How Web Scraping Changes Automotive Industry Market?

Web Scraping

Web scraping is the method of fetching the information from the targeted website that includes useful information, scraping it all, and saving it in the format the user requires such as CSV, or excel.

Previously, if a business wanted to extract information from the database, they would use the copy-pasting method. However, when dealing with enormous amounts of data, copy-pasting takes a long time, and when copy-pasting large amounts of data, the webpage slows down, notifying the owner of suspicious activity.

This isn't the case with web scraping. You can simply extract data without slowing down the site in only a few minutes, that's why it has become so popular. It is simple to undertake web scraping activities; all you need to do is:

• Choose the website or source you'd like to scrape.
• Choose the information you want to scrape.
• Execute the web scraping script.
• Save everything to your computer.

What is the Importance of Web Scraping for the Business Industry?

1. Extracts Appropriate Information from the Targeted Website

There are large numbers of data points available on the internet, and not all of them are useful to your company's growth. As a company, you know what is required for your brand's development, therefore web scraping can help brands achieve faster in their lead generation initiatives by obtaining only that fraction of essential data.

For example, if you wish to extract data from your competitors' websites, web scraping allows you to do so without causing the competitor's webpage to slow down or putting any brand at risk of being caught.

2. Enhancement of Brand Solutions

When a company has the correct data, which demonstrates the requirements for improving the solution, it can improve it. Because all of the data is gathered from authorities that have been monitoring it or from platforms where prospects have expressed their thoughts on their expectations for a certain product, web scraping gives accurate data.

All of this provides significant data to brands that can be used to help them develop their offering. Because brands have a better understanding of what their customers expect, it's easier to adjust to changes and plan for future changes before the new trend emerges.

3. Retaining Brand Success

A band's brand reputation can only be sustained if it continues to meet the needs of all of its customers, regardless of changing trends. With the data gathered, brands may get a sense of how their solution will be seen in a few years. It won't be an accurate prediction, but it will be a straightforward analysis.

Not only that, but brands can also research how their prospects will react to a product.

For example, if the latest product that offers a brand is great, which has great features and prices, then by utilizing web scraping, you will be able to achieve information that helps in monitoring various prospects you are looking for.

Web scraping ensures that you can fetch the data that will assist you to meet the requirements ignoring the changes and yet retaining the success crown for a longer time.

How Does Web Scraping Assist in Automotive Industry?

Using Web Scraping, the Automobile Industry benefits in various ways:

1. Staying Updated with Market Changes

If the existing automobile industry fails to embrace what its current target audience wants, the sector will never be able to grow at a faster pace. Because market conditions are constantly changing, a trend that is significant now will be irrelevant in five years.

For instance, the Pajero car was a hit in the 2000s, but now the newer options of Jeep are more in demand for many target audiences. As a result, it is critical to have market data that will aid in the development of effective solutions that will assist in serving your target audience when the new change comes. Web scraping makes this simpler.

Web scraping will extract all market-related data, allowing you to quickly keep track of what's going on. You may easily design solutions that will most likely be accepted by present clients owing to their preferences taste with regular web scraping operations. You'll be able to boost the quality of the solution as well because market conditions influence what each car type is required to do in the customer's eyes.

2. Creating a Solution with Client’s Preference

Your clients will always have various requirements. When you can fulfill those requirements, you can become bigger and better. With various options and a huge target audience, if you will be able to meet each customer’s requirement, you will get the clients that will engage with you on daily basis.

This is where web scraping comes in handy. Web scraping will collect all of the information that your target audience requires, including which model they want to buy, what they expect from the model, what colors they prefer, what features they desire, and the price range. While you have all of this knowledge, you are more likely to build a solution that many of your target audience wants to buy.

3. Enhancing Solutions via User Feedback

Automobile vendors all over the world are well conscious about how a single piece of feedback can either destroy or make them develop as a customer-centric company. Feedback is critical since it allows brands to enhance their solutions and develop a flawless product that appeals to their target audience.

However, in 2019, this move has gone in the wrong direction. Many evaluations or feedbacks are made today with the erroneous or fraudulent purpose in order to win the competition, and these needs are addressed sooner rather than later. As a result, web scraping can address both issues.

For example, Web scraping can filter through all of your target audience's input on relevant models and models that are comparable to the ones you generate. When this occurs, you can keep a careful eye on what your target audience expects from a certain model on the market.

Second, Web scraping can assist you to scrape all of the negative reviews and erasing them before your target audience sees them. Regularly conducting this will help you maintain a stronger brand identity in the public eye while also meeting the specific needs of the various target audiences on your list.

4. Keeps an Eye on Pricing

Pricing is another element that has an impact on the vehicle industry. In the automobile sector, pricing is extremely important because it provides for a competitive feel and may sell the target audience the correct value for the proper solutions being delivered.

When it comes to selling a vehicle, it is critical to remember how you can set any price; the price must meet market standards and account for all relevant charges from both the buyer and the manufacturer. As a result, prices must be legitimate and add value.

This activity is ensured using web scraping. You can quickly monitor pricing strategies and design however you want your pricing schemes to look by extracting all of the information.

Web scraping also allows you to look at your competitors' pricing tactics, which can help you better deal with them. You may readily observe how they develop pricing plans and, most likely, enhance your current valuation aspects. When you can offer competitive pricing, there is no reason to have any reservations about buying any of the vehicles on offer.

5. Innovative Approach to Designs

The 2019 market will not have the same style as it did five years ago; your target audience's preferences and choices will have completely changed to match the new expectations of 2019.

In the case of automotive, a vehicle can represent a variety of aspects that entice your target audience to devote their attention and then their money to it; these factors include design, color, amenities, and so on. A design that was fashionable decades ago may or may not still be popular in the current market.

As a result, data on client preferences in terms of design, creative approaches to vehicle structure, features, and a variety of other elements must be acquired using web scraping so that optimum solutions may be made.

How Proxy Servers can Enhance Web Scraping Actions?

Proxy servers act as a middleman between a user and the website they want to see. When a user requests access to a protected website, the proxy server receives it first and then forwards it to the website. Because it changes the IP address, the proxy server receives it first.

As your IP address identifies the location you are already in, you are frequently prevented when accessing restricted sources of information. A proxy server helps to solve this problem by masking your true identity and providing you with a new one.

Proxy servers are helpful when it comes to web scraping for business purposes. When you continue to scrape several websites, proxy servers keep your identity secret and your scraping activities moving faster.

For more details regarding how web scraping will change the Automotive Industry market, contact X-Byte Enterprise Crawling today!!

HOW TO SCRAPE HERTZ CAR INVENTORY USING PYTHON?

Hertz Global Holdings or HTZ has substantial media coverage because of a current bankruptcy filing and also an effort to get an extra $500 million in the equity after bankruptcy announcement. Hertz had filed for Chapter 11 Bankruptcy expecting a reformation. The move of raising equity has got stopped by as well as noted by current Hertz 8Q filing as of 18th June 2020.

Summary

This blog shows study using a Python programming language for both downloading data from Hertz website to scrape car data and also save it in “DataFrames”, which we had exported to “.csv” files, which were manipulated with Excel. The code utilized is given in the appendix of this blog.

For extract car API data, a search was done on Hertz website for different vehicles for sale inside 10,000 miles of the St. Louis, MO. The similar 10,000 radius download was implemented for New York City, NY as well as San Francisco, CA, except otherwise given statistics related to “St. Louis” search. The search yielded a few descriptive details, however, not the comprehensive view about the size of fleet portfolio, which is for sale.

Search results primarily showed total 26,054 vehicles for St. Louis search however, after extracting relevant search found are only 11,994 vehicles before site search results started showing blank. It brings question about the accuracy of search options. Different online sources mention that Hertz has originally put more than 50,000 vehicles for sale, although, without a complete universe, which contains distinctive Vehicle Identification Numbers (VINSs), attached with an organized scraping approach, we just cannot validate with inevitability the size of fleet for sale. Here, this indicates that whereas a search gathered 26,054 results, scanning through these results indicates that specific results have ended after 11,994 vehicles. Given in one more manner, the whole suggested search results did not match with the accessible displayed results. It is most expected related to programming structure or display structure of the used car sales website.

Data

The data given briefly indicates some data from St. Louis search. A few descriptions are a bit longer than others. For instance, we have a couple of entries in the St. Louis data, which list ‘1992’ like a complete description having mileage of ‘1 mile’ as well as no other details. It is usual with larger datasets and not concern for us as this is not material depending on the total data size from the search. Also, we have 215 entries for vehicles, which need manual calling to the locations for price quotes. So, all these 215 items have got removed from the St. Louis Data. Just go through the sample statistics for St. Louis data:

Example (St. Louis Zip Code Search):

• Vehicle Title & Description: “2019 CADILLAC XT5 Premium Luxury SUV”
• Listed Price: $33,279
• Mileage: 24,664

Descriptive Statistics Overview:

• Total Vehicles: 11,994
• Average Price: $18,472
• Total Sum of Sales Value: $217,583,305
• Maximum Price: 2019 Chevrolet Corvette Z06 3LZ Coupe: $67,995
• Minimum Price: 2016 Kia Rio LX Sedan: $6,877
• Average Year: 2018.70
• Average Mileage: 33,350

Image 1: Portfolio Sales Value in terms of Make

Figure 2: Vehicle Data Buckets Summary Statistics in terms of Search Location

Closing

The data given shows that an average mileage, price, as well as age across the samples is comparable. On an average, a car, which Hertz is selling is worth anywhere around $18,500. Also, an average mileage is around around 33,000–34,000 miles for cars putting for sale as well as most cars are of 2018–2019 models. Also, we observe that Hertz significantly favors Chevrolet car models. Although, without getting a list of all unique vehicles given for sale we just cannot go too far.

Next Steps:

The following step is try and get complete idea about how much money Hertz could raise through selling the portfolio as well as how that might affect them while going forward. For doing that, the given steps would be tried:

Accumulate a complete world of accessible to sell cars

Depending on the universe, just calculate an expected value of the whole sales portfolio

Utilize financial statements for determining what effect it might have for on Hertz debt holders for recoveries because Hertz continues the bankruptcy proceedings

In the bankruptcy reorganization, many factors could affect recoveries depending on which your securities assemble in the company’s capital structure. Generally, debt holders would get some part of the total principal in the recoveries or equity in a newly formed company. That estimated or perceived amount has a huge impact on market value of present securities. Modeling possible recoveries can provide insights about what present exceptional securities are valued.

Image 3: Example of a Sliced File Format

Image 4: Python Code

from requests import get
from bs4 import BeautifulSoup
import pandas as pd
url_insert = 0 # a variable to increment by 35 results per page
count_one = 0
count_two = 0
#create an empty dataframe, the "Split" columns are placeholders for when we split our multi-word title into
#components so in Excel we can analyze by make, model, etc.
df_middle = pd.DataFrame(columns = ['Description', 'Price', 'Mileage', 'Split 1', "Split 2", "Split 3", "Split 4",
                                    "Split 5", "Split 6", "Split 7", "Split 8", "Split 9", "Split 10"])
#at 35 results per page using a p of 800 means we can search up to 28,000 (35 * 800) results.  The most results we got after searching
#was just over 26,000 even though we did not download all of them because blank pages started just before 12,000 at the time
# of our St. Louis 10,000 mile radius search
for p in range(0,800):
    url = 'https://www.hertzcarsales.com/used-cars-for-sale.htm?start=' + str(url_insert) + '&geoRadius=10000&geoZip=10007'
    response = get(url)
    html_soup = BeautifulSoup(response.text, 'html.parser')
    type(html_soup)
    li_class = html_soup.find_all('li', class_ = 'item hproduct clearfix closed certified primary')
    count_one = count_one + 1
    print(count_one)
    print(url)
    url_insert = url_insert + 35
    for i in li_class:
        title_long = i.find('a', class_ ='url')
        title_long = title_long.text
        title_long = str(title_long)
        title_long = title_long.rstrip("\n")
        title_long = title_long.lstrip("\n")
        title_split = title_long.split()
        #our pre-split placeholder list, when split up to 14 columns are available, one for each word based on the number of
        #words in the title
        blank_list = ["none", "none", "none", "none", "none", "none", "none", "none", "none", "none", "none", "none", "none", "none"]
        for a in range(len(title_split)):
            blank_list[a]=title_split[a]
        price = i.find('span', class_='value')
        price = price.text
        price = str(price)
        price = price.rstrip("\n")
        price = price.lstrip("\n")
        #print(price)
        data = i.find('div', class_='gv-description')
        mileage = data.span.span
        mileage = mileage.text
        mileage = mileage.rstrip("\n")
         qmileage = mileage.lstrip("\n")
        #the line below removes the text "miles" from our mileage column
        mileage_clean = ''.join([i for i in mileage if i.isdigit()])
        #print(mileage.text)
        #print("----------------------------------------------------")
        list_current = {'Description':[title_long], 'Price':[price], 'Mileage':[mileage_clean], 'Split 1':blank_list[0], 'Split 2':blank_list[1], 'Split 3':blank_list[2],
        'Split 4': blank_list[3], 'Split 5':blank_list[4], 'Split 6':blank_list[5], 'Split 7':blank_list[6], 'Split 8':blank_list[7], 'Split 9':blank_list[8],
        'Split 10':blank_list[9], 'Split 11':blank_list[10], 'Split 12':blank_list[11], 'Split 13':blank_list[12], 'Split 14':blank_list[13]}
        df_current = pd.DataFrame(data = list_current)
        count_two = count_two + 1
        print(count_two)
        #print(df_current)
        df_middle = df_middle.append(df_current)
df_middle.to_csv('C:\\Users\\james\\PycharmProjects\\workingfiles\\Webscraping\\Hertz\\output_NewYorkCity.csv')

If you want to know more about how to scrape car data or extract car API data using Python, you can contact X-Byte Enterprise Crawling or ask for a free quote!

