Update README.md

README.md

@@ -47,9 +47,9 @@ The features in this dataset encompass all of the data columns except for DITM_I
 
 The target variables in this dataset are DITM_IV, ITM_IV, sITM_IV, ATM_IV, sOTM_IV, OTM_IV, and DOTM_IV. These represent implied volatilities for different categories of options, which are what we would be interested in predicting in a regression or classification model. Implied volatility is a key concept in options trading as it reflects the market's expectation of future volatility of the underlying security's price.
 
-This dataset can also be used in dimensionality reduction machine learning models. These models aim to reduce the number of input variables in a dataset, while preserving as much of the relevant information as possible. Dimensionality reduction can be particularly useful when dealing with high-dimensional data, and can help improve model performance, speed up training times, and mitigate issues such as the curse of dimensionality.
+This dataset can also be used in dimensionality reduction machine learning models. These models aim to reduce the number of input variables in a dataset, while preserving as much of the relevant information as possible. 
 
-This dataset has been shared specifically for the course "Applied Artificial Intelligence" at UCEMA. Students in this course can use this dataset to practice building and evaluating different types of predictive models, as well as working with real-world financial data. Remember, the application of AI and machine learning in finance is a complex field and should be approached with careful understanding of both the financial and the technical aspects.
+This dataset has been shared specifically for the course "Applied Artificial Intelligence" at UCEