Introduction:

Employee attrition is a critical concern in today's business landscape, with significant implications for organizations worldwide. High attrition rates result in increased costs, reduced productivity, and diminished morale. To address this issue, you must understand the underlying factors driving attrition and develop effective retention strategies. This capstone project focuses on analyzing the Employee Attrition dataset for a tech company, which provides comprehensive information on employee demographics, job roles, satisfaction levels, compensation, and work-life balance. By utilizing data analysis techniques, you can gain valuable insights into the factors influencing attrition, leading to the proposal of potential solutions.

The impact of high attrition rates extends beyond individual organizations and has significant economic consequences. Recruiting, hiring, and training new employees entail substantial costs, compounded by the loss of institutional knowledge. Attrition also disrupts productivity and hampers organizational growth, affecting economic performance at micro and macro levels. By exploring the factors contributing to attrition and developing effective retention strategies, this project contributes to the formulation of robust economic and fiscal policies that   foster stability, productivity, and sustainable growth within the business sector.

The capstone project focuses on developing your proficiency in key technical tools essential for data analysis and modeling. These tools encompass data cleaning and preprocessing techniques to ensure data quality. Additionally, you will gain expertise in analyzing data distributions through visualization, examining correlations, and conducting targeted analysis to extract meaningful business insights. Furthermore, the project involves constructing predictive models using the Attrition dataset, evaluating model performance, identifying the most significant features, and interpreting statistical terms to understand their practical implications.

These technical skills enable you to extract valuable insights from employee data and propose effective retention strategies that contribute to organizational stability and sustainable growth.

Completing this capstone project will result in a comprehensive understanding of employee attrition and its organizational impact. You will cultivate essential data analysis skills, enabling you to extract valuable insights from employee data. By proposing effective retention  strategies, the project will contribute to the formulation of sound economic and fiscal policies that enhance organizational stability and foster sustainable growth. Ultimately, the outcomes of this project will empower businesses by providing you with actionable insights to improve employee retention, enhance productivity, and cultivate a positive work environment.

Questions & Tasks:

Question 1: Data Overview ( 10 points) (3+3+4 = 10 pts)

1)   Examine the data's shape and identify any missing values in each column.

2)   Apply appropriate data-cleaning techniques to remove anomaly columns.(columns which only have one distinct value)

3)   Consider converting data types as needed. Note that some numerical features should be treated as categorical variables.

Question 2: Data Distribution Analysis (20 points) (5 points each)

1)   Analyze and visualize the distributions of both numerical and categorical features in the dataset using suitable plots such as histograms, bar plots, or pie charts.

2)   Examine the correlation between numerical features and identify the variables with the highest correlation. Provide insights into the reasons for this correlation.

3)   Draw meaningful conclusions from the insights generated for the categorical features.

4)   Explore and visualize the distribution of the response variable 'Attrition.' Do you think the dataset is balanced or not?

Question 3: Targeted Analysis (20 points) (6+6+8 = 20 pts)

1)   Determine which job role has the highest percentage of attrition and provide an explanation. Visualize your results.

2)   Investigate gender disparities in monthly income. Visualize your results.

3)   Compare attrition amounts or rates among different education levels and visualize the results. Visualize your results.

Question 4: Model Construction (40 points) (6+8+9+5+ 12 = 40 pts)

1)   Convert categorical variables into dummy variables to prepare the data for modeling.

2)   Conduct a linear probability model or logistic regression model with 'Attrition' as the response variable.

3)   Evaluate the model's performance using appropriate evaluation metrics or visualizations.

4)   List the five most significant features based on the model evaluation results.