#zillow api python

[martinsage24]

API Endpoint

Do you know what is API endpoint? It is one particular part where two different APIs meet and execute the API functionality for the end user. It leads you to better and effective purposes of an app. Ensure to give it a progressive direction with the help of APIs and high on demand plugins.

[paulowens72]

API Call

Have you ever noticed a food or online apparel app offering you the facility to call the service provider or customer right from the app? Well it is because of the API call that lets you call either the customer or service provider right from the app itself. Not only this, it also offers series of record keeping benefits helping the app user in many ways.

[andysummer84]

Python Google Finance API

Different app types offer different purposes. As a result, the process to develop different apps is unique. For instance python google finance API works when prepared using python and other supporting programming languages.  Ensure that you have ample knowledge about different languages to excel it and see how it works the best.

[patrickamos71]

Android App With Javascript

These days developing an app or solution require developers to get through all possible factors contributing in the final impact of the app. That's where pro of this industry suggest going for android app with javascript which strengthens the app quality to a considerable extent.

[iwebscrapingblogs]

Real Estate Web Scraping | Scrape Data From Real Estate Website

In the digital age, data is king, and nowhere is this more evident than in the real estate industry. With vast amounts of information available online, web scraping has emerged as a powerful tool for extracting valuable data from real estate websites. Whether you're an investor looking to gain insights into market trends, a real estate agent seeking to expand your property listings, or a developer building a property analysis tool, web scraping can provide you with the data you need. In this blog, we'll explore the fundamentals of web scraping in real estate, its benefits, and how to get started.

What is Web Scraping? Web scraping is the automated process of extracting data from websites. It involves using software to navigate web pages and collect specific pieces of information. This data can include anything from property prices and descriptions to images and location details. The scraped data can then be analyzed or used to populate databases, allowing for a comprehensive view of the real estate landscape.

Benefits of Web Scraping in Real Estate Market Analysis: Web scraping allows investors and analysts to gather up-to-date data on property prices, rental rates, and market trends. By collecting and analyzing this information, you can make informed decisions about where to buy, sell, or invest.

Competitive Intelligence: Real estate agents and brokers can use web scraping to monitor competitors' listings. This helps in understanding the competitive landscape and adjusting marketing strategies accordingly.

Property Aggregation: For websites and apps that aggregate property listings, web scraping is essential. It enables them to pull data from multiple sources and provide users with a wide selection of properties to choose from.

Automated Updates: Web scraping can be used to keep databases and listings up-to-date automatically. This is particularly useful for platforms that need to provide users with the latest information on available properties.

Detailed Insights: By scraping detailed property information such as square footage, amenities, and neighborhood details, developers and analysts can provide more nuanced insights and improve their decision-making processes.

Getting Started with Real Estate Web Scraping Step 1: Identify the Target Website Start by choosing the real estate websites you want to scrape. Popular choices include Zillow, Realtor.com, and Redfin. Each website has its own structure, so understanding how data is presented is crucial. Look for listings pages, property details pages, and any relevant metadata.

Step 2: Understand the Legal and Ethical Considerations Before diving into web scraping, it's important to understand the legal and ethical implications. Many websites have terms of service that prohibit scraping, and violating these can lead to legal consequences. Always check the website’s robots.txt file, which provides guidance on what is permissible. Consider using APIs provided by the websites as an alternative when available.

Step 3: Choose Your Tools Web scraping can be performed using various tools and programming languages. Popular choices include:

BeautifulSoup: A Python library for parsing HTML and XML documents. It’s great for beginners due to its ease of use. Scrapy: An open-source Python framework specifically for web scraping. It's powerful and suitable for more complex scraping tasks. Selenium: A tool for automating web browsers. It’s useful when you need to scrape dynamic content that requires interaction with the webpage. Step 4: Develop Your Scraping Script Once you have your tools ready, the next step is to write a script that will perform the scraping. Here’s a basic outline of what this script might do:

Send a Request: Use a tool like requests in Python to send an HTTP request to the target website and retrieve the page content. Parse the HTML: Use BeautifulSoup or another parser to extract specific data from the HTML. This might include property prices, addresses, descriptions, and images. Store the Data: Save the extracted data in a structured format such as CSV or a database for further analysis. Step 5: Handle Dynamic Content and Pagination Many modern websites load content dynamically using JavaScript, or they may paginate their listings across multiple pages. This requires handling JavaScript-rendered content and iterating through multiple pages to collect all relevant data.

For Dynamic Content: Use Selenium or a headless browser like Puppeteer to render the page and extract the dynamic content. For Pagination: Identify the pattern in the URL for paginated pages or look for pagination controls within the HTML. Write a loop in your script to navigate through all pages and scrape the data. Step 6: Clean and Analyze the Data After collecting the data, it’s essential to clean and normalize it. Remove duplicates, handle missing values, and ensure consistency in the data format. Tools like pandas in Python can be incredibly helpful for this step. Once the data is clean, you can begin analyzing it to uncover trends, insights, and opportunities.

[kaylabrooke82]

Zillow API

Those who have been considering and always thinking to have a different and conceptual mark in the major identification measures of API processes should be familiar with how a zillow API processes and works in the long term. This comes with a new functionality feature and other such resources which make it easier to handle API requests.

[theresawelchy]

Reviewing 2018 and Previewing 2019

TLDR

Kaggle ended 2018 with 2.5MM members, up from 1.4M at the end of 2017 and 840K when we were acquired in March 2017.

We had 1.55MM logged-in-users visit Kaggle in 2018, up 73% from 895K in 2017.

In 2019, we aim to grow the community passed 4MM members.

  Kaggle Kernels

Kaggle Kernels is our hosted data science environment. It allows our users to author, execute, and share code written in Python and R.

Kaggle Kernels entered 2018 as a data science scratchpad. In 2018, we added key pieces of functionality that make it a powerful environment. This includes the ability to use a GPU backend and collaborate with other users.  

We had 346K users author kernels in 2018, up 3.1x from 111K in 2017.

Some of the most upvoted kernels from this year were:

A notebook that explore which embeddings are most powerful in a competition for Quora to detect insincere questions

A kernel that compiles many of the top performing solutions to Kaggle competitions

An introduction to time series methods

  Datasets

Kaggle’s datasets platform allows our community to share datasets with each other. We currently have ~14K datasets that have been shared publicly by our community.

We entered the year only supporting public datasets, which limited the use cases for our datasets. In 2018, we added the ability for datasets to be kept private or shared with collaborators. This makes Kaggle a good destination for projects that aren’t intended to be publicly shared. We had 78K private datasets upload in 2018.

We had 11K public datasets uploaded to Kaggle in 2018, up from 3.4K 2017. 731K users downloaded datasets in 2018, up 2.2x from 335K downloaded in 2017.

Some of the most downloaded datasets from this year include:

A rich dataset on Google Play Store Apps, including metadata (e.g. ratings, genre) and app reviews.

A rich gun violence dataset, which includes date, location, number of injuries and fatalities

Historical data on FIFA World Cups, including player and match data going back to the 1930s.

  Competitions

Machine learning competitions were Kaggle’s first product. Companies and researchers post machine learning problems and our community competes to build the most accurate algorithm.

We launched 52 competitions in 2018, up from 38 in 2017. We had 181K users make submissions, up 48% from 122K in 2017.

One of the most exciting competitions of 2018 was the second round of the $1.15MM Zillow Prize to improve the Zestimate home valuation algorithm.

The competitions team focused its product efforts towards support for kernels-only competitions, where users submit code rather than predictions. We launched 8 Kernels-only competitions in 2018. In 2019, we’re aiming to harden kernels-only support and use it for an increasing portion of our competitions, including targeting newer areas of AI such as reinforcement learning and GANs.

Kaggle InClass is a free version of our competitions platform that allows professors to host competitions for their students. In 2018, we hosted competitions for 2,247 classes, up from 1,217 in 2017.

We had 55K students submit to InClass competitions, up 77% from the 31K in 2017.

  Kaggle Learn

We launched Kaggle Learn in 2018. Kaggle Learn is ultra short-form data science education inside Kaggle Kernels. Kaggle Learn grew from 3 courses at launch to 11 courses by year end. 143K users did the Kaggle Learn exercises in 2018.

  Other highlights

As the amount of content on Kaggle increased dramatically in 2018, we have started putting meaningful emphasis on improving the discoverability of that content. This year, we added notifications, revamped our newsfeed and made improvements to search. Improving discoverability is going to continue to be a big theme in 2019.

We added an API to allow our users to programmatically interact with the major parts of our site.

We hosted our second annual machine learning and data science survey. With 24K responses, it is the world’s largest ML survey.

  Focus for 2019

In 2019, we will continue to grow the community, with a goal of passing 4MM members. We aim to do this by:

adding functionality that makes Kaggle Kernels and our datasets platform useful beyond learning and hobby projects; ie for real world problems.

improve discoverability of the content on Kaggle: we have a huge number of kernels and datasets that users can build off but it’s often hard for our users to find what they’re looking for

transition competitions to start to run newer competition types (kernels-only, RL and GANs related competitions)

continue to create Kaggle Learn content to bring new machine learners to Kaggle

  How You Can Help

Continue sharing your thoughts on our product, community, and platform. User feedback is invaluable in our development roadmap.

  Thanks for being here!

Team Kaggle 2018

No Free Hunch published first on No Free Hunch