How AI is changing the daily life

Artificial Intelligence (AI) is changing the current era in numerous ways, as it has the potential to revolutionize various fields of human life. Here are some of the ways AI is changing the current era:

1. Automation: AI has the potential to automate many routine tasks, which can improve efficiency and reduce errors. This can lead to significant cost savings for businesses and increase productivity for individuals.
2. Personalization: AI can personalize services and experiences for individuals by analyzing their data and behavior. This can lead to a better customer experience, increased loyalty, and improved satisfaction.
3. Healthcare: AI can help in early disease detection, personalized treatments, and drug development. AI can also improve patient outcomes, reduce costs, and improve the overall healthcare system's efficiency.
4. Finance: AI can analyze financial data, detect fraud, and provide financial recommendations. It can also automate financial services and provide personalized investment advice.
5. Education: AI can personalize learning experiences for students, provide intelligent tutoring, and improve the overall quality of education.
6. Transportation: AI can improve traffic flow, reduce accidents, and optimize transportation routes. It can also improve the efficiency of transportation systems and reduce travel times.
7. Environment: AI can help in predicting and mitigating natural disasters, monitoring climate change, and optimizing resource usage. It can also help in reducing waste and improving energy efficiency.

Overall, AI has the potential to transform various aspects of human life, making it more efficient, personalized, and sustainable. However, it is essential to ensure that the development and deployment of AI are done responsibly and ethically to avoid any negative impacts. 

MI vs PBKS HIGHLIGHTS

 

MI vs PBKS HIGHLIGHTS, IPL 2021: Hardik, Pollard steer Mumbai to first win in phase 2 vs Punjab.

PBKS vs MI HIGHLIGHTS, IPL 2021: Get the live IPL score updates of today's IPL match between Punjab Kings and Mumbai Indians from Abu Dhabi.

COMMENTARY

KL Rahul, losing captain: "Good fight from the bowlers, but we didn't score enough. This was the best deck we have played on. We should've got 170. The next three games will be really interesting for us. We haven't been able to handle the pressure over the tournament. Take it one game at a time will be the chat. We will also try to enjoy the games and not worry about the results. All the UAE games for us have gone to the wire."

 Points Table watch: IPL 2021 Points Table: MI beats PBKS, fifth on points table  

Mumbai stays alive in the playoffs race as it earns it first two points in the second-leg. The MI contingent is elated as they run onto the field in jubilation.

MI 131/4 in 19 overs: Shami continues to dish it short to Hardik and this time he gets off the firing zone with a dazzling stroke behind point. He dances down and move to his right and cuts it with poise to the boundary.  Hardik drags one to the on-side and finds another boundary. 5 to win off 7. Hardik slogs and the long-on fielder slips it through his hands for six! Mumbai races home with an over to spare! Anti-climax for Shami after a fine effort to start off. Hardik's final flourish was too good for the fielders in the deep and MI and the Indian managament will heave a sigh of relief tonight.

MI needs 16 in 2 overs. Shami's back on.

MI 120/4 in 18 overs: Arshdeep begins with three wide yorkers to Hardik conceding just a single. Pollard blitz to follow as he cashes in on two loose balls. A wide low full toss and Pollard's astounding reach helps him to cream it through extra cover for four. Arshdeep hits the slot and Pollard smokes it for six over long-off. The equation takes a massive slide in MI's favour.

MI needs 29 in 18 balls

MI 107/4 in 17 overs: Smoked by Hardik! Shami starts with a nagging bouncer that rises to hit Pandya on the chest. He balls short again and Hardik swats it to midwicket for four. Shami strays to the pads next and Pandya absolutely wallops it over midwicket for a splendid maximum. Glimpses of his very best there! Shami does well to keep him in check with a couple of short stuff outside the off-stump.

Shami is back

40 in 24 balls. Pollard and Pandya at the crease. Surely, MI to take this home, isn't it folks?

MI 96/4 in 16 overs: Ellis to Tiwary. OUT! Sheepish dismissal for Tiwary!  A slower one well outside his reach on the off-side and Tiwary wafts his blade at it. The ball takes a faint edge and KL Rahul tumbles forward to grab the ball. A fine knock comes to an end. Saurabh Tiwary c †Rahul b Ellis 45 (37b 3x4 2x6)

MI 92/3 in 15 overs: Cracking six from Tiwary! Bishnoi fends into the slot and Tiwary sits on the crease and slogs it handsomely over midwicket for six. Mumbai is picking up the pace and things slip away once again from Punjab's grip.

MI 84/3 in 14 overs: Hardik and Tiwary are DROPPED in the same over! Arshdeep induces the leading-edge as Pandya attempts to slice through point. The ball lobs up and brushes the point fielder's fingers before slipping away. Tiwary gets a reprieve as he pops one straight to Arshdeep off a straight ball. Arshdeep extends his arms but fails to latch onto it.

MI 78/3 in 13 overs: Brilliant use of his variations as Ellis builds the pressure on the batters. Ellis hits the deck hard and Hardik fails to put it past the inner-circle.  Ellis follows up with a slower-one and Hardik does well to keep it a bit mellow. MI needs 58 runs in 42 balls.

Ellis is back

MI 75/3 in 12 overs: Arshdeep clogs the over with dot balls and puts Tiwary in further misery. Arshdeep hits the blockhole and Tiwary steps out to tuck it back to him. Arshdeep collects the ball and throws it back straight to Tiwary's groin, forcing a break. Tiwary's up and responds in style. He leans into the drive with ease and smashes a fine boundary.

MI needs 68 in 54 balls. Arshdeep returns

MI 68/3 in 11 overs: Nervy start for Hardik as Bishnoi skims past the outside-edge in succession after the first ball gives him a massive scare. The ball clips the inside-edge off the googly and runs past short fine leg for four.

Bishnoi replaces Brar

MI 62/3 in 10 overs: Shami to QDK, OUT! Inside-edge onto the stumps! KL's man delivers right on time. de Kock never got going tonight and not even a freebie hammered to midwicket for four off the previous helps him. Shami draws a similar heave next ball but this time the length was a touch fuller. The ball springs up and clips the inside-edge and jags back to the stumps. Hardik's the new man at 5. Quinton de Kock b Mohammed Shami 27 (29b 2x4)

Shami is back

MI 54/2 in 9 overs: Fifty up for MI with a cracking six from Tiwary! Brar wafts one in from over the wicket and Tiwary dances the wicket to toss it over long-on for a massive six. 94 metres and MI's slowly gaining back steam in the chase here.

Brar into the attack

MI 44/2 in 8 overs: High into the sky and Markram drops it at long off. de Kock attempts for a lofted stroke over the boundary but mistimes it.  Markram makes quick ground to his right and dives to grab the ball but misses it as he tumbles to the floor. Talk about the woes, the ball skims to the boundary for four!

Bishnoi is back

MI 35/2 in 7 overs: Rahul continues to operate with Markram who does a fine job. QDK and Tiwary resort to singles. Punjab deftly mounting pressure on the batters.

MI 30/2 in 6 overs: Tiwary's off the blocks as he finds the fine leg boundary with a clip off the pads. Powerplay done and dusted and MI is teetering a bit here. Meanwhile, Bishnoi is off the field after a cut on his finger.

Nathan Ellis into the attack

MI 25/2 in 5 overs: de Kock finds the boundary after an eternity. Markram keeps it tight through the over before tossing one wide of off. de Kock slices it through the off-side and beats the point fielder for four.

Not a happy sight for MI and India.   -  SPORTZPICS

 

Markram's back

MI 18/2 in 4 overs: Bishnoi to Rohit. OUT! Easy for the mid-on fielder as Rohit falls to leg-spin, once again! The leg-break is drifted in and Rohit slams against the spin and perish. SKY, the new man. BOWLED! Bishnoi with a beauty to smash the stumps! A snorter to beat the loose defences of Suryakumar and Bishnoi is on a roll. The odds are tilting once again and Punjab's having a good start here.  Rohit Sharma c Mandeep Singh b Ravi Bishnoi 8 (10b 1x4); Suryakumar Yadav b Ravi Bishnoi 0 (1b)

Bishnoi replaces Shami

MI 14/0 in 3 overs: de Kock checks his drive as Arshdeep his the deck and finds two runs. A bit iffy on the drive there as Arshdeep tails the ball in to folllow. de Kock struggles to churn out the runs as he is cramped for room with the ball following his bat swing. Excellent start for Arshdeep, just two runs from it!

Arshdeep into the attack

MI 12/0 in 2 overs: Shami tails one in and it passes close to Rohit's sliding blade. Rahul goes up in a loud shout but the umpire isn't interested. Rohit follows up with a fierce drive through square for a beautiful boundary. Shami continues to attack Rohit down leg as another ball brushes the pads and Rahul's dive prevents four runs.

Mohammed Shami from the opposite end

MI 6/0 in 1 over: Rohit off the mark with a single to midwicket. Markram slides one in to QDK who finds a couple of runs by opening the face of the bat towards point. Decent start with the ball as the MI openers keep their anchors down.

Surprise, surprise. Aiden Markram to begin with the new ball. Rohit on strike.

Back for the chase. Rohit Sharma and Quinton de Kock walk in for Mumbai. KL Rahul's men are in their positions.

An evening where all of Rohit's plans fell on the spot. Rohit, known for encouraging match-ups began with Krunal in the Powerplay against KL Rahul and Mandeep Singh who have struggled against him.  Pollard followed soon after with a lone over where he dismissed Gayle and Rahul and Punjab had already fallen into a point of return. Markram and Hooda stitched a deft fightback with a solid partnership to squeeze Punjab past 100 before it again stooped to 135. Mumbai should be confident of chasing this down and return to winning ways.

PBKS 135/6  in 20 overs: Excellent finish for Mumbai as NCN's accuracy does the job. Four yorkers in the over as the batter resort to singles. He misses the yorker off the final ball and Ellis hits the ball to long-on to finish with two more runs on the board.

PBKS 127/6 in 19 overs: Bumrah to Hooda. OUT! Skier and Pollard holds onto it easily at long-on! Succumbs to the pressure. Brar misses a few deliveries off Bumrah before Hooda retains strike. He flashes hard at a fuller one but the ball takes the toe edge and flies into the sky. Deepak Hooda c Pollard b Bumrah 28 (26b 1x4 1x6)

PBKS 122/5 in 18 overs: Superb from Coulter-Nile! He begins with the sharp one off the deck and rolls in a cutter to Hooda. NCN fires in a swinging yorker and Hooda just about keeps it off the stumps. Brar and Hooda struggle to up the ante.

NCN returns

PBKS 118/5 in 17 overs: Bumrah mixes up the length but fires the ball off the same pace. Brar works a couple to the off-side and sneaks in a couple of runs. Bumrah finishes with a fine bouncer that moves away off the length.

Bumrah's back

PBKS 112/5 in 16 overs: Chahar to Markram. OUT! Cleaned him up! Too full to play a sweep as Chahar yorks Markram out. A lapse in judgement and Punjab's back on square one.  Aiden Markram b Chahar 42 (29b 6x4)

PBKS 105/4 in 15 overs: Beautiful shotmaking from Markram! Boult angles the ball away on the full and Markram slams him to the left of the deep point fielder for four. He reduces the length by a tinge and Markram cuts it to the right of point for another boundary. Hooda joins the act as Boult's shorter ball is flaked over backward point for the third four in the over. Excellent revival this for Punjab.

Boult to resume

PBKS 90/4 in 14 overs: Chahar slides one in full towards the off-stump. Markram steps out to his right and paddles the ball fine for two runs. Chahar lures him into a false drive once again as he floats one in full outside off. The thickish-edge skims the ball towards third man and the batters continue to rotate strike with ease. Timeout to follow.

PBKS 83/4 in 13 overs: Hooda milks a couple of twos off Krunal. Excellent running from the pair. Markram follows up with a couple of singles too as Punjab continues to crawl towards the three-figure mark.

Krunal to bowl out

PBKS 75/4 in 12 0vers: Chahar keeps it tucked in straight. Hooda and Markram tread cautiously and rotate strike through the over with six singles.

