ardbramantyo / MachineLearning-Employee-Attrition

The project tries to develop & compare 3 different Machine Learning methods that could better predict in employee attrition.

Comparative Study of 3 Different Machine Learning Techniques to Predict Employee Attrition

Data Source: Kaggle

Overview

The project is aimed to develop Machine Learning models and make comparative prediction from "IBM HR Analytics Employee Attrition & Performance" fictional data (1470 rows of data) that could better predict in employee attrition.

Tools: Pandas, Numpy, Seaborn, Matplotlib, Scikit-Learn, Tensorflow, Keras

Exploratory Data Analysis

Data Cleaning

To avoid AI misunderstanding when interpreting data, 2 variables (X) are made based on their data type and converting categorical variable (X_cat) into numerical using scikit-learn and concatenate both of them back.

Variables:

1. Categorical(X_cat): Anything from fields exclude Attrition that has object data type
2. Numerical(X_numerical): Anything from fields that has numerical data type.

from sklearn.preprocessing import OneHotEncoder
onehotencoder = OneHotEncoder()
X_cat = onehotencoder.fit_transform(X_cat).toarray()

Machine Learning Methods Used for This Case:

1. Logistic Regression
2. Random Forest
3. Deep Learning Model

Accuracy Measurement Method:

• Training: 1102 (75%)
• Test: 368 (25%)

1. Logistics Regression Model

• Logistic regression is best used to predict binary outputs with two possible values labeled "0" or "1".
• Logistic model output can be one of two classes: stayed/left, pass/fail, win/lose, etc.
• Logistic regression algorithm works by implementing a linear equation first with independent predictors to predict a value.

model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

2. Random Forest Classifier Model

• Decision Trees are supervised Machine Learning technique where the data is split according to a certain condition/parameter.
• Random Forest Classifier is a type of ensemble algorithm.
• It creates a set of decision trees from randomly selected subset of training set.
• It then combines votes from different decision trees to decide the final class of the test object.

model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

3. Deep Learning Model

Parameter for training:

1. Input layer = 50 (from table fields)
2. Hidden layer = 3 layers (dense, 500 neurons each, relu activation function)
3. Output = 1 (sigmoid activation function)
4. Epochs = 100
5. Batch size = 50

Deep Learning Performance

Confusion Matrix Comparison

Confusion Matrix: Logistic Regression(left), Random Forest(mid), and Deep Learning(right)

Method	Accuracy (%)
Logistic Regression	89
Random Forest	85
Deep Learning	83

Conclusion

Based on analysis with 3 different Machine Learning Methods, Logistic Regression has highest Accuracy (89%) and best suitable to be applied to predict employee attriction.

Reference:

1. https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
2. https://matplotlib.org/3.5.0/plot_types/index.html
3. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
4. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html
5. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
6. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
7. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
8. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
9. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
10. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html
11. https://seaborn.pydata.org/generated/seaborn.heatmap.html
12. https://seaborn.pydata.org/generated/seaborn.countplot.html
13. https://seaborn.pydata.org/generated/seaborn.kdeplot.html
14. https://www.tensorflow.org/api_docs/python/tf/keras/Sequential
15. https://www.tensorflow.org/guide/keras/train_and_evaluate
16. https://www.tensorflow.org/api_docs/python/tf/keras/Model
17. https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense
18. https://towardsdatascience.com/a-practical-guide-to-implementing-a-random-forest-classifier-in-python-979988d8a263