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:
- Find the rows that contain missing values for `'CPI'` and `'Unemployment'`
- Fill in the missing values using previous row values


# A. Finding the missing values

We previously noted that both the `'CPI'` and `'Unemployment'` features contain 585 missing values. We'll find the row indexes containing these missing values by first converting the feature columns in the `na_values` boolean DataFrame to integers, i.e. 0 and 1.

We then use the `nonzero` function to find the locations of the 1's, which correspond to the `True` values.

import numpy as np  # NumPy library

na_cpi_int = na_values['CPI'].astype(int)
na_indexes_cpi = na_cpi_int.to_numpy().nonzero()[0]
na_une_int = na_values['Unemployment'].astype(int)
na_indexes_une = na_une_int.to_numpy().nonzero()[0]

print(np.array_equal(na_indexes_cpi, na_indexes_une))

The row indexes are located in the `na_indexes_cpi` and `na_indexes_une` NumPy arrays, which you can see contain the exact same row indexes (sorted in ascending order). Now let's take a closer look at the exact rows that contain the missing values.


Fill in missing data for features that only have a few missing values.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Filling In Data

Chapter Goals:

A. Finding the missing values