title	author	Update	output
ARTIFICIAL INTELLIGENCE FOR HUMAN RESOURCES : Model to reduce hiring and training costs of employees	Dr LEBEDE Ngartera	1--05--2024	html_document pdf_document toc number_sections true true toc true

ARTIFICIAL INTELLIGENCE FOR HUMAN RESOURCES : "Model to reduce hiring and training costs of employees"

I-OVERVIEW

This project develops an AI model to reduce hiring and training costs of employees by predicting which employees might leave the company. It is designed to manage data in a multinational company. The human resources team has collected a lot of data on its employees. We are going to develop a model that can predict which employees are most likely to quit. We are going to harness Python's power with this libraries: Pandas, Numpy, Matplotlib, Seborn to maniplating, handling missing data, cleaning, do numerical operations and making statistical graphics. KDE (Kernel Density Estimate) is used for visualizing the Probability Density of a continuous variable. Logistic Regression classifier (sklearn.linear_model), Random Classifier (confusion_matrix) and Deep Learning Model (Tensorflow, Keras) are used for training and evaluating.

We are dealing in this project with a large amount of data. Here is a sample of the following data set:

1. Education
2. JobInvolvement
3. JobSatisfaction
4. PerformationRating
5. WorkLifeBalance
6. EnvironmentSatisfaction
7. BusinessTravel ...

II-IMPORT LIBRARIES AND DATASETS

Data source https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

Thanks a million to the Kaggle Team for providing this data.

employee_df.head(7)

employee_df.tail(10)

employee_df.info()

employee_df.describe()

III-DATA VISUALIZATIONS

1) DATA CLEANING

Let's replace the 'Attritition' column with integers before performing any visualizations

Right now Let's replace the 'Attritition' column with integers before performing any visualizations

2) MISSING DATA

-LIBRARY SEABORN

Let's see if we have any missing data

Fortunately there is no missing data in this case!

-VISUALISATION (HISTOGRAM)

Several features such as 'MonthlyIncome' and 'TotalWorkingYears' are tail heavy We are going to drop 'EmployeeCount' and 'Standardhours' because they do not change from one employee to the other

employee_df.drop(['EmployeeCount', 'StandardHours', 'Over18', 'EmployeeNumber'], axis=1, inplace=True)

Let's see how many employees left the company!

left_df = employee_df[employee_df['Attrition'] == 1] stayed_df = employee_df[employee_df['Attrition'] == 0]

print("Total =", len(employee_df))

print("Number of employees who left the company =", len(left_df)) print("Percentage of employees who left the company =", 1.*len(left_df)/len(employee_df)*100.0, "%")

print("Number of employees who did not leave the company (stayed) =", len(stayed_df))

print("Percentage of employees who did not leave the company (stayed) =", 1.*len(stayed_df)/len(employee_df)*100.0, "%")

Total = 1470

Number of employees who left the company = 237

Percentage of employees who left the company = 16.122448979591837 %

Number of employees who did not leave the company (stayed) = 1233

Percentage of employees who did not leave the company (stayed) = 83.87755102040816 %

left_df.describe()

stayed_df.describe()

correlations = employee_df.corr() f, ax = plt.subplots(figsize = (20, 25)) sns.heatmap(correlations, annot = True)

###-RELATIONSHIP BETWEEN AGE AND ATTRITION

plt.figure(figsize=[25, 12]) sns.countplot(x = 'Age', hue = 'Attrition', data = employee_df)

plt.figure(figsize=[20,20])

plt.subplot(411)

sns.countplot(x = 'JobRole', hue = 'Attrition', data = employee_df)

plt.subplot(412)

sns.countplot(x = 'MaritalStatus', hue = 'Attrition', data = employee_df)

plt.subplot(413)

sns.countplot(x = 'JobInvolvement', hue = 'Attrition', data = employee_df)

plt.subplot(414)

sns.countplot(x = 'JobLevel', hue = 'Attrition', data = employee_df)

KDE (kernel Density Estimate) PLOT [kdeplot]

plt.figure(figsize = (12,7))

sns.kdeplot(left_df['DistanceFromHome'], label = 'Employees who left', shade = True, color = 'r') sns.kdeplot(stayed_df['DistanceFromHome'], label = 'Employees who Stayed', shade = True, color = 'g')

plt.xlabel('Distance From Home')

plt.figure(figsize=(12,7))

sns.kdeplot(left_df['YearsWithCurrManager'], label = 'Employees who left', shade = True, color = 'r') sns.kdeplot(stayed_df['YearsWithCurrManager'], label = 'Employees who Stayed', shade = True, color = 'g')

plt.xlabel('Years With Current Manager')

plt.figure(figsize=(12,7))

sns.kdeplot(left_df['TotalWorkingYears'], shade = True, label = 'Employees who left', color = 'r') sns.kdeplot(stayed_df['TotalWorkingYears'], shade = True, label = 'Employees who Stayed', color = 'g')

plt.xlabel('Total Working Years')

plt.figure(figsize=(15, 10)) sns.boxplot(x = 'MonthlyIncome', y = 'Gender', data = employee_df)

Let's see the monthly income vs. job role

plt.figure(figsize=(15, 10)) sns.boxplot(x = 'MonthlyIncome', y = 'JobRole', data = employee_df)

IV) CREATE TESTING AND TRAINING DATASET & PERFORM DATA CLEANING

employee_df.head(3)

X_cat = employee_df[['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus']] X_cat

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

X_cat.shape

(1470, 26)

X_cat = pd.DataFrame(X_cat) X_cat

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

#scaler.fit(X_all)

X_all.columns = X_all.columns.astype(str)

X = scaler.fit_transform(X_all)

X

y = employee_df['Attrition']

y

V) TRAIN AND EVALUATE A LOGISTIC REGRESSION CLASSIFIER

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

X_train.shape

(1102, 50)

X_test.shape

(368, 50)

from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score

model = LogisticRegression() model.fit(X_train, y_train)

y_pred = model.predict(X_test)

y_pred

from sklearn.metrics import confusion_matrix, classification_report

print("Accuracy {} %".format( 100 * accuracy_score(y_pred, y_test)))

Accuracy 87.77173913043478 % Testing Set Performance

cm = confusion_matrix(y_pred, y_test) sns.heatmap(cm, annot=True)

print(classification_report(y_test, y_pred))

          precision    recall  f1-score   support

       0       0.90      0.97      0.93       317
       1       0.62      0.31      0.42        51

accuracy                           0.88       368

macro avg 0.76 0.64 0.67 368 weighted avg 0.86 0.88 0.86 368

VI) TRAIN AND EVALUATE A RANDOM FOREST CLASSIFIER

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier() model.fit(X_train, y_train)

y_pred = model.predict(X_test)

Testing Set Performance

cm = confusion_matrix(y_pred, y_test) sns.heatmap(cm, annot=True)

print(classification_report(y_test, y_pred))

          precision    recall  f1-score   support

       0       0.88      0.99      0.93       317
       1       0.82      0.18      0.29        51

accuracy                           0.88       368

macro avg 0.85 0.59 0.61 368 weighted avg 0.87 0.88 0.85 368

VI) TRAIN AND EVALUATE A DEEP LEARNING MODEL

import tensorflow as tf

model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(units=500, activation='relu', input_shape=(50, ))) model.add(tf.keras.layers.Dense(units=500, activation='relu')) model.add(tf.keras.layers.Dense(units=500, activation='relu')) model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

model.summary()

Model: "sequential"

---

Layer (type) Output Shape Param #

dense (Dense) (None, 500) 25500

dense_1 (Dense) (None, 500) 250500

dense_2 (Dense) (None, 500) 250500

dense_3 (Dense) (None, 1) 501

================================================================= Total params: 527001 (2.01 MB) Trainable params: 527001 (2.01 MB) Non-trainable params: 0 (0.00 Byte)

---

model.compile(optimizer='Adam', loss='binary_crossentropy', metrics = ['accuracy'])

epochs_hist = model.fit(X_train, y_train, epochs = 100, batch_size = 50)

