GRE Verbal | Barron’s 800 Destroyed | Day 7

It’s such a beautiful day, I already learnt 30 words and I would like to continue. Let’s learn Barron’s 800 words for GRE in the most layman and simple way

161. emollient – mollify; soothing

At least there are 4 words which means soothing, first of all mollify, then alleviate( to lessen), then eMOLLIent

You gotta remember it this way

162. endemic – belonging to a particular area;

There are few diseases which pertains to a particular area. May be Ebola was one of them

163. Pandemic – Belonging to a range of countries

Corona is a pandemic i.e. a lot of countries are affected by this virus

164. entomology – Scientific study of insects

ent – ANT which is an insect

165. ephemeral – short lived

Ephemeral is short lived and perennial is long lived.
Ephemeral sounds like ‘abhi maral’ i.e. he died just now 😛

166. etymology – Origin of history of a word

Have you read the book Word Power Made Easy by Norman Lewis?
It has a very interesting way to learn words by teaching the origin of each word.
It’s definitely an awesome book to start with

167. euphoria – feeling of extreme happiness

Remember Palash Sen? The singer? Kabhi aana tu meri gali
His band was Euphoria and it used to make me very happy 🙂

168. Euthanasia – Mercy Killing

This is a very very specific term, in Asia Japan is the only country which allows or basically have no law against Mercy killing

Japan is the only country to entertain Euthanasia in Asia

169. Exorcise – to expel evil spirits;

Emily Exorcism, to expel evil spirit from Emily

170. Extrapolation – to estimate the projection for future

It’s an easy term to remember if you are form a Data Science Background.
Linear Regression extrapolate the values for future by using the historic data

171. fervor – warmth and intensity of emotion

fever – warmth of body, so is fervor

These animals will love and hate with equal fervor

172. flora – plants of a region

Flora and Fauna, easy

173. Flux – flowing; a continuous moving

If you are mechanical, then it’s easy for you
in-flux and out-flux

174. forestall – to prevent; delay

fore means ahead of time and the word forestall
He forestalled critics by offering a defense of the project.

175. fresco – a painting done on a wall

What a beautiful word, a very very specific word, much like Euthanasia (mercy killing)

Wall painting

176. fusion – union

Atomic fusion, easy
Don’t know why the words am choosing are easy and frequently used !!

177. garrulous – very talkative

Though it looks like the word is something to do with anger, but remember this word has come along with loquacious as synonyms.

What is the antonym of loquacious ? Laconic

178. gerrymander – to divide an area into voting districts in a way that favours a political party

Another special word with special meaning,

Gotta by-heart like this only

179. gregarious – outgoing; sociable

Most of the extroverts are gregarious in nature

180. guileless – not cunning

So, guile means cunning. Guileless means not cunning and so does the word artless.

I met a girl at the airport who was artless 🙂

181. herbivorous – plant eater

Zero-brainer

182. homogeneous – uniform in composition

Zero-brainer

183. hyperbole – purposeful exaggeration for effect

My aunt is a bit of a drama queen, and she uses hyperbole in almost every sentence
Little children often speak almost exclusively in hyperbole.

Hyper – jyada
Bola – Speaking

Hyperbole – jo jyada bolta hai

184. idolatry – idol worship

India follows idolatry

185. impair – to damage; injure

to pair is to grow string, to impair is to damage or injure someone

186. impede – To block; arrest

Remember, the story about arrest?
Arrest is to stop or block and so is impede i.e. it is to stop from exceed

187. ingenuous – Naive and idiot

188. ingenious – Genius

The above two words look the same, the only difference is u vs i
i = Genius
u = Idiot

189. intangible – which can’t be touched

Human feelings are intangible

190. introspective – contemplating one’s own thoughts and feelings

John liked him because he was not introspective or self-critical and quite free from self-consciousness.

There were some easy words and some difficult.

Do keep on revising the words. It’s gonna be a long road, but the end product is definitely rewarding.

Keep Learning 🙂
Target 330


GRE Verbal | Barron’s 800 Destroyed | Day 6

Learn Barron 800 words in simple and layman way for GRE

We will start with 133 and will learn a lot of words today

133. Abscission – Act of cutting; the natural separation of a leaf or other part of a plant

Take a scissor and cut the leaf into two parts – It’s called ab’scission’

134. aesthetic – related to beauty or art?
The aesthetic beauty of the place was mesmeric

135. amulet – ornament worn as a charm against evil spirit

If you are a bollywood movie fan then you can connect amulet with Taabeez or what the actors wore in the movie Zaani Dushman 😛

136. analgesic – medicine that reduces pain; pain-relieving

anaesthetic is used to create partial loss of feeling, same ways analgesic is used to relieve or alleviate(Barron Word) pain

Let’s talk about few pathy related words which are asked in GRE

137. empathy – understand and share feelings of others
138. apathy – Looks like sharing feeling or a good word but it actually means LACK OF INTEREST. He showed apathy in Mathematics from beginning

139. antipathy – pathy means liking, empathy means same feeling, apathy means DISLIKE, what is left? Dislike??
Antipathy is feeling of dislike

140. archeology – Study of material evidence of past Human Life

Easy fizzgy

141. arrest – to stop or block; IMPEDE (learn it, ye aage aane wala hai)

arrest is mostly used to show that someone is captured, but why will Police capture anyone?
Because the Police wanted to stop that person

142. artifact – Items made by human craft

What ever is hand made is an artifact(mostly of cultural importance)

This artifact was created to pour ‘Cutting-Chai’ or Masala Chai to Akbar

143. ascetic

143 means I LOVE YOU in teenage argot(Barron 800, means a language specific to a group).

Ascetic is someone who practices self-denial. What is the name of this website?
The Data MONK – Monk practices ascetic lifestyle

144. astringent – Harsh and severe

When ever I had to write a letter to principal to complain about a student. I always ended the letter like this

‘Sir, Please five stern and stringent punishment to Amit for his bad behaviour’

If you have a friend name Harsha, and if he is stern then the word to describe him is astringent

145. asylum – Place of Refugee

Easy hai

146. austere – very simple; stern

In this pandemic of corona, everyone decided to celebrate an austere christmas

147. bifurcate – to divide into two parts

Machine Learning algorithms are bifurcated into Supervised and Unsupervised algorithms

148. bovine – cow like

Though it’s unlikely that GRE uses this word but remember the first three alphabets

bov – b+1 o v+1 = cow like

149. broach – to mention for the first time

how do you ask a person who is new to your team?
‘Hey Broo, Let me coach you’
Broach – to mention for the first time

150. chivalry – bravery and good behaviour towards women

Easy hai

151. complement – something that completes or makes up whole

In a right angle triangle the two non-90 degree angles are complementary


152. Connoisseur – Expert in matters of taste

I would love to be a chocolate/wine connoisseur

153. Converge – Tend to meet; towards something

Easy hai, converge and diverge

154. Deride – To belittle someone or to mock

Below are two eggs deriding the middle one

155. Derivative – something that is derived; unoriginal

To derive from something i.e. It was not original. When you derive a formula, you start from something basic and then go ahead to prove some other formula/theory

156. discrepancy – difference

There is a discrepancy between the numbers which you are reporting and the numbers which Harish is reporting to the CEO

157. Egotistical – Excessively Self-centred

Bahut jyada ego, matlabe hadd se jyada ego.
I myself is an egotistical human

158. Elegy – Poem or expression of grief

In the complete Barron 800 series, there are three words which is related to death – Dirge (song sung when someone dies), eulogy(speech after someone dies), and Elegy(song of grief)

Learn these

159. Elixir – A substance believed to have the power to cure ills

Have you ever played the game Clash of the Clans?
It’s a well known strategy mobile game, there we have normal blue elixir and dark elixir.
Blue elixir was used to heal normal troop
Dark elixir was used to heal heavy troop.

160. embellish – enhance
I added a lot of bells in order to embellish the Christmas tree

You can comment and add more things to help everyone understand

Keep Learning 🙂
Target 330

One Hot Encoding – Feature Engineering

So, I just started solving the latest Hackathon on Analytics Vidhya, Women in the loop . Be it a real-life Data Science problem or a Hackathon, one-hot encoding is one of the most important part of your data preparation.

If you don’t know about it yet, then you are definitely missing out on something which can boost your rank.

One hot encoding is a representation of categorical variables as binary vectors. What this means is that we want to transform a categorical variable or variables to a format that works better with classification and regression algorithms.

This is how One Hot Encoding works

How not to do a categorical division?
Basically, if you have a column with Course Details like. Data Science, Software Development, Testing, etc. and you want to use these categorical variable in your model, then the best way to do is to make a column with binary variable with all the variables. So, you will have Data Science, Software Development, Testing will be new columns with values as 0, 1, 2, etc.

Now the problem is that 2>1>0 and the model might treat it as this way. So, to get things sorted you need to specify this to the model that ‘bro, these are all categorical numbers and you dare not treat it as numbers’

What to do?
Create new column as binary column. So, Data Science, Software Development, Testing, etc. with 0 and 1. This whole process is called One Hot Encoding.

Example below

There was some JSON error while directly posting the code, so pasting the screenshot

Sales is the name of the column which we need to predict, splitting the sample into 8:2 and putting it into train and test
Initial column names, here Course Domain and Course Type are the two columns which need One Hot Encoding Treatment
ohe <- c("Course_Domain","Course_Type")
train_data = as.data.frame(train_data)

Put the name of the variables which need OHE treatment at one place and convert the training_data into data frame
dummies_train = dummyVars(~ Course_Domain+Course_Type , data = train_data)

df_ohe = as.data.frame(predict(dummies,newdata = train_data))

Here we are creating and converting the variables into dummy variables. Let's see how the columns are names in the data frame df_ohe
colnames(df_ohe)
[1] "Course_Domain.Business" "Course_Domain.Development"
[3] "Course_Domain.Finance & Accounting" "Course_Domain.Software Marketing"
[5] "Course_Type.Course" "Course_Type.Degree"
[7] "Course_Type.Program"

So, all the variables in the two column were given a new name and each have the value 0 or 1..Awesome !!

df_train_ohe = cbind(train_data[,-c(which(colnames(train_data) %in% ohe))],df_ohe)
colnames(df_train_ohe)
The new list of columns in your training data set are below
colnames(df_train_ohe)
[1] "ID" "Day_No"
[3] "Course_ID" "Short_Promotion"
[5] "Public_Holiday" "Long_Promotion"
[7] "User_Traffic" "Competition_Metric"
[9] "Sales" "Course_Domain.Business"
[11] "Course_Domain.Development" "Course_Domain.Finance & Accounting"
[13] "Course_Domain.Software Marketing" "Course_Type.Course"
[15] "Course_Type.Degree" "Course_Type.Program"

You started with 11 variables, and now you have 16 columns, feed it in your XGB or Linear Regression..By the way, you still have 7 more days for the Hacathon..Try it 🙂

Keep Learning 🙂

The Data Monk

GRE Verbal | Barron’s 800 Destroyed | Day 5

You should revise the previous 120 words first !!
But no one follows the rule, so let’s proceed 😛

Today will we be 150 words strong 🙂

121. aberrant – deviating from what is normal
Imagine, there is a line of ants going to eat a cube of sugar, but suddenly one of the ant, named Gaitonde, breaks the chain to meet Cuckoo.
Now that’s an aberrant behaviour

122. abstemious – moderate in appetite i.e. one who eats less

Who eats less?
A monk? But, definitely not the Data Monk 😛
Bonus word – Stoicism is the practice of having no interest in world affairs.

While practicing stoicism he led an abstemious life

Have you ever observed someone skinny? He looks like a stem and he always have a visible but weak abs , why? because he don’t eat much

abstemious – having low appetite

123. adamant – Ziddi;, uncompromising

Who is adamant?
A small kid when he wants a particular toy – that’s an adamant behaviour

Easy tha 😛

124. admonish – scolding

How did I learn this word?
I had a friend, Manish, and he used to scold every person. The moment I saw the word and it’e meaning, I imagined him and it was easy to learn

Search your scolding adMONISH

125. adulterate – To make impure

When you add rat’s poison in weed, then you adulterate the drug 😛
When you add small pebbles in Pulses, then that is adulteration

126. affinity – fondness; liking

Easy hai yeh bhi..Negative has affinity towards positive charge
His affinity towards coding made him what he is today

127. ambrosia – the food of the God

Brooo, I got some food for the God..

Don’t have a good example here..Help me out in the comment section

128. artless – Natural

I met a girl at the airport and she was as artless as a bird. Warm and happy.

129. argot – the jargon of a particular group

Yoo Bro, Wassup Manhh, Yoo, I ain’t doin that shit no more

The above is teenage argot

130. apex – Highest Point

Facebook was at it’s apex in 2018
I am just choosing easy words because am too tired today 🙁

131. Autonomous – Self governing

There are many colleges which are autonomous in nature i.e. exam, paper-correction, etc. are done in the college itself.

In non-autonomous colleges, exam paper are set by a group of college professors and the answer sheets are corrected by teachers from other colleges

132. Capricious – Unexpected change in mind; whimsical; sudden change in mind

132 was my roll number in college and I was capricious. One moment I am in the class, and after 30 mins I might be at Big Bazaar 😛

People born in the date range 21st Dec – 19th Jan are Capricorns and trust me they have a capricious mind

Not feeling good..Will continue tomorrow 🙂

Keep Learning !!

Target 330

GRE Verbal | Barron’s 800 Destroyed | Day 4

Target 330

We have already covered 90 words i.e. more than 10% of the word list. Let’s make it 15% today

91. audacious – bold
You know where did I always hear this word?
I hear it from Ravi Shashtri, “This is an audacious move by Dhoni to start the innings with R.Ashwin”
“What an audacious shot to complete his 300” said Ravi when Shehwag hit a six to get to 300 at Multan

92.aver – to affirm
The only thing which I can relate with aver is

“Please aver your attendance at the marriage”
Suppose Gayathri is getting married, so she will send a card to all her friends with a note to aver if they are coming as she has to make arrangement for the guests.

93. banter – playful conversation

Banter is two step before a person actually files an official complain against someone for “passing sexual/inappropriate comment”

But his completely depend on the situation. A person can be of a jolly nature and banter with his colleagues and this could be acceptable

Ritesh was bashed for his banter behaviour

94. bucolic – characteristic of the countryside
Natural view, serene
The church is there in the bucolic setting

Remember this view? This is Windows wallpaper and this is bucolic

95. cacophonous – unpleasant or harsh-sounding
I will let you imagine the sound(though the word is quite frequently used)

Nail rubbing against marble
High note sound of a mike when someone is actually testing it
Really irritating sound(mostly high pitch)

Caco – Bad
Phonous – Voice (Wonder where the word phone came from?)

96. cadge – to beg; sponge
This one is a bit hard, because I could not find a way to learn it. I cadge you to make me remember the word.

One of my friend actually cadged for some money to buy weed. So, don’t do drugs.

cadge – CAn David GEt me something? I beg you

97. canard – false; rumour

My girlfriend helped me remember the word.
She cooked a story saying that Canara Bank spread a rumour that it’s shutting down, but Yes Bank did.

