{"id":22631438,"url":"https://github.com/lijesh010/employeeanalysis-","last_synced_at":"2026-04-12T11:35:55.533Z","repository":{"id":168300571,"uuid":"643840159","full_name":"lijesh010/EmployeeAnalysis-","owner":"lijesh010","description":"This is a simple data analysis project developed as part of my Entri Elevate Data Science and Machine Learning course. 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The dataset consists of 458 rows and 9 columns. The objective is to provide a detailed report and explanation of the employees in each team, as well as answer several specific questions about the dataset.\n\n### Preprocessing the Dataset\nBefore analyzing the dataset, some preprocessing steps were performed. One of the columns, \"height,\" contained incorrect data. To address this issue, the data in the height column was replaced with random numbers between 150 and 180, representing the height of the employees.\n\n### Analysis and Findings\n#### 1.Team Distribution:\nThe first step was to determine the number of employees in each team and the percentage split with respect to the total number of employees. This analysis provides insights into the team composition within the company.\n\n#### 2.Employee Position Segregation:\nThe dataset was further analyzed to segregate employees based on their positions within the company. This analysis helps identify the distribution and frequency of different roles.\n\n#### 3.Age Group Distribution:\nTo understand the age demographics of the employees, an analysis was performed to determine the age group to which most of the employees belong. This insight provides an understanding of the age distribution within the company.\n\n#### 4.High-Spending Teams and Positions:\nAnother important aspect analyzed was the relationship between team, position, and salary spending. By identifying the teams and positions where the salary spending is high, the company can gain insights into areas where financial resources are allocated more significantly.\n\n#### 5.Age and Salary Correlation:\nA correlation analysis was conducted to examine the relationship between age and salary. This analysis helps determine whether there is any correlation or pattern between these two variables. To enhance understanding, the correlation was represented visually.\n\n### Conclusion\nThis project involved the analysis of an employee dataset from the ABC company. By performing preprocessing and various analyses, valuable insights were obtained regarding team distribution, position segregation, age group distribution, high-spending teams and positions, and the correlation between age and salary. These findings can aid in making informed decisions regarding team composition, resource allocation, and understanding the characteristics of the employees within the company.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flijesh010%2Femployeeanalysis-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flijesh010%2Femployeeanalysis-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flijesh010%2Femployeeanalysis-/lists"}