Hypothesis Problem Solving Using Natural Language Processing

Getting answers to the questions about hypotheses can be difficult because it requires careful consideration to determine which data are essential for analysis and which hypothesis tests should be used. Obtaining relevant data can be time-consuming and calls for expertise in the relevant domain, whereas selecting the appropriate test is dependent on understanding the problem statement and the context in which it exists. These complexities frequently result in errors, delays, and inefficiencies in the decision-making process, which impedes the efficient problem-solving of hypothesis-based issues. The power of natural language processing (NLP) and various keyword detection techniques is leveraged by our proposed solution so that it can successfully overcome these challenges. Our system automates the extraction of crucial information, identifies the necessary data for analysis, and suggests the most suitable hypothesis test by applying NLP algorithms to problem statements. This automated method simplifies the process, increases accuracy, and lessens the load on the users, which enables efficient and reliable hypothesis problem-solving. Our solution improves decision-making processes and speeds up hypothesis analysis by combining natural language processing (NLP) and keyword detection, which enables more timely and accurate conclusions to be drawn. This allows our system to save time and improve accuracy to 85.7
^%
.