COURSE OBJECTIVES:
To make the student to get a clear understanding of the core concepts of python like import data in various formats for statistical computing, data manipulation, business analytics, machine learning algorithms and data visualization etc.
COURSE OUTCOMES:
After successful completion of this course, the students will be able to:
CO 1: Gain proficiency in cleaning, transforming, and visualizing data.
CO 2: Understand the importance of preprocessing for effective ML modeling.
CO 3: Be able to extract meaningful insights and prepare data for ML pipelines.
CO 4: Proficiency in applying various supervised learning algorithms.
CO 5: Ability to evaluate models and tune hyperparameters.
CO 6: Hands-on experience with regression and classification tasks.
CO 7: Capability to build end-to-end supervised learning pipelines.
CO 8: Gain hands-on experience with various unsupervised learning techniques.
CO 9: Understand how to extract meaningful patterns and reduce dimensionality in data.
CO10: Develop skills to evaluate and compare clustering algorithms.
CO11: Learn to apply unsupervised learning to real-world problems.
CO12: Understand and implement key deep learning architectures.
CO 13: Train and evaluate models on image, text, and sequence data.
CO 14: Gain proficiency in advanced topics like transfer learning, GANs, and transformers.
CO 15: Deploy deep learning solutions to real-world problems
1. Preprocessing and Exploratory Data Analysis (EDA) [ CO1 - CO3]
1. Data Cleaning
- Objective: Learn to handle missing, duplicate, and inconsistent data.
- Tasks:
- Dataset Suggestions: Titanic dataset, Loan Prediction dataset.
2. Data Transformation
- Objective: Understand how to transform raw data for analysis.
- Tasks:
- Dataset Suggestions: Car Price dataset, Iris dataset.
3. Handling Outliers
- Objective: Learn techniques to detect and handle outliers.
- Tasks:
- Visualize outliers using box plots and scatter plots.
- Detect outliers using Z-scores and the Interquartile Range (IQR).
- Remove or cap outliers using statistical methods.
- Dataset Suggestions: Boston Housing dataset.
4. Feature Engineering
- Objective: Create new features to improve model performance.
- Tasks:
- Generate polynomial features for non-linear relationships.
- Combine existing features (e.g., creating a "total income" column by summing two income-related columns).
- Perform feature selection using correlation and variance threshold.
- Dataset Suggestions: Employee Attrition dataset.
5. Data Visualization
- Objective: Use visualization techniques to explore data patterns.
- Tasks:
- Create histograms to analyze the distribution of numerical variables.
- Plot scatter plots to study relationships between variables.
- Build heat maps to analyze feature correlations.
- Visualize categorical data using bar plots and pie charts.
- Dataset Suggestions: Superstore dataset, Sales dataset.
6. Dealing with Imbalanced Data
- Objective: Handle datasets with imbalanced target classes.
- Tasks:
- Identify imbalance in the target variable.
- Perform under sampling, oversampling, and SMOTE (Synthetic Minority Over-sampling Technique).
- Dataset Suggestions: Credit Card Fraud Detection dataset.
7. Time Series Preprocessing
- Objective: Prepare time-series data for modeling.
- Tasks:
- Handle missing timestamps and interpolate missing values.
- Perform seasonal decomposition of time-series data.
- Normalize and scale time-series data.
- Dataset Suggestions: Air Passenger dataset, Weather dataset.
8. Text Preprocessing
- Objective: Process textual data for NLP tasks.
- Tasks:
- Convert text to lowercase and remove punctuation.
- Tokenize text into words and remove stopwords.
- Apply stemming and lemmatization.
- Create a bag-of-words or TF-IDF matrix.
- Dataset Suggestions: IMDb Reviews dataset.
9. Dimensionality Reduction
- Objective: Reduce the dimensionality of datasets while preserving meaningful information.
- Tasks:
- Apply Principal Component Analysis (PCA) to reduce dimensions.
- Use t-SNE for visualizing high-dimensional data.
- Dataset Suggestions: MNIST dataset, Customer segmentation dataset.
10. Feature Importance Analysis
- Objective: Identify the most important features in the dataset.
- Tasks:
- Analyze feature importance using decision trees or random forests.
- Use permutation importance to rank features.
- Visualize feature importance using bar plots.
- Dataset Suggestions: Insurance dataset, Medical dataset.
11. Data Augmentation (for Image Data)
- Objective: Generate additional data samples for better model training.
- Tasks:
- Perform image rotation, flipping, and scaling.
- Use Python libraries like OpenCV or Keras for augmentation.
- Dataset Suggestions: CIFAR-10, Plant Village dataset.
12. Histogram Equalization and Edge Detection
- Objective: Enhance image data for better feature extraction.
- Tasks:
- Perform histogram equalization to adjust image contrast.
- Apply edge detection using Sobel, Prewitt, or Canny operators.
- Dataset Suggestions: Plant Village dataset, Facial Recognition dataset.
13. Exploratory Data Analysis (Full Pipeline)
- Objective: Perform comprehensive EDA on a given dataset.
- Tasks:
- Summarize data using descriptive statistics.
- Visualize distributions, relationships, and trends.
- Create a report summarizing key insights and recommendations.
- Dataset Suggestions: Any large, real-world dataset (e.g., Kaggle datasets).
2. Supervised Learning and It's Evaluation [CO4-CO7]
1. Linear Regression
- Objective: Predict continuous variables using Linear Regression.
- Tasks:
- Dataset Suggestions: Salary_Data.csv, Car Price Prediction dataset.
For Multiple Linear Regression Click Here
2. Polynomial Regression
- Objective: Extend Linear Regression to handle non-linear relationships.
- Tasks:
- Transform features into polynomial features.
- Train and compare the model with simple Linear Regression.
- Plot the polynomial curve for better visualization.
- Dataset Suggestions: Any dataset with non-linear patterns (e.g., advertising vs. sales).
3. Logistic Regression
4. k-Nearest Neighbors (k-NN)
- Objective: Classify data using the k-NN algorithm.
- Tasks:
- Implement k-NN for multi-class classification.
- Analyze the effect of different values of on model performance.
- Visualize decision boundaries (if working with 2D features).
- Dataset Suggestions: Iris dataset, MNIST subset.
5. Support Vector Machines (SVM)
- Objective: Train and evaluate SVM for classification tasks.
- Tasks:
- Implement SVM for binary and multi-class classification.
- Use linear and non-linear kernels (RBF, polynomial).
- Visualize decision boundaries for simple datasets.
- Dataset Suggestions: Iris dataset, Breast Cancer dataset.
6. Decision Trees
- Objective: Build interpretable models using Decision Trees.
- Tasks:
- Train a Decision Tree classifier or regressor.
- Visualize the decision tree structure.
- Prune the tree to avoid overfitting.
- Dataset Suggestions: Titanic dataset, California Housing dataset.
7. Random Forest
- Objective: Use Random Forest for robust predictions.
- Tasks:
- Train a Random Forest model for regression or classification.
- Analyze the effect of the number of trees () on performance.
- Extract feature importances and visualize them.
- Dataset Suggestions: Loan Prediction dataset, Weather dataset.
8. Gradient Boosting Algorithms
- Objective: Learn advanced tree-based models.
- Tasks:
- Experiment 1: Train and evaluate Gradient Boosting.
- Experiment 2: Use XGBoost for faster and more accurate results.
- Experiment 3: Compare Gradient Boosting, XGBoost, and Random Forest.
- Dataset Suggestions: Customer Churn dataset, House Price Prediction dataset.
9. Naive Bayes Classifier
- Objective: Apply probabilistic classification using Naive Bayes.
- Tasks:
- Train a Naive Bayes classifier for text or numerical data.
- Compare Gaussian, Multinomial, and Bernoulli Naive Bayes models.
- Dataset Suggestions: Spam Detection dataset, Sentiment Analysis dataset.
10. Multi-Class Classification
- Objective: Classify data into multiple categories.
- Tasks:
- Implement any classifier (e.g., Logistic Regression, k-NN) for multi-class problems.
- Compare "One-vs-Rest" and "One-vs-One" approaches.
- Dataset Suggestions: Iris dataset, Digits dataset.
11. Model Evaluation and Cross-Validation
- Objective: Learn evaluation and validation techniques.
- Tasks:
- Implement k-Fold Cross-Validation.
- Compare results with Train-Test Split.
- Use metrics like accuracy, F1-score, MSE, and R² Score.
- Dataset Suggestions: Any dataset used in earlier experiments.
12. Regularization Techniques
- Objective: Avoid overfitting in regression models.
- Tasks:
- Train Ridge and Lasso regression models.
- Compare results with ordinary Linear Regression.
- Analyze the impact of regularization parameters ().
- Dataset Suggestions: Any regression dataset.
13. Imbalanced Data Handling
- Objective: Improve model performance on imbalanced datasets.
- Tasks:
- Train a classifier on imbalanced data.
- Apply resampling techniques:
- Oversampling (SMOTE)
- Undersampling
- Evaluate and compare results before and after balancing the data.
- Dataset Suggestions: Credit Card Fraud dataset, Customer Churn dataset.
14. Ensemble Learning
- Objective: Combine multiple models for improved performance.
- Tasks:
- Implement Bagging (e.g., Bagging Classifier).
- Train a Voting Classifier with multiple models (e.g., Logistic Regression, SVM, Random Forest).
- Compare ensemble models with individual classifiers.
- Dataset Suggestions: Heart Disease Prediction dataset.
15. Hyperparameter Tuning
- Objective: Optimize model performance using hyperparameter tuning.
- Tasks:
- Perform Grid Search and Randomized Search for hyperparameter optimization.
- Compare tuned models with default ones.
- Dataset Suggestions: Any dataset from previous experiments.
16. End-to-End Supervised Learning Pipeline
- Objective: Build a complete supervised learning pipeline.
- Tasks:
- Perform data preprocessing and EDA.
- Train and evaluate multiple supervised learning models.
- Compare models and select the best one.
- Deploy the model using Flask or Streamlit.
- Dataset Suggestions: Any real-world dataset (e.g., Kaggle datasets)
Unsupervised Learning and It's Evaluation [CO8-CO11]
1. k-Means Clustering
- Objective: Group data into clusters using k-Means.
- Tasks:
- Implement the k-Means algorithm.
- Choose the optimal number of clusters using the Elbow Method or Silhouette Score.
- Visualize clusters in 2D/3D space.
- Dataset Suggestions: Iris dataset, Customer Segmentation dataset.
2. Hierarchical Clustering
- Objective: Cluster data using hierarchical methods.
- Tasks:
- Perform Agglomerative and Divisive clustering.
- Visualize the dendrogram to decide the number of clusters.
- Compare results with k-Means.
- Dataset Suggestions: Wholesale Customer dataset, Mall Customer Segmentation dataset.
3. Principal Component Analysis (PCA)
- Objective: Reduce the dimensionality of high-dimensional data.
- Tasks:
- Apply PCA to reduce dimensions.
- Visualize principal components in a 2D/3D plot.
- Analyze variance explained by each principal component.
- Dataset Suggestions: MNIST dataset, Wine dataset.
4. t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Objective: Visualize high-dimensional data in 2D/3D space.
- Tasks:
- Apply t-SNE for dimensionality reduction.
- Visualize clusters formed in the reduced space.
- Compare with PCA for visualization.
- Dataset Suggestions: Fashion MNIST dataset, Digits dataset.
5. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Objective: Perform density-based clustering.
- Tasks:
- Implement DBSCAN and tune parameters ( and ).
- Identify and visualize core, border, and noise points.
- Compare results with k-Means and Hierarchical Clustering.
- Dataset Suggestions: Any dataset with non-spherical clusters (e.g., Moons dataset).
6. Gaussian Mixture Models (GMM)
- Objective: Use probabilistic clustering.
- Tasks:
- Fit a Gaussian Mixture Model to data.
- Compare results with k-Means.
- Visualize cluster probabilities.
- Dataset Suggestions: Iris dataset, Synthetic datasets.
7. Anomaly Detection
- Objective: Detect anomalies in data using clustering or density estimation.
- Tasks:
- Use k-Means or DBSCAN for anomaly detection.
- Implement Gaussian-based anomaly detection.
- Evaluate the model using precision and recall for anomalies.
- Dataset Suggestions: Credit Card Fraud dataset, Network Intrusion dataset.
8. Association Rule Mining
- Objective: Discover patterns and relationships in transactional data.
- Tasks:
- Implement Apriori or FP-Growth algorithms.
- Generate association rules with confidence and support thresholds.
- Analyze relationships between items.
- Dataset Suggestions: Market Basket dataset, Online Retail dataset.
9. Self-Organizing Maps (SOM)
- Objective: Implement a neural network-based clustering approach.
- Tasks:
- Train a Self-Organizing Map.
- Visualize the clusters and feature map.
- Analyze how the SOM organizes similar data points.
- Dataset Suggestions: Iris dataset, Customer Segmentation dataset.
10. Autoencoders
- Objective: Use neural networks for dimensionality reduction and anomaly detection.
- Tasks:
- Train an autoencoder on high-dimensional data.
- Reconstruct data from the compressed representation.
- Use reconstruction error to detect anomalies.
- Dataset Suggestions: MNIST dataset, Fraud Detection dataset.
11. Clustering Text Data
- Objective: Cluster text data into meaningful groups.
- Tasks:
- Preprocess text data (tokenization, stopword removal, TF-IDF).
- Apply k-Means or DBSCAN to cluster documents.
- Visualize clusters using word clouds.
- Dataset Suggestions: 20 Newsgroups dataset, Social Media Posts dataset.
12. Image Segmentation
- Objective: Cluster image pixels into segments.
- Tasks:
- Use k-Means or DBSCAN for image segmentation.
- Apply PCA or t-SNE for dimensionality reduction before clustering.
- Visualize segmented images.
- Dataset Suggestions: Any image dataset (e.g., Satellite Images, Plant Village).
13. Feature Grouping
- Objective: Identify groups of related features in high-dimensional data.
- Tasks:
- Apply k-Means or Hierarchical Clustering to feature correlations.
- Visualize grouped features using a heatmap.
- Dataset Suggestions: Any dataset with many features (e.g., Genomic data, Sensor data).
14. Visualizing Clusters with UMAP
- Objective: Use UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction and visualization.
- Tasks:
- Reduce dimensions using UMAP.
- Visualize clusters in 2D or 3D.
- Compare with t-SNE and PCA.
- Dataset Suggestions: MNIST dataset, Fashion MNIST dataset.
15. Comparing Clustering Algorithms
- Objective: Evaluate and compare clustering methods.
- Tasks:
- Implement multiple clustering algorithms (k-Means, DBSCAN, GMM).
- Evaluate using metrics like Silhouette Score, Davies-Bouldin Index.
- Analyze the strengths and weaknesses of each method.
- Dataset Suggestions: Synthetic datasets with various cluster shapes (e.g., Scikit-learn's
make_blobs
,make_moons
).
16. End-to-End Unsupervised Learning Pipeline
- Objective: Combine preprocessing, clustering, and visualization into a single pipeline.
- Tasks:
- Perform preprocessing (scaling, feature extraction).
- Apply clustering and dimensionality reduction techniques.
- Present a report with key findings and visualizations.
- Dataset Suggestions: Any large dataset from Kaggle or UCI Machine Learning Repository.
Deep Learning and It's Evaluation [CO12-CO15]
1. Introduction to Artificial Neural Networks (ANN)
- Objective: Build and train a simple ANN for classification tasks.
- Tasks:
- Implement a feedforward neural network using a framework (TensorFlow/Keras or PyTorch).
- Train on a small dataset and visualize loss and accuracy curves.
- Evaluate the model using metrics like accuracy and confusion matrix.
- Dataset Suggestions: Iris dataset, MNIST (binary classification subset).
2. Activation Functions
- Objective: Understand and experiment with different activation functions.
- Tasks:
- Implement an ANN using ReLU, Sigmoid, Tanh, and Softmax.
- Compare their impact on training performance and convergence.
- Dataset Suggestions: Any small classification dataset.
3. Multi-Layer Perceptron (MLP)
- Objective: Train a fully connected network for multi-class classification.
- Tasks:
- Build an MLP with multiple hidden layers.
- Use dropout and batch normalization to prevent overfitting.
- Evaluate the model on unseen data.
- Dataset Suggestions: MNIST dataset, Fashion MNIST dataset.
4. Convolutional Neural Networks (CNNs)
- Objective: Train CNNs for image classification.
- Tasks:
- Implement a simple CNN for image recognition.
- Use techniques like max pooling, dropout, and data augmentation.
- Visualize feature maps and filters.
- Dataset Suggestions: CIFAR-10, Cats vs. Dogs dataset.
5. Transfer Learning
- Objective: Use pre-trained models to solve a new problem.
- Tasks:
- Fine-tune pre-trained models like VGG16, ResNet50, or MobileNet.
- Train on a small dataset for specific tasks like flower classification.
- Compare results with models trained from scratch.
- Dataset Suggestions: Flowers dataset, Plant Village dataset.
6. Recurrent Neural Networks (RNNs)
- Objective: Apply RNNs for sequence modeling.
- Tasks:
- Build an RNN to predict sequential data (e.g., temperature or stock prices).
- Use GRU and LSTM variants and compare their performance.
- Dataset Suggestions: Air Passenger dataset, Stock Price Prediction dataset.
7. Natural Language Processing (NLP) with Deep Learning
- Objective: Train deep learning models for text classification or generation.
- Tasks:
- Preprocess text data (tokenization, word embeddings).
- Train an LSTM or GRU for sentiment analysis or next-word prediction.
- Use pre-trained embeddings like Word2Vec or GloVe.
- Dataset Suggestions: IMDb Reviews dataset, News Categorization dataset.
8. Autoencoders
- Objective: Learn dimensionality reduction and anomaly detection using autoencoders.
- Tasks:
- Implement a basic autoencoder for dimensionality reduction.
- Use the reconstruction error to detect anomalies.
- Dataset Suggestions: MNIST dataset, Credit Card Fraud dataset.
9. Generative Adversarial Networks (GANs)
- Objective: Generate new data samples using GANs.
- Tasks:
- Build a basic GAN for generating images.
- Train the generator and discriminator models iteratively.
- Generate synthetic images and evaluate their quality.
- Dataset Suggestions: MNIST dataset, Fashion MNIST dataset.
10. Image Segmentation using U-Net
- Objective: Train a U-Net for pixel-wise image segmentation.
- Tasks:
- Implement U-Net for medical image segmentation or object detection.
- Evaluate segmentation results using metrics like IoU and Dice coefficient.
- Dataset Suggestions: Medical image datasets, Satellite image datasets.
11. Object Detection using YOLO or SSD
- Objective: Detect and classify objects in images.
- Tasks:
- Implement object detection using a pre-trained YOLO or SSD model.
- Fine-tune the model for a custom dataset.
- Dataset Suggestions: COCO dataset, Traffic Sign dataset.
12. Sequence-to-Sequence (Seq2Seq) Models
- Objective: Train Seq2Seq models for tasks like translation or summarization.
- Tasks:
- Build an encoder-decoder architecture using LSTM.
- Train the model for English-to-French translation or text summarization.
- Dataset Suggestions: OpenSubtitles dataset, Text Summarization dataset.
13. Attention Mechanisms and Transformers
- Objective: Understand attention mechanisms and implement transformers.
- Tasks:
- Build a basic attention-based sequence model.
- Use a pre-trained transformer like BERT or GPT for NLP tasks.
- Dataset Suggestions: IMDb dataset, SQuAD dataset.
14. Model Regularization and Optimization
- Objective: Experiment with regularization techniques and optimizers.
- Tasks:
- Use L1/L2 regularization, dropout, and batch normalization.
- Compare optimizers like SGD, Adam, and RMSprop.
- Dataset Suggestions: Any small dataset.
15. Hyperparameter Tuning
- Objective: Optimize deep learning models.
- Tasks:
- Use grid search or random search for hyperparameter optimization.
- Experiment with learning rates, activation functions, and layer configurations.
- Dataset Suggestions: Any dataset from previous experiments.
16. Time-Series Forecasting with CNN-LSTM
- Objective: Combine CNNs and LSTMs for time-series predictions.
- Tasks:
- Extract features using CNNs and predict using LSTMs.
- Forecast future values in time-series data.
- Dataset Suggestions: Air Passenger dataset, Energy Consumption dataset.
17. End-to-End Deep Learning Pipeline
- Objective: Build and deploy a complete deep learning model.
- Tasks:
- Perform data preprocessing and build the model.
- Train, evaluate, and deploy the model using Flask or Streamlit.
- Deploy a web-based interface for predictions.
- Dataset Suggestions: Any real-world dataset (e.g., Kaggle datasets).
