Implementating an options trading web application Making predictions using the trained modelĭeveloping an options trading web app using Q-learning Reinforcement versus supervised and unsupervised learning Step 6 - Build an ALS user product matrixĬhapter 7: Options Trading Using Q-learning and Scala Play Framework Step 5 - Prepare the data for building the recommendation model using ALS Step 4 - Prepare training and test rating data and check the counts Step 3 - Explore and query for related statistics Step 2 - Register both DataFrames as temp tables to make querying easier
#How to unzip file mac 10.7.5 izip movie
Step 1 - Import packages, load, parse, and explore the movie and rating dataset Step 1 - Importing necessary libraries and creating a Spark session Item-based collaborative filtering for movie similarity Other topic models versus the scalability of LDAĬhapter 6: Developing Model-based Movie Recommendation Engines Step 8 - Measuring the likelihood of two documents Step 3 - Instantiate the LDA model before training Step 2 - Creating vocabulary and tokens count to train the LDA after text pre-processing Topic modeling with Spark MLlib and Stanford NLP Using random forest for ethnicity predictionĬhapter 5: Topic Modeling - A Better Insight into Large-Scale Texts Spark-based K-means for population-scale clusteringĭetermining the number of optimal clusters Population scale clustering and geographic ethnicityġ000 Genomes Projects dataset descriptionĪDAM for large-scale genomics data processingĭata pre-processing and feature engineering Predicting prices and evaluating the modelĭemo prediction using Scala Play frameworkĬhapter 4: Population-Scale Clustering and Ethnicity Prediction Real-time data through the Cryptocompare API
#How to unzip file mac 10.7.5 izip series
Transformation of historical data into a time series
Historical and live-price data collection High-level data pipeline of the prototype State-of-the-art automated trading of Bitcoin
Why do we perform churn analysis, and how do we do it?Įxploratory analysis and feature engineeringĬhapter 3: High Frequency Bitcoin Price Prediction from Historical and Live Dataīitcoin, cryptocurrency, and online trading Spark-based model deployment for large-scale datasetĬhapter 2: Analyzing and Predicting Telecommunication Churn Random Forest for classification and regressionĬomparative analysis and model deployment GBT regressor for predicting insurance severity claimsīoosting the performance using random forest regressor LR for predicting insurance severity claimsĭeveloping insurance severity claims predictive model using LR Hyperparameter tuning and cross-validationĪnalyzing and predicting insurance severity claims Chapter 1: Analyzing Insurance Severity Claims