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Answered on 03 Apr IT Courses/Data Science

Sniffer Search

Yes,we have sarted such program. The course is designed to make you expert in 4 month time(60 Hourse course+60 Hours project work) 1)Machine Learning 2) Deep learning ,NLP and Speech to text with expert knowledge building in six type of neural network 3) Product Development(building real time A.I powered... read more

Yes,we have sarted such program.

The course is designed to make you expert in 4 month time(60 Hourse course+60 Hours project work)

1)Machine Learning

2) Deep learning ,NLP and Speech to text with expert knowledge building in six type of neural network

3) Product Development(building real time A.I powered Chatbot to automate recruitment process)

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Answered on 28 Feb IT Courses/Data Science

Sujan Edudemy Gowda

Chief Trainer

Easiest way to get started is with simlpe tools like excel and regression. Doesn't require programming language, basic maths and statistics would suffice to get the grasp at beginner level. Next, more advanced tools like SPSS, Tableau, linear algebra techniques and languages like R, Python could be learnt... read more

Easiest way to get started is with simlpe tools like excel and regression. Doesn't require programming language, basic maths and statistics would suffice to get the grasp at beginner level. Next, more advanced tools like SPSS, Tableau, linear algebra techniques and languages like R, Python could be learnt at more advanced stage. To summarise, start with analytics with excel course. Then move onto data science and engineering, as stated above. Hope this helps. 

Feel free to contact for more clarity.

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Lesson Posted on 24 Feb IT Courses/Data Science

Data Science: Case Studies

Reachout Analytics Pvt Ltd

I have 20 years strong experience & Alumni of MBA-IIMC SQC-ISI Building Statistical Predictive models...

Modules Training Practice Case Studies Module 2: Data Visualization and Summarization 10 15 1. Crime Data 2. Depression & anxiety 3. Customer Demographic Data Module 3: Data Preparation and Quality Check 5 10 3.... read more

Modules

Training

Practice

Case Studies

Module 2: Data Visualization and Summarization

10

15

1. Crime Data

2. Depression & anxiety

3. Customer Demographic Data

Module 3: Data Preparation and Quality Check

5

10

3. Sales Target Fixing

4. Hyper Market Data

5. Customer Attitude

6. Continuation of Depression & anxiety

Module 4: Predictive & Estimation Models (Supervised earning)

15

20

7 .Samsung Dubai Sales Modeling

8. Credit Card Fraudulent

9. Cancer Prediction

Module 5: Advanced Big Data Analytics

10

10

10. Hadoop Data Clustering

11. Speed Dating  modeling

12. E-commerce Customer Sentiment  Analysis

Module 6: Data Mining (Machine Learning)

5

15

Above  6, 7, 8, 9, 10, 11, 12 Case studies repeat in Machine learning  tools

Capstone project 

0

30

Data to Decision Making

Total 

45 hours

100 hours

 

 

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Lesson Posted on 22/12/2017 IT Courses/Data Science IT Courses/Machine Learning IT Courses/R Programming

Decision Tree or Linear Model For Solving A Business Problem

Ashish R.

SAS certified analytics professionals, more than 11 years of industrial and 12 years of teaching experience....

When do we use linear models and when do we use tree based classification models? This is common question often been asked in data science job interview. Here are some points to remember: We can use any algorithm. It is purely depends on the type of business problem we are solving and who is end user... read more

When do we use linear models and when do we use tree based classification models? This is common question often been asked in data science job interview. Here are some points to remember:

We can use any algorithm. It is purely depends on the type of business problem we are solving and who is end user of the model and how he is going to consume the model’s output. Let’s look at some key factors which will help you to decide which model to use:

  1. If the relationship between dependent & independent variable is well approximated by a linear model, linear regression will outperform tree based model. No doubt in this aspect. If the realationship is not linear then tree model is better to choose as lot of complicated transformation might be required on the independent variables to make the relationship linear.
  2. If there is a higher degree of non-linearity between dependent & independent variables, a tree model will perform better than Linear Regression Model. How do you check the linearity? Simply create the bivariate plot of dependent variable and independent variables and study the plots to determine what kind of relationship is between Y and the chosen X variable.
  3. Decision tree models do not require too much data cleaning (missing value and outlier effect). Hence easy and fast to develop and easy to explain to our customers as well.
  4. If your business problem demands the possible cause or path to reach to the target variable then tree is easy to explain whereas finding the nature of relationship of the predictor variables with the target variable Linear regression is a better choice.
  5. Decision tree models are even easier to interpret from a layman point of view.
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Lesson Posted on 22/12/2017 IT Courses/Data Science IT Courses/Machine Learning IT Courses/R Programming

Basics Of R Programming 1

Ashish R.

SAS certified analytics professionals, more than 11 years of industrial and 12 years of teaching experience....

# To know the working directory which is assigned by defaultgetwd()# set the working directory from where you would like to take the files setwd("C:/Mywork/MyLearning/MyStuddocs_UrbanPro/Data") # Assign the path as per the location where you want to allocate getwd() # to see the list of files in your... read more

# To know the working directory which is assigned by default
getwd()
# set the working directory from where you would like to take the files
setwd("C:/Mywork/MyLearning/MyStuddocs_UrbanPro/Data") # Assign the path as per the location where you want to allocate

getwd()

# to see the list of files in your working directory- just assigned above
dir() ## Lists files in the working directory

# Creating a folder in C drive
dir.create("C:/Mywork/MyLearning/MyStuddocs_UrbanPro/Data/Nov26")


#install.packages("car")
#install.packages("Hmisc")
#install.packages("reshape")
#install.packages('pastecs')
#install.packages('gtools')
#install.packages('gmodels')
#install.packages('caret')
#install.packages('MASS')


##-----------------------------------------------------------
## Load required libraries
##-----------------------------------------------------------
# calling the libraries in each active session is very much required
#if we want to use the functions in the library

library(foreign)
library(MASS) # for stepAIC()
library(Hmisc) # for describe()
library(boot)
library(pastecs) # for stat.desc()
library(gmodels)
library(gtools)
library(lattice)
library(ggplot2)
library(caret)
library(car)
library(foreign)
library(reshape)
library(Hmisc)

version # to check what version u are using

# import world data set
world

dim(world) # check how many rows and columns

View(world) # to View the data frame

trans<-read.csv("TransactionMaster.csv")

View(trans)

cust<-read.csv("CustomerMaster.csv")

View(cust)

dim(cust)

str(cust) # to check the structure/meta data of the data frame

# carbon copy of the file

cust_copy<-cust[,]

#save as a R file

saveRDS(cust_copy,"C:/Mywork/MyLearning/MyStuddocs_UrbanPro/Data/customerdata")

# take a sample of 100 rows and all the columns and create a sample file
# 1:100 stands for 100 rows and after comma blank means all columns to pick up
cust_sample<-cust[1:100,]

dim(cust_sample)


# take all the rows and specific columns from teh source file "cust"
samplefile

# take all rows and specific column numbers 1,8,9
samplefile

# do the frequency distribution of the City variable
table(cust$City)

# do a cross table freqency distribution of City and State variable
table(cust$State,cust$City )

 

table(world$deathCat, world$birthCat)


# calculate average value of energy_use_percapita variable from the world
mean(world$energy_use_percapita, na.rm=T)

#calculate median value of gni_per_capita
median(world$gni_per_capita) # 50th percentile


# to check the type of the R objects
class(world)
class(cust)
class(trans)

is.vector(world)
is.factor(world)
is.data.frame(world)
is.matrix(cust)

length(world) # display the number of cloumns : partcularly use for vectors

head(trans) # display first 6 rows in console

head(trans, n = 2) # Display top 2 rows

tail(trans) # display last 6 rows of a data frame

tail(trans,n=1)

firstfewrows

View(firstfewrows)


# to store the country names in lower case letters

world$country_name<-tolower(world$country_name)

# dropping the first column from a data frame and create a new one

world_1<-world[,-c(1)]

# filter out the atlanta customers

atlantaCustomers


# filter out atlanta or hollywood customers : | OR operator & AND opearator

atlantaHollyCustomers <-cust[which(cust$City == "ATLANTA" | cust$City == "HOLLYWOOD" ) , ]

## Selecting specific cloumns
atlantaCustomers1


# filtering out data with multiple conditions

highSales_mod<-trans[which(trans$Sales_Amount >= 100 & trans$Sales_Amount <= 150 ),]


max(highSales_mod$Sales_Amount)

min(highSales_mod$Sales_Amount)

###------------------------------------------------------------
### Basic Date functions in R
###------------------------------------------------------------

Sys.Date() # Current date

today

class(today)

Sys.time() # Current date and time with time zone
time<-Sys.time()

class(time)

 

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Lesson Posted on 22/12/2017 IT Courses/Data Science IT Courses/Machine Learning IT Courses/R Programming

Market Basket Analysis

Ashish R.

SAS certified analytics professionals, more than 11 years of industrial and 12 years of teaching experience....

