Create a Logistic model using #python

#This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.
#The objective of the dataset is to diagnostically predict whether or not a patient has diabetes,
#based on certain diagnostic measurements included in the dataset.
#Several constraints were placed on the selection of these instances from a larger database.
#In particular, all patients here are females at least 21 years old of Pima Indian heritage.

#The datasets consists of several medical predictor variables and one target variable.
#Predictor variables includes the number of pregnancies the patient has had, their BMI,
#insulin level, age, and so on.

#import the data set from the directory you want.

import os
os.chdir("C:\\Users\\satya\\Desktop\\python\\Assignment")

 

import pandas as pd #for data read

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
import seaborn as sns #visualize
import matplotlib.pyplot as plt #for tight layout

import pandas as pd

Medical_DATA = pd.read_csv("pima.csv")

#take a look of the dataset

print(Medical_DATA.shape) #768,9

 

#to check the 10 0observations of the data

Medical_DATA.head(10)

 

The dependant variables and independent variables - 

# 1. Number of times pregnant 
# 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 
# 3. Diastolic blood pressure (mm Hg) 
# 4. Triceps skin fold thickness (mm) 
# 5. 2-Hour serum insulin (mu U/ml) 
# 6. Body mass index (weight in kg/(height in m)^2) 
# 7. Diabetes pedigree function 
# 8. Age (years) 
# 9. Class variable (0 or 1)

#output variable - 
diabet=0/1



To see the data - 

print(Medical_DATA.describe())

 

#find the standard deviation of each column ( less variance is required)

print(Medical_DATA.std())

 

#plot the histograms - ( see the skew nessof the columns) #diabetic data is imbalance here

Medical_DATA.hist()

plt.tight_layout()
plt.show()

 

#in the above case we see the target variable is imbalance so we have to make the data balance.

#check the count of the target variable in graphical format

sns.countplot(x='Diabet', data=Medical_DATA , palette ='hls')
plt.show()
plt.savefig('count_plot')

 

Model Creation –

#separating the predictors and response

independentVar=['NPG','PGL','DIA','TSF','INS','BMI','DPF','AGE']

x=Medical_DATA[independentVar] #predictor
y=Medical_DATA['Diabet'] #Response

 

#breaking data in train and test (70-30 ratio)

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)

#fitting logistic regression model

logreg=LogisticRegression()
logreg.fit(x_train,y_train)


#warning - in next version it will changed lbfgs

 

#using the above created model to predict

y_pred=logreg.predict(x_test)

#accuracy on test

print('Accuracy of logistics regression on test set :{:.2f}'.
format (metrics.accuracy_score(y_test,y_pred)))

 

#import modeule to get the confusion matrix

from sklearn.metrics import confusion_matrix
confusion_matrix = confusion_matrix(y_test,y_pred) #just give "ytest" and "predicted y"
print(confusion_matrix)

#check the classification report 

from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred))

#importing the ROC curve

from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve

logit_roc_auc = roc_auc_score(y_test,y_pred)  #this is to calculate AUC

fpr,tpr,thresholds = roc_curve(y_test, logreg.predict_proba(x_test)[:,1])

#plot properties

plt.figure()
plt.plot(fpr,tpr,label = 'logistics regression (area = %0.2f)' % logit_roc_auc)
plt.plot([0,1] ,[0,1],'r--')
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.05])
plt.xlabel('false positive rate')
plt.ylabel('true positive rate')
plt.title('receiver operating characterstics')
plt.legend(loc="lower right")
plt.savefig('log_roc')
plt.show()

Steps for Logistic Regression Model

 

• Step 1: Data Cleansing & Treatment (Missing Value, Floor/Cap to remove outliers)
• Step 2: Variable Selection
• Using IV or Variable Clustering
• Check VIF
• Run Stepwise Logistic for final set of variables for optimization
• Step 3: Create Model Training (Development) and Validation (In-time & Out-of-time) datasets
• Step 4: Run Logistic Regression for Optimization
• Check for p-value
• Check for Odds Ratio
• Check for Concordant / Discordant
• Check for KS metrics

 

The code which we mostly use  –

Code:-

• Variable Selection using Variable Clustering (Proc VarClus)

proc varclus data=model_sample maxeigen=1 short hi;
var x1 x2 x3 x4 x5 x6 x7 x8 x9 x10;
run;

