Dataset: IBM HR Analytics Employee Attrition & Performance Here's the link for the dataset: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
- Analyze the dataset to understand its structure and features. It contains various attributes related to employee demographics, job roles, satisfaction levels, performance ratings, etc., along with a target variable indicating whether an employee has left the company (Yes or No).
- Perform preprocessing steps such as handling missing values, encoding categorical variables, and scaling numerical features if necessary.
- Split the dataset into training and testing sets.
- Choose suitable machine learning algorithms (e.g., logistic regression, random forest, support vector machine) for binary classification.
- Implement the selected algorithm(s) using Python libraries like scikit-learn or pytorch.
- Train the model(s) on the training data and evaluate their performance using metrics such as accuracy, precision, recall, and F1-score.
- Analyze the performance of the trained model(s) using evaluation metrics.
- Explore techniques for model optimization, such as hyperparameter tuning, feature selection, or model ensemble methods, to improve performance.
- Optimize the model parameters and evaluate the impact on model performance.