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Regression techniques in Machine Learning

Machine learning has become the sexiest and very trendy technology in this world of technologies, Machine learning is used every day in our life such as Virtual assistance, for making future predictions, Videos surveillance, Social media services, spam mail detection, online customer support, search engine resulting prediction, fraud detection, recommendation systems, etc. In machine learning, Regression is the most important topic that needed to be learned. There are different types of Regression techniques which we will know in this article.

Introduction:

Regression algorithms such as Linear regression and Logistic regression are the most important algorithms that people learn while they study about Machine learning algorithms. There are numerous forms of regression that are used to perform regression and each has its own specific features, that are applied accordingly. The regression techniques are used to find out the relationship between the dependent and independent variables or features. It is a part of data analysis that is used to analyze the infinite variables and the main aim of this is forecasting, time series analysis, modeling.

What is Regression?

Regression is a statistical method that mainly used for finance, investing and sales forecasting, and other business disciplines that make attempts to find out the strength and relationship among the variables.

There are two types of the variable into the dataset for apply regression techniques:

  1. Dependent Variable that is mainly denoted as Y
  2. Independent variable that is denoted as x.

And, There are two types of regression

  1. Simple Regression: Only with a single independent feature /variable
  2. Multiple Regression: With two or more than two independent features/variables.

Indeed, in all regression studies, mainly seven types of regression techniques are used firmly for complex problems.

  • Linear regression
  • Logistics regression
  • Polynomial regression
  • Stepwise Regression
  • Ridge Regression
  • Lasso Regression

Linear regression:

It is basically used for predictive analysis, and this is a supervised machine learning algorithm. Linear regression is linear approach to modeling the relationship between scalar response and the parameters or multiple predictor variables. It focuses on the conditional probability distribution. The formula for linear regression is Y = mX+c.

Where Y is the target variable, m is the slope of the line, X is the independent feature, and c is the intercept.

Simple Linear Regression in Machine learning - Javatpoint

Additional points on Linear regression:

  1. There should be a linear relationship between the variables.
  2. It is very sensitive to Outliers and can give a high variance and bias model.
  3. The problem of occurring multi colinearity with multiple independent features

Logistic regression:

It is used for classification problems with a linear dataset. In layman’s term, if the depending or target variable is in the binary form (1 0r 0), true or false, yes or no. It is better to decide whether an occurrence is possibly either success or failure.

 

Logistic Regression

Additional point:

  1. It is used for classification problems.
  2. It does not require any relation between the dependent and independent features.
  3. It can after by the outliers and can occur underfitting and overfishing.
  4. It needs a large sample size to make the estimation more accurate.
  5. It needs to avoid collinearity and multicollinearity.

Polynomial regression:

The polynomial regression technique is used to execute a model that is suitable for handling non-linear separated data. It gives a curve that is best suited to data points, rather than a straight line.
The polynomial regression suits the least-squares form. The purpose of an analysis of regression to model the expected y value for the independent x of the dependent variable. 
The formula for this Y=  β0+ β0x1+e
Polynomial Regression - Towards Data Science
Additional  features: 
Look particularly for curve towards the ends to see if those shapes to patterns make logical sense. More polynomials can lead to weird extrapolation results. 

Step-wise Regression:

It is used for statistical model fitting regression with predictive models. It is done automatically. 
The variable is supplemented or removed from the explanatory variable set at every step. The main approaches for the regression are reverse elimination and bidirectional elimination and step by step approaches. 
The formula for this: b = b(sxi/sy)
Additional points: 
  1. This regression provides two things, the very first one is to add prediction for each steep and remove predictors fro each step.
  2. It starts with the most significant predictor into the ML model and then adds features for each step.
  3. The backward elimination starts with all the predictors into the model and then removes the least significant variable.

Ridge Regression: 

It is a method that used when the dataset having multicollinearity which means, the independent variables are strongly related to each other. Although the least-squares estimates are unbiased in multicollinearity, So after adding the degree of bias to the regression, ridge regression can reduce the standard errors.
Ridge Regression for Better Usage - Towards Data Science

Additional points:

  1. In this regression, normality is not to be estimated the same as Least squares regression.
  2. In this regression, the value could be varied but doesn’t come to zero.
  3. This uses the l2 regularization method as it is also a regularization method.

Lasso Regression:

Lasso is an abbreviation of the Least Absolute shrinkage and selection operator. This is similar to the ridge regression as it also analyzes the absolute size of the regression coefficients. And the additional features of that are it is capable of reducing the accuracy and variability of the coefficients of the Linear regression models.

Lasso regression in matlab - Stack Overflow

 

Additional points: 
  1. Lasso regression shrinks the coefficients aero, which will help in feature selection for building a proper ML model.
  2. It is also a regularization method that uses l1 regularization.
  3. If there are many correlated features, it picks only one of them and shrinks it to the zero.

 

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R, and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Top 50 interview question on Statistics

1. What are the different types of Sampling?
Ans: Some of the Common sampling ways are as follows:

  • Simple random sample: Every member and set of members have an equal chance of being included in the sample. Technology, random number generators, or some other sort of change process is needed to get a simple random sample.

Example—A teacher puts students’ names in a hat and chooses without looking to get a sample of students.

Why it’s good: Random samples are usually fairly representative since they don’t favor certain members.

  • Stratified random sample: The population is first split into groups. The overall sample consists of some members of every group. The members of each group are chosen randomly.

Example—A student council surveys 100100100 students by getting random samples of 252525 freshmen, 252525 sophomores, 252525 juniors, and 252525 seniors.

Why it’s good: A stratified sample guarantees that members from each group will be represented in the sample, so this sampling method is good when we want some members from every group.

  • Cluster random sample: The population is first split into groups. The overall sample consists of every member of the group. The groups are selected at random.

Example—An airline company wants to survey its customers one day, so they randomly select 555 flights that day and survey every passenger on those flights.

Why it’s good: A cluster sample gets every member from some of the groups, so it’s good when each group reflects the population as a whole.

  • Systematic random sample: Members of the population are put in some order. A starting point is selected at random, and every nth member is selected to be in the sample.

Example—A principal takes an alphabetized list of student names and picks a random starting point. Every 20th student is selected to take a survey.

2. What is the confidence interval? What is its significance?

Ans: A confidence interval, in statistics, refers to the probability that a population parameter will fall between two set values for a certain proportion of times. Confidence intervals measure the degree of uncertainty or certainty in a sampling method. A confidence interval can take any number of probabilities, with the most common being a 95% or 99% confidence level.

3. What are the effects of the width of the confidence interval?

  • The confidence interval is used for decision making
  •  The confidence level increases the width of
  • The confidence interval also increases 
  • As the width of the confidence interval increases, we tend to get useless information also. 
  • Useless information – wide CI
  • High risk – narrow CI

4.  What is the level of significance (Alpha)?

Ans: The significance level also denoted as alpha or α, is a measure of the strength of the evidence that must be present in your sample before you will reject the null hypothesis and conclude that the effect is statistically significant. The researcher determines the significance level before conducting the experiment.

The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. Lower significance levels indicate that you require stronger evidence before you will reject the null hypothesis.

Use significance levels during hypothesis testing to help you determine which hypothesis the data support. Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant. In other words, the evidence in your sample is strong enough to be able to reject the null hypothesis at the population level.

5. What are Skewness and Kurtosis? What does it signify?

Ans: Skewness: It is the degree of distortion from the symmetrical bell curve or the normal distribution. It measures the lack of symmetry in the data distribution. It differentiates extreme values in one versus the other tail. The asymmetrical distribution will have a skewness of 0.

There are two types of Skewness: Positive and Negative

Positive Skewness means when the tail on the right side of the distribution is longer or fatter. The mean and median will be greater than the mode.

Negative Skewness is when the tail of the left side of the distribution is longer or fatter than the tail on the right side. The mean and median will be less than the mode.

So, when is the skewness too much?

The rule of thumb seems to be:

  • If the skewness is between -0.5 and 0.5, the data are fairly symmetrical.
  • If the skewness is between -1 and -0.5(negatively skewed) or between 0.5 and 1(positively skewed), the data are moderately skewed.
  • If the skewness is less than -1(negatively skewed) or greater than 1(positively skewed), the data are highly skewed.

Example

Let us take a very common example of house prices. Suppose we have house values ranging from $100k to $1,000,000 with the average being $500,000.

If the peak of the distribution was left of the average value, portraying a positive skewness in the distribution. It would mean that many houses were being sold for less than the average value, i.e. $500k. This could be for many reasons, but we are not going to interpret those reasons here.

If the peak of the distributed data was right of the average value, that would mean a negative skew. This would mean that the houses were being sold for more than the average value.

Kurtosis: Kurtosis is all about the tails of the distribution — not the peakedness or flatness. It is used to describe the extreme values in one versus the other tail. It is actually the measure of outliers present in the distribution.

High kurtosis in a data set is an indicator that data has heavy tails or outliers. If there is a high kurtosis, then, we need to investigate why do we have so many outliers. It indicates a lot of things, maybe wrong data entry or other things. Investigate!

Low kurtosis in a data set is an indicator that data has light tails or a lack of outliers. If we get low kurtosis(too good to be true), then also we need to investigate and trim the dataset of unwanted results.

Mesokurtic: This distribution has kurtosis statistics similar to that of the normal distribution. It means that the extreme values of the distribution are similar to that of a normal distribution characteristic. This definition is used so that the standard normal distribution has a kurtosis of three.

Leptokurtic (Kurtosis > 3): Distribution is longer, tails are fatter. The peak is higher and sharper than Mesokurtic, which means that data are heavy-tailed or profusion of outliers.

Outliers stretch the horizontal axis of the histogram graph, which makes the bulk of the data appear in a narrow (“skinny”) vertical range, thereby giving the “skinniness” of a leptokurtic distribution.

Platykurtic: (Kurtosis < 3): Distribution is shorter; tails are thinner than the normal distribution. The peak is lower and broader than Mesokurtic, which means that data are light-tailed or lack of outliers. The reason for this is because the extreme values are less than that of the normal distribution.

6. What are Range and IQR? What does it signify?

Ans: Range: The range of a set of data is the difference between the highest and lowest values in the set.

IQR(Inter Quartile Range): The interquartile range (IQR) is the difference between the first quartile and the third quartile. The formula for this is:

IQR = Q3 – Q1

The range gives us a measurement of how spread out the entirety of our data set is. The interquartile range, which tells us how far apart the first and third quartile is, indicates how to spread out the middle 50% of our set of data is.

7.  What is the difference between Variance and Standard Deviation? What is its significance?

Ans: The central tendency mean gives you the idea of an average of the data points( i.e center location of the distribution) And now you want to know how far are your data points from mean So, here comes the concept of variance to calculate how far are your data points from mean (in simple terms, it is to calculate the variation of your data points from mean)

 Standard deviation is simply the square root of variance. And the standard deviation is also used to calculate the variation of your data points (And you may be asking, why do we use standard deviation when we have variance. Because in order to maintain the calculations in same units i.e suppose mean is in 𝑐𝑚/𝑚, then the variance is in 𝑐𝑚2/𝑚2, whereas standard deviation is in 𝑐𝑚/𝑚, so we use standard deviation most)

8.  What is selection Bias? Types of Selection Bias?

Ans: Selection bias is the phenomenon of selecting individuals, groups, or data for analysis in such a way that proper randomization is not achieved, ultimately resulting in a sample that is not representative of the population.

Understanding and identifying selection bias is important because it can significantly skew results and provide false insights about a particular population group.

Types of selection bias include:

  • Sampling bias: a biased sample caused by non-random sampling
  • Time interval: selecting a specific time frame that supports the desired conclusion. e.g. conducting a sales analysis near Christmas.
  • Exposure: includes clinical susceptibility bias, protopathic bias, indication bias. Read more here.
  • Data: includes cherry-picking, suppressing evidence, and the fallacy of incomplete evidence.
  • Attrition: attrition bias is similar to survivorship bias, where only those that ‘survived’ a long process are included in an analysis, or failure bias, where those that ‘failed’ are only included
  • Observer selection: related to the Anthropic principle, which is a philosophical consideration that any data we collect about the universe is filtered by the fact that, in order for it to be observable, it must be compatible with the conscious and sapient life that observes it.

Handling missing data can make selection bias worse because different methods impact the data in different ways. For example, if you replace null values with the mean of the data, you adding bias in the sense that you’re assuming that the data is not as spread out as it might actually be.

9.  What are the ways of handling missing Data?

  • Delete rows with missing data
  • Mean/Median/Mode imputation
  • Assigning a unique value
  • Predicting the missing values using Machine Learning Models
  • Using an algorithm that supports missing values, like random forests.

10.  What are the different types of the probability distribution? Explain with example?

Ans: The common Probability Distribution is as follows:

  1. Bernoulli Distribution
  2. Uniform Distribution
  3. Binomial Distribution
  4. Normal Distribution
  5. Poisson Distribution

1. Bernoulli Distribution: A Bernoulli distribution has only two possible outcomes, namely 1 (success) and 0 (failure), and a single trial. So the random variable X which has a Bernoulli distribution can take value 1 with the probability of success, say p, and the value 0 with the probability of failure, say q or 1-p.

Example: whether it’s going to rain tomorrow or not where rain denotes success and no rain denotes failure and Winning (success) or losing (failure) the game.

2. Uniform Distribution: When you roll a fair die, the outcomes are 1 to 6. The probabilities of getting these outcomes are equally likely and that is the basis of a uniform distribution. Unlike Bernoulli Distribution, all the n number of possible outcomes of a uniform distribution are equally likely.

Example: Rolling a fair dice.

3. Binomial Distribution: A distribution where only two outcomes are possible, such as success or failure, gain or loss, win or lose and where the probability of success and failure is the same for all the trials is called a Binomial Distribution.

  • Each trial is independent.
  • There are only two possible outcomes in a trial- either a success or a failure.
  • A total number of n identical trials are conducted.
  • The probability of success and failure is the same for all trials. (Trials are identical.)

Example: Tossing a coin.

4. Normal Distribution: Normal distribution represents the behavior of most of the situations in the universe (That is why it’s called a “normal” distribution. I guess!). The large sum of (small) random variables often turns out to be normally distributed, contributing to its widespread application. Any distribution is known as Normal distribution if it has the following characteristics:

  • The mean, median, and mode of the distribution coincide.
  • The curve of the distribution is bell-shaped and symmetrical about the line x=μ.
  • The total area under the curve is 1.
  • Exactly half of the values are to the left of the center and the other half to the right.

5. Poisson Distribution: A distribution is called Poisson distribution when the following assumptions are valid:

  • Any successful event should not influence the outcome of another successful event. 
  • The probability of success over a short interval must equal the probability of success over a longer interval. 
  • The probability of success in an interval approaches zero as the interval becomes smaller.

Example: The number of emergency calls recorded at a hospital in a day.

 

11. What are the statistical Tests? List Them.

Ans: Statistical tests are used in hypothesis testing. They can be used to:

  • determine whether a predictor variable has a statistically significant relationship with an outcome variable.
  • estimate the difference between two or more groups.

Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then they determine whether the observed data fall outside of the range of values predicted by the null hypothesis.

Common Tests in Statistics:

    1. T-Test/Z-Test
    2. ANOVA
    3. Chi-Square Test
    4. MANOVA

 

12. How do you calculate the sample size required?

Ans: You can use the margin of error (ME) formula to determine the desired sample size.

  • t/z = t/z score used to calculate the confidence interval
  • ME = the desired margin of error
  • S = sample standard deviation

 

13. What are the different Biases associated when we sample?

Ans: Potential biases include the following:

  • Sampling bias: a biased sample caused by non-random sampling
  • Under coverage bias: sampling too few observations
  • Survivorship bias: error of overlooking observations that did not make it past a form of the selection process.

 

14.  How to convert normal distribution to standard normal distribution?

Standardized normal distribution has mean = 0 and standard deviation = 1

To convert normal distribution to standard normal distribution we can use the

formula: X (standardized) = (x-µ) / σ

 

 

15. How to find the mean length of all fishes in a river?

  • Define the confidence level (most common is 95%)
  • Take a sample of fishes from the river (to get better results the number of fishes > 30)
  • Calculate the mean length and standard deviation of the lengths
  • Calculate t-statistics
  • Get the confidence interval in which the mean length of all the fishes should be.

 

16.  What do you mean by the degree of freedom?

  • DF is defined as the number of options we have 
  • DF is used with t-distribution and not with Z-distribution
  • For a series, DF = n-1 (where n is the number of observations in the series)

 

17. What do you think if DF is more than 30?

  • As DF increases the t-distribution reaches closer to the normal distribution
  • At low DF, we have fat tails
  • If DF > 30, then t-distribution is as good as the normal distribution.

 

18. When to use t distribution and when to use z distribution?

  • The following conditions must be satisfied to use Z-distribution
  • Do we know the population standard deviation?
  • Is the sample size > 30?
  • CI = x (bar) – Z*σ/√n to x (bar) + Z*σ/√n
  • Else we should use t-distribution
  • CI = x (bar) – t*s/√n to x (bar) + t*s/√n

 

19. What are H0 and H1? What is H0 and H1 for the two-tail test?

  • H0 is known as the null hypothesis. It is the normal case/default case.

                               For one tail test x <= µ

                               For two-tail test x = µ

  • H1 is known as an alternate hypothesis. It is the other case.

                               For one tail test x > µ

                               For two-tail test x <> µ

 

20. What is the Degree of Freedom? 

DF is defined as the number of options we have: 

DF is used with t-distribution and not with Z-distribution

For a series, DF = n-1 (where n is the number of observations in the series)

 

21. How to calculate p-Value?

Ans: Calculating p-value:

Using Excel:

  1. Go to the Data tab
  2. Click on Data Analysis
  3. Select Descriptive Statistics
  4. Choose the column
  5. Select summary statistics and confidence level (0.95)

By Manual Method:

  1. Find H0 and H1
  2. Find n, x(bar) and s
  3. Find DF for t-distribution
  4. Find the type of distribution – t or z distribution
  5. Find t or z value (using the look-up table)
  6. Compute the p-value to the critical value

 

22. What is ANOVA?

Ans: ANOVA expands to the analysis of variance, is described as a statistical technique used to determine the difference in the means of two or more populations, by examining the amount of variation within the samples corresponding to the amount of variation between the samples. It bifurcates the total amount of variation in the dataset into two parts, i.e. the amount ascribed to chance and the amount ascribed to specific causes.

It is a method of analyzing the factors which are hypothesized or affect the dependent variable. It can also be used to study the variations amongst different categories, within the factors, that consist of numerous possible values. It is of two types:

One way ANOVA: When one factor is used to investigate the difference between different categories, having many possible values.

Two way ANOVA: When two factors are investigated simultaneously to measure the interaction of the two factors influencing the values of a variable.

 

23.  What is ANCOVA?

Ans: ANCOVA stands for Analysis of Covariance, is an extended form of ANOVA, that eliminates the effect of one or more interval-scaled extraneous variable, from the dependent variable before carrying out research. It is the midpoint between ANOVA and regression analysis, wherein one variable in two or more populations can be compared while considering the variability of other variables.

When in a set of independent variables consist of both factor (categorical independent variable) and covariate (metric independent variable), the technique used is known as ANCOVA. The difference independent variables because of the covariate are taken off by an adjustment of the dependent variable’s mean value within each treatment condition.

This technique is appropriate when the metric independent variable is linearly associated with the dependent variable and not to the other factors. It is based on certain assumptions which are:

  • There is some relationship between the dependent and uncontrolled variables.
  • The relationship is linear and is identical from one group to another.
  • Various treatment groups are picked up at random from the population.
  • Groups are homogeneous in variability.

 

24.  What is the difference between ANOVA and ANCOVA?

Ans: The points given below are substantial so far as the difference between ANOVA and ANCOVA is concerned:

  • The technique of identifying the variance among the means of multiple groups for homogeneity is known as Analysis of Variance or ANOVA. A statistical process which is used to take off the impact of one or more metric-scaled undesirable variable from the dependent variable before undertaking research is known as ANCOVA.
  • While ANOVA uses both linear and non-linear models. On the contrary, ANCOVA uses only a linear model.
  • ANOVA entails only categorical independent variables, i.e. factor. As against this, ANCOVA encompasses a categorical and a metric independent variable.
  • A covariate is not taken into account, in ANOVA, but considered in ANCOVA.
  • ANOVA characterizes between-group variations, exclusively to treatment. In contrast, ANCOVA divides between-group variations to treatment and covariate.
  • ANOVA exhibits within-group variations, particularly individual differences. Unlike ANCOVA, which bifurcates within-group variance in individual differences and covariate.

 

25.  What are t and z scores? Give Details.

T-Score vs. Z-Score: Overview: A z-score and a t score are both used in hypothesis testing. 

T-score vs. z-score: When to use a t score:

The general rule of thumb for when to use a t score is when your sample:

Has a sample size below 30,

Has an unknown population standard deviation.

You must know the standard deviation of the population and your sample size should be above 30 in order for you to be able to use the z-score. Otherwise, use the t-score.

Z-score

Technically, z-scores are a conversion of individual scores into a standard form. The conversion allows you to more easily compare different data. A z-score tells you how many standard deviations from the mean your result is. You can use your knowledge of normal distributions (like the 68 95 and 99.7 rule) or the z-table to determine what percentage of the population will fall below or above your result.

The z-score is calculated using the formula:

  • z = (X-μ)/σ

Where:

  • σ is the population standard deviation and
  • μ is the population mean.
  • The z-score formula doesn’t say anything about sample size; The rule of thumb applies that your sample size should be above 30 to use it.

T-score

Like z-scores, t-scores are also a conversion of individual scores into a standard form. However, t-scores are used when you don’t know the population standard deviation; You make an estimate by using your sample.

  • T = (X – μ) / [ s/√(n) ]

Where:

  • s is the standard deviation of the sample.

If you have a larger sample (over 30), the t-distribution and z-distribution look pretty much the same. 

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Model vs Algorithm in ML

Machine Learning works with “models” and “algorithms”, and both play an important role in machine learning where the algorithm tells about the process and model is built by following those rules.

Algorithms have derived by the statistician or mathematician very long ago and those algorithms are studies and applied by the individuals for their business purposes.

A model in machine learning nothing but a function that is used to take some certain input, perform a certain operation which is told by algorithms to its best on the given input, and gives a suitable output.

Some of the machine learning algorithms are:

  1. Linear regression
  2. Logistic regression
  3. Decision tree
  4. Random forest
  5. K-nearest neighbor
  6. K-means learning

What is an algorithm in Machine learning?

An algorithm is a step by step approach powered by statistics that guides the machine learning in its learning process. An algorithm is nothing but one of the several components that constitute a model.

There are several characteristics of machine learning algorithms:

  1. Machine learning algorithms can be represented by the use of mathematics and pseudo code.
  2. The effectiveness of machine learning algorithms can be measured and represented.
  3. With any of the popular programming languages, machine learning algorithms can be implemented.

What is the Model in Machine learning?

The model is dependent on factors such as features selection, tuning parameters, cost functions along with the algorithm the model just not fully dependent on algorithms.

Model is the result of an algorithm when we implement the algorithm with the code when we train the algorithms with the real data. A model is something that tells what your program learned from the data by following the rules of those algorithms. The model is used to predict the future result that is observed by the algorithm implementation of small data.

                Model = Data + Algorithm 

A model contains four major steps that are:

  1. Data preprocessing
  2. Feature engineering
  3. Data management
  4. performance measurement.

How the model and algorithms work together in machine learning?

For example:

y = mx+c is an equation for a line where m is the slope of the line and c is the y-intercept, this is nothing but linear regression with only one variable.
similarly, the decision tree and random forest have something like the Gini index and K-nearest having Euclidean distance formula.

So take the linear regression algorithm:

  1. Start with a training set with x1, x2,…, and y.
  2. Find out the parameters c0, c1, c2 with the random variables.
  3. Find out the learning rate alpha
  4. Then repeat the following updates such as c0 = co-alpha +h(x)-y and for c1, c2 also.
  5. Repeat these processes till converged.

when you employing this algorithm, you are employing these exact 5 steps in your model without changing the steps, your model initiated by the algorithm and also treat all the dataset same.

If you want to apply that algorithm to the model, the model finds out the value of m and c that we don’t know, then how will you find out?
suppose you have 3 variables that are having values of x and y now your model will find the value of m1, m2, m3, and c1, c2, c3 for three variables.
The model will work with three slopes and three intercepts to find out the result of the dataset to predict the future.

The “algorithm” might be treating all the data the same but it is the “model” that actually solves the problems. An algorithm is something that you use to train the model on the data.

After building a model, a data science enthusiasts test it to get the accuracy of that model and fine-tuning to improve the results.

This article may help you yo understand about the algorithm and model in Machine learning, In summary, an algorithm is a process or a technique that we follow to get the result or to find the solution of a problem.
And a model is a computation or a formula that formed as an output of an algorithm that takes some input, so you can say that you are building a model using a given algorithm.

 

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Gaussian and Normal distribution

Gaussian distribution is a bell-shaped curve, it follows the normal distribution with the equal number of measurements right side and left side of the mean value. Mean is situated in the center of the curve, the right side values from the mean are greater than mean value and the left side values from mean are smaller than the mean. It is used for mean, median, and mode for continuous values. You all know the basic meaning of mean, median, and mod. The mean is an average of the values, the median is the center value of the distribution and the mode is the value of the distribution which is frequently occurred. In the normal distribution, the values of mean, median, and are all same. If the values generate skewness than it is not normally distributed. The normal distribution is very important in statistics because it fits for many occurrences such as heights, blood pressure, measurement error, and many numerical values.

A gaussian and normal distribution is the same in statistics theory. Gaussian distribution is also known as a normal distribution. The curve is made with the help of probability density function with the random values. F(x) is the PDF function and x is the value of gaussian & used to represent the real values of random variables having unknown distribution.

There is a property of Gaussian distribution which is known as Empirical formula which shows that in which confidence interval the value comes under. The normal distribution contains the mean value as 0 and standard deviation 1.

The empirical rule also referred to as the three-sigma rule or 68-95-99.7 rule, is a statistical rule which states that for a normal distribution, almost all data falls within three standard deviations (denoted by σ) of the mean (denoted by µ). Broken down, the empirical rule shows that 68% falls within the first standard deviation (µ ± σ), 95% within the first two standard deviations (µ ± 2σ), and 99.7% within the first three standard deviations (µ ± 3σ).

Python code for plotting the gaussian graph:

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math

mu = 0
variance = 1
sigma = math.sqrt(variance)
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
plt.plot(x, stats.norm.pdf(x, mu, sigma))
plt.show() 

The above code shows the Gaussian distribution with 99% of the confidence interval with a standard deviation of 3 with mean 0.

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

 

What is Supervised, unsupervised learning, and reinforcement learning in Machine learning

The supervised learning algorithm is widely used in the industries to predict the business outcome, and forecasting the result on the basis of historical data. The output of any supervised learning depends on the target variables. It allows the numerical, categorical, discrete, linear datasets to build a machine learning model. The target variable is known for building the model and that model predicts the outcome on the basis of the given target variable if any new data point comes to the dataset.

The supervised learning model is used to teach the machine to predict the result for the unseen input. It contains a known dataset to train the machine and its performance during the training time of a model. And then the model predicts the response of testing data when it is fed to the trained model. There are different machine learning models that are suitable for different kinds of datasets. The supervised algorithm uses regression and classification techniques for building predictive models.

For example, you have a bucket of fruits and there are different types of fruits in the bucket. You need to separate the fruits according to their features and you know the name of the fruits follow up its corresponding features the features of the fruits are independent variables and name of fruits are dependent variable that is out target variable. We can build a predicting model to determine the fruit name.

There are various types of Supervised learning:

  1. Linear regression
  2. Logistic regression
  3. Decision tree
  4. Random forest
  5. support vector machine
  6. k-Nearest neighbors

Linear and logistic regression is used when we have continuous data. Linear regression defines the relationship between the variables where we have independent and dependent variables. For example, what would be the performance percentage of a student after studying a number of hours? The numbers of hours are in an independent feature and the performance of students in the dependent features. The linear regression is also categorized in types
those are simple linear regression, multiple linear regression, polynomial regression. 

Classification algorithms help to classify the categorical values. It is used for the categorical values, discrete values, or the values which belong to a particular class. Decision tree and Random forest and KNN all are used for the categorical dataset. Popular or major applications of classification include bank credit scoring, medical imaging, and speech recognition. Also, handwriting recognition uses classification to recognize letters and numbers, to check whether an email is genuine or spam, or even to detect whether a tumor is benign or cancerous and for recommender systems.