So canara bank spreads rumors i.e. Canara Canard stories

98. caucus – meeting

Did it sound like caco and you thought it’s about evil or bad??
No it is not

Caucus is not a simple meeting, it’s a special meeting of SUPPORTERS

Couldn’t find a good way to remember the word. Help me out in the comment box

99. centrifugal – moving away from a centre
You are preparing for GRE , that means you are an engineer from India. And being an engineer you got to know the meaning of these two forces

centrifugal – towards the centre

100. centripetal – moving towards a centre

centripetal force – away from the centre

101. coalesce – to cause to become one

There is a function in SQL – coalesce(abc,1) it changes NULL present in the variable abc to 1, you know why? Because coalesce believes in equality and this thing adds consistency to the column’s data type

Coalesce synonyms – Join, union, fuse, etc.
and JOIN and UNION are used in SQL as well 😛

102. compendium – brief, comprehensive summary
What is a podium? Podium is a small structure in which your principal used to stand and deliver his speech. Podium is something brief

So is compendium !! It’s a brief summary

Podium

103. credo – statement of belief; creed
Have you ever read the Preamble(the introductory article) of the constitution?
It says ‘The government should not discriminate on the basis of caste, creed, religion, and sex.

Creed is a statement of belief which might or might not be relate

104. deference – respect; regard for another’s wish
deference comes from a beautiful word defer which means to agree to someone’s point of view due to respect.

My old uncle believes that today’ media is good for nothing. Though i did not agree to him, but I defer to him

If you and your dad disagree about the best route to the grocery store, you might defer to him, and take his route

105. detraction – the act of taking away
Easy fizzy – sounds like distraction and distraction takes you away, so does detraction 😛

106. digression – act of straying away from the main point
What is linear regression?
It is a fit of the data points towards a particular line

What is digression?
Act of staying away from the main pint 😛

107. discomfit – to make uneasy; feel embarrassed
not comfortable.

Manager’s mean look served to discomfit me during the interview

108. discordant – not in tune
Concordant – both positive or both negative
Discordant – Opposite i.e. not aligned or not in tune

Discordant vs Concordant


109. facilitate – to make less difficult
There were vegetable shops open after every 2 km during Corona virus outburst to facilitate availability of essentials to the common people

110. fallacious – based on false idea or fact
Fallacy mans false or having some problem
Fallacious is the same

111. fawning – seeking favours by flattering
Chaplusi krna

If you are a good manager, you will yawn when someone fawn you

Fawning


112. foment – arouse
fomentation is a process of stirring up,
the leader fomented the crowd against the government i.e. he arouse people against the government

113. paragon – model of perfection
These are the iconic paragon slippers which are made of perfection.
My complete family has wore these chappals(it’s so damn good)



114. preamble – preliminary statement
Preamble of Constitution – Introductory statement

115. precursor – forerunner; predecessor

pre – pehle aane wala
a person or thing that comes before another of the same kind; a forerunner.

a three-stringed precursor of the violin

116. presumptuous – rude; improperly bold
It has nothing to do with assumption

Presumptuous is rude, improperly bold

Help me learn this word also please

117. prodigal – extravagant
Prodigy is a person who is very intelligent or very skilful at a very early age. Example – Visvanathan Anand, Sachin Tendulkar, and definitely Magnus Carlsen

But what about prodigal?
This is completely opposite. It means someone who spends too much.
Everyone has at least one friend who is prodigal in nature.

118. puissant – powerful
Who is a pussy ? Some one who is afraid.
I am not a pussy, I ain’t a pussy. I am puissant i.e. powerful

119. quibble – to argue over insignificant and irrelevant details
I always love words with very specific meaning, this is one of it.

Example – The only quibble about the book is the price.
Matlab itni achchi book aur tumhara concern sirf chota sa price hai

120. quiescent – inactive; still
Remember the word dormant?
It’s almost the same – quiescent is inactive

strikes were headed by groups of workers who had previously been quiescent

Keep Learning 🙂
Target

GRE Verbal | Barron’s 800 Destroyed | Day 3

Target 330

Disclaimer – I have used all the references to remember the meaning of the words. There are a few very naive ways down there. So, please ignore

61. abscond – to depart secretly

One of the classic example of the word is S.C.Bose
S.C.Bose absconded from India when Britishers were after them. He was then functional from some other country

S.C. Bose absconded from India when Britishers were after them.


62. abstinence – the giving up of certain pleasure
Have you ever given up on Television to prepare for something?? may be IIT JEE or some semester exam?
When you absent yourself from some sort of pleasure ~ Abstinence

63. verbose – wordy (For GRE)

With a lot of verbal..the Reading Comprehensions in CAT,GMAT, and GRE are verbose

64. alleviate –
to relieve
Crocin alleviates headache
Hot Water bag alleviates period pain for females

65. appellation – name; Title
If you have cheated in your engineering examinations, then you don’t deserve the appellation of an Engineer. And thus India lost all it’s 100Million engineers in one go

66. Brazen – Bold; Shameless

Most of you would have visited the website brazzers..bus wahi se yaad kr lena..
Brazen = Bold or shameless


67. Bedizen – To dress in vulgar manner
How do you dress for bed?
I won’t say vulgar, but it’s not that decent as well plus you already know that brazen is bold and bedizen sounds a lot like brazen so it would also mean the same..

Brazen ~ Bedizen ~ bold/Shameless/Vulgar

68. Belie – To contradict
I have a beautiful story for this

Anil Kapoor contradicts his age, right?
Anil Kapoor belie his age

I was unable to learn the word with this trick, so I searched for Anil Kapoor belie dance on youtube and wooooo, it was there..Now I won’t forget the word

Anil Kapoor doing bellly dance. He belie his age.


69. Cant – insincere talk

This is hard for me. Actually there is a youtube channel where an Indian couple shows their life style and all.
The modern cant


70. Circuitous – roundabouts
Circuitous ~ Circus, and what do we have in circus??
Round abouts

71. Churlish – Boorish
Churchil was a badass. He is famous for his leadership skills during World War.
I wonder if the hindi word ‘boore’ is used pan India, but boore means someone who is bad and boorish means rude

Churchil ~ Churlish ~ Boorish ~Boore ~ Rude

72. Boorish – Rude

You are boorish if you have ignored word number 71

73. Cognizant – Aware; Informed
Most of you must have heard the name of this company Cognizant. Everyone know the company Cognizant, it means that everyone is informed about CTS i.e. everyone is cognizant about CTS

74. Codify – To systematise
Codify ~ Codes i.e. a bunch of lines of instructions in a systematic manner

75. Coda – Last stanza of a music composition or a poem

This is one more beautify word. I love those words which are having very very specific meaning. One of those is coda which means the last stanza of a song or poem. One of my fav song is ‘Jaane De’ from the movie Qareeb Qareeb single staring Irfan Khan. The coda of the song is definitely the best part of the song
मालूम है जहाँ दर्द हैवही फिर भी क्यूँ जाएँ
वही कशमकश, वही उलझने
वही टीस क्यूँ लाएँ
बेहतर तो ये होता
हम मिले ही ना होते
मगर जाने दे

76. Clique – A small exclusive group
There are few groups active on the internet who deletes child porn available on the dark web. These are secret groups (may be 31 people) and can be considered as Clique.

Clique -> Click -> Dark Web Website -> A small and EXCLUSIVE group of people/hackers deleting child porn

77. Coagulate – Thicken
Easy if you are a science students
Few things coagulate. If you add lemon to milk then it will coagulate and will then change to Paneer/Cottage cheese

78. Contiguous – Touching; Neighbouring
If you are reading this article in March 2020 you already know what is contiguous. It’s CORONA which is contiguous disease

79. Cosmology – Study of the universe

Study of cosmos i.e. universe

80. Dearth – Scarcity
Earth is full of resources but when you Dearth something then you feel the scarcity
There is a great dearth of water in Rajasthan
Rural areas in India witnesses a dearth of technological advancement.

81. Dogmatic – Orthodox
Sorry to say, but few people in Rajasthan holds dogmatic thinking and marries their daughters at a very early age

82. Effrontery – Shameless boldness

Jeff Efron worked in Baywatch, why was he selected as the hero of the movie?

Correct..Because he was effrontery

Effrontery Efron

83.Condone – To overlook voluntarily; forgive
The S.C. should never condone the rights of any particular community of India.
The class teacher should not condone the work of back-benchers


84. decorum – proper behaviour
Maintain decorum when you are with your client during Client visit (I.T. People can easily connect with the above statement)

85. Coquette – women who flirts
When your female CO-worker calls you cute(QUETTE), she is actually a coquette herself.

Don’t judge me with my sentences. We need to learn it.

86. Diatribe – bitter verbal attack
Australian players used

87. Discredit – To dishonour; disgrace
Credit – Acknowledgment or to honour people of their efforts
Discredit – To dishonour


88. Disparage – To belittle i.e. kisi ko neecha dikhana
Hear this out

‘English is a medium of communication and not a measure of intellect’s
So never ever disparage someone because of poor english

89. Dormant – Inactive
There are seeds which are in dormant stage for a few months until they start growing up.
Sometimes criminals are in dormant state

A dormant volcano


90. Equivocate – Intentionally use vague language

Equi means two equal things and vocal means language
So when a person is equivocal then he uses a language which has two different meanings to confuse people or to keep the topics vague


Bonus words

Hegemony – Leadership or dominance
Holocaust – Slaughter on a large scale. Like the one during India vs Pakistan division

USA shows it’s hegemony on the World.

Day 3, Total 90 words destroyed

Keep Learning 🙂
Target330

GRE Verbal | Barron’s 800 Destroyed | Day 2

Target 330

31. Vendetta – prolonged bitter quarrel against someone
Remember the movie V for Vendetta ?
Vendetta means a prolonged bitter quarrel against someone.
I have not watched the movie but I guess there was a story in Bollywood where Amir Khan took an age old revenge

32. Vertigo – A sense of dizziness
It’s mostly because of ear problem or when you get down from 10-12 floors

A person going through vertigo

33. Vapid – Tasteless
34. Insipid – lack of flavour

Will tackle both of these together
Don’t know about other areas but in Gurgaon people drink Cucumber juice. So one day I took a SIP and IN it did not had any flavour i.e.it was insipid or VAPID

Vapid could be something or someone i.e.very boring/dull person –

B.S. – One who is not Rapid is Vapid..Matlab kas=ise v yaad kro yaar..If possible comment something interesting for VAPID

35. Untoward – Unexpected
Toward resembles expected, it’s intuitive that untoward is unexpected or not favorable or troublesome

In the last semester project no of my teammates had any clues about the project but everyone acted as nothing untoward has happened

36. Usury – A practice of lending money at exorbitant rates

Remember the word penury from Day 1? It meant very poorThe moment I saw the word usury I had a hunch that it has something to do with money..Here it is, lending money at heavy rates.

Old Jamindars in India USed their Treasury for usury

37. Vogue – Prevailing fashion
Ever heard of the vogue magazine ?
It contains information about new fashion

Ex. – Wearing bright clothes has become a vogue.
Vogue – Ranveer Singh !!

38. Vaunt – To boast
Remember daunt from Day 1? It meant to frighten or demoralise
So Vaunt is to boast

He knows nothing but loves to vaunt about his skills which eventually daunts the co-workers

39. Supersede – To replace
I always used to get confused with this word, thinking that it has something to do with being superior
This word is used quite frequently, so if you know the meaning then it’s fine.
Else – Supreme court decision will supersede the decision of high court
The Prince will supersede his father

40. Toady – Flatter , Yes Man

My colleague Amrit became a toady by bringing sweets only for the Manager.
He is such a toady and wishes the class-teacher every time she crosses him

B.S. – Matlab teddy bear ki tarah sab ko khush krna

41. Sundry – Various
I just love the sundry appetisers and kebabs at Barbeque Nation 😛

42. Supplant – to replace

Implant is to fix something and mostly used with breast. In implant you fix breast and in supplant you replace it. I know it’s a very inappropriate example, but do comment a better one and I will remove it at the earliest.

43. Terrestrial – Earthly, commonplace

Terrestrial animals – Animals who live on earth

44. Uncanny – Mysterious

Jo cheez can main band hai wo mysterious hi hoga na..
She has an uncanny way of talking to people, she talks in a very soft and stable voice which sometimes scare the shit out of me

45. Torpor – Lethargy

This one is my favourite.
Chote nache tor-phor kar ke thak gye and they went to a state of torpor

46. Tortuous – Having many twists and turns

Though it looks like the word means something related to torture but have you ever travelled up the hill?
I was once travelling from Kodai Kanal (A hill station in Karnataka) and while coming back i had a very unpleasant experience because of the tortuous road

47. Sylvan – related to the woods or forest

I don’t know why but I feel there is some forest by this name or some tall trees. Sylvan is related to woods

48. tome – books, usually large and academic

49. Soliloquy – Dramatic speech by one character when he is talking to oneself

This is one of the interesting words. When you. talk to yourself it’s called monologue, when you talk to someone on stage it’s called dialogue.
And when you talk to yourself on a stage then it’s called soliloquy

50. simile – comparison of one thing with another thing using “like” or “as”
Simile ~ similar
Take a not of the spelling, I made two mistakes while writing simile.

51. Stigma – Disgrace

You are a disgrace to the society
You are a stigma to the society.

Easy hin hai ye toh

52. savour – to enjoy; have a distinctive flavour or smell

Dekho(Look), there are two meaning to it, first to enjoy i.e. Nitin savour each moment of his life
The soup has a savour of onion

53. sage – wise

There is this beautiful song by Ritwiz which goes by the name of sage. This is definitely a wise song. Lyrics below

दिल की धड़कन से तो आए जाना
हमरी सब-सब के हो जाए आना
कभी-कभी तू मुझसे घबराना
सभी-सभी को तुम ही समझाना
अभी-अभी तो हमने ही जाना

Ek baar sun loge toh sayad yaad v ho jaey 😛

54. rebu – puzzle in which pictures or symbols represent words

This is rebu..Now guess the movie name (Bollywood)..Answer at the end of the article

55. rococo – extravagantly ornate;
extravagant means to to spend too much and that to on decorating things

Rococo style wedding
Indians mostly goes for Big Fat Wedding in a rococo style

56. extravagant – bahut kharch krne wala
He is extravagant.
Normal hi word hai..someone who spends ‘extra’

57. reverent – expressing deep respect; worshipful

The following two words come together

58. Dirge – Funeral Hymn
Don’t have much idea

59. Eulogy – High praise to a death person
A toast for a person who died

60. Expatriate – To send into exile
My friend is an expatriate who left her luxurious life to live on the mountains

With this we have covered 60 of the 800 words

Answer to the bollywood movie – Agni Path 😛

Keep Learning 🙂
Target 330

GRE Verbal | Barron’s 800 Destroyed | Day 1

Aim – 160+ in GRE
Resources – Barron’s 800 and 100 RCs

B.S – It stands for Bullshit Story

Day 1
1. Abate – to decrease

After a 15 days of lockdown, the Corona virus cases suddenly abated, bringing smile and relief on everyone’s face

B.S. – Ab’ate’ – Since you ate something, it will decrease, right ? Right !! Awesome 

2. Bawdy – Dealing with sexual matter in a comical way; obscene

List of Bawdy movies –
-Kya Super Kool Hain Hum
-Grand Masti
-American Pie(The only Bawdy movie I liked :P)

3. Callous – Having a cruel disregard for others; insensitive

Have you watched the lawyer defending the accused of Nirbhaya Rape Case?
He was defending the rape and his callous comments will make you shiver

4. Daunt – To discourage; demoralize; Fearful

People living in villages are mostly daunted by new technologies

B.S. – Daunt sounds like daatna i.e. to scold and I get demoralised when someone daunts me 

5. Ebullient – Overly enthusiastic

I met a girl traveling solo in Goa, she was ebullient and happy.