HOW STOCK SENTIMENT ANALYSIS AND SUMMARIZATION IS CONDUCTED USING WEB SCRAPING?

 

For some, the stock market represents a tremendous risk since they lack the necessary information to make better selections. People spend a lot of time picking which Café to visit, but not nearly as much time deciding which stock to invest in. It is due to the fact that individuals have far less time, but this is where AI can help. Automatic summarization and online scraping appear to help us obtain the knowledge we need to make the best decisions.

Module References

1. Web Scraping Modules

Requests Module

For web scrapers, the request module is a blessing. It enables developers to retrieve the target webpage's HTML code.

BeautifulSoup

Unless you're a web developer, BeautifulSoup will come in handy because it breaks down a complex HTML page into a legible and scrapable soup object.

2. Standard Modules

Pandas Module

It's a well-known technique in a data developer's toolbox for dealing with enormous amounts of data and gaining inference or seeking information through direct correlation, combining, filtering, and expanded data analysis.

Numpy Module

To put it another way, it makes doing mathematical operations on the data. The heart of this module is the use of matrices and array calculations. Pandas is also based on it.

Matplotlib

Consumers, of obviously, like to see cool images, and visuals communicate a fair bit better than text on a screen. Matplotlib will take care of the rest.

3. Sentiment Analyzer Module

NLTK

It works by analyzing text data and inferring feelings from it. When it comes to Natural Language Processing, Hugging Face Robots and NLTK have a competitive advantage in the current market.

Textblob

During the first phase of my project, you can employ a light-weight sentiment analyzer.

Transformers Pipeline Sentiment

Transformer's arsenal includes a sentiment analyzer.

4. Article Summarization

Newspaper3K

It's a simple abstractive summary python module that assists you in summarizing a text.

Transformers(Financial-Summarization-Pegasus)

A deep learning toolkit primarily for NLP projects. Pegasus financial summary will be used in this project.

1. Install and Import Dependencies

Install pip... Essentially, we're just using run command in the background to download the latest the appropriate packages in our system so that we can access them in our code.

For the sake of convenience, pip will install all of the required packages for this project.

2. Summarization Modules

The summarizing models reduce the provided material to a logical and succinct summary.

Example: Financial-summarization-pegasus (Huggingface): It is pre-trained on financial language in order to extract the best summary from financial data.

Input:

In the largest financial buyout this year, National Commercial Bank (NCB), Saudi Arabia's top lender by assets, agreed to buy rival Samba Financial Group for $15 billion. According to a statement issued on Sunday, NCB will pay 28.45 riyals (US$7.58) each Samba share, valuing the company at 55.7 billion riyals. NCB will issue 0.739 new shares for every Samba share, which is at the lower end of the 0.736–0.787 ratio agreed upon by the banks when they signed an initial framework deal in June. The offer represents a 3.5 percent premium over Samba's closing price of 27.50 riyals on Oct. 8 and a 24 percent premium over the level at which the shares traded before the talks were made public. The merger talks were initially reported by Bloomberg News. The new bank will have total assets of more than 220 billion dollars, making it the third-largest lender in the Gulf area. The entity's market capitalization of 46 billion dollars is almost identical to Qatar National Bank's.

Output:

The NCB will pay 28.45 riyals per Samba share. The deal will create the third-largest lender in the Gulf area.

3. A News and Sentiment Pipeline: Finiviz website

Finiviz is the website that is being considered in this pipeline. It's a web-based application that lists securities and the most recent stock stories in chronological order. The goal of this pipeline is to extract the URLs, as well as their headlines and dates, and do sentiment analysis on the headlines.

User Defined Functions used in Pipeline 1:

1. Function: Finiviz_parser_data(ticker):

Using the requests library, this method collects data from the Finviz website. The downloaded item should thereafter have a response code of at least 200.

The HTML response is parsed and returned as soup using the Beautiful Soup class. It should be mentioned that soup is a bs4 food. BeautifulSoup.

