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# -*- coding: utf-8 -*-
"""skipwithpredictor.159
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1C7AO89jheeQ3C61BPsSdIfK5tCgcL7IT
"""
import pandas as pd
import numpy as np
df = pd.read_csv('/content/online_course_engagement_data.csv')
df.dtypes
df.info()
df.isnull().sum()
df.drop('UserID', axis=1,inplace=True)
df['CourseCategory'].unique()
cat_mapping={
'Heatlh': 1,
'Arts': 2,
'Science': 3,
'Programming': 4,
'Business': 5
}
df['CourseCategory'] = df['CourseCategory'].map(cat_mapping)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df['QuizScores'] = scaler.fit_transform(df[['QuizScores']])
df['CompletionRate'] = scaler.fit_transform(df[['CompletionRate']])
df.head(15)
df.dtypes
import matplotlib.pyplot as plt
import seaborn as sns
int_col = df.select_dtypes(include='int').columns
float_col = df.select_dtypes(include='float').columns
plt.figure(figsize=(15,15))
for i, col in enumerate(int_col, 1):
plt.subplot(3,2,i)
counts = df[col].value_counts()
plt.bar(counts.index, counts)
plt.title(f'Bar Chart of {col}')
plt.xlabel(col)
plt.ylabel('Frequency')
for x, y in zip(counts.index, counts):
plt.text(x, y, str(y), ha='center', va='bottom')
plt.tight_layout()
plt.show
plt.figure(figsize=(12, 6))
for i, col in enumerate(float_col, 1):
plt.subplot(1, 3, 1)
sns.boxplot(y=df[col])
plt.title(f'Box Plot of {col}')
plt.ylabel(col)
plt.tight_layout()
plt.show()
cor = df.corr()
plt.figure(figsize=(10, 6))
sns.heatmap(cor,annot=True, cmap="coolwarm", fmt=".2f")
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import xgboost as xgb
import lightgbm as lgb
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
X = df.drop('CourseCompletion', axis=1)
y = df['CourseCompletion']
seed = 42
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=seed)
models = {
'RandomForest': RandomForestClassifier(random_state=seed),
'XGBoost': xgb.XGBClassifier(random_state=seed),
'LightGBM': lgb.LGBMClassifier(random_state=seed)
}
result = {}
for name, model in models.items():
model.fit(Xtrain, ytrain)
y_pred = model.predict(Xtest)
accuracy = accuracy_score(ytest, y_pred)
result[name] = accuracy
print(f'{name} Accuracy: {accuracy:.2f}')
print('Classification Report:')
print(classification_report(ytest, y_pred))
print('Confusion Matrix:')
print(confusion_matrix(ytest, y_pred)) |