B.S. – ‘bull’ is always full of energy, and this is the only word in 800 words with ‘bull’ in it. So be ebullient

6. Facetious – Humorous (not seriously meaning what you are saying, mostly in the humorous way); Witty

‘We should spend all our money in Poker and Pizza’ she said facetiously

B.S.
Facetious means witty and whenever someone says witty, my mind rushes to Shahrukh Khan, the wittiest person on stage

I would like to be a person as facetious as SRK

7. Gainsay – To deny; An opposer

I was home alone, I can dance to my own music, eat what I want and no one is there to gainsay me 

8. Halcyon – Happy; Prosperous

cryon? – It makes you happy and if you are happy you are prosperous

This is a genus of Kingfisher and its name is Halcyon. Kingfisher makes you happy 

9. Iconoclastic – Criticising cherished beliefs

This is interesting, iconoclastic person criticises the common beliefs. It’s like in a community everyone considers drinking alcohol as a taboo, lekin main toh iconoclast hun na, I will drink.

When drunk, my uncle is an iconoclast who insults the belief of others

10.Junta – A group of people

Quite easy, Janta ~ Junta

11. Laconic – Using few words

My favourite, it actually means a person who uses very few words. And the next word in the list is loquacious which means someone who talks a lot.

So your ex- best friend who used to be loquacious is now laconic 

12. Loquacious – Talkative

13. Maelstrom – Turmoil

So there is one more term which is associated with turmoil i.e. whirlpool.
Now let’s learn these words.
Whirlpool is famous for it’s washing machine. We have turmoil or rotating movement in washing machine, right?
That motion is like called maelstrom or turmoil

14. Malinger – pretend to be ill in order to escape duty or work.

Do you watch cricket?
recently a photo of Malinga was released where he was topless and had a big belly. How is that possible for a cricketer?
Because he was malinga, he pretended to be ill to escape duty i.e. practice session 😛

MI is the only team I support and in the same tournament Malinga helped MI win the trophy. I <3 Malinga


15. Maverick – Independent or Unorthodox person

Top Gun Maverick ? So maverick is a type of gun which was one of the most used gun in Counter Strike. If you have a maverick, then you are independent 

16. Misogynist – One who hates women

Gynaecologist looks after problems related to women.
Miso means hate (my assumption)
If you hate women – misogynist
If you hate marriage – misogamist
If you hate mankind – misanthrope
If you hate me – Misidiot

17. Misogamist – One who hates marriage
18. Misanthrope – One who hates mankind
19. Nostalgia – Sentimental longing for a past time

Hohoho – This one is easy !!

20. Nugatory – Invalid; of no importance;

It’s a nugatory hypothesis
It’s a nugatory observation

When you look at the word, you already know that word is negative.
This one is tricky, so you can comment a good way to learn this 

21. Oligarchy – Type of govt. in which power belongs to only a few leaders

Oligo means very few in Greek
Archy comes from archein which means ‘to rule’

22. Anarchy – A state of disorder

The new bill or the spread of Corona left the country in anarchy

23. Officious – Too helpful (Itna ki irritate kr de/so much so that it irritates the person)

Officious – It’s an official call which gets you furious i.e. itna call krne credit card bechne ko ki even though they are providing a service, they become irritating

Officious – Official + Furious

24. Plummet – to fall or plunge

To drop sharply
The 10 days lock in period plummeted the spread of Corona virus

Plum sounds like someone/something which is too heavy aur wo dham se gir gya..Plummet!!!

25. Penury – Extreme poverty

Matlab pen kharidne ke bhi paise nai thain, he could not face that penury 

26. Penchant – Inclination

Easy hai, intuitive hai
I have a penchant for dogs 

27. Phlegmatic – Unemotional

Phlegmatic people are good to others, may be soft spoken, but they are unemotional.

Have you watched Leon – The Professional or Bicchoo (Bobby Deol)
The hero was mostly Phlegmatic.

28. Phoenix – a person or thing which is uniquely remarkable in some respect

Phoenix mall ? It’s in Bangalore as well as in Pune – It is remarkable because of it’s size

29. Platonic – Affectionate but not sexual

I simple love specific words, words which are apt. to a particular situation. Platonic is one of those words.

Their relationship is platonic i.e. people can think otherwise that they are dating and are into physical relationship but NOPE, it’s platonic

30. Forestall – To prevent using an advance action

Forestall to check and mate Corona
He forestalled the police

Fore means before in my mind so I always assume it has something to do with an advance action.

You went through 30 words. Which one was your favourite ?
Mine was Laconic, loquacious, and platonic.

If you have a favourite word, do comment below…is se yaad ache se hota hai

Keep learning 

GRE330

Feature Engineering in Data Science

Have you ever wondered why two different people gets different accuracy while using the same algorithm?
We all know that XGBoost can help us get a very good result in our Hackathons, but then also only few people achieve a decent rank using the same algorithm, why?

Well !! The answer is feature engineering i.e. creating more features/data points from the fixed number of given data set.

Feature engineering is the art of extracting more information from existing data. You are not adding any new data here, but you are actually making the data you already have more useful

Let’s take some examples:-
We had this Titanic Dataset (most used data set in Data Science domain)
Problem Statement – Given the name, age, class, sex, cabin type, and number of family members traveling in Titanic. Can you predict which passenger survived and which did not?
It’s obviously a supervised learning questions and you already have a data set with the output.
All you need to do is to predict for a test data set

We are not going too deep into the solution. You can find the solution here.