2. Function: correct_time_formatting(time_data)

This function converts the Finiviz website's incorrect date and time format to a standardized format.

Before Execution
0 Sep-20–21 07:53AM
1 06:48AM
2 06:46AM
3 12:01AM
4 Sep-19–21 06:45AM
5 Sep-18–21 05:50PM
6 10:34AM
</br>
After Execution
0 Sep-20–21 07:53AM
1 Sep-20–21 06:48AM
2 Sep-20–21 06:46AM
3 Sep-20–21 12:01AM
4 Sep-19–21 06:45AM
5 Sep-18–21 05:50PM
6 Sep-18–21 10:34AM

3. Function: finviz_create_write_data(soup,file_name=’’MSFT”)

The file_name is customizable since the soup is supplied as a position argument and the file name is passed as a keyword parameter.

Finviz_create_write_data (soup, file name="Amazon") is an example.

The code extracts the URL, time, News Reporter, and News headline, among other things.

It uses Pandas to generate a data frame, publishes it to a CSV, then returns the data frame.

4. Function: create_csv_ticker_list(ticker_list):

This program simplifies the process of adding several stocks to a ticker list.

5. Function: def finviz_view_pandas_dataframe(ticker)

This function assists in the analysis process when an analyst has to do calculations on the data frame from a certain stock.

Take an example of Google stock and analysis

6. Function: clean_data(df, column_filter=”News Headline’, othe_column=Time”)

When the text is cleaned, such as lower casing, eliminating punctuation marks, removing stop words, and lemmatizing the text, the emotion analyzer that we employ, if efficient like transformers or lower effecient analyzers, performs much better.

7. Function: (Optional)find_unnecessary_stop_words(df, count) & cleaning_secondry(df, apply_column = “lemmatized”):

The other stop phrases must be found manually, and these functions help with that.

8. Function: sentiment_analyzer(df, column_applied_df = “final_sentiment_cleaned”, other_column-=”Time_pdformat’)

With df as input, the programme basically employs sentiment analyzers like nltk vader and textblob.

Steps to Reproduce

Step 1:

Using the user created functions finviz_parser_data and finviz_create_write_data, make a tesla stock CSV file.

Step 2:

Create a ticker list of at least the stocks you want and provide it to the function create_csv_ticker_list as an argument.

Step 3:

To perform individual analysis on your selected stock, establish a stock data frame.

Step 4:

Pandas includes a function that converts a data time item to a timestamp. Using pd.to datetime on the data frame's time column.

Step 5:

Import Stop Words in The Desired Language

Step 6:

Clean the Data Frame by passing it via preset clean functions.

Step 7:

Conduct sentiment analysis on the cleansed data's last column and assess the results.

Step 8:

Remember that we wrote a predefined method to analyze sentiment from CSV data.

Step 9:

The next step is to extract the news article summaries from the extracted URLS. Because some articles may result in a 403 ERROR, all of them cannot be scraped properly.

Example of one of the files:

Summarizing Pipeline 1:

• To download our ticker's CSV file, we passed a ticker value to the function.
• Created a ticker list, which was then utilized to scrape several tickers and their related CSV files.
• Obtained stock data for a particular ticker.
• Removed the information from the News headline.
• (Optional) Using the function provided, manually declare the other stop words list and eliminate those words.
• Run sentiment analysis on the News Headlines that have been cleansed.
• Used a basic scatter plot to analyze the emotion.
• Using a data frame and a csv file, you can scrape news items.

Functions Used in Pipeline 2

1. **def

google search stocknews (ticker,num=100,site=”yahoo+finance”) **: The "ticker" is used as a positional argument, "num" is the number of pages to search, and "site" can be any trustworthy website.

2. **def strip_unwanted_urls(urls)**:

It removes the dirty urls from the list and filters the urls that fit the standard, as the name implies.

3. **def scrape_articles(URLs):**

The method scrapes the Url for text and parses it to a maximum of 350 words.

4. **def create_csv(summaries, scores, final_urls_lists):**

As we export all of the needed information to a CSV file, this is self-explanatory.

Reproducing Steps:

Step 1: Creating a ticker list and passing it to the function 1:

Step 2:

Step 3: To build the final URLs list, remove any unneeded URLs:

Summarizing Pipeline 2:

• Scrape the corresponding ticker and Nerws agency URLs.
• Remove any URLs that you don't want from the URL list.
• Look for comparable URLs in news articles.
• Using the Pegasus model, summarized the scraped articles.
• Make a CSV file with all of the required fields.

For any further queries, contact X-Byte Enterprise Crawling today or request for a quote!!!