Combine what you've learned so far to analyze a real-world case from start to finish.

Machine Learning with Python Libraries

In this module, we'll work on an industry-level machine learning project based on predicting weekly retail sales with different given factors.  We’ll learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this module, we’ll 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 module 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. This module requires basic knowledge of Python and TensorFlow as its prerequisites. By the end of this module, we’ll have hands-on experience in creating machine learning projects.

TensorFlow

## Chapter Goals:
- Create a bar plot to visualize the correlation between dataset features and weekly sales

## A. Categorical feature plots

For categorical features, it doesn't make sense to create a scatter plot. Instead, when we're using a categorical feature as the independent variable (with a numeric feature as the dependent variable), we'll make a bar plot.

When using weekly sales as the dependent variable, we'll create bar plots that show the average weekly sale amount for each category in a categorical feature.


# Chapter Goals:
- Create a bar plot to visualize the correlation between dataset features and weekly sales

# A. Categorical feature plots

For categorical features, it doesn't make sense to create a scatter plot. Instead, when we're using a categorical feature as the independent variable (with a numeric feature as the dependent variable), we'll make a bar plot.

When using weekly sales as the dependent variable, we'll create bar plots that show the average weekly sale amount for each category in a categorical feature.


Learn how to create bar plots comparing categorical features to sales.

Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Bar Plot

Chapter Goals:

A.