Machine Learning and Data Science Blueprints for Finance
- Autorzy:
- Hariom Tatsat, Sahil Puri, Brad Lookabaugh
- Ocena:
- Bądź pierwszym, który oceni tę książkę
- Stron:
- 432
- Dostępne formaty:
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ePubMobi
Opis ebooka: Machine Learning and Data Science Blueprints for Finance
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).
Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.
This book covers:
- Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
- Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
- Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
- Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
- Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
- NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
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Szczegóły ebooka
- ISBN Ebooka:
- 978-14-920-7300-0, 9781492073000
- Data wydania ebooka:
- 2020-10-01 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
- Język publikacji:
- angielski
- Rozmiar pliku ePub:
- 10.6MB
- Rozmiar pliku Mobi:
- 24.8MB
Spis treści ebooka
- Preface
- Who This Book Is For
- How This Book Is Organized
- Part I: The Framework
- Part II: Supervised Learning
- Part III: Unsupervised Learning
- Part IV: Reinforcement Learning and Natural Language Processing
- Conventions Used in This Book
- Using Code Presented in the Book
- Python Libraries
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
- Special Thanks from Hariom
- Special Thanks from Sahil
- Special Thanks from Brad
- I. The Framework
- 1. Machine Learning in Finance:
The Landscape
- Current and Future Machine Learning Applications
in Finance
- Algorithmic Trading
- Portfolio Management and Robo-Advisors
- Fraud Detection
- Loans/Credit Card/Insurance Underwriting
- Automation and Chatbots
- Risk Management
- Asset Price Prediction
- Derivative Pricing
- Sentiment Analysis
- Trade Settlement
- Money Laundering
- Current and Future Machine Learning Applications
in Finance
- Machine Learning, Deep Learning, Artificial Intelligence, and Data Science
- Machine Learning Types
- Supervised
- Classification
- Regression
- Supervised
- Unsupervised
- Dimensionality reduction
- Clustering
- Reinforcement Learning
- Natural Language Processing
- Chapter Summary
- 2. Developing a Machine Learning
Model in Python
- Why Python?
- Python Packages for Machine Learning
- Python and Package Installation
- Steps for Model Development in Python Ecosystem
- Model Development Blueprint
- 1. Problem definition
- 2. Loading the data and packages
- 2.1. Load libraries
- 2.2. Load data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Model Development Blueprint
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Feature selection
- 4.3. Data transformation
- 5. Evaluate models
- 5.1. Training and test split
- 5.2. Identify evaluation metrics
- 5.3. Compare models and algorithms
- 6. Model tuning
- 7. Finalize the model
- 7.1. Performance on the test set
- 7.2. Model/variable intuition
- 7.3. Save/deploy
- Chapter Summary
- 3. Artificial Neural Networks
- ANNs: Architecture, Training, and Hyperparameters
- Architecture
- Neurons
- Layers
- Input layer
- Hidden layers
- Output layer
- Neuron weights
- Architecture
- ANNs: Architecture, Training, and Hyperparameters
- Training
- Forward propagation
- Backpropagation
- Hyperparameters
- Number of hidden layers and nodes
- Learning rate
- Activation functions
- Cost functions
- Optimizers
- Epoch
- Batch size
- Creating an Artificial Neural Network Model in Python
- Installing Keras and Machine Learning Packages
- Importing the packages
- Loading data
- Model constructiondefining the neural network architecture
- Compiling the model
- Fitting the model
- Evaluating the model
- Installing Keras and Machine Learning Packages
- Running an ANN Model Faster: GPU and Cloud Services
- GPU
- Cloud services such as Kaggle and Google Colab
- Chapter Summary
- II. Supervised Learning
- 4. Supervised Learning: Models and Concepts
- Supervised Learning Models: An Overview
- Linear Regression (Ordinary Least Squares)
- Implementation in Python
- Training a model
- Grid search
- Advantages and disadvantages
- Linear Regression (Ordinary Least Squares)
- Regularized Regression
- Logistic Regression
- Hyperparameters
- Advantages and disadvantages
- Supervised Learning Models: An Overview
- Support Vector Machine
- Hyperparameters
