Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Table of Contents (10 chapters)

Implementing logistic regression using scikit-learn

In this section, you will learn how you can implement and quickly evaluate a logistic regression model for your dataset. We will be using the same dataset that we have already cleaned and prepared for the purpose of predicting whether a particular transaction was fraudulent. In the previous chapter, we saved this dataset as fraud_detection.csv. The first step is to load this dataset into your Jupyter Notebook. This can be done by using the following code:

import pandas as pd

# Reading in the dataset

df = pd.read_csv('fraud_prediction.csv')

Splitting the data into training and test sets

The first step to building any machine learning model with scikit-learn is to...