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Introduction to Evernote Notes

Web development, programming languages, Software testing & others

Evernote Uses

We all have warranty cards and user manuals of the different products we own and we cannot even through them out and don’t even open them a single time in years. Now, we can download their pdf versions and save them to Evernote. After saving them in Evernote, now we can search for any word in those warranty cards and user manuals and can find the word in seconds.

Make Evernote your memorabilia. We can use Evernote,s camera to save our memories whether it can be ticket stubs, love letters, theatre programs, travel brochure, and the list never ends. After saving these memories in Evernote, we can rewatch those memories any time and can get a peek into the past. This will make your evernote into a digital scrapbook.

There is an increasing trend of keeping pet animals but we fail to keep the documents related to our pet safe which makes it difficult for us when our pet needs care. Now, using Evernote, we can save all of the pet information. We can save the pet’s adoption papers, pet sitter’s contact, licenses, veterinary care information, vaccination records data in Evernote which would help us to keep every document related to our furry friends in a single place.

Save your kid’s work of art. We can create a virtual refrigerator door that can be shared to our friends and family. We can scan our kid’s works of art and can save them for our lives, moreover, it also saves us from all the clutter. Using Evernote, we can make a scrapbook of our kid’s art and can gift them on their graduation day.

Use Evernote as your spending tracker. We can track our everyday expenses on Evernote which would eventually help in making a personal financial budget. We can use the email forwarding or camera feature of Evernote to send our receipts to Evernote’s notebook. If we buy a large number of goods from a single seller then we can also make a tag for the seller. Now at the end of the year, we can check our spending in our favorite stores.

We all have discount coupons from the different stores of e-commerce platforms. Now, there are different ways to use coupons with Evernote. We can use web clipper to snap the photos of coupons, we can also take screenshots of the coupons or can use email forwarding. Now, we can keep a tag with coupons stored on Evernote and can set a reminder for the expiry date of those coupons.

Gone are the days of writing dear diaries offline. Now people find it more helpful to write journals online and Evernote is one such application that can help us in this. Make Evernote your journal which can be accessed anywhere and anytime.

Make Evernote your day planner or calendar. Now, if we need to keep track of something in terms of date, we can do it through Evernote. Moreover, Evernote has some awesome templates for us to keep an eye on our year, month, week, or day. This will help us in keeping the track of various things even if we are not at home or at the workplace.

Keep your secret recipe collection. Scan and save the old recipes of your family, extract good recipes from the web or snap photos of your favorite recipes from a book. We can also add notes for our help while preparing the dishes. If required, we can share these recipes to our family and friends as well.

Make Evernote your idea box. Save the things which inspire you by grabbing articles, social media posts or images via web clipper. Use the saved articles and images whenever looking out for inspiration or ideas anytime and anyplace. We can use web clippers on our mobile phones as well making it comfortable for the users to clip anything and anywhere.

We can make Evernote our newsletter destination. We can use Evernote’s email address for receiving all of the newsletters directly in Evernote instead of cluttering and spamming our mailboxes.

Conclusion

On the basis of the above article, we understood about Evernote. We went through the different uses of Evernote which would help us in using Evernote in its most efficient way. This article would help anyone who is looking for an online platform where they can store their important documents, set reminders, monitor their meals and calories, and do many other tasks.

Different Types Of Foundation And Their Uses

The structural basis that underpins a building or other structure is known as a foundation. Depending on the soil conditions, climate, and kind of construction, there are numerous types of foundations, each with specific qualities and applications. Shallow foundations, deep foundations, pile foundations, and raft foundations are a few examples of frequent foundation types.

The choice of foundation type is influenced by a number of elements, including the soil’s ability to support weight, the construction materials used, the depth of the water table, and the existence of subterranean utilities or obstructions. For a construction to be stable and secure, picking the correct foundation is essential.

What Is Makeup Foundation?

One type of face makeup is foundation, as we already said. To lay down an even foundation for the rest of your makeup, use this product all over your face. This item should serve as the foundation for your complete makeup appearance. With the right formula, you can hide flaws, even out your skin tone, reduce the appearance of defects, and produce an overall smooth and even canvas.

What Kinds of Foundations Are There? Write Its Uses.

The application of foundation makeup is not universally applicable. You can choose a formula based on your preferred consistency, desired level of coverage, and chosen finish. Following is a list of foundations and its uses explained −

Liquid Foundation − This common type of foundation comes in a liquid form and is offered in a range of coverage intensities, from light to full. It has a natural-looking finish and is simple to apply with a brush, sponge, or fingertips. Most skin types, including dry, normal, and combo skin, can use liquid foundation. It can be used to balance out skin tone, cover flaws, and offer a flawless foundation for makeup. Additionally, it comes in a variety of formulations, including oil-free, matte, and dewy options, to accommodate various skin types and preferences.

Powder Foundation − A type of foundation that is available in powder form, powder foundation is perfect for those with oily skin or those who like a matte look. It offers a subtle, natural-looking finish and is simple to apply with a brush. Moreover, powder foundation comes in a range of coverage options, from light to full. It may be applied alone or over liquid foundation for more coverage, and it’s ideal for touch-ups throughout the day.

Cream Foundation − Cream foundation is a type of foundation that is best for dry skin types or those who like a dewier finish. It comes in a cream or mousse-like texture. It can be used with a brush, sponge, or fingertips to apply and offers a smooth, moisturizing coverage. Cream foundation can be used to conceal flaws, even out skin tone, and give the complexion a dewy, radiant finish. It comes in varying coverage levels, from light to full.

Stick Foundation − Stick foundation is a kind of foundation that is simple to use on the skin and comes in a solid stick form. It’s excellent for those who desire thoroughness and accuracy in their application. Stick foundation can be used as a smooth base for makeup, to hide flaws, and to even out skin tone. It’s also practical for touch-ups while on the go and is perfect for those with combination or oily complexion. While some stick foundations are designed to have a matte finish, others have a more dewy or organic look.

Mineral Powder − Mineral powder foundation is a type of foundation that offers a natural-looking finish and is manufactured with natural minerals like zinc oxide and titanium dioxide. As it is free of harsh chemicals and scents that might irritate the skin, it is excellent for persons with sensitive or acne-prone skin. Mineral powder foundation can be applied with a brush for a natural-looking finish and comes in a range of coverage intensities, from light to full. Due to its ability to absorb excess oil and give skin a matte appearance, it is also appropriate for those with oily or mixed skin.

Tinted Moisturizer − Tinted moisturizer is a form of foundation that hydrates the skin while offering a thin, light coverage. It helps hydrate and soothe the skin, making it ideal for persons with dry or aged skin. A natural, little makeup appearance is also acceptable and can be achieved with tinted moisturizer. It’s simple to apply with your fingertips or a sponge and gives skin a dewy, organic appearance. Some tinted moisturizers provide SPF, which makes them a great option for daily usage.

BB Cream − A form of tinted moisturizer that offers coverage, moisture, and sun protection is known as BB cream, often referred to as beauty balm. It’s a multipurpose product that combines numerous steps into one to streamline your cosmetic application. SPF is typically used in BB creams to protect skin from UV rays, along with antioxidants and other skincare compounds to hydrate and brighten the skin. It offers sheer to medium coverage and is excellent for those who prefer a finish that looks natural. All skin types can use BB cream, which can be applied with either fingers or a sponge.

CC Cream − Often referred to as colour correcting cream, CC cream is a kind of foundation made to even out the colour of the skin by reducing redness, dullness, and other flaws. It offers a more focused method of coverage than BB cream and is perfect for persons with particular skin issues. Each shade of CC cream is intended to address a particular issue, such as redness, sallowness, or black spots. Moreover, it is lightweight and offers a natural-looking finish, making it appropriate for daily wear. Hyaluronic acid and vitamin C are two skincare components that are frequently found in CC creams to enhance the overall health and look of the skin.

The multi-purpose BB cream offers hydration, sun protection, and coverage. A color-correcting foundation that focuses on particular skin issues is called CC cream. Your skin type, skin issues, and personal preferences will ultimately determine the sort of foundation you select. For a natural-looking result, it’s critical to choose a shade that complements your skin tone and blend it effectively. You may get a beautiful, bright complexion by using the appropriate foundation.

Conclusion

Any building or structure needs foundations to stand on. To guarantee the security, sturdiness, and durability of a project, it is essential to choose the right sort of foundation. While choosing the type of foundation, architects, engineers, and builders must take into account a variety of criteria, such as the soil characteristics, load-bearing capability, building materials, water table depth, and underground obstructions.

The foundation can offer a sturdy base for a construction that lasts for many years with careful planning and design. By distributing the load of the structure to the earth below, the foundation is essential to a building’s structural stability. The inappropriate foundation type or improper foundation design can cause structural issues like settling, cracking, or even collapse.

15 Best Uses Of Chatgpt

ChatGPT is fun and has several practical uses to integrate into your daily routine.

Did you know ChatGPT is being rampantly adopted in claims and customer service because it substantially reduces the risk of human error and time lag?

The best uses of ChatGPT include providing virtual assistance, assisting with creative writing and translations, conducting research, and handling complex tasks such as claims support, debugging coding, playing games, and learning languages.

Therefore, this Chatbot is more than you may imagine; it has many daily applications.

Continue reading to learn about some of the best and worst uses of ChatGPT.

15 Best Uses of ChatGPT [Unleashing AI’s Potential]

ChatGPT has become a one-stop virtual assistant with its vast neural network and parameters, supervised and reinforced learning from available texts.

Like commanding a genie for a wish, you can command ChatGPT to create almost anything.

But a Genie only offers three wishes, whereas chatGPT will fulfill your limitless desires.

Let us look at a few compelling examples and best uses of ChatGPT.

1. Write and Debug Code

Did you know that you can create complete software using ChatGPT?

However, the software would only be a miniature version of games or applications but a robust office program.

Nonetheless, you can create, debug, and explain Codes with ChatGPT to narrow down the problem within your Code.

You can ask ChatGPT to write a block of functional Code snippets from scratch. Here is an example.

However, examine them before copying or deploying them in your software Codes.

In fact, it knows over 20 different programming languages, including Python, Java, C++, PHP, etc.

