Teacher Promotion Prediction App using Python Machine Learning
Abstract :
The Teacher Promotion Prediction App Using Python , Machine learning to provide accurate predictions on Teacher promotions. We will explore the various aspects of this project, including the HR dataset used, the challenges of working with imbalanced data, and the integration of advanced machine learning algorithms such as Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM). The proposed system of Teacher promotion prediction and discover how this app can streamline the promotion process for businesses. With a focus on data management and predictive modeling, our app aims to revolutionize the way Teacher promotions are determined in the education sector.
Teacher Promotion Prediction App using Python Machine Learnin
Software Requirements: –
- Operating System : Windows OS
- Front-End : HTML, CSS, and JS
- Back-End : Python 3.10 ,
- Framework : Flask
- Tool : Miniconda3-latest-Windows-x86_64, Python 3.10
Hardware Requirements: –
- System : Dual Core
- Hard Disk : 40 GB
- Monitor : 15 VGA Colour
- Mouse : Logitech
- RAM : 2 GB
Technique :
Package and Libraries:
- Flask
- Flask-WTF
- flask_wtf
- numpy
- scikit-learn
- joblib
Existing System
An Existing System for The Teacher_Promotion_Prediction App is designed to analyze a vast HR dataset to predict whether a teacher is likely to be promoted. The dataset contains various features such as qualifications, years of experience, performance ratings, and more. However, one of the challenges faced in this project is an imbalanced dataset, where the number of promoted teachers is significantly lower than the number of non-promoted teachers. This imbalance can lead to biased predictions and inaccurate results.
DISADVANTAGE
- Potential Bias in Imbalanced Datasets: Imbalanced datasets in HR can lead to biased predictions, favoring certain groups or demographics. The Teacher_Promotion_Prediction App needs to address this issue by implementing techniques to mitigate bias and ensure fair promotion predictions.
- Limited Human Judgment: While machine learning algorithms provide efficient predictions, they may lack the human judgment and context that HR professionals possess. The app should be used as a tool to support decision-making rather than replacing human expertise entirely.
- Dependency on Data Quality: The accuracy of predictions heavily relies on the quality and completeness of the HR dataset. Inaccurate or incomplete data can lead to unreliable predictions. Regular data cleansing and validation processes should be implemented to mitigate this risk.
- Data Privacy and Security: Since the app deals with sensitive HR datasets, it is crucial to ensure proper data privacy and security measures are in place. Users must take precautions to protect the confidentiality of the data and comply with relevant data protection regulations
PROPOSED SYSTEM
- A Proposed System for The proposed system offers several benefits for educational institutions and decision-makers. Firstly, it provides a fair and transparent evaluation process for teacher promotions, based on objective data-driven criteria. This reduces biases and ensures equal opportunities for all teachers. Secondly, it saves time and effort for HR departments by automating the prediction process. Instead of manually reviewing each teacher’s performance, the system generates predictions instantly, allowing HR personnel to focus on other important tasks.
- One of the key challenges in this task is dealing with imbalanced datasets. In most cases, the number of teachers who get promoted is significantly lower than those who do not. This creates an imbalance that can affect the performance of traditional machine learning algorithms. To address this issue, our system employs techniques such as oversampling, undersampling, and Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset and improve prediction accuracy.
Advantages :
- Time and Cost Savings: By automating the promotion prediction process, the Teacher_Promotion_Prediction App eliminates the need for manual analysis, saving time and reducing costs associated with HR decision-making. The app provides quick and efficient results, enabling HR departments to focus on other important tasks.
- Improved Decision-Making: By leveraging machine learning techniques, the app provides valuable insights into employee promotion prediction. This enables HR managers and decision-makers to make informed choices about promotions, leading to better talent management and overall organizational success.
- Data Management: The app efficiently manages large and complex datasets, ensuring data integrity and accuracy. It provides a centralized platform for storing, organizing, and retrieving HR data, making it easier for HR professionals to access and analyze the information.
System Modules:
- Get Following Input Set From User Like Firstname, Lastname,Age,Experience,Grade,Lastpromotion, Promotion1,2,3 Performance.
- process the Input data
- Load the Model Using Joblib
- Find the Model Prediction Using RandomForestClassifier Algorithm
- Get the Eligibility Result For Teacher Promotion
