Assignment 1

Problem : Time Series Forecasting with Missing Data
Prompt:
•	Dataset: A time series dataset with missing data points.
•	Task: Predict future values of the time series.
Questions:
•	Imputation: Discuss different methods for handling missing data in time series (e.g., mean imputation, interpolation, time series forecasting models).
•	Feature Engineering: Explore techniques to create new features from the time series data, such as lagged values, moving averages, and seasonal components.
•	Model Selection: Compare the performance of various time series forecasting models (e.g., ARIMA, SARIMA, LSTM, Prophet) on the dataset. Evaluate using metrics like RMSE, MAE, and MAPE.
•	Hyperparameter Tuning: How would you tune the hyperparameters of your chosen model to optimize its performance on the time series data?

Assignment 2

•	Given the following classification results: 50 true positives, 30 false positives, 10 false negatives, and 100 true negatives, calculate the accuracy, precision, recall, and F1-score.
•	Describe how you would implement a Support Vector Machine (SVM) model to classify emails as 'Urgent' or 'Non-Urgent' based on their content. Specify the types of features you would consider for this classification task.

Assignment 3

•	You have the following data points:
Point A: (1, 2) Point B: (2, 3) Point C: (4, 5) Point D: (6, 7)
Implement a dendrogram using the single-linkage method.
•	You have the following transaction dataset:
Transaction 1: {A, B, C}
Transaction 2: {A, C}
Transaction 3: {A, B}
Transaction 4: {B, C}
Transaction 5: {A, B, C}
Compute the support, confidence, and lift for the rule {A, B} -> {C}.

Assignment 4
•	Given a dataset comprising both spam and non-spam emails, outline the process of employing Support Vector Machines (SVM) for email classification. Discuss the role of hyperplanes and how to select the appropriate kernel function for this task.
•	To create a deep learning model for classifying images, design a Convolutional Neural Network (CNN) architecture. Detail the layers and their functions in the CNN designed for image classification.

Assignment 5

•	Design a framework for employee scheduling in a hospital that guarantees sufficient staffing while accommodating individual preferences and constraints.

•	Formulate a fitness function for a genetic algorithm aimed at optimizing delivery routes for a logistics company, incorporating factors such as distance, fuel usage, and delivery time.

•	Create a genetic algorithm to enhance the design of an eco-friendly vehicle, with a fitness function that considers factors such as fuel efficiency, material costs, and carbon footprint.