Kindly watch both the webinar and summarize both usecases and present it as a powerpoint presentation
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Hospital Readmission Rates: Calculate and analyze hospital readmission rates. Identify factors that contribute to high readmission rates and propose strategies to reduce them. Dataset
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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
/* Get the latitude and longitude from address: | |
Author : Bastin Robins J | |
*/ | |
// Add the link to webpage | |
<script src="https://maps.googleapis.com/maps/api/js?v=3.exp&sensor=false"></script> | |
//Function to covert address to Latitude and Longitude | |
var getLocation = function(address) { | |
var geocoder = new google.maps.Geocoder(); |
# 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) |
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:
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
package main | |
import ( | |
"encoding/json" | |
"fmt" | |
"io/ioutil" | |
"log" | |
"net/http" | |
) |
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) |
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: