Multiple regression modelling in Python and Google Colab

Machine Learning Programming Assignment 1 The aim of this assignment is to introduce you to Python’s popular machine learning libraries. Python is a programming language like Java, first introduced in 1991. One of its best features is its open-source libraries available for users. Users can choose from a range of machine learning algorithms and quickly build models, without having to program from a scratch. Here are some popular libraries. 1. NumPy – for matrix manipulation and linear algebra 2. Pandas – this library is built on top of NumPy and is used for preparing data sets for machine learning algorithms. It has functions for analyzing, cleaning, exploring, and manipulating data. It is used to create two-dimensional data sets called ‘data frames’. 3. Scikit-learn – it has most of the supervised and unsupervised machine learning algorithms. It is built on top of NumPy and SciPy libraries. 4. TensorFlow – it is a library for high performance numerical computation but its focus is on deep neural networks. 5. Keras – it is built on top of TensorFlow and offers a user-friendly interface for quick experimentation and prototyping. 6. PyTorch – it is based on C programming language framework, Torch and is used in machine language applications involving natural language processing and computer vision. 7. MatPlotLib – it is for data visualization. Users can create graphs, plots, histograms and bar charts. It is compatible with SciPy, NumPy and Pandas 8. Seaborn – this library is based on Matplotlib but focuses on Pandas data structures. Another aim of this assignment is to introduce you to Google’s Colaboratory (Colab). In it you can create Jupyter Notebooks for your machine learning programs and execute them online. You can access it through your Google account and your files get stored in your Google Drive. There is no requirement for any setup in your laptop or PC and it provides online access to free GPU and TPU resources. You can read more about Colab at this link: https://colab.google. To access the Colab associated with your google account go to this link: Welcome to Colaboratory – Colab (google.com). In this assignment you are required to build a multi-variate linear regression model, for selling price prediction, from the given housing data set. Choose the following features of the houses to induce the model: area, bedrooms, bathrooms, number of stories, hot water availability and air conditioning. Use the learned model to make selling price prediction for a house with feature values of your choice. Take the code for univariate regression example discussed in the class as an example.