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
dataframe = read_csv(path, names=names)
array = dataframe.values
Now, we can use StandardScaler class to rescale the data.
As the name suggests, this is the technique with the help of which we can make our data binary. We can use a binary threshold for making our data binary. The values above that threshold value will be converted to 1 and below that threshold will be converted to 0. For example, if we choose threshold value = 0.5, then the dataset value above it will become 1 and below this will become 0. That is why we can call it binarizing the data or thresholding the data. This technique is useful when we have probabilities in our dataset and want to convert them into crisp values.
We can binarize the data with the help of Binarizer class of scikit-learn Python library.
Example
In this example, we will rescale the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded and then with the help of Binarizer class it will be converted into binary values i.e. 0 and 1 depending upon the threshold value. We are taking 0.5 as threshold value.
The first few lines of following script are same as we have written in previous chapters while loading CSV data.
from pandas import read_csv
from sklearn.preprocessing import Binarizer
path =r'C:\pima-indians-diabetes.csv'
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
dataframe = read_csv(path, names=names)
array = dataframe.values
Now, we can use Binarize class to convert the data into binary values.
This technique is used to convert categorical variables into numerical representations. Some common encoding techniques include one-hot encoding, label encoding and target encoding.
Label Encoding
Most of the sklearn functions expect that the data with number labels rather than word labels. Hence, we need to convert such labels into number labels. This process is called label encoding. We can perform label encoding of data with the help of LabelEncoder() function of scikit-learn Python library.
Example
In the following example, Python script will perform the label encoding.
First, import the required Python libraries as follows −
import numpy as np
from sklearn import preprocessing
Now, we need to provide the input labels as follows −
This technique is usually used in handling skewed data. It involves apply natural logarithmic function for all values in the dataset to modify the scale of numeric values.
Data Reduction
Data Reduction is a technique to reduce the size of the dataset by selecting a subset of features or observations that are most relevant for the analysis. This can help to reduce noise and improve the accuracy of the model.
This is useful when the dataset is very large or when a dataset contains large amount of irrelevant data.
One of the most common technique used is Dimensionality Reduction, which reduces the size of the dataset without loosing the important information. Other method is the Discretization, where continuous values like time and temperature are converted to discrete categories which simplifies the data.
Data Splitting
Data Splitting is the last step in the preparation of data for machine learning, where the data is split into different sets –
Training − subset which is used by the machine learning model for learning patterns.
Validation − subset used to evaluate the performance of machine learning model while training.
Testing − subset used to evaluate the performance and efficiency of the trained model.
Python Example
Let’s check an example of data preparation using the breast cancer dataset −
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# load the dataset
data = load_breast_cancer()# separate the features and target
X = data.data
y = data.target
# split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# normalize the data using StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
In this example, we first load the breast cancer dataset using load_breast_cancer function from scikit-learn. Then we separate the features and target, and split the data into training and testing sets using train_test_split function.
Finally, we normalize the data using StandardScaler from scikit-learn, which subtracts the mean and scales the data to unit variance. This helps to bring all the features to a similar scale, which is particularly important for models like SVM and neural networks.
Data Preparation and Feature Engineering
Feature engineering involves creating new features from the existing data that may be more informative or useful for the analysis. It can involve combining or transforming existing features, or creating new features based on domain knowledge or insights. Both data preparation and feature engineering go hand-in-hand in the overall data preprocessing pipeline.
While working with machine learning projects, usually we ignore two most important parts called mathematics and data. What makes data understanding a critical step in ML is its data driven approach. Our ML model will produce only as good or as bad results as the data we provided to it.
Data understanding basically involves analyzing and exploring the data to identify any patterns or trends that may be present.
The data understanding phase typically involves the following steps −
Data Collection − This involves gathering the relevant data that you will be using for your analysis. The data can be collected from various sources such as databases, websites, and APIs.
Data Cleaning − This involves cleaning the data by removing any irrelevant or duplicate data, and dealing with missing data values. The data should be formatted in a way that makes it easy to analyze.
Data Exploration − This involves exploring the data to identify any patterns or trends that may be present. This can be done using various statistical techniques such as histograms, scatter plots, and correlation analysis.
Data Visualization − This involves creating visual representations of the data to help you understand it better. This can be done using tools such as graphs, charts, and maps.
Data Preprocessing − This involves transforming the data to make it suitable for use in machine learning algorithms. This can include scaling the data, transforming it into a different format, or reducing its dimensionality.
Understand the Data before Uploading It in ML Projects
Understanding our data before uploading it into our ML project is important for several reasons −
Identify Data Quality Issues
By understanding your data, you can identify data quality issues such as missing values, outliers, incorrect data types, and inconsistencies that can affect the performance of your ML model. By addressing these issues, you can improve the quality and accuracy of your model.
Determine Data Relevance
You can determine if the data you have collected is relevant to the problem you are trying to solve. By understanding your data, you can determine which features are important for your model and which ones can be ignored.
Select Appropriate ML Techniques
Depending on the characteristics of your data, you may need to choose a particular ML technique or algorithm. For example, if your data is categorical, you may need to use classification techniques, while if your data is continuous, you may need to use regression techniques. Understanding your data can help you select the appropriate ML technique for your problem.
Improve Model Performance
By understanding your data, you can engineer new features, preprocess your data, and select the appropriate ML technique to improve the performance of your model. This can result in better accuracy, precision, recall, and F1 score.
Data Understanding with Statistics
In the previous chapter, we discussed how we can upload CSV data into our ML project, but it would be good to understand the data before uploading it. We can understand the data by two ways, with statistics and with visualization.
In this chapter, with the help of following Python recipes, we are going to understand ML data with statistics.
Looking at Raw Data
The very first recipe is for looking at your raw data. It is important to look at raw data because the insight we will get after looking at raw data will boost our chances to better pre-processing as well as handling of data for ML projects.
