Write a python program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set.

You work for a company that has warehouses of items. The items are all packaged in boxes that are roughly the same size. Each warehouse has some shelves in it. The shelves all hold the same number of boxes. To keep things from getting confusing, a shelf will only contain one kind of item on it, and a warehouse will have at most one shelf storing a given kind of item. (If the shelf is full, that item would go to a different warehouse to make sure that items are evenly distributed, but your code won’t track multiple warehouses.) You will create a Warehouse class to simulate a warehouse according to the above description. If you would like to make use of another class of your design, declare it inside the Warehouse class (this is called an inner class). It is possible to create an efficient solution without having to do this. You can imagine each shelf sitting in a long line; what kind of data structure have we seen that can take advantage of this? If each shelf has multiple properties,…

Q8.1 In MATLAB, implement the cellular automaton simulation where configurations 001, 011, 100 and 110 give rise to a live cell at the next step. Use 521 cells and take 255 steps. Set the initial states to be zero, except for a single 1 in the middle (i.e. at column 260). Use either "dead cell" "wrap around" boundary conditions. Provide the final state of the simulation as your answer. It will be a single row with 521 columns. Just copy and paste this single row as your answer A1_final. If you've called your simulation array A in MATLAB, then the command disp(num2str(A(end, :))) will give you the last row in ready-to-paste form (but remember the square brackets when you enter it). By the way, be sure to visualise the full array in MATLAB - it's a picture of a very famous fractal!

Implement a simple linear regression model using Python without using any machine learning libraries like scikit-learn. Your model should take a dataset of input features X and corresponding target values y, and it should output the coefficients w and b for the linear equation y =wX + b