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 the final dataset by merging the training and features DataFrames

## A. The final dataset

For the same organizational reasons we had in merging the features and stores DataFrames, we'll now merge the training and combined features DataFrames.

Remember that the stores DataFrame contains potentially useful features listed weekly by store, and the stores DataFrame contains the type and size of each store.


# Chapter Goals:
- Create the final dataset by merging the training and features DataFrames

# A. The final dataset

For the same organizational reasons we had in merging the features and stores DataFrames, we'll now merge the training and combined features DataFrames.

Remember that the stores DataFrame contains potentially useful features listed weekly by store, and the stores DataFrame contains the type and size of each store.


Merge the main training dataset with its corresponding feature data.

Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Merging Data

Chapter Goals:

A. The final dataset