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:
- Create a scatter plot to visualize the relationships between dataset features and weekly sales


# A. Plotting data

The most important part of interpreting data is being able to visualize trends and correlations between dataset features. We do this through data plots, which allows us to easily discover interesting patterns in the dataset and decide whether the dataset is feasible for training a machine learning model.

We create the data plots using the [pyplot](https://matplotlib.org/api/pyplot_api.html) API of Matplotlib. One of the most common plots for data analysis is the 2-D scatter plot. It's used for plotting the relationship between a numeric dependent feature (Y-axis) and a numeric independent feature (X-axis). Since we're trying to predict weekly sales for stores, we'll make plots with the `'Weekly_Sales'` feature as the dependent feature.


Learn how to create scatter plots comparing numerical features to sales.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Scatter Plot

Chapter Goals:

A. Plotting data