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:
- Write the training and evaluation set data into TFRecords files

## A. Writing Example data

Now that we've completed the function to convert each DataFrame row into an Example object, we can create the efficient input pipeline storage for both the training and evaluation sets. The data storage will be in the form of TFRecords files, which hold serialized Example objects.

The `write_tfrecords` function (shown below) writes the data from a given DataFrame into a TFRecords file. It uses the `create_example` function from the previous chapter to convert each row of the dataset into an Example object. Each Example object is then serialized and written into the TFRecords file.

# Chapter Goals:
- Write the training and evaluation set data into TFRecords files

# A. Writing Example data

Now that we've completed the function to convert each DataFrame row into an Example object, we can create the efficient input pipeline storage for both the training and evaluation sets. The data storage will be in the form of TFRecords files, which hold serialized Example objects.

The `write_tfrecords` function (shown below) writes the data from a given DataFrame into a TFRecords file. It uses the `create_example` function from the previous chapter to convert each row of the dataset into an Example object. Each Example object is then serialized and written into the TFRecords file.

Write serialized Example objects into TFRecords files.

Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Writing TFRecords

Chapter Goals:

A. Writing Example data