Pandas: For data manipulation and analysis.
Scikit-learn: For machine learning models and evaluation metrics.
XGBoost: For gradient boosting machine learning.
TensorFlow: For building and training neural networks.
NumPy: It's likely used internally by Pandas and other libraries for numerical operations.
To install these dependencies, you can use pip, Python's package manager
pip install pandas scikit-learn xgboost tensorflow numpy
Execute
Ibm_Employee_Attrition.py
Ibm_Employee_Attrition.ipynb
The code utilizes various machine learning models for classification tasks. Here's a list of the models used:
Random Forest Classifier: Ensemble learning method based on decision trees.
Logistic Regression: Linear model for binary classification.
Support Vector Machine (SVM): Supervised learning algorithm for classification.
XGBoost (Extreme Gradient Boosting): Gradient boosting framework for classification and regression.
AdaBoost (Adaptive Boosting): Ensemble learning method that combines multiple weak classifiers to form a strong classifier.
Decision Tree Classifier: Tree-like model where an observation is classified based on feature values.
Naive Bayes Classifier: Probabilistic classifier based on Bayes' theorem with strong independence assumptions.
K-Nearest Neighbors (KNN): Instance-based learning method that classifies data points based on the majority class of their neighbors.
Gradient Boosting Classifier: Ensemble learning technique that builds models sequentially to correct the errors of the previous models.
Neural Network Classifier: Deep learning model with multiple layers of interconnected neurons, used for complex pattern recognition.
These models are trained and evaluated in the code for the classification task on the given dataset.
