Analysis of Employee Dataset: Insights on Demographics, Job Satisfaction, and Experience

Aug 13, 2024, 4:59 AM

sample_excel.xlsx

Missing file

Give me all the insights from this excel sheet

The dataset contains the following columns:
  1. Employee ID: Unique identifier for each employee.
  2. Department: The department where the employee works.
  3. Age: The age of the employee.
  4. Gender: The gender of the employee.
  5. Years of Experience: The number of years the employee has worked.
  6. Job Satisfaction: The job satisfaction rating of the employee on a scale from 1 to 10.
Let's proceed with some insights from this dataset.

Insights from the Dataset

  1. Basic Statistics:
    • The dataset contains 1000 records.
    • The average age of employees is approximately 43.52 years with a standard deviation of 12.61 years.
    • The average years of experience is approximately 19.59 years with a standard deviation of 11.73 years.
    • The average job satisfaction rating is 5.59 on a scale of 1 to 10.
  2. Gender Distribution:
    • The dataset includes three gender categories: Male, Female, and Non-binary.
    • The distribution is visualized in the pie chart.
  3. Department Distribution:
    • Employees are distributed across five departments: Finance, Sales, IT, HR, and Marketing.
    • The distribution is visualized in the bar chart.
  4. Job Satisfaction by Department:
    • The average job satisfaction rating varies across departments.
    • This is visualized in the bar chart showing average job satisfaction by department.
  5. Years of Experience vs Job Satisfaction:
    • There is a scatter plot showing the relationship between years of experience and job satisfaction.
  6. Age Distribution:
    • The age distribution of employees is visualized in the histogram.
Would you like to explore any specific aspect of this dataset further or perform any additional analysis?

Which department had the most and least job satisfaction?

The department with the most job satisfaction is Sales with an average job satisfaction rating of 5.76.
The department with the least job satisfaction is HR with an average job satisfaction rating of 5.37.
Would you like to explore any other insights or perform additional analysis?

Can you analyze the correlation between age and job satisfaction?

The correlation coefficient between age and job satisfaction is approximately 0.021.
This indicates a very weak positive correlation between age and job satisfaction, suggesting that age has almost no linear relationship with job satisfaction in this dataset.
Would you like to visualize this relationship or explore any other aspects of the data?

I want to know many groups of employees are there. I want to get insights regarding the groups

Could you please clarify what you mean by 'groups of employees'?
Are you referring to groups based on specific criteria such as department, age range, years of experience, or any other specific attribute?

Yes. By, groups I mean clusters.

The Elbow Method plot helps us determine the optimal number of clusters by looking at the point where the Within-Cluster Sum of Squares (WCSS) starts to decrease more slowly, forming an 'elbow'.
Based on the plot, we can identify the optimal number of clusters. How many clusters would you like to use for the analysis?

What would be your suggestion? How many clusters should I select for my analysis

Based on the Elbow Method plot, it appears that the 'elbow' is around 3 clusters. This suggests that 3 clusters might be a good choice for the analysis.
Would you like to proceed with 3 clusters for the clustering analysis?
The 3D scatter plot above visualizes the employee clusters based on their Age, Years of Experience, and Job Satisfaction. Each color represents a different cluster.
The first few rows of the dataframe with the assigned cluster labels are displayed above.
Would you like to perform any further analysis or gain more insights on these clusters?

I want to know the characteristics of each clusters,based on their Age, Years of Experience, and Job Satisfaction. How they differ from each other.

The table below shows the mean values for each cluster based on Age, Years of Experience, and Job Satisfaction:
ClusterAgeYears of ExperienceJob Satisfaction
051.6130.215.53
130.3119.235.61
251.798.665.63

Characteristics of Each Cluster:

  • Cluster 0: Employees in this cluster are generally older (average age ~51.61 years) with a high number of years of experience (~30.21 years). Their job satisfaction is moderate (~5.53).
  • Cluster 1: This cluster consists of younger employees (average age ~30.31 years) with moderate years of experience (~19.23 years). Their job satisfaction is also moderate (~5.61).
  • Cluster 2: Similar to Cluster 0 in terms of age (average age ~51.79 years), but with significantly fewer years of experience (~8.66 years). Their job satisfaction is moderate (~5.63).
Would you like to explore any other aspects of the data or perform further analysis?

Any more insights will you suggest me?

Here are a few more insights and analyses that could be valuable:
  1. Distribution of Clusters by Department and Gender:
    • Analyze how the clusters are distributed across different departments and genders.
  2. Job Satisfaction Analysis:
    • Explore the distribution of job satisfaction within each cluster to identify any patterns or outliers.
  3. Experience vs. Age Analysis:
    • Visualize the relationship between years of experience and age within each cluster to see if there are any interesting trends.
  4. Cluster Size:
    • Determine the size of each cluster to understand the distribution of employees across clusters.
  5. Correlation Analysis:
    • Perform a correlation analysis to see how different features (age, years of experience, job satisfaction) are related within each cluster.
Would you like to proceed with any of these analyses or have any other specific questions in mind?

Do all this analysis and let me know the insights

Insights from the Analysis

  1. Cluster Characteristics:
    • Cluster 0: Older employees (average age ~51.61 years) with high experience (~30.21 years) and moderate job satisfaction (~5.53).
    • Cluster 1: Younger employees (average age ~30.31 years) with moderate experience (~19.23 years) and moderate job satisfaction (~5.61).
    • Cluster 2: Older employees (average age ~51.79 years) with low experience (~8.66 years) and moderate job satisfaction (~5.63).
  2. Correlation Analysis:
    • Within each cluster, the correlations between age, years of experience, and job satisfaction are generally low, indicating weak relationships among these variables.
  3. Cluster Size:
    • Cluster 1: 382 employees
    • Cluster 0: 320 employees
    • Cluster 2: 298 employees
  4. Distribution by Department and Gender:
    • The distribution of clusters across departments and genders shows some variation, but no strong patterns are immediately evident.
  5. Job Satisfaction Distribution:
    • The box plot shows the distribution of job satisfaction within each cluster, indicating some variability but generally moderate satisfaction levels.
  6. Experience vs. Age:
    • The scatter plot visualizes the relationship between years of experience and age within each cluster, showing distinct patterns for each cluster.
Would you like to explore any other aspects of the data or perform further analysis?
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