What we want to discuss is the opportunities to create new columns.
I have seen people using the following types of columns in the data set. Before reading forward, remember it’s not about how good the new data point is? It’s about whether you can think out of the box.

Columns created by different solution submitter:-
1- Title of the passenger(Dr.,Mr.,Miss,etc.)
2-Creating blocks of ages rather than using actual age
3-With or without wife – Binary variable which suggests whether the person was with or without his wife
4-Number of children traveling
5-Number of alphabets in the name – Yes people did use the length of the name to try and test if this was useful. Not good enough, but brave enough 🙂

Why to create more variables when we already have a handful?
The performance of a predictive model is heavily dependent on the quality of the features in the dataset used to train that model. If you are able to create new features that help in providing more information to the model about the target variable, it’s performance will go up
Spend a considerable amount of time in pre-processing and feature engineering. You need to concentrate a lot on this since this can make a huge difference in the scores.

Better features means flexibility.

You can choose “the wrong models” (less than optimal) and still get good results. Most models can pick up on good structure in data. The flexibility of good features will allow you to use less complex models that are faster to run, easier to understand and easier to maintain. This is very desirable.

Better features means simpler models.

With well engineered features, you can choose “the wrong parameters” (less than optimal) and still get good results, for much the same reasons. You do not need to work as hard to pick the right models and the most optimized parameters.

With good features, you are closer to the underlying problem and a representation of all the data you have available and could use to best characterize that underlying problem

How to do feature engineering?

The feature engineering process might look as follows:

  1. Brainstorm features: Really get into the problem, look at a lot of data, study feature engineering on other problems and see what you can steal.
  2. Devise features: Depends on your problem, but you may use automatic feature extraction, manual feature construction and mixtures of the two.
  3. Select features: Use different feature importance scorings and feature selection methods to prepare one or more “views” for your models to operate upon.
  4. Evaluate models: Estimate model accuracy on unseen data using the chosen features.

You can start with any hackathon at Analytics Vidhya and try to create more and more columns, feed your algorithm with these variables and evaluate the model.

Keep Learning 🙂

The Data Monk

Correlation and Collinearity explained in layman terms

Correlation tells you have two numerical variables relate to each other. It will tell you whether data points that have a higher than average value for one variable will also likely have a higher than average value for the other variable (positive correlation) or smaller than average (negative correlation) or if there is no such relationship (correlation close to zero).

Some examples:

  • Height of a person and weight of a person have a high correlation: tall people tend to be heavier than shorter people. The value will be positive, and
  • Height of a person and the number formed by the last 4 digits of their phone number are uncorrelated (the correlation will be close to 0) because they are independent of each other.
  • The traffic density (number of cars driving at a given time) is negatively correlated with the average speed (if there is more traffic, there will be longer queues at traffic lights, and more people taking turns or moving in and out of traffic).

What is the difference between collinearity and correlation?

Correlation means two variables vary together, if one changes so does the other. Correlation gives no indication of strength, just how noisy this relationship is and its direction.

Correlation is an operator, meaning that we can talk about the correlation between height and weight. The correlation can be positive, negative, or 0.

Collinearity is a phenomenon related to regression, in which some of the predictor variables are highly correlated among themselves. This makes one or more of these variables redundant in our analysis. For example: if you wish to regress “Household expenditure” on “Household income” and “Tax paid in the last year”, the income and tax paid will be highly correlated (or there will be collinearity in this setup). It would be best to regress “Expenditure” on either “income” or “tax paid”.

If in multiple regression analysis, one of the predictors is linearly associated/dependent on other predictor, then this issue is known as collinearity.

For example, let’s consider the linear model

Y = αx1 + β1×1 + β2×2 … (1)

If predictor x1 can be expressed as linear combination of x2, say, x1 = 3*x2

Then this is known as collinearity among the predictors. Note that there will be perfect (or very high) correlation between the predictors as opposed to the assumption of linear regression model (All predictors are assumed to be independent).

Essentially it means that one of the independent variables is not really necessary to the model because its effect/impact on the model is already captured by some of the other variables. This variable is not contributing anything extra to the predictions and can be removed. If we have true collinearity (perfect correlation as in the example above), the one of the predictor is automatically deleted by some of the software’s like R, other shows an error or warning for the same.

The effects of collinearity are seen in the variances of the parameter estimates, not in the parameter estimates themselves.

How could I test whether a calculated correlation coefficient between two variables is meaningful or not?

The correlation coefficient R lies between -1 to +1.

In general if |R| >= 0.75, we say that the variables are highly correlated. And similarly, poor and moderate correlation for |R| <= 0.25 and 0.25 <= |R| <= 0.75 respectively.

The coefficient of determination R squared measures the ratio of explained variation (in one variable due to the change on other) to total variation.

For example, if R = 0.8 (high correlation), then R squared = 0.64.

Hence only 64 % of variation in one variable is due to the other variable. Rest of the variation (36 %) is caused by other factors.

So it is suggested to interpreted your result after calculating R squared.

Also, great care should be taken (using ROL/ expert opinion/ judgement based on common sense) while making decision on the basis of above mentioned two measures.

As sometimes, we may get a high value of R and R squared between two variables just by chance. For example correlation between amount of rain in a particular city in last one year and number of deaths due to cancer in that city.

For more details, see non- sense correlation/ spurious correlation.

Keep Learning 🙂

The Data Monk