PBKS 69/4 in 11 overs: Hooda slams Boult for a biggie! Boult aims for a bumper on length but Hooda sets up well for it. He pulls with disdain and sends the ball way over midwicket for six. Boult follows up with a fiery delivery into the blockhole to finish.

Boult replaces Chahar

PBKS 62/4 in 10 overs: NCN goes full and Markram lashes one through cover and gets a fierce boundary. He hits the deck hard and Hooda fails to get his timing right. Just the five runs and the pressure continues to mount on Punjab.

NCN returns

PBKS 57/4 in 9 overs: Tossed up from Chahar and Markram slams one beside cover for four. A crucial partnership this for Punjab. Not a lot of batting to follow and the batters must also keep the scoreboard ticking. The run-rate's taken a huge crash.

Rahul Chahar into the attack

PBKS 50/4 in 8 overs: Bumrah to Pooran. OUT! Fired into the blockhole and slams the pads. Pooran's a goner as the umpire raises his finger straightaway. Seething pace from Bumrah as Hooda gets a fast bowler's welcome with a fiery bumper.  Nicholas Pooran lbw b Bumrah 2 (3b)

Bumrah is back

PBKS 46/3 in 7 overs: Pollard to Gayle. OUT! Straight to long-on! The bowling change works for MI as the Universe Boss falls cheaply.  In the slot to be hit but Gayle aims to parry the ball over the longer boundary. He fails to get the desired timing on it and Hardik Pandya takes a comfortable catch on the fence. Rahul on strike. OUT! There's another one! Rahul pulls it straight to short fine leg!

 

Rohit rolling in the changes. Kieron Pollard into the attack

PBKS 38/1 in 6 overs: Krunal to Mandeep. OUT! Trapped in front with a straighter one. The ball moves in towards the off-stump and Mandeep takes a huge stride to the off-side, aiming for a paddlesweep. He misses it to Krunal's guile and it hits plumb in front. Gayle, on strike. Krunal drills in the blockhole and Gayle tucks it back straight to KL. The ball hits him and deflects behind Krunal. KL is shook by the ball. Krunal flicks the bails in a flash and goes up in an appeal for a moment. The umpire opts for a replay but Krunal plays it down and asks to play on! He gets Rohit's support and KL sends a thumbs up to the MI skipper. Mandeep Singh lbw b KH Pandya 15 (14b 2x4)

Krunal is back

PBKS 35/0 in 5 overs: Mandeep nearly holes out to midwicket! Probing over from NCN and he induces the false stroke. He hits the deck hard and builds the pressure and slides in a slower-one on length. Mandeep aims to slam it over midwicket for six but mistimes it altogether. Boult steams in from the fence but fails to cover enough ground despite a slide.

Coulter-Nile into the attack

PBKS 28/0 in 4 overs: Overpitched and Rahul crashes the off-side boundary. Bumrah begins with a real freebie outside off and Rahul punishes it through covers for four. Mandeep finds another boundary behind the keeper as Bumrah bowls short. The bouncer from Boom takes the top-edge and races over for four.

Bumrah replaces Boult

PBKS 21/0 in 3 overs: Krunal tightens his length onto the stumps but oversteps. Mandeep fails to make the most of the freehit as the ball hits the deck and holds on. Mandeep slams it straight to the cover fielder. A bit loose from Krunal to finish as he offers a fulltoss to Mandeep who cashes in this time with a drive through covers for four.

PBKS 12/0 in 2 overs: Cracking stroke from Rahul! Boult tails in a couple of deliveries onto the pads and quickly moves one the other way. Rahul flawlessly moves into it and drills it through cover for a gorgeous boundary.

Trent Boult into the attack

PBKS 4/0 in 1 over: Krunal maintains a similar line throughout the over. Angling the ball into the pads of both batters. Rahul and Mandeep work the ball to the on-side and find four singles.

Krunal up with the new ball. Interesting!

Game On! Mandeep Singh and KL Rahul walk out to the middle. Rohit's men take their positions.                                                                                                

 

7:08PM IST: Massive call from MI to drop Ishan Kishan. We're under a month away from the T20 WC, what does this suggest about the road ahead for India and MI, folks?

