Big Data five V types - By Kapil Sharma

Big Data five V types:- 

1. Volume:- Amount of data (Like in GB or PB in data size)
2. Value:- Useful information for users
3. Veracity:-  Conformity to facts; accuracy of data
4. Variety:- Type of data (Structured, Semi- and Un-Structured)  
5. Velocity:- Speed of data generation (Bills Data, Social Media feeds, All type of streaming)

RBI - Central Bank Digital Currency, India

 RBI - Central Bank Digital Currency, India

India has made impressive progress towards innovation in digital payments. India has enacted a separate law for Payment and Settlement Systems which has enabled an orderly development of the payment eco-system in the country. The present state-of-the-art payment systems that are affordable, accessible, convenient, efficient, safe, secure and available 24x7x365 days a year are a matter of pride for the nation. 

This striking shift in payment preference has been due to the creation of robust round the clock electronic payment systems such as Real Time Gross Settlement (RTGS) and National Electronic Funds Transfer (NEFT) that has facilitated seamless real time or near real time fund transfers. In addition, the launch of Immediate Payment Service (IMPS) and Unified Payments Interface (UPI) for instant payment settlement, the introduction of mobile based payment systems such as Bharat Bill Payment System (BBPS), and National Electronic Toll Collection (NETC) to facilitate electronic toll payments have been the defining moments that has transformed the payments ecosystem of the country and attracted international recognition. 

The convenience of these payment systems ensured rapid acceptance as they provided consumers an alternative to the use of cash and paper for making payments. The facilitation of non-bank FinTech firms in the payment ecosystem as PPI issuers, Bharat Bill Payment Operating Units (BBPOUs) and third-party application providers in the UPI platform have furthered the adoption of digital payments in the country. Throughout this journey, the Reserve Bank has played the role of a catalyst towards achieving its public policy objective of developing and promoting a safe, secure, sound, efficient and interoperable payment system.

As CBDCs are electronic form of sovereign currency, it should imbibe all the possible features of physical currency. The design of CBDC is dependent on the functions it is expected to perform, and the design determines its implications for payment systems, monetary policy as well as the structure and stability of the financial system. One of the main considerations is that the design features of CBDCs should be least disruptive.

CBDCs being digital in nature, technological consideration will always remain at its core. The infrastructure of CBDCs can be on a conventional centrally controlled database or on Distributed Ledger Technology. The two technologies differ in terms of efficiency and degree of protection from single point of failure. The technology considerations underlying the deployment of CBDC needs to be forward looking and must have strong cybersecurity, technical stability, resilience and sound technical governance standards. While crystallizing the design choices in the initial stages, the technological considerations may be kept flexible and open-ended in order to incorporate the changing needs based on the evolution of the technological aspects of CBDCs.

The Reserve Bank of India (RBI), which is India's central bank, has been exploring the possibility of issuing a Central Bank Digital Currency (CBDC) for several years. In January 2021, the RBI announced that it was working on a phased implementation strategy for a digital version of the Indian rupee.

The RBI's CBDC project is still in the development phase, and details about the design, functionality, and timeline for the digital currency have not yet been finalized. However, the RBI has stated that its CBDC will be a legal tender and will be introduced alongside physical currency.

The RBI has cited several potential benefits of a CBDC, including increased financial inclusion, reduced transaction costs, and enhanced efficiency in payment systems. A digital rupee could also provide a more secure and traceable alternative to physical currency, and it could help combat the use of illicit funds and money laundering.

However, the RBI has also acknowledged that a CBDC would present several challenges, including ensuring data privacy and security, preventing cyber-attacks, and balancing the need for innovation with financial stability and regulatory concerns.

Overall, the RBI's CBDC project is still in its early stages, and it remains to be seen when and how a digital version of the Indian rupee will be introduced. However, given the growing interest in CBDCs worldwide, it is likely that the RBI will continue to explore this technology as a means of modernizing India's payment systems and promoting financial inclusion.

RBI Public News Source: https://rbi.org.in/

https://bitcoin.org/en/

Apple 14 Pro

Apple 14 Pro 

 

Apple iPhone 14

 Apple iPhone 14 

Air pod Pro 2022

 Air pod Pro 2022 

Apple Watch Series 8

 

Apple Watch Series 8 

Kaggle ( Titanic ML competition) - Kapil Sharma (Class-103/S2022)

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

from sklearn.ensemble import RandomForestClassifier

train_data = pd.read_csv("/kaggle/input/titanic/train.csv") 

test_data = pd.read_csv("/kaggle/input/titanic/test.csv")

train_data.head()

women = train_data.loc[train_data.Sex == 'female']["Survived"]

rate_women = sum(women)/len(women)

print("% of women who survived:", rate_women)

train_data[train_data.Sex == 'male']

men = train_data.loc[train_data.Sex == 'male']["Survived"]

rate_men = sum(men)/len(men)

print("% of men who survived:", rate_men)

target = train_data["Survived"]

features = ["Sex","Parch","SibSp"]

X = pd.get_dummies(train_data[features])

X_test = pd.get_dummies(test_data[features])

model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)

model.fit(X, target)

predictions = model.predict(X_test)

output = pd.DataFrame({"PassengerId": test_data.PassengerId, "Survived": predictions})

output.to_csv("my_submission.csv", index=False)

output.head()

Dash Python Installation - Kapil Sharma (Class-102/S2022)

Commands to install Dash

Open command line and make sure that python already installed in it than install the below commands.    

1. pip install dash

2. pip install jupyter-dash

3. pip install pandas

Python Dash: A new approach towards data science - Kapil Sharma (Class-101/S2022)

Dash is the original low-code framework for rapidly building data apps in Python. Dash is an open source library released under the permissive MIT license. 

Dash is the new standard for AI & data science apps.

Source: https://plotly.com/