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.

Analyze employee performance and productivity using SQL and Python. Build HR dashboards to evaluate departmental KPIs, efficiency trends, and workload balance. Generates actionable insights through data visualization and KPI aggregation for business decision-making.

In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.

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Attendance management is important to every single organization such as employees and students to keep a track of their daily schedule and maximize their performance. Managing student attendance during lecture periods has become a difficult challenge and major task as manual computation produces errors, and wastes a lot of time. For the stated reason, an efficient Android application for attendance management system is designed to track student's activity in the class. This application takes attendance electronically and the records of the attendance are stored in a database. Google Firebase is used for the Application Database to develop iOS,Android and Web Apps. Firebase provides tools for tracking the analytics, reporting and fixing app crashes. Tensorflow is used for an image classification with pertained models and image segmentation to predict whether the pixels of image is associated with a certain class trained with peoples, places and more. Insertions, deletions, and changes of data in the system can do straightforward via the designed GUI without interacting with the tables. Different presentation of information is obtainable from the system. The test case of the system exposed that the system is working enormously and is ready to use to manage to attend students for any department of the University.

Business Problem IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees. Attrition is a problem that impacts all businesses, irrespective of geography, industry and size of the company. Employee attrition leads to significant costs for a business, including the cost of business disruption, hiring new staff and training new staff. As such, there is great business interest in understanding the drivers of, and minimizing staff attrition. In this context, the use of classification models to predict if an employee is likely to quit could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition. While this model can be routinely run to identify employees who are most likely to quit, the key driver of success would be the human element of reaching out the employee, understanding the current situation of the employee and taking action to remedy controllable factors that can prevent attrition of the employee. This data set presents an employee survey from IBM, indicating if there is attrition or not. The data set contains approximately 1500 entries. Given the limited size of the data set, the model should only be expected to provide modest improvement in indentification of attrition vs a random allocation of probability of attrition. While some level of attrition in a company is inevitable, minimizing it and being prepared for the cases that cannot be helped will significantly help improve the operations of most businesses. As a future development, with a sufficiently large data set, it would be used to run a segmentation on employees, to develop certain “at risk” categories of employees. This could generate new insights for the business on what drives attrition, insights that cannot be generated by merely informational interviews with employees. Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists. Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor' EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding' RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best' IBM HR Analytics Employee Attrition & Performance Predict attrition of your valuable employees IBM HR Analytics Employee Attrition & Performance IBM HR Analytics Employee Attrition & Performance

IBM HR Analytics Employee Attrition & Performance Analysis

IBM HR Analytics Employee Attrition & Performance

Attrition is a critical issue and pretty high in the industry these days. It’s the major problem which highlights in all the organizations. Though the term ‘ATTRITION’ is common, many would be at a loss to define what actually Attrition is, “Attrition is said to be the gradual reduction in the number of employees through retirement, resignation or death. It can also be said as Employee Turnover or Employee Defection” Whenever a well-trained and well-adapted employee leaves the organization, it creates a vacuum. So, the organization loses key skills, knowledge and business relationships. Modern managers and personnel administrators are greatly interested in reducing Attrition in the organization, in such a way that it will contribute to the maximum effectiveness, growth, and progress of the organization. Retaining employees is a critical and ongoing effort. One of the biggest challenges in having managers in the place that understands it is their responsibility to create and sustain an environment that fosters retention. Staff requires reinforcement, direction and recognition to grow and remain satisfied in their positions. Managers must recognize this and understand that establishing such fundamentals demonstrates their objectives to support nature and motivate their employees. The main objectives of this study are to know the reasons, why attrition occurs, to identify the factors which make employees dissatisfy, to know the satisfactory level of employees towards their job and working conditions and to find the areas where companiesare lagging behind.