Similarly, you can ask the Chatbot to debug your Code and spot vulnerabilities whenever in doubt.

2. Use ChatGPT as a Linux Terminal

You can use the ChatGPT interface as a Linux terminal, a text interface, or a terminal where you input commands and check the result as text.

If you wonder, you can use complex Linux commands, create files and directories, or even compile and execute Codes.

Those familiar with Linux terminals can try different Linux commands in the ChatGPT.

Here is an example.

3. Play Tic-Tac-Toe With ChatGPT

Did you know you can play Tic-Tac-Toe with ChatGPT?

Simply type let’s play tic-tac-toe in ChatGPT and be ready to play against ChatGPT.

Although the gaming UI is not intuitive like the computer games, you can send custom commands to direct your next move.

Here is an example:

Similarly, you can create the entire Tic-Tac-Toe program in ChatGPT itself.

As a part of language learning, it can create viable products based on any programming Codes.

You can ask ChatGPT to write the Tic-Tac-Toe Code in Python. Here is a snippet from the same.

4. Creative Writing Jobs

You can use the ChatGPT well for creating anything from essays, resume and cover letter, articles, blogs, and academic papers.

The supervised and reinforced learning model can quickly generate distinctive texts for various contexts.

If you are currently job hunting, consider creating a quick resume through ChatGPT.

You can simply input your requirements, such as the type of job applied, years of experience, special skills, etc., and the system will generate a relevant resume on the go.

Similarly, you can create a cover letter by submitting your requirements. Here is an example of the cover letter created with ChatGPT.

It also comes in handy for creating articles and essays, but creating academic papers would require scrutiny as it is likely to copy from genuine and false sources.

5. Write and Tell Jokes, Poems, Songs

ChatGPT is a wholesome content creator who can come up with anything from jokes, news pieces, inspirational speeches, songs, and poems.

Do you not trust? Here is an example: I asked ChatGPT to create a joke about love.

Similarly, you can command it to write original poems and lyrics as well that too in any of your preferred languages.

Here, I asked ChatGPT to write a four-line poem on “Courage” that too in Spanish.

Therefore, if you need anything creative, ChatGPT is your safe bet.

6. Prepare for a Job Interview

Did you know ChatGPT can help you prepare by providing assertive answers to all possible questions that might be asked in an interview?

You can use the platform to generate hypothetical scenarios to raise possible questions and their intelligent replies.

ChatGPT’s huge knowledge base and natural language learning ability will retrieve the most knowledgeable answers to any possible question.

In fact, you can use ChatGPT for both scenarios. As an interviewer, you can use ChatGPT to create unique questions.

Here is an example of the same.

Another handy feature of ChatGPT’s creative content tool is that you can generate news pieces and updates on the go.

If you want a news piece, of any length, about any event occurring, accidents, products, etc., you can do so with ease.

Here is an example of a news piece ChatGPT generated for “Rising Covid-109 cases in a single paragraph.”

8. Create Viral Social Media Post

Like creating unique and sellable news pieces, you can generate social media posts for your marketing Campaign.

The best thing about ChatGPT is that it can generate a niche social media post about anything within your desired word limit.

Here is an example of a social media post about a kebab roll stand that recently opened in the Bronx, NY, in 50 words and 25 words simultaneously.

9. Get Personalized Recommendations

ChatGPT works like Google, Siri, and Alexa to provide personalized recommendations about almost anything.

You can ask it for recommendations on the nearest cafeteria, pizzeria, tourist spots, or shops.

Here is an example:

Make ChatGPT your virtual tour guide or assistant by asking for information about the popular place you visit.

The information can range from historical background, exciting facts, and relevant details.

However, remember that ChatGPT does not retrieve the latest information, and the recommended place may not exist anymore.

10. Dictionary, Spell, and Grammar Check

Did you know ChatGPT is your go-to dictionary, thesaurus, and spell and grammar checker?

You can retrieve meanings, synonyms, and antonyms for a word existing in any language.

Here is an example:

ChatGPT uses natural language processing technology like Grammarly to provide a quick spell and grammar check.

If you provide any sentence or paragraph to ChatGPT for proofreading, it will do so quickly and provide feedback for improvement.

However, ChatGPT is not a substitute for Grammarly, which boasts robust tools and resources focused on improving writing quality.

11. Generic or Specific Research

ChatGPT is a one-stop solution for researching almost anything.

The Chatbot provides prompt responses and assistance based on training data and programming.

If you are wondering about its information, it is limitless, so you can ask about almost anything related to academics, movies, business, history, etc.

Here is an interesting question about medieval history.

Here is another question about stem cell research.

12. Create Travel Planning

ChatGPT is a free travel planner that you should undertake to plan your next trip to any popular destinations or attractions.

It is pretty good at amassing information about different places and summarizing them to create a custom itinerary.

Here is an example of a custom itinerary for two days trip to Cape Cod.

Besides you can also ask ChatGPT to maximize your budget by planning a trip within limited expenses.

13. Explain Complex Topics

If Googling complex topics or reading academic papers does not give a clear understanding, you should ask ChatGPT the same thing.

However, please make a point to explain it to you in simpler terms.

For example, I asked the ChatGPT to explain momentum and gravity like I was five years old.

Similarly, you can ask ChatGPT to summarize any article, topic, or video you like.

14. Solve Tricky Math Questions

Are you a math geek or failing to get good grades in mathematics?

Why not ask ChatGPT to solve tricky math questions in a way you would understand?

Moreover, it shows how each equation works and gives helpful explanations, so you can learn how to solve the problem yourself.

Here is an example.

15. Language Learning and Translations

ChatGPT is equally trained in many different languages, where it can respond in the language of your choice.

You can learn a foreign language using ChatGPT’s assistance as a tutor.

Here is an example.

Otherwise, you can ask the Chatbot to create sentences, articles, or poems in a foreign language.

An exciting thing about ChatGPT is its conversational skill, where it can rephrase everything in a sentence.

Some of the Worst Examples of ChatGPT Uses

As an AI language model, ChatGPT is designed to assist with various tasks, but specific uses could be considered inappropriate or unethical.

Here are some examples of the worst uses of ChatGPT:

Creating fake news or propaganda to post on social media.

Generating hate speech or discriminatory content against any religion, culture, or group.

Engaging in phishing or other forms of online fraud

Generating automated spam or unsolicited marketing messages.

Developing bots, malicious Code, and malware for inappropriate uses

Cheating on school assignments and academic papers.

Producing plagiarized content or academic papers with inauthentic references.

Engaging in cyber stalking or other forms of online harassment

Misrepresenting the capabilities or intentions of ChatGPT to deceive or manipulate others.

It is essential to understand that ChatGPT has the potential to be used in both good and bad ways.

Read on to learn why it is unethical to bypass ChatGPT filters.

Final Thoughts

There are many beneficial daily applications of ChatGPT, which makes it quite a useful AI tool.

In fact, you can use the Chatbot’s assistance to reduce human effort and get complex things done in the shortest time possible.

However, you must always be aware of possible fake and plagiarized information that may come your way, requiring further research.

Frequently Asked Questions Are ChatGPT Results Accurate?

OpenAI ensures to provide accurate information almost all the time.

However, the Chatbot may retrieve information from sources that may be compromised, which even OpenAI may fail to monitor.

You, as a user, must scrutinize the ChatGPT responses before applying.

Is ChatGPT a Life Coach?

It becomes your therapist if you give it the appropriate prompts, but you must carefully use them without analyzing them beforehand.

Can ChatGPT Become a Full-Fledged Developer?

Nope, ChatGPT is not a replacement for experienced developers.

Although the AI tool is trained to Code snippets from scratch, it may fail to create a complete, robust program or software.

Continue reading to learn about some of the best ChatGPT chrome extensions and ChatGPT alternatives.

Learning Different Techniques Of Anomaly Detection

This article was published as a part of the Data Science Blogathon.

Introduction

As a data scientist, in many cases of fraud detection in the bank for a transaction, Smart meters anomaly detection,

Have you ever thought about the bank where you make several transactions and how the bank helps you by identifying fraud?

Someone logins to your account, and a message is sent to you immediately after they notice suspicious activity at a place to confirm if it’s you or someone else.

What is Anomaly Detection?

Suppose we own a otice the font end data errors, even when our company supplies the same service, but the sales are declining. Here come the errors, which are termed anomalies or outliers.

Let’s take an example that will further clarify what an means.

Source: Canvas

Here in this example, a bird is an outlier or noise.

Have you ever thought about the bank where you make several transactions, How the bank helps you by identifying fraud detection?

If the bank manager notices unusual behavior in your accounts, it can block the card. For example, spending a lot of amount in one day or another sum amount on another day will send a message of alert or will block your card as it’s not related to how you were spending previously.

Two AI firms detect an anomaly inside the bank. One is Fedzai’s detection firm, and another one by Ayasdi’s solution.

Let’s take another example of shopping e end of the month, the shopkeeper puts certain items on sale and offers you a scheme where you can buy two at less rate.

Now how do we describe the sales data compared to the start-of-month data? Do sale data validate data concerning monthly sales at the start of selling? It’s not vali

Outliers are “something I should remove from the dataset so that it doesn’t skew the model I’m building,” typically because they suspect that the data in question is flawed and that the model they want to construct shouldn’t need to take into account.

Outliers are most commonly caused by:

Intentional (dummy outliers created to test detection methods)

Data processing errors (data manipulation or data set unintended mutations)

Sampling errors (extracting or mixing data from wrong or various sources)

Natural (not an error, novelties in the data)

An actual data point significantly outside a distribution’s mean or median is an outlier.

An anomaly is a false data point made by a different process than the rest of the data.

If you construct a linear regression model, it is less likely that the model generated points far from the regression line. The likelihood of the data is another name for it.

Outliers are data points with a low likelihood, according to your model. They are identical from the perspective of modeling.

For instance, you could construct a model that describes a trend in the data and then actively looks for existing or new values with a very low likelihood. When people say “anomalies,” they mean these things. The anomaly detection of one person is the outlier of another!

Extreme values in your data series are called outliers. They are questionable. One student can be much more brilliant than other students in the same class, and it is possible.

However, anomalies are unquestionably errors. For example, one million degrees outside, or the air temperature won’t stay the same for two weeks. As a result, you disregard this data.

An outlier is a valid data point, and can’t be ignored or removed, whereas noise is garbage that needs removal. Let’s take another example to understand noise.

Suppose you wanted to take the average salary of employees and in data added the pay of Ratan Tata or Bill Gate, all the employer’s salary averages will show an increase which is incorrect data.

2. Uni-variate – Uni-variate a variable with different values in the dataset.

3. Multi-variate – It is defined by the dataset by having more than one variable with a different set of values.

We will now use various techniques which will help us to find outliers.

Anomaly Detection by Scikit-learn

We will import the required library and read our data.

import seaborn as sns import pandas as pd titanic=pd.read_csv('titanic.csv') titanic.head()

We can see in the image many null values. We will fill the null values with mode.

titanic['age'].fillna(titanic['age'].mode()[0], inplace=True) titanic['cabin'].fillna(titanic['cabin'].mode()[0], inplace=True) titanic['boat'].fillna(titanic['boat'].mode()[0], inplace=True) titanic['body'].fillna(titanic['body'].mode()[0], inplace=True) titanic['sex'].fillna(titanic['sex'].mode()[0], inplace=True) titanic['survived'].fillna(titanic['survived'].mode()[0], inplace=True) titanic['home.dest'].fillna(titanic['home.dest'].mode()[0], inplace=True)

Let’s see our data in more detail. When we look at our data in statistics, we prefer to know its distribution types, whether binomial or other distributions.

titanic['age'].plot.hist( bins = 50, title = "Histogram of the age" )

This distribution is Gaussian distribution and is often called a normal distribution.

Mean and Standard Deviation are considered the two parameters. With the change in mean values, the distribution curve changes to left or right depending on the mean values.

Standard Normal distribution means mean(μ = 0) and standard deviation (σ) is one. To know the probability Z-table is already available.

Z-Scores

We can calculate Z – Scores by the given formula where x is a random variable, μ is the mean, and σ is the standard deviation.

Why do we need Z-Scores to be calculated?

It helps to know how a single or individual value lies in the entire distribution.

For example, if the maths subject scores mean is given to us 82, the standard deviation σ is 4. We have a value of x as 75. Now Z-Scores will be calculated as 82-75/4 = 1.75. It shows the value 75 with a z-score of 1.75 lies below the mean. It helps to determine whether values are higher, lower, or equal to the mean and how far.

Now, we will calculate Z-Score in python and look at outliers.

We imported Z-Scores from Scipy. We calculated Z-Score and then filtered the data by applying lambda. It gives us the number of outliers ranging from the age of 66 to 80.

from scipy.stats import zscore titanic["age_zscore"] = zscore(titanic["age"]) titanic["outlier"] = titanic["age_zscore"].apply( lambda x: x = 2.8 ) titanic[titanic["outlier"]]

We will now look at another method based on clustering called Density-based spatial clustering of applications with noise (DBSCAN).

DBSCAN

As the name indicates, the outliers detection is on clustering. In this method, we calculate the distance between points.

Let’s continue our titanic data and plot a graph between fare and age. We made a scatter graph between age and fare variables. We found three dots far away from the others.

Before we proceed further, we will normalize our data variables.

There are many ways to make our data normalize. We can import standard scaler by sklearn or min max scaler.

titanic['fare'].fillna(titanic['fare'].mean(), inplace=True) from sklearn.preprocessing import StandardScaler scale = StandardScaler() fage = scale.fit_transform(fage) fage = pd.DataFrame(fage, columns = ["age", "fare"]) fage.plot.scatter(x = "age", y = "fare")

We used Standard Scaler to make our data normal and plotted a scatter graph.

Now we will import DBSCAN to give points to the clusters. If it fails, it will show -1.

from sklearn.cluster import DBSCAN outlier = DBSCAN( eps = 0.5, metric="euclidean", min_samples = 3, n_jobs = -1) clusters = outlier.fit_predict(fage) clusters array([0, 1, 1, ..., 1, 1, 1])

Now we have the results, but how do we check which value is min, max and whether we have -1 values? We will use the arg min value to check the smallest value in the cluster.

value=-1 index = clusters.argmin() print(" The element is at ", index) small_num = np.min(clusters) print("The small number is : " , small_num) print(np.where(clusters == small_num)) The element is at: 14 The small number is : -1 (array([ 14, 50, 66, 94, 285, 286], dtype=int64),)

We can see from the result six values which are -1.

Lets now plot a scatter graph.

from matplotlib import cm c = cm.get_cmap('magma_r') fage.plot.scatter( x = "age", y = "fare", c = clusters, cmap = c, colorbar = True )

The above methods we applied are on uni-variate outliers.

For Multi-variates outliers detections, we need to understand the multi-variate outliers.

For example, we take Car readings. We have seen two reading meters one for the odometer, which records or measures the speed at which the vehicle is moving, and the second is the rpm reading which records the number of rotations made by the car wheel per minute.

Suppose the odometer shows in the range of 0-60 mph and rpm in 0-750. We assume that all the values which come should correlate with each other. If the odometer shows a 50 speed and the rpm shows 0 intakes, readings are incorrect. If the odometer shows a value more than zero, that means the car was moving, so the rpm should have higher values, but in our case, it shows a 0 value. i.e., Multi-variate outliers.

Mahalanobis Distance Method

In DBSCAN, we used euclidean distance metrics, but in this case, we are talking about the Mahalanobis distance method. We can also use Mahalanobis distance with DBSCAN.

DBSCAN(eps=0.5, min_samples=3, metric='mahalanobis', metric_params={'V':np.cov(X)}, algorithm='brute', leaf_size=30, n_jobs=-1)

Why is Euclidean unfit for entities cor-related to each other? Euclidean distance cannot find or will give incorrect data on how close are the two points.

Mahalanobis method uses the distance between points and distribution that is clean data. Euclidean distance is often between two points, and its z-score is calculated by x minus mean and divided by standard deviation. In Mahalanobis, the z-score is x minus the mean divided by the covariance matrix.

Therefore, what effect does dividing by the covariance matrix have? The covariance values will be high if the variables in your dataset are highly correlated.

Similarly, if the covariance values are low, the distance is not significantly reduced if the data are not correlated. It does so well that it addresses both the scale and correlation of the variables issues.

Code

df = pd.read_csv('caret.csv').iloc[:, [0,4,6]] df.head()

We defined the function distance as x= None, data= None, and Covariance = None. Inside the function, we took the mean of data and used the covariance value of the value there. Otherwise, we will calculate the covariance matrix. T stands for transpose.

For example, if the array size is five or six and you want it to be in two variables, then we need to transpose the matrix.

np.random.multivariate_normal(mean, cov, size = 5) array([[ 0.0509196, 0.536808 ], [ 0.1081547, 0.9308906], [ 0.4545248, 1.4000731], [ 0.9803848, 0.9660610], [ 0.8079491 , 0.9687909]]) np.random.multivariate_normal(mean, cov, size = 5).T array([[ 0.0586423, 0.8538419, 0.2910855, 5.3047358, 0.5449706], [ 0.6819089, 0.8020285, 0.7109037, 0.9969768, -0.7155739]])

We used sp.linalg, which is Linear algebra and has different functions to be performed on linear algebra. It has the inv function for the inversion of the matrix. NumPy dot as means for the multiplication of the matrix.

import scipy as sp def distance(x=None, data=None, cov=None): x_m = x - np.mean(data) if not cov: cov = np.cov(data.values.T) inv_cov = sp.linalg.inv(cov) left = np.dot(x_m, inv_cov) m_distance = np.dot(left, x_m.T) return m_distance.diagonal() df_g= df[['carat', 'depth', 'price']].head(50) df_g['m_distance'] = distance(x=df_g, data=df[['carat', 'depth', 'price']]) df_g.head() B. Tukey’s method for outlier detection

Tukey method is also often called Box and Whisker or Box plot method.

Tukey method utilizes the Upper and lower range.

Upper range = 75th Percentile -k*IQR

Lower range = 25th Percentile + k* IQR

Let us see our Titanic data with age variable using a box plot.

sns.boxplot(titanic['age'].values)

We can see in the image the box blot created by Seaborn shows many dots between the age of 55 and 80 are outliers not within the quartiles. We will detect lower and upper range by making a function outliers_detect.

def outliers_detect(x, k = 1.5): x = np.array(x).copy().astype(float) first = np.quantile(x, .25) third = np.quantile(x, .75) # IQR calculation iqr = third - first #Upper range and lower range lower = first - (k * iqr) upper = third + (k * iqr) return lower, upper outliers_detect(titanic['age'], k = 1.5) (2.5, 54.5) Detection by PyCaret

We will be using the same dataset for detection by PyCaret.

from pycaret.anomaly import * setup_anomaly_data = setup(df)

Pycaret is an open-source machine learning which uses an unsupervised learning model to detect outliers. It has a get_data method for using the dataset in pycaret itself, set_up for preprocessing task before detection, usually takes data frame but also has many other features like ignore_features, etc.

Other methods create_model for using an algorithm. We will first use Isolation Forest.

ifor = create_model("iforest") plot_model(ifor) ifor_predictions = predict_model(ifor, data = df) print(ifor_predictions) ifor_anomaly = ifor_predictions[ifor_predictions["Anomaly"] == 1] print(ifor_anomaly.head()) print(ifor_anomaly.shape)

Anomaly 1 indicates outliers, and Anomaly 0 shows no outliers.

The yellow color here indicates outliers.