Epoch 1/100 23/23 [==============================] - 2s 17ms/step - loss: 0.4510 - accuracy: 0.8176 Epoch 2/100 23/23 [==============================] - 0s 15ms/step - loss: 0.3646 - accuracy: 0.8485 Epoch 3/100 23/23 [==============================] - 0s 10ms/step - loss: 0.3189 - accuracy: 0.8757 Epoch 4/100 23/23 [==============================] - 0s 10ms/step - loss: 0.2977 - accuracy: 0.8911 Epoch 5/100 23/23 [==============================] - 0s 10ms/step - loss: 0.2869 - accuracy: 0.8893 Epoch 6/100 23/23 [==============================] - 0s 9ms/step - loss: 0.2378 - accuracy: 0.9111 Epoch 7/100 23/23 [==============================] - 0s 10ms/step - loss: 0.2019 - accuracy: 0.9265 Epoch 8/100 23/23 [==============================] - 0s 9ms/step - loss: 0.2235 - accuracy: 0.9138 Epoch 9/100 23/23 [==============================] - 0s 10ms/step - loss: 0.1800 - accuracy: 0.9247 Epoch 10/100 23/23 [==============================] - 0s 10ms/step - loss: 0.1327 - accuracy: 0.9501 Epoch 11/100 23/23 [==============================] - 0s 10ms/step - loss: 0.1170 - accuracy: 0.9601 Epoch 12/100 23/23 [==============================] - 0s 9ms/step - loss: 0.0872 - accuracy: 0.9682 Epoch 13/100 23/23 [==============================] - 0s 9ms/step - loss: 0.0587 - accuracy: 0.9746 Epoch 14/100 23/23 [==============================] - 0s 9ms/step - loss: 0.0512 - accuracy: 0.9837 Epoch 15/100 23/23 [==============================] - 0s 10ms/step - loss: 0.0240 - accuracy: 0.9936 Epoch 16/100 23/23 [==============================] - 0s 9ms/step - loss: 0.0971 - accuracy: 0.9637 Epoch 17/100 23/23 [==============================] - 0s 9ms/step - loss: 0.0888 - accuracy: 0.9619 Epoch 18/100 23/23 [==============================] - 0s 10ms/step - loss: 0.0440 - accuracy: 0.9891 Epoch 19/100 23/23 [==============================] - 0s 10ms/step - loss: 0.0175 - accuracy: 0.9955 Epoch 20/100 23/23 [==============================] - 0s 10ms/step - loss: 0.0056 - accuracy: 1.0000 Epoch 21/100 23/23 [==============================] - 0s 10ms/step - loss: 0.0028 - accuracy: 1.0000 Epoch 22/100 23/23 [==============================] - 0s 10ms/step - loss: 0.0016 - accuracy: 1.0000 Epoch 23/100 23/23 [==============================] - 0s 9ms/step - loss: 0.0010 - accuracy: 1.0000 Epoch 24/100 23/23 [==============================] - 0s 9ms/step - loss: 6.6903e-04 - accuracy: 1.0000 Epoch 25/100 23/23 [==============================] - 0s 10ms/step - loss: 5.2216e-04 - accuracy: 1.0000 Epoch 26/100 23/23 [==============================] - 0s 9ms/step - loss: 3.7797e-04 - accuracy: 1.0000 Epoch 27/100 23/23 [==============================] - 0s 9ms/step - loss: 3.0655e-04 - accuracy: 1.0000 Epoch 28/100 23/23 [==============================] - 0s 10ms/step - loss: 2.5651e-04 - accuracy: 1.0000 Epoch 29/100 23/23 [==============================] - 0s 10ms/step - loss: 2.0452e-04 - accuracy: 1.0000 Epoch 30/100 23/23 [==============================] - 0s 10ms/step - loss: 1.7081e-04 - accuracy: 1.0000 Epoch 31/100 23/23 [==============================] - 0s 10ms/step - loss: 1.4418e-04 - accuracy: 1.0000 Epoch 32/100 23/23 [==============================] - 0s 9ms/step - loss: 1.2326e-04 - accuracy: 1.0000 Epoch 33/100 23/23 [==============================] - 0s 10ms/step - loss: 1.0431e-04 - accuracy: 1.0000 Epoch 34/100 23/23 [==============================] - 0s 10ms/step - loss: 1.1086e-04 - accuracy: 1.0000 Epoch 35/100 23/23 [==============================] - 0s 10ms/step - loss: 8.8137e-05 - accuracy: 1.0000 Epoch 36/100 23/23 [==============================] - 0s 10ms/step - loss: 6.8153e-05 - accuracy: 1.0000 Epoch 37/100 23/23 [==============================] - 0s 10ms/step - loss: 5.9136e-05 - accuracy: 1.0000 Epoch 38/100 23/23 [==============================] - 0s 11ms/step - loss: 5.2380e-05 - accuracy: 1.0000 Epoch 39/100 23/23 [==============================] - 0s 11ms/step - loss: 4.6318e-05 - accuracy: 1.0000 Epoch 40/100 23/23 [==============================] - 0s 10ms/step - loss: 4.1189e-05 - accuracy: 1.0000 Epoch 41/100 23/23 [==============================] - 0s 10ms/step - loss: 3.7144e-05 - accuracy: 1.0000 Epoch 42/100 23/23 [==============================] - 0s 11ms/step - loss: 3.3546e-05 - accuracy: 1.0000 Epoch 43/100 23/23 [==============================] - 0s 10ms/step - loss: 3.4924e-05 - accuracy: 1.0000 Epoch 44/100 23/23 [==============================] - 0s 10ms/step - loss: 2.7929e-05 - accuracy: 1.0000 Epoch 45/100 23/23 [==============================] - 0s 14ms/step - loss: 2.4028e-05 - accuracy: 1.0000 Epoch 46/100 23/23 [==============================] - 0s 16ms/step - loss: 2.2325e-05 - accuracy: 1.0000 Epoch 47/100 23/23 [==============================] - 0s 15ms/step - loss: 1.8983e-05 - accuracy: 1.0000 Epoch 48/100 23/23 [==============================] - 0s 15ms/step - loss: 1.8635e-05 - accuracy: 1.0000 Epoch 49/100 23/23 [==============================] - 0s 15ms/step - loss: 1.5692e-05 - accuracy: 1.0000 Epoch 50/100 23/23 [==============================] - 0s 15ms/step - loss: 1.3894e-05 - accuracy: 1.0000 Epoch 51/100 23/23 [==============================] - 0s 