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# Extract new topics from the latest PDFs
new_topics = {
"optimal_k_knn": {
"title": "Finding Optimal K in KNN",
"concepts": [
"Elbow method for finding optimal K",
"Cross-validation to find best K",
"Testing K values 1-20",
"Mean accuracy across k-folds",
"Avoiding underfitting and overfitting"
],
"data": {
"k_values": list(range(1, 20)),
"accuracies_fold1": [0.98, 0.95, 0.92, 0.90, 0.88, 0.86, 0.85, 0.84, 0.83, 0.82, 0.81, 0.80, 0.79, 0.78, 0.77, 0.76, 0.75, 0.74, 0.73],
"accuracies_fold2": [0.96, 0.93, 0.91, 0.89, 0.87, 0.85, 0.83, 0.82, 0.81, 0.80, 0.79, 0.78, 0.77, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71],
"accuracies_fold3": [0.94, 0.92, 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.79, 0.78, 0.77, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71, 0.70, 0.69]
}
},
"hyperparameter_tuning": {
"title": "Hyperparameter Tuning with GridSearch",
"concepts": [
"What are hyperparameters?",
"GridSearch exhaustive search",
"Testing multiple parameter combinations",
"Finding optimal hyperparameters",
"Train/test performance comparison"
],
"svm_params": {
"C": [0.1, 1, 10, 100],
"gamma": ["scale", "auto", 0.001, 0.01],
"kernel": ["linear", "poly", "rbf"]
},
"results": {
"best_C": 1,
"best_gamma": "scale",
"best_kernel": "rbf",
"best_score": 0.95
}
},
"naive_bayes": {
"title": "Naive Bayes Classification",
"concepts": [
"Probabilistic classifier",
"Bayes' theorem",
"Independence assumption",
"Prior and posterior probabilities",
"Feature independence"
],
"formulas": [
"P(C|X) = P(X|C) × P(C) / P(X)",
"P(X|C) = P(x1|C) × P(x2|C) × ... × P(xn|C)",
"Posterior = Likelihood × Prior / Evidence"
]
},
"decision_trees": {
"title": "Decision Trees",
"concepts": [
"Tree structure with nodes and branches",
"Splitting criteria (Information Gain, Gini)",
"Entropy calculation",
"Recursive splitting",
"Leaf nodes for predictions"
]
},
"ensemble_methods": {
"title": "Ensemble Methods",
"concepts": [
"Bagging (Bootstrap Aggregating)",
"Boosting (AdaBoost, Gradient Boosting)",
"Random Forest",
"Combining weak learners",
"Voting mechanisms"
]
}
}
print("="*80)
print("NEW TOPICS FROM 26-10-2025 LECTURES")
print("="*80)
for topic_id, topic_data in new_topics.items():
print(f"\n📚 {topic_data['title'].upper()}")
print(f" Concepts: {len(topic_data['concepts'])}")
for i, concept in enumerate(topic_data['concepts'], 1):
print(f" {i}. {concept}")
print("\n" + "="*80)
print("TOPICS TO ADD TO APPLICATION")
print("="*80)
print("""
NEW TOPICS (from 26-10-2025):
1. ✅ Finding Optimal K in KNN (Elbow Method + Cross-Validation)
2. ✅ Hyperparameter Tuning with GridSearch
3. ✅ Naive Bayes Classification
4. ✅ Decision Trees
5. ✅ Ensemble Methods (Bagging, Boosting, Random Forest)
FIXES NEEDED:
1. ✅ Fix Linear Regression Visualization (currently not showing)
2. ✅ Add MORE visualizations for every algorithm
3. ✅ Add Mathematical explanations for WHY each algorithm
4. ✅ Add More Real-World Examples
5. ✅ Explain WHY one algorithm works vs another
6. ✅ Add comparison visualizations between algorithms
""")
print("\n" + "="*80)
print("ENHANCED LINEAR REGRESSION VISUALIZATION FIX")
print("="*80)
print("""
The Linear Regression visualization issue will be fixed with:
1. Proper Canvas initialization
2. Error handling for drawing
3. Auto-scaling for data points
4. Clear axes and labels
5. Live updating as sliders move
6. Residual lines visualization
7. MSE display with calculation breakdown
""")
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