MACHINE LEARNING

Analyzing the causes of employee attrition and building an ML model for future forecasts

Employee Attrition is the loss of employees through other means than sacking without replacement. Natural causes such as retirement, resignation, health reasons etc. all fall under causes of employee attrition.

Apart from the natural causes of employee attrition, there are other causes that could influence an employee to leave their current employment. A lot of these reasons are related to job satisfaction and personal fulfilment.

When employees feel dissatisfied with the working conditions or they feel less accomplished working at these jobs, they are more likely…


DATA ANALYTICS

An analysis of the economy of some nations in Africa

Photo by Imelda on Unsplash

The economy of a nation is measured by the average standard if living of its populace. The more economically stable a country is, the better or more even the standard of living of each citizen is.

There are indicators which reflect the economic conditions of a country such as: Inflation rate, Gross Domestic Product (GDP), exchange rate etc. Each of these indicators are reflective of each other that is, a country with a high inflation rate will have a very high exchange rate and vice versa.

Yemen, Somalia, South Sudan…


DATA ANALYTICS

Arsenal’s best and worst seasons yet

Photo by Nelson Ndongala on Unsplash

Arsenal Football Club (Arsenal FC), is an elite football club based in Islington, London. The club competes in the highest level of the English Professional league — the Premier League(PL).

The Premier League is the professional level of the English football played by 20 teams. It operates on the system of promotion and relegation in the English Football league.

Each of the twenty teams play each other twice — home and away which means there are 38 matches to be played by each team. Each match is valued at 3 points, if they match…


DATA ANALYTICS

Data Analytics Using Tableau

Photo by Ibrahim Rifath on Unsplash

On the 31st day of January 2020, the United Kingdom (UK) officially withdrew from the European Union(EU) and the European Atomic Energy Community. Prior to that day, there were 28 member countries of the EU who carried out trade amongst other things within themselves.

The EU is the UK’s biggest trading partner accounting for 47% of the country’s total trades. The EU consumes about 42.6% of UK’s total exports while providing 51.8% of their total imports (visualcapitalist.com)

One of the greatest benefits of being part of the EU is free movement of goods, services, labour and…


Data Analytics

Making the most of your dataset

Photo by Markus Spiske on Unsplash

The hallmark of Data Science is the ability to explore your data properly. You could have robust data with plenty of latent information but if not properly explored, you will leave such information largely unharnessed. In Data Analytics, data is deeply explored through wrangling to provide insight to history, to answer the questions of why and how and to also make accurate predictions about the future by finding the relationship in the dataset.

No data can be properly explored without it being properly cleaned. Truthfully, some issues of tidiness and quality may escape your focus during the initial cleaning but…


The easiest way to overcome cancer is by early detection through timely identification of cancerous cells in the body. According to records, 10–20% of people with cancer are misdiagnosed and 28% of 583 cases were life threatening or life altering (google). Therefore, in order to win this war against cancer, it is important that we greatly cut down the rate of misdiagnosis.

In this article, I will analyze the common traits of cancerous and non-cancerous cells using a sample data from Kaggle on breast cancer — cancer and non cancer classification.

The data contains cell characteristics such as radius, concavity…


Hi there, I see you have been wondering the same thing as I. In the field of Data Science, mathematics can best be described as a tool in manipulating your data and deriving the best possible analyses and relationship between the data. Some of the most frequently used concepts are Standard deviation, Variance, z-score, etc. As an example, we would like to look at two concepts: Standard Deviation and Z-score and how it can be used as a Financial Analyst or Stockbroker.

We have a sample data of the returns of a company stock over the last 5 years: 10%…

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Data Science, Software Development and lots of Satire...

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