Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

Problems

Problem 1: Cloud storage prediction cost: Our software application generates data on a monthly basis and stores this data in cloud storage, together with the data from the previous months. We are given the following bills for cloud storage, and would like to estimate the running costs for the first year of using it:

 Months using cloud storage Monthly bill in euros 1 120.00 2 131.20 3 142.10 4 152.90 5 164.30 1 to 12 ?

Problem 2: Fahrenheit and Celsius conversion: In the example that we looked at earlier in this chapter, we devised a formula for converting degrees Fahrenheit to degrees Celsius. Devise a formula converting degrees Celsius into degrees Fahrenheit.

Problem 3: Flight time duration prediction based on distance: Why do you think that a linear regression model resulted in an estimation of the speed of 1,192 km/h, as opposed to the real speed of about 850 km/h? Can you suggest a way to better model the estimation of the flight duration based on flight distances and times?

Problem 4: Bacteria...