Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Index

Training the baseline model


As you know, we have selected the RandomForestRegressor algorithm. We will be using the scikit-learn library to train the model. These are the steps we need to follow:

  1. Splitting the training and testing dataset

  2. Splitting prediction labels for the training and testing dataset

  3. Converting sentiment scores into the numpy array

  4. Training the ML model

So, let's implement each of these steps one by one.

Splitting the training and testing dataset

We have 10 years of data values. So for training purposes, we will be using 8 years of the data, which means the dataset from 2007 to 2014. For testing purposes, we will be using 2 years of the data, which means data from 2015 and 2016. You can refer to the code snippet in the following screenshot to implement this:

Figure 2.22: Splitting the training and testing dataset

As you can see from the preceding screenshot, our training dataset has been stored in the train dataframe and our testing dataset has been stored in the test dataframe...