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ARTICLE

From ASP.NET Core in Action, Second Edition by Andrew Lock

This article discusses the advantages of ASP.NET Core and why you should (or shouldn’t) choose to use it for your application.

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When to choose ASP.NET Core

This article assumes that you have a general grasp of what ASP.NET …


ARTICLE

From Fighting Churn with Data by Carl Gold

This article discusses using machine learning algorithms to forecast churn risks.

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Continued from Part 1, in which the XGBoost machine learning model and how it can be used to predict churn is presented. Below, listing 1 shows XGBoost cross-validation for regression.

Listing 1. XGBoost cross-validation

import pandas as pd
import pickle
from sklearn.model_selection import GridSearchCV, TimeSeriesSplit
from sklearn.metrics import make_scorer
import xgboost as xgb #A

from listing_8_2_logistic_regression import prepare_data
from listing_9_2_top_decile_lift import calc_lift


def crossvalidate_xgb(data_set_path,n_test_split):

X,y = prepare_data(data_set_path,ext='',as_retention=False) #B
tscv = TimeSeriesSplit(n_splits=n_test_split)
score_models = {'lift': make_scorer(calc_lift, needs_proba=True), 'AUC': 'roc_auc'}

xgb_model = xgb.XGBClassifier(objective='binary:logistic') #C
test_params = { 'max_depth': [1,2,4,6], #D
'learning_rate': [0.1,0.2,0.3,0.4], #E
'n_estimators': [20,40,80,120], #F
'min_child_weight' : [3,6,9,12]} #G

gsearch = GridSearchCV(estimator=xgb_model,n_jobs=-1, scoring=score_models, #H
cv=tscv, verbose=1, return_train_score=False,
param_grid=test_params,refit='AUC') #I
gsearch.fit(X.values,y) …

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From Succeeding with AI by Veljko Krunic

This article discusses how to get practical business results from AI and how this book will help you learn how to do it.

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Guiding AI to business results

I don’t promise results from buying frameworks that solve all your problems, nor am I believer in catalogues of case studies showing you how someone else made money using AI. If you’re hoping that there are some parts of AI algorithms that you poorly understand but which will make you a ton of money, you’ll be disappointed at the end of the road. I call this approach the “rainbow and unicorns approach to AI.”[1] Instead of having a clear idea of where we want to take our business, we too often abdicate responsibility to AI and wish that some mystic part of AI would solve our problems. …


ARTICLE

From Five Lines of Code by Christian Clausen

This article delves into when a function should call a method on an object or pass it on as an argument.

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Our First Rule, Why Five Lines

To answer this question we introduce the most fundamental rule in this book: Five Lines. This is a simple rule stating that no method should have more than five lines. In this book Five Lines is the ultimate goal, because, adhering to this rule alone is a huge improvement.

Rule: Five Lines

Statement

A method should not contain more than five lines, excluding { and }. …


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From Fighting Churn with Data by Carl Gold

This article discusses using machine learning algorithms to forecast churn risks.

_____________________________________________________________

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_____________________________________________________________

Forecasting churn risk with machine learning

You can forecast churn with a regression in which predictions are made by multiplying metrics by a set of weights. You can also predict churn with other kinds of forecasting models which are collectively known as machine learning. |No official definition of what constitutes a machine learning model exists, but for the purpose of this article, I use the following:

DEFINITION A machine learning model is any predictive algorithm which has the following two characteristics: 1) the algorithm learns to make the prediction by processing sample data (as compared with making predictions with rules set by a human programmer), and 2) the algorithm isn’t the regression algorithm.


ARTICLE

From Learn Docker in a Month of Lunches by Elton Stoneman

Logging is usually the most boring part of learning a new technology, but it’s not the case with Docker. The basic principle is simple: you need to make sure your application logs are written to the standard output stream, because this is where Docker looks for them. This can be achieved in a couple of ways, which we’ll cover in this article, and then the fun begins.

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Docker has a pluggable logging framework — you need to make sure your application logs are coming out from the container, and then Docker can send them to different places. That lets you build a powerful logging model, where the application logs from all your containers are sent to a central log store with a searchable UI on top of it — all using open source components, all running in containers. …


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