 

Mumbai Indians:  Rohit Sharma (c), Quinton de Kock (wk), Saurabh Tiwary, Suryakumar Yadav, Kieron Pollard, Hardik Pandya, Krunal Pandya, Nathan Coulter-Nile, Rahul Chahar, Jasprit Bumrah,  Toss, to bowl first

 

                    

 

 

 

 

Digital Health ID Card

 

Digital Health ID Card: All You Need To Know About Ayushman Bharat Digital Mission

New Delhi: Prime Minister Narendra Modi today launched the Ayushman Bharat Digital Mission through a video conference.

Speaking on the occasion, the Prime Minister said that the campaign of strengthening health facilities that have been going for the last seven years is entering a new phase today. “Today we are launching a mission that has the potential of bringing a revolutionary change in India’s health facilities”, said the Prime Minister.

The Prime Minister underlined the fact that with 130 crore Aadhaar numbers, 118 crore mobile subscribers, about 80 crore internet users, about 43 crore Jan Dhan bank accounts, there is no such big connected infrastructure anywhere in the world. This digital infrastructure is bringing everything from ration to administration (Ration to Prashasan)  to the common Indian in a fast and transparent manner. “The way technology is being deployed in governance reforms today is unprecedented”, said the Prime Minister.

The Prime Minister remarked that the Arogya Setu app helped a lot in preventing the spread of corona infection. He lauded Co-WIN for its role in making India achieve a record administration of about 90 crore vaccine doses today, under the free vaccine campaign.

Continuing with the theme of the use of technology in health, the Prime Minister said that there has also been an unprecedented expansion of telemedicine during the Corona period, so far about 125 crores, remote consultations have been completed through e-Sanjeevani. This facility is connecting thousands of countrymen living in far-flung parts of the country every day with doctors of big hospitals of cities while sitting at home, said the Prime Minister.

The Prime Minister remarked that Ayushman Bharat-PMJAY has addressed a key headache in the lives of the poor. So far more than 2 crore countrymen have availed the facility of free treatment under this scheme, half of which are women. The Prime Minister emphasized that diseases are one of the key reasons to push families into the vicious cycle of poverty and women of the families are the worst sufferers as they always relegate their health issues to the background. Modi said that he has made it a point to personally meet some beneficiaries of Ayushman and he has experienced the benefits of the scheme during the interactions. He said, “these healthcare solutions are a big investment in the present and future of the country.”

The Prime Minister said Ayushman Bharat – Digital Mission, will now connect the digital health solutions of hospitals across the country with each other. The Mission will not only make the processes of hospitals simplified but also will increase ease of living, he added.  Under this, every citizen will now get a digital health ID and their health record will be digitally protected.

The Prime Minister informed that India is working on a health model that is holistic and inclusive. A model which stresses preventive healthcare and, in case of disease, easy, affordable and accessible treatment. He also discussed unprecedented reforms in health education and said a much larger number of doctors and par medical manpower is being created in India now as compared to 7-8 years ago. A comprehensive network of AIIMS and other modern health institutions is being established in the country and work on establishing one medical college in every three Lok Sabha constituencies is going on. He also talked about strengthening health facilities in villages and informed that in the villages, primary health centre networks and wellness centres are being strengthened. More than 80 thousand such centres have already been operationalized, said the Prime Minister.

Noting that today’s program is being organized on World Tourism Day, the Prime Minister remarked that health has a very strong relationship with tourism. Because when our health infrastructure is integrated, strengthened, it also improves the tourism sector.

NEET PG 2021 Result

 

NEET PG 2021 Result DECLARED - Check cut-off scores, direct link to view detailed results with rank

The link to check result for NEET PG 2021 will soon be active on the official portal www.nbe.edu.in.

The National Board of Examinations (NBE) has announced the result of the National Eligibility cum Entrance Test - Postgraduate (NEET PG 2021).

The link to check result for NEET PG 2021 will soon be active on the official portal www.nbe.edu.in.

Furthermore, NBE has also declared the NEET PG cut-offs which will be the basis on which aspirants get admission to medical colleges.

The NEET PG 2021 cut-off scores are  

• 302 (general category)
• 265 (reserved categories including the Scheduled Castes, Scheduled Tribes and Other Backward Classes)
• 283 (persons with disability in the unreserved category)

As per NBE’s latest post on Twitter, the result scores are out and detailed results with ranks will follow.

General category students will need to score 50th percentile while reserved categories students will need 40th percentile to qualify. PwD category Students will require 45th percentile.

Step-by-step guide to check NEET PG 2021 result from NBE website

• Log on to the official NBE website at www.nbe.edu.in
• Click the NEET PG 2021 result link on homepage to reach new login page
• Fill details as required
• Click submit
• Check NEET PG 2021 result
• Download, save and print result.

Aspirants can directly check the NEET 2021 PG result once active via this link. 

Telecom Churn - Exploratory Data Analysis - Technical Document

 

Abstract:

The telecoms industry is a highly competitive sector which is constantly challenged by customer churn or attrition. In order to remain steadfast in the consumer business, companies need to have sophisticated churn management strategies that will harness valuable data for business intelligence. Data mining and machine learning are tools which can be used by telecoms companies to monitor the churn behaviour of customers.

This study implemented exploratory data analysis and feature engineering in a public domain Telecoms dataset and this study discussed how these results are essential in reducing customer churn and improving customer service.

Introduction:

Customer churn is a good indicator of service quality and customer service satisfaction. The telecommunications industry is a dynamic business sector that is primarily composed of companies operating in a subscription-based model. These companies are constantly pressured with higher rates of customers who churned and shifted to rival companies that offer competitive products and services. Thus, some of them employ measures in determining the reasons why their customers churn and seek innovative strategies to improve customer satisfaction and increase the customer base.

Customer Relationship Management (CRM) is a strategic process of managing customer relations and customer retention. Some companies mine customers’ data to better understand the behavior of their customers and gain actionable insights that help improve customer service . When machine learning is embedded in a CRM software, it can track churn rates, identify churn determinants, and pinpoint customers who are at risk of churning. It can also help a company decide and employ proactive retention strategies.

Types of Churners:

From the problem statement we conclude the following possible type of churners are there.

                                                                                                                                                                                     Voluntary Churner - Voluntary Churn occurs when a customer makes a conscious decision to cancel your services. This could be due to a variety of reasons like dissatisfaction with the service, unhappiness with the pricing, or the perceived value of your service. If your clients are cancelling in droves or you receive multiple customer complaints, it could be a sign of an underlying problem. When this kind of churn happens, it is essential to find the root cause of the churn and update your strategy as voluntary churn drives up the customer acquisition cost. It is again of two types

 a)   Deliberate Churn – Deliberate churn happens when a customer decides to leave. This could be because they find a better deal, product or service with a competitor or because they are unsatisfied with what your business offers. You might hear them saying

b)   Incidental Churn – Incidental churn is when a customer is no longer able to remain with you. For example, they move somewhere you do not service or they no longer have the financial funds to keep purchasing what you sell. These reasons can be hard, but not impossible for a business to overcome.

Involuntary churn - 

     When most companies create strategies to combat churn, they mostly consider voluntary churn as it’s the one that’s easier to notice. But there is another type of churn that may drain a huge amount of revenue and can become an issue if it goes unchecked.                                               

    Involuntary churn occurs when a customer’s payment attempt fails, without them noticing. If the customer misses multiple payments their subscription isn’t renewed resulting in churn.

     Reasons for customer churning:

·      Price: Pricing promotions abound to entice customers to flee from one carrier to a competitor

·      Service quality: Lack of connection capabilities may make a customer go with a carrier with wider network coverage

·      Lack of customer service: Slow or no response to customer complaints makes a customer more likely to switch carriers

·      Billing disputes

·      New competitors entering the market

·      Competitors introducing new products or technology

 Business Problem Overview:

Using the data provided, this research aims to analyse the data to determine what variables are correlated with customer churn.
The telecom industry experiences an average of 15-25% annual churn rate and it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition,
To reduce customer churn rate telecom companies, need to predict which customers are at high risk of churn.
 

