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