Market Basket Analysis (MBA): Market Basket Analysis (MBA), also known as affinity analysis, is a technique to identify items likely to be purchased together. The introduction of electronic point of sale systems has led to collection of large amount of data. Simple, yet powerful - MBA is an inexpensive... read more

Market Basket Analysis (MBA):

Market Basket Analysis (MBA), also known as affinity analysis, is a technique to identify items likely to be purchased together. The introduction of electronic point of sale systems has led to collection of large amount of data. Simple, yet powerful - MBA is an inexpensive technique to identify cross-sell opportunities mostly in CPG industries. A classic example is toothpaste and tuna. It seems that people who eat tuna are more prone to brush their teeth right after finishing their meal. So, why it is important for retailers to get a good grasp of the product affinities? This information is critical to appropriately plan for promotions because reducing the price on some items may cause a spike on related high-affinity items without the need to further promote these related items.

Market Basket Analysis (MBA) is a data mining technique which is widely used in the consumer package goods (CPG) industry to identify which items are purchased together. The classic example of MBA is diapers and beer: "An apocryphal early illustrative example for this was when one super market chain discovered in its analysis that customers that bought diapers often bought beer as well, have put the diapers close to beer coolers, and their sales increased dramatically. Although this urban legend is only an example that professors use to illustrate the concept to students, the explanation of this imaginary phenomenon might be that fathers that are sent out to buy diapers often buy a beer as well, as a reward."

The example may or may not be true, but it illustrates the point of MBA.

The analysis can be applied in various ways:

  • Develop combo offers based on products sold together
  • Organize and place associated products/categories nearby inside a store
  • Determine the layout of the catalog of an ecommerce site
  • Control inventory based on product demands and what products sell together
  • Credit card transactions: items purchased by credit card give insight into other products the customer is likely to purchase.
  • Supermarket purchases: common combinations of products can be used to inform product placement on supermarket shelves.
  • Telecommunication product purchases: commonly associated options (call waiting, caller display, etc) help determine how to structure product bundles which maximize revenue
  •  Banking services: the patterns of services used by retail customers are used to identify other services they may wish to purchase.
  •  Insurance claims: unusual combinations of insurance claims can be a sign of fraud.
  • Medical patient histories: certain combinations of conditions can indicate increased risk of various complications.

Three common terminologies are used a lot in the market basket analysis, which is mostly based on classical definition of probability:

i. Support, Confidence and Lift:

There are several measures used to understand various aspects of associated products. Let's understand the measures with the help of an example. In a store, there are 1000 transactions overall. Item A appears in 80 transactions and Item B occurs in 100 transactions. Items A and B appear in 20 transactions together.

a. Support: The simplest one, Support is the ratio of number of times two or more items occur together to the total number of transactions. Support of A = P(A) = 80/1000 = 8% and Support of B = P(B) = 100/1000 = 10%.

Support of a product or product bundle indicates the popularity of the product or product bundle in the transaction set. Higher the support, more popular is the product or product bundle. This measure can help in identifying driver of traffic to the store. Hence, if Barbie dolls have a higher support then they can be appropriately priced to entice traffic to a store.

b. Confidence is a conditional probability that a randomly selected transaction will include Item A given Item B. Confidence of A = P(A|B) = 20/100 = 20%.

i.e. to measure the probability of bundling/selling propensity of a product A when it is bundled with B.

Confidence can be used for product placement strategy and increasing profitability. Place high-margin items with associated high selling (driver) items. If Market Basket Analysis indicates that customers who bought high selling Barbie dolls also bought high-margin candies, then candies should be placed near Barbie dolls.

c. Lift can be expressed as the ratio of the probability of Items A and B occurring together to the multiple of the two individual probabilities for Item A and Item B. Lift = P(A,B) / P(A).P(B) = (20/1000)/((80/1000)*(100/1000)) = 2.5.

Lift indicates the strength of an association rule over the random co-occurrence of Item A and Item B, given their individual support. Lift provides information about the change in probability of Item A in presence of Item B. Lift values greater than 1.0 indicate that transactions containing Item B tend to contain Item A more often than transactions that do not contain Item B.

In order to gain better insights, differentiate Market Basket Analysis based on:

  • Weekend vs weekday sales.
  • Month beginning vs month-end sales.
  • Different seasons of the year.
  • Different stores.
  • Different customer profiles.

Based on the content and value of the basket, it is useful to classify the trip. Variables such as total basket value, number of items, number of category X vs. category Y items, help in developing rules to map each of the baskets to a previously defined classification. Understanding what kind of shopping trips a customer performs at a particular store at a particular time is critical for planning purposes. This data provides a unique window into what is happening at the store and enables advanced applications such as labor scheduling, product readiness and even temporary layout changes.

