In [1]:
# Importing the pandas library for working with datasets/dataframes
import pandas as pd
# Importing numpy for numerical operations
import numpy as np
# Importing tree module from scikit-learn library (machine learning tools)
from sklearn import tree
# Importing the DecisionTreeClassifier class for building a decision tree model
from sklearn.tree import DecisionTreeClassifier
# Importing matplotlib to create plots and visualisations
import matplotlib.pyplot as plt
# Importing from sklearn to split the data to train on a smaller test
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
accuracy_score,
precision_score,
recall_score,
f1_score,
roc_auc_score,
classification_report
)
In [2]:
# Loading the gaming data dataset
df = pd.read_csv(r"C:\Users\miike\Documents\UWTSD\Machine Learning\online_gaming_behavior_dataset.csv")
In [3]:
# To show the data which is in the dataset
print(df)
PlayerID Age Gender Location GameGenre PlayTimeHours \
0 9000 43 Male Other Strategy 16.271119
1 9001 29 Female USA Strategy 5.525961
2 9002 22 Female USA Sports 8.223755
3 9003 35 Male USA Action 5.265351
4 9004 33 Male Europe Action 15.531945
... ... ... ... ... ... ...
40029 49029 32 Male USA Strategy 20.619662
40030 49030 44 Female Other Simulation 13.539280
40031 49031 15 Female USA RPG 0.240057
40032 49032 34 Male USA Sports 14.017818
40033 49033 19 Male USA Sports 10.083804
InGamePurchases GameDifficulty SessionsPerWeek \
0 0 Medium 6
1 0 Medium 5
2 0 Easy 16
3 1 Easy 9
4 0 Medium 2
... ... ... ...
40029 0 Easy 4
40030 0 Hard 19
40031 1 Easy 10
40032 1 Medium 3
40033 0 Easy 13
AvgSessionDurationMinutes PlayerLevel AchievementsUnlocked \
0 108 79 25
1 144 11 10
2 142 35 41
3 85 57 47
4 131 95 37
... ... ... ...
40029 75 85 14
40030 114 71 27
40031 176 29 1
40032 128 70 10
40033 84 72 39
EngagementLevel
0 Medium
1 Medium
2 High
3 Medium
4 Medium
... ...
40029 Medium
40030 High
40031 High
40032 Medium
40033 Medium
[40034 rows x 13 columns]
In [4]:
# Showing the information for each column in the data
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 40034 entries, 0 to 40033 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PlayerID 40034 non-null int64 1 Age 40034 non-null int64 2 Gender 40034 non-null object 3 Location 40034 non-null object 4 GameGenre 40034 non-null object 5 PlayTimeHours 40034 non-null float64 6 InGamePurchases 40034 non-null int64 7 GameDifficulty 40034 non-null object 8 SessionsPerWeek 40034 non-null int64 9 AvgSessionDurationMinutes 40034 non-null int64 10 PlayerLevel 40034 non-null int64 11 AchievementsUnlocked 40034 non-null int64 12 EngagementLevel 40034 non-null object dtypes: float64(1), int64(7), object(5) memory usage: 4.0+ MB
In [5]:
# Converting all text columns to numeric values
g = {'Male': 1, 'Female': 2}
df['Gender'] = df['Gender'].map(g)
l = {'Asia': 1, 'Europe': 2, 'USA': 3, 'Other': 4}
df['Location'] = df['Location'].map(l)
gg = {'Action': 1, 'RPG': 2, 'Simulation': 3, 'Sports': 4, 'Strategy': 5}
df['GameGenre'] = df['GameGenre'].map(gg)
gd = {'Easy': 1, 'Medium': 2, 'Hard': 3}
df['GameDifficulty'] = df ['GameDifficulty'].map(gd)
el = {'Low': 1, 'Medium': 2, 'High': 3}
df['EngagementLevel'] = df['EngagementLevel'].map(el)
In [6]:
# Print data to show the conversions that have been made for text columns
print(df)
PlayerID Age Gender Location GameGenre PlayTimeHours \
0 9000 43 1 4 5 16.271119
1 9001 29 2 3 5 5.525961
2 9002 22 2 3 4 8.223755
3 9003 35 1 3 1 5.265351
4 9004 33 1 2 1 15.531945
... ... ... ... ... ... ...
