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Types of Probability Distributions Every Data Science Expert Should know

By KnowledgeHut

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution. What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values. Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm import matplotlib.pyplot as mpl import numpy as np def normalDist() -> None: fig, ax = mpl.subplots(1, 1) mean, var, skew, kurt = norm.stats(moments = 'mvsk') x = np.linspace(norm.ppf(0.01), norm.ppf(0.99), 100) ax.plot(x, norm.pdf(x), 'r-', lw = 5, alpha = 0.6, label = 'norm pdf') ax.plot(x, norm.cdf(x), 'b-', lw = 5, alpha = 0.6, label = 'norm cdf') vals = norm.ppf([0.001, 0.5, 0.999]) np.allclose([0.001, 0.5, 0.999], norm.cdf(vals)) r = norm.rvs(size = 1000) ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2) ax.legend(loc = 'best', frameon = False) mpl.show() normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial. Probability Mass Function of a Bernoulli’s Distribution is: where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli import seaborn as sb def bernoulliDist(): data_bern = bernoulli.rvs(size=1200, p = 0.7) ax = sb.distplot( data_bern, kde = True, color = 'g', hist_kws = {'alpha' : 1}, kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'}) ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution') bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random import matplotlib.pyplot as mpl import seaborn as sb def uniformDist(): sb.distplot(random.uniform(size = 1200), hist = True) mpl.show() uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl def lognormalDist(): muu, sig = 3, 1 s = np.random.lognormal(muu, sig, 1000) cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y') x = np.linspace(min(bins), max(bins), 10000) calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2)) / (x * sig * np.sqrt(2 * np.pi))) mpl.plot(x, calc, linewidth = 2.5, color = 'g') mpl.axis('tight') mpl.show() lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np from matplotlib import pyplot as plt from scipy.stats import pareto def paretoDist(): xm = 1.5 alp = [2, 4, 6] x = np.linspace(0, 4, 800) output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp]) plt.plot(x, output.T) plt.show() paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random import matplotlib.pyplot as mpl import seaborn as sb def expDist(): sb.distplot(random.exponential(size = 1200), hist = True) mpl.show() expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random import matplotlib.pyplot as mpl import seaborn as sb def binomialDist(): sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal') sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial') plt.show() binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl def probability_to_occur_at(attempt, probability): return (1-p)**(attempt - 1) * probability p = 0.3 attempt = 4 attempts_to_show = range(21)[1:] print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p)) mpl.xlabel('Number of Trials') mpl.ylabel('Probability of the Event') barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show) barlist[attempt].set_color('g') mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson import seaborn as sb import numpy as np import matplotlib.pyplot as mpl def poissonDist(): mpl.figure(figsize = (10, 10)) data_binom = poisson.rvs(mu = 3, size = 5000) ax = sb.distplot(data_binom, kde=True, color = 'g', bins=np.arange(data_binom.min(), data_binom.max() + 1), kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'}) ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency') mpl.show() poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np import matplotlib.pyplot as mpl np.random.seed(99) n = 12 pvalue = [0.3, 0.46, 0.22] s = [] p = [] for size in np.logspace(2, 3): outcomes = np.random.multinomial(n, pvalue, size=int(size)) prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes) p.append(prob) s.append(int(size)) fig1 = mpl.figure() mpl.plot(s, p, 'o-') mpl.plot(s, [0.0248]*len(s), '--r') mpl.grid() mpl.xlim(xmin = 0) mpl.xlabel('Number of Events') mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments. Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl import numpy as np from scipy.stats import nbinom x = np.linspace(0, 6, 70) gr, kr = 0.3, 0.7 g = nbinom.ppf(x, gr, kr) s = nbinom.pmf(x, gr, kr) mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course https://www.knowledgehut.com/data-science-courses.

Types of Probability Distributions Every Data Science Expert Should know

By KnowledgeHut

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution. What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values. Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm import matplotlib.pyplot as mpl import numpy as np def normalDist() -> None: fig, ax = mpl.subplots(1, 1) mean, var, skew, kurt = norm.stats(moments = 'mvsk') x = np.linspace(norm.ppf(0.01), norm.ppf(0.99), 100) ax.plot(x, norm.pdf(x), 'r-', lw = 5, alpha = 0.6, label = 'norm pdf') ax.plot(x, norm.cdf(x), 'b-', lw = 5, alpha = 0.6, label = 'norm cdf') vals = norm.ppf([0.001, 0.5, 0.999]) np.allclose([0.001, 0.5, 0.999], norm.cdf(vals)) r = norm.rvs(size = 1000) ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2) ax.legend(loc = 'best', frameon = False) mpl.show() normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial. Probability Mass Function of a Bernoulli’s Distribution is: where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli import seaborn as sb def bernoulliDist(): data_bern = bernoulli.rvs(size=1200, p = 0.7) ax = sb.distplot( data_bern, kde = True, color = 'g', hist_kws = {'alpha' : 1}, kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'}) ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution') bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random import matplotlib.pyplot as mpl import seaborn as sb def uniformDist(): sb.distplot(random.uniform(size = 1200), hist = True) mpl.show() uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl def lognormalDist(): muu, sig = 3, 1 s = np.random.lognormal(muu, sig, 1000) cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y') x = np.linspace(min(bins), max(bins), 10000) calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2)) / (x * sig * np.sqrt(2 * np.pi))) mpl.plot(x, calc, linewidth = 2.5, color = 'g') mpl.axis('tight') mpl.show() lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np from matplotlib import pyplot as plt from scipy.stats import pareto def paretoDist(): xm = 1.5 alp = [2, 4, 6] x = np.linspace(0, 4, 800) output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp]) plt.plot(x, output.T) plt.show() paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random import matplotlib.pyplot as mpl import seaborn as sb def expDist(): sb.distplot(random.exponential(size = 1200), hist = True) mpl.show() expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random import matplotlib.pyplot as mpl import seaborn as sb def binomialDist(): sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal') sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial') plt.show() binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl def probability_to_occur_at(attempt, probability): return (1-p)**(attempt - 1) * probability p = 0.3 attempt = 4 attempts_to_show = range(21)[1:] print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p)) mpl.xlabel('Number of Trials') mpl.ylabel('Probability of the Event') barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show) barlist[attempt].set_color('g') mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson import seaborn as sb import numpy as np import matplotlib.pyplot as mpl def poissonDist(): mpl.figure(figsize = (10, 10)) data_binom = poisson.rvs(mu = 3, size = 5000) ax = sb.distplot(data_binom, kde=True, color = 'g', bins=np.arange(data_binom.min(), data_binom.max() + 1), kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'}) ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency') mpl.show() poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np import matplotlib.pyplot as mpl np.random.seed(99) n = 12 pvalue = [0.3, 0.46, 0.22] s = [] p = [] for size in np.logspace(2, 3): outcomes = np.random.multinomial(n, pvalue, size=int(size)) prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes) p.append(prob) s.append(int(size)) fig1 = mpl.figure() mpl.plot(s, p, 'o-') mpl.plot(s, [0.0248]*len(s), '--r') mpl.grid() mpl.xlim(xmin = 0) mpl.xlabel('Number of Events') mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments. Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl import numpy as np from scipy.stats import nbinom x = np.linspace(0, 6, 70) gr, kr = 0.3, 0.7 g = nbinom.ppf(x, gr, kr) s = nbinom.pmf(x, gr, kr) mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course https://www.knowledgehut.com/data-science-courses.

What Is Statistical Analysis and Its Business Applications?

