1 Introduction

The decision to change employers is influenced by a variety of factors, including pay, work environment, benefits, etc. All of these factors depend on how a business is doing on a daily basis. Given these factors, the performance statistics of various organizations from various sectors vary. For instance, when a company prepares to go public, it not only indicates that it will begin its initial public offering (IPO), but also that it has the necessary revenue to do so.

1.0.1 And how was this income produced?

This relies on how much money the business made from selling its goods or services in the market after deducting the costs (salaries provided to their employees depending upon size of the company and other miscellaneous expenditures). Which sector a company operates in has a significant impact on the costs incurred. In light of this, we have chosen to estimate a firm’s sector utilizing a variety of variables from labelled record data and a variety of classification methods.

Using text data, we were able to guess the firm’s identity based on tweets about salaries. It’s true that no two companies offer a candidate applying for a job the same wage. Perhaps the CTC supplied, the workplace atmosphere, and perhaps the size of the organization influence how employees speak about each employer. In order to illustrate the name of the company, we used such statements or tweets and once more applied machine learning classification methods.

Due to the competition around, every individual has higher expectations and goals. But the harsh truth is that we cannot randomly provide everyone their desired salary.

Therefore the question arises:

1.0.2 What if there was a system that would help employees decide which company to work for so that it’s not just best option they choose for their employment but is fruitful for them in future too?

This project revolves around the idea of answering the above question such that it can be used as a guide when determining sectors and firms since it leads to reasonable predictions when given information about certain features. Data transformation and machine learning will be used to create a model.

In order to gain useful insights into the job recruitment, we compare different strategies and machine learning models. Some of the parameters/features that were collected from company data are:

Job Designation
Year the company was founded
Size range of the firm
Salary range of the employees within the company
Location of headquarters

Calculations will be performed for working of this proposed system to predict the sector/company with results.

2 Data Science Questions

What does employment data mean?
What are the current employment trends?
What are the major factors that influence employment trends?
Why salary prediction is important ?
How can your model benefit both the employer and employee?
Do people feel underpaid for the jobs they work at?
Which sector/industry employees are paid the highest?
Which companies/organizations an employee must work for if they want to earn more than the industry standard/average?
How can this model help employers decide upon the base pay and furthermore the CTC?
What are the important features an employee should consider before deciding to accept an offer from a company?
How much accuracy does your model display for predicting sectors and firms when compared to the real-life scenario?

--- author: - name: Raghav Sharma url: https://github.com/anly501/anly-501-project-raghavSharmaCode/tree/main/501-project-website --- # Introduction The decision to change employers is influenced by a variety of factors, including pay, work environment, benefits, etc. All of these factors depend on how a business is doing on a daily basis. Given these factors, the performance statistics of various organizations from various sectors vary. For instance, when a company prepares to go public, it not only indicates that it will begin its initial public offering (IPO), but also that it has the necessary revenue to do so. ### And how was this income produced? This relies on how much money the business made from selling its goods or services in the market after deducting the costs (salaries provided to their employees depending upon size of the company and other miscellaneous expenditures). Which sector a company operates in has a significant impact on the costs incurred. In light of this, we have chosen to estimate a firm's sector utilizing a variety of variables from labelled record data and a variety of classification methods. Using text data, we were able to guess the firm's identity based on tweets about salaries. It's true that no two companies offer a candidate applying for a job the same wage. Perhaps the CTC supplied, the workplace atmosphere, and perhaps the size of the organization influence how employees speak about each employer. In order to illustrate the name of the company, we used such statements or tweets and once more applied machine learning classification methods. Due to the competition around, every individual has higher expectations and goals. But the harsh truth is that we cannot randomly provide everyone their desired salary. <b>Therefore the question arises:</b> ### What if there was a system that would help employees decide which company to work for so that it's not just best option they choose for their employment but is fruitful for them in future too? This project revolves around the idea of answering the above question such that it can be used as a guide when determining sectors and firms since it leads to reasonable predictions when given information about certain features. Data transformation and machine learning will be used to create a model. In order to gain useful insights into the job recruitment, we compare different strategies and machine learning models. Some of the parameters/features that were collected from company data are: 1. Job Designation 2. Year the company was founded 3. Size range of the firm 4. Salary range of the employees within the company 5. Location of headquarters Calculations will be performed for working of this proposed system to predict the sector/company with results. # Data Science Questions 1. What does employment data mean? 2. What are the current employment trends? 3. What are the major factors that influence employment trends? 4. Why salary prediction is important ? 5. How can your model benefit both the employer and employee? 6. Do people feel underpaid for the jobs they work at? 7. Which sector/industry employees are paid the highest? 8. Which companies/organizations an employee must work for if they want to earn more than the industry standard/average? 9. How can this model help employers decide upon the base pay and furthermore the CTC? 10. What are the important features an employee should consider before deciding to accept an offer from a company? 11. How much accuracy does your model display for predicting sectors and firms when compared to the real-life scenario?