Feature Extraction and Feature Selection Techniques
1. Feature Extraction Using Principal Component Analysis (PCA)
- Objective: Extract features by reducing dimensionality using PCA.
- Tasks:
- Perform PCA on high-dimensional data.
- Retain components explaining a significant percentage of variance.
- Visualize the transformed features in 2D or 3D.
- Dataset Suggestions: MNIST dataset, Wine dataset.
2. Feature Extraction Using Linear Discriminant Analysis (LDA)
- Objective: Extract discriminative features for classification tasks.
- Tasks:
- Apply LDA to labeled data.
- Visualize the separability of classes using the extracted features.
- Dataset Suggestions: Iris dataset, CIFAR-10 (simplified subset).
3. Deep Feature Extraction Using Pre-trained CNNs
- Objective: Extract deep features using layers from pre-trained networks like VGG16, ResNet50, or EfficientNet.
- Tasks:
- Use a pre-trained model to extract feature maps.
- Apply these features to a downstream classification task.
- Dataset Suggestions: Plant Village dataset, Fashion MNIST dataset.
4. Gabor Filter-Based Feature Extraction
- Objective: Extract texture features using Gabor filters.
- Tasks:
- Apply Gabor filters to extract frequency and orientation-based features.
- Use these features for texture classification tasks.
- Dataset Suggestions: Brodatz texture dataset, Image classification datasets.
5. Feature Extraction Using Wavelet Transform
- Objective: Extract time-frequency domain features using wavelet transforms.
- Tasks:
- Apply discrete wavelet transform (DWT) to time-series or image data.
- Analyze the transformed features for pattern recognition.
- Dataset Suggestions: ECG Signal dataset, Traffic Flow dataset.
6. Statistical Feature Extraction
- Objective: Extract statistical features (mean, standard deviation, skewness, kurtosis) for analysis.
- Tasks:
- Compute statistical features for numerical or time-series data.
- Use these features for clustering or classification.
- Dataset Suggestions: Air Quality dataset, Financial datasets.
7. Mutual Information-Based Feature Selection (Probability-Based)
- Objective: Select features based on their mutual information with the target variable.
- Tasks:
- Compute mutual information scores for features.
- Select features with the highest scores for classification tasks.
- Dataset Suggestions: Titanic dataset, Health datasets.
8. Recursive Feature Elimination (RFE)
- Objective: Select the most relevant features using an iterative approach.
- Tasks:
- Implement RFE with classifiers like SVM or Random Forest.
- Evaluate model performance with selected features.
- Dataset Suggestions: Breast Cancer dataset, UCI Classification datasets.
9. Feature Selection Using Chi-Square Test (Probability-Based)
- Objective: Select features that have a strong association with the target variable.
- Tasks:
- Perform chi-square tests on categorical features.
- Retain features with significant p-values.
- Dataset Suggestions: Titanic dataset, Census Income dataset.
10. L1 Regularization for Feature Selection
- Objective: Use Lasso regression to penalize irrelevant features.
- Tasks:
- Train a Lasso model and observe the coefficients.
- Select non-zero coefficient features for further analysis.
- Dataset Suggestions: Boston Housing dataset, Financial datasets.
11. Feature Selection Using Tree-Based Models
- Objective: Use feature importance scores from tree-based models.
- Tasks:
- Train a Random Forest or Gradient Boosting model.
- Use feature importance to select the top-k features.
- Dataset Suggestions: Customer Segmentation dataset, Weather dataset.
12. Boruta Algorithm for Feature Selection
- Objective: Implement an all-relevant feature selection approach.
- Tasks:
- Apply the Boruta algorithm to identify relevant features.
- Visualize feature importance and evaluate selected features.
- Dataset Suggestions: Any medium-sized classification dataset.
13. ReliefF Algorithm for Feature Selection
- Objective: Select features based on their ability to distinguish between classes.
- Tasks:
- Implement ReliefF to calculate feature weights.
- Retain features with weights above a threshold.
- Dataset Suggestions: Gene Expression datasets, Image datasets.
14. Information Gain and Gain Ratio (Probability-Based)
- Objective: Select features based on information gain with respect to the target variable.
- Tasks:
- Compute information gain for each feature.
- Use gain ratio to address bias in multi-valued features.
- Dataset Suggestions: Census Income dataset, Social Media datasets.
15. Feature Selection Using ANOVA (Probability-Based)
- Objective: Use Analysis of Variance (ANOVA) for feature selection in regression tasks.
- Tasks:
- Perform one-way ANOVA to assess the relationship between features and the target.
- Select features with low p-values.
- Dataset Suggestions: Boston Housing dataset, Climate datasets.
16. Embedded Feature Selection with XGBoost or LightGBM
- Objective: Use gradient-boosted decision trees for feature importance.
- Tasks:
- Train an XGBoost or LightGBM model.
- Use the feature importance scores for selection.
- Dataset Suggestions: Tabular classification datasets.
17. Deep Feature Selection with Autoencoders
- Objective: Use autoencoders to learn a reduced feature representation.
- Tasks:
- Train an autoencoder to reconstruct input data.
- Use the bottleneck layer as a reduced feature set.
- Dataset Suggestions: MNIST dataset, Fashion MNIST dataset.
18. Unsupervised Feature Selection Using Variance Thresholding
- Objective: Remove features with low variance.
- Tasks:
- Apply a variance threshold to identify and remove redundant features.
- Observe model performance after feature reduction.
- Dataset Suggestions: Any dataset with numerical features.
19. Fisher Score for Feature Selection
- Objective: Rank features based on their Fisher score.
- Tasks:
- Calculate Fisher scores for each feature.
- Select top-ranked features for classification tasks.
- Dataset Suggestions: UCI datasets with class imbalance.
20. Correlation-Based Feature Selection
- Objective: Select features that are less correlated with each other but highly correlated with the target.
- Tasks:
- Compute a correlation matrix.
- Use a threshold to filter features.
- Dataset Suggestions: Stock Market dataset, Sensor datasets.
Advanced Research-Based Methods
- SHAP (SHapley Additive exPlanations): Analyze feature importance using explainable AI techniques.
- t-SNE/UMAP for Feature Selection: Use embeddings for dimensionality reduction.
- Hybrid Methods: Combine filter and wrapper methods (e.g., using PCA followed by RFE).
Ensemble Based Learning
1. Bagging with Random Forest
- Objective: Use Random Forest to combine decision trees for improved classification or regression performance.
- Tasks:
- Train a Random Forest model.
- Analyze the effect of the number of trees (
n_estimators
) on accuracy. - Compare with a single decision tree model.
- Dataset Suggestions: Titanic dataset, Boston Housing dataset.
2. Bagging with Bootstrap Aggregation (Generic)
- Objective: Implement bagging with base estimators like Decision Tree or K-Nearest Neighbors.
- Tasks:
- Manually create bagging ensembles using bootstrapped samples.
- Evaluate and compare performance with non-ensemble models.
- Dataset Suggestions: Iris dataset, Weather dataset.
3. Boosting with AdaBoost
- Objective: Use AdaBoost to create a weighted ensemble of weak learners (e.g., decision stumps).
- Tasks:
- Train an AdaBoost model with decision stumps.
- Analyze the impact of the number of estimators and learning rate on performance.
- Compare results with Bagging.
- Dataset Suggestions: Heart Disease dataset, Wine Quality dataset.
4. Gradient Boosting for Regression
- Objective: Use Gradient Boosting for predicting continuous targets.
- Tasks:
- Train a Gradient Boosting model for regression.
- Tune hyperparameters such as learning rate, number of estimators, and maximum depth.
- Evaluate and compare performance with Random Forest Regression.
- Dataset Suggestions: California Housing dataset, Energy Efficiency dataset.
5. XGBoost for Classification
- Objective: Apply XGBoost for high-performance classification tasks.
- Tasks:
- Train an XGBoost classifier.
- Perform hyperparameter tuning using grid search or random search.
- Compare performance with Gradient Boosting and Random Forest.
- Dataset Suggestions: Churn Prediction dataset, Customer Segmentation dataset.
6. Stacking Ensemble (Blending Multiple Models)
- Objective: Combine predictions from different base models using a meta-model.
- Tasks:
- Use diverse base models (e.g., Logistic Regression, Decision Tree, SVM).
- Train a meta-model (e.g., Logistic Regression or Random Forest) on predictions from base models.
- Compare performance with individual models.
- Dataset Suggestions: Spam Email dataset, Pima Indians Diabetes dataset.
7. Voting Ensemble (Hard and Soft Voting)
- Objective: Combine predictions from multiple classifiers using voting mechanisms.
- Tasks:
- Train base models (e.g., SVM, Logistic Regression, KNN).
- Implement hard voting (majority rule) and soft voting (probability averaging).
- Evaluate and compare results with individual models.
- Dataset Suggestions: Iris dataset, MNIST (simplified subset).
8. CatBoost for Categorical Features
- Objective: Use CatBoost, which is optimized for datasets with categorical features.
- Tasks:
- Train a CatBoost model on a dataset with categorical variables.
- Compare its performance with XGBoost and LightGBM.
- Analyze training speed and accuracy.
- Dataset Suggestions: Titanic dataset, Loan Prediction dataset.
9. LightGBM for Large Datasets
- Objective: Train a LightGBM model optimized for speed and performance on large datasets.
- Tasks:
- Use LightGBM for classification or regression tasks.
- Evaluate performance on both small and large datasets.
- Compare with Random Forest and XGBoost.
- Dataset Suggestions: Higgs Boson dataset, Airline Delay dataset.
10. Bagging with Extra Trees (Extremely Randomized Trees)
- Objective: Use Extra Trees for creating more randomized decision tree ensembles.
- Tasks:
- Train an Extra Trees classifier.
- Compare performance with Random Forest.
- Analyze the impact of randomness on bias and variance.
- Dataset Suggestions: Wine dataset, Credit Card Fraud dataset.
11. Hybrid Ensemble (Bagging + Boosting)
- Objective: Combine Bagging and Boosting techniques for better performance.
- Tasks:
- Train a Random Forest model.
- Train an XGBoost or Gradient Boosting model.
- Combine predictions using stacking or averaging.
- Dataset Suggestions: Customer Retention dataset, Plant Disease dataset.
12. Ensemble for Imbalanced Data (SMOTE + Ensemble)
- Objective: Handle imbalanced datasets using resampling techniques with ensemble methods.
- Tasks:
- Apply SMOTE (Synthetic Minority Oversampling Technique) to balance classes.
- Train Random Forest, XGBoost, or AdaBoost on the resampled dataset.
- Compare results with non-ensemble models.
- Dataset Suggestions: Credit Card Fraud dataset, Medical Diagnosis dataset.
13. Bayesian Model Averaging
- Objective: Combine predictions probabilistically using Bayesian Model Averaging.
- Tasks:
- Train multiple models (e.g., Naive Bayes, Logistic Regression).
- Use Bayesian techniques to assign weights to model predictions.
- Compare performance with traditional voting ensembles.
- Dataset Suggestions: Sentiment Analysis dataset, E-commerce datasets.
14. Random Forest Feature Importance Analysis
- Objective: Use feature importance scores from Random Forest for feature selection.
- Tasks:
- Train a Random Forest model.
- Extract and analyze feature importance scores.
- Train a new model using only the selected features and evaluate performance.
- Dataset Suggestions: Heart Disease dataset, Marketing datasets.
15. Ensemble of Neural Networks
- Objective: Combine multiple deep learning models for improved accuracy.
- Tasks:
- Train multiple neural networks (e.g., CNN, MLP) on the same dataset.
- Combine predictions using averaging or majority voting.
- Compare performance with individual networks.
- Dataset Suggestions: MNIST dataset, CIFAR-10 dataset.
16. Advanced Research: Dynamic Ensemble Selection (DES)
- Objective: Select the most appropriate models dynamically for each test instance.
- Tasks:
- Implement a DES approach using k-NN or clustering.
- Evaluate the performance on imbalanced or noisy datasets.
- Dataset Suggestions: Sensor Fault Detection dataset, Anomaly Detection datasets.
Data Sets Required can download from below
3. 7_wine.csv
6. Data.csv
7. diabetes.csv
8. DTree.csv
10. id3.csv
11. id3_test.csv
12. pima-indians.csv
13. heart.csv
14. Titanic dataset
15. Loan Prediction dataset
16. Car Price dataset
17. Iris dataset
18. Boston Housing dataset.
19.Employee Attrition dataset.
20. Superstore dataset
21.Sales dataset.
22. Advertising_Sales
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