Project Report
After the analysis of the findings, I found that the employee earnings analysis has vital implications for organizational management. It highlights the need to address income inequality, prompting a review of compensation structures. Insights into departmental earnings inform strategic financial management and resource allocation. Overtime policies should be evaluated for fairness, and the identification of alternative income sources suggests opportunities for diversification. Implementing these measures can enhance employee satisfaction, engagement, and overall strategic human resource planning.
Later, I started compiling all the issues, findings, their implications, and the charts and plots used to analyze the data together for the report.
Project report
Today I started working on the Project report. Here I started working on the issues for the EMPLOYEE EARNINGS REPORT 2022 dataset. The issues I want to work on are 1. distribution of income from the Salaries of the employees, 2. distribution of total gross earnings of the employees, 3. Departments with the employees having the highest and lowest earnings 4. Job titles with the employees having the highest and lowest earnings through salary, 5. Departments with the employees having the highest and lowest total earnings, 6. Departments with the highest overtime earnings, 7. Departments with the employees having the highest income sources other than salary 8. Departments with the highest overtime earnings.
Later on, I started working on compiling the findings together. These findings are as follows:
The analysis of employee earnings data reveals several key findings. Regular and total earnings distributions are right-skewed, indicating a concentration of employees with lower earnings and a smaller group earning significantly more. The Boston Police Department has the highest total earnings, with top earners predominantly from the Police and Fire Departments. Job titles show considerable variation in average earnings etc.
Project report work
Today I started working on a predictive model to find out if the income of the employee influences where the employee lives. To build a logistic regression first I removed rows with NaN values in the ‘REGULAR’ and ‘TOTAL GROSS’ and ‘POSTAL’ columns. After that I trained a logistic regression model to predict postal codes based on regular earnings, resulting in an accuracy of approximately 8%. Later I attempted training of another logistic regression model to predict postal codes based on total gross earnings, resulting in an accuracy of approximately 7%. The confusion matrix is extensive and indicates that the model’s predictions are mostly zeros, which suggests that the model may not be performing well. This could be due to a variety of factors, including a possible imbalance in the dataset or the complexity of predicting postal codes from a single feature.
1 Dec
Now I will proceed with visualizing the distribution of earnings within each department. This will provide a clear visual representation of the spread of earnings. This may help in identifying the patterns and outliers in the data.
Next today I analyzed Employee Earnings reports from 2011 to 2022, To understand the increase in total payroll between 2011 to 2022, the number of employees decreased during the same period, and the growth in average earnings per employee.
Next, I’m planning to plot the distribution of earnings on the map using the zip code and will try to find out if the income of the employee influences where the employee lives.