• Creating development & validation datasets

/*Selecting Customer IDs*/

Proc sql;
Create table data_id
As select distinct cust_ud
From model_sample;
Run;



/*Select random sample for dev & create dev & itv data*/

Proc surveyselect data=data_id
Method =srs n=1000 out=data_id_dev;
Run; /*the value of n should be 60% of the total observations in the main data*/


/*For stratified sampling*/

proc surveyselect data=Customers
method=srs n=1000
seed=1953 out=SampleStrata;
strata age gender;
run;
/*After this create dev & itv data basis the cust_id included in 
the sample selection above*/
/*Logistic Regression – variable selection & model creation*/

Proc logistic data=dev_data descending;
Model ins = hhincome age gender…… state

/ link= logit stb expb corrb

  Covb lackfit rsquare ctable pcorr selection=stepwise slentry=0.01

  Slstay=0.1 hierarchy=single technique=fisher;

Output out=logistic_stats predicted=pred_ lower=lcl_ upper=ucl_

Reschi=reschi_ resdev=resdev_ reslik=reslik_ stdReschi=stdReschi_

stdResdev=stdResdev_ difChiSq=difChiSq_ difdev=difdev_ h=leverage_

score out=logistic_scores;

run;

SAS – for beginners

What SAS stands for –

“Statistical Analysis System” is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics  –  ( Source – Wikipedia)

 

Mostly used for various purposes such data mining, report writing, statistical analysis, business modeling, applications development and data warehousing. SAS is considered as the diamond in the present scenario.

SAS a Leader in 2016 Gartner Magic Quadrant for Data Integration Tools 

 

Before we start SAS please install SAS from SAS site ( university or cloud edition )

Let’s start playing with SAS

The 1st step for SAS is to create a library and we want to keep all our data to that library.

Format for library is

Libname <>  <Path> ;

Libname Aug_2017 'F:\Base SAS';
Run;

Shortcut keys in SAS

CTRL+? to comment*//*
CTRL+SHIFT+? to uncomment*//*
Press F3 or F8 to run the program*/

1st program in SAS  

Format - 
Data dataset name 
input;
cards;
run;

Data Stu_Enq;
Input Stu_ID $ Stu_Name $ Age Academics $ Occupation $ DOE Course $;
Informat DOE MMDDYY10.;
Format DOE MMDDYY10.;
Cards;
S1 Rachit 25 Btech IT 01-01-2018 SAS_AnalyticsS2
Manish 30 Btech IT 02-01-2018 SAS_AnalyticsS3
Sunny 24 Btech IT 03-01-2018 SAS_AnalyticsS4
Varun 21 Btech IT 04-01-2018 SAS_AnalyticsS5
Chandru 29 MCA IT 05-01-2018 SAS_AnalyticsS6
Ashu 25 MCA IT 06-01-2018 SAS_AnalyticsS7
Shasank 26 MCA IT 07-01-2018 SAS_AnalyticsS8
Namrata 25 MBA IT 08-01-2018 SAS_AnalyticsS9
Surya 27 MBA IT 09-01-2018 SAS_AnalyticsS10
Lavina 27 MBA IT 10-01-2018 SAS_Analytics;
Run;

 

When we run the program the dataset is created into WORK library ,
/*Note: if we don’t define library and given reference to the library, the dataset will default into work library*/

let’s discuss about the programming
In the above program we use many statements

1.Data- To create a dataset and every statement ends with the semicolon

2.Input- To define column input and every statement ends with the semicolon

3.Informat- To read data into SAS format and every statement ends with the semicolon

4.Format- To write data into SAS format and every statement ends with semicolon

5.cards- is a statement is used for putting temporary data and every statement ends with semicolon

6.Run- is a statement is used for executing the program and every statement ends with semicolon

 

USES Informat and Format  - 
Data Stu_Enq;
Input Stu_ID $ Stu_Name $ Age Academics $ Occupation $ DOE Course $;
Informat DOE MMDDYY10.;
Format DOE MMDDYY10.;
Cards;
S1 Rachit 25 Btech IT 01-01-1960 SAS_Analytics
S2 Manish 30 Btech IT 01-10-1960 SAS_Analytics
S3 Sunny 24 Btech IT 01-01-1959 SAS_Analytics
S4 Varun 21 Btech IT 01-01-1961 SAS_Analytics
S5 Chandru 29 MCA IT 05-01-2018 SAS_Analytics
S6 Ashu 25 MCA IT 06-01-2018 SAS_Analytics
S7 Shasank 26 MCA IT 07-01-2018 SAS_Analytics
S8 Namrata 25 MBA IT 08-01-2018 SAS_Analytics
S9 Surya 27 MBA IT 09-01-2018 SAS_Analytics
S10 Lavina 27 MBA IT 10-01-2018 SAS_Analytics;
Run;

