#datanalysis

[nareshitechnologiesofficial]

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Programming (think Python & R) 💻

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Learn Python 🐍 (trust me, it's beginner-friendly)

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Build cool projects like price predictors or recommendation engines

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[tutort-academy]

Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages-it can be used for better decision making, preventing fraudulent activities, among other things👩‍💻 ➡️ To know more about what the areas are where big data analytics is used, Check out here 👇 ➡️ If you want to start your career into the field of data science, ML and AI visit here-- www.tutort.net

[thelightinglamp]

SQL is everywhere, from small-scale Industries to large-scale Industries. Companies like Google, Instagram, Quora, and other tech company use SQL to query data. Is SQL is still in demand? Indeed advertised more than 30,000 job vacancies by 2021. 57% of Data Analyst jobs demand SQL as a primary skill. According to Stack over flow’s 2020 developer survey, SQL is the most popular language among PHP, Java, JavaScript, Python, C, and C++. Practicing is the best way to learn SQL. Here, The Lighting Lamp gives you practical sessions with live projects and a Superior Learning Experience. Because we know learning SQL is valuable and essential to start a career in data.

[onleitechnologies]

What is Exploratory Data Analysis in Data Science ?

Exploratory Data Analysis In Data Science

For Instance : https://onleitechnologies.com/what-is-exploratory-data-analysis-in-data-science/

[swetha-sunil]

Principal Component Analysis using R

INTRODUCTION

The Principal Component Analysis approach is very beneficial for data processing with multi-collinearity between variables. When the dimensions of the input characteristics are large, PCA can be applied. PCA may also be used for data reduction and noise removal. When we initially investigate a dataset, we perform PCA to determine which observations in the data are most comparable to one another.

During PCA, there are various procedures that must be followed:

1. The data is normalised

2. The covariance matrix must be computed.

3. Obtaining eigenvectors and eigenvalues

4. Identifying the Principal Component

5. Minimizing the data set's size

The dataset is associated with a Portuguese financial institution's direct marketing programs. The various factors that help in predicting client subscription of term deposits are age of the client, type of job, marital status of the client, education level, credit in default, call duration, day and month of call/contact, number of calls/contacts performed during this campaign to the client, number of days that passed by after the client was last contacted, number of calls/contacts performed before this campaign and outcome of the previous marketing campaign.

The purpose of this research is to minimize the dimensionality of the database while enhancing accurateness and reducing information loss using principal component analysis (PCA). It does this by randomly creating additional uncorrelated factors that eventually increase variance.

Program Code:

1) A library (readxl) was implemented to allow R to open files in xlsx format.

Code:

library(readxl)

2) Loading the data and viewing the column names:

Code:

data=read_excel("C:/Users/sweth/Downloads/Bank Marketing Dataset.xlsx")

View(data)

colnames(data)

Output:

[1] "Age"

[2] "Type of Job"

[3] "Marital Status"

[4] "Educational Qualification"

[5] "Credit in Default"

[6] "Housing Loan"

[7] "Personal Loan"

[8] "Contact Communication Type"

[9] "Month Contacted"

[10] "Day of the Week"

[11] "Last Contacted Duration (In Seconds)"

[12] "Campaign"

[13] "Days Passed since Last contact"

[14] "Previous"

[15] "Outcome (Previous Marketing Campaign)"

[16] "Employment Variation Rate"

[17] "Consumer Price Index"

[18] "Consumer Confidence Index"

[19] "Euribor (Quarterly rate)"

[20] "Number of Employees"

[21] "y = Customer Subscription"

There are 21 columns with 41188 observations including both numeric and non-numeric data.

3) The non-numeric columns were removed using the following code.

Code:

data_extracted<-data [, -c (2:11,15)]

colnames(data_extracted)

Output:

[1] "Age" "Campaign"

[3] "Days Passed since Last contact" "Previous"

[5] "Employment Variation Rate" "Consumer Price Index"

[7] "Consumer Confidence Index" "Euribor (Quarterly rate)"

[9] "Number of Employees" "y = Customer Subscription"

We have 10 variables to perform Principal Component Analysis (PCA).

4) Standardization is critical in data analysis and interpretation; without it, the conclusions we obtain are likely to be skewed and erroneous. Standardization is the process of scaling data such that all factors and their values fall within a same range.

Code:

data_extracted$`y = Customer Subscription`<-scale(data_extracted$`y = Customer Subscription`)

5) To obtain the correlation matrix between the variables, the cor() function was employed. Using the eigen() function, this correlation matrix is then utilised to calculate the eigen value and eigen vector matrix. It is critical to identify strongly dependent variables that include redundancy and misleading information that might change the outcome and degrade the quality of any given system.

Code:

corr_data<-cor(data_extracted)

corr_data

Output:

eig_data<-eigen(corr_data)

eigen_value<-eig_data$values

eigen_vec<-eig_data$vectors

view(eigen_vec)

eigen_value

sqrt(eigen_value)

Output:

Correlation Matrix

Eigen Vector Matrix

6) The explained variance is determined as the ratio of the eigenvalue (eigenvector) of an individual main component to the total eigenvalues.

Code:

explained_var<-(eigen_value/sum(eigen_value))

explained_var

Output:

7) The prcomp() function for pca is used to display the plots before displaying the scree plot.

Code:

pr<-prcomp(data_extracted)

sum_pr<-summary(pr)

biplot(pr)

plot(eigen_value,type="b")

abline(h=1, col = 'blue')

A PCA biplot shows sample PC scores (dots) as well as factor loadings. The longer the distance between any of these vectors and a PC's origin, the greater their impact on that PC. Loading plots also show whether variables correlate with each other: a small angle shows a positive correlation, a big angle shows a negative correlation, and then a 90° angle shows no association between two variables.

Scree Plot

Interpretation

· Eigenvalues are assigned to Principal Components 1, 2, and 3.

· PC1 explains 40.37% of variance and PC2 explains 14.37% of the total variance. Cumulative variance explained by PC1 and PC2 is 54.74% approximately.

· Looking at Component 3, you can observe an "elbow" joint. At this point, it may be counterproductive to proceed with component extraction. We use the scree plot to choose three components that explain roughly 80% of the data variability. This also implies that overfitting is avoided. PC1, PC2, and PC3 are above the abline().

As a result, knowing a sample's place in reference to merely PC1, PC2, and PC3 may offer a highly true depiction as to where it stands in respect to other instances, as PC1, PC2, and PC3 can explain about 80% of the variation.

Swetha Sunil Kumar,

Course: Master of Business Administration

Amrita School of Business, Coimbatore

Amrita Vishwa Vidyapeetham.

"This blog is a part of the assignments done in the course Data Analysis using R and Python"

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[datamahadev]

Link in Bio. Introducing #word2vec model and #wordembedding for beginners. https://datamahadev.com/introducing-word2vec-word-embedding-detailed-explanation/ #datamahadev #pythoneer #pythonista #datascience #python #picoftheday #instacool #follow4followback #followbackinstantly #likeforlikes #machinelearning #deeplearning #datanalysis #nlp #naturallanguageprocessing (at Mountain View, California) https://www.instagram.com/p/CErVrM2JgMw/?igshid=1gm4ifmhj3qul

[excelalgorithms]

ARRAY (diapazon) formulu

ARRAY exceldə ən maraqlı, çox istifadə edilən və biraz da çətin mövzulardan biridir. ARRAY formulları birdən çox qiymətlə əməliyyatlar edir. Formulun nəticəsi tək qiymət və ya diapazon ola bilər. Bu formulun necə qurulmağından asılıdır. Formulun düzgün işləməsi üçün CTRL+ALT+ENTER düymələri sıxılmaqla daxil edilməlidir. Formul bu yolla daxil edildikdə, görəcəksiz ki, formula barda formulun…

[tecnoetica]

L'identità degli italiani attraverso domande con risposte opposte

[incegna]

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