By Abhresh Sugandhi

Statistics is a science concerned with collection, analysis, interpretation, and presentation of data. In Statistics, we generally want to study a population. You may consider a population as a collection of things, persons, or objects under experiment or study. It is usually not possible to gain access to all of the information from the entire population due to logistical reasons. So, when we want to study a population, we generally select a sample. In sampling, we select a portion (or subset) of the larger population and then study the portion (or the sample) to learn about the population. Data is the result of sampling from a population.Major ClassificationThere are two basic branches of Statistics – Descriptive and Inferential statistics. Let us understand the two branches in brief. Descriptive statistics Descriptive statistics involves organizing and summarizing the data for better and easier understanding. Unlike Inferential statistics, Descriptive statistics seeks to describe the data, however, it does not attempt to draw inferences from the sample to the whole population. We simply describe the data in a sample. It is not developed on the basis of probability unlike Inferential statistics. Descriptive statistics is further broken into two categories – Measure of Central Tendency and Measures of Variability. Inferential statisticsInferential statistics is the method of estimating the population parameter based on the sample information. It applies dimensions from sample groups in an experiment to contrast the conduct group and make overviews on the large population sample. Please note that the inferential statistics are effective and valuable only when examining each member of the group is difficult. Let us understand Descriptive and Inferential statistics with the help of an example. Task – Suppose, you need to calculate the score of the players who scored a century in a cricket tournament. Solution: Using Descriptive statistics you can get the desired results. Task – Now, you need the overall score of the players who scored a century in the cricket tournament. Solution: Applying the knowledge of Inferential statistics will help you in getting your desired results. Top Five Considerations for Statistical Data AnalysisData can be messy. Even a small blunder may cost you a fortune. Therefore, special care when working with statistical data is of utmost importance. Here are a few key takeaways you must consider to minimize errors and improve accuracy. Define the purpose and determine the location where the publication will take place.  Understand the assets to undertake the investigation. Understand the individual capability of appropriately managing and understanding the analysis.  Determine whether there is a need to repeat the process.  Know the expectation of the individuals evaluating reviewing, committee, and supervision. Statistics and ParametersDetermining the sample size requires understanding statistics and parameters. The two being very closely related are often confused and sometimes hard to distinguish.  StatisticsA statistic is merely a portion of a target sample. It refers to the measure of the values calculated from the population.  A parameter is a fixed and unknown numerical value used for describing the entire population. The most commonly used parameters are: Mean Median Mode Mean : The mean is the average or the most common value in a data sample or a population. It is also referred to as the expected value. Formula: Sum of the total number of observations/the number of observations. Experimental data set: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 Calculating mean:  (2 + 4 + 6 + 8 + 10 + 12 + 14 + 16 + 18 + 20)/10 = 110/10  = 11 Median: In statistics, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. It’s the mid-value obtained by arranging the data in increasing order or descending order. Formula:  Let n be the data set (increasing order) When data set is odd: Median = n+1/2th term Case-I: (n is odd) Experimental data set = 1, 2, 3, 4, 5 Median (n = 5) = [(5 +1)/2]th term     = 6/2 term      = 3rd term  Therefore, the median is 3 When data set is even: Median = [n/2th + (n/2 + 1)th] /2 Case-II: (n is even) Experimental data set = 1, 2, 3, 4, 5, 6  Median (n = 6) = [n/2th + (n/2 + 1)th]/2    = ( 6/2th + (6/2 +1)th]/2    = (3rd + 4th)/2     = (3 + 4)/2 = 7/2 = 3.5 Therefore, the median is 3.5 Mode: The mode is the value that appears most often in a set of data or a population. Experimental data set= 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4,4,5, 6 Mode = 3 (Since 3 is the most repeated element in the sequence.) Terms Used to Describe DataWhen working with data, you will need to search, inspect, and characterize them. To understand the data in a tech-savvy and straightforward way, we use a few statistical terms to denote them individually or in groups.  The most frequently used terms used to describe data include data point, quantitative variables, indicator, statistic, time-series data, variable, data aggregation, time series, dataset, and database. Let us define each one of them in brief: Data points: These are the numerical files formed and organized for interpretations. Quantitative variables: These variables present the information in digit form.  Indicator: An indicator explains the action of a community's social-economic surroundings.  Time-series data: The time-series defines the sequential data.  Data aggregation: A group of data points and data set. Database: A group of arranged information for examination and recovery.  Time-series: A set of measures of a variable documented over a specified time. Step-by-Step Statistical Analysis ProcessThe statistical analysis process involves five steps followed one after another. Step 1: Design the study and find the population of the study. Step 2: Collect data as samples. Step 3: Describe the data in the sample. Step 4: Make inferences with the help of samples and calculations Step 5: Take action Data distributionData distribution is an entry that displays entire imaginable readings of data. It shows how frequently a value occurs. Distributed data is always in ascending order, charts, and graphs enabling visibility of measurements and frequencies. The distribution function displaying the density of values of reading is known as the probability density function. Percentiles in data distributionA percentile is the reading in a distribution with a specified percentage of clarifications under it. Let us understand percentiles with the help of an example.  