Following is a Python script implemented by using head() function of Pandas DataFrame on Pima Indians diabetes dataset to look at the first 10 rows to get better understanding of it −
Example
from pandas import read_csv
path =r"C:\pima-indians-diabetes.csv"
headernames =['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(path, names=headernames)print(data.head(10))
We can observe from the above output that first column gives the row number which can be very useful for referencing a specific observation.
Checking Dimensions of Data
It is always a good practice to know how much data, in terms of rows and columns, we are having for our ML project. The reasons behind are −
Suppose if we have too many rows and columns then it would take long time to run the algorithm and train the model.
Suppose if we have too less rows and columns then it we would not have enough data to well train the model.
Following is a Python script implemented by printing the shape property on Pandas Data Frame. We are going to implement it on iris data set for getting the total number of rows and columns in it.
Example
from pandas import read_csv
path =r"C:\iris.csv"
data = read_csv(path)print(data.shape)
Output
(150, 4)
We can easily observe from the output that iris data set, we are going to use, is having 150 rows and 4 columns.
Getting Each Attributes Data Type
It is another good practice to know data type of each attribute. The reason behind is that, as per to the requirement, sometimes we may need to convert one data type to another. For example, we may need to convert string into floating point or int for representing categorial or ordinal values. We can have an idea about the attributes data type by looking at the raw data, but another way is to use dtypes property of Pandas DataFrame. With the help of dtypes property we can categorize each attributes data type. It can be understood with the help of following Python script −
Example
from pandas import read_csv
path =r"C:\iris.csv"
data = read_csv(path)print(data.dtypes)
From the above output, we can easily get the datatypes of each attribute.
Statistical Summary of Data
We have discussed Python recipe to get the shape i.e. number of rows and columns, of data but many times we need to review the summaries out of that shape of data. It can be done with the help of describe() function of Pandas DataFrame that further provide the following 8 statistical properties of each & every data attribute −
Count
Mean
Standard Deviation
Minimum Value
Maximum value
25%
Median i.e. 50%
75%
Example
from pandas import read_csv
from pandas import set_option
path =r"C:\pima-indians-diabetes.csv"
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(path, names=names)
set_option('display.width',100)
set_option('precision',2)print(data.shape)print(data.describe())
From the above output, we can observe the statistical summary of the data of Pima Indian Diabetes dataset along with shape of data.
Reviewing Class Distribution
Class distribution statistics is useful in classification problems where we need to know the balance of class values. It is important to know class value distribution because if we have highly imbalanced class distribution i.e. one class is having lots more observations than other class, then it may need special handling at data preparation stage of our ML project. We can easily get class distribution in Python with the help of Pandas DataFrame.
Example
from pandas import read_csv
path =r"C:\pima-indians-diabetes.csv"
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(path, names=names)
count_class = data.groupby('class').size()print(count_class)
Output
Class
0 500
1 268
dtype: int64
From the above output, it can be clearly seen that the number of observations with class 0 are almost double than number of observations with class 1.
Reviewing Correlation between Attributes
The relationship between two variables is called correlation. In statistics, the most common method for calculating correlation is Pearsons Correlation Coefficient. It can have three values as follows −
Coefficient value = 1 − It represents full positive correlation between variables.
Coefficient value = -1 − It represents full negative correlation between variables.
Coefficient value = 0 − It represents no correlation at all between variables.
It is always good for us to review the pairwise correlations of the attributes in our dataset before using it into ML project because some machine learning algorithms such as linear regression and logistic regression will perform poorly if we have highly correlated attributes. In Python, we can easily calculate a correlation matrix of dataset attributes with the help of corr() function on Pandas DataFrame.
Example
from pandas import read_csv
from pandas import set_option
path =r"C:\pima-indians-diabetes.csv"
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(path, names=names)
set_option('display.width',100)
set_option('precision',2)
correlations = data.corr(method='pearson')print(correlations)
The matrix in above output gives the correlation between all the pairs of the attribute in dataset.
Reviewing Skew of Attribute Distribution
Skewness may be defined as the distribution that is assumed to be Gaussian but appears distorted or shifted in one direction or another, or either to the left or right. Reviewing the skewness of attributes is one of the important tasks due to following reasons −
Presence of skewness in data requires the correction at data preparation stage so that we can get more accuracy from our model.
Most of the ML algorithms assumes that data has a Gaussian distribution i.e. either normal of bell curved data.
In Python, we can easily calculate the skew of each attribute by using skew() function on Pandas DataFrame.
Example
from pandas import read_csv
path =r"C:\pima-indians-diabetes.csv"
names =['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(path, names=names)print(data.skew())
Output
preg 0.90
plas 0.17
pres -1.84
skin 0.11
test 2.27
mass -0.43
pedi 1.92
age 1.13
class 0.64
dtype: float64
From the above output, positive or negative skew can be observed. If the value is closer to zero, then it shows less skew.
Suppose if you want to start a ML project then what is the first and most important thing you would require? It is the data that we need to load for starting any of the ML project.
In machine learning, data loading refers to the process of importing or reading data from external sources and converting it into a format that can be used by the machine learning algorithm. The data is then preprocessed to remove any inconsistencies, missing values, or outliers. Once the data is preprocessed, it is split into training and testing sets, which are then used for model training and evaluation.
The data can come from various sources such as CSV files, databases, web APIs, cloud storage, etc. The most common file formats for machine learning projects is CSV (Comma Separated Values).
Consideration While Loading CSV data
CSV is a plain text format that stores tabular data, where each row represents a record, and each column represents a field or attribute. It is widely used because it is simple, lightweight, and can be easily read and processed by programming languages such as Python, R, and Java.
In Python, we can load CSV data into ML projects with different ways but before loading CSV data we must have to take care about some considerations.
In this chapter, let’s understand the main parts of a CSV file, how they might affect the loading and analysis of data, and some consideration we should take care before loading CSV data into ML projects.
File Header
This is the first row of the CSV file, and it typically contains the names of the columns in the table. When loading CSV data into an ML project, the file header (also known as column headers or variable names) can play an important role in data analysis and model training. Here are some considerations to keep in mind regarding the file header −
Consistency − The header row should be consistent across the entire CSV file. This means that the number of columns and their names should be the same for each row. Inconsistencies can cause issues with parsing and analysis.
Meaningful names − Column names should be meaningful and descriptive. This can help with understanding the data and building more accurate models. Avoid using generic names like “column1”, “column2”, etc.
Case sensitivity − Depending on the tool or library being used to load the CSV file, the column names may be case sensitive. It’s important to ensure that the case of the header row matches the expected case sensitivity of the tool or library being used.
Special characters − Column names should not contain any special characters, such as spaces, commas, or quotation marks. These characters can cause issues with parsing and analysis. Instead, use underscores or camelCase to separate words.
Missing header − If the CSV file does not have a header row, it’s important to specify the column names manually or provide a separate file or documentation that includes the column names.
Encoding − The encoding of the header row can affect its interpretation when loading the CSV file. It’s important to ensure that the encoding of the header row is compatible with the tool or library being used to read the file.
Comments
These are optional lines that begin with a specified character, such as “#” or “//”, and are ignored by most programs that read CSV files. They can be used to provide additional information or context about the data in the file.
Comments in a CSV file are not typically used to represent data that would be used in a machine learning project. However, if comments are present in a CSV file, it’s important to consider how they might affect the loading and analysis of the data. Here are some considerations −
Comment markers − In a CSV file, comments can be indicated using a specific marker, such as “#” or “//”. It’s important to know what marker is being used, so that the loading process can ignore comments properly.
Placement − Comments should be placed in a separate line from the actual data. If a comment is included in a line with actual data, it may cause issues with parsing and analysis.
Consistency − If comments are used in a CSV file, it’s important to ensure that the comment marker is used consistently throughout the entire file. Inconsistencies can cause issues with parsing and analysis.
Handling comments − Depending on the tool or library being used to load the CSV file, comments may be ignored by default or may require a specific parameter to be set. It’s important to understand how comments are handled by the tool or library being used.
Effect on analysis − If comments contain important information about the data, it may be necessary to process them separately from the data itself. This can add complexity to the loading and analysis process.
Delimiter
This is the character that separates the fields in each row. While the name suggests that a comma is used as the delimiter, other characters such as tabs, semicolons, or pipes can also be used depending on the file.
The delimiter used in a CSV file can significantly affect the accuracy and performance of a machine learning model, so it is important to consider the following while loading data into an ML project −
Delimiter choice − The delimiter used in a CSV file should be carefully chosen based on the data being used. For example, if the data contains commas within the values (e.g. “New York, NY”), then using a comma as a delimiter may cause issues.In this case, a different delimiter, such as a tab or semicolon, may be more appropriate.
Consistency − The delimiter used in the CSV file should be consistent throughout the entire file. Mixing different delimiters or using whitespace inconsistently can lead to errors and make it difficult to parse the data accurately.
Encoding − The delimiter can also be affected by the encoding of the CSV file. For example, if the CSV file uses a non-ASCII delimiter and is encoded in UTF-8, it may not be correctly read by some machine learning libraries or tools. It is important to ensure that the encoding and delimiter are compatible with the machine learning tools being used.
Other considerations − In some cases, the delimiter may need to be customized based on the machine learning tool being used. For example, some libraries may require a specific delimiter or may not support certain delimiters. It is important to check the documentation of the machine learning tool being used and customize the delimiter as needed.
Quotes
These are optional characters that can be used to enclose fields that contain the delimiter character or newlines. For example, if a field contains a comma, enclosing the field in quotes ensures that the comma is treated as part of the field and not as a delimiter. When loading CSV data into an ML project, there are several considerations to keep in mind regarding the use of quotes −
Quote character − The quote character used in a CSV file should be consistent throughout the file. The most commonly used quote character is the double quote (“) but some files may use single quotes or other characters. It’s important to make sure that the quote character used is consistent with the tool or library being used to read the CSV file.
Quoted values − In some cases, values in a CSV file may be enclosed in quotes to differentiate them from other values. For example, if a field contains a comma, it may be enclosed in quotes to prevent it from being interpreted as a new field. It’s important to make sure that quoted values are properly handled when loading the data into an ML project.
Escaping quotes − If a field contains the quote character used to enclose values, it must be escaped. This is typically done by doubling the quote character. For example, if the quote character is double quote (“) and a field contains the value “John “the Hammer” Smith”, it would be enclosed in quotes and the internal quotes would be escaped like this: “John “”the Hammer”” Smith”.
Use of quotes − The use of quotes in CSV files can vary depending on the tool or library being used to generate the file. Some tools may use quotes around every field, while others may only use quotes around fields that contain special characters. It’s important to make sure that the quote usage is consistent with the tool or library being used to read the file.
Encoding − The use of quotes can also be affected by the encoding of the CSV file. If the file is encoded in a non-standard way, it may cause issues when loading the data into an ML project. It’s important to make sure that the encoding of the CSV file is compatible with the tool or library being used to read the file.
Various Methods of Loading a CSV Data File
While working with ML projects, the most crucial task is to load the data properly into it. As told earlier, the most common data format for ML projects is CSV and it comes in various flavors and varying difficulties to parse.
In this section, we are going to discuss some common approaches in Python to load CSV data file into machine learning project −
Using the CSV Module
This is a built-in module in Python that provides functionality for reading and writing CSV files. You can use it to read a CSV file into a list or dictionary object. Below is its implementation example in Python −
import csv
withopen('mydata.csv','r')asfile:
reader = csv.reader(file)for row in reader:print(row)
This code reads a CSV file called mydata.csv and prints each row in the file.
Using the Pandas Library
This is a popular data manipulation library in Python that provides a read_csv() function for reading CSV files into a pandas DataFrame object. This is a very convenient way to load data and perform various data manipulation tasks. Below is its implementation example in Python −
import pandas as pd
data = pd.read_csv('mydata.csv')
This code reads a CSV file called mydata.csv and loads it into a pandas DataFrame object called data.