Proc print data=Stu_Enq;

Run;

In the above dataset we have DOE is starting from 01-01-1960 hence the informat reads the date value as 0and in order to read the date value to date format we use sas format statement
In SAS PC, we have three windows

1. Editor window- Where we write syntaxes/Programs
2. Log window- Where we check the error, warning and successful program running msg
3. Output window- Where to print the result

SAS Programming is based on 3 things   —

1. statements
2. options
3. functions

/*Going forward we will have more examples of statements, options, and functions*/
/*Lets talk about SAS Programming steps*//*

There are two steps to learn and grow in SAS programming

1. Data step- To create a dataset and starts with data statement and ends with a run statement

2. Proc step- To print the output and starts with proc statement and ends with run statement
/*Data step*/

Data Stu_Enq;
Input Stu_ID $ Stu_Name $ Age Academics $ Occupation $ DOE Course $;
Informat DOE MMDDYY10.;Format DOE Date9.;
Cards;
S1 Rachit 25 Btech IT 01-01-1960 SAS_Analytics
S2 Manish 30 Btech IT 01-10-1960 SAS_Analytics
S3 Sunny 24 Btech IT 01-01-1959 SAS_Analytics
S4 Varun 21 Btech IT 01-01-1961 SAS_Analytics
S5 Chandru 29 MCA IT 05-01-2018 SAS_Analytics
S6 Ashu 25 MCA IT 06-01-2018 SAS_Analytics
S7 Shasank 26 MCA IT 07-01-2018 SAS_Analytics
S8 Namrata 25 MBA IT 08-01-2018 SAS_Analytics
S9 Surya 27 MBA IT 09-01-2018 SAS_Analytics
S10 Lavina 27 MBA IT 10-01-2018 SAS_Analytics;
Run;

---

/*Proc step*//

 Proc print data=Stu_Enq;Run;
 

Lets do more table using Cards and Data lines
 
Data School;
Input Roll_No Name $ Age Class $ Year;
Cards;
1 Rachit 25 Std1 20102 Manish 30 Std1 20103 Sunny 26 Std2 2010;
Run;
 Proc print data=School;
run;


Data School;
Input Roll_No Name $ Age Class $ Year;Datalines;
1 Rachit 25 Std1 20102 Manish 30 Std1 20103 Sunny 26 Std2 2010;
Run;
Proc print data=School;
run;

Till now we learn if we dont define any library dataset goes to work library

How to Create our own library ?

Libname <Libref> 'Path';
Run;

 

Libname Aug_2017 ‘F:\Base SAS’;

Run;

How to define dataset into your library ?

Data Aug_2017.Stu_Enq;Input Stu_ID $ Stu_Name $ Age Academics $ Occupation $ DOE Course $;
Informat DOE MMDDYY10.;
Format DOE Date9.;
Cards;
S1 Rachit 25 Btech IT 01-01-1960 SAS_AnalyticsS2 
Manish 30 Btech IT 01-10-1960 SAS_AnalyticsS3 
Sunny 24 Btech IT 01-01-1959 SAS_AnalyticsS4 
Varun 21 Btech IT 01-01-1961 SAS_AnalyticsS5 
Chandru 29 MCA IT 05-01-2018 SAS_AnalyticsS6 
Ashu 25 MCA IT 06-01-2018 SAS_AnalyticsS7
 Shasank 26 MCA IT 07-01-2018 SAS_AnalyticsS8 
Namrata 25 MBA IT 08-01-2018 SAS_AnalyticsS9 
Surya 27 MBA IT 09-01-2018 SAS_AnalyticsS10 
Lavina 27 MBA IT 10-01-2018 SAS_Analytics;
Run;

proc print data=Aug_2017.Stu_Enq ;
run;

While creating library, we need to follow some criteria – 

1. Library name should not be more than 8 charcter long

2. It should not be started with a number or any special character apart from underscore

3. It can be started with an alphabet or an underscore only. and followed by number or an underscore only
Ex:  – ABCD_123456778 —-No

ABCD_123– YES

_ABC123–YES

ABC@1234—NO

/Aug_2017–YES
Criteria to be followed for creating dataset name and the variable name –
1. Dataset and Variable name should not be more than 32 charcter long

2. It should not be started with a number or any special character apart from underscore

3. It can be started with an alphabet or an underscore only. and followed by number or an underscore only*/
Ex –

Jan_2019_Cuttack_Sales —–YES

First Name———--No

First_Name———–YES

Last_Name————Yes

Academics———–-Yes

Jan sales_2010——–No

 
How many types of variables are there ?  

Variables are two types

Numeric and Character 
Character Varuable – 

1. default characger varuable length is 8 characters*//*2. maximum character variable length could be 32,767*/
Numeric Variable – 

1. Default is 8 bytes*//*2. 1 byte equals to 2 digits. Now default numeric variable length would be 16 digits*/

/*Example - 
 Data Stu_Enq;

Input Stu_ID $ Stu_Name $ Age Academics $ Occupation $ DOE Course $;
Informat DOE MMDDYY10. ;
Format DOE Date9. ;
Cards;
S1 Rachit 25 Btech IT 01-01-1960 SAS_AnalyticsS2
Manish 30 Btech IT 01-10-1960 SAS_AnalyticsS3
Sunny 24 Btech IT 01-01-1959 SAS_AnalyticsS4
Varun 21 Btech IT 01-01-1961 SAS_AnalyticsS5
Chandru 29 MCA IT 05-01-2018 SAS_AnalyticsS6
Ashu 25 MCA IT 06-01-2018 SAS_AnalyticsS7
Shasank 26 MCA IT 07-01-2018 SAS_AnalyticsS8
Namrata 25 MBA IT 08-01-2018 SAS_AnalyticsS9
Surya 27 MBA IT 09-01-2018 SAS_AnalyticsS10
Lavina 27 MBA IT 10-01-2018 SAS_Analytics;
Run;

proc print data=Stu_Enq;

Run;

 

In the above program Course reads character upto 8  *//*so output is “SAS_Analy”

/*If we want to read full, we need to define length of the character variable*/

Data Stu_Enq;
Input Stu_ID $ Stu_Name $ Age Academics $ Occupation : $10. DOE Course : $20.;
Informat DOE MMDDYY10.;
Format DOE Date9.;
Cards;
S1 Rachit 25 Btech IT 01-01-1960 SAS_AnalyticsS2
Manish 30 Btech IT 01-10-1960 SAS_AnalyticsS3
Sunny 24 Btech IT 01-01-1959 SAS_AnalyticsS4
Varun 21 Btech IT 01-01-1961 SAS_AnalyticsS5
Chandru 29 MCA IT 05-01-2018 SAS_AnalyticsS6
Ashu 25 MCA IT 06-01-2018 SAS_AnalyticsS7
Shasank 26 MCA IT 07-01-2018 SAS_AnalyticsS8
Namrata 25 MBA IT 08-01-2018 SAS_AnalyticsS9
Surya 27 MBA IT 09-01-2018 SAS_AnalyticsS10
Lavina 27 MBA IT 10-01-2018 SAS_Analytics;
Run;

proc print data=Stu_Enq;
Run;

 

Every sas dataset has two portions – 
1. descriptor portion- to describe dataset

2.data portion to show the dataset and the observations*/

/*Descriptor Portion of the dataset*/
Proc contents data=Stu_Enq;
run;
Proc contents data=Stu_Enq varnum;
run;
Proc contents data=Stu_Enq varnum short;
run;

 

Varnum– is an option use in proc contents to get the variable list in the datset sequence

Short – is an option use in proc contents to put variable header list

/*Descriptor Portion of the library*/Proc contents data=Aug_2017._all_ ;run;

Data Aug_2017.School;
Input Roll_No Name $ Age Class $ Year;
Cards;
1 Rachit 25 Std1 20102 
Manish 30 Std1 20103 
Sunny 26 Std2 2010;
Run;

Proc contents data=Aug_2017._all_ ;
run;

Proc contents data=Aug_2017._all_ NODS;
run;

