Upload skipwithpredictor_159.py

skipwithpredictor_159.py

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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))