Add missing footnote url

papers/jyotika_singh/main.tex

@@ -33,7 +33,7 @@ \subsection{Tool selection}\label{tool-selection}
 
 NLP has gained a lot of popularity over the last decade. This has been accompanied by several useful open-source tools and models that can be leveraged for a variety of tasks. Several paid services from cloud providers have been launched in this space as well, creating a plethora of options of businesses to easily plug their data into models, without putting in the work to create them from scratch. There still exist a lot of use cases requiring custom model building, but the availability of many great options have led to the creation of a standard workflow of approaching solution building in NLP. First, the data science / machine learning developer gauges the probelm statement, understand available data, and evaluation based on business goals. Then, when it comes to building the solution, the developer first explores existing tools and models. If existing tools have gaps, then further work can be done to fill those gaps. If the existing solutions don't work, training a custom model is the next step. 
 
-Data Scientists spend at least 20\% of their time in model selection and training. This time is even larger when there is a new/unfamiliar problem to solve. The problem may have existed, but it is the developer's first time trying to build a solution for it that fits their data. However, with so many available tools to choose from, how can the choice be made? For this, the developer needs to read-up a lot about available options and then find similar patterns to what might work on their data, then proceeding with experimentation with trial and errors. The process of tool selection has a gap in the market today and can be made quicker. Usually, as a developer gains experience, this process gets easier and less time-consuming over time, which this problem remains bigger for early career professionals. To solve this problem, this section explores some common NLP tasks and how you can short-list tools based on your data. The nlprw\_toolkit integrates this knowledge and shares a way to make this assessment using the toolkit directly. This section sheds more light on decision making while selecting tools and shares code samples of doing so using the toolkit.
+Data Scientists spend at least 20\% of their time in model selection and training \footnote{/url{https://businessoverbroadway.com/2019/02/19/how-do-data-professionals-spend-their-time-on-data-science-projects)}}. This time is even larger when there is a new/unfamiliar problem to solve. The problem may have existed, but it is the developer's first time trying to build a solution for it that fits their data. However, with so many available tools to choose from, how can the choice be made? For this, the developer needs to read-up a lot about available options and then find similar patterns to what might work on their data, then proceeding with experimentation with trial and errors. The process of tool selection has a gap in the market today and can be made quicker. Usually, as a developer gains experience, this process gets easier and less time-consuming over time, which this problem remains bigger for early career professionals. To solve this problem, this section explores some common NLP tasks and how you can short-list tools based on your data. The nlprw\_toolkit integrates this knowledge and shares a way to make this assessment using the toolkit directly. This section sheds more light on decision making while selecting tools and shares code samples of doing so using the toolkit.
 
 
 \subsubsection{Based on desired task}\label{based-on-desired-task}