Now let us see another algorithm, K Nearest Neighbors (KNN)

knn = create_model("knn") plot_model(knn) knn_pred = predict_model(knn, data = df) print(knn_pred) knn_anomaly = knn_pred[knn_pred["Anomaly"] == 1] knn_anomaly.head() knn_anomaly.shape

Now we will use a clustering algorithm.

clus = create_model("cluster") plot_model(clus) clus_pred = predict_model(clus, data = df) print(clus_pred) clus_anomaly = clus_predictions[clus_pred["Anomaly"] == 1] print(clus_anomaly.head()) clus_anomaly.shape Anomaly Detection by PyOD

PyOD is a python library for the detection of outliers in multivariate data. It is good both for supervised and unsupervised learning.

from pyod.models.iforest import IForest from chúng tôi import KNN

We imported the library and algorithm.

from chúng tôi import generate_data from chúng tôi import evaluate_print from pyod.utils.example import visualize train= 300 test=100 contaminate = 0.1 X_train, X_test, y_train, y_test = generate_data(n_train=train, n_test=test, n_features=2,contamination=contaminate,random_state=42) cname_alg = 'KNN' # the name of the algorithm is K Nearest Neighbors c = KNN() c.fit(X_train) #Fit the algorithm y_trainpred = c.labels_ y_trainscores = c.decision_scores_ y_testpred = c.predict(X_test) y_testscores = c.decision_function(X_test) print("Training Data:") evaluate_print(cname_alg, y_train, y_train_scores) print("Test Data:") evaluate_print(cname_alg, y_test, y_test_scores) visualize(cname_alg, X_train, y_train, X_test, y_test, y_trainpred,y_testpred, show_figure=True, save_figure=True)

We will use the IForest algorithm.

fname_alg = 'IForest' # the name of the algorithm is K Nearest Neighbors f = IForest() f.fit(X_train) #Fit the algorithm y_train_pred = c.labels_ y_train_scores = c.decision_scores_ y_test_pred = c.predict(X_test) y_test_scores = c.decision_function(X_test) print("Training Data:") evaluate_print(fname_alg, y_train_pred, y_train_scores) print("Test Data:") evaluate_print(fname_alg, y_test_pred, y_test_scores) visualize(fname_alg, X_train, y_train, X_test, y_test_pred, y_train_pred,y_test_pred, show_figure=True, save_figure=True) Anomaly Detection by Prophet import prophet from prophet import forecaster from prophet import Prophet m = Prophet() data = pd.read_csv('air_pass.csv') data.head() data.columns = ['ds', 'y'] data['y'] = np.where(data['y'] != 0, np.log(data['y']), 0)

The Log of the y column enables no negative value. We split our data into train, test, and stored the prediction in the variable forecast.

train, test= train_test_split(data, random_state =42) m.fit(train[['ds','y']]) forecast = m.predict(test) def detect(forecast): forcast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].copy() forcast['real']= data['y'] forcast['anomaly'] =0 forcast.loc[forcast['real']< forcast['yhat_lower'], 'anomaly']=-1 forcast['imp']=0 in_range = forcast['yhat_upper']-forcast['yhat_lower'] forcast.loc[forcast['anomaly']==1, 'imp'] = forcast['real']-forcast['yhat_upper']/in_range forcast.loc[forcast['anomaly']==-1, 'imp']= forcast['yhat_lower']-forcast['real']/in_range return forcast detect(forecast)

We took the anomaly as -1.

Conclusion

The process of finding outliers in a given dataset is called anomaly detection. Outliers are data objects that stand out from the rest of the object values in the dataset and don’t behave normally.

Anomaly detection tasks can use distance-based and density-based clustering methods to identify outliers as a cluster.

We here discuss anomaly detection’s various methods and explain them using the code on three datasets of Titanic, Air passengers, and Caret to

Key Points

1. Outliers or anomaly detection can be detected using the Box-Whisker method or by DBSCAN.

2. Euclidean distance method is used with the items not correlated.

3. Mahalanobis method is used with Multivariate outliers.

4. All the values or points are not outliers. Some are noises that ought to be garbage. Outliers are valid data that need to be adjusted.

5. We used PyCaret for outliers detection by using different algorithms where the anomaly is one, shown in yellow colors, and no outliers where the outlier is 0.

6. We used PyOD, which is the Python Outlier detection library. It has more than 40 algorithms. Supervised and unsupervised techniques it is used.

7. We used Prophet and defined the function detect to outline the outliers.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Example And Types Of Leveraged Loans With Its Uses

Introduction to Leveraged Loans

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These are simple loans; their distribution and arrangement are the only difference. They are arranged for the companies already having debts and are set by the syndicate banks. These are managed by private equity firms, hedge funds, and other players.

Example of Leveraged Loans Types of Leverage Loans

There are three types :

Underwritten Deals: In this type, the arranger guarantees the entire loan amount by entering into the underwriting deal. The arranger must bear any left-out loan if the investors do not subscribe to the loan amount. He can try to sell the remaining loan in the market later on. Even if the market conditions are down in the future, the arranger is the only one who bears losses by selling the loan even at a discounted rate.

Best- Efforts: Contrary to the above, instead of committing the entire amount of underwriting of the loan, the arranger group commits to underwrite less than the whole amount. Any undersubscribed amount can be adjusted per the market variations, or it can be left as a credit. If, after the changes also, the loan continues to be unsubscribed, the lower amount of the loan has to be accepted by the borrower to close the deal.

Club Deal: This type of deal is usually for private equity players. The private equity players can acquire targets previously held by more prominent strategic players anytime in the past while distributing the exposure risk. These are larger-sized loans than own funding by the lenders and are used for M&A activity.

Leveraged Loans Index Uses of Leveraged Loans

There are many M&A deals where leveraged buyout (LBO) is used. Leveraged loans form an essential and significant portion of LBO. Hence, these are used in many M& A deals.