Using the Numpy Library
This is a numerical computing library in Python that provides a genfromtxt() function for loading CSV files into a numpy array. Below is its implementation example in Python −
import numpy as np
data = np.genfromtxt('mydata.csv', delimiter=',')
This code reads a CSV file called mydata.csv and loads it into a numpy array called ‘data’.
Using the Scipy Library
This is a scientific computing library in Python that provides a loadtxt() function for loading text files, including CSV files, into a numpy array. Below is its implementation example in Python −
import numpy as np
from scipy import loadtxt
data = loadtxt('mydata.csv', delimiter=',')
This code reads a CSV file called mydata.csv and loads it into a numpy array called ‘data’.
Using the Sklearn Library
This is a popular machine learning library in Python that provides a load_iris() function for loading the iris dataset, which is a commonly used dataset for classification tasks. Below is its implementation example in Python −
from sklearn.datasets import load_iris
data = load_iris().data
This code loads the iris dataset, which is included in the sklearn library, and loads it into a numpy array called data.
Categorical data in Machine Learning refers to data that consists of categories or labels, rather than numerical values. These categories may be nominal, meaning that there is no inherent order or ranking between them (e.g., color, gender), or ordinal, meaning that there is a natural ordering between the categories (e.g., education level, income bracket).
Categorical data is often represented using discrete values, such as integers or strings, and is frequently encoded as one-hot vectors before being used as input to machine learning models. One-hot encoding involves creating a binary vector for each category, where the vector has a 1 in the position corresponding to the category and 0s in all other positions.
Techniques for Handling Categorical Data
Handling categorical data is an important part of machine learning preprocessing, as many algorithms require numerical input. Depending on the algorithm and the nature of the categorical data, different encoding techniques may be used, such as label encoding, ordinal encoding, or binary encoding etc.
In the subsequent sections of this chapter, we will discuss the following different techniques for handling categorical data in machine learning along with their implementations in Python.
One-Hot Encoding
Label Encoding
Frequency Encoding
Target Encoding
Binary Encoding
Let’s understand the each of the above mentioned techniques to handle categorical data in machine learning.
1. One-Hot Encoding
One-hot encoding is a popular technique for handling categorical data in machine learning. It involves creating a binary vector for each category, where each element of the vector represents the presence or absence of the category. For example, if we have a categorical variable for color with values red, blue, and green, one-hot encoding would create three binary vectors: [1, 0, 0], [0, 1, 0], and [0, 0, 1] respectively.
Example
Below is an example of how to perform one-hot encoding in Python using the Pandas library −
import pandas as pd
# Creating a sample dataset with a categorical variable
data ={'color':['red','green','blue','red','green']}
df = pd.DataFrame(data)# Performing one-hot encoding
one_hot_encoded = pd.get_dummies(df['color'], prefix='color')# Combining the encoded data with the original data
df = pd.concat([df, one_hot_encoded], axis=1)# Drop the original categorical variable
df = df.drop('color', axis=1)# Print the encoded dataprint(df)
Output
This will create a one-hot encoded dataframe with three binary variables (“color_blue,” “color_green,” and “color_red”) that take the value 1 if the corresponding color is present and 0 if it is not. This encoded data, output given below, can then be used for machine learning tasks such as classification and regression.
One-Hot Encoding technique works well for small and finite categorical variables but can be problematic for large categorical variables as it can lead to a high number of input features.
2. Label Encoding
Label Encoding is another technique for handling categorical data in machine learning. It involves assigning a unique numerical value to each category in a categorical variable, with the order of the values based on the order of the categories.
For example, suppose we have a categorical variable “Size” with three categories: “small,” “medium,” and “large.” Using label encoding, we would assign the values 0, 1, and 2 to these categories, respectively.
Example
Below is an example of how to perform label encoding in Python using the scikit-learn library −
from sklearn.preprocessing import LabelEncoder
# create a sample dataset with a categorical variable
data =['small','medium','large','small','large']# create a label encoder object
label_encoder = LabelEncoder()# fit and transform the data using the label encoder
encoded_data = label_encoder.fit_transform(data)# print the encoded dataprint(encoded_data)
This will create an encoded array with the values [0, 1, 2, 0, 2], which correspond to the encoded categories “small,” “medium,” and “large.” Note that the encoding is based on the alphabetical order of the categories by default, but you can change the order by passing a custom list to the LabelEncoder object.
Output
[2 1 0 2 0]
Label encoding can be useful when there is a natural ordering between the categories, such as in the case of ordinal categorical variables. However, it should be used with caution for nominal categorical variables because the numerical values may imply an order that does not actually exist. In these cases, one-hot encoding is a safer option.
3. Frequency Encoding
Frequency Encoding is another technique for handling categorical data in machine learning. It involves replacing each category in a categorical variable with its frequency (or count) in the dataset. The idea behind frequency encoding is that categories that appear more frequently may be more important or informative for the machine learning algorithm.
Example
Below is an example of how to perform frequency encoding in Python −
import pandas as pd
# create a sample dataset with a categorical variable
data ={'color':['red','green','blue','red','green']}
df = pd.DataFrame(data)# calculate the frequency of each category in the categorical variable
freq = df['color'].value_counts(normalize=True)# replace each category with its frequency
df['color_freq']= df['color'].map(freq)# drop the original categorical variable
df = df.drop('color', axis=1)# print the encoded dataprint(df)
This will create an encoded dataframe with one variable (“color_freq”) that represents the frequency of each category in the original categorical variable. For example, if the original variable had two occurrences of “red” and three occurrences of “green,” then the corresponding frequencies would be 0.4 and 0.6, respectively.
Output
color_freq
0 0.4
1 0.4
2 0.2
3 0.4
4 0.4
Frequency encoding can be a useful alternative to one-hot encoding or label encoding, especially when dealing with high-cardinality categorical variables (i.e., variables with a large number of categories). However, it may not always be effective, and its performance can depend on the particular dataset and machine learning algorithm being used.
4. Target Encoding
Target Encoding is another technique for handling categorical data in machine learning. It involves replacing each category in a categorical variable with the mean (or other aggregation) of the target variable (i.e., the variable you want to predict) for that category. The idea behind target encoding is that it can capture the relationship between the categorical variable and the target variable, and therefore improve the predictive performance of the machine learning model.
Example
Below is an example of how to perform target encoding in Python with the Scikit-learn library by using a combination of a label encoder and a mean encoder −
import pandas as pd
from sklearn.preprocessing import LabelEncoder
# create a sample dataset with a categorical variable and a target variable
data ={'color':['red','green','blue','red','green'],'target':[1,0,1,0,1]}
df = pd.DataFrame(data)# create a label encoder object and fit it to the data
label_encoder = LabelEncoder()
label_encoder.fit(df['color'])# transform the categorical variable using the label encoder
df['color_encoded']= label_encoder.transform(df['color'])# create a mean encoder object and fit it to the transformed data
mean_encoder = df.groupby('color_encoded')['target'].mean().to_dict()# map the mean encoded values to the categorical variable
df['color_encoded']= df['color_encoded'].map(mean_encoder)# print the encoded dataprint(df)
In this example, we first create a Pandas DataFrame df with a categorical variable ‘color’ and a target variable ‘target’. We then create a LabelEncoder object from scikit-learn and fit it to the ‘color’ column of df.
Next, we transform the categorical variable ‘color’ using the label encoder by calling the transform method on the label encoder object and assigning the resulting encoded values to a new column ‘color_encoded‘ in df.
Finally, we create a mean encoder object by grouping df by the ‘color_encoded’ column and calculating the mean of the ‘target’ column for each group. We then convert this mean encoder object to a dictionary and map the mean encoded values to the original ‘color’ column of df.
Output
color target color_encoded
0 red 1 0.5
1 green 0 0.5
2 blue 1 1.0
3 red 0 0.5
4 green 1 0.5
Target encoding can be a powerful technique for improving the predictive performance of machine learning models, especially for datasets with high-cardinality categorical variables. However, it is important to avoid overfitting by using cross-validation and regularization techniques.
5. Binary Encoding
Binary encoding is another technique used for encoding categorical variables in machine learning. In binary encoding, each category is assigned a binary code, where each digit represents whether the category is present (1) or not (0). The binary codes are typically based on the position of the category in a sorted list of all categories.
Example
Here’s an example Python implementation of binary encoding using the category_encoders library −
import pandas as pd
import category_encoders as ce
# create a sample dataset with a categorical variable
data ={'color':['red','green','blue','red','green']}
df = pd.DataFrame(data)# create a binary encoder object and fit it to the data
binary_encoder = ce.BinaryEncoder(cols=['color'])
binary_encoder.fit(df['color'])# transform the categorical variable using the binary encoder
encoded_data = binary_encoder.transform(df['color'])# merge the encoded variable with the original dataframe
df = pd.concat([df, encoded_data], axis=1)# print the encoded dataprint(df)
In this example, we first create a Pandas DataFrame df with a categorical variable ‘color’. We then create a BinaryEncoder object from the category_encoders library and fit it to the ‘color’ column of df.
Next, we transform the categorical variable ‘color’ using the binary encoder by calling the transform method on the binary encoder object and assigning the resulting encoded values to a new DataFrame encoded_data.
Finally, we merge the encoded variable with the original DataFrame df using the concat method along the column axis (axis=1). The resulting DataFrame should have the original ‘color’ column along with the encoded binary columns.
Output
When you run the code, it will produce the following output −
color color_0 color_1
0 red 0 1
1 green 1 0
2 blue 1 1
3 red 0 1
4 green 1 0
The binary encoding works best for categorical variables with a moderate number of categories, as it can quickly become inefficient for variables with a large number of categories.
Machine learning models are only as good as the data they are trained on. Therefore, obtaining good quality and relevant datasets is a critical step in the machine learning process. There are many open-source repositories, like Kaggle, from where you can download datasets. You can even purchase data, scrap a website, or collect data independently. Let’s see some different sources of datasets for machine learning and how to obtain them.
What is a dataset?
Dataset is a collection of data in a structured and organized manner. It is typically used to simplify tasks like analysis, storage or processing, machine learning model training, etc. Datasets can be stored in multiple formats like CSV, JSON, zip files, Excel, etc.
Types of Datasets
Datasets are generally categorized based on the information they consist of. Some common types of datasets are:
Tabular Datasets: They are structured collections of data organized into rows and columns, similar to a table.
Time Series Datasets: These include data between a period, for example, stock price analysis, climatic information and many more.
Image datasets: These include images as the data, which are used for computer vision tasks such as image classification, object detection and image segmentation.
Text datasets: These include textual information like numeric, characters and alphabets. They are used in NLP techniques like sentiment analysis and text classification.
Getting Datasets for Machine Learning
Getting a dataset is a very important step while developing a solution for a machine learning problem. Data is the key necessity in training a machine learning model. The quality, quantity and diversity of the data collected would highly impact the performance of machine learning models.
There are different ways or sources to get datasets for machine learning. Some of them are listed as below −
Open Source Datasets
Data Scraping
Data Purchase
Data Collection
Let’s discuss each of the above sources of dataset for machine learning in detail −
Popular Open Source/ Public Datasets
There are many publicly available open-source datasets that you can use for machine learning. Some popular sources of public datasets include Kaggle, UCI Machine Learning Repository, Google Dataset Search, and AWS Public Datasets. These datasets are often used for research and are open to the public.