- Advantages and disadvantages
- K-Nearest Neighbors
- Hyperparameters
- Advantages and disadvantages
- Linear Discriminant Analysis
- Implementation in Python and hyperparameters
- Advantages and disadvantages
- Classification and Regression Trees
- Representation
- Learning a CART model
- Stopping criterion
- Pruning the tree
- Implementation in Python
- Hyperparameters
- Advantages and disadvantages
- Ensemble Models
- Random forest
- Implementation in Python
- Hyperparameters
- Advantages and disadvantages
- Extra trees
- Implementation in Python
- Adaptive Boosting (AdaBoost)
- Implementation in Python
- Hyperparameters
- Advantages and disadvantages
- Gradient boosting method
- Implementation in Python and hyperparameters
- Advantages and disadvantages
- ANN-Based Models
- ANN using sklearn
- Hyperparameters
- Deep neural network
- Advantages and disadvantages
- Using ANNs for supervised learning in finance
- Model Performance
- Overfitting and Underfitting
- Cross Validation
- Evaluation Metrics
- Mean absolute error
- Mean squared error
- R metric
- Adjusted R metric
- Selecting an evaluation metric for supervised regression
- Classification
- Accuracy
- Precision
- Recall
- Area under ROC curve
- Confusion matrix
- Selecting an evaluation metric for supervised classification
- Model Selection
- Factors for Model Selection
- Model Trade-off
- Chapter Summary
- 5. Supervised Learning: Regression
(Including Time Series Models)
- Time Series Models
- Time Series Breakdown
- Autocorrelation and Stationarity
- Autocorrelation
- Stationarity
- Differencing
- Traditional Time Series Models (Including the ARIMA Model)
- ARIMA
- Time Series Models
- Deep Learning Approach to Time Series Modeling
- RNNs
- Long short-term memory
- Modifying Time Series Data for Supervised Learning Models
- Case Study 1: Stock Price Prediction
- Blueprint for Using Supervised Learning Models to Predict a Stock Price
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- 3.3. Time series analysis
- Blueprint for Using Supervised Learning Models to Predict a Stock Price
- 4. Data preparation
- 5. Evaluate models
- 5.1. Train-test split and evaluation metrics
- 5.2. Test options and evaluation metrics
- 5.3. Compare models and algorithms
- 5.3.1. Machine learning models from Scikit-learn
- 5.3.2. Time seriesbased models: ARIMA and LSTM
- 6. Model tuning and grid search
- 7. Finalize the model
- 7.1. Results on the test dataset
- Conclusion
- Case Study 2: Derivative Pricing
- Blueprint for Developing a Machine Learning Model for Derivative Pricing
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Defining functions and parameters
- Volatility and option pricing functions
- 2.3. Data generation
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Blueprint for Developing a Machine Learning Model for Derivative Pricing
- 4. Data preparation and analysis
- 4.1. Univariate feature selection
- 5. Evaluate models
- 5.1. Train-test split and evaluation metrics
- 5.2. Compare models and algorithms
- 6. Model tuning and finalizing the model
- 7. Additional analysis: removing the volatility data
- Conclusion
- Case Study 3: Investor Risk Tolerance and Robo-Advisors
- Blueprint for Modeling Investor Risk Tolerance and Enabling a Machine LearningBased Robo-Advisor
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Data preparation and feature selection
- 3.1. Preparing the predicted variable
- 3.2. Feature selectionlimit the feature space
- 3.2.1. Feature elimination
- Blueprint for Modeling Investor Risk Tolerance and Enabling a Machine LearningBased Robo-Advisor
- 4. Evaluate models
- 4.1. Train-test split
- 4.2. Test options and evaluation metrics
- 4.3. Compare models and algorithms
- 5. Model tuning and grid search
- 6. Finalize the model
- 6.1. Results on the test dataset
- 6.2. Feature importance and features intuition
- 6.3. Save model for later use
- 7. Additional step: robo-advisor dashboard
- Input for investor characteristics
- 7.2 Asset allocation and portfolio performance
- Conclusion
- Case Study 4: Yield Curve Prediction
- Blueprint for Using Supervised Learning Models to Predict the Yield Curve
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- 4. Data preparation and analysis
- 5. Evaluate models
- 5.1. Train-test split and evaluation metrics
- 5.2. Compare models and algorithms
- Blueprint for Using Supervised Learning Models to Predict the Yield Curve
- 6. Model tuning and grid search.
- Prediction comparison
- Conclusion
- Chapter Summary
- Exercises
- 6. Supervised Learning: Classification
- Case Study 1: Fraud Detection
- Blueprint for Using Classification Models to Determine Whether a Transaction Is Fraudulent
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Blueprint for Using Classification Models to Determine Whether a Transaction Is Fraudulent
- 4. Data preparation
- 5. Evaluate models
- 5.1. Train-test split and evaluation metrics
- 5.2. Checking models
- Case Study 1: Fraud Detection
- 6. Model tuning
- 6.1. Model tuning by choosing the correct evaluation metric
- 6.2. Model tuningbalancing the sample by random under-sampling
- Conclusion
- Case Study 2: Loan Default Probability
- Blueprint for Creating a Machine Learning Model for Predicting Loan Default Probability
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Data preparation and feature selection
- 3.1. Preparing the predicted variable
- 3.2. Feature selectionlimit the feature space
- 3.2.1. Feature elimination based on significant missing values
- 3.2.2. Feature elimination based on intuitiveness
- 3.2.3. Feature elimination based on the correlation
- Blueprint for Creating a Machine Learning Model for Predicting Loan Default Probability
- 4. Feature selection and exploratory analysis
- 4.1. Feature analysis and exploration
- 4.1.1. Analyzing the categorical features
- 4.1.2. Analyzing the continuous features
- 4.2. Encoding categorical data
- 4.3. Sampling data
- 5. Evaluate algorithms and models
- 5.1. Train-test split
- 5.2. Test options and evaluation metrics
- 5.3. Compare models and algorithms
- 6. Model tuning and grid search
- 7. Finalize the model
- 7.1. Results on the test dataset
- 7.2. Variable intuition/feature importance
- Conclusion
- Case Study 3: Bitcoin Trading Strategy
- Blueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin Market
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- Blueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin Market
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Preparing the data for classification
- 4.3. Feature engineering
- 4.4. Data visualization
- 5. Evaluate algorithms and models
- 5.1. Train-test split
- 5.2. Test options and evaluation metrics
- 5.3. Compare models and algorithms
- 5.3.1. Models
- 6. Model tuning and grid search
- 7. Finalize the model
- 7.1. Results on the test dataset
- 7.2. Variable intuition/feature importance
- 7.3. Backtesting results
- Conclusion
- Chapter Summary
- Exercises
- III. Unsupervised Learning
- 7. Unsupervised Learning:
Dimensionality Reduction
- Dimensionality Reduction Techniques
- Principal Component Analysis
- Eigen decomposition
- Singular value decomposition
- Principal Component Analysis
- Kernel Principal Component Analysis
- t-distributed Stochastic Neighbor Embedding
- Dimensionality Reduction Techniques
- Case Study 1: Portfolio Management: Finding an Eigen Portfolio
- Blueprint for Using Dimensionality Reduction for Asset Allocation
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Blueprint for Using Dimensionality Reduction for Asset Allocation
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Data transformation
- 5. Evaluate algorithms and models
- 5.1. Train-test split
- 5.2. Model evaluation: applying principal component analysis
- 5.2.1. Explained variance using PCA
- 5.2.2. Looking at portfolio weights
- 5.2.3. Finding the best eigen portfolio
- 5.2.4. Backtesting the eigen portfolios
- Conclusion
- Case Study 2: Yield Curve Construction and Interest Rate Modeling
- Blueprint for Using Dimensionality Reduction to Generate a Yield Curve
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Blueprint for Using Dimensionality Reduction to Generate a Yield Curve
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Data transformation
- 5. Evaluate algorithms and models
- 5.2. Model evaluationapplying principal component analysis
- 5.2.1. Explained variance using PCA
- 5.2.2. Intuition behind the principal components
- 5.2.3. Reconstructing the curve using principal components
- Conclusion
- Case Study 3: Bitcoin Trading: Enhancing Speed and Accuracy
- Blueprint for Using Dimensionality Reduction to Enhance a Trading Strategy
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 3. Exploratory data analysis
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Preparing the data for classification
- 4.3. Feature engineering
- 4.4. Data visualization
- Blueprint for Using Dimensionality Reduction to Enhance a Trading Strategy
- 5. Evaluate algorithms and models
- 5.1. Train-test split
- 5.2. Singular value decomposition (feature reduction)
- 5.2.1. Basic visualization of reduced features
- 5.3. t-SNE visualization
- 5.4. Compare models with and without dimensionality reduction
- 5.4.1. Models
- Conclusion
- Chapter Summary
- Exercises
- 8. Unsupervised Learning: Clustering
- Clustering Techniques
- k-means Clustering
- k-means hyperparameters
- Implementation in Python
- k-means Clustering
- Hierarchical Clustering
- Implementation in Python
- Clustering Techniques
- Affinity Propagation Clustering
- Implementation in Python
- Case Study 1: Clustering for Pairs Trading
- Blueprint for Using Clustering to Select Pairs
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Blueprint for Using Clustering to Select Pairs
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Data transformation
- 5. Evaluate algorithms and models
- 5.1. k-means clustering
- 5.1.1. Finding the optimal number of clusters
- 5.1.2. Clustering and visualization
- 5.2. Hierarchical clustering (agglomerative clustering)
- 5.2.1. Building hierarchy graph/dendrogram
- 5.2.2. Clustering and visualization
- 5.3. Affinity propagation
- 5.4. Cluster evaluation
- Visualizing the return within a cluster
- 6. Pairs selection
- Cointegration and pair selection function
- Conclusion
- Case Study 2: Portfolio Management: Clustering Investors
- Blueprint for Using Clustering for Grouping Investors
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- 3.2. Data visualization
- Blueprint for Using Clustering for Grouping Investors
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Data transformation
- 5. Evaluate algorithms and models
- 5.1. k-means clustering
- 5.1.1. Finding the optimal number of clusters
- 5.1.2. Clustering and visualization
- 5.2. Affinity propagation
- 5.3. Cluster evaluation
- 6. Cluster intuition
- Conclusion
- Case Study 3: Hierarchical Risk Parity
- Blueprint for Using Clustering to Implement Hierarchical Risk Parity
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 3. Exploratory data analysis
- 4. Data preparation
- 4.1. Data cleaning
- 4.2. Data transformation
- Blueprint for Using Clustering to Implement Hierarchical Risk Parity
- 5. Evaluate algorithms and models
- 5.1. Building a hierarchy graph/dendrogram
- 5.2. Steps for hierarchical risk parity
- 5.2.1. Quasi-diagonalization
- 5.2.2. Recursive bisection
- 5.3. Comparison against other asset allocation methods
- 5.4. Getting the portfolio weights for all types of asset allocation
- 6. Backtesting
- In-sample results
- Out-of-sample results
- Conclusion
- Chapter Summary
- Exercises
- IV. Reinforcement Learning and Natural Language Processing
- 9. Reinforcement Learning
- Reinforcement LearningTheory and Concepts
- RL Components
- Policy
- Value function (and Q-value)
- Model
- RL components in a trading context
- RL Components
- RL Modeling Framework
- Bellman equations
- Markov decision processes
- Temporal difference learning
- Artificial neural network and deep learning
- Reinforcement LearningTheory and Concepts
- Reinforcement Learning Models
- Model-based algorithms
- Model-free algorithms
- Q-Learning
- SARSA
- Deep Q-Network
- Policy gradient
- Key Challenges in Reinforcement Learning
- Case Study 1: Reinforcement LearningBased Trading Strategy
- Blueprint for Creating a Reinforcement LearningBased Trading Strategy
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 4. Data preparation
- 4.1. Data cleaning
- Blueprint for Creating a Reinforcement LearningBased Trading Strategy
- 5. Evaluate algorithms and models
- 5.1. Train-test split
- 5.2. Implementation steps and modules
- Modules and functions
- 5.3. Agent class
- 5.4. Helper functions
- 5.5. Training the model
- 5.6. Model tuning
- 6. Testing the data
- Conclusion
- Case Study 2: Derivatives Hedging
- Blueprint for Implementing a Reinforcement LearningBased Hedging Strategy
- 1. Problem definition
- 2. Getting started
- 2.1. Loading the Python packages
- 2.2. Generating the data
- 3. Exploratory data analysis
- 4. Evaluate algorithms and models
- 4.1. Policy gradient script
- 4.2. Training the data
- Blueprint for Implementing a Reinforcement LearningBased Hedging Strategy
- 5. Testing the data
- 5.1. Helper functions for comparison against Black-Scholes
- 5.1.1. Black-Scholes price and delta
- 5.1.2. Test results and plotting
- 5.1.3. Hedging error for Black-Scholes replication
- 5.2. Comparison between Black-Scholes and reinforcement learning
- 5.2.1. Test at 99% risk aversion
- 5.2.2. Changing moneyness
- 5.2.3. Changing drift
- 5.2.4. Shifted volatility
- Conclusion
- Case Study 3: Portfolio Allocation
- Blueprint for Creating a Reinforcement LearningBased Algorithm for Portfolio Allocation
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Exploratory data analysis
- 3.1. Descriptive statistics
- Blueprint for Creating a Reinforcement LearningBased Algorithm for Portfolio Allocation
- 4. Evaluate algorithms and models
- 4.1. Agent and cryptocurrency environment script
- 4.3. Training the data
- 5. Testing the data
- Conclusion
- Chapter Summary
- Exercises
- 10. Natural Language Processing
- Natural Language Processing: Python Packages
- NLTK
- TextBlob
- spaCy
- Natural Language Processing: Python Packages
- Natural Language Processing: Theory and Concepts
- 1. Preprocessing
- 1.1. Tokenization
- 1.2. Stop words removal
- 1.3. Stemming
- 1.4. Lemmatization
- 1.5. PoS tagging
- 1.6. Named entity recognition
- 1.7. spaCy: All of the above steps in one go
- 1. Preprocessing
- 2. Feature Representation
- 2.1. Bag of wordsword count
- 2.2. TF-IDF
- 2.3. Word embedding
- 2.3.1. Pretrained model: Via spaCy
- 2.3.2. Pretrained model: Word2Vec using gensim package
- 3. Inference
- 3.1. Supervised learning exampleNaive Bayes
- 3.2. Unsupervised learning example: LDA
- Case Study 1: NLP and Sentiment AnalysisBased Trading Strategies
- Blueprint for Building a Trading Strategy Based on Sentiment Analysis
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 2.2. Loading the data
- 3. Data preparation
- 3.1. Preprocessing news data
- Blueprint for Building a Trading Strategy Based on Sentiment Analysis
- 4. Evaluate models for sentiment analysis
- 4.1. Predefined modelTextBlob package
- 4.2. Supervised learningclassification algorithms and LSTM
- 4.3. Unsupervisedmodel based on a financial lexicon
- 4.4. Exploratory data analysis and comparison
- 5. Models evaluationbuilding a trading strategy
- 5.1. Setting up a strategy
- 5.2. Results for individual stocks
- 5.3. Results for multiple stocks
- 5.4. Varying the strategy time period
- Conclusion
- Case Study 2: Chatbot Digital Assistant
- Blueprint for Creating a Custom Chatbot Using NLP
- 1. Problem definition
- 2. Getting startedloading the libraries
- 2.1. Load libraries
- 3. Training a default chatbot
- 4. Data preparation: Customized chatbot
- 4.1. Data construction
- 4.2. Training data
- 5. Model creation and training
- Blueprint for Creating a Custom Chatbot Using NLP
- 5.1. Model optimization function
- 5.2. Custom logic adapter
- 5.3. Model usagetraining and testing
- Conclusion
- Case Study 3: Document Summarization
- Blueprint for Using NLP for Document Summarization
- 1. Problem definition
- 2. Getting startedloading the data and Python packages
- 2.1. Loading the Python packages
- 3. Data preparation
- 4. Model construction and training
- 5. Visualization of topics
- 5.1. Topic visualization
- 5.2. Word cloud
- Blueprint for Using NLP for Document Summarization
- Conclusion
- Chapter Summary
- Exercises
- Index
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