/*NODS= NO Description, we use supress the descriptor portion of dataset*/

*//*Data Portion*/

Proc print data=Stu_Enq;
Run;
Proc print data=Stu_Enq (obs=5);
Run;
Proc print data=Stu_Enq (firstobs=5 obs=10);
Run;
Proc print data=Stu_Enq (firstobs=10 obs=10);
Run;

Proc print data=Stu_Enq ;
var Stu_ID  Age Academics DOE Course;
Run;
Proc print data=Stu_Enq ;
var Stu_ID  Age Academics DOE Course;
Where Academics='Btech';
Run;

Data manipulation in SAS

/*Importing MED_New_2016.csv file*/
Proc Import Out=Aug_2017.MED
datafile='F:\Aug_Batch_2017\a5. SAS Base and Advanced\Base SAS\Data\MED_New_2016.csv' 
dbms=csv replace;
Run;

 

/*in the log window we got,*/
 data AUG_2017.MED_Infile ;

infile 'F:\Aug_Batch_2017\a5. SAS Base and Advanced\Base SAS\Data\MED_New_2016.csv' delimiter=',' MISSOVER DSD lrecl=32767 firstobs=2 ;
informat CUSTOMER_ID best32. ;
informat Company $9. ;
informat CARD_REG_DATE anydtdtm40. ;
informat CARD_ACTIVE $1. ;
informat FIRST_USE_DTE anydtdtm40. ;
informat firstSTOR best32. ;
informat TITLE $4. ;
informat GENDER $6. ;
informat max_spent best32. ;
informat DOB ddmmyy10. ;
informat FTD anydtdtm40. ;
informat Age best32. ;
informat STATE_CODE $3. ;
informat POST_CODE best32. ;
informat CUST_STAT $6. ;
informat Avgsize_spent best32. ;
informat CARD_STAT $10. ;
informat RGSTN_TYPE_IND $6. ;
informat NO_OF_TRIPS best32. ;
informat TOWN $14. ;
informat EMAIL_IND $1. ;
informat CONTACT_PREF $5. ;
informat Average_Qty_PER_ACC best32. ;
informat Spent_amount best32. ;
format CUSTOMER_ID best12. ;
format Company $9. ;
format CARD_REG_DATE datetime. ;
format CARD_ACTIVE $1. ;
format FIRST_USE_DTE datetime. ;
format firstSTOR best12. ;
format TITLE $4. ;
format GENDER $6. ;
format max_spent best12. ;
format DOB ddmmyy10. ;
format FTD datetime. ;
format Age best12. ;
format STATE_CODE $3. ;
format POST_CODE best12. ;
format CUST_STAT $6. ;
format Avgsize_spent best12. ;
format CARD_STAT $10. ;
format RGSTN_TYPE_IND $6. ;
format NO_OF_TRIPS best12. ;
format TOWN $14. ;
format EMAIL_IND $1. ;format CONTACT_PREF $5. ;format Average_Qty_PER_ACC best12. ;format Spent_amount best12. ;inputCUSTOMER_IDCompany $CARD_REG_DATECARD_ACTIVE $FIRST_USE_DTEfirstSTORTITLE $GENDER $max_spentDOBFTDAgeSTATE_CODE $POST_CODECUST_STAT $Avgsize_spentCARD_STAT $RGSTN_TYPE_IND $NO_OF_TRIPSTOWN $EMAIL_IND $CONTACT_PREF $Average_Qty_PER_ACCSpent_amount;

run;

 

Pune Diaries…. :)

My 1^st trip to Pune, well it was an unplanned trip. I was in Pune for 3 days almost fell in love with the city. I had covered pretty much all touristy things, and loved them all. But this trip was reserved for pure unplanned exploration and I was determined to walk the walk, like the locals do.

1^st night :- The day I reach at Pune airport #Biju was there to pick me up with a great smile though it was a fake smile :P. Biju prepare #Maggi for me as dinner it was delicious !

2^nd day – I did my best to see and experience the local life and vibe during my Pune trip. The city is young and the streets are always -ALWAYS – buzzing with energy. Started with a bang to have some breakfast at #Novotel ended with a sad face as it was already 12 O’clock. Then we plan for to go NH-37 Dhaba.

 

The city is young and the streets are always -ALWAYS – buzzing with energy.

Breathing the air as people walk all around you (almost half with tiny fans, yes) amidst happy chatter – makes you feel a part of it.

Pune is not the same as Bangalore. Far from it, actually. Politically, I don’t want to comment on it in much detail because that’s not a discussion. But the city totally different from Bangalore, full of tress and hills. City may be small but I feel the city for full of youths.

After lunch we planned for a #Lavasa trip, well we have to drop #Biju at his den as he was getting late for his office. We started the trip around 4.30 Pm. It was nice long drive with known people and some unknown friends. Took around 2 hours to reach the place it was really awesome. Manmade lake, hotels, chai-point, Open air auditorium which pulls tourist from nearby place of #Pune and #Mumbai.

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2^nd Day Planned properly woke up at 7.30 and get ready by 9. Planned to have breakfast at #GermanBakery. Nice place filled with foreigners. Well we ordered some salad, Garlic bread, Soup and Juices. Chit chat started as usual about the school colleges. The best moment came when #Biju reveal about his love-story, it was a short and simple love story but we loved it.!

We moved to #Hoppipolla for snacks and drinks, that was the best part of the trip.

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We suddenly plan for Lonavala trip at night !

Next morning I have to catch a flight as per plan #Jaga dropped at airport!

I hope it help you in planning a Pune trip – for experience this super vibrant destination. And a dream destination it is!  So what you waiting for? Plan your trip and let me know if you need help in preparing the best Pune trip itineraries for Indian travellers, I’ll do the best I can to help out.

Happy Travelling!

Why I lyk #BLR

When I first came to #Bengaluru, I was lost. Literally! I get down at majestic waiting for my friend. The traffic, autobhaiya & long distances, I hated it all and couldn’t wait to go back to my city.

But just in 3 months of internship, the city has become a close part of my life.

Then by some way I got a job in #BLR, my 1st job I was really excited for that, new people and the eagerness to explore the unexplored part of #BLR.

This is the day (May 6th) I completed 2yrs 6 mnth in this city, it started from from “Kannada gothilla” (I don’t know Kannada) to “nanu Kannada kalita-iddini” (I can speak Kannada), I have traveled a long way!

There are so may things why I love #blr, So, here is the list of 5 things that we all absolutely love about Bangalore-

Well connected with Buses – The Volvo is all you need to travel in a hot summer day and Vayu Vajra will make sure you never miss a flight!

 

Green city of India – full of tress and gardens you will love it.

Brigade Road- The integral part of the city

People used say if you want to see the real #Bengaluru plz visit #brigade road. PC- Govind Saji (Ace photographer & Budding Cinema director)

India’s Silicon Valley and StartUp Hub

Full of startups emerging Ideas – Investment – Ventures- Entrepreneurs

Nyc to see my friend here.

Work Hard and Party Harder

Work more if  want to party more.

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If U know something awesome about Bangalore that should be included in the list? Leave your inputs in the comments below follow me @satya_majhi

 

 

My #1st Flight…….:)

Well that was the day when I get a call from my mom can you come down to #bbsr if you are free as there was a family get together.

First flight experience is unforgettable for any one. I was bit scared what to do what not to do. Time flies very fast during masters.

I chose to fly by #Indigo Airways since there’s a direct flight from #blr to #bbsr and it allows one additional baggage of 20 kgs. I made a travel plan in 15 days advance. Finally the day comes I have to board the airport bus from. It took me hardly 3 hours to reach at airport.

As soon as I reached the airport the security guard guided me to the #Indigo counter #33. Here comes another twist, when I was booking ticket in #indigo office, the staff mentioned that my travel is through check-in and I do not have to worry about my luggage . At that time I was really scared.

I have to wait at waiting room and just watching all the digital ads…still I have 1 hour to board the flight. At 11.30 they started opening the gate and gradually my no comes. As I entered there is an angel who is waiting to greet us. Then she guided me to my seat “c 5”. As soon as the flight is full they started their daily routine of work …..I mean how to tie a seat belt and blabla….

After 30 mins they started selling goods and food at a tag price which is quite higher than the normal price. As a new flyer I tried a nutshell just for a memory.

 

 

My co-passenger was from same place and from same Enng. college also. We have some chit chat about our college and the jobs.

The moment they started announcing the landing timings, I was literally excited. Finally the moment came and we landed safely on time.

