loading reading lists ...
');
function adjustReadingListIcon(isInReadingList){
$readingListToggle.toggleClass("fa-plus", !isInReadingList);
$readingListToggle.toggleClass("fa-check", isInReadingList);
var tooltipMessage = isInReadingList ? "edit in reading lists" : "add to reading list";
$readingListToggle.attr("title", tooltipMessage);
$readingListToggle.attr("data-original-title", tooltipMessage);
}
$.ajax({
url: "/readingList/isInReadingList",
data: {
productId: 1538
}
}).done(function (data) {
adjustReadingListIcon(data && data.hasProductInReadingList);
}).catch(function(e){
console.log(e);
adjustReadingListIcon(false);
});
$readingListToggle.on("click", function(){
if(codePromise == null){ showToast() }
loadCode().then(function(store){
store.requestReadingListSpecificationForProduct({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId,
title: window.readingListsServerVars.title
});
ReadingLists.ReactDOM.render(
ReadingLists.React.createElement(ReadingLists.ManningOnlineReadingListModal, {
store: store,
}),
document.getElementById("reading-lists-modal")
);
}).catch(function(e){
console.log("Error loading code reading list code");
});
});
var codePromise
var readingListStore
function loadCode(){
if(codePromise) { return codePromise }
return codePromise = new Promise(function (resolve, reject){
$.getScript(window.readingListsServerVars.libraryLocation).done(function(){
hideToast()
readingListStore = new ReadingLists.ReadingListStore(
new ReadingLists.ReadingListProvider(
new ReadingLists.ReadingListWebProvider(
ReadingLists.SourceApp.marketplace,
getDeploymentType()
)
)
);
readingListStore.onReadingListChange(handleChange);
readingListStore.onReadingListModalChange(handleChange);
resolve(readingListStore);
}).catch(function(){
hideToast();
console.log("Error downloading reading lists source");
$readingListToggle.css("display", "none");
reject();
});
});
}
function handleChange(){
if(readingListStore != null) {
adjustReadingListIcon(readingListStore.isInAtLeastOneReadingList({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId
}));
}
}
var $readingListToast = $("#reading-list-toast");
function showToast(){
$readingListToast.css("display", "flex");
setTimeout(function(){
$readingListToast.addClass("shown");
}, 16);
}
function hideToast(){
$readingListToast.removeClass("shown");
setTimeout(function(){
$readingListToast.css("display", "none");
}, 150);
}
function getDeploymentType(){
switch(window.readingListsServerVars.deploymentType){
case "development":
case "test":
return ReadingLists.DeploymentType.dev;
case "qa":
return ReadingLists.DeploymentType.qa;
case "production":
return ReadingLists.DeploymentType.prod;
case "docker":
return ReadingLists.DeploymentType.docker;
default:
console.error("Unknown deployment environment, defaulting to production");
return ReadingLists.DeploymentType.prod;
}
}
});
}
});
- prerequisites
- intermediate Python • basics of scikit-learn • basics of pandas • basics of data science
- skills learned
- using machine learning to analyze and visualize location-based and time dependent data to make predictions • interactive data visualization • evaluating risks of bias and discrimination in production models
pro $24.99 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose one free eBook per month to keep
- exclusive 50% discount on all purchases
lite $19.99 per month
- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
Look inside
A big city receives thousands of daily calls to its emergency services, reporting everything from illegal parking to life-threatening emergencies. In this liveProject, you’ll take on the role of a data scientist called in to advise on how the city can better allocate resources for these safety events. Your challenge is to create a machine learning model that can predict when and where different emergencies will occur. To do this, you’ll analyze data to identify trends, build and enhance a predictive model, and make your model explainable with model interpretability tools. You’ll also perform checks to ensure that the model won’t lead to bias or discrimination, and tweak your model so it can account for major lockdown events such as a pandemic.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
liveProject mentor Hadrien Lacroix shares what he likes about the Manning liveProject platform.
features
Predicting Urban Events with scikit-learn project for free
