JJRyan0

bnp-paribas-cardif-claims-management-problem-accelarating-claims-management-process

Machine learning system designed to categorise claims in order to speed up the claim denial or approval system in an accelerated automated way.

code-R-notebooks-Rshiny-apps

code-sql-scripts

credit-card-default-prediction-logistic-regression

Overview Question: How do we predict future Credit card default payments with the measurements provided? The data set provides many categorical and continous variables that allow for the opportunity to implement Machine Learning models to accurately predict future tranactional habits of customers.

gradient-boosting-machines-vs-decision-tree-demo-99.30-acc-sklearn

ibm-watson-visual-recognition-system-identifying-junction-types

Watson Image recognition system built to identify road junction types based on road characteristics and markings from a large number of traffic incident images to help the assessment of automated digital motor claims.

outlier-detection-algorithm-isolation-forests

BNP Paribas Kaggle Data Set Data source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management Outlier Detection- Ensemble unsupervised learning method - Isolation Forest The isolation algorithm is an unsupervised machine learning method used to detect abnormal anomalies in data such as outliers. This is once again a randomized & recursive partition of the training data in a tree structure. The number of sub samples and tree size is specified and tuned appropriately. The distance to the outlier is averaged calculating an anomaly detection score: 1 = outlier 0 = close to zero are normal data.

Predicting-GoSales-Transactions-with-Logistic-Regression

Question: How do we predict future GoSales transactions of outdoor equipment with the measurements provided? The data set provides many categorical and continous variables that allow for the opportunity to implement Machine Learning models to accurately predict future purchasing habits of customers.

JJRyan0/outlier-detection-algorithm-isolation-forests

BNP Paribas Kaggle Data Set Data source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management Outlier Detection- Ensemble unsupervised learning method - Isolation Forest The isolation algorithm is an unsupervised machine learning method used to detect abnormal anomalies in data such as outliers. This is once again a randomized & recursive partition of the training data in a tree structure. The number of sub samples and tree size is specified and tuned appropriately. The distance to the outlier is averaged calculating an anomaly detection score: 1 = outlier 0 = close to zero are normal data.

JJRyan0/Predicting-GoSales-Transactions-with-Logistic-Regression

Question: How do we predict future GoSales transactions of outdoor equipment with the measurements provided? The data set provides many categorical and continous variables that allow for the opportunity to implement Machine Learning models to accurately predict future purchasing habits of customers.

JJRyan0/bnp-paribas-cardif-claims-management-problem-accelarating-claims-management-process

Machine learning system designed to categorise claims in order to speed up the claim denial or approval system in an accelerated automated way.

JJRyan0/code-R-notebooks-Rshiny-apps

JJRyan0/code-sql-scripts

JJRyan0/credit-card-default-prediction-logistic-regression

Overview Question: How do we predict future Credit card default payments with the measurements provided? The data set provides many categorical and continous variables that allow for the opportunity to implement Machine Learning models to accurately predict future tranactional habits of customers.

JJRyan0/ibm-watson-visual-recognition-system-identifying-junction-types

Watson Image recognition system built to identify road junction types based on road characteristics and markings from a large number of traffic incident images to help the assessment of automated digital motor claims.

JJRyan0/john-python-jupyter-notebooks

JJRyan0/network-graphs-clustering-for-community-detection

R script to create a simple network, force network graph with a cluster edge betweeness & Progating Labels algorithm for imlementation of group/community detection

JJRyan0/gradient-boosting-machines-vs-decision-tree-demo-99.30-acc-sklearn

JJRyan0/Apache-Spark-MLlib---Extracting-Text-Features-for-TF-IDF

This tutorial brings you trough the most import fundamentals of running spark programmes. When begining every pyspark session the following process is required: Import and create RDDs from external data sources. Transform - develop new RDDs using transformations such as filter(). persist - possible RDDs that will need further use later need to be persisted a. Actions - Initiate parellel computation executed by Spark using take(), count() etc..

JJRyan0/apache-spark-pyspark-extracting-text-features-for-tf-idf

This tutorial brings you trough the most import fundamentals of running spark programmes. When begining every pyspark session the following process is required: Import and create RDDs from external data sources. Transform - develop new RDDs using transformations such as filter(). persist - possible RDDs that will need further use later need to be persisted a. Actions - Initiate parellel computation executed by Spark using take(), count() etc..

JJRyan0/Apache-Spark-with-Python-Running-SQL-Queries-on-Spark-DataFrames

This notebook is designed to introduce some basic concepts and help get you familiar with using Spark in Python. In this notebook, we will load and explore the titanic dataset. Specifically, this tutorial covers: Loading data in memory Creating SQLContext Creating Spark DataFrame Group data by columns Operating on columns Running SQL Queries from a Spark DataFrame

JJRyan0/bnp-paribas-cardif-claims-management-problem-accelarating-claims-management-process-update

JJRyan0/business-analyst-projects

JJRyan0/code-java

Sample Java code

JJRyan0/code-py-scripts

JJRyan0/eclipse-birt-report

JJRyan0/h2o-3

Open Source Fast Scalable Machine Learning API For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles...)

JJRyan0/jasper-soft-reports

JJRyan0/jjryan0

JJRyan0/linux-cmd

JJRyan0/sci-kit-learn-image-classification-predicting-digits-stocastic-gradient-descent-sgd

1. Preprocessing data, creating a simple stocastic gradient descent binary classifier and performing K-fold Cross Validation.

JJRyan0/vehicle-purchasing-insights-linear-regression-random-forest-regression

JJRyan0/wave-energy-flux-prediction-system

JJRyan0/wisconson-breast-cancer-diagnosis

For the purpose of this analysis we will look to machine learning as a method to predict diagnosis of cancers. Question: How do we predict the cancer status of a patient given their health measurements?