Predicting Employees Under Stress For Pre-Emptive Remediation Using Machine Learning AlgorithmLast Updated on May 3, 2021
With the ongoing COVID-19 pandemic, businesses and organizations have acclimated to unconventional and different working ways and patterns, like working from home, working with limited employees at office premises. With the new normal here to stay for the recent future, employees have also adapted to different working environments and customs, which has also resulted in psychological stress and lethargy for many, as they adapt to the new normal and adjust their personal and professional lives. In this work, data visualization techniques and machine learning algorithms have been used to predict employees stress levels. Based on data, we can develop a model that will assist to predict if an employee is likely to be under stress or not. Here, the XGB classifier is used for the prediction process and the results are presented showing that the method facilitates getting a more reliable model performance. After performing interpretation utilizing XGB classifier it is determined that working hours, workload, age, and, role ambiguity have a significant and negative influence on employee performance. The additional factors do not hold much significance when associated to the above discussed. Therefore, It is concluded that concluded that increasing working hours, role ambiguity, the workload would diminish employee representation in all perspectives.
Link for paper: https://ieeexplore.ieee.org/document/9315726?denied=
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Churn PredictionLast Updated on May 3, 2021
Predicting Customer Churn at a Fictitious Wireless Telecom Company
Churn Management has gotten great attention among the telecommunication Industry because it is proved that instead of going for advertisements to find new customers it’s better to find a technique, solution, and all the available resources in our service to figure out a pattern to make customers stay in the company. Every telecommunication company has huge competition and due to easy access of the plans and services provided by all the companies, a customer can switch the company anytime. For churn Prediction, it is most required to identify the customer who has the highest probability of leaving the service of the company and it will be effective if it’s done at the right time. Through this company can make a decision on what service to provide to make the customer not leave the service.
Here, we can reformulate the given problem as a Classification problem. My goal is to build a Classification model that can predict if Customers will stay with the company or not from the given features. To achieve this, first, I did data analysis and data cleaning, data preparation for training, and then model building. After this, based on the performance I find the best parameters of our model through GridSearchCV which best suits for the given data and gave the expected result.
Iris Flower PredictionLast Updated on May 3, 2021
Understanding the scenario
Let’s assume that a hobby botanist is interested in distinguishing the species of some iris flowers that she has found. She has collected some measurements associated with each iris, which are:
- the length and width of the petals
- the length and width of the sepals, all measured in centimetres.
She also has the measurements of some irises that have been previously identified by an expert botanist as belonging to the species setosa, versicolor, or virginica. For these measurements, she can be certain of which species each iris belongs to. We will consider that these are the only species our botanist will encounter.
The goal is to create a machine learning model that can learn from the measurements of these irises whose species are already known, so that we can predict the species for the new irises that she has found.
- SkLearn is a pack of Python modules built for data science applications (which includes machine learning). Here, we’ll be using three particular modules:
- load_iris: The classic dataset for the iris classification problem. (NumPy array)
- train_test_split: method for splitting our dataset.
- KNeighborsClassifier: method for classifying using the K-Nearest Neighbor approach.
- NumPy is a Python library that makes it easier to work with N-dimensional arrays and has a large collection of mathematical functions at its disposal. It’s’ base data type is the “numpy.ndarray”.
Building our model
As we have measurements for which we know the correct species of iris, this is a supervised learning problem. We want to predict one of several options (the species of iris), making it an example of a classification problem. The possible outputs (different species of irises) are called classes. Every iris in the dataset belongs to one of three classes considered in the model, so this problem is a three-class classification problem. The desired output for a single data point (an iris) is the species of the flower considering it’s features. For a particular data point, the class / species it belongs to is called its label.
As already stated, we will use the Iris Dataset already included in scikit-learn.
Now, let’s print some interesting data about our dataset:
ACCURACY we get an accuracy of 93%
OUTPUT IN THIS CASE as we have 2 samples [[3,5,4,2], [2,3,5,4]]
so the iris type predicted by our model based on the given features are
predictions: ['versicolor', 'virginica']
for more details this is my Github repository
ResistarLast Updated on May 3, 2021
Augmented Reality Circuit Visualizer and Solver.
TartanHacks Grand Prize
Facebook Company Prize
Attempted Prize Categories
Duolingo’s Social Impact Prize (Educational)
GoDaddy’s Social Impact Prize (“Best app that improves STEM education”)
Long hours spent on ECE problem sets and frustration visualizing convoluted circuits caused these four CMU undergrads to create a circuit visualization system that would also help them solve circuits. A member of the team is currently in the intro ECE course: "Well it's not bad, I guess." - Team Member
What it does
ResistAR is an Augmented Reality Circuit Visualizer and Solver. A user can place down circuit elements in parallel and series configurations and ResistAR will solve the current through and voltage across each element of the circuit. It gives the user an easy way to see (sharp) the circuit.
How we built it
We first began with 3D printed chassis for the VuMark targets. These targets are identified and parsed by the program and cross checked against our cloud database on Vuforia. We then created 3D, textured, models in Blender that will hover over the VuMark targets. We then wrote the code in Unity that will calculate voltage and current values using concepts from vector calculus and matrix algebra.
Challenges we ran into
The math was very difficult and attempting to rush a 3D printed design was also difficult but there was a rush because 3D printing would be a very time consuming process. Thus we also had to create a lot of our latter designs around the already 3D printed parts. VuMarks were also difficult to create. VuMarks must be very easily distinguishable from each other and non-symmetric along any axis, and therefore took a while to get finely tuned and calibrated. Finally the math was a very difficult thing to visualize. We had to go from 3D space to 2D space and there were some difficulties with projections. The coders did end up writing relatively bug-free code, but not before a long, arduous thinking process.
Accomplishments that we're proud of
The two logic/algorithm gods that we had on our team solved an extremely complex math problem very quickly. Also our 3D printed parts are actually fire though. Just saying.
What we learned
Two 5 hour energies in 72 hours is actually not as bad an idea as some might think. Math is hard.
What's next for ResistAR
Norton and Thevenin Equivalents. Yikes.
Predicting Credit Card ApprovalsLast Updated on May 3, 2021
Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this notebook, we will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do! I have used the Credit Card Approval dataset from the UCI Machine Learning Repository.
The structure of this notebook is as follows: First, we will start off by loading and viewing the dataset. We will see that the dataset has a mixture of both numerical and non-numerical features, that it contains values from different ranges, plus that it contains a number of missing entries. We will have to preprocess the dataset to ensure the machine learning model we choose can make good predictions. After our data is in good shape, we will do some exploratory data analysis to build our intuitions. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted.
Permission Management SystemLast Updated on May 3, 2021
Permission Management System is web project developed for the newly joined employees to get their resumes validated by the manager and if manager is impressed or feels that the employee is fit for the job, he grants permission to access their official site as an employee where employee can manage his work and gets permission to view all the details of that job. The aim of this project is to make the tasks of newly joining employees and manager of the company easy. Many employees apply for the job and the manager need to validate all the details of these employees, so our project aim is to provide a database that can store all the applied employee resumes and remove all the resumes which are not fit for the job and validate the selected employee resumes. If he/she is selected for the job then the manager will be able to give access to the official site, where the employee can view all the other employees available in company and previous employee details and he/she can manage data in the official site. And even manager can give permission whether the employee is just an employee for the job or the admin to handle all the tasks in official site. The main objective of this project is to grant permissions for the newly joined employees based on his/her resume and work. The project is developed as a web-based application which works for a company to maintain their records and grant permissions for new employees. But later on, the project can be modified to all the companies by making partial changes to the site by providing their company details in the site online. Permission Management System utilitarian scope is enabled through the concepts of computing mainly Database management and the user interface aspects enabled through various web interfaces and technologies. Who can use this application in real life? 1. Employees who are willing to apply for a job. 2. Manager who gives permissions for the employee to give a job.