Linkedin API using data scraping
Budget: $250 – $750 USD
We are looking for an experienced developer to help us with an API Integration project. Your main responsibility will be to develop and implement an API that will allow us to extract and analyze data from LinkedIn. We need someone who is able to work independently and deliver high-quality results. Specifically, we want an API that can identify law firm partners that have moved law firms at least once and have been at their current firm for at least 3 years. We are essentially wanting to create a predictive model of law partners that are likely to leave their law firm in the next 12 months. To be considered for this role, you should be proficient in ChatGPT, API, Automation, and Web Crawling. Experience in Data Extraction, and Data Mining. Additionally, any experience with Open AI would be considered a plus. As a candidate, we would like you to submit a proposal outlining how you can help us with this project. Please provide us with details of similar projects you have worked on in the past and any relevant links to completed projects. We are looking for someone who has excellent communication skills, is able to work collaboratively with our team, and can deliver high-quality results within the stipulated timeframe. If you believe you have the necessary skills and experience to assist us with this project, we encourage you to submit your proposal as soon as possible.
Requirement:
1. collect relevant data from LinkedIn, specifically focusing on law firm partners at the firms in the attached document, including information such as employment history, duration at current firm, past firm movements, education, skills, recommendations, and any other available professional http://information.
2. Once it is collected, it needs to be preprocessed to ensure its quality and consistency. .
3. conduct exploratory data analysis to understand the data patterns, relationships, and identifying potential predictors.
4. create visualizations and statistical analysis techniques to gain insights and assess the relevance of different features.
5. Based on preprocessed data and identified features, create a predictive model.
6. Once the model is finalized, deploy as an api that accepts relevant input data and provides predictions.
7. regular monitoring of the model's performance and updating it with new data will help ensure its accuracy and relevance over time.
Please restrict it to the law firms identified in this chart up to No. 84 and remove #82, #75, #65, and #66. Also, only search for partners in the United States.
https://en.wikipedia.org/wiki/List_of_largest_law_firms_by_profits_per_partner
List of largest law firms by profits per partner - Wikipedia
Frame: Node.js
Database: MongoDB
Requirement:
1. collect relevant data from LinkedIn, specifically focusing on law firm partners at the firms in the attached document, including information such as employment history, duration at current firm, past firm movements, education, skills, recommendations, and any other available professional http://information.
2. Once it is collected, it needs to be preprocessed to ensure its quality and consistency. .
3. conduct exploratory data analysis to understand the data patterns, relationships, and identifying potential predictors.
4. create visualizations and statistical analysis techniques to gain insights and assess the relevance of different features.
5. Based on preprocessed data and identified features, create a predictive model.
6. Once the model is finalized, deploy as an api that accepts relevant input data and provides predictions.
7. regular monitoring of the model's performance and updating it with new data will help ensure its accuracy and relevance over time.
Please restrict it to the law firms identified in this chart up to No. 84 and remove #82, #75, #65, and #66. Also, only search for partners in the United States.
https://en.wikipedia.org/wiki/List_of_largest_law_firms_by_profits_per_partner
List of largest law firms by profits per partner - Wikipedia
Frame: Node.js
Database: MongoDB