Chapter 12: Data Preparation for Fraud Analytics: Project: Human Recourses Analysis - Human_Resources.csv
Project: Human Recourses Analysis - Human_Resources.csv Description: The dataset, named "Human_Resources.csv", is a comprehensive collection of employee records from a fictional company. Each row represents an individual employee, and the columns represent various features associated with that employee. The dataset is rich, highlighting features like 'Age', 'MonthlyIncome', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department', 'EducationField', 'JobSatisfaction', and many more. The main focus is the 'Attrition' variable, which indicates whether an employee left the company or not. Employee data were sourced from various departments, encompassing a diverse array of job roles and levels. Each employee's record provides an in-depth look into their background, job specifics, and satisfaction levels. The dataset further includes specific indicators and parameters that were considered during employee performance assessments, offering a granular look into the complexities of each employee's experience. For privacy reasons, certain personal details and specific identifiers have been anonymized or fictionalized. Instead of names or direct identifiers, each entry is associated with a unique 'EmployeeNumber', ensuring data privacy while retaining data integrity. The employee records were subjected to rigorous examination, encompassing both manual assessments and automated checks. The end result of this examination, specifically whether an employee left the company or not, is clearly indicated for each record.
Steps to reproduce
Human_Resources.csv Data Acquisition: - Obtain the dataset titled "Human_Resources" from the provided link. - Download and store the dataset locally for easy access during subsequent steps. - Data Loading & Initial Exploration: - Use Python's Pandas library to load the dataset into a DataFrame. Code: hr_df = pd.read_csv('Human_Resources.csv') print(hr_df.head()) - Inspect the initial rows, data types, and summary statistics to get an understanding of the dataset's structure. Data Cleaning & Pre-processing: -Handle missing values, if any. Strategies may include imputation or deletion based on the nature of the missing data. Identify and handle outliers. Exploratory Data Analysis (EDA): -Utilize visualization libraries such as Matplotlib and Seaborn in Python for graphical exploration. -Examine distributions, correlations, and patterns in the data, especially between features and the target variable 'Attrition'. -Feature Engineering & Selection: -Utilize the best-performing model to make predictions on unseen data. Software & Tools: -Programming Language: Python -Libraries: Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn. -Environment: Jupyter Notebook or any Python IDE. Categories These lines of code read in a CSV file called "Human_Resources.csv" and store it in a Pandas DataFrame called "hr_df". The DataFrame is then printed to the console to provide an overview of the dataset. The dataset shows various features such as: -Employee Ages -Employees' Monthly Income -Attrition: Indicates if the employee left the company -Business Travel: Frequency of travel -Daily Rate -Department: The department the employee belongs to -Education Field -Job Satisfaction -Gender -Hourly Rate -Job Involvement -Job Level -And many more features. In this analysis, the primary goal is to delve deeply into the HR dataset, extracting actionable insights and understanding employee dynamics.