Some of the most popular sources where structured and valuable data is available are −
Kaggle Datasets
AWS Datasets
Google Dataset Search Engine
UCI Machine learning Repository
Microsoft Datasets
Scikit-learn Dataset
HuggingFace Datasets Hub
Government Datasets
Kaggle Datasets
Kaggle is a popular online community for data science and machine learning. It hosts more than 23,000 public datasets. It is the most chosen platform for getting datasets as it allows users to search, download, and publish data easily. It provides high-quality pre-processed dataset which fits right for almost all machine learning models based on the user’s requirement.
Kaggle also provides notebooks with the algorithms and different types of pre-trained models.
AWS Datasets
You can search, download and share the datasets that are publicly available in the registry of open data on AWS. Though they are accessed through AWS, the datasets are maintained and updated by government organizations, businesses and researchers.
Google Dataset Search Engine
Google Dataset Search is a tool developed by Google that allows users to search for datasets from different sources across the web. It is a search engine specially designed for datasets.
UCI Machine learning Repository
The UCI machine learning repository is a dataset repository developed by the University of California, Irvine exclusively for machine learning. It covers 100s of datasets from a wide range of domains. You can find datasets related to time series, classification, regression, or recommendation systems.
Microsoft Dataset
Microsoft Research Open Data, launched by Microsoft in 1918, provides a data repository in the cloud.
Scikit-learn Dataset
Scikit-learn is a popular Python library that provides a few datasets like the Iris dataset, Boston housing dataset, etc., for trial and error. These datasets are open and can be used to learn and experiment with machine learning models.
Syntax to use Scikit-learn dataset −
from sklearn.datasets import load_iris
iris = load_iris()
In the above code snippet, we loaded the iris dataset to our Python script.
HuggingFace Datasets hub
HuggingFace datasets hub provides major public datasets such as image datasets, audio datasets, text datasets, etc. You can access these datasets by installing “datasets” using the following command −
pip install datasets
You can use the following simple syntax to get any dataset to use in you program −
from datasets import load_dataset
ds = load_dataset(dataset_name)
For exmaple you can use the following command to load iris dataset −
from datasets import load_dataset
ds = load_dataset("scikit-learn/iris")
Government Datasets
Each country has a source where government related data is available for public use, which is collected from various departments. The goal of these sources is to increase the transparency of government and to use them for productive research work.
Followings are some government dataset links −
Indian Government Public Datasets
U.S. Government’s Open Data
World Bank Data Catalog
European Union Open Data
U.K. Open Data
Data Scraping
Data scraping involves automatically extracting data from websites or other sources. It can be a useful way to obtain data that is not available as a pre-packaged dataset. However, it is important to ensure that the data is being scraped ethically and legally, and that the source is reliable and accurate.
Data Purchase
In some cases, it may be necessary to purchase a dataset for machine learning. Many companies sell pre-packaged datasets that are tailored to specific industries or use cases. Before purchasing a dataset, it is important to evaluate its quality and relevance to your machine learning project.
Data Collection
Data collection involves manually collecting data from various sources. This can be time-consuming and requires careful planning to ensure that the data is accurate and relevant to your machine learning project. It may involve surveys, interviews, or other forms of data collection.
Strategies for Acquiring High Quality Datasets
Once you have identified the source of your dataset, it is important to ensure that the data is of good quality and relevant to your machine learning project. Below are some Strategies for obtaining good quality datasets −
Identify the Problem You Want to Solve
Before obtaining a dataset, it is important to identify the problem you want to solve with machine learning. This will help you determine the type of data you need and where to obtain it.
Determine the Size of the Dataset
The size of the dataset depends on the complexity of the problem you are trying to solve. Generally, the more data you have, the better your machine learning model will perform. However, it is important to ensure that the dataset is not too large and contains irrelevant or duplicate data.
Ensure the Data is Relevant and Accurate
It is important to ensure that the data is relevant and accurate to the problem you are trying to solve. Ensure that the data is from a reliable source and that it has been verified.
Preprocess the Data
Preprocessing the data involves cleaning, normalizing, and transforming the data to prepare it for machine learning. This step is critical to ensure that the machine learning model can understand and use the data effectively.
In the world of artificial intelligence, two terms that are often used interchangeably are machine learning and deep learning. While both of these technologies are used to create intelligent systems, they are not the same thing. Machine learning is a subset of artificial intelligence (AI) that enables machines to learn without being explicitly programmed while deep learning is a subset of machine learning that uses neural networks to process complex data. In this chapter, we will explore the differences between machine learning and deep learning and how they are related.
Let us first understand both the terms and then their differences in detail.
What is Machine Learning?
Machine learning, abbreviated as ML, is a subfield of artificial intelligence that automatically enables machines to learn from experience. In machine learning, algorithm development is core work. These algorithms are trained on data to learn the hidden patterns and make predictions based on what they learned. The whole process of training the algorithms is sometimes termed model building.
When we say “machine learning enables the machine to learn from experience,” what does it mean by experience? We often hear about training machine learning algorithms with data. This training of algorithms with data is termed as experience. Like we humans learn from experience, machines learn from data when we train them.
In other words, machine learning is a technique to implement solutions for artificial intelligence related problems.
There are many ways to implement solutions for AI problems. One of the ways is machine learning.
There are mainly four approaches to making a machine learn from data: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
Supervised learning is one of the most important approaches to making machines learn from labeled data. Supervised learning is best suited for tasks related to classification and regression. Again, there are different methods or algorithms to implement supervised learning in machine learning. Among these algorithms, linear regression, k-nearest neighbors, random forests, etc., are well known.
With neural networks, machine learning has reached its highest accuracy. Neural networks can be classified as a complex supervised learning approach. Deep learning is another approach to implementing machine learning solutions. Deep learning uses neural networks to learn the complex relations in data. Let’s learn more in detail about deep learning in the next section.
What is Deep Learning?
Deep learning is a type of machine learning that uses neural networks to process complex data. In other words, deep learning is a process by which computers can automatically learn patterns and relationships in data using multiple layers of interconnected nodes or artificial neurons. Deep learning algorithms are designed to detect and learn from patterns in data to make predictions or decisions.
Deep learning is particularly well-suited to tasks that involve processing complex data, such as image and speech recognition, natural language processing, and self-driving cars. Deep learning algorithms are able to process vast amounts of data and can learn to recognize complex patterns and relationships in that data.
Examples of deep learning include facial recognition, voice recognition, and self-driving cars.
Difference between Machine Learning and Deep Learning
The following table highlights the significant differences between machine learning and deep learning −
Basis
Machine Learning
Deep Learning
Definition
Machine learning is a subfield of AI that allows machines to learn without being explicitly programmed. ML uses algorithms to learn hidden patterns from data and make decisions and predictions based on new data.
Deep learning is a subfield of machine learning that uses neural networks to process complex data.
Complexity
Machine learning uses simpler methods such as decision trees or linear regression to learn hidden patterns from data and make decisions and predictions based on new data.
Deep learning uses complex methods found in neural networks.
Amount of Data
Machine learning needs a large amount of data. It is also useful for small amounts of data.
Deep learning requires larger data than ML requires. The accuracy increases with the increase of the amount of data.
Training Methods
Machine learning has four training methods: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Deep learning has complex training methods such as convolutional neural networks, recurrent neural networks, generative adversarial networks, etc.
Hardware Dependencies
As machine learning uses simpler methods, it needs less storage and computational power.
Because deep learning is more complex and larger data, deep learning models require more storage and computational power.
Feature Engineering
In machine learning, you need to perform feature engineering manually.
In Deep learning, deep learning models are capable to of performing feature engineering tasks.
Problem Solving Approach
Machine learning follows a standard approach, and uses statistics and mathematics to solve a problem.
Deep learning models use statistics and mathematics with neural network architecture.
Execution Time
Machine learning algorithms require less execution time than deep learning models.
Deep learning requires a lot of time to train models as it has a lot of parameters to be trained on more complex data.
Best Suited For
Machine learning is best suited for structured data.
Deep learning is also best for complex and unstructured data.
Machine Learning Vs. Deep Learning: Key Comparisons
Now that we have a basic understanding of what machine learning and deep learning are, let’s dive deeper into the differences between the two.
Firstly, machine learning is a broad category that encompasses many different types of algorithms, including deep learning. Deep learning is a specific type of machine learning algorithm that uses neural networks to process complex data.
Secondly, while machine learning algorithms are designed to learn from data and improve their accuracy over time, deep learning algorithms are designed to process complex data and recognize patterns and relationships in that data. Deep learning algorithms are able to recognize complex patterns and relationships that other machine learning algorithms may not be able to detect.
Thirdly, deep learning algorithms require a lot of data and processing power to train. Deep learning algorithms typically require large datasets and powerful hardware, such as graphics processing units (GPUs), to train effectively. Machine learning algorithms, on the other hand, can be trained on smaller datasets and less powerful hardware.
Finally, deep learning algorithms can provide highly accurate predictions and decisions, but they can be more challenging to understand and interpret than other machine learning algorithms. Deep learning algorithms can process vast amounts of data and recognize complex patterns and relationships in that data, but it can be difficult to understand how the algorithm arrived at its conclusion.
Machine learning and deep learning are both techniques to solve artificial intelligence-related problems. Without using machine learning or deep learning, we can implement artificial intelligence in real words. When we are not using machine learning to implement AI, we will be using rule-based algorithms to implement it.
When discussing machine and deep learning methods, all deep learning methods fall under machine learning but not vice versa.
Machine learning and neural networks are two important technologies in the field of artificial intelligence (AI). While they are often used together, they are not the same thing. Here, we will explore the differences between machine learning and neural networks and how they are related.
Let us first understand both the terms in detail and then their differences.
What is Machine Learning?
Machine Learning is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do.
In simple words, ML is a type of artificial intelligence that extracts patterns out of raw data by using an algorithm or method.
Machine learning can be classified into three different categories on the basis of human supervision. These categories are supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, machine learning algorithms are trained with labeled data sets to perform tasks related to classification and regression. Some of the used supervised learning algorithms are linear regression, K-nearest neighbors, decision trees, random forest, etc.
In unsupervised learning, the models are trained on unlabeled datasets. Unsupervised learning is mainly used for tasks related to clustering, association rule mining, and dimensionality reduction. Some of the most used unsupervised algorithms include K-means clustering, apriori algorithm, etc.
Reinforcement learning is somehow similar to supervised learning where an agent (algorithm or software entity) learns to interact environment by performing actions and monitoring results. Learning is based on rewards and penalties. There are various algorithms used in reinforcement learning, such as Q-learning, policy gradient methods, the Monte Carlo method, and many more.
What are Neural Networks?
Neural networks are a type of machine learning algorithm that is inspired by the structure of the human brain. They are designed to simulate how the brain works by using layers of interconnected nodes, or artificial neurons. Each neuron takes in input from the neurons in the previous layer and uses that input to produce an output. This process is repeated for each layer until a final output is produced.
Neural networks can be used for a wide range of tasks, including image recognition, speech recognition, natural language processing, and prediction. They are particularly well-suited to tasks that involve processing complex data or recognizing patterns in data.
The following diagram represents the general model of ANN followed by its processing.
Machine Learning vs. Neural Networks
Now that we have a basic understanding of what machine learning and neural networks are. Let’s dive deeper into the differences between the two.
Firstly, machine learning is a broad category that encompasses many different types of algorithms, including neural networks. Neural networks are a specific type of machine learning algorithm that is designed to simulate the way the brain works.
Secondly, while machine learning algorithms can be used for a wide range of tasks, neural networks are particularly well-suited to tasks that involve processing complex data or recognizing patterns in data. Neural networks can recognize complex patterns and relationships in data that other machine learning algorithms may not be able to detect.
Thirdly, neural networks require a lot of data and processing power to train. Neural networks typically require large datasets and powerful hardware, such as graphics processing units (GPUs), to train effectively. Machine learning algorithms, on the other hand, can be trained on smaller datasets and less powerful hardware.