Suppose you have scored 90th percentile on a math test. A basic interpretation is that merely 4-5% of the scores were higher than your scores. Right? The median is 50th percentile because the assumed 50% of the values are higher than the median. Dispersion Dispersion explains the magnitude of distribution readings anticipated for a specific variable and multiple unique statistics like range, variance, and standard deviation. For instance, high values of a data set are widely scattered while small values of data are firmly clustered. Histogram The histogram is a pictorial display that arranges a group of data facts into user detailed ranges. A histogram summarizes a data series into a simple interpreted graphic by obtaining many data facts and combining them into reasonable ranges. It contains a variety of results into columns on the x-axis. The y axis displays percentages of data for each column and is applied to picture data distributions. Bell Curve distribution Bell curve distribution is a pictorial representation of a probability distribution whose fundamental standard deviation obtained from the mean makes the bell, shaped curving. The peak point on the curve symbolizes the maximum likely occasion in a pattern of data. The other possible outcomes are symmetrically dispersed around the mean, making a descending sloping curve on both sides of the peak. The curve breadth is therefore known as the standard deviation. Hypothesis testingHypothesis testing is a process where experts experiment with a theory of a population parameter. It aims to evaluate the credibility of a hypothesis using sample data. The five steps involved in hypothesis testing are:  Identify the no outcome hypothesis.  (A worthless or a no-output hypothesis has no outcome, connection, or dissimilarities amongst many factors.) Identify the alternative hypothesis.  Establish the importance level of the hypothesis.  Estimate the experiment statistic and equivalent P-value. P-value explains the possibility of getting a sample statistic.  Sketch a conclusion to interpret into a report about the alternate hypothesis. Types of variablesA variable is any digit, amount, or feature that is countable or measurable. Simply put, it is a variable characteristic that varies. The six types of variables include the following: Dependent variableA dependent variable has values that vary according to the value of another variable known as the independent variable.  Independent variableAn independent variable on the other side is controllable by experts. Its reports are recorded and equated.  Intervening variableAn intervening variable explicates fundamental relations between variables. Moderator variableA moderator variable upsets the power of the connection between dependent and independent variables.  Control variableA control variable is anything restricted to a research study. The values are constant throughout the experiment. Extraneous variableExtraneous variable refers to the entire variables that are dependent but can upset experimental outcomes. Chi-square testChi-square test records the contrast of a model to actual experimental data. Data is unsystematic, underdone, equally limited, obtained from independent variables, and a sufficient sample. It relates the size of any inconsistencies among the expected outcomes and the actual outcomes, provided with the sample size and the number of variables in the connection. Types of FrequenciesFrequency refers to the number of repetitions of reading in an experiment in a given time. Three types of frequency distribution include the following: Grouped, ungrouped Cumulative, relative Relative cumulative frequency distribution. Features of FrequenciesThe calculation of central tendency and position (median, mean, and mode). The measure of dispersion (range, variance, and standard deviation). Degree of symmetry (skewness). Peakedness (kurtosis). Correlation MatrixThe correlation matrix is a table that shows the correlation coefficients of unique variables. It is a powerful tool that summarises datasets points and picture sequences in the provided data. A correlation matrix includes rows and columns that display variables. Additionally, the correlation matrix exploits in aggregation with other varieties of statistical analysis. Inferential StatisticsInferential statistics use random data samples for demonstration and to create inferences. They are measured when analysis of each individual of a whole group is not likely to happen. Applications of Inferential StatisticsInferential statistics in educational research is not likely to sample the entire population that has summaries. For instance, the aim of an investigation study may be to obtain whether a new method of learning mathematics develops mathematical accomplishment for all students in a class. Marketing organizations: Marketing organizations use inferential statistics to dispute a survey and request inquiries. It is because carrying out surveys for all the individuals about merchandise is not likely. Finance departments: Financial departments apply inferential statistics for expected financial plan and resources expenses, especially when there are several indefinite aspects. However, economists cannot estimate all that use possibility. Economic planning: In economic planning, there are potent methods like index figures, time series investigation, and estimation. Inferential statistics measures national income and its components. It gathers info about revenue, investment, saving, and spending to establish links among them. Key TakeawaysStatistical analysis is the gathering and explanation of data to expose sequences and tendencies.  Two divisions of statistical analysis are statistical and non-statistical analyses. Descriptive and Inferential statistics are the two main categories of statistical analysis. Descriptive statistics describe data, whereas Inferential statistics equate dissimilarities between the sample groups. Statistics aims to teach individuals how to use restricted samples to generate intellectual and precise results for a large group.  Mean, median, and mode are the statistical analysis parameters used to measure central tendency.   Conclusion Statistical analysis is the procedure of gathering and examining data to recognize sequences and trends. It uses random samples of data obtained from a population to demonstrate and create inferences on a group. Inferential statistics applies economic planning with potent methods like index figures, time series investigation, and estimation. Statistical analysis finds its applications in all the major sectors – marketing, finance, economic, operations, and data mining. Statistical analysis aids marketing organizations in disputing a survey and requesting inquiries concerning their merchandise.