Rice University - IBM HR Analytics Employee - Attrition & Performance Analysis

This repository contains the source code and assets for an HR Analytics Dashboard developed using Power BI. The dashboard provides insights into various aspects of human resources management, including employee demographics, performance metrics, leave balances, and more. Leveraging a monochromatic design approach

📊 Employee Task Management System: A sleek Python-Django web app for seamless task management, 🔒 secure user authentication, and powerful 📊 data analytics with Matplotlib, ensuring enhanced productivity and performance.

AdventureWorks is a fictitious multinational manufacturing company. The data set used for this report is from the AdventureWorks2014 version. The goal of this project is to demonstrate how building powerful and beautiful (I hope :)) reports in Power BI by following the data visualization best practices and the data modelling patterns, showing the awesome features of Power BI like Report Page Tooltip, Calculated Groups, Forecasting, What-If parameters, Time Intelligence Functions, complex DAX code, custom charts and how it is easy to integrate Power BI with Python/R script in order to build machine learning models and so creating advanced and predictive analytics reports. Reports Pages are as follow: - P&L Overview: Profit & Loss Report, in which I analyzed the economic status of the company from the point of view of Revenues and Expenditures. In this report page I compared the trend of actual amount and budget amount, unfortunately for 2011 year only (missing data for other years). You can notice the powerful feature of “report page tooltip” in this page, by hovering over the bar charts. - Internet Sales Overview: in this report page I analyzed the sales of products to customers via Internet. You can notice the forecasting feature of Power BI and the use of what-if parameters in order to build advanced analytics reports. Also I made use of calculated groups (for further info about visit https://www.sqlbi.com/articles/introducing-calculation-groups/). - Reseller Sales Overview: similar to the previous one, but this time the focus is on the sales of products to resellers. Things to notice in this report page are the comparison of current sales vs last year sales, the comparison of actual sales amount vs budget/quota sales amount, and the running total and moving/running average charts. - Product Inventory Overview: inventory management analysis of the company. I analyzed the current stock on hold vs the recent sales revenue, showing the value of current stock, the current units in stock and the stock ratio. - Products Overview: in this report page I analyzed the products by category, color, size, “ABC” class too. Two very interesting and powerful things to notice: the basket analysis using DAX and the basket/association rules analysis using R script and apriori machine learning model. I made use of the custom chart called “Network Chart” to represent the association rules between products. Through the basket analysis the user could know which product is likely to be bought with another one. - Customers Overview: customer analysis by age group, location, status, and so on. The customer could be classified as “Bike Buyer” and “Non Bike Buyer”. So I decided to build a machine learning model (in this case I used XGBoost algorithm in Python) in order to predict if a (new) customer would be a bike buyer or not. The model has a prediction accuracy of about 84% which it is not bad. - Employees Overview: employee analysis by sales location, title, age. Thing to notice is the ribbon chart through which I can analyze the performance of employees over time. - Reseller Overview: analysis of the performance of the resellers. Important dimensions to analyze are the business type, the product line, the reseller location.

Predict attrition of your valuable employees

Final project for Statistical Learning

HR Analytics Dashboard to help organization to improve employee performance and retention

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%

Project machine learning for school lab.

IBM HR Analytics Employee Attrition & Performance

HR Analytics Dashboard: Employee Attrition, Performance, and Satisfaction Insights

🚀 CoreConnect - Streamline employee management! 🌟 Manage profiles, attendance, leave, payroll, and performance all in one app! 🎯 Custom reports, secure access, and insightful analytics to boost productivity and team growth! 📊 Empower your organization with ease! 👥 #EmployeeManagement #HR

HR Analytics Project with Data Preprocessing, EDA, SQL Queries, and Power BI Dashboard

IBM HR Analytics Employee Attrition & Performance

Predicting employee attrition using data mining and machine learning techniques — includes data preprocessing, EDA, and model comparison.

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Predict attrition of your valuable employees

🚀 Employee Performance Analysis using Python – Analyzing key factors like training hours, overtime, and salary to understand their impact on employee performance using Pandas, Seaborn, and NumPy. 📊✨

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Analyzing IBM HR Analytics Employee Attrition & Performance using Decision Tree and Keras Deep Learning