The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

Analysis of employee data in the dataset "HR_comma_sep.csv" to find out what contributes to employees leaving the company.

This file is contain univariate, bivariate, multivariate analysis of HR dataset with insights.

This project performs an exploratory data analysis (EDA) on an HR dataset focused on job changes among data science professionals. It aims to uncover insights that can help HR departments understand the key factors behind employee retention and job switching behavior.

Employee Turnover is one of the key market challenges in Human Resource (HR) Analytics. Organizations usually invest a greater amount of money and time in the hiring of staff and nursing them in the hope to receive value addition. When an employee leaves the company, the reduction of opportunity costs is borne by the company. Turnover is especially prevalent in large-scale recruitment agencies. The risk of replacing workers remains important for most employers. This is due to the amount of time spent recruiting and selecting a successor, the sign-on incentives, and the lack of morale for several months as the new employee gets used to the new job. The tangible costs of workforce turnover will be the cost of recruiting new staff, the cost of recruitment and hiring, the time of transition, future product or service quality issues, the cost of temporary staff, the cost of training, the cost of lack of production, the cost of lost expertise and the cost of the job being empty before an acceptable replacement is found. We find that the attributes of workers such as Job Position, overtime, work level affect significantly attrition. Various classification methods are introduced such as logistic regression, linear discriminate analysis, ridge classification, lasso classification, decision trees, random forests to forecast and concurrently measure the likelihood of turnover of every new employee. Data from an HR department of the company available at Kaggle were used to estimate the employee turnover. The dataset includes 10 different attributes of 1470 personnel. Dataset specifies if the personnel is leaving or staying based on the attributes. Now, to construct a prediction model based on the previously mentioned machine learning algorithms with 90 percent of the total personnel's attributes and the rest for model testing. The best performing performance algorithm yields the best accuracy of Decision Tree Classifier is 93 percent and the worst accuracy of Logistic Regression is 0.18%

Data analysis for Kaggle's HR dataset

The objective of the project is to create an engaging dashboard designed to visualize meaningful insights derived from HR data. This dashboard encompasses comprehensive employee information, including job status, performance metrics, reasons for resignation, and more.

Project files for the Human Resources Analysis Dataset from kaggle.com (https://www.kaggle.com/ludobenistant/hr-analytics)

This project is based on a company's HR data. Regression analysis with Random Forest is performed and visualization to tell the story behind the data

A comprehensive Data analysis project using SQL for data cleaning and pre-processing and Tableau for visualization, focusing on key HR KPIs. Features interactive dashboards and detailed insights.

HR Attrition Analysis dashboard built using Power BI and Excel datasets.

Exploratory Data Analysis of HR Analytics dataset to understand employee engagement, attrition patterns, and workforce insights using Python, Pandas, Seaborn, and Matplotlib.

This repository presents an in-depth analysis of employee health and compensation, with the primary objective of optimizing HR decisions and promoting employee well-being. The project combines data integration and visualization techniques to derive actionable insights from multiple datasets.

In-depth analysis of employee demographics, tenure, and turnover within a corporate setting. The dataset comprises HR data spanning from the year 2000 to 2020, totaling over 22,000 rows. The data was meticulously cleaned using MySQL, and the insights were visualized using Tableau.

This is a personal project aimed at practicing data analytics skills by analyzing a simulated dataset on employee turnover and recruitment trends within an organization. This project involved data collection, cleaning, analysis, and visualization using Python and Power BI to uncover insights into HR data trends.

Using Mysql Data Analysis on HR_Datasets

HR Employee Database Analysis using MySQL Performed SQL-based EDA on HR dataset to analyze employee distribution by department, marital status, and employment status (active vs. terminated). Queries include total employee count, current vs. old employees, and department-wise breakdown using MySQL Workbench.

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Carried out exploratory data analysis using pandas and scikit-learn, and imputed missing values using the KNN algorithm and performed data preprocessing using data mining methods to convert imbalanced dataset to balanced dataset using Python  Constructed Logistic Regression, Decision Tree, Random Forest Classifier models using PySpark, scikit-learn, pandas and matplotlib that uses current credentials, demographics, experience data to predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision

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The ultimate goal of the project is to leverage data analysis techniques to provide valuable insghits ,that can help optimize HR strategies, foster employee satisfaction,and contribute to the overall success of the organization.

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Here, I have analyzed the Human Resource dataset of a US Company so that the company can make critical decisions to drive employee’s productivity.

Large-scale HR analytics project performing exploratory data analysis on 2M+ employee records to uncover compensation fairness, workforce distribution, department structure, and data-driven insights for strategic HR decision-making.

Explanation of this project file is in Bahasa

The Project is to develop an attractive Dashboard, which helps in visualizing the insights out of the HR Data. It consists of Employees detail about their job status, performance, resignation reason, etc. Using these attributes insights are developed out of data.

Deep dive analysis of HR dataset using SQL

HR Dataset for Workforce Analysis in Power BI

- Help companies to be prepared for future employee-loss - Evaluating possible trends and reasons for employee attrition, in order to prevent valuable employees from leaving. - We analyzed the numeric and categorical data with the use of Machine Learning models to identify the main variables contributing to the attrition of employees - This project was completed and carried out by three DSAI students Angelin Grace Wijaya, Agarwala Pratham, Krishna Shivangi

This project uses HR data to analyze employee attrition, focusing on demographics, work history, compensation, performance, and job satisfaction. Through EDA and machine learning, we identify attrition patterns and predict flight risk using Logistic Regression and Gradient Boosting, providing insights for better HR decision-making.