## Linear Regression-

In Machine Learning,

• Linear Regression is a supervised machine learning algorithm.
• It tries to find out the best linear relationship that describes the data you have.
• It assumes that there exists a linear relationship between a dependent variable and independent variable(s).
• The value of the dependent variable of a linear regression model is a continuous value i.e. real numbers.

## Representing Linear Regression Model-

Linear regression model represents the linear relationship between a dependent variable and independent variable(s) via a sloped straight line. The sloped straight line representing the linear relationship that fits the given data best is called as a regression line.

It is also called as best fit line.

## Types of Linear Regression-

Based on the number of independent variables, there are two types of linear regression- 1. Simple Linear Regression
2. Multiple Linear Regression

## 1. Simple Linear Regression-

In simple linear regression, the dependent variable depends only on a single independent variable.

For simple linear regression, the form of the model is-

Y = β0 + β1X

Here,

• Y is a dependent variable.
• X is an independent variable.
• β0 and β1 are the regression coefficients.
• β0 is the intercept or the bias that fixes the offset to a line.
• β1 is the slope or weight that specifies the factor by which X has an impact on Y.

There are following 3 cases possible-

### Case-01: β1 < 0

• It indicates that variable X has negative impact on Y.
• If X increases, Y will decrease and vice-versa. ### Case-02: β1 = 0

• It indicates that variable X has no impact on Y.
• If X changes, there will be no change in Y. ### Case-03: β1 > 0

• It indicates that variable X has positive impact on Y.
• If X increases, Y will increase and vice-versa. ## 2. Multiple Linear Regression-

In multiple linear regression, the dependent variable depends on more than one independent variables.

For multiple linear regression, the form of the model is-

Y = β0 + β1X1 + β2X2 + β3X3 + …… + βnXn

Here,

• Y is a dependent variable.
• X1, X2, …., Xn are independent variables.
• β0, β1,…, βn are the regression coefficients.
• βj (1<=j<=n) is the slope or weight that specifies the factor by which Xj has an impact on Y.

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## Machine Learning-

Before you go through this article, make sure that you have gone through the previous article on Machine Learning.

We have discussed-

• Machine learning is building machines that can adapt and learn from experience.
• Machine learning systems are not explicitly programmed.

## Machine Learning Workflow-

Machine learning workflow refers to the series of stages or steps involved in the process of building a successful machine learning system.

The various stages involved in the machine learning workflow are- 1. Data Collection
2. Data Preparation
3. Choosing Learning Algorithm
4. Training Model
5. Evaluating Model
6. Predictions

Let us discuss each stage one by one.

### 1. Data Collection-

In this stage,

• Data is collected from different sources.
• The type of data collected depends upon the type of desired project.
• Data may be collected from various sources such as files, databases etc.
• The quality and quantity of gathered data directly affects the accuracy of the desired system.

### 2. Data Preparation-

In this stage,

• Data preparation is done to clean the raw data.
• Data collected from the real world is transformed to a clean dataset.
• Raw data may contain missing values, inconsistent values, duplicate instances etc.
• So, raw data cannot be directly used for building a model.

Different methods of cleaning the dataset are-

• Ignoring the missing values
• Removing instances having missing values from the dataset.
• Estimating the missing values of instances using mean, median or mode.
• Removing duplicate instances from the dataset.
• Normalizing the data in the dataset.

This is the most time consuming stage in machine learning workflow.

### 3. Choosing Learning Algorithm-

In this stage,

• The best performing learning algorithm is researched.
• It depends upon the type of problem that needs to solved and the type of data we have.
• If the problem is to classify and the data is labeled, classification algorithms are used.
• If the problem is to perform a regression task and the data is labeled, regression algorithms are used.
• If the problem is to create clusters and the data is unlabeled, clustering algorithms are used.

The following chart provides the overview of learning algorithms- ### 4. Training Model-

In this stage,

• The model is trained to improve its ability.
• The dataset is divided into training dataset and testing dataset.
• The training and testing split is order of 80/20 or 70/30.
• It also depends upon the size of the dataset.
• Training dataset is used for training purpose.
• Testing dataset is used for the testing purpose.
• Training dataset is fed to the learning algorithm.
• The learning algorithm finds a mapping between the input and the output and generates the model. ### 5. Evaluating Model-

In this stage,

• The model is evaluated to test if the model is any good.
• The model is evaluated using the kept-aside testing dataset.
• It allows to test the model against data that has never been used before for training.
• Metrics such as accuracy, precision, recall etc are used to test the performance.
• If the model does not perform well, the model is re-built using different hyper parameters.
• The accuracy may be further improved by tuning the hyper parameters. ### 6. Predictions-

In this stage,

• The built system is finally used to do something useful in the real world.
• Here, the true value of machine learning is realized.

To gain better understanding about Machine Learning Workflow,

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Next Article- Linear Regression

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## Machine Learning-

 Learning is a continuous process of improvement over experience.

Machine learning is building machines that can adapt and learn from experience without being explicitly programmed.

In machine learning,

• There is a learning algorithm.
• Data called as training data set is fed to the learning algorithm.
• Learning algorithm draws inferences from the training data set.
• It generates a model which is a function that maps input to the output. ## Machine Learning Applications-

Some important applications of machine learning are-

• Spam Filtering
• Fraudulent Transactions
• Credit Scoring
• Recommendations

## Machine Learning Algorithms-

There are three types of machine learning algorithms- 1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

## 1. Supervised Learning-

In this type of machine learning algorithm,

• The training data set is a labeled data set.
• In other words, the training data set contains the input value (X) and target value (Y).
• The learning algorithm generates a model.
• Then, new data set consisting of only the input value is fed.
• The model then generates the target value based on its learning. ### Example-

Consider a sample database consisting of two columns where-

• The first column specifies mails.
• The second column specifies whether those emails are spam or not.

 Mails (X) IsSpam (Y) Mail-1 Yes Mail-2 No Mail-3 No Mail-4 No

In this training data set, emails categorized as spam or not are done by a supervisor’s knowledge.

So, it is supervised learning algorithm.

### Applications-

Some real-life applications are-

• Spam Filtering
• House Price Prediction
• Credit Scoring (high risk or a low risk customer while lending loans by the banks)
• Face Recognition etc

### Types of Supervised Learning Algorithm-

There are two types of supervised learning algorithm- 1. Regression
2. Classification

### Regression-

Here,

• The target variable (Y) has continuous value.
• Example- house price prediction

### Classification-

Here,

• The target variable (Y) has discrete values such as Yes or No, 0 or 1 and many more.
• Example- Credit Scoring, Spam Filtering

## 2. Unsupervised Learning-

In this type of machine learning algorithm,

• The training data set is an unlabeled data set.
• In other words, the training data set contains only the input value (X) and not the target value (Y).
• Based on the similarity between data, it tries to draw inference from the data such as finding patterns or clusters. ### Applications-

Some real-life applications are-

• Document Clustering
• Finding fraudulent transactions

## 3. Reinforcement Learning-

In this type of machine learning algorithm,

• The agent acts in an environment in order to maximize the rewards and minimize the penalty.
• Unlike supervised learning, no data is provided to the agent.
• The agent itself takes action or sequence of actions whether right or wrong to perform a task and learn from the experience.

### Applications-

Some real-life applications are-

• Game Playing

To gain better understanding about Machine Learning & its Algorithms,

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Next Article- Machine Learning Workflow

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