Finally, neural networks can provide highly accurate predictions and decisions, but they can be more difficult to understand and interpret than other machine learning algorithms. The way that neural networks make decisions is not always transparent, which can make it difficult to understand how they arrived at their conclusions.
Artificial Intelligence and Machine Learning are two buzzwords that are commonly used in the world of technology. Although they are often used interchangeably, they are not the same thing. Artificial intelligence (AI) and machine learning (ML) are related concepts, but they have different definitions, applications, and implications. In this article, we will explore the differences between machine learning and artificial intelligence and how they are related.
What is Artificial Intelligence?
Artificial intelligence is a broad field that encompasses the development of intelligent machines that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making. In simple terms, AI is the ability of machines to perform tasks that normally require human intervention or intelligence.
There are two types of AI: narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, such as speech recognition or image recognition, while general AI is designed to be able to perform any intellectual task that a human can do. Currently, we only have narrow AI in use, but the goal is to develop general AI that can be applied to a wide range of tasks.
Branches of AI
AI is like a basket containing several branches, the important ones being Machine Learning (ML), Robotics, Expert Systems, Fuzzy Logic, Neural Networks, Computer Vision, and Natural Language Processing (NLP).
Here is a brief overview of the other important branches of AI:
Robotics Robots are primarily designed to perform repetitive and tedious tasks. Robotics is an important branch of AI that deals with designing, developing and controlling the application of robots.
Computer Vision It is an exciting field of AI that helps computers, robots, and other digital devices to process and understand digital images and videos, and extract vital information. With the power of AI, Computer Vision develops algorithms that can extract, analyze and comprehend useful information from digital images.
Expert Systems Expert systems are applications specifically designed to solve complex problems in a specific domain, with humanlike intelligence, precision, and expertise. Just like human experts, Expert Systems excel in a specific domain in which they are trained.
Fuzzy Logic We know computers take precise digital inputs like True (Yes) or False (No), but Fuzzy Logic is a method of reasoning that helps machines to reason like human beings before taking a decision. With Fuzzy Logic, machines can analyze all intermediate possibilities between a YES or NO, for example, “Possibly Yes”, “Maybe No”, etc.
Neural Networks Inspired by the natural neural networks of the human brain, Artificial Neural Networks (ANN) can be considered as a group of highly interconnected group of processing elements (nodes) that can process information by their dynamic state response to external inputs. ANNs use training data to improve their efficiency and accuracy.
Natural Language Processing (NLP) NLP is a field of AI that empowers intelligent systems to communicate with humans using a natural language like English. With the power of NLP, one can easily interact with a robot and instruct it in plain English to perform a task. NLP can also process text data and comprehend its full meaning. It is heavily used these days in virtual chatbots and sentiment analysis.
Examples of AI include virtual assistants, autonomous vehicles, facial recognition, natural language processing, and decision-making systems.
What is Machine Learning?
Machine learning is a subset of artificial intelligence that focuses on teaching machines how to learn from data. In other words, machine learning is a process by which computers can automatically learn patterns and relationships in data without being explicitly programmed to do so. Machine learning algorithms are designed to detect and learn from patterns in data to make predictions or decisions.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is when the machine is trained on labeled data with known outcomes. Unsupervised learning is when the machine is trained on unlabeled data and is asked to find patterns or similarities. Reinforcement learning is when the machine learns by trial and error through interactions with the environment.
Examples of machine learning include image recognition, speech recognition, recommendation systems, fraud detection, and natural language processing.
Artificial Intelligence Vs. Machine Learning Overview
Now that we have a basic understanding of what machine learning and artificial intelligence are, let’s dive deeper into the differences between the two.
Firstly, machine learning is a subset of artificial intelligence, meaning that machine learning is a part of the larger field of AI. Machine learning is a technique used to implement artificial intelligence.
Secondly, while machine learning focuses on developing algorithms that can learn from data, artificial intelligence focuses on developing intelligent machines that can perform tasks that normally require human intelligence. In other words, machine learning is more focused on the process of learning from data, while AI is more focused on the end goal of creating machines that can perform intelligent tasks.
Thirdly, machine learning algorithms are designed to learn from data and improve their accuracy over time, while artificial intelligence systems are designed to learn and adapt to new situations and environments. Machine learning algorithms require a lot of data to be trained effectively, while AI systems can adapt and learn from new data in real-time.
Finally, machine learning is more limited in its capabilities compared to AI. Machine learning algorithms can only learn from the data they are trained on, while AI systems can learn and adapt to new situations and environments. Machine learning is great for solving specific problems that can be solved through pattern recognition, while AI is better suited for complex, real-world problems that require reasoning and decision-making.
Difference Between Artificial Intelligence and Machine Learning
The following table highlights the important differences between Machine Learning and Artificial Intelligence
Key
Artificial Intelligence
Machine Learning
Definition
Artificial Intelligence refers to the ability of a machine or a computer system to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, and making decisions.
Machine Learning is a type of Artificial Intelligence that allows a system to learn and improve from experience without being explicitly programmed. It articulates how a machine can learn and apply its knowledge to improve its decisions.
Concept
Artificial Intelligence revolves around making smart and intelligent devices.
Machine Learning revolves around making a machine learn/decide and improve its results.
Goal
The goal of Artificial Intelligence is to simulate human intelligence to solve complex problems.
The goal of Machine Learning is to learn from data provided and make improvements in machine’s performance.
Includes
Artificial Intelligence has several important branches including Artificial Neural Networks, Natural Language Processing, Fuzzy Logic, Robotics, Expert Systems, Computer Vision, and Machine Learning.
Machine Learning training methods include supervised learning, unsupervised learning, and reinforcement learning.
Development
Artificial Intelligence is leading to the development of such machines which can mimic human behavior.
Machine Learning is helping in the development of self-learning algorithms.