In this project we will analyze customer level data of a leading telecom company to predict the customer churn

     Data description:

   Data volume:

    Since we don’t know the features that could be useful to predict the churn, we had to work on all the data that reflect the customer behavior in general. We used data sets related to calls, charges with all related information like total day, evening, night call and international calls and their charges and other. The volume of the data is (3333,20).

  Unbalanced dataset:

    The generated dataset was unbalanced since it is a special case of the classification problem where the distribution of a class is not usually homogeneous with other classes. the dominant class is called the basic class, and the other is called the secondary class. the data set is unbalanced if one of its categories is 10% or less compared to the other one.

    We found that Orange S.A Telecom dataset is unbalanced since the percentage of the secondary class that represents churn customers is about 14% of the whole dataset.

Steps Involved:

 

Ø Exploratory Data Analysis

     After data collection, several steps are carried out to explore the data. Goal of this step is to get an understanding of the data structure, conduct initial pre-processing, clean the data, identify patterns and inconsistencies in the data (i.e., skewness, outliers, missing values) and build and validate hypotheses. . This process helped us figuring out various aspects and relationships among the target and the independent variables

It gave us a better idea of which feature behaves in which manner compared to the target variable.

Ø Missing Values:

   There is a representation of each service and product for each customer. Missing values may occur because not all customers have the same subscription. Some of them may have a number of services and others may have something different. In addition, there are some columns related to system configurations and these columns may have null values.

    But in Orange S.A Telecom data set there are no null values.

Ø Univariate Analysis:

    Univariate analysis looks at one feature at a time. When we analyse a feature independently, we are usually mostly interested in the distribution of its values and ignore other features in the dataset.

    Below, we will consider different statistical types of features and the corresponding tools for their individual visual analysis.

    In our case, the data is not balanced; that is, our two target classes, loyal and disloyal customers, are not represented equally in the dataset. Only a small part of the clients cancelled their subscription to the telecom service. As we will see in the following articles, this fact may imply some restrictions on measuring the classification performance, and, in the future, we may want to additionally penalize our model errors in predicting the minority "Churn" class

Bar plots

Histograms are best suited for looking at the distribution of numerical variables while bar plots are used for categorical features

Histograms and density plots

In the above plot, we see that the variable Total day minutes is normally distributed, while Total intl calls is prominently skewed right (its tail is longer on the right).

 

A histogram groups values into bins of equal value range. The shape of the histogram may contain clues about the underlying distribution type: Gaussian, exponential, etc. You can also spot any skewness in its shape when the distribution is nearly regular but has some anomalies. Knowing the distribution of the feature values becomes important when you use Machine Learning methods that assume a particular type.

Box plot

Another useful type of visualization is         a box plot

We can see that a large number of international calls is quite rare in our data.

The box by itself illustrates the interquartile spread of the distribution; its length is determined by the 25th(Q1)25th(Q1) and 75th(Q3)75th(Q3) percentiles. The vertical line inside the box marks the median (50%50%) of the distribution.

 

The whiskers are the lines extending from the box. They represent the entire scatter of data points, specifically the points that fall within the interval 

(Q1−1.5⋅IQR,Q3+1.5⋅IQR)(Q1−1.5⋅IQR,Q3+1.5⋅IQR), where IQR=Q3−Q1IQR=Q3−Q1 is the interquartile range.

Ø Multivariate Analysis:

Multivariate plots allow us to see relationships between two and more different variables, all in one figure. Just as in the case of univariate plots, the specific type of visualization will depend on the types of the variables being analyzed.

Correlation matrix

Let's look at the correlations among the numerical variables in our dataset. This information is important to know as there are Machine Learning algorithms (for example, linear and logistic regression) that do not handle highly correlated input variables well.

First, we will use the method corr () on a DataFrame that calculates the correlation between each pair of features. Then, we pass the resulting correlation matrix to heatmap () from seaborn, which renders a color-coded matrix for the provided values:

Scatter plot

The scatter plot displays values of two numerical variables as Cartesian coordinates in 2D space. Scatter plots in 3D are also possible.

We get an uninteresting picture of two normally distributed variables. Also, it seems that these features are uncorrelated because the ellipse-like shape is aligned with the axes.