In March of last year, COVID shut everything down…including my one-man consulting company. Clients’ attentions were justifiably diverted to more pressing matters and I suddenly found myself with…
We have a total of 1470 employees information with 35 features which shows the information corresponding to each employee. The Attrition column indicates the employee status showing that the employee stayed at the company or leave the company. In this column, 0 indicates that the employee still working at the company, and 1 indicates that the employee leaves the company. Based on this data we are going look for the pattern that if the same profile of people leaving the company and finally we are going to predict who will leave the company using the ML algorithm.
Distribution plots help us understand the data better, with this visualization we are able to see how our data is distributed and if there are any outliers in the data sets. For this purpose, we are going to use the histogram for every numerical feature in the dataset.
In this project, we are going to use the seaborn for insulation but as an example of Plotly, I am going to plot the attrition count and pie chart for the percentage of attrition using Plotly.
We are going to write a function to investigate the categorical and numerical columns separately and plot them.
We also look at the numerical columns correlation with each other for this we are going to use the heat map for after applying core() function.
XR Web is a blockchain-based project aiming to make extended reality (XR) as the most advanced approach in business and social interactions. Get to know more about this exceptional project by…
District 5 of the Knox County Democratic Party is committed to keeping our nation safe and fighting for equal opportunity for every American. We are outraged and sickened by the murder of George…
Feeling Sad? Your birthday is coming up but still, stuck in lockdown? Thinking of how you will go to celebrate your birthday. Can’t go to Restaurants, Clubs, Not able to meet friends? Guest what you…