Gain insights into predicting retail sales with TensorFlow. Delve into data analysis, training models, and extracting insights using industry techniques. Explore efficient model evaluation with experts from top tech companies.

APLCS.tar.gz

case-study

In this course, you'll work on an industry-level machine learning project based on predicting weekly retail sales given different factors. You will learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this course, you will be able to take on industry-level machine learning projects, from data analysis to creating efficient models and providing results and insights.

The code for this course is built around the TensorFlow framework, which is one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Basic knowledge of Python and TensorFlow are prerequisites. To get some experience with TensorFlow, try our course: Machine Learning for Software Engineers.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Industry Case Study with TensorFlow

# Chapter Goals:
- Set up the regression function for the model

# A. Classification vs. regression
There are two main uses of MLPs in industry: classification and regression. Classification refers to predicting a *class* for a data observation, given its feature data. Examples of classification tasks include:
- Classifying email as real or spam
- Classifying voters as Republican, Democrat, and Independent
- Classifying dolphins into a particular subspecies

The Deep Learning with Tensorflow section of [Machine Learning for Software Engineers](/courses/machine-learning-for-software-engineers) Lab goes over the details of using MLP models for classification.

The other main usage of MLPs in industry is regression. Regression refers to predicting a real number value for a data observation, given its feature data. Examples of regression tasks include:
- Predicting housing prices based on factors like region and national unemployment rate
- Predicting the weather in Celsius/Fahrenheit
- Predicting the number of points a basketball team will score in a game

For our project, we'll be creating a regression model to predict weekly sales for the departments of each store.

# B. Regression loss
For regression models, there are two main loss functions that can used to train the model: mean absolute error and mean squared error. Mean absolute error takes the average absolute difference between the labels (in this case, the actual value for the weekly sales) and the model's predicted values. Mean squared error takes a similar approach, but uses the squared difference rather than the absolute difference.


$$\text{MAE} = \frac{1}{n}\sum_{i = 1}^n |label - prediction|$$

$$\text{MSE} = \frac{1}{n}\sum_{i = 1}^n (label - prediction)^2$$

>Mean absolute error (MAE) vs. mean squared error (MSE) over a batch of *n* data observations.

The MSE (also known as L2 loss) amplifies large error values (e.g. a difference of 1000) and minimizes fractional error values (e.g. a difference of 0.01), due to the squaring operation. Since we're predicting sales values that range from about 10,000 to 40,000 dollars, the error values will be in the thousands even when the model is relatively good.

Therefore, we'll use the MAE (also known as L1 loss) to avoid unnecessary error amplification.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Regression Function

Chapter Goals:

A. Classification vs. regression