40029 49029 32 1 3 5 20.619662
40030 49030 44 2 4 3 13.539280
40031 49031 15 2 3 2 0.240057
40032 49032 34 1 3 4 14.017818
40033 49033 19 1 3 4 10.083804
InGamePurchases GameDifficulty SessionsPerWeek \
0 0 2 6
1 0 2 5
2 0 1 16
3 1 1 9
4 0 2 2
... ... ... ...
40029 0 1 4
40030 0 3 19
40031 1 1 10
40032 1 2 3
40033 0 1 13
AvgSessionDurationMinutes PlayerLevel AchievementsUnlocked \
0 108 79 25
1 144 11 10
2 142 35 41
3 85 57 47
4 131 95 37
... ... ... ...
40029 75 85 14
40030 114 71 27
40031 176 29 1
40032 128 70 10
40033 84 72 39
EngagementLevel
0 2
1 2
2 3
3 2
4 2
... ...
40029 2
40030 3
40031 3
40032 2
40033 2
[40034 rows x 13 columns]
In [7]:
# I then need to identify the feature columns and the target column
features = ['Age', 'Gender', 'Location', 'GameGenre','PlayTimeHours','InGamePurchases','GameDifficulty',
'SessionsPerWeek', 'AvgSessionDurationMinutes', 'PlayerLevel', 'AchievementsUnlocked']
X = df[features]
y = df['EngagementLevel']
print(X)
print(y)
Age Gender Location GameGenre PlayTimeHours InGamePurchases \
0 43 1 4 5 16.271119 0
1 29 2 3 5 5.525961 0
2 22 2 3 4 8.223755 0
3 35 1 3 1 5.265351 1
4 33 1 2 1 15.531945 0
... ... ... ... ... ... ...
40029 32 1 3 5 20.619662 0
40030 44 2 4 3 13.539280 0
40031 15 2 3 2 0.240057 1
40032 34 1 3 4 14.017818 1
40033 19 1 3 4 10.083804 0
GameDifficulty SessionsPerWeek AvgSessionDurationMinutes \
0 2 6 108
1 2 5 144
2 1 16 142
3 1 9 85
4 2 2 131
... ... ... ...
40029 1 4 75
40030 3 19 114
40031 1 10 176
40032 2 3 128
40033 1 13 84
PlayerLevel AchievementsUnlocked
0 79 25
1 11 10
2 35 41
3 57 47
4 95 37
... ... ...
40029 85 14
40030 71 27
40031 29 1
40032 70 10
40033 72 39
[40034 rows x 11 columns]
0 2
1 2
2 3
3 2
4 2
..
40029 2
40030 3
40031 3
40032 2
40033 2
Name: EngagementLevel, Length: 40034, dtype: int64
In [8]:
dtree = DecisionTreeClassifier()
dtree = dtree.fit(X,y)
tree.plot_tree(dtree, feature_names=features)
Out[8]:
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In [9]:
#Using the train_test_split to create train and test sets.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 47, test_size = 0.25)
In [10]:
# Train the model
dtree = DecisionTreeClassifier(random_state=47)
dtree.fit(X_train, y_train)
Out[10]:
DecisionTreeClassifier(random_state=47)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.
DecisionTreeClassifier(random_state=47)
In [11]:
# Make predictions based on the test set which has been defined
y_pred = dtree.predict(X_test)
In [12]:
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Accuracy: 0.8290538515336198
In [13]:
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
Precision: 0.8297774947129604 Recall: 0.8290538515336198 F1 Score: 0.8293500763671461
In [14]:
print(classification_report(y_test, y_pred))
precision recall f1-score support
1 0.78 0.79 0.79 2539
2 0.87 0.85 0.86 4926
3 0.80 0.82 0.81 2544
accuracy 0.83 10009
macro avg 0.82 0.82 0.82 10009
weighted avg 0.83 0.83 0.83 10009
In [15]:
y_prob = dtree.predict_proba(X_test)
In [16]:
auc_roc = roc_auc_score(
y_test,
y_prob,
multi_class='ovr',
average='weighted'
)
print("AUC-ROC:", auc_roc)
AUC-ROC: 0.8656814694608668
In [17]:
# Hyperparameter tuning to tweak the range of max depth
depths = range(1, 16) # 1 to 15
train_acc = []
test_acc = []
test_auc = []
for depth in depths:
dtree = DecisionTreeClassifier(max_depth=depth, random_state=47)
dtree.fit(X_train, y_train)
# Predictions
y_train_pred = dtree.predict(X_train)
y_test_pred = dtree.predict(X_test)
y_test_prob = dtree.predict_proba(X_test)
# Accuracy
train_acc.append(accuracy_score(y_train, y_train_pred))
test_acc.append(accuracy_score(y_test, y_test_pred))
# AUC-ROC
test_auc.append(roc_auc_score(y_test, y_test_prob, multi_class='ovr', average='weighted'))
# Plot Accuracy and AUC vs Max Depth
plt.figure(figsize=(10,6))
plt.plot(depths, train_acc, marker='o', label='Train Accuracy')
plt.plot(depths, test_acc, marker='o', label='Test Accuracy')
plt.plot(depths, test_auc, marker='x', label='Test AUC-ROC')
plt.xlabel('Max Depth')
plt.ylabel('Score')
plt.title('Decision Tree Performance vs Max Depth')
plt.legend()
plt.grid(True)
plt.show()
In [18]:
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, roc_auc_score, accuracy_score
# Final tuned model
dtree_tuned = DecisionTreeClassifier(
max_depth=7,
random_state=47
)
dtree_tuned.fit(X_train, y_train)
# Predictions
y_pred = dtree_tuned.predict(X_test)
y_prob = dtree_tuned.predict_proba(X_test)
# Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print("AUC-ROC:", roc_auc_score(y_test, y_prob, multi_class='ovr', average='weighted'))
print("\nClassification Report:\n")
print(classification_report(y_test, y_pred))
Accuracy: 0.8829053851533619
AUC-ROC: 0.930463688814985
Classification Report:
precision recall f1-score support
1 0.87 0.82 0.85 2539
2 0.87 0.93 0.90 4926
3 0.92 0.85 0.88 2544
accuracy 0.88 10009
macro avg 0.89 0.87 0.88 10009
weighted avg 0.88 0.88 0.88 10009
In [19]:
from sklearn.model_selection import GridSearchCV
param_grid = {
'max_depth': [6, 7, 8],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10],
'criterion': ['gini', 'entropy']
}
dtree = DecisionTreeClassifier(random_state=47)
grid = GridSearchCV(
estimator=dtree,
param_grid=param_grid,
cv=5,
scoring='roc_auc_ovr_weighted',
n_jobs=-1
)
grid.fit(X_train, y_train)
print("Best parameters:", grid.best_params_)
Best parameters: {'criterion': 'entropy', 'max_depth': 7, 'min_samples_leaf': 4, 'min_samples_split': 2}
In [20]:
best_dtree = grid.best_estimator_
y_pred = best_dtree.predict(X_test)
y_prob = best_dtree.predict_proba(X_test)
print("Optimised Accuracy:", accuracy_score(y_test, y_pred))
print("Optimised AUC-ROC:",
roc_auc_score(y_test, y_prob, multi_class='ovr', average='weighted'))
print("\nOptimised Classification Report:\n")
print(classification_report(y_test, y_pred))
Optimised Accuracy: 0.8890998101708463
Optimised AUC-ROC: 0.9387732639148653
Optimised Classification Report:
precision recall f1-score support
1 0.89 0.84 0.86 2539
2 0.88 0.93 0.90 4926
3 0.92 0.86 0.89 2544
accuracy 0.89 10009
macro avg 0.89 0.88 0.88 10009
weighted avg 0.89 0.89 0.89 10009
In [21]:
# Support Vector Machine (SVM Model)
In [22]:
# Importing the required tools for SVM
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
# Step 1 for SVM: Feature scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
In [23]:
# Step 2 - Create and train a baseline SVM model
svm_model = SVC(
kernel='rbf',
probability=True,
random_state=47
)
svm_model.fit(X_train_scaled, y_train)
Out[23]:
SVC(probability=True, random_state=47)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.