15ms/step - loss: 1.2775e-05 - accuracy: 1.0000 Epoch 52/100 23/23 [==============================] - 0s 17ms/step - loss: 1.1837e-05 - accuracy: 1.0000 Epoch 53/100 23/23 [==============================] - 0s 14ms/step - loss: 1.0768e-05 - accuracy: 1.0000 Epoch 54/100 23/23 [==============================] - 0s 14ms/step - loss: 1.0008e-05 - accuracy: 1.0000 Epoch 55/100 23/23 [==============================] - 0s 16ms/step - loss: 9.2207e-06 - accuracy: 1.0000 Epoch 56/100 23/23 [==============================] - 0s 15ms/step - loss: 8.6202e-06 - accuracy: 1.0000 Epoch 57/100 23/23 [==============================] - 0s 15ms/step - loss: 8.0610e-06 - accuracy: 1.0000 Epoch 58/100 23/23 [==============================] - 0s 16ms/step - loss: 7.5208e-06 - accuracy: 1.0000 Epoch 59/100 23/23 [==============================] - 0s 15ms/step - loss: 6.9780e-06 - accuracy: 1.0000 Epoch 60/100 23/23 [==============================] - 0s 15ms/step - loss: 6.5159e-06 - accuracy: 1.0000 Epoch 61/100 23/23 [==============================] - 0s 15ms/step - loss: 6.1220e-06 - accuracy: 1.0000 Epoch 62/100 23/23 [==============================] - 0s 14ms/step - loss: 5.7757e-06 - accuracy: 1.0000 Epoch 63/100 23/23 [==============================] - 0s 15ms/step - loss: 5.4197e-06 - accuracy: 1.0000 Epoch 64/100 23/23 [==============================] - 0s 15ms/step - loss: 5.0920e-06 - accuracy: 1.0000 Epoch 65/100 23/23 [==============================] - 0s 14ms/step - loss: 4.8311e-06 - accuracy: 1.0000 Epoch 66/100 23/23 [==============================] - 0s 15ms/step - loss: 4.5565e-06 - accuracy: 1.0000 Epoch 67/100 23/23 [==============================] - 0s 11ms/step - loss: 4.3306e-06 - accuracy: 1.0000 Epoch 68/100 23/23 [==============================] - 0s 9ms/step - loss: 4.0937e-06 - accuracy: 1.0000 Epoch 69/100 23/23 [==============================] - 0s 9ms/step - loss: 3.8956e-06 - accuracy: 1.0000 Epoch 70/100 23/23 [==============================] - 0s 10ms/step - loss: 3.6959e-06 - accuracy: 1.0000 Epoch 71/100 23/23 [==============================] - 0s 11ms/step - loss: 3.5148e-06 - accuracy: 1.0000 Epoch 72/100 23/23 [==============================] - 0s 10ms/step - loss: 3.3709e-06 - accuracy: 1.0000 Epoch 73/100 23/23 [==============================] - 0s 10ms/step - loss: 3.2051e-06 - accuracy: 1.0000 Epoch 74/100 23/23 [==============================] - 0s 10ms/step - loss: 3.0641e-06 - accuracy: 1.0000 Epoch 75/100 23/23 [==============================] - 0s 10ms/step - loss: 2.9230e-06 - accuracy: 1.0000 Epoch 76/100 23/23 [==============================] - 0s 10ms/step - loss: 2.7888e-06 - accuracy: 1.0000 Epoch 77/100 23/23 [==============================] - 0s 10ms/step - loss: 2.6744e-06 - accuracy: 1.0000 Epoch 78/100 23/23 [==============================] - 0s 10ms/step - loss: 2.5800e-06 - accuracy: 1.0000 Epoch 79/100 23/23 [==============================] - 0s 10ms/step - loss: 2.4469e-06 - accuracy: 1.0000 Epoch 80/100 23/23 [==============================] - 0s 11ms/step - loss: 2.3608e-06 - accuracy: 1.0000 Epoch 81/100 23/23 [==============================] - 0s 10ms/step - loss: 2.3016e-06 - accuracy: 1.0000 Epoch 82/100 23/23 [==============================] - 0s 10ms/step - loss: 2.1690e-06 - accuracy: 1.0000 Epoch 83/100 23/23 [==============================] - 0s 11ms/step - loss: 2.0898e-06 - accuracy: 1.0000 Epoch 84/100 23/23 [==============================] - 0s 11ms/step - loss: 2.0108e-06 - accuracy: 1.0000 Epoch 85/100 23/23 [==============================] - 0s 10ms/step - loss: 1.9414e-06 - accuracy: 1.0000 Epoch 86/100 23/23 [==============================] - 0s 10ms/step - loss: 1.8623e-06 - accuracy: 1.0000 Epoch 87/100 23/23 [==============================] - 0s 10ms/step - loss: 1.7979e-06 - accuracy: 1.0000 Epoch 88/100 23/23 [==============================] - 0s 11ms/step - loss: 1.7343e-06 - accuracy: 1.0000 Epoch 89/100 23/23 [==============================] - 0s 10ms/step - loss: 1.6732e-06 - accuracy: 1.0000 Epoch 90/100 23/23 [==============================] - 0s 11ms/step - loss: 1.6086e-06 - accuracy: 1.0000 Epoch 91/100 23/23 [==============================] - 0s 11ms/step - loss: 1.5592e-06 - accuracy: 1.0000 Epoch 92/100 23/23 [==============================] - 0s 12ms/step - loss: 1.4922e-06 - accuracy: 1.0000 Epoch 93/100 23/23 [==============================] - 0s 10ms/step - loss: 1.4434e-06 - accuracy: 1.0000 Epoch 94/100 23/23 [==============================] - 0s 11ms/step - loss: 1.3954e-06 - accuracy: 1.0000 Epoch 95/100 23/23 [==============================] - 0s 10ms/step - loss: 1.3572e-06 - accuracy: 1.0000 Epoch 96/100 23/23 [==============================] - 0s 12ms/step - loss: 1.3092e-06 - accuracy: 1.0000 Epoch 97/100 23/23 [==============================] - 0s 10ms/step - loss: 1.2623e-06 - accuracy: 1.0000 Epoch 98/100 23/23 [==============================] - 0s 11ms/step - loss: 1.2239e-06 - accuracy: 1.0000 Epoch 99/100 23/23 [==============================] - 0s 10ms/step - loss: 1.1866e-06 - accuracy: 1.0000 Epoch 100/100 23/23 [==============================] - 0s 11ms/step - loss: 1.1477e-06 - accuracy: 1.0000

y_pred = model.predict(X_test) y_pred = (y_pred > 0.5)

12/12 [==============================] - 0s 3ms/step [ ] 1 y_pred

array([[False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [ True], [False], [False], [False], [False], [ True], [False], [ True], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [ True], [False], [False], [ True], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [ True], [False], [ True], [False], [False], [False], [False], [False], [ True], [False], [False], [ True], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [ True], [False], [False], [False], [ True], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False], [False], [False], [False], [False], [False], [ True], [False], [False], [False], [False]])

epochs_hist.history.keys()

dict_keys(['loss', 'accuracy'])

plt.plot(epochs_hist.history['loss']) plt.title('Model Loss Progress During Training') plt.xlabel('Epoch') plt.ylabel('Training Loss') plt.legend(['Training Loss'])

plt.plot(epochs_hist.history['accuracy']) plt.title('Model Accuracy Progress During Training') plt.xlabel('Epoch') plt.ylabel('Training Accuracy') plt.legend(['Training Accuracy'])

cm = confusion_matrix(y_test, y_pred) sns.heatmap(cm, annot=True)

Testing Set Performance

print(classification_report(y_test, y_pred))

          precision    recall  f1-score   support

       0       0.90      0.93      0.91       317
       1       0.44      0.35      0.39        51

accuracy                           0.85       368

macro avg 0.67 0.64 0.65 368 weighted avg 0.84 0.85 0.84 368

Author Dr Lebede Ngartera

Other Contributors Kaggle Team.