The support vector machine is used for both classification and regression problems. It uses the regression method to create a hyperplane to classify the category of the datapoint. sentiment analysis of a subject is determined with the help of SVM whether the statement is positive or negative.

Unsupervised learning algorithms

Unsupervised learning is a technique in which we need to supervise the model as we have not any target variable or labeled dataset. It discovers its own information to predict the outcome. It is used for the unlabeled datasets. Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning methods. It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention.

For example, We have a bucket of fruits and we need to separate them accordingly, and there no target variable available to determine whether the fruit is apple, orange, or banana. Unsupervised learning categorizes these fruits to make a prediction when new data comes.

Types of unsupervised learning:

  1. Hierarchical clustering
  2. K-means clustering
  3. K-NN (k nearest neighbors)
  4. Principal Component Analysis
  5. Singular Value Decomposition
  6. Independent Component Analysis

Hierarchical clustering is an algorithm that builds a hierarchy of clusters. It begins with all the data which is assigned to a cluster of their own. Here, two close clusters are going to be in the same cluster. This algorithm ends when there is only one cluster left.

K-means and KNN is also a clustering method to classify the dataset. k-means is an iterative method of clustering and also used to find the highest value for every iteration, we can select the numbers of clusters. You need to define the k cluster for making a good predictive model. K- nearest neighbour is the simplest of all machine learning classifiers. It differs from other machine learning techniques, in that it doesn’t produce a model. It is a simple algorithm that stores all available cases and classifies new instances based on a similarity measure.

PCA(Principal component analysis) is a dimensionality reduction algorithm. For example, you have a dataset with 200 of the features/columns. You need to reduce the number of features for the model with only an important feature. It maintains the complexity of the dataset.

Reinforcement learning is also a type of Machine learning algorithm. It provides a suitable action in a particular situation, and it is used to maximize the reward. The reward could be positive or negative based on the behavior of the object. Reinforcement learning is employed by various software and machines to find the best possible behavior in a situation.

Main points in Reinforcement learning –

  • Input: The input should be an initial state from which the model will start
  • Output: There are much possible output as there are a variety of solution to a particular problem
  • Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.
  • The model keeps continues to learn.
  • The best solution is decided based on the maximum reward.

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Decision Tree

Decision tree:

The decision tree is the classification algorithm in ML(machine learning). A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

To understand the algorithm of the decision tree we need to know about the classification.

What is Classification?

Classification is the process of dividing the datasets into different categories or groups by adding a label. It adds the data point to a particular labeled group on the basis of some condition.

As we see in daily life there are three categories in an email(Spam, Promotions, Personal) they are classified to get the proper information. Here decision tree is used to classify the mail type and fix it the proper one.

Types of classification 

  • DECISION TREE
  • RANDOM FOREST
  • NAIVE BAYES
  • KNN

Decision tree:

  1. Graphical representation of all the possible solutions to a decision.
  2. A decision is based on some conditions.
  3. The decision made can be easily explained.

There are following steps to get a decision with the decision tree

1. Entropy:

Entropy is basically used to create a tree. We find our entropy from attribute or class. A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogeneous). ID3 algorithm uses entropy to calculate the homogeneity of a sample.

2.Information Gain:

The information gain is based on the decrease in entropy after a data-set is split on an attribute. Constructing a decision tree is all about finding an attribute that returns the highest information gain.

  • The information gain is based on the decrease in entropy after a dataset is split on an attribute.
  • Constructing a decision tree is all about finding an attribute that returns the highest information gain (i.e., the most homogeneous branches).
  • Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
  • We intend to choose the attribute, splitting by which information gain will be the most
  • Next step is calculating information gain for all attributes
Here the short example of a Decision tree:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
play_data=pd.read_csv('data/tennis.csv.txt')
print(play_data)
play_data=pd.read_csv('data/tennis.csv.txt')
play_data

Output:

outlook temp humidity windy play
0 sunny hot high False no
1 sunny hot high True no
2 overcast hot high False yes
3 rainy mild high False yes
4 rainy cool normal False yes
5 rainy cool normal True no
6 overcast cool normal True yes
7 sunny mild high False no
8 sunny cool normal False yes
9 rainy mild normal False yes
10 sunny mild normal True yes
11 overcast mild high True yes
12 overcast hot normal False yes
13 rainy mild high True no 

Entropy of play:

  • Entropy(play) = – p(Yes) . log2p(Yes) – p(No) . log2p(No)

play_data.play.value_counts()
Entropy_play=-(9/14)*np.log2(9/14)-(5/14)*np.log2(5/14)
print(Entropy_play)

output:
0.94028595867063114

Information Gain on splitting by Outlook

  • Gain(Play, Outlook) = Entropy(Play) – ∑ [ p(Play|Outlook) . Entropy(Play|Outlook) ]
  • Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] – [ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]

play_data[play_data.outlook == 'sunny'] 

# Entropy(Play|Outlook=Sunny)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
Entropy_Play_Outlook_Sunny
play_data[play_data.outlook == 'overcast'] # Entropy(Play|Outlook=overcast)
# Since, it's a homogenous data entropy will be 0
play_data[play_data.outlook == 'rainy'] # Entropy(Play|Outlook=rainy)
Entropy_Play_Outlook_Rain = -(2/5)*np.log2(2/5) - (3/5)*np.log2(3/5)
print(Entropy_play_Outlook_Rain)
# Entropy(Play_Sunny|)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
#Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] –
#[ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]

Other gains

  • Gain(Play, Temperature) – 0.029
  • Gain(Play, Humidity) – 0.151
  • Gain(Play, Wind) – 0.048