Not Just Retail:

Although Market Basket Analysis reminds pictures of shopping carts and supermarket shoppers, there are many other areas in which it can be applied. These include:

For a financial services company:

  • Analysis of credit and debit card purchases.
  • Analysis of cheque payments made.
  • Analysis of services/products taken e.g. a customer who has taken executive credit card is also likely to take personal loan of $5,000 or less.

For a telecom operator:

  • Analysis of telephone calling patterns.
  • Analysis of value-add services taken together. Rather than considering services taken together at a point in time, it could be services taken over a period of, let's say, six months.

A predictive market basket analysis can be used to identify sets of products/services purchased (or events) that generally occur in sequence — something of interest to direct marketers, criminologists and many others.

Advanced Market Basket Analysis provides an excellent way to get to know the customer and understand the different behaviors. This insight, in turn, can be leveraged to provide better assortment, design a better planogram and devise more promotions that can lead to more traffic and profits.

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Lesson Posted on 19/12/2017 IT Courses/Machine Learning IT Courses/Data Science

What Is Cart?

Ashish R.

SAS certified analytics professionals, more than 11 years of industrial and 12 years of teaching experience....

CART means classification and regression tree. It is a non-parametric approach for developing a predictive model. What is meant by non-parametric is that in implementing this methodology, we do not have any assumption that our target variable has to follow certain probability distribution. This is a... read more

CART means classification and regression tree. It is a non-parametric approach for developing a predictive model. What is meant by non-parametric is that in implementing this methodology, we do not have any assumption that our target variable has to follow certain probability distribution. This is a big relaxation from developing parametric models (for example linear regression models etc.) where we need to check the model's adequacy by assessing the various underlying assumption. When our target variable is categorical, then we say it is a classification problem and when our target variable is continuous i.e quantitative by nature then we call it is a regression problem.

Combination of classification and regression problem together is termed as CART. CART models are supervised models in terms of Machine Learning literature. These type of models are basically tree based (classification or regression) models. In developing such models, each node is basically splitted in binary fashion (not more than 2 split). For regression tree, target value of any new observation is estimated by averaging out the value of the training set observations that followed a particular branch where as in classification problem the predicted class is determined with certain probability. 

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Answered on 11/12/2017 IT Courses IT Courses/Data Science

Sniffer Search

Python is leading popularity wise and number of job in data science in 2017. BI people have statistics background which help to get fundamental understanding. Now need to understand some python package like numpy,skipy,pandas ,matplotlib and start implementing with python and machine learning algo... read more
Python is leading popularity wise and number of job in data science in 2017. BI people have statistics background which help to get fundamental understanding. Now need to understand some python package like numpy,skipy,pandas ,matplotlib and start implementing with python and machine learning algo which are already implemented in python package .Do analysis,data wrangling,exploration,implement on train data some fit algorithm and test for accuracy. read less
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Answered on 30 Jan Tuition/Class IX-X Tuition Tuition/Class IX-X Tuition/Science IT Courses/Data Science

Sujoy D.

Tutor

Newton's First Law states that an object will remain at rest or in uniform motion in a straight line unless acted upon by an external force. It may be seen as a statement about inertia, that objects will remain in their state of motion unless a force acts to change the motion. 2. Newton's second law... read more

Newton's First Law states that an object will remain at rest or in uniform motion in a straight line unless acted upon by an external force. It may be seen as a statement about inertia, that objects will remain in their state of motion unless a force acts to change the motion. 2. Newton's second law of motion can be formally stated as follows: The acceleration of an object as produced by a net force is directly proportional to the magnitude of the net force, in the same direction as the net force, and inversely proportional to the mass of the object. 3. Newton's third law is: For every action, there is an equal and opposite reaction. The statement means that in every interaction, there is a pair of forces acting on the two interacting objects. The size of the forces on the first object equals the size of the force on the second object.

Newton's laws of motion are three physical laws that, together, laid the foundation for classical mechanics. They describe the relationship between a body and the forces acting upon it, and its motion in response to those forces. They have been expressed in several different ways, over nearly three centuries and can be summarised as follows. First law: In an inertial reference frame, an object either remains at rest or continues to move at a constant velocity, unless acted upon by a net force. Second law: In an inertial reference frame, the vector sum of the forces F on an object is equal to the mass m of that object multiplied by the acceleration a of the object: F = ma. Third law: When one body exerts a force on a second body, the second body simultaneously exerts a force equal in magnitude and opposite in direction on the first body.

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