Measures of Dispersion: All You Need to Know

By Abhresh Sugandhi

What is Dispersion in StatisticsDispersion in statistics is a way of describing how spread out a set of data is. Dispersion is the state of data getting dispersed, stretched, or spread out in different categories. It involves finding the size of distribution values that are expected from the set of data for the specific variable. The statistical meaning of dispersion is “numeric data that is likely to vary at any instance of average value assumption”.Dispersion of data in Statistics helps one to easily understand the dataset by classifying them into their own specific dispersion criteria like variance, standard deviation, and ranging.Dispersion is a set of measures that helps one to determine the quality of data in an objectively quantifiable manner.The measure of dispersion contains almost the same unit as the quantity being measured. There are many Measures of Dispersion found which help us to get more insights into the data: Range Variance Standard Deviation Skewness IQR Image SourceTypes of Measure of DispersionThe Measure of Dispersion is divided into two main categories and offer ways of measuring the diverse nature of data. It is mainly used in biological statistics. We can easily classify them by checking whether they contain units or not. So as per the above, we can divide the data into two categories which are: Absolute Measure of Dispersion Relative Measure of DispersionAbsolute Measure of DispersionAbsolute Measure of Dispersion is one with units; it has the same unit as the initial dataset. Absolute Measure of Dispersion is expressed in terms of the average of the dispersion quantities like Standard or Mean deviation. The Absolute Measure of Dispersion can be expressed in units such as Rupees, Centimetre, Marks, kilograms, and other quantities that are measured depending on the situation. Types of Absolute Measure of Dispersion: Range: Range is the measure of the difference between the largest and smallest value of the data variability. The range is the simplest form of Measure of Dispersion. Example: 1,2,3,4,5,6,7 Range = Highest value – Lowest value   = ( 7 – 1 ) = 6 Mean (μ): Mean is calculated as the average of the numbers. To calculate the Mean, add all the outcomes and then divide it with the total number of terms. Example: 1,2,3,4,5,6,7,8 Mean = (sum of all the terms / total number of terms)                = (1 + 2 + 3 + 4 + 5 + 6 + 7 + 8) / 8                = 36 / 8                = 4.5 Variance (σ2): In simple terms, the variance can be calculated by obtaining the sum of the squared distance of each term in the distribution from the Mean, and then dividing this by the total number of the terms in the distribution. It basically shows how far a number, for example, a student’s mark in an exam, is from the Mean of the entire class. Formula: (σ2) = ∑ ( X − μ)2 / N Standard Deviation: Standard Deviation can be represented as the square root of Variance. To find the standard deviation of any data, you need to find the variance first. Formula: Standard Deviation = √σ Quartile: Quartiles divide the list of numbers or data into quarters. Quartile Deviation: Quartile Deviation is the measure of the difference between the upper and lower quartile. This measure of deviation is also known as interquartile range. Formula: Interquartile Range: Q3 – Q1. Mean deviation: Mean Deviation is also known as an average deviation; it can be computed using the Mean or Median of the data. Mean deviation is represented as the arithmetic deviation of a different item that follows the central tendency. Formula: As mentioned, the Mean Deviation can be calculated using Mean and Median. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Median: ∑ | X – X1 | / N Relative Measure of DispersionRelative Measures of dispersion are the values without units. A relative measure of dispersion is used to compare the distribution of two or more datasets. The definition of the Relative Measure of Dispersion is the same as the Absolute Measure of Dispersion; the only difference is the measuring quantity. Types of Relative Measure of Dispersion: Relative Measure of Dispersion is the calculation of the co-efficient of Dispersion, where 2 series are compared, which differ widely in their average. The main use of the co-efficient of Dispersion is when 2 series with different measurement units are compared.  1. Co-efficient of Range: it is calculated as the ratio of the difference between the largest and smallest terms of the distribution, to the sum of the largest and smallest terms of the distribution. Formula: L – S / L + S  where L = largest value S= smallest value 2. Co-efficient of Variation: The coefficient of variation is used to compare the 2 data with respect to homogeneity or consistency. Formula: C.V = (σ / X) 100 X = standard deviation  σ = mean 3. Co-efficient of Standard Deviation: The co-efficient of Standard Deviation is the ratio of standard deviation with the mean of the distribution of terms.  Formula: σ = ( √( X – X1)) / (N - 1) Deviation = ( X – X1)  σ = standard deviation  N= total number 4. Co-efficient of Quartile Deviation: The co-efficient of Quartile Deviation is the ratio of the difference between the upper quartile and the lower quartile to the sum of the upper quartile and lower quartile.  Formula: ( Q3 – Q3) / ( Q3 + Q1) Q3 = Upper Quartile  Q1 = Lower Quartile 5. Co-efficient of Mean Deviation: The co-efficient of Mean Deviation can be computed using the mean or median of the data. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Mean: ∑ | X – X1 | / N Why dispersion is important in a statisticThe knowledge of dispersion is vital in the understanding of statistics. It helps to understand concepts like the diversification of the data, how the data is spread, how it is maintained, and maintaining the data over the central value or central tendency. Moreover, dispersion in statistics provides us with a way to get better insights into data distribution. For example, 3 distinct samples can have the same Mean, Median, or Range but completely different levels of variability. How to Calculate DispersionDispersion can be easily calculated using various dispersion measures, which are already mentioned in the types of Measure of Dispersion described above. Before measuring the data, it is important to understand the diversion of the terms and variation. One can use the following method to calculate the dispersion: Mean Standard deviation Variance Quartile deviation For example, let us consider two datasets: Data A:97,98,99,100,101,102,103 Data B: 70,80,90,100,110,120,130 On calculating the mean and median of the two datasets, both have the same value, which is 100. However, the rest of the dispersion measures are totally different as measured by the above methods. The range of B is 10 times higher, for instance. How to represent Dispersion in Statistics Dispersion in Statistics can be represented in the form of graphs and pie-charts. Some of the different ways used include: Dot Plots Box Plots Stems Leaf Plots Example: What is the variance of the values 3,8,6,10,12,9,11,10,12,7? Variation of the values can be calculated using the following formula: (σ2) = ∑ ( X − μ)2 / N (σ2) = 7.36 What is an example of dispersion? One of the examples of dispersion outside the world of statistics is the rainbow- where white light is split into 7 different colours separated via wavelengths. Some statistical ways of measuring it are- Standard deviation Range Mean absolute difference Median absolute deviation Interquartile change Average deviation Conclusion: Dispersion in statistics refers to the measure of variability of data or terms. Such variability may give random measurement errors where some of the instrumental measurements are found to be imprecise. It is a statistical way of describing how the terms are spread out in different data sets. The more sets of values, the more scattered data is found, and it is always directly proportional. This range of values can vary from 5 - 10 values to 1000 - 10,000 values. This spread of data is described by the range of descriptive range of statistics. The dispersion in statistics can be represented using a Dot Plot, Box Plot, and other different ways.

