Applied Supervised Learning with Python: Use Scikit-Learn to Build Predictive Models from Real-world Datasets and Prepare Yourself for the Future of Machine Learning

Description

Explore the exciting world of machine learning with the fastest growing technology in the world Key Features Understand various machine learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into how you can use machine learning in everyday life Book Description Machine learning--the ability of a machine to give right answers based on input data--has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit--from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learn Understand the concept of supervised learning and its applications Implement common supervised learning algorithms using machine learning Python libraries Validate models using the k-fold technique Build your models with decision trees to get results effortlessly Use ensemble modeling techniques to improve the performance of your model Apply a variety of metrics to compare machine learning models Who this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries. Cover FM Copyright Table of Contents Preface Chapter 1: Python Machine Learning Toolkit Introduction Supervised Machine Learning When to Use Supervised Learning Why Python? Jupyter Notebooks Exercise 1: Launching a Jupyter Notebook Exercise 2: Hello World Exercise 3: Order of Execution in a Jupyter Notebook Exercise 4: Advantages of Jupyter Notebooks Python Packages and Modules pandas Loading Data in pandas Exercise 5: Loading and Summarizing the Titanic Dataset Exercise 6: Indexing and Selecting Data Exercise 7: Advanced Indexing and Selection pandas Methods Exercise 8: Splitting, Applying, and Combining Data Sources Lambda Functions Exercise 9: Lambda Functions Data Quality Considerations Managing Missing Data Class Imbalance Low Sample Size Activity 1: pandas Functions Summary Chapter 2: Exploratory Data Analysis and Visualization Introduction Exploratory Data Analysis (EDA) Exercise 10: Importing Libraries for Data Exploration Summary Statistics and Central Values Standard Deviation Percentiles Exercise 11: Summary Statistics of Our Dataset Missing Values Finding Missing Values Exercise 12: Visualizing Missing Values Imputation Strategies for Missing Values Exercise 13: Imputation Using pandas Exercise 14: Imputation Using scikit-learn Exercise 15: Imputation Using Inferred Values Activity 2: Summary Statistics and Missing Values Distribution of Values Target Variable Exercise 16: Plotting a Bar Chart Categorical Data Exercise 17: Datatypes for Categorical Variables Exercise 18: Calculating Category Value Counts Exercise 19: Plotting a Pie Chart Continuous Data Exercise 20: Plotting a Histogram Exercise 21: Skew and Kurtosis Activity 3: Visually Representing the Distribution of Values Relationships within the Data Relationship between Two Continuous Variables Exercise 22: Plotting a Scatter Plot Exercise 23: Correlation Heatmap Exercise 24: Pairplot Relationship between a Continuous and a Categorical Variable Exercise 25: Bar Chart Exercise 26: Box Plot Relationship between Two Categorical Variables Exercise 27: Stacked Bar Chart Activity 4: Relationships Within the Data Summary Chapter 3: Regression Analysis Introduction Regression and Classification Problems Data, Models, Training, and Evaluation Linear Regression Exercise 28: Plotting Data with a Moving Average Activity 5: Plotting Data with a Moving Average Least Squares Method The scikit-learn Model API Exercise 29: Fitting a Linear Model Using the Least Squares Method Activity 6: Linear Regression Using the Least Squares Method Linear Regression with Dummy Variables Exercise 30: Introducing Dummy Variables Activity 7: Dummy Variables Parabolic Model with Linear Regression Exercise 31: Parabolic Models with Linear Regression Activity 8: Other Model Types with Linear Regression Generic Model Training Gradient Descent Exercise 32: Linear Regression with Gradient Descent Exercise 33: Optimizing Gradient Descent Activity 9: Gradient Descent Multiple Linear Regression Exercise 34: Multiple Linear Regression Autoregression Models Exercise 35: Creating an Autoregression Model Activity 10: Autoregressors Summary Chapter 4: Classification Introduction Linear Regression as a Classifier Exercise 36: Linear Regression as a Classifier Logistic Regression Exercise 37: Logistic Regression as a Classifier – Two-Class Classifier Exercise 38: Logistic Regression – Multiclass Classifier Activity 11: Linear Regression Classifier – Two-Class Classifier Activity 12: Iris Classification Using Logistic Regression Classification Using K-Nearest Neighbors Exercise 39: K-NN Classification Exercise 40: Visualizing K-NN Boundaries Activity 13: K-NN Multiclass Classifier Classification Using Decision Trees Exercise 41: ID3 Classification Exercise 42: Iris Classification Using a CART Decision Tree Summary Chapter 5: Ensemble Modeling Introduction Exercise 43: Importing Modules and Preparing the Dataset Overfitting and Underfitting Underfitting Overfitting Overcoming the Problem of Underfitting and Overfitting Bagging Bootstrapping Bootstrap Aggregation Exercise 44: Using the Bagging Classifier Random Forest Exercise 45: Building the Ensemble Model Using Random Forest Boosting Adaptive Boosting Exercise 46: Adaptive Boosting Gradient Boosting Exercise 47: GradientBoostingClassifier Stacking Exercise 48: Building a Stacked Model Activity 14: Stacking with Standalone and Ensemble Algorithms Summary Chapter 6: Model Evaluation Introduction Exercise 49: Importing the Modules and Preparing Our Dataset Evaluation Metrics Regression Exercise 50: Regression Metrics Classification Exercise 51: Classification Metrics Splitting the Dataset Hold-out Data K-Fold Cross-Validation Sampling Exercise 52: K-Fold Cross-Validation with Stratified Sampling Performance Improvement Tactics Variation in Train and Test Error Hyperparameter Tuning Exercise 53: Hyperparameter Tuning with Random Search Feature Importance Exercise 54: Feature Importance Using Random Forest Activity 15: Final Test Project Summary Appendix Index