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The dataset is the sample dataset if we know the difference between the sample and the population dataset then we may know that sample is drawn randomly from the population and this sample dataset has the customers who have left the telecom company.telecom = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')
Now while using the head function we can see that beginning records.telecom.head()
From the shape attribute, we can see the shape of the data i.e number of records and number of columns in the dataset like (1200,13) so that particular dataset will have 1200 rows and 13 columns.telecom.shape
Now let’s see the columns in our dataset.telecom.columns.values
Output:array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'], dtype=object)
Here we will be checking what type of data our dataset holds.telecom.dtypes
Now let’s see the statistics part of our data i.e. mean, standard deviation, and so on.telecom.describe()
Senior citizen is actually categorical hence the 25%-50%-75% distribution is not proper
We can also conclude that 75% of people have tenure.
Average Monthly charges are USD 64.76 whereas 25% of customers pay more than USD 89.85 per monthtelecom['Churn'].value_counts().plot(kind='barh', figsize=(8, 6)) plt.xlabel("Count", labelpad=14) plt.ylabel("Target Variable", labelpad=14) plt.title("Count of TARGET Variable per category", y=1.02)
Output:No 73.463013 Yes 26.536987 Name: Churn, dtype: float64
Let’s see the value count of our target variabletelecom['Churn'].value_counts()
Output:No 5174 Yes 1869 Name: Churn, dtype: int64
Inference: From the above analysis we can conclude that.
In the above output, we can see that our dataset is not balanced at all i.e. Yes is 27 around and No is 73 around
So we analyze the data with other features while taking the target values separately to get some insights.
This code will return us valid and valuable information about the dataset also we can see that the verbose mode is on so it will give all the hidden information also.telecom.info(verbose = True)
Output:missing = pd.DataFrame((telecom.isnull().sum())*100/telecom.shape).reset_index() plt.figure(figsize=(16,5)) ax = sns.pointplot('index',0,data=missing) plt.xticks(rotation =90,fontsize =7) plt.title("Percentage of Missing values") plt.ylabel("PERCENTAGE") plt.show()
Output:Missing Data – Initial Intuition
Here, we don’t have any missing data.
General Thumb Rules:
When we see a lot of outliers in the dataset then don’t really use mean because if the dataset has lots of outliers then we would be in the situation where the regression analysis will have drastic changes.
And if we get features that have a high number of missing values then it’s better to drop them.
As there’s no thumb rule on what criteria do we delete the columns with a high number of missing values, still one can delete the columns, if you have more than 30-40% of missing values.
1. Here we will be copying the telecom data to preprocess it further.telco_data = telecom.copy()
2. Total Charges should be numeric amounts. So it’s better to convert them to numeral types.telco_data.TotalCharges = pd.to_numeric(telco_data.TotalCharges, errors='coerce') telco_data.isnull().sum()
3. Here we can see that there are a lot of missing values in the Total charges column.telco_data.loc[telco_data ['TotalCharges'].isnull() == True]
4. Missing Value Treatment
Since the % of these records compared to a total dataset is very low ie 0.15%, it is safe to ignore them from further processing.
Removing missing valuestelco_data.dropna(how = 'any', inplace = True) #telco_data.fillna(0)
5. Now we will be dividing the persons e.g. for tenure < 12 months: assign a tenure group of 1-12, for tenure between 1 to 2 Yrs, tenure group of 13-24; so on.
Get the max tenureprint(telco_data['tenure'].max())
Here we will try to group the tenurestelco_data['tenure_group'].value_counts()
Output:1 - 12 2175 61 - 72 1407 13 - 24 1024 49 - 60 832 25 - 36 832 37 - 48 762 Name: tenure_group, dtype: int64
6. Remove columns not required for processing
Drop column customerID and tenuretelco_data.drop(columns= ['customerID','tenure'], axis=1, inplace=True) telco_data.head()
Data ExplorationUnivariate Analysis for i, predictor in enumerate(telco_data.drop(columns=['Churn', 'TotalCharges', 'MonthlyCharges'])): plt.figure(i) sns.countplot(data=telco_data, x=predictor, hue='Churn')
2. Here as we know we can’t have character values for our ML model so hence we should convert it into binary numerical values i.e. Yes=1; No = 0telco_data['Churn'] = np.where(telco_data.Churn == 'Yes',1,0) telco_data.head()
3. Now we have to convert the categorical data into dummy variables with the getting dummies function.telco_data_dummies = pd.get_dummies(telco_data) telco_data_dummies.head()
Output:sns.lmplot(data=telco_data_dummies, x='MonthlyCharges', y='TotalCharges', fit_reg=False)
Inference: Here from the above graph it is clear that as the monthly charges are increasing we can experience the total charges also increase which shows the positive correlation too.Mth = sns.kdeplot(telco_data_dummies.MonthlyCharges[(telco_data_dummies["Churn"] == 0) ], color="Red", shade = True) Mth = sns.kdeplot(telco_data_dummies.MonthlyCharges[(telco_data_dummies["Churn"] == 1) ], ax =Mth, color="Blue", shade= True) Mth.legend(["No Churn","Churn"],loc='upper right') Mth.set_ylabel('Density') Mth.set_xlabel('Monthly Charges') Mth.set_title('Monthly charges by churn')
Insight: Here it is evident that when the churn is high then the charges are high.Tot = sns.kdeplot(telco_data_dummies.TotalCharges[(telco_data_dummies["Churn"] == 0) ], color="Red", shade = True) Tot = sns.kdeplot(telco_data_dummies.TotalCharges[(telco_data_dummies["Churn"] == 1) ], ax =Tot, color="Blue", shade= True) Tot.legend(["No Churn","Churn"],loc='upper right') Tot.set_ylabel('Density') Tot.set_xlabel('Total Charges') Tot.set_title('Total charges by churn')
Inference: Here we get the surprising insight that as we can see more churn is there with lower charges.
Tenure, Monthly Charges & Total Charges then the picture is a bit clear:- Higher Monthly Charge at lower tenure results into lower Total Charge. Hence, all these 3 factors viz Higher Monthly Charge, Lower tenure, and Lower Total Charge are linked to High Churn.
We can also analyze the heat map of our complete dataset.plt.figure(figsize=(12,12)) sns.heatmap(telco_data_dummies.corr(), cmap="Paired")
These are some of the quick insights on churn analysis from this exercise:
Electronic check mediums are the highest churners
Contract Type – Monthly customers are more likely to churn because of no contract terms, as they are free-to-go customers.
No Online security, No Tech Support category are high churners
Non-senior Citizens are high churners
Note: There could be many more such insights, so take this as an assignment and try to get more insights.About Me
Greeting to everyone, I’m currently working in TCS and previously, I worked as a Data Science Analyst in Zorba Consulting India. Along with full-time work, I’ve got an immense interest in the same field, i.e. Data Science, along with its other subsets of Artificial Intelligence such as Computer Vision, Machine Learning, and Deep learning; feel free to collaborate with me on any project on the domains mentioned above (LinkedIn).
Here you can access my other articles, which are published on Analytics Vidhya as a part of the Blogathon (link).
The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.
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In the defense of many businesses, they do hear the same words coming from both the marketer and the technology company. Both companies use the words “SEO”, “optimization”, “paid search”, and many other terms, which on the surface sound the same. Thus, a business that doesn’t understand the digital medium would not know there is a difference between these two types of companies.Evolution of Disciplines on the Web
If I think back 20 years ago when we first put a website together using HTML 0.0 we could bold text, change colors, make text flash and use an animated gif for a mailbox that opened and closed. It was all pretty exciting stuff, at the time.
Since then the disciplines have evolved and matured to the point where there are:
Technology companies that know how to manage servers and networks
Technology specialists who understand databases, programming, and website assembly
Creative Designers who understand how to make a website look good and lay it out
If we break the disciplines down to these areas, it might be easier to understand who has the competency in what areas.Putting the Marketing Processes & Concepts to Practice
Think of a marketing process like a cycle, which starts with research, strategy, and planning. If you have a strategy, then you must also have a way to measure the success of this strategy. Measuring the strategy is important to understanding the initial plan so we know what tasks are necessary in the process and how those tasks will be completed.
This middle area tends to be where there is an overlap between marketers and technology firms when it comes to the execution.
The last area falls into gathering the measurements, assembling the measurements into a report(s), analyzing multiple data points, and ultimately interpreting the results. The interpretation is then used to communicate and collaborate with the client through a dialogue, which will be used to improve the results.
This dialogue is not only to present the marketing results, but also to learn from the client how the results have impacted them and what is happening in their business that needs to be incorporated into the marketing process. More importantly, the meeting must look at not only what is working, but also what is not working. Usually this is the area that requires the most discussion and collaboration.