As promised by Indigo we are #ontime #indigo #6E #bestflight #bestcabincrew #bestfood #nycculture #nycairhostess #bangalore #bhubaneswar #firstflight.

 

If you like this blog post, please do comment. Your encouragement now helps me more than ever to get rid of my writers block.

A chilling Sunday #nandihills

Source: A chilling Sunday #nandihills

A chilling Sunday #nandihills

A chilling Sunday @nandihills

That was the last weekend of the year #2015, we are planning what to do….on this long weekend. As usual we planned for a road trip non other than #nandihills. The main concern was the picking all the friends at early morning any way this time #rajesh and #nitin managed that well and good enough.

So we start around 4 am, on the way we pick #anurag. Then we directly headed towards #nandi hills

For more info click here

Nandi hills is 60 km from Majestic, Bangalore (75 kms from #marathalli) at an altitude of 1478 (approx.) metres above sea level. It’s a cluster of small hills with marvellous cliffs. To note Bangalore is 949 mtrs above sea level. So we had to travel 529 mtrs to reach Nandi Hills.

By now, we had already crossed 42 kms of the 60 km journey but there was no sign of hills. There was our 1^st stop where we had a cup of tea. This bothered me along with the fact that the lesser the distance left, the steep will be the ride. More over for me it was 7^th time I’m visiting that place,

As we move towards our destination we can figure out two huge cliffs at some distance from the road. My driver guessed, it might be the Nandi Hills. Without bothering what they were, we moved forward and then we realised that the roads had started to twist and turn. The turns and steepness wasn’t those scary ones as compare to #Munnar or #Drajeeling .

Our driver managed to drive through the hills, after all for him it was the 1^st time, so we are bit confused whether he can drive or not. It took almost 2 hours to reach there ….

 

On the way I have taken some pic ….it’s chilling 😛

 

The ticket counter…….!

 

If you having a car just pay 100 bucks.

 

The entrance ……!

 

The place next to #ganesh temple……!

 

 

This is supposed to be the execution point during Tipu’s rule. As a punishment, Criminals were pushed down the cliff, from here.

 

By now, the clouds started to moving fast ……and are waiting for the right time to take snaps ……….We could see surrounding hills and curled roads down beneath. What a view. After some snaps and posses we started to move towards the restaurant to have snacks. Price was bit high but it doesn’t matter.

Some picks of ours ……

 

 

Well it was a great trip……thanks to #nitinji , #rajesh and #anurag.

@sbz rocks

Entrepreneurship is the ‘engine’ of any economy…

When I think about the Entrepreneurship one name comes to mind that is “Bansal Families”.

Even from my child hood the name “Bansal”  is brand name which gives IIT coaching but today it is the common factor binding the who’s who of India’s fledgling e-commerce sector.

Flipkart,Lenskart,Myntra,Snapdealn led by Bansals. Here I have attached the info graphics.

The Bansal Family
*source HT

What if India’s house old name “Tata” will enter into e-commerce and India’s stalwart Ratan Tata entered into e-commerce with a bang. He invested in 8 companies within a year. He is using his personal income to boost the India’s start-up ecosystem. In 2014 he invested in 4 co and they are as follows- Urbanladder, Snapdeal, Bluestone, Swachbharat. In 2015 he invested in another 4 companies and they are as follows- Grameen capital, Paytm, Cardekho, Xiaomi.

On Sunday, Xiaomi founder and CEO Lei Ju said “He (Tata) is one of the most well-respected business leaders in the world. An investment by him is an affirmation of the strategy we have undertaken in India so far.”

In my view is TATA is the only co. who is investing aggressively in the burgeoning online retail sector.

#Myntra #Flipkart #TATA #Lenskart #Xiaomi

IS INDIA really prepared for the #gosf

#GOSF- Great Online Shopping Festival….

When I ask my friend about this he gives a look which is obvious, he replied suddenly wat’s that is that a short hand you are using in chatting / SMS.

Well this time we all Indians are really heading for this online shopping festival initiative by Google to make an Indian version of Black Friday with the collaboration of various online stores in India, which gives the Indian consumers an opportunity to grab multitude of products from several popular shopping websites with big discounts.

This time almost 100 e-commerce companies are participating in this event.

Really it’s interface is very much appealing just click it –

https://www.gosf.in/ 

Hope all will enjoy the shopping during those days and save more for the coming new year.

#advance #newyear