- Implement Linear Regression problem. For example, based on a dataset comprising of existing set of prices and area/size of the houses, predict the estimated price of a given house.
- Based on multiple features/variables perform Linear Regression. For example, based on a number of additional features like number of bedrooms, servant room, number of balconies, number of houses of years a house has been built predict the price of a house.
- Implement a classification/ logistic regression problem. For example based on different features of students data, classify, whether a student is suitable for a particular activity. Based on the available dataset, a student can also implement another classification problem like checking whether an email is spam or not.
- Use some function for regularization of dataset based on problem 14.
- Use some function for neural networks, like Stochastic Gradient Descent or backpropagation
- algorithm to predict the value of a variable based on the dataset of problem 14.