Finally, action steps, which are documented in meeting minutes, to improve the marketing process are implemented and the cycle starts over again.
An interesting side benefit of the marketing process is the education of the client to help them better understand the digital medium. Equally, the marketing firm is given the opportunity to learn about the client’s business and industry as well.Where Do The Differences Lie?
If you think about the labor time it takes to implement a successful marketing process, the biggest difference between the marketing firms and technology firms will be cost.
If your company is able to hire a marketing firm to manage the strategy, understand the results and lead the plan with discipline, then you’re able to remove yourself from “doing the work”.
On the flip side, if your company is only able to hire a technology firm, then you have to accept that you will be giving the orders, be more involved in the work, and need to interpret the reports you will be getting. Meetings tend to be few and far between.Three Examples To Think About
One primary situation I have come across is businesses that say they have tried everything and nothing works. After some listening and some questions, I find they have been working with technology firms and usually both sides are equally frustrated with the other.
When it comes to meetings, they tend to not exist or, if they do, the discussion is about the reports that are missing information and not coordinated with a strategic plan. We have seen clients having to submit trouble tickets through a website to get any communication and certainly there is no consistency in a contact person who has learned about the client’s business.In Conclusion
There is one thing a web marketing agency does or should be doing… accepting responsibility for when things do not go right. Instead of blaming yourself when you hire a technology firm, wouldn’t it make more sense to hire someone you can blame things on when they don’t go right?
Featured Image: Created by Author
Two clear trends are developing in the automotive space: The first is the move away from internal-combustion engines and toward battery-electric vehicles, and the second is the pursuit of autonomy. Cars that can drive themselves come from the likes of Waymo and other companies, while automakers such as Tesla boast about their driver-assistance features.
One company planning on tinkering at the intersection of these trends is called Lightmatter, which is going to build the brains of a self-driving prototype vehicle. The company’s microchips set them apart from others in the tech industry: The chips that will do the computing for this experimental self-driving car will be light-based, unlike the traditional chips that employ electrons and transistors.
The company, in conjunction with Harvard University and Boston University, has received $4.8 million in funding from a government organization called IARPA. That stands for Intelligence Advanced Research Projects Activity, and can be thought of as an analog of DARPA—which funds defense-related research—but from the Office of the Director of National Intelligence.
Lightmatter, unlike outfits such Zoox or Rivian, is definitely not a car or transportation company, so don’t expect to see Lightmatter-branded vehicles passing you on the highway. It’s a chip company, and the photonic chips they make are specifically geared towards powering artificial intelligence computations in an efficient way.
“We are building something that’s not a general-purpose processor—it’s a processor targeted at AI,” says Nicholas Harris, the CEO and co-founder of Lightmatter. Running the neural networks that can execute AI tasks, such as what goes on in computer-vision processing, takes math. “It’s a lot of multiplication and addition, and it’s a lot of moving data around the chip at very high speeds—so we do all of those operations using integrated photonics,” he says.
[Related: Why this Amazon-owned company is bringing its autonomous vehicles to Seattle]
A photonics chip uses light waves, whereas traditional computer chips are based on electric circuits and transistors. One very simple way to think about the difference is this: “The basic concept is really destructive and constructive interference of light in one case, and controlling the path of electrons using transistors as switches in the other case,” explains Ajay Joshi, an associate professor at Boston University who is also involved in the project.
A photonics-based chip. Lightmatter
So why try to use light-based chips on a self-driving car, even just as an experiment? Since Lightmatter boasts that its chips are both faster and more efficient than a traditional chip, the idea here is that running the brains of a self-driving car with their chips could more efficiently use the limited electrical resources onboard an autonomous EV. “Our vision, as that [AV] market grows, is that we’re going to preserve battery range while providing the enormous amount of compute you need to rip out the steering wheel,” says Harris.
He says that their light-based chips will “serve as the brain for a buggy.” The entire experimental vehicle won’t run on light-based chips (so technically, the system will be a combination of photonic chips and traditional ones), but the part of the computing hub handling the AI would. That brain would probably operate “multiple neural nets that are running back-to-back,” he adds, helping the buggy figure out what its perception system is seeing and what the car should do next.
The company is not planning to actually create a car from scratch. “We’re just going to buy something off the shelf,” Harris says. “We’re not trying to innovate on anything to do with the car—it’s just the brain.” The light-based chips, in other words, could work as the central compute unit for a self-driving buggy or even a golf-cart-type contraption. The ultimate goal is that if that brain can do its AI-operations efficiently, then the self-driving buggy could do more with its batteries.
Credit analysis is an important part of businesses that provide credit to their customers in order to expand their business portfolio. However, like all other protocols of business, credit analysis is also based on some important factors.
To get rid of losses created by bad debt and lengthy payments, businesses can depend on these factors to understand and apply credit analysis to their potential customers.5 C’s of Credit Analysis
There are select parameters on which the credit analysis process rests. These are known as the 5 C’s of credit analysis. These are the following −Character
Lenders want to judge the financial character of their customers before lending them credit. It is important for the lenders to know how diligent the financial character of the customer has been in order to minimize the risk of bad debt.
The character of a firm that wants to avail credit is judged by credit rating or credit scores. The better the rating or the score, the better the potential of the firm to return the credit in time.
The character can be improved by firms by paying back older debts in a systematic and organized manner. Staying professional and dealing systematically to impress the lenders go a long way in making credit character superior. In the case there is a need, making a professional rapport can also be good.Capacity
Capacity is the ability of the firm to pay back the debt availed and it shows how much credit is good enough for a particular firm. It shows the lenders the profit made by the applicants of credit lately which is a measure that is very important for the lenders.
The capacity of a firm is assessed by analyzing the cash flow statements and their projections, debt service coverage ratio (DSCR), bank statements, and debt to income ratio (DTI).
To increase capacity, a firm should lower its expenses and increase its income. Professional software can be used to do this.Capital
It is the money a credit-seeking firm has already invested in the business and the amount it wants to invest more. In general, the more the capacity of a firm, the more its ability to raise more credit from lenders. This is so because a big firm usually has a sound business process that is able to pay back the loans within a given period of time.
To make an impression related to capital a firm should invest money in a business and earn some profit before applying for credit. Keeping the plan of using the credit amount can also help the firms in obtaining a loan readily.Conditions
Conditions refer to the terms that are put by lenders on the credit management process to minimize their anticipated losses in the process. It may include the credit rules and regulations a credit-seeking firm has to follow to get the credit approved.
Lenders create conditions depending on industry practices, market segments, risks involved in the business, and competitors in the market.
In order to be eligible, firms should explain how they will spend the money in the businesses they have. Applying for a loan when the cash flow is superb can increase the chances of getting loans many times.Collaterals
Collaterals include assets, such as jewelry, a car, a home, and land that can be pledged as security against the loan. Collaterals make loans secured as the valuables can be seized if the credit-seeking firm fails to pay the credits.
The collaterals asked by the lenders differ from organization to organization. While some may ask for property some lenders ask for bank liens or personal guarantees.
It is important to learn about the depreciation and market price of the collaterals being used for getting the credit. Looking for lenders that offer loans on favorable terms is also helpful for the firms who apply for loans.Conclusion
The 5 C’s of Credit Analysis are basically the key factors used by financial institutions to determine a potential borrower’s creditworthiness; to decide whether a borrower is eligible for the credit and what would be the interest rates.
The modern business environment is highly competitive and filled with uncertainties that organizations cannot avoid experiencing risks from time to time. Fortunately, the proper application of risk management strategies such as risk mitigation and risk remediation can help businesses to minimize the damage or eliminate threats to their operations. Risk mitigation and risk remediation help organizations improve their efficiency and performance by identifying and addressing security vulnerabilities and risks that threaten the success of projects and business operations.What’s Risk Mitigation?
Businesses should engage in risk mitigation because it helps them to establish action plans for existing and future security vulnerabilities. It also allows companies to focus and achieve their objectives and goals by creating strategies for controlling risks. Risk mitigation helps minimize risks and facilitate better business decision-making, which increases project success.
Consequently, risk mitigation allows businesses to improve communication and protect their employees, team members, and stakeholders against risks. It also promotes compliance, reduces legal liability, and protects the company’s reputation.What’s Risk Remediation?
Risks arise from unfixed vulnerabilities within a system. Therefore, risk remediation involves steps taken by a business to identify vulnerabilities that present risks and stop them before they become significant threats.
What are The Key Differences Between Mitigation and Remediation?