SVC(probability=True, random_state=47)
In [24]:
# Step 3 - Make predictions on the test set
y_pred_svm = svm_model.predict(X_test_scaled)
y_prob_svm = svm_model.predict_proba(X_test_scaled)
In [25]:
from sklearn.metrics import (
accuracy_score,
precision_score,
recall_score,
f1_score,
classification_report,
roc_auc_score
)
print("SVM Accuracy:", accuracy_score(y_test, y_pred_svm))
print("SVM Precision:",
precision_score(y_test, y_pred_svm, average='weighted'))
print("SVM Recall:",
recall_score(y_test, y_pred_svm, average='weighted'))
print("SVM F1 Score:",
f1_score(y_test, y_pred_svm, average='weighted'))
print("\nSVM Classification Report:\n")
print(classification_report(y_test, y_pred_svm))
print("SVM AUC-ROC:",
roc_auc_score(y_test, y_prob_svm,
multi_class='ovr',
average='weighted'))
SVM Accuracy: 0.8947946847836947
SVM Precision: 0.8959700164278291
SVM Recall: 0.8947946847836947
SVM F1 Score: 0.8941709585311922
SVM Classification Report:
precision recall f1-score support
1 0.90 0.84 0.87 2539
2 0.88 0.95 0.91 4926
3 0.92 0.85 0.89 2544
accuracy 0.89 10009
macro avg 0.90 0.88 0.89 10009
weighted avg 0.90 0.89 0.89 10009
SVM AUC-ROC: 0.9414586234342287
In [26]:
from sklearn.model_selection import GridSearchCV
# Step 4 for SVM: Hyperparameter tuning
param_grid_svm = {
'C': [0.1, 1, 10],
'gamma': ['scale', 0.01, 0.1, 1],
'kernel': ['rbf']
}
In [27]:
svm = SVC(
probability=True,
random_state=47
)
grid_svm = GridSearchCV(
estimator=svm,
param_grid=param_grid_svm,
cv=5,
scoring='roc_auc_ovr_weighted',
n_jobs=-1
)
grid_svm.fit(X_train_scaled, y_train)
Out[27]:
GridSearchCV(cv=5, estimator=SVC(probability=True, random_state=47), n_jobs=-1,
param_grid={'C': [0.1, 1, 10], 'gamma': ['scale', 0.01, 0.1, 1],
'kernel': ['rbf']},
scoring='roc_auc_ovr_weighted')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.
GridSearchCV(cv=5, estimator=SVC(probability=True, random_state=47), n_jobs=-1,
param_grid={'C': [0.1, 1, 10], 'gamma': ['scale', 0.01, 0.1, 1],
'kernel': ['rbf']},
scoring='roc_auc_ovr_weighted')SVC(C=10, gamma=0.01, probability=True, random_state=47)
SVC(C=10, gamma=0.01, probability=True, random_state=47)
In [3]:
# Using the results from the SVM GridSearchCV to save re-running code
from sklearn.svm import SVC
best_svm = SVC(
C=10,
gamma=0.01,
kernel='rbf',
probability=True,
random_state=47
)
best_svm.fit(X_train_scaled, y_train)
Out[3]:
SVC(C=10, gamma=0.01, probability=True, random_state=47)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.
SVC(C=10, gamma=0.01, probability=True, random_state=47)
In [4]:
# Evaluate optimised SVM
y_pred_svm = best_svm.predict(X_test_scaled)
y_prob_svm = best_svm.predict_proba(X_test_scaled)
from sklearn.metrics import accuracy_score, classification_report, roc_auc_score, precision_score, recall_score, f1_score
print("Optimised SVM Accuracy:", accuracy_score(y_test, y_pred_svm))
print("Optimised SVM Precision:", precision_score(y_test, y_pred_svm, average='weighted'))
print("Optimised SVM Recall:", recall_score(y_test, y_pred_svm, average='weighted'))
print("Optimised SVM F1 Score:", f1_score(y_test, y_pred_svm, average='weighted'))
print("\nOptimised SVM Classification Report:\n")
print(classification_report(y_test, y_pred_svm))
print("Optimised SVM AUC-ROC:", roc_auc_score(y_test, y_prob_svm, multi_class='ovr', average='weighted'))
Optimised SVM Accuracy: 0.8904985513038266
Optimised SVM Precision: 0.8916336966740668
Optimised SVM Recall: 0.8904985513038266
Optimised SVM F1 Score: 0.8899108405437465
Optimised SVM Classification Report:
precision recall f1-score support
1 0.89 0.82 0.85 2539
2 0.88 0.94 0.91 4926
3 0.93 0.86 0.89 2544
accuracy 0.89 10009
macro avg 0.90 0.88 0.88 10009
weighted avg 0.89 0.89 0.89 10009
Optimised SVM AUC-ROC: 0.9413189530747609
In [ ]:
# Clustering Algorithms
In [2]:
# Features used for clustering (no target variable needed)
cluster_features = [
'Age',
'PlayTimeHours',
'InGamePurchases',
'SessionsPerWeek',
'AvgSessionDurationMinutes',
'PlayerLevel',
'AchievementsUnlocked'
]
X_cluster = df[cluster_features]
In [6]:
# Check for missing values
print(X_cluster.isnull().sum())
# Quick statistics to see ranges and distributions
print(X_cluster.describe())
Age 0
PlayTimeHours 0
InGamePurchases 0
SessionsPerWeek 0
AvgSessionDurationMinutes 0
PlayerLevel 0
AchievementsUnlocked 0
dtype: int64
Age PlayTimeHours InGamePurchases SessionsPerWeek \
count 40034.000000 40034.000000 40034.000000 40034.000000
mean 31.992531 12.024365 0.200854 9.471774
std 10.043227 6.914638 0.400644 5.763667
min 15.000000 0.000115 0.000000 0.000000
25% 23.000000 6.067501 0.000000 4.000000
50% 32.000000 12.008002 0.000000 9.000000
75% 41.000000 17.963831 0.000000 14.000000
max 49.000000 23.999592 1.000000 19.000000
AvgSessionDurationMinutes PlayerLevel AchievementsUnlocked
count 40034.000000 40034.000000 40034.000000
mean 94.792252 49.655568 24.526477
std 49.011375 28.588379 14.430726
min 10.000000 1.000000 0.000000
25% 52.000000 25.000000 12.000000
50% 95.000000 49.000000 25.000000
75% 137.000000 74.000000 37.000000
max 179.000000 99.000000 49.000000
In [3]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_cluster_scaled = scaler.fit_transform(X_cluster)
# Quick check
print("Mean of each feature after scaling:", X_cluster_scaled.mean(axis=0))
print("Std of each feature after scaling:", X_cluster_scaled.std(axis=0))
Mean of each feature after scaling: [-7.91582305e-17 3.45207978e-17 -3.30121769e-17 1.51572038e-16 -1.41987857e-16 -3.36333737e-17 8.37728359e-17] Std of each feature after scaling: [1. 1. 1. 1. 1. 1. 1.]
In [4]:
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
wcss = []
k_values = range(1, 11)
for k in k_values:
kmeans = KMeans(n_clusters=k, random_state=47)
kmeans.fit(X_cluster_scaled)
wcss.append(kmeans.inertia_)
plt.figure(figsize=(8,5))
plt.plot(k_values, wcss, marker='o')
plt.xlabel('Number of clusters (k)')
plt.ylabel('WCSS (Inertia)')
plt.title('Elbow Method for Optimal k')
plt.grid(True)
plt.show()
In [6]:
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
k_values = [4, 5, 6, 7, 8]
silhouette_scores = []
for k in k_values:
kmeans = KMeans(n_clusters=k, random_state=47)
labels = kmeans.fit_predict(X_cluster_scaled)
score = silhouette_score(X_cluster_scaled, labels)
silhouette_scores.append(score)
print(f"K = {k}, Silhouette Score = {score:.4f}")
K = 4 → Silhouette Score = 0.1180 K = 5 → Silhouette Score = 0.1183 K = 6 → Silhouette Score = 0.1154 K = 7 → Silhouette Score = 0.1152 K = 8 → Silhouette Score = 0.1155 ================================================== Best number of clusters according to silhouette score: → k = 5 (score = 0.1183)
In [5]:
from sklearn.cluster import KMeans
kmeans = KMeans(
n_clusters=6,
random_state=47,
n_init=20
)
cluster_labels = kmeans.fit_predict(X_cluster_scaled)
In [6]:
df['Cluster'] = cluster_labels
# Check how many players are in each cluster
print(df['Cluster'].value_counts().sort_index())
Cluster 0 8036 1 6309 2 6256 3 6196 4 6554 5 6683 Name: count, dtype: int64
In [7]:
# CLuster Profiling
cluster_profile = (
df
.groupby('Cluster')[cluster_features]
.mean()
.round(2)
)
print(cluster_profile)
Age PlayTimeHours InGamePurchases SessionsPerWeek \
Cluster
0 31.99 11.94 1.0 9.53
1 24.45 9.25 0.0 5.28
2 35.92 17.35 0.0 13.70
3 23.30 10.85 0.0 13.95
4 34.31 17.66 0.0 4.85
5 41.23 5.33 0.0 9.79
AvgSessionDurationMinutes PlayerLevel AchievementsUnlocked
Cluster
0 94.49 50.02 24.53
1 95.36 72.15 25.12
2 93.12 71.77 25.13
3 97.72 29.67 25.40
4 95.41 29.88 23.14
5 92.87 45.20 23.95
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