# [OLD] scikit-learn: Predict Sales Revenue using Multiple Linear Regression

This project will cover how to implement multiple linear regression using Python. We will alternatively use the `statsmodels` and `sklearn`modules for calculating the linear regression, while using `pandas` for data management, and `seaborn` for plotting. We will be working with the very popular advertising dataset to predict sales revenue based on advertising spending on different mediums.

Duration (mins)

Learners

#### NA / 5

Rating

We will cover the following tasks in 1 hour and 11 minutes:

### Introduction and Libraries

We will understand the Rhyme interface and our learning environment. You will be provided with a cloud desktop with Jupyter Notebooks and all the software you will need to complete the project. Jupyter Notebooks are very popular with Data Science and Machine Learning Engineers as one can write code in cells and use other cells for documentation.

We will also introduce the model we will be building as well the dataset for this Project. Finally, we will import libraries and helper functions that will be essential later in the project.

In this task, we will load the very popular advertising dataset about various costs incurred on advertising by different mediums such as through TV, radio, newspaper, and the sales for a particular product. Next, we will briefly explore the data to get some basic information about what we are going to be working with.

### Data Cleaning

Cleaning and preprocessing data is a vital process in data analysis and machine learning. In this task, we will take a look at our imported data and ascertain what to remove and what to keep for further analysis.

### Multiple Linear Regression - Estimating Coefficients

Multiple linear regression is an approach for modeling the relationship between a scalar dependent response variable y and two or more predictors (or independent variables) denoted X.

In this task, we are going to use the `statsmodels.api` to create our ordinary least squares (OLS) linear regression model. We will use `sklearn` to split our data into training and test sets.

### Interpreting Coefficients

In this task, we will understand the significance of the model coefficients as they relate to the advertising data. We have already learned to interpret them geometrically, and now we will look at how the coefficients of the predictors affect the response variable `sales.

### Feature Selection

Although one can include all available predictors as features or regressors, that is most often not the smart choice. In this task, we will implement various models and determine which features to include for our inference.

### Model Evaluation Metrics

Here we will learn the theory and implementation details behind Mean Absolute Error, Mean Squared Error, and Root Mean Square Error. We will then make the appropriate choice of metrics for our model.

### Model Evaluation Using Train/Test Split and RMSE

Armed with our arsenal of model metrics, we will evaluate the multiple regression model on the test set to determine out of sample performance.

## Watch Preview

Preview the instructions that you will follow along in a hands-on session in your browser.

## About the Host (Snehan Kekre)

Snehan Kekre is a Machine Learning and Data Science Instructor at Coursera. He studied Computer Science and Artificial Intelligence at Minerva Schools at KGI, based in San Francisco. His interests include AI safety, EdTech, and instructional design. He recognizes that building a deep, technical understanding of machine learning and AI among students and engineers is necessary in order to grow the AI safety community. This passion drives him to design hands-on, project-based machine learning courses on Rhyme.

##### How is this different from YouTube, PluralSight, Udemy, etc.?
In Rhyme, all projects are completely hands-on. You don't just passively watch someone else. You use the software directly while following the host's (Snehan Kekre) instructions. Using the software is the only way to achieve mastery. With the "Live Guide" option, you can ask for help and get immediate response.
##### Can I buy Rhyme sessions for my company or learning institution?
Absolutely. We offer Rhyme for workgroups as well larger departments and companies. Universities, academies, and bootcamps can also buy Rhyme for their settings. You can select projects and trainings that are mission critical for you and, as well, author your own that reflect your own needs and tech environments. Please email us at help@rhyme.com
##### What kind of accessibility options does Rhyme provide?
Rhyme strives to ensure that visual instructions are helpful for reading impairments. The Rhyme interface has features like resolution and zoom that will be helpful for visual impairments. And, we are currently developing a close-caption functionality to help with hearing impairments. Most of the accessibility options of the cloud desktop's operating system or the specific application can also be used in Rhyme. If you have questions related to accessibility, please email us at accessibility@rhyme.com
##### Why don't you just use containers or virtual browsers?
We started with windows and linux cloud desktops because they have the most flexibility in teaching any software (desktop or web). However, web applications like Salesforce can run directly through a virtual browser. And, others like Jupyter and RStudio can run on containers and be accessed by virtual browsers. We are currently working on such features where such web applications won't need to run through cloud desktops. But, the rest of the Rhyme learning, authoring, and monitoring interfaces will remain the same.
##### I have a different question
Please email us at help@rhyme.com and we'll respond to you within one business day.

## More Projects by Snehan Kekre

45 minutes
###### scikit-learn: Logistic Regression for Sentiment Analysis
1 hour and 26 minutes
###### Project: Predictive Modelling with Azure Machine Learning Studio
1 hour and 6 minutes
###### Project: Named Entity Recognition using LSTMs with Keras
1 hour and 10 minutes
###### Project: Support Vector Machines with scikit-learn
1 hour and 40 minutes
57 minutes
###### Project: Image Super Resolution using Autoencoders in Keras
1 hour and 4 minutes
58 minutes
52 minutes
###### Project: Predict Employee Turnover with scikit-learn
1 hour and 4 minutes
###### Logistic Regression with NumPy and Python
1 hour and 3 minutes
###### Project: Real-Time Object Detection with YOLOv3
1 hour and 5 minutes
47 minutes
53 minutes
55 minutes
53 minutes
52 minutes
###### Project: Generate Synthetic Images with DCGANs in Keras
1 hour and 7 minutes
47 minutes
47 minutes
###### Create Interactive Dashboards with Streamlit and Python
1 hour and 33 minutes
###### Project: Facial Expression Recognition in Keras
1 hour and 24 minutes
58 minutes
43 minutes
###### Build a Data Science Web App with Streamlit and Python
1 hour and 21 minutes
###### Project: Anomaly Detection in Time Series Data with Keras
1 hour and 3 minutes
41 minutes
###### Project: Perform Feature Analysis with Yellowbrick
1 hour and 15 minutes
48 minutes
52 minutes
52 minutes
52 minutes
###### Build a Machine Learning Web App with Streamlit and Python
1 hour and 21 minutes
59 minutes
58 minutes
###### Project: Regression Analysis with Yellowbrick
1 hour and 10 minutes
43 minutes
57 minutes
42 minutes