Q1: Feature Transformation (Any one) A. Use the PCA algorithm for dimensionality reduction. You have a dataset that includes measurements for different variables on wine (alcohol, ash, magnesium, and so on). Apply the PCA algorithm and transform this data so that most variations in the measurements of the variables are captured by a small number of principal components, making it easier to distinguish between red and white wine by inspecting these principal components. Dataset link: https://media.geeksforgeeks.org/wp-content/uploads/Wine.csv B. Apply the LDA algorithm on the Iris dataset and classify which species a given flower belongs to. Dataset link: https://www.kaggle.com/datasets/uciml/iris View Notebook A View Notebook B
Q2: Regression Analysis (Any one) A. Predict the price of an Uber ride from a given pickup point to the agreed drop-off location. Perform the following tasks: 1. Pre-process the dataset. 2. Identify outliers. 3. Check the correlation. 4. Implement linear regression, Ridge regression, and Lasso regression models. 5. Evaluate the models and compare their respective scores, such as R2 and RMSE. Dataset link: https://www.kaggle.com/datasets/yasserh/uber-fares-dataset B. Use the UCI diabetes dataset and Pima Indians diabetes dataset to perform the following: a. Univariate analysis: frequency, mean, median, mode, variance, standard deviation, skewness, and kurtosis. b. Bivariate analysis: linear and logistic regression modeling. c. Multiple regression analysis. d. Compare the results of the above analyses for the two datasets. Dataset link: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database View Notebook A View Notebook B
Q3: Classification Analysis (Any one) A. Implement Support Vector Machines (SVM) to classify images of handwritten digits into their respective numerical classes (0 to 9). B. Implement the K-Nearest Neighbours algorithm on the Social Network Ads dataset. Compute the confusion matrix, accuracy, error rate, precision, and recall on the given dataset. Dataset link: https://www.kaggle.com/datasets/rakeshrau/social-network-ads View Notebook A View Notebook B
Q4: Clustering Analysis (Any one) A. Implement K-Means clustering on the Iris CSV dataset. Determine the number of clusters using the elbow method. Dataset link: https://www.kaggle.com/datasets/uciml/iris B. Implement the K-Medoid algorithm on a credit card dataset. Determine the number of clusters using the silhouette method. Dataset link: https://www.kaggle.com/datasets/arjunbhasin2013/ccdata View Notebook A View Notebook B
Q5: Ensemble Learning (Any one) A. Implement a Random Forest classifier to predict the safety of a car. Dataset link: https://www.kaggle.com/datasets/elikplim/car-evaluation-data-set B. Use different voting mechanisms and apply AdaBoost (Adaptive Boosting), Gradient Tree Boosting (GBM), and XGBoost classification on the Iris dataset. Compare the performance of the three models using different evaluation measures. Dataset link: https://www.kaggle.com/datasets/uciml/iris View Notebook A View Notebook B
Q6: Reinforcement Learning (Any one) A. Implement reinforcement learning using an example of a maze environment that the agent needs to explore. B. Solve the Taxi problem using reinforcement learning, where the agent acts as a taxi driver to pick up a passenger at one location and drop them off at their destination. C. Build a Tic-Tac-Toe game using reinforcement learning in Python by performing the following tasks: a. Set up the environment. b. Define the Tic-Tac-Toe game. c. Build the reinforcement learning model. d. Train the model. e. Test the model. View Notebook A View Notebook B View Notebook C