Sirai Juan
In the context of the final challenge of the Data Science course (Digital House Argentina), we developed a descriptive and predictive analysis of energy consumption in UK. The data set was provided by kaggle.com and consists of daily consumption measurements on smart meters. This dataset, consists in a refactorised version of the data from the London data store, that contains the energy consumption readings for a sample of 5,567 London Households that took part in the UK Power Networks led Low Carbon London project between November 2011 and February 2014. The data from the smart meters seems associated only to the electrical consumption.
Organizations are maded of people. Inside the company, we can find individuals
with different backgrounds, experiences and with different objetives.
Sometimes, what the individual wants for him or her career, is aligned with the organization objetives.
In that case, the person is able to pursuit his/her personal objetive,
while following the organization one. In other times, the organizational objetive is not aligned with
the individual one, so he or she may enter an internal conflict.
Organizations often put a lot of effort into desperate "talent retention" strategies but ...
maybe it's time to think and rethink, in strategies with a long-term goal:
"build the conditions so that people want to stay."
If you want, I invite you to follow me in this trip, triying to get the first insights about
"Why people left their jobs?", and think in ways to don't lose our main value.
The sinking of the Titanic is one of the most infamous shipwrecks in
history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS
Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for
everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were
more likely to survive than others.
In this notebook, I developed a predictive model that answers the question: “what sorts of people
were more likely to survive?” using passenger data (ie name, age, gender, socio-economic
class, etc)..
Akadelivers is a home delivery company specialized in delivering packages in less than 1 hour.
This company has a mobile application with which its users can choose from a catalog of products
from local stores in your city and have them delivered in less than 10 minutes to the address
of your choice.
In this Data Science Challenge, we need to answer the questions:
- The 3 countries in which the most orders are made
- The hours in which more orders are made in Spain
- Average price per store order with ID 12513
- Optimal shift distribution.
- Predictive model for cancelled orders.
© 2022 Juan Sirai.