Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data

Description

Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event. Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasets Book Description Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through chapters, you will study the basic functions, data structures, and... Preface About the Book About the Authors Learning Objectives Audience Approach Minimum Hardware Requirements Software Requirements Installation and Setup Using Kaggle for Faster Experimentation Conventions Installing the Code Bundle Chapter 1 Introduction to Data Science and Data Pre-Processing Introduction Python Libraries Roadmap for Building Machine Learning Models Data Representation Independent and Target Variables Exercise 1: Loading a Sample Dataset and Creating the Feature Matrix and Target Matrix Data Cleaning Exercise 2: Removing Missing Data Exercise 3: Imputing Missing Data Exercise 4: Finding and Removing Outliers in Data Data Integration Exercise 5: Integrating Data Data Transformation Handling Categorical Data Exercise 6: Simple Replacement of Categorical Data with a Number Exercise 7: Converting Categorical Data to Numerical Data Using Label Encoding Exercise 8: Converting Categorical Data to Numerical Data Using One-Hot Encoding Data in Different Scales Exercise 9: Implementing Scaling Using the Standard Scaler Method Exercise 10: Implementing Scaling Using the MinMax Scaler Method Data Discretization Exercise 11: Discretization of Continuous Data Train and Test Data Exercise 12: Splitting Data into Train and Test Sets Activity 1: Pre-Processing Using the Bank Marketing Subscription Dataset Supervised Learning Unsupervised Learning Reinforcement Learning Performance Metrics Summary Chapter 2 Data Visualization Introduction Functional Approach Exercise 13: Functional Approach – Line Plot Exercise 14: Functional Approach – Add a Second Line to the Line Plot Activity 2: Line Plot Exercise 15: Creating a Bar Plot Activity 3: Bar Plot Exercise 16: Functional Approach – Histogram Exercise 17: Functional Approach – Box-and-Whisker plot Exercise 18: Scatterplot Object-Oriented Approach Using Subplots Exercise 19: Single Line Plot using Subplots Exercise 20: Multiple Line Plots Using Subplots Activity 4: Multiple Plot Types Using Subplots Summary Chapter 3 Introduction to Machine Learning via Scikit-Learn Introduction Introduction to Linear and Logistic Regression Simple Linear Regression Exercise 21: Preparing Data for a Linear Regression Model Exercise 22: Fitting a Simple Linear Regression Model and Determining the Intercept and Coefficient Exercise 23: Generating Predictions and Evaluating the Performance of a Simple Linear Regression Model Multiple Linear Regression Exercise 24: Fitting a Multiple Linear Regression Model and Determining the Intercept and Coefficients Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model Logistic Regression Exercise 25: Fitting a Logistic Regression Model and Determining the Intercept and Coefficients Exercise 26: Generating Predictions and Evaluating the Performance of a Logistic Regression Model Exercise 27: Tuning the Hyperparameters of a Multiple Logistic Regression Model Activity 6: Generating Predictions and Evaluating Performance of a Tuned Logistic Regression Model Max Margin Classification Using SVMs Exercise 28: Preparing Data for the Support Vector Classifier (SVC) Model Exercise 29: Tuning the SVC Model Using Grid Search Activity 7: Generating Predictions and Evaluating the Performance of the SVC Grid Search Model Decision Trees Activity 8: Preparing Data for a Decision Tree Classifier Exercise 30: Tuning a Decision Tree Classifier Using Grid Search Exercise 31: Programmatically Extracting Tuned Hyperparameters from a Decision Tree Classifier Grid Search Model Activity 9: Generating Predictions and Evaluating the Performance of a Decision Tree Classifier Model Random Forests Exercise 32: Preparing Data for a Random Forest Regressor Activity 10: Tuning a Random Forest Regressor Exercise 33: Programmatically Extracting Tuned Hyperparameters and Determining Feature Importance from a Random Forest Regressor Grid Search Model Activity 11: Generating Predictions and Evaluating the Performance of a Tuned Random Forest Regressor Model Summary Chapter 4 Dimensionality Reduction and Unsupervised Learning Introduction Hierarchical Cluster Analysis (HCA) Exercise 34: Building an HCA Model Exercise 35: Plotting an HCA Model and Assigning Predictions K-means Clustering Exercise 36: Fitting k-means Model and Assigning Predictions Activity 12: Ensemble k-means Clustering and Calculating Predictions Exercise 37: Calculating Mean Inertia by n_clusters Exercise 38: Plotting Mean Inertia by n_clusters Principal Component Analysis (PCA) Exercise 39: Fitting a PCA Model Exercise 40: Choosing n_components using Threshold of Explained Variance Activity 13: Evaluating Mean Inertia by Cluster after PCA Transformation Exercise 41: Visual Comparison of Inertia by n_clusters Supervised Data Compression using Linear Discriminant Analysis (LDA) Exercise 42: Fitting LDA Model Exercise 43: Using LDA Transformed Components in Classification Model Summary Chapter 5 Mastering Structured Data Introduction Boosting Algorithms Gradient Boosting Machine (GBM) XGBoost (Extreme Gradient Boosting) Exercise 44: Using the XGBoost library to Perform Classification XGBoost Library Controlling Model Overfitting Handling Imbalanced Datasets Activity 14: Training and Predicting the Income of a Person External Memory Usage Cross-validation Exercise 45: Using Cross-validation to Find the Best Hyperparameters Saving and Loading a Model Exercise 46: Creating a Python Pcript that Predicts Based on Real-time Input Activity 15: Predicting the Loss of Customers Neural Networks What Is a Neural Network? Optimization Algorithms Hyperparameters Keras Exercise 47: Installing the Keras library for Python and Using it to Perform Classification Keras Library Exercise 48: Predicting Avocado Price Using Neural Networks Categorical Variables One-hot Encoding Entity Embedding Exercise 49: Predicting Avocado Price Using Entity Embedding Activity 16: Predicting a Customer's Purchase Amount Summary Chapter 6 Decoding Images Introduction Images Exercise 50: Classify MNIST Using a Fully Connected Neural Network Convolutional Neural Networks Convolutional Layer Pooling Layer Adam Optimizer Cross-entropy Loss Exercise 51: Classify MNIST Using a CNN Regularization Dropout Layer L1 and L2 Regularization Batch Normalization Exercise 52: Improving Image Classification Using Regularization Using CIFAR-10 images Image Data Preprocessing Normalization Converting to Grayscale Getting All Images to the Same Size Other Useful Image Operations Activity 17: Predict if an Image Is of a Cat or a Dog Data Augmentation Generators Exercise 53: Classify CIFAR-10 Images with Image Augmentation Activity 18: Identifying and Augmenting an Image Summary Chapter 7 Processing Human Language Introduction Text Data Processing Regular Expressions Exercise 54: Using RegEx for String Cleaning Basic Feature Extraction Text Preprocessing Exercise 55: Preprocessing the IMDB Movie Review Dataset Text Processing Exercise 56: Creating Word Embeddings Using Gensim Activity 19: Predicting Sentiments of Movie Reviews Recurrent Neural Networks (RNNs) LSTMs Exercise 57: Performing Sentiment Analysis Using LSTM Activity 20: Predicting Sentiments from Tweets Summary Chapter 8 Tips and Tricks of the Trade Introduction Transfer Learning Transfer Learning for Image Data Exercise 58: Using InceptionV3 to Compare and Classify Images Activity 21: Classifying Images using InceptionV3 Useful Tools and Tips Train, Development, and Test Datasets Working with Unprocessed Datasets pandas Profiling TensorBoard AutoML Exercise 59: Get a Well-Performing Network Using Auto-Keras Model Visualization Using Keras Activity 22: Using Transfer Learning to Predict Images Summary Appendix Chapter 1: Introduction to Data Science and Data Preprocessing Activity 1: Pre-Processing Using the Bank Marketing Subscription Dataset Chapter 2: Data Visualization Activity 2: Line Plot Activity 3: Bar Plot Activity 4: Multiple Plot Types Using Subplots Chapter 3: Introduction to Machine Learning via Scikit-Learn Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model Activity 6: Generating Predictions and Evaluating Performance of a Tuned Logistic Regression Model Activity 7: Generating Predictions and Evaluating the Performance of the SVC Grid Search Model Activity 8: Preparing Data for a Decision Tree Classifier Activity 9: Generating Predictions and Evaluating the Performance of a Decision Tree Classifier Model Activity 10: Tuning a Random Forest Regressor Activity 11: Generating Predictions and Evaluating the Performance of a Tuned Random Forest Regressor Model Chapter 4: Dimensionality Reduction and Unsupervised Learning Activity 12: Ensemble k-means Clustering and Calculating Predictions Activity 13: Evaluating Mean Inertia by Cluster after PCA Transformation Chapter 5: Mastering Structured Data Activity 14: Training and Predicting the Income of a Person Activity 15: Predicting the Loss of Customers Activity 16: Predicting a Customer's Purchase Amount Chapter 6: Decoding Images Activity 17: Predict if an Image Is of a Cat or a Dog Activity 18: Identifying and Augmenting an Image Chapter 7: Processing Human Language Activity 19: Predicting Sentiments of Movie Reviews Activity 20: Predicting Sentiments from Tweets Chapter 8: Tips and Tricks of the Trade Activity 21: Classifying Images using InceptionV3 Activity 22: Using Transfer Learning to Predict Images