Conclusion – Outlook is winner & thus becomes root of the tree

Time to find the next splitting criteria

play_data[play_data.outlook == 'overcast'] play_data[play_data.outlook == 'sunny'] # Entropy(Play_Sunny|)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
print(Entropy_Play_Outlook_Sunny)
# Entropy(Play_Sunny|)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
print(Entropy_Play_Outlook_Sunny)

Information Gain for humidity

#Entropy for attribute high = 0, also entropy for attribute normal = 0
Entropy_Play_Outlook_Sunny - (3/5)*0 - (2/5)*0 

Information Gain for windy

  • False -> 3 -> [1+ 2-]
  • True -> 2 -> [1+ 1-]

Entropy_Wind_False = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)
print(Entropy_Wind_False)
Entropy_Play_Outlook_Sunny - (3/5)* Entropy_Wind_False - (2/5)*1  

Information Gain for temperature

  • hot -> 2 -> [2- 0+]
  • mild -> 2 -> [1+ 1-]
  • cool -> 1 -> [1+ 0-]

Entropy_Play_Outlook_Sunny - (2/5)*0 - (1/5)*0 - (2/5)* 1]

Conclusion : Humidity is the best choice on sunny branch:

play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'high')] 

Output:

outlook temp humidity windy play
0 sunny hot high False no
1 sunny hot high True no
7 sunny mild high False no 

play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'normal']

Output:
outlook temp humidity windy play
8 sunny cool normal False yes
10 sunny mild normal True yes

Splitting the rainy branch:

play_data[play_data.outlook == 'rainy'] # Entropy(Play_Rainy|)
Entropy_Play_Outlook_Rainy =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)outlook temp humidity windy play
3 rainy mild high False yes
4 rainy cool normal False yes
5 rainy cool normal True no
9 rainy mild normal False yes
13 rainy mild high True no 

Information Gain for temp

  • mild -> 3 [2+ 1-]
  • cool -> 2 [1+ 1-]

Entropy_Play_Outlook_Rainy - (3/5)*0.918 - (2/5)*1

Output:
0.020150594454668602

Information Gain for Windy:

Entropy_Play_Outlook_Rainy - (2/5)*0 - (3/5)*0

Output:
0.97095059445466858 

Information Gain for Humidity

  • High -> 2 -> [1+ 1-]
  • Normal -> 3 -> [2+ 1-]

Entropy_Play_Outlook_Rainy_Normal = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)
Entropy_Play_Outlook_Rainy_Normal
Entropy_Play_Outlook_Rainy - (2/5)*1 - (3/5)*Entropy_Play_Outlook_Rainy_Normal
Entropy_Play_Outlook_Rainy_Normal
Entropy_Play_Outlook_Rainy_Normal

Output:
0.91829583405448956
0.019973094021974891 

Final tree:

Decision trees are popular among non-statisticians as they produce a model that is very easy to interpret. Each leaf node is presented as an if/then rule. Cases that satisfy the if/then the statement is placed in the node. Are non-parametric and therefore do not require normality assumptions of the data. Parametric models specify the form of the relationship between predictors and response. An example is a linear relationship for regression. In many cases, however, the nature of the relationship is unknown. This is a case in which non-parametric models are useful. Can handle data of different types, including continuous, categorical, ordinal, and binary. Transformations of the data are not required. It can be useful for detecting important variables, interactions, and identifying outliers. It handles missing data by identifying surrogate splits in the modeling process. Surrogate splits are splitting highly associated with the primary split. In other models, records with missing values are omitted by default.

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Necessity of Machine Learning in Retail

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a thorough analysis of a vast amount of data allows influencing or rather manipulating the customers decisions. Numerous flows of information, along with channels of communication, are used for this purpose.

The sphere of the retail develops rapidly. The retailers manage to analyze data and develop a peculiar psychological portrait of a customer to learn his or her sore points. Thereby, a customer tends to be easily influenced by the tricks developed by the retailers.

This article presents top 10 data science use cases in the retail, created for you to be aware of the present trends and tendencies.

  1. Recommendation engines

    Recommendation engines proved to be of great use for the retailers as the tools for customers’ behavior prediction. The retailers tend to use recommendation engines as one of the main leverages on the customers’ opinion. Providing recommendations enables the retailers to increase sales and to dictate trends.Recommendation engines manage to adjust depending on the choices made by the customers. Recommendation engines make a great deal of data filtering to get the insights. Usually, recommendation engines use either collaborative or content-based filtering. In this regard, the customer’s past behavior or the series of the product characteristics are under consideration. Besides, various types of data such as demographic data, usefulness, preferences, needs, previous shopping experience, etc. go via the past data learning algorithm.Then the collaborative and content filtering association links are built. The recommendation engines compute a similarity index in the customers’ preferences and offer the goods or services accordingly. The up-sell and cross-sell recommendations depend on the detailed analysis of an online customer’s profile.

  2. Market basket analysis

    Market basket analysis may be regarded as a traditional tool of data analysis in the retail. The retailers have been making a profit out of it for years. This process mainly depends on the organization of a considerable amount of data collected via customers’ transactions. Future decisions and choices may be predicted on a large scale by this tool. Knowledge of the present items in the basket along with all likes, dislikes, and previews is beneficial for a retailer in the spheres of layout organization, prices making and content placement. The analysis is usually conducted via rule mining algorithm. Beforehand the data undertakes transformation from data frame format to simple transactions. A specially tailored function accepts the data, splits it according to some differentiating factors and deletes useless. This data is input. On its basis, the association links between the products are built. It becomes possible due to the association rule application.The insight information largely contributes to the improvement of the development strategies and marketing techniques of the retailers. Also, the efficiency of the selling efforts reaches its peak.

  3. Warranty analytics
    Warranty analytics entered the sphere of the retail as a tool of warranty claims monitoring, detection of fraudulent activity, reducing costs and increasing quality. This process involves data and text mining for further identification of claims patterns and problem areas. The data is transformed into actionable real-time plans, insight, and recommendations via segmentation analysis.The methods of detecting are quite complicated, as far as they deal with vague and intensive data flows. They concentrate on the detecting anomalies in the warranty claims. Powerful internet data platforms speed up the analysis process of a significant amount of warranty claims. This is an excellent chance for the retailers to turn warranty challenges into actionable intelligence.
  4. Price optimization
    Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitors’ offers. The tools for data analysis bring this issue to a new level of its approaching. Price optimization tools include numerous online tricks as well as secret customers approach. The data gained from the multichannel sources define the flexibility of prices, taking into consideration the location, an individual buying attitude of a customer, seasoning and the competitors’ pricing. The computation of the extremes in values along with frequency tables are the appropriate instruments to make the variable evaluation and perfect distributions for the predictors and the profit response.The algorithm presupposes customers segmentation to define the response to changes in prices. Thus, the costs that meet corporate goals may be determined. Using the model of a real-time optimization the retailers have an opportunity to attract the customers, to retain the attention and to realize personal pricing schemes.
  5. Inventory management
    Inventory, as it is, concerns stocking goods for their future use. Inventory management, in its turn, refers to stocking goods in order to use them in time of crisis. The retailers aim to provide a proper product at a right time, in a proper condition, at a proper place. In this regard, the stock and the supply chains are deeply analyzed. Powerful machine learning algorithms and data analysis platforms detect patterns, correlations among the elements and supply chains. Via constantly adjusting and developing parameters and values the algorithm defines the optimal stock and inventory strategies. The analysts spot the patterns of high demand and develop strategies for emerging sales trends, optimize delivery and manage the stock implementing the data received.
  6. Location of new stores
    Data science proves to be extremely efficient about the issue of the new store’s location. Usually, to make such a decision a great deal of data analysis is to be done. The algorithm is simple, though very efficient. The analysts explore the online customers’ data, paying great attention to the demographic factor. The coincidences in ZIP code and location give a basis for understanding the potential of the market. Also, special settings concerning the location of other shops are taken into account. As well as that, the retailer’s network analysis is performed. The algorithms find the solution by connection all these points. The retailer easily adds this data to its platform to enrich the analysis opportunities for another sphere of its activity.
  7. Customer sentiment analysis
    Customer sentiment analysis is not a brand-new tool in this industry. However, since the active implementation of data science, it has become less expensive and time-consuming. Nowadays, the use of focus groups and customers polls is no longer needed. Machine learning algorithms provide the basis for sentiment analysis.The analysts can perform the brand-customer sentiment analysis by data received from social networks and online services feedback. Social media sources are readily available. That is why it is much easier to implement analytics on social platforms. Sentiment analytics uses language processing to track words bearing a positive or negative attitude of a customer. These feedback become a background for services improvement.

    The analysts perform sentiment analysis on the basis of natural language processing, text analysis to extract defining positive, neutral or negative sentiments. The algorithms go through all the meaningful layers of speech. All the spotted sentiments belong to certain categories or buckets and degrees. The output is the sentiment rating in one of the categories mentioned above and the overall sentiment of the text.

  8. Merchandising
    Merchandising has become an essential part of the retail business. This notion covers a vast majority of activities and strategies aimed at increase of sales and promotion of the product. The implementation of the merchandising tricks helps to influence the customer’s decision-making process via visual channels. Rotating merchandise helps to keep the assortment always fresh and renewed. Attractive packaging and branding retain customers attention and enhance visual appeal. A great deal of data science analysis remains behind the scenes in this case.The merchandising mechanisms go through the data picking up the insights and forming the priority sets for the customers, taking into account seasonality, relevancy and trends.
  9. Lifetime value prediction
    In retail, customer lifetime value (CLV) is a total value of the customer’s profit to the company over the entire customer-business relationship. Particular attention is paid to the revenues, as far as they are not so predictable as costs. By the direct purchases two significant customer methodologies of lifetime predictions are made: historical and predictive.All the forecasts are made on the past data leading up to the most recent transactions. Thus the algorithms of a customer’s lifespan within one brand are defined and analyzed. Usually, the CLV models collect, classify and clean the data concerning customers’ preferences, expenses, recent purchases and behavior to structure them into the input. After processing this data we receive a linear presentation of the possible value of the existing and possible customers. The algorithm also spots the inter dependencies between the customer’s characteristics and their choices. The application of the statistical methodology helps to identify the customer’s buying pattern up until he or she stops making purchases. Data science and machine learning assure the retailer’s understanding of his customer, the improvement in services and definition of priorities.
  10. Fraud detection
    The detection of fraud and fraud rings is a challenging activity of a reliable retailer. The main reason for fraud detection is a great financial loss caused. And this is only a tip of an iceberg. The conducted profound National Retail Security Survey goes deeply into details. The customer might suffer from fraud in returns and delivery, the abuse of rights, the credit risk and many other fraud cases that do nothing but ruin the retailer’s reputation. Once being a victim of such situations may destroy a precious trust of the customer forever.The only efficient way to protect your company’s reputation is to be one step ahead of the fraudsters. Big data platforms provide continuous monitoring of the activity and ensure the detection of the fraudulent activity. The algorithm developed for fraud detection should not only recognize fraud and flag it to be banned but to predict future fraudulent activities. That is why deep neural networks prove to be so efficient. The platforms apply the common dimensionality reduction techniques to identify hidden patterns, to label activities and to cluster fraudulent transactions. Using the data analysis mechanisms within fraud detection schemes brings benefits and somewhat improves the retailer’s ability to protect the customer and the company as it is.
Learnbay is a Data Science and Artificial Intelligence training institute that provides the essential and highly recommended topics of Machine Learning.

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GET THE LEARNBAY ADVANTAGE FOR YOUR CAREER
Note : Our programs are suitable for working professionals(any domain). Fresh graduates are not eligible.