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Case study summarization

Kindly watch both the webinar and summarize both usecases and present it as a powerpoint presentation

Case Study - 1

Case Study - 2

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Chirst-CleverInsight Datastorytellers Community

Usecases

• Hospital Readmission Rates: Calculate and analyze hospital readmission rates. Identify factors that contribute to high readmission rates and propose strategies to reduce them. Dataset

• Control Attrition: ACME Corp Hospital is facing high attrition of employees. Identify factors that contribute to attrition and stop attrition and also try to retain the employees. Dataset

Evaluation Criteria

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# import config.
# You can change the default config with `make cnf="config_special.env" build`
cnf ?= config.env
include $(cnf)
export $(shell sed 's/=.*//' $(cnf))

# import deploy config
# You can change the default deploy config with `make cnf="deploy_special.env" release`
dpl ?= deploy.env
include $(dpl)

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We have been provided with the dataset from XYZ Labs Network Intrusion Logs, Our network protection classifier was able to detect Anamoly vs Normal requests. But as a cyber security expert, your duty is to find out the following details.

• Which services has highest anamoly detected.
• Which protocal has highest anamoly detected
• How many private services had anamoly and which type of protocol used.
• Does total request counts has any correlation with anamoly.
• Which are most important variables which contributes to an anamoly.

Download Dataset:

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The data comes from the U.S. International Air Passenger and Freight Statistics Report. As part of the T-100 program, USDOT receives traffic reports of US and international airlines operating to and from US airports. There are two datasets available:

Departures: Data on all flights between US gateways and non-US gateways, irrespective of origin and destination. Each observation provides information on a specific airline for a pair of airports, one in the US and the other outside. Three main columns record the number of flights: Scheduled, Charter, and Total. Passengers: Data on the total number of passengers for each month and year between a pair of airports, as serviced by a particular airline.

U.S. International Air Passenger and Freight data are confidential for a period of 6 months, after which it can be released. As a result, quarterly reports and the year to date/calendar year raw data files available here will always lag by two quarters. Questions that can be answered with data

• Top 10 busiest airp

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package main

import (
	"encoding/json"
	"fmt"
	"io/ioutil"
	"log"
	"net/http"
)

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Python Learning Plan
- How to install python on Mac
- Install VSCode editor on Mac
- How to Run python code using terminal
- Variable and constants
- Scope of Variable
- Variable data types
	- Integer {0,1,2,1000}
	- Float { Decimal - 10.1, 2.01 }
	- Boolean (True, False)

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Squashing Git Commits

The easy and flexible way

This method avoids merge conflicts if you have periodically pulled master into your branch. It also gives you the opportunity to squash into more than 1 commit, or to re-arrange your code into completely different commits (e.g. if you ended up working on three different features but the commits were not consecutive).

Note: You cannot use this method if you intend to open a pull request to merge your feature branch. This method requires committing directly to master.

Switch to the master branch and make sure you are up to date:

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1. Which district has more cases?
2. Total survived cases across country?
3. Geographical pattern of pandemic spread?
4. Rate of survival across districts?
5. What is the recovery rate across countries, districts, state?
6. Rate of new cases across districts?
7. Total active cases in India?
8. Datewise new cases in India?
9. To understand the relation between new and death cases

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Implementation of Hierarichal Clustering (Agglomeration Cluster)

Hierarchical clustering algorithms group similar objects into groups called clusters. There are two types of hierarchical clustering algorithms:

• Agglomerative — Bottom up approach. Start with many small clusters and merge them together to create bigger clusters.
• Divisive — Top down approach. Start with a single cluster than break it up into smaller clusters.

Create a Hierarical clustering and experiment the following tutorial