Our project lies in research and develop category which is Learning based Spider Search and Analysis tool, which takes user?s website as input, compare it to currently top ranked websites and generate a comparative analysis based on various search engine optimization features. Also, it recommen
Deep learning-based Spider Search and Analysis tool
Our project lies in research and develop category which is Learning based Spider Search and Analysis tool, which takes user’s website as input, compare it to currently top ranked websites and generate a comparative analysis based on various search engine optimization features. Also, it recommends the improvements in meta tags and in overall website automatically; that facilitate the organizations to bring traffic to their websites and ranked the website on top google page.
As we all know that the Google and Bing are the primary tools that most people used to retrieve information from the Internet. The fact that it is a convenient means for communication and information search has made it extremely popular. This fact led companies to start using online advertising by creating corporate websites. The competition to appear on the first search engine results page (SEERP) is continually increasing and as a result, driving traffic to websites has become more difficult.
So, basically we are trying to automate SEO in a long run involing machine learning and deep learning concepts in it. We are proposing a solution considering the fact that google never disclose its algorithm of ranking the websites, so our system will get the website as input and compare it to currently top ranked websites and generate a comparative analysis based on various search engine optimization features and also automatically recommends the improvements in meta tags and in overall website. Meta tags are snippets of code that tell search engines important information about your web page, such as how they should display it in search results.
Moreover, our system firstly focuses on generating the current rules from top ranked websites than analyze the website which is input by the users and after the comparative analysis it will suggest the changes in the website in order to rank the website in the google page. Furthermore, we will also try to automatically apply those recommendations on the website which is input by the user of the system and changed the website according to the rules and suggestion which is previously generated by our system and optimize the whole website. It will result in displaying the website in the top three organic results.
Our main aim is to optimize the website and get it listed in Google's top pages.
Uptill now we used different python libraies in order to extract features from the websites, Created automatically updated dataset with 27 features by using python libraries. Build our own crawler and trained the model on the dataset collected. We are working with ANN model to predict the customer's website predicted feature weights.
We are working with our own crawler and spider which help us to generate our large dataset and automatically updated it in a given time. In our to train the dataset we are working with ANN appoarch so that we can get the predicted weights of the features extracted from user's websites.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Laptop | Equipment | 1 | 60000 | 60000 |
| Evo device | Equipment | 1 | 3000 | 3000 |
| Total in (Rs) | 63000 |
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