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Take a Deep Look on How Machine Learning Boosts Business Growth!

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Take a Deep Look on How Machine Learning Boosts Business Growth!

Machine Learning is the technology of the future and the rise of it is, well, shocking! Numerous businesses have already started adopting Machine Learning into their business strategy which is ultimately culminating towards their growth. You can also get the most of Machine Learning by going for the best Machine Learning course in India without wasting hours on the internet.

This new and improving technology is showing marked results in making a particular business more efficient, enhancing customer relationships and driving more sales than ever. You can get right on to Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows and learn about Machine Learning and its rising curve.

Here we have decided to discuss in details about the ways how Machine Learning is helping business touch great heights:

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Natural Language

One of the major setbacks in the industry of computer science was the inability of computers to comprehend our natural language or the way we speak in our everyday life. This is slowly changing with the rapid growth and considerable research and development on Machine Learning. 

It looks like we have come a long way from the crude search terms that we used to generate the results that we wanted. The AI-driven programs of now, with the help of Machine Learning, can figure out the essence of our conversations and also capitalizing largely on the nuances of our language. Most importantly, they learn from past experiences, which is highly progressive.

Logistics

The retail industry and that of logistics are largely relying on Python for Data Analysis and this in turn, is making them future-proof.

Retail giants like Amazon are encouraging the use of Machine Learning to sharpen the efficiency of their company with new features and technology like “anticipatory shopping” protocol. Retail analytics using Python is becoming formidable.

Even in the field of logistics, the inclusion of Machine Learning is proving a boon!

Manufacturing Industry

Innumerable manufacturing companies are adopting the budding technology of Machine Learning and utilizing it in almost every stage of production, simply because the AI-driven technology reduces unnecessary expenses. 

Companies like Seebo, are taking up Python seriously to build accurate data analytics software. Moreover, machine learning is estimated to cut down on the delivery times by 30% and surprisingly save fuel by 12%. According to the reports, the programs fed on AI would even reduce the maintenance costs by 20 – 30%.

Deep Learning and AI using Python

Consumer Data

We have already seen a world of data collection which has been on a rise for years. Now, finally, with the rise of machine learning, the companies are looking forward to making some use of all these data that they have accumulated. In the coming years, we will see AI improving powered by Machine Learning to make the world productive and smart all the more.

You can take a look at A DISCUSSION ABOUT ARTIFICIAL INTELLIGENCE: KNOWING AI CLOSELY if you are interested in AI. Stay glued to our website for more updates and information from the world of technology!

 

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Calculating the Standard Deviation Using R & Python

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Calculating the Standard Deviation Using R & Python

When it comes to summarizing the data, standard deviation (σ) is the value which tells us about the spread of the data. More specifically, it gives information about the dispersion of each observation from the mean of the data. Now, if you are interested in understanding Mean and knowing how to calculate it, then we have shown you in CALCULATING GEOMETRIC MEAN USING R AND PYTHON And APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

Thus, in essence standard deviation gives us valuable information about the robustness of the mean. The deviation is in both positive and negative direction of the mean.

Therefore, it is desirable for the standard deviation to be a low value in comparison to the mean. This would indicate a smaller spread.

Mathematically speaking, standard deviation is known as the second moment about Mean. Variance is standard deviation squared. The variance does not have any mathematical significance on its own. Think of the variance as a mere mathematical maneuver.

The formula for the Variance is:

Application:

An investor wants to calculate the Standard Deviation experience by his investment portfolio in last 12 months (Year 2017-2018).  The returns are:-

Month (Year 2017-18)

Returns (%)

April

12%

May

10%

June

-8%

July

4%

August

12.25%

September

18%

October

13%

November

-9%

December

-4%

January

3%

February

9%

March

11.05%

Calculate Standard Deviation in R:

Examining the Standard Deviation of the investment portfolio returns of a year in R, we get the deviation = 8.803533 or, 8.81% (Approx).

Calculate Standard Deviation in Python:

First, create a Data Frame in Python.

Now, calculate Standard Deviation of the returns,

Examining the Standard Deviation of the investment portfolio returns of a year in Python, we get the deviation = 8.803533209439092 or, 8.81% (Approx)

Standard Deviation is a key part of calculating margins of errors.

Standard deviation shows the variation from the mean. A low standard deviation indicates that the observations (series of number) are very close to the mean. A high standard deviation indicates that the observations (series of numbers) are spread out over a large range.

In this data the mean of the returns is 5.95%, and standard deviation is 8.81% which is close to the mean. So, the deviation of the data is low.

Thus, the investor now knows that the returns of his portfolio fluctuate by approximately 8.81% month-over-month. The information can be used to modify the portfolio to better the investor’s attitude towards risk. If the investor is risk-loving and is comfortable with investing in higher-risk, higher-return securities and can tolerate a higher standard deviation, he/she may consider adding in some small-cap stocks or high-yield bonds. Conversely, an investor who is more risk-averse may not be comfortable with this standard deviation and would want to add in safer investments such as large-cap stocks or mutual funds.

Endnotes

This article will surely help you to figure out the standard deviation with R and Python. However, if you want to have a general idea about Central tendency, about Mean, Median and Mode, then go through our blog on STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY.

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Application of Median Using R And Python: Calculating Median On the Go

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Application of Median Using R And Python: Calculating Median On the Go

This blog is in continuation of STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY and takes you through a comprehensive way to calculate the Median in R and Python.

The term ‘Median’ is derived from the Latin word – ‘Medius’ means the center of something. In mathematics, Median is treated is that unique observation which would divide your data set into two equal halves.

If you are still unclear about Mean and/or seeking easier ways to calculate Mean using R & Python, then check APPLICATION OF HARMONIC MEAN USING R AND PYTHON and CALCULATING GEOMETRIC MEAN USING R AND PYTHON.

Median is special because unlike its rival, the Mean, Median is not ridiculed by the curse of extreme values. To illustrate the curse of extreme values, we bring you the following example:

Imagine I had the following data about the average annual salaries:

In Lacs

8.5
9
11
7
8
8.5
36

The mean of the above data set is: 88/7 = 12.57 lacs.

Whereas, to get the median we would have to first arrange the data into ascending order and look for the midpoint of my data i.e.,(1/2 + n/2)th observation. Where “n” is the number of observations.

The median would then be:

7
8
8.5
8.5
9
11
36

Median is the 4th observation, which is 8.5 lacs.

Looking at the mean and median, it would be fair to conclude that median is the better choice to accurate summarizing the data set whenever extreme values are present. However, this may be a crude generalization which should be taken with a pinch of salt. Despite its flaws, the mean still has statistical properties used in predictive analytics which the median lacks.

Application:

A construction company gave wages to their 10 labor (Let name A to J)  as a weekly basis, the wages are 2000, 2100, 1900, 2150, 2500, 2450, 1800, 2600, 2200, 2300. Compute the Median wages of the construction company.

Sr.NoLaborsWages (Weekly)
1A2000
2B2100
3C1900
4D2150
5E2500
6F2450
7G1800
8H2600
9I2200
10J2300

Calculation Median in R:

Python Certification

The Median wage is 2175, calculate in R.

Calculate Median in Python:

Create a data frame of the data in Python.

R Programming Certification

Now, calculate Median in Python.

R Programming Certification

The Median wage is 2175, calculated in R.

This concludes the post. If you have any queries with regards to this post, you can reach us at Dexlab Analytics. Furthermore, you can also look up for interesting and quality courses of R Programming Certification, Python Certification. Also, you can enroll with us for our combined courses of Data Science with Python Certification, Deep Learning and AI using Python, among others. So, hurry up and grab the best course!

 

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Application of Mode using R and Python

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Application of Mode using R and Python

Mode, for a given set of observations, is that value of the variable, where the variable occurs with the maximum or the highest frequency.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will elucidate the Mode and its application using Python and R.

Mode is the most typical or prevalent value, and at times, represents the true characteristics of the distribution as a measure of central tendency.

Application:

The numbers of the telephone calls received in 245 successive one minute intervals at an exchange are shown in the following frequency distribution table:

 

No of Calls
Frequency
0
14
1
21
2
25
3
43
4
51
5
40
6
51
7
51
8
39
9
12
Total
245

 

 [Note: Here we assume total=245 when we calculate Mean from the same data]

Evaluate the Mode from the data.

Evaluate the Mode from the data

Calculate Mode in R:

Calculate mode in R from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Calculate Median in Python:

First, make a data frame for the data.

Now, calculate the mode from the data frame.

Calculate mode in Python from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Mode is used in business, because it is most likely to occur. Meteorological forecasts are, in fact, based on mode calculations.

The modal wage of a group of the workers is the wages which the largest numbers of workers receive, and as such, this wage may be considered as the representative wage of the group.

In this particular data set we use the mode function to know the occurrence of the highest number of phone calls.

It will thus, help the Telephone Exchange to analyze their data flawlessly.

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Note – As you have already gone through this post, now, if you are interested to know about the Harmonic Mean, you can check our post on the APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

Dexlab Analytics is a formidable institute for Deep learning for computer vision with Python. Here, you would also find more information about courses in Python, Deep Learning, Machine Learning, and Neural Networks which will come with proper certification at the end.

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Application of Harmonic Mean using R and Python

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Application of Harmonic Mean using R and Python

Harmonic mean, for a set of observations is the number of observations divided by the sum of the reciprocals of the values and it cannot be defined if some of the values are zero.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will discover Harmonic mean and its application using Python and R.

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Application:

A milk company sold milk at the rates of 10,16.5,5,13.07,15.23,14.56,12.5,12,30,32, 15.5, 16 rupees per liter in twelve different months (January-December), If an equal amount of money is spent on milk by a family in the ten months. Calculate the average price in rupees per month.

Table for the problem:

Month

Rates (Rupees/Liter)

January

10

February

16.5

March

5

April

13.07

May

15.23

June

14.56

July

12.5

August

12

September

30

October

32

November

15.5

December

16

Calculate Harmonic Mean in R:-

So, the average rate of the milk in rupees/liter is 12.95349 = 13 Rs/liter (Approx)

We get this answer from the Harmonic Mean, calculated in R.

Calculate Harmonic Mean in Python:-

First, make a data frame of the available data in Python.

Now, calculate the Harmonic mean from the following data frame.

So, the average rate of the milk in rupees/liter is 12.953491609077956 = 13 Rs/Liter (Approx)

We get this answer from Harmonic mean, calculated in Python.

Summing it Up:

In this data, we have a few large values which are putting an effect on the average value, if we calculate the average in Arithmetic mean, but in Harmonic mean, we get a perfect average from the data, and also for calculating the average rate.

Use of Harmonic mean is very limited. Harmonic mean gives the largest value to the smallest item and smallest value to the largest item.

Where there are a few extremely large or small values, Harmonic mean is preferable to Arithmetic mean as an average.

The Harmonic mean is mainly useful in averages involving time, rate & price.

Deep Learning and AI using Python

Note – If you want to learn the calculation of Geometric Mean, you can check our post on CALCULATING GEOMETRIC MEAN USING R AND PYTHON.

Dexlab Analytics is a peerless institute for Python Certification Training in Delhi. Therefore, for tailor-made courses in Python, Deep Learning, Machine Learning, Neural Networks, reach us ASAP!

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A Deeper Understanding of Deep Learning

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A Deeper Understanding of Deep Learning

To define Deep Learning, it can be summed up as a machine learning technique used to teach computers all those things which comes to humans quite instinctively. This is a sub-classification of the umbrella term Machine Learning and is based on artificial neural networks.

The technology of driver-less cars, of computers with the knowledge of lampposts and trees as non-living entities and with their discretion of differentiating between a pedestrian and a lamppost, all are being developed from Deep Learning. Besides, the voice assistant you find nowadays, that comes with the smartphones, tablets, TVs and hands-free electronic gadgets, everything is matured by Deep Learning.

Deep Learning is an immensely effective technique with huge prospective. Thus, Deep Learning is a highly regarded technology and more and more people are looking forward to finding their career in it.

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Deep Learning: The Path of Success

Among the ever-changing technologies, Deep Learning has its path paved to stand strong in the long run. Now, this is possible primarily because of the high accuracy levels that it has reached.

Pin-pointed Accuracy

With the convincing accuracy levels reached, Deep Learning is believed to be steadfast in situations which involves high risks and which calls for the least margin of errors. For example – driver-less cars.

Extensive Library

If you aim Deep Learning for computer vision with Python, you should be ready with enormous information that it can go through and process quite effortlessly, hence, putting forth an all-inclusive library to be used in real-time. For instance, millions of images, days of video and data should be fed to the system going forward to develop the technology of the driverless car.

Powerful Computing

If we talk about the power that Deep Learning needs, it is astonishingly unreal, the amount of power that this technology expects to perform in its optimum. None other than immensely powerful GPUs are used to get the best results.

As Deep Learning is quite a new thing, unknown in most of its dimensions, here are a few of the fields which have already absorbed or are trying to infuse Deep Learning in constructively.

  • Automobiles – As we have already mentioned that the automobile industry has already taken Deep Learning quite seriously and is effective moving in the direction, where, soon we would witness cars without any human drivers.
  • Defence and Aerospace – Deep learning is constantly taken into account when determining the objects that the satellites bring us. Via Deep Learning we can be sure of the areas/objects in the space. Furthermore, whether a particular zone is fit for the soldiers or not, can also be easily determined by Deep Learning.
  • Pharmacy – Deep Learning is highly significant even in modern medical science. For example, this technology is used to detect cancerous cells.

Deep Learning and AI using Python

With these being said, Deep Learning is simply superb in how it has performed still and the promise that it is showing to be on par with the age. Therefore, if you are seeking for the Deep learning for computer vision course, you can simply avail of Deep Learning for computer vision Training Center in Delhi NCR.

 

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Python is the Leader in Data Science: Know Why

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Python is the Leader in Data Science: Know Why

From being simple and effective to being updated and thereby, solving almost everything that the booming industry of Data Science of today can look up to, Python boasts of it all.

It’s not a shock that Python is finding its uses in an array of industries. It is, in fact, the language that the Data Scientists rely on. Thus, our tailored courses of Python Certification Training in Delhi would be helpful for all in this digital age.

Let’s see some more of the advantages for which Python stands distinguished among the other programming languages:

Handling Data without a Hassle

The field of Data Science is entrusted with the handling of incredibly large amounts of data which is found to be intricate to compute. However, with Python, it is now simpler than ever. Any of the other high-level programming languages would make it rather difficult and messy compared to the peerless Python, if we talk about analytical and quantitative computing.

Open Source Programming Language

Python is an open-source programming language. Wonder why this programming language is the most preferred still?

It truly opens a whole lot of opportunities that the language can build upon, being open-source in nature. Furthermore, there is not a single restriction regarding Python. Thus, you can be as creative as you wish on this programming language.

It is Powerful and Easy to Use

Python is an easy language right from the start for which it has become so popular. Any of the beginners with just the rudimentary knowledge can start fine with Python. Besides, once you are on with this programming language, you can start progressing with it day by day at your own pace.

The implementation of the code has a slower approach in the languages: Java, C and C#, but if you try Python, you would discover that it is fast to debug and effective to perform. The prompt results in coding would aid with an added boost in your work.

In the Library of Python

Python is an all-absorbing language that even supports the cutting edge technologies of Machine Learning and Artificial Intelligence. And on top of it, Python also offers its users a colossal database of libraries. Therefore, you can simply check in the libraries, import them and then implement all of them in your day to day coding.

It is Highly Scalable

In the parameter of scalability, Python superbly stands out. The programming languages: R and Java certainly falls short in this factor. Thus, with the ease of scalability and quicker turnaround times, data scientists and nearly all of the organisations exploring Data Science, are choosing Python over any other existing languages.

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It is Peerless in Visualisation and Graphics

As the smooth rendering of quality graphics and visualisation is the demand of the age, Python fits in quite comfortably here. With an exhaustive range of options for visualisation, which are simple and efficient, the world of Data Science is rooting for Python.

With all the benefits that you can reap, Python for data analysis is a must, if you want to be absorbed in the industry of Data Science.


.

Calculating Geometric Mean Using R and Python

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Calculating Geometric Mean Using R and Python

In this blog, we are going to discuss the Geometric Mean and its application using Python and R.

Geometric Mean of group of ‘n’ observations is the nth root of their product. It is defined only when all observations have the same sign and none of them is zero.

Application:

Calculate the Geometric Mean of the salary increment of 12 employees. From the following table, calculate the average salary increment of the year (2019-2020):-

 

Name

Salary Increment in

Percentage (%)

Ritesh

10.09%

Heena

15.45%

Kritika

9%

Anuradha

13.06%

Gaurav

20%

Prakash

14%

Aarti

16%

Meena

6.25%

Utkarsh

12.85%

Chirag

10%

Neha

18%

Smrita

21.36%

 

Calculate the Geometric Mean in R:

So, from the data of the employee’s in R we calculate the G.M. and get that the average salary increment in the year (2019-2020) = 13.17618 or 13.18% (approx).

Calculate the Geometric Mean in Python:

First, make a data frame in Python from the following table.

Now, calculate the Geometric Mean from the data-frame.