The Loans are used to better prepare the company’s balance sheet in case of its stock repurchase.

Debts of the companies can be refinanced with the help of these.

The company can use it for its day-to-day operations and acquiring various long-term assets.

Leveraged Loans vs High Yield

Leveraged Loans are secured loans guaranteed by the company’s assets, whereas high-yield bonds are not secured.

As the Leveraged Loans are secured, they prioritize getting paid in case of the company’s insolvency, whereas the High Yield bonds are paid after the Leveraged Loans.

Advantages

The loan amount obtained through these loans can push the company’s capital, and if that amount is used correctly, it can make the company achieve its dream heights.

When the business has objectives of acquisition, management buyout, shares buy-back, or a one-time dividend, leveraged loans suit the best because there are additional costs and risks of bulking up on debt.

The company takes these Loans in addition to other debts, i.e., short-term and long-term debts. It brings the company to a higher than average debt level, and in the long run, it possesses an increased leverage risk.

The interest rates paid in these loans are higher; hence, this type of funding proves costly for the company.

The process of taking, dealing with, and managing leveraged loans is much more complex; thus, the management must invest much time.

Conclusion Recommended Articles

Learn The Different Examples Of Sqlite Function

Introduction to SQLite functions

SQLite provides different kinds of functions to the user. Basically, SQLite has different types of inbuilt functions, and that function we easily use and handle whenever we require. All SQLite functions work on the string and numeric type data. All functions of SQLite are case sensitive that means we can either use functions in uppercase or lowercase. By using the SQLite function, we sort data as per the user requirements. SQLite functions have a different category, such as aggregate functions, data functions, string functions, and windows functions, and that function we can use as per the requirement.

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SQLite functions

Now let’s see the different functions in SQLite as follows.

1. Aggregate Functions

AVG: It is used to calculate the average value of a non-null column in a group.

COUNT: It is used to return how many rows from the table.

MAX: It is used to return the maximum value from a specified

MIN: It is used to return the minimum value from a specified

SUM: is used to calculate the sum of non-null columns from the specified table.

GROUP_CONCAT: It is used to concatenate the null value from the column.

2. String Functions

SUBSTR: It is used to extract and return the substring from the specified column with predefined length and also its specified position.

TRIM: It is used to return the copy of the string, and it removes the start the end character.

LTRIM: It is used to return the copy of the string that removed the starting character of the string.

RTRIM: It is used to return the copy of the string that removed the ending character of the string.

LENGTH: It is used to return how many characters in the string.

REPLACE: It is used to display the copy of the string with each and every instance of the substring that is replaced by the other specified string.

UPPER: It is used to return the string with uppercase that means it is used to convert the all character into the upper cases.

LOWER: It is used to return the string with a lowercase, which means converting all character into lower cases.

INSTR: It is used to return the integer number that indicates the very first occurrence of the substring.

3. Control Flow Functions

COALESCE: It is used to display the first non-null argument.

IFNULL: It is used to implement if-else statements with the null values.

IIF: By using this, we can add if – else into the queries.

NULLIF: It is used to return the null if first and second the element is equal.

4. Data and Time Function

DATE: It is used to determine the date based on the multiple data modifiers.

TIME: It is used to determine the time based on the multiple data modifiers.

DATETIME: It is used to determine the date and time based on the multiple data modifiers.

STRFTIME: That returns the date with the specified format.

5. Math Functions

ABS: It is used to return the absolute value of the number.

RANDOM: It is used to return the random floating value between the minimum and maximum integer.

ROUND: It is used to specify the precision.

Examples

Now let’s see the different examples of SQLite functions as follows.

create table comp_worker(worker_id integer primary key, worker_name text not null, worker_age text, worker_address text, worker_salary text);

Explanation

In the above example, we use the create table statement to create a new table name as comp_worker with different attributes such as worder_id, worker_name, worker_age, worker_address, and worker_salary with different data types as shown in the above example.

Now insert some record for function implementation by using the following insert into the statement as follows.

insert into comp_worker(worker_id, worker_name, worker_age, worker_address, worker_salary) values(1, "Jenny", "23", "Mumbai", "21000.0"), (2, "Sameer", "31", "Pune", "25000.0"), (3, "John", "19", "Mumbai", "30000.0"), (4, "Pooja", "26", "Ranchi", "50000.0"), (5, "Mark", "29", "Delhi", "45000.0");

Explanation

In the above statement, we use to insert it into the statement. The end output of the above statement we illustrate by using the following screenshot as follows.

Now we can perform the SQLite different functions as follows.

a. COUNT Function

Suppose users need to know how many rows are present in the table at that time; we can use the following statement.

select count(*) from comp_worker;

Explanation

In the above example, we use the count function. The end output of the above statement we illustrate by using the following screenshot.

b. MAX Function

Suppose we need to know the highest salary of the worker so that we can use the following statement as follows.

select max(worker_salary) from comp_worker;

Explanation

In the above example, we use the max function to know the max salary of a worker from the comp_worker table. The end output of the above statement we illustrate by using the following screenshot.

c. MIN Function select min(worker_salary) from comp_worker;

Explanation