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https://github.com/vidhi1290/hr_employee_prediction
"Welcome to the HR Employee Promotion Prediction project! This repository contains the code and resources for a machine learning project that focuses on predicting employee promotions. By analyzing various employee attributes, this project aims to provide valuable insights for HR decision-making and talent recognition within organizations.
https://github.com/vidhi1290/hr_employee_prediction
data-exploration data-science data-visualization docker hr-employee-prediction hyperparameter-tuning machine-learning matplot model-building numpy pandas scikit-learn seaborn streamlit streamlit-webapp
Last synced: 7 days ago
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"Welcome to the HR Employee Promotion Prediction project! This repository contains the code and resources for a machine learning project that focuses on predicting employee promotions. By analyzing various employee attributes, this project aims to provide valuable insights for HR decision-making and talent recognition within organizations.
- Host: GitHub
- URL: https://github.com/vidhi1290/hr_employee_prediction
- Owner: Vidhi1290
- Created: 2023-02-17T05:57:51.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-29T09:37:33.000Z (over 1 year ago)
- Last Synced: 2024-12-08T08:12:55.904Z (2 months ago)
- Topics: data-exploration, data-science, data-visualization, docker, hr-employee-prediction, hyperparameter-tuning, machine-learning, matplot, model-building, numpy, pandas, scikit-learn, seaborn, streamlit, streamlit-webapp
- Language: Python
- Homepage:
- Size: 9.66 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# HR Employee Promotion Prediction :chart_with_upwards_trend:
Welcome to the HR Employee Promotion Prediction project repository! 🚀 In this project, we delve into the exciting realm of machine learning to predict employee promotions. By analyzing a comprehensive set of employee attributes, we aim to assist HR professionals in identifying and recognizing potential candidates for promotion within an organization.
## Project Overview :clipboard:
This project is designed to provide a robust solution for predicting employee promotions based on a diverse range of features. Our journey begins with a meticulous exploration and preprocessing of the data, enabling us to gain crucial insights. We then move on to visualizing these insights, leveraging powerful libraries to visualize relationships, patterns, and distributions within the data.
The core of this project lies in the construction of a highly accurate machine learning model. This model is meticulously trained using a rich set of features and is evaluated using various metrics. By harnessing the power of machine learning, we strive to enhance the decision-making process in HR departments.
## Libraries Used 📚
We have harnessed the capabilities of a variety of Python libraries for this project:
- Pandas
- NumPy
- Seaborn
- Matplotlib
- Scikit-learn
## Project Details :mag_right:
### Data Exploration and Preprocessing
Our journey begins with an in-depth exploration of the training and testing datasets. We leave no stone unturned in identifying and addressing any missing values. Categorical variables are skillfully transformed through label encoding, and irrelevant columns are pruned from the dataset.
### Data Visualization
We believe that a picture is worth a thousand words. Through intricate visualizations, we unravel insights that would otherwise remain hidden. This visual journey guides us through the data distribution, correlations, and inherent patterns, providing us with a comprehensive understanding.
### Model Building and Evaluation
Our dataset is meticulously partitioned into training and validation sets. With a focused determination, we construct a powerful Random Forest Classifier to make predictions. A rigorous evaluation ensues, involving metrics such as accuracy, classification report, and a comprehensive confusion matrix.
### Hyperparameter Tuning
In our quest for excellence, we turn to hyperparameter tuning. Leveraging advanced techniques like RandomizedSearchCV, we fine-tune our model to maximize performance. This endeavor ensures that our model captures the essence of the data with precision.
### Model Deployment
The culmination of our efforts is the deployment of our trained model using Streamlit. This results in an engaging and interactive web app. Users can effortlessly input employee attributes through this app, and in return, receive instantaneous predictions regarding the likelihood of promotion.
## Usage :computer:
To immerse yourself in this project's intricacies:
1. Clone this repository to your local machine.
2. Install the necessary libraries using `pip install -r requirements.txt`.
3. Immerse yourself in the immersive world of machine learning by exploring `HR_EMPLOYEEE_PROMOTION.ipynb`.
4. Experience the future of HR analytics by running the Streamlit app with `streamlit run app.py`, allowing you to interact with our employee promotion prediction tool.
Feel free to tweak the code, experiment with different models, or introduce novel features to elevate the prediction accuracy!
## Contributing 👥
We wholeheartedly welcome contributions! If you come across any issues or have ideas to enhance this project, submit a pull request. Let's collaboratively shape this project into a remarkable achievement.
## About the Author :raising_hand:
This project is authored by Vidhi Waghela. Feel free to reach out and connect with me on [LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/) to discuss the project, share insights, or explore exciting collaborations.
Let's embark on this journey of predicting employee promotions and empowering HR decisions with the magic of data! :bar_chart::rocket: