https://github.com/ahmed122000/ml_model_deployment

The HR Analytics: Job Change Predictor is a Flask-based web application that uses machine learning to predict whether an employee will stay with a company or leave. It allows users to train models, evaluate their performance, and make predictions based on employee data, providing valuable insights for HR decision-making.
https://github.com/ahmed122000/ml_model_deployment

classification flask machine-learning python3 rest-api scikit-learn

Last synced: 3 days ago
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The HR Analytics: Job Change Predictor is a Flask-based web application that uses machine learning to predict whether an employee will stay with a company or leave. It allows users to train models, evaluate their performance, and make predictions based on employee data, providing valuable insights for HR decision-making.

• Host: GitHub
• URL: https://github.com/ahmed122000/ml_model_deployment
• Owner: Ahmed122000
• Created: 2022-01-10T15:49:29.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-12-25T20:52:53.000Z (about 1 month ago)
• Last Synced: 2024-12-25T21:21:30.318Z (about 1 month ago)
• Topics: classification, flask, machine-learning, python3, rest-api, scikit-learn
• Language: HTML
• Homepage:
• Size: 619 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# HR Analytics: Job Change Predictor

This is a Flask-based web application that predicts whether a data scientist will stay with a company or leave. The project provides tools for training machine learning models, evaluating their performance, and making predictions based on user inputs. The application is designed for HR analytics and aims to assist in decision-making processes.

## Features

- **Train Machine Learning Models:** Train Logistic Regression, K-Nearest Neighbors, or SVM models on different datasets (normal, oversampled, or undersampled).

- **Evaluate Models:** View evaluation metrics, including train/test scores and a detailed classification report.

- **Make Predictions:** Predict an employee's likelihood of staying or leaving based on their features.

- **User-Friendly Interface:** Interact with the application through a clean and intuitive web interface.

## Installation

### Prerequisites

- Python 3.7+

- Flask

- scikit-learn

- pandas

- numpy

- matplotlib

- joblib

### Steps

1. Clone the repository:

   ```bash

   git clone https://github.com/your-username/hr-analytics-predictor.git

   cd hr-analytics-predictor

   ```

2. Create and activate a virtual environment (optional but recommended):

   ```bash

   python -m venv venv

   source venv/bin/activate   # On Windows: venv\Scripts\activate

   ```

3. Install dependencies:

   ```bash

   pip install -r requirements.txt

   ```

4. Place your datasets (`normal_data.csv`, `oversample.csv`, `undersample_data.csv`) in the `data` folder.

5. Run the application:

   ```bash

   python main.py

   ```

6. Open your browser and navigate to:

   [http://127.0.0.1:5000](http://127.0.0.1:5000)

## Project Structure

```

hr-analytics-predictor/

├── main.py                 # Main Flask application

├── train.py               # Handles model training and evaluation

├── predict.py             # Handles predictions

├── templates/             # HTML templates for the web interface 

├── data/                  # Folder for datasets

├── static/                # Static files (CSS, JS, etc.)

└── requirements.txt       # Python dependencies

```

## Usage

### 1. Train a Model

- Navigate to the **Train Models** page.

- Select a dataset type (normal, oversampled, or undersampled).

- Choose a model (Logistic Regression, KNN, or SVM).

- Train the model and view its evaluation metrics.

### 2. Make Predictions

- Navigate to the **Predict** page.

- Input the employee's details (e.g., city development index, gender, experience, etc.).

- Submit the form to get the prediction result.

## Datasets

The project expects datasets in CSV format with the following columns:

- `city_development_index`

- `gender`

- `relevant_experience`

- `enrolled_university`

- `education_level`

- `major_discipline`

- `experience`

- `company_size`

- `company_type`

- `last_new_job`

- `training_hours`

- `target` (0: Stays, 1: Leaves)

## Acknowledgements

- [scikit-learn](https://scikit-learn.org/) for machine learning tools

- [Flask](https://flask.palletsprojects.com/) for the web framework

- [Kaggle](https://www.kaggle.com/) for providing the HR Analytics dataset