Why Should You Start a Career in Machine Learning?

By Abhresh Sugandhi

If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it. Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques. We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses. This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation. Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment. The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of $112,000. Huge numbers of jobs are expected to be created in the coming years. Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch $20.83B in 2024, according to Forbes. We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising. A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer. Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses. Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.

What Are the Differences Between Pandas Vs Numpy?

By Punit Shah

This blog post covers the two most widely used and discussed libraries of the Python programming language in the context of Data manipulation, Feature engineering and Data wrangling. We will be discussing Pandas and NumPy.By the end of this post, you should have a clear understanding of What are Pandas and NumPy? What are the data objects they offer? What are the features and areas of application for Pandas and NumPy? How to install and use them in Python Relation and differences between Pandas and NumPy What makes them such popular libraries? When to use Pandas and when to use NumPy?Let's get started. What is NumPy? NumPy stands for Numerical Python. NumPy is the most powerful and fundamental open source third party (external) Python library for creating and manipulating numerical objects. It was created by Travis Oliphant in 2005.  Let’s decompose and understand this complicated introduction! It is powerful, as it provides super high performance multi-dimensional homogenous data objects, which are called NumPy Arrays. It is super-fast, because NumPy is partially written in C/ C++ and partially in Python. It leverages the capability of pointer calculations and memory operations of C/C++. It is open source, which makes it possible for us to use it free of cost. We refer to NumPy as fundamental because NumPy provides an easy and effective framework to work with large datasets. NumPy is the base library for many other powerful libraries such Pandas, Matplotlib, Seaborn, TensorFlow, Keras etc. I refer to NumPy as a third party (external) library because it's not part of the standard installation of Python, hence you will have to explicitly install it on your own.NumPy is NOT part of the standard Python installation, however you can easily install the latest version of the NumPy library from the Python repository using PIP (Python utility to manage external libs) as shown below:One of the most fundamental data objects provided by NumPy is Multi-Dimensional Array and it is called ndarray (nd - "N" dimensional) in Python. NumPy also has many built-in operations/functions which operate on ndarray, such as getting random samples, sorting, searching, string operations. It provides a lot of statics around these arrays. How to create Arrays in NumPy? In NumPy, ndarrays or arrays can be created in a few different ways: Array building using user defined values Array building from existing (other) data objects such as list, tuple etc Array building using in-built functions. Array building using user defined values We can create and array with user defined values using the built-in syntax.In the very first line, we are importing the NumPy library and using alias as np for easy access at a later time. In the second line, we are defining array using the built-in function array and passing a list of numbers as the argument. Upon printing we should see the array printed on the screen.Some of the fundamental attributes of a NumPy object are: ndim: It showcases the number of dimensions of the array object. Shape: It returns the size of the array Size: It returns the total number of elements in the NumPy array NumPy provides various built-in stationary functions, which demonstrate meta-data about an array object. We can access any elements of an array using the "index" mechanism. Indexes represent the address or position of elements in an array. In Python, the index position starts from 0.As seen in the above image, accessing an array object with 0 index (enclosed in square bracket) returns 1 (which is the first element of an array). Array building from existing (other) data objects We can choose to create an array from existing data structures such as List or Tuple. As we can see, the built-in function to create an array (np.array) remained the same and only the passed argument has changed. In the first instance, we passed an object of List and in the second instance we passed an object of Tuple. Array building using in-built functions Lastly, we have the option to create an array using alternative or built-in methods. This option provides a great variety of variations to the user.Here, we are creating an array with range of values using built-in function np.arangeWe can also create an array with all elements initialized to either 0 or 1. We can create an array which follows specific data distributions. This is especially helpful in initializing weights in neural networks.Features of NumPy The NumPy library provides tons of features which help users of all backgrounds such as Data Analysts, Data Scientists, Researchers or even novice users to work with large and complex data and also extract meaningful insights out of it. Below is the list of some features provided by NumPy (This is by NO means an exhaustive list)Easy and fast framework for working on homogeneous datasets Helps create data objects with N dimensions Arrays, which are a fundamental unit of data for Machine Learning or Neural Networks Broadcasting or Vectorization of applied operations Robust matrix manipulation methods NumPy is the base package for various other packages such as Matplotlib, Seaborn, Pandas, which makes working with them easier and more efficientWhat is Pandas? Pandas stands for Python Data Analysis Library. It is also an open source and third-party library which is fundamentally used for data manipulation, wrangling and data exploration. Pandas was released in 2008 by Wes McKinney. Pandas provide a framework to read data from multiple sources such as Excel, CSV, JSON, SQL and many more. Fundamentally, Pandas provide two types of data objects:- Pandas Series :- It is a one-dimensional labelled array which can hold heterogenous types of data. The series can be compared to columns in MS-Excel. Pandas DataFrame :- It is a two dimensional, mutable and tabular data structure with labelled axes (rows and columns) DataFrames are generally compared with excel, SQL tables.Individual columns are referred to as Series, and multiple series are collectively called the “DataFrame”. As Pandas is not part of a standard Python installation, we have to externally install it using PIP utility. We can choose to read data from any format from a list of built-in methods in Pandas.As we can see, a DataFrame is created from an existing CSV file and the first few records are printed using built-in functions head. DataFrame objects are accessible from both row and column levels as they are labelled. Pandas provides the below special functions (this list is not exhaustive), which help the user to know data better. info – This method allows the user to access various useful information about data such as:- Number of NULL values in each column Data types of each column Memory size consumed by data. describe – This method generates a 5-point data summary for ONLY numerical columns, which include: - Min Max Count Average Standard Deviation shape – This method returns the number of rows and columns in the DataFrame. isnull(col) – This method helps in determining whether the supplied column has any NULL value or not. Accessing the DataFrame using row or column index becomes easy for an analyst or data scientist, as it allows them to select the subset of data and perform dedicated operations or logic on top of it.Features of Pandas Pandas is THE most widely used package when it comes to data manipulation and data transformation. The availability of built-in functions and support for various user defined operations makes it very easy for users across all groups to prepare their data for downstream tasks. Apart from these above-mentioned features, given below are a few more which contribute to the popularity of Pandas. Representation of data in tabular format. Built-in methods like loc & iloc, allows users to access any subsection of data to apply custom logic or processing. loc – Allows user to select rows/columns based on labels iloc – Allows user to select rows/columns based on integer index positions Support for Group-By clause Support for built-in data visualization Support for apply and lambda functions, which allows users to apply user specific functions to each and every element of the column Built-in functions for identifying and operating on NULL and MISSING values Easy and user-friendly way to join and append different DataFrame objects.Differences between Pandas and NumPyPandasNumPyData ObjectsUsed for creating two-dimensional data objectsUsed for creating “N” dimensional objectsTypes of Data ObjectsPandas creates heterogenous type of objects.NumPy creates homogenous type of objects.External DataPandas objects are created from external data such as CSV, Excel or SQLNumPy generally uses data created by user or built-in functionsApplicationPandas objects are primarily used for data manipulation and data wranglingNumPy objects are used to create matrices or arrays which are used in creating ML or DL modelsData AccessData can be accessed using index positions or index labelsData is accessed using ONLY index positionsOperationsPandas provides special utilities such as groupby, loc, iloc & apply to access and manipulate different subsets of dataNumPy doesn’t provide any such functionalities, however subset can be selected using indexes or conditional formattingSpeedDataFrames are relatively slower than ArrayNumPy arrays are faster than DataFramesUsageCommonly used for holding external user data and performing analysis on it to understand the data wellCommonly used for building components for ML or DL modelsConclusionWe have understood the importance and usage of two of the most widely used packages of Python. We also have understood why these packages are so useful and efficient. In the conclusion I would say, both libraries have their own use, and they cannot be replaced or interchanged. These libraries play fundamental roles in data analyses, understanding, manipulation and preparation for further downstream tasks. If you are dealing with simpler and more homogenous data which requires a lot of mathematical operations, I would suggest that you use NumPy. On the other hand, if you are using data from a client or a similar entity and your end goal is to understand the data, manipulate and transform it, then the clear choice should be Pandas.

Machine Learning Projects for Beginners

By Abhresh Sugandhi

There is probably no one who hasn’t heard of Artificial Intelligence. AI was once compared to the discovery of fire, a discovery which changed human race forever. Similar to fire, AI has permeated every part of our lives and is changing it for the better. Machine learning is a branch of AI; it's all about creating an algorithm, analyzing data, learning from data, processing data, identifying and applying patterns on data with minimal intervention by humans.What is Machine Learning, and why are ML projects interesting? Moving towards the definition of Machine Learning, “Machine Learning is the application or branch of Artificial Intelligence (AI) that is the ability to learn from data, train data, identify patterns, and improve overall user experience. It focuses on developing the computer program which can easily analyze the data.” Machine learning and its projects are fascinating because it involves real-time data analysis, management, and learning data. It helps to solve real-time and human-related problems. It can be said that a machine learning program is the program that writes the other program, and then they write another; this process is continuous and never-ending. As a programmer, you are probably fascinated by its wide category of problem statements and state of the art solutions. It involves image classification, image detection, image recognition, voice recognition, and many other study fields. While dealing with the problem statement, you need to understand the problem, recognize its algorithm, develop the most suitable set of techniques, and apply it to large sets of data with different problems with a little bit of tweaking. When you go for a more practical approach, everything out there becomes more interesting and easier to learn. As a beginner, you should start with some basic projects so that you can brush up your skills and get in-depth knowledge of the required algorithm.Some features of a Machine Learning Project: It exposes you to a large variety of real-world problems of the business. It helps you perform automated data visualization. It provides the best automation tools for processing. It provides user engagement and better relationships. It provides accurate and precise data analytics. More business intelligence and exposure. The easiest way to predict for decision-making and business insights. Some key points to remember before moving toward the machine learning project: To understand machine learning's basic concepts, you can opt for many free or paid courses available online. After developing the concepts, move towards developing the basic level projects. Once you develop an understanding of basic projects and gain complete knowledge about the algorithm and its workflow, move towards intermediate projects. Then move to advanced level projects, where you can develop systems based on machine learning algorithms and techniques.Some projects based on Machine Learning:These small-scale projects will help you create your base and develop an understanding of the fundamentals of machine learning. Before moving towards big datasets, one should be familiar with working with a small dataset and create a graph and learning curve. Wine Quality Test Project: Here, you have to understand the chemical composition of the mixture, how the wine is made, and then you have to apply the machine learning model on the data to obtain the quality of the wine. The data source you can refer to: Wine quality: This dataset is composed of the different qualities of wine and their chemical composition. There are 2 datasets that contain red and white wine data samples from the north of Portugal. Fake News Detection: Social media has contributed to the proliferation of fake news. It is really very hard to understand the quality and correctness of the content present in social media. According to surveys, 3 out of 5 messages in social media are fake. Using this model you can understand the ambiguity of the news present in our world. Fake news is like wildfire, and spreads uncontrollably. The data source you can refer to: Fake news dataset: find out the data present in social media, which is fake and predict data or information that is the legitimate source. Kinetics project: This project identifies human actions and reactions by observing their behavior during activities. This dataset contains 3 different datasets, each of which is kinetic with a different collection of URLs and high-quality images and videos. The data source you can refer to: Kinetics Dataset: This contains about 650,000 video clips with 400-600-700 different classes of human action divided into subclasses, with different data set versions. Top 10 Machine Learning projects for beginnersAny ML project should be interesting, true-to-life, and meaningful. When you try to understand the basics of any technology, you must work on it hands-on to understand and take a deep dive into the subject. Here we will try to cover machine learning projects, which can be a great starting point for you to learn about machine learning, or which can be added to your portfolio of projects to make your resume stand out. Sales Forecasting with Walmart: Walmart is an American multinational retail corporation with hypermarkets, discount department stores, and grocery stores in its chain. Kaggle organizes a challenge for sales forecasting, in which aspiring data scientists can participate. You can find the sample data set on GitHub or from their official site. Sales forecasting data increases day by day and minute by minute, and this is a good place to apply machine learning and data analysis. It is very helpful in practising data visualization, analysis, and exploratory analysis. Data sources you can refer to: Walmart sales forecasting: the dataset available from the “Walmart store sales forecasting” project that was available on Kaggle. It contains weekly sales data for more than 40+ stores and 99 departments over a 3-year period. Kaggle Walmart sales forecasting: Kaggle organizes a challenge where you can participate and help them to organize their dataset and files and apply machine learning on the required dataset. Stock price predictions: The stock market exchange is a candy shop for data scientists who are interested in the finance sector. There are numerous data sets that you can choose from and perform analysis on. You can apply predictions on the prices, fundamentals, value investing, and future forecasting and arbitraging. Data sources you can refer to: Financial and economic data: Here, you can find the free as well as premium data for financial and economic analyses. It provides bulk amounts of data from the federal reserve. Data from US Companies: It has 5+ years of data from the US companies, which contain more than 5000+ records and value edit services. Human Activity Recognition with Smartphones Data: It is a classification problem where the sequence of accelerometer data has been recorded by the specialized harnesses or smart phones into known well-defined movements. For more information on the project and to develop more insights, you can visit the tutorial and then move onto the project. To visit the tutorial, click here. Human Activity Recognition is where you find what the person is doing and trace their activity and perform analysis and exploration of the data set. The data source you can refer to: Human Activity recognition: This will provide you with insights into affordable wearable equipment and portable computing devices. It includes the UCI machine learning repository and dataset.  Kaggle Human Activity Recognition: This contains the record of 30+ study participants, their daily activities and living standards. Investigation on Enron data: It was the largest corporate meltdown in history. In the year 2000, they were called out for fraud. But luckily, for us, their database, which contains 500 thousand emails between employee, senior executive, and customers is still available. Data scientists have been using that data for education and research purposes for years. The data source you can use: Enron Email dataset: This set of data was managed and prepared by the organization known as Cognitive Assistant that Learns and Organizes (CALO), which contains 150 users’ data, maintained in different folders. An off-balance sheet of Enron: it is an assets liability that does not appear on the company’s balance sheet. This sheet contains typically those datasets which do not contain any direct obligation relating to most operating and significant values. Chatbot Intents Dataset: this is a basic machine learning project which you can undertake to develop a better understanding of the libraries and natural language processing. It contains the JSON file structure, which will respond to your chat with a defined pattern and syntax. This is a useful machine learning project for beginners with source code in Python.   The data source you can refer to: JSON Dataset link: this JSON dataset file contains tags like goodbye, greeting, good morning, pharmacy search, and nearby hospital search, etc. Python source code: Chatbots help in business organizations and also in customer communication. Chatbots come under Natural language Processing, which involves Natural Language Understanding and Natural Language Generation. Flickr 30K Dataset: Flickr is a platform that provides an opportunity to upload, organize, and share your photos and videos. Flickr contains a 30k dataset; it has become a standard benchmark for sentence-based image processing. It contains about 158k captions and 244k coreference chains. This is used to create a more accurate model. The data source you can refer to: Flickr image source by Kaggle: this paper contains records from Flickr, which has a 30k image dataset, captions, and co-references. Emojify: (helps in creating your emoji with the help of Python) This performs a mapping operation between facial expressions and emojis. You are required to create a neural network to recognize the facial expression and map it down into the expression. An emoji or avatar indicates a non-verbal cue; these cues are increasing as a part of our chatting and messaging world. It is used to describe your emotion, behavior and mood in your conversation. The data source you can refer to: Emojify dataset: This dataset contains less amount of classification which is the best fit for a beginner; try it if you are at the initial stage of Machine Learning, then move on to the next dataset. ML PROJECT BY KAGGLE: it is used to solve the sentimental classification problem, and has loads of data. You can visit Kaggle to work on the challenge. Mall customer dataset: The mall customer dataset contains all the entries about the customers visiting the mall, their names, age, gender, recommendations, a product they buy, issues they face etc. Using the data's different characteristics, we can gain insights into the data and divide the data into different attributes and group them into different groups, based on their behaviour. The data source you can refer to: Customer dataset: this datasheet contains several sets of data and metadata you can go thorough to understand more about the dataset. Source code: trying to do the project in real-time? Visit the source code for all your references. The code is segmented according to the customer with the Machine Learning model's help. Boston Housing: the most famous and used dataset is the Boston housing dataset; many machine learning tutorials take this dataset as an example dataset. This is used for pattern recognition; it contains 500+ observations with 14 attributes or distribution variables. The common logic behind this project is to predict the new house's cost using the regression model of machine learning. The data source you can refer to: Boston Housing Dataset: The dataset is the natural dataset, which is being collected by the US service and housing management system. MNIST Digit Classification: MNIST stands for Modified National Institute of Standards and Technology; it is the dataset of 60+ thousand grayscale images of handwriting. In this project, you'll be able to recognize the handwriting digits using simple Python and machine learning algorithms. This is very useful in computer vision. As this dataset contains flat and relational data, this data is the best fit for beginners to learn more about the algorithmic strategy.  The data source you can refer to: Digital handwriting recognition: here, you can easily find the pre-requisites for project development. The Machine Learning model is trained using Convolutional Neural Network, best known as CNN's. This data set is the best fit for users dealing with less memory space. Source code: Handwriting recognition: this drive contains the complete source code of the project. ConclusionMachine Learning automates analytical modelling and building decisions. You can opt for different free or premium courses, which help you understand the space and create your projects. Aforementioned are the collection of top machine learning projects available online, which are easy to use and develop. The project contains complete guidelines you can refer to. This will help you to learn new algorithms and master your machine learning skills. If you want to gain expertise, dive into the concept and figure out how the module works. Machine learning is the future and if you have set yourself up for a career in this space then building a solid resume with a project portfolio is the right way to go about it.

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