Although both risk mitigation and remediation are steps conducted during risk management, they involve different concepts and activities as follows;
Risk mitigation and risk remediation address different amounts of risk.
Risk mitigation and risk remediation focus on different issues.
Risk mitigation and risk remediation have different implementation processes.
Why Do Businesses Need to Pay Attention to Both? How to Implement Risk Mitigation and Risk Remediation Processes Risk Mitigation
Businesses can tackle the implementation of risk mitigation using five main processes;
When the consequence and effects of the risks are severe, a business may choose risk avoidance by canceling a project altogether or making schedule changes. However, a business may opt for risk acceptance when the project is ongoing, and the risk is already affecting the business without a possible remedy.
There are instances where the effects of the risk can be reduced and are not strong enough to warrant avoidance; hence businesses implement risk reduction. Businesses can implement strategies such as additional safety procedures during risk reduction to reduce the consequences of the risk. Another strategy for handling risk mitigation is risk transfer.
Businesses can tackle adding risk remediation using the four-step vulnerability risk remediation process, which involves;
Find – The first step is to find, which involves using scans and tests to identify and detect the vulnerabilities within the organization’s systems.
Prioritize – The business will prioritize and classify vulnerabilities based on the level of urgency and handle issues that pose a significant threat.
Fix – involves fixing and resolving vulnerabilities through strategies such as system upgrades to resolve data breaches.
Monitoring – involves tracking and monitoring vulnerabilities to ensure they do not transform into significant threats.
Apple’s China problem may require a new type of iPhone designed specifically for the country, say some former Apple execs.
Apple has already taken one small step in this direction, with a physical dual-SIM model of the iPhone XS/Max available only in mainland China. It’s also widely believed that Apple’s decision to offer gold-colored phones was driven by China and the Middle East. But it’s being suggested the company may need to do much more …Apple’s China problem
There’s no denying the problem Apple faces in China. Apple’s sales there fell 27% in the quarter, and was responsible for the guidance warning that promoted a 30% slide in the company’s share price. In the letter giving the downgraded guidance – borne out in the subsequent earnings report – CEO Tim Cook specifically identified China as the issue.
Cook also points to China as a pain point for Apple’s first quarter results [saying] that revenue in China accounts for “over 100 percent of our year-over-year worldwide revenue decline.”
Part of that is the Chinese economy. Smartphone sales as a whole fell by somewhere in the 12-15.5% range. Apple’s fall was steeper, however.
Apple is also addressing pricing to some extent, promising to absorb price increases caused by currency fluctuations in key overseas markets, and it’s likely that China will be on the list.A China-specific iPhone
But the WSJ quotes former Apple execs suggesting that won’t be enough.
Some former employees and analysts suggest a more radical change: End Apple’s one-size-fits-all-markets approach for products and aggressively push to differentiate its gadgets and software in China from its offerings elsewhere […]
Over the years, the company’s “Designed by Apple in California. Assembled in China.” ethos meant staff in China couldn’t always deliver iPhones that fit well with local apps and user habits.
“They’re not adapting quick enough,” said Carl Smit, a former Apple retail executive in Asia who is now a strategic sales consultant. “These apps and systems are how people communicate in China, and if you don’t have seamless integration, the Chinese manufacturers have an edge” […]
“We’d say, ‘Here’s what my consumer wants,’” said Veronica Wu, who worked in sales for Apple in China before becoming a venture capitalist at Hone Capital. But Apple’s product executives “were a black box,” she said, and the phones that were later unveiled didn’t have the features gaining traction in China.
The time it took Apple to introduce a dual-SIM model was given as an example of the company’s slow-paced response to local brands like Xiaomi and Huawei. More than 90% of smartphones sold in China have two physical SIMs, but it took Apple until this year to do something local execs had been requesting for years.
QR code support is another example. These are seen everywhere for use by WeChat’s popular mobile wallet platform, but the iPhone’s Camera app didn’t add native support for QR code scanning until last year. That likely came about as a result of Apple appointing its first exec with specific responsibility for ensuring Apple offers the right features for the Chinese market.
Others, however, counsel caution in addressing Apple’s China problem.
“When you sell a brand with cachet, you don’t want to do anything that diminishes that,” said Richard Kramer, an analyst tracking Apple for Arete Research. “Apple can’t afford to substitute local tastes for the massive premiums they get.”
It was recently suggested that changes to WeChat could have serious ramifications for Apple.
iPhone concept image: Ben Geskin
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