Machine Learning Lab – List of Core Experiments

Machine Learning Lab Experiments

Machine Learning Lab provides hands-on experience with fundamental ML algorithms and techniques. These experiments cover supervised learning (classification and regression), unsupervised learning (clustering), and essential ML concepts like evaluation metrics and model validation.

Each experiment is designed to teach specific ML concepts while building practical skills in using Python libraries like scikit-learn, pandas, and matplotlib. Understanding these experiments thoroughly prepares you for real-world ML applications and advanced ML courses.

Complete List of ML Lab Experiments

1. Implement linear regression to predict house prices – Learn supervised learning, regression problems, cost function, gradient descent, and model evaluation. Understand the relationship between features and continuous target variables. Practice with real estate datasets and interpret regression coefficients.
2. Logistic regression for binary classification – Master classification problems, sigmoid function, decision boundaries, and probability estimation. Use datasets like diabetes prediction, spam detection, or medical diagnosis. Understand how logistic regression differs from linear regression.
3. KNN classifier for Iris dataset – Learn instance-based learning, distance metrics (Euclidean, Manhattan), k-value selection, and lazy learning algorithms. The Iris dataset is perfect for understanding multi-class classification and feature visualization.
4. Naive Bayes classifier for text classification – Understand probabilistic classification, Bayes theorem, feature independence assumption, and text processing. Apply to spam detection, sentiment analysis, or document classification. Learn about bag-of-words and TF-IDF representations.
5. Decision tree and random forest for classification – Master tree-based algorithms, information gain, entropy, Gini impurity, and ensemble methods. Understand how decision trees split data and how random forests reduce overfitting. Visualize decision boundaries and feature importance.
6. Support Vector Machine for non-linear decision boundaries – Learn about maximum margin classifiers, kernel functions (RBF, polynomial), hyperparameter tuning, and handling non-linearly separable data. Understand the concept of support vectors and how kernels transform feature space.
7. K-means clustering for customer segmentation – Master unsupervised learning, centroid-based clustering, distance metrics, and cluster evaluation. Apply to customer segmentation, image compression, or anomaly detection. Understand the difference between supervised and unsupervised learning.
8. Principal Component Analysis (PCA) for dimensionality reduction – Learn about feature reduction, variance preservation, eigen decomposition, and visualization of high-dimensional data. Understand when and why to use dimensionality reduction. Apply PCA before classification to improve performance.
9. Evaluation metrics: accuracy, precision, recall, F1-score, confusion matrix – Master model evaluation techniques. Understand when to use which metric, how to interpret confusion matrices, and handle imbalanced datasets. Learn about ROC curves, AUC, and precision-recall curves.
10. Train–test split and cross-validation – Learn about model validation, overfitting prevention, k-fold cross-validation, stratified sampling, and hyperparameter tuning. Understand the importance of proper train-test splits and how cross-validation provides more reliable performance estimates.

Experiment Structure

For each experiment, include the following components:

• Objective – Clear statement of what the experiment aims to achieve
• Theory – Mathematical foundation, algorithm explanation, and key concepts
• Dataset Description – Source, features, target variable, data preprocessing steps
• Algorithm – Step-by-step algorithm or methodology
• Code – Complete, well-commented Python code with explanations
• Output – Screenshots, graphs, tables, and results
• Result/Discussion – Analysis of results, observations, limitations, and improvements

Essential Python Libraries

Learning Outcomes

After completing these experiments, you should be able to:

• Implement and apply various ML algorithms to real-world problems
• Preprocess data and handle missing values, outliers, and categorical variables
• Evaluate model performance using appropriate metrics
• Visualize data and model results effectively
• Understand when to use which algorithm for different problem types
• Apply cross-validation and hyperparameter tuning
• Interpret model results and make informed decisions

Frequently Asked Questions

Q1: What programming language should I use for ML lab?

Python is the standard language for ML due to its extensive libraries (scikit-learn, pandas, numpy) and ease of use. R is also used but Python is more common in industry and academia.

Q2: Where can I find datasets for these experiments?

Use datasets from UCI Machine Learning Repository, Kaggle, scikit-learn's built-in datasets (load_iris, load_diabetes), or create synthetic datasets. Always cite your data sources.

Q3: How do I choose the right algorithm for a problem?

Consider: problem type (classification vs regression), data size, interpretability requirements, and performance needs. Start with simple algorithms (linear/logistic regression) and move to complex ones if needed.

Q4: What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data (input-output pairs) to learn a mapping function. Unsupervised learning finds patterns in unlabeled data. Classification and regression are supervised; clustering is unsupervised.

Q5: How important is data preprocessing in ML?

Very important! Poor data quality leads to poor models. Preprocessing includes handling missing values, encoding categorical variables, scaling features, and removing outliers. Often, 80% of ML work is data preparation.