So, from the data of the employee’s in Python we calculate the G.M. and get that the average salary increment in the year (2019-2020) = 13.176183416401196 or 13.18% (approx).

We use Geometric Mean for calculating ratios, rates and percentages. And it is not affected by the extreme value or outlier. In this particular problem, we use Geometric Mean because an average of the salary increment of the employee’s not affected by the extreme highest or extreme lowest value, that’s why the salary increment rates of Meena and Smrita do not have any effect on the total average rate.

Geometric Mean gives small value than Arithmetic Mean.

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Note: This is a continuation of the blog: Statistical Application in R & Python: Chapter 1 – Measure of Central Tendency. It would be better to go through the first installment and then read this one. More blogs are to be followed, so stay tuned.

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Statistical Application In R & Python: Chapter 1 – Measure Of Central Tendency

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Statistical Applcation In R & Python: Chapter 1 – Measure Of Central Tendency

Statistical analysis helps explore data relationship and develop high-end models to frame better decisions. It’s an intricate process of collecting and evaluating data to define the nature of data that has to be analyzed.

Below, we dig into the basics of statistical application in R and Python using the measure of central tendency.

  • Introduction:-

As body methods for the study of numerical data, if some rows or columns are too long, in such cases, it becomes necessary to summarize data in an easily manageable form. The purpose is to serve by classifying the data in the form of frequency distribution and various graphs. When data relate to a variable, the process of summarization can be taken a step further by using certain descriptive measures. The dim is to focus on certain features that are central frequency and description.

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  • Central Tendency :

In a set of data, they have a tendency, notwithstanding their variability, to cluster-around a central value and the tendency of the quantitative statistical observations is called central tendency.

The three measures of the central tendency are commonly used is:-

  • Mean
  • Median
  • Mode

The description of these 3 estimators start below:-

  • Mean:-

Mean is the average of central tendency and is the most commonly used measures.

The concept of mean is divided into three parts:-

  • Arithmetic mean.
  • Geometric mean.
  • Harmonic mean.

Mainly the mean refers to an arithmetic mean.

  • Arithmetic Mean (A.M.):-

The arithmetic mean of a set of observations is defined to be their sum, divided by the number of observations.

For n numbers of observation (x1,x2,… ,xn )

  • Weighted A.M.

For frequency distribution where  have  frequencies. (i=1,2,3…)

  • Application of A.M.:-

Let’s, calculate the mean of Age, Height & Weight from the given data.

NameSexAgeHeightWeight
RiteshM246.9112.5
HeenaF235.6584
KritikaF236.5398
AnuradhaF246.28102.5
GauravM246.35102.5
PrakashM225.7383
AartiF225.9884.5
MeenaF256.25112.5
UtkarshM236.2584
ChiragM225.999.5
NehaF215.1350.5
SmritaF246.4390

Calculating Mean in Python:

Therefore,

Age (Mean) = 23.08333333, Height (Mean) = 6.12, weight(Mean) = 85.625

Calculating Mean in R:

  • Application of Weighted A.M.:-

The weighted mean is denoted that the mean with frequency.

Data to solve:

Calculate the average price per ton of coal purchased by the industry for the half-year.

Month

Price Per TonTons Purchased

January

Rs. 52.4926

February

Rs. 62.2334
MarchRs. 87.26

40

AprilRs. 45.25

54

MayRs. 78.56

13

June

Rs. 69.25

45

Data to solve:

Month

Price (Rs)

Per Ton

(x)

Tons

Purchased

(f)

fx=y

(Main Data)

January

 52.49261364.74

February

 62.2334

2115.82

March

 87.2640

3490.4

April

45.2554

2443.5

May

 78.5613

1021.28

June

69.2545

3116.25

Total395.04N=212

13551.99

 

The price is denoted as x (52.49, 62.23, 87.26, 45.25, 78.56, 69.25 [in Rs.])=395.04

The amount of purchased (frequency) is denoted by f (26, 34, 40, 54, 13, 45) = 212 (N)

Then multiply the x and f and we get the total amount which is denoted by y, fx(y) = 13551.99

Calculate Weighted Mean in R:

Calculate Weighted Mean in Python:

To calculate the weighted mean from R & Python we get the same result = 63.9244811.

Want to know more about the nature of data? Keen to perform high-end statistical analysis using Python and R? Follow DexLab Analytics, an excellent Python training center in Gurgaon, India. Our team of consultants will help you learn the basics of R and Python in the easiest manner possible.

 

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