In [1]:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, accuracy_score
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
csv_path = 'employee_churn_data.csv'
turnover_df = pd.read_csv(csv_path)
turnover_df.head()
Out[2]:
| department | promoted | review | projects | salary | tenure | satisfaction | bonus | avg_hrs_month | left | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | operations | 0 | 0.577569 | 3 | low | 5.0 | 0.626759 | 0 | 180.866070 | no |
| 1 | operations | 0 | 0.751900 | 3 | medium | 6.0 | 0.443679 | 0 | 182.708149 | no |
| 2 | support | 0 | 0.722548 | 3 | medium | 6.0 | 0.446823 | 0 | 184.416084 | no |
| 3 | logistics | 0 | 0.675158 | 4 | high | 8.0 | 0.440139 | 0 | 188.707545 | no |
| 4 | sales | 0 | 0.676203 | 3 | high | 5.0 | 0.577607 | 1 | 179.821083 | no |
In [3]:
turnover_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9540 entries, 0 to 9539 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 department 9540 non-null object 1 promoted 9540 non-null int64 2 review 9540 non-null float64 3 projects 9540 non-null int64 4 salary 9540 non-null object 5 tenure 9540 non-null float64 6 satisfaction 9540 non-null float64 7 bonus 9540 non-null int64 8 avg_hrs_month 9540 non-null float64 9 left 9540 non-null object dtypes: float64(4), int64(3), object(3) memory usage: 745.4+ KB
In [4]:
#categorical columns turned into numerical
label_encoder = LabelEncoder()
turnover_df['salary'] = label_encoder.fit_transform(turnover_df['salary'])
turnover_df['department'] = label_encoder.fit_transform(turnover_df['department'])
turnover_df['left'] = label_encoder.fit_transform(turnover_df['left'])
In [5]:
# Split features and target variable
X = turnover_df.drop('left', axis=1)
y = turnover_df['left']
y
Out[5]:
0 0
1 0
2 0
3 0
4 0
..
9535 1
9536 1
9537 1
9538 1
9539 1
Name: left, Length: 9540, dtype: int32
In [6]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [7]:
#scaling our data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.fit_transform(X_test)
In [8]:
#creating model
classifier = LogisticRegression(solver='lbfgs',
max_iter=1000,
random_state=1)
classifier
Out[8]:
LogisticRegression(max_iter=1000, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LogisticRegression(max_iter=1000, random_state=1)
In [11]:
#fitting model on our unscaled data
classifier.fit(X_train, y_train)
Out[11]:
LogisticRegression(max_iter=1000, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LogisticRegression(max_iter=1000, random_state=1)
In [12]:
classifier.fit(X_train_scaled, y_train)
Out[12]:
LogisticRegression(max_iter=1000, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(max_iter=1000, random_state=1)
In [13]:
#getting predictions for our scaled test data
predictions = classifier.predict(X_test_scaled)
In [15]:
#test data set
print(f"Testing Accuracy Score: {accuracy_score(y_test, predictions)}")
print("\nClassification Report:")
print(classification_report(y_test, predictions, target_names = ['not leaving', 'leaving']))
print("\nConfusion Matrix:")
print(confusion_matrix(y_test, predictions))
Testing Accuracy Score: 0.7311320754716981
Classification Report:
precision recall f1-score support
not leaving 0.74 0.95 0.83 1330
leaving 0.67 0.22 0.34 578
accuracy 0.73 1908
macro avg 0.70 0.59 0.58 1908
weighted avg 0.72 0.73 0.68 1908
Confusion Matrix:
[[1265 65]
[ 448 130]]
In [16]:
train_predictions = classifier.predict(X_train_scaled)
In [17]:
#training data reports
print(f' Training Accuracy Score: ', accuracy_score(y_train, train_predictions))
print("\nClassification Report:")
print(classification_report(y_train, train_predictions, target_names = ['not leaving', 'leaving']))
print("\nConfusion Matrix:")
print(confusion_matrix(y_train, train_predictions))
Training Accuracy Score: 0.7333595387840671
Classification Report:
precision recall f1-score support
not leaving 0.75 0.94 0.83 5426
leaving 0.61 0.22 0.32 2206
accuracy 0.73 7632
macro avg 0.68 0.58 0.58 7632
weighted avg 0.71 0.73 0.69 7632
Confusion Matrix:
[[5108 318]
[1717 489]]
In [18]:
#train importance df
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
model = LogisticRegression()
model.fit(X_train_scaled, y_train)
# Get the feature importance (coefficients)
feature_importance = model.coef_[0]
# Create a DataFrame for easy viewing
feature_names = X.columns # Assuming X is a DataFrame
importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importance})
importance_df = importance_df.sort_values(by='Importance', ascending=False)
print(importance_df)
Feature Importance 2 review 0.933413 6 satisfaction 0.385084 8 avg_hrs_month 0.367450 4 salary 0.032709 0 department -0.013433 3 projects -0.014418 7 bonus -0.024395 1 promoted -0.097614 5 tenure -0.108770
In [19]:
plt.figure(figsize=(10, 6))
sns.barplot(data=importance_df, x='Importance', y='Feature')
plt.title('Feature Importance')
plt.xlabel('Importance')
plt.ylabel('Features')
plt.show()
In [22]:
#test importance df
scaler = StandardScaler()
X_test_scaled = scaler.fit_transform(X_test)
model = LogisticRegression()
model.fit(X_test_scaled, y_test)
# Get the feature importance (coefficients)
feature_importance = model.coef_[0]
# Create a DataFrame for easy viewing
feature_names = X.columns # Assuming X is a DataFrame
importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importance})
importance_df = importance_df.sort_values(by='Importance', ascending=False)
print(importance_df)
Feature Importance 2 review 1.023313 5 tenure 0.406741 6 satisfaction 0.404325 0 department 0.055922 4 salary 0.004134 7 bonus -0.020784 1 promoted -0.087659 3 projects -0.144251 8 avg_hrs_month -0.153577
In [23]:
plt.figure(figsize=(10, 6))
sns.barplot(data=importance_df, x='Importance', y='Feature')
plt.title('Feature Importance')
plt.xlabel('Importance')
plt.ylabel('Features')
plt.show()
