alyamamah Multiple regression modelling in Python and Google Colab

Machine LearningProgramming 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.