Creation of a neural network to predict whether employees are likely to leave a company
Creating neural network trained using employee attrician data that HR can use to predict whether employees are likely to leave; Model also predicts each employee's fit across all departments using a branched neural network.
The primary skills demonstrated include the ability to:
- Prepare data for neural network model use
- Compile and evaluate a prediction model using neural network
- Create, train, and evaluate neural network models using TensorFlow
- Use KerasTuner to assist with finding optimal neural network structures
- Save trained TensorFlow models for later use
Python Libraries and Functions
- pandas
- pathlib
- tensorflow
- Dense (from tensorflow.keras.layers)
- Sequential (from tensorflow.keras.models)
- train_test_split (from klearn.model_selection)
- classification_report (from sklearn.metrics)
- StandardScaler (from sklearn.preprocessing)
Jamie Bond | [email protected] | Connect on Linkedin
Adapted from instructions starter files provided by The Artificial Intelligence Boot Camp at UNC Charlotte, including:
- Data: https://static.bc-edx.com/ai/ail-v-1-0/m19/lms/datasets/attrition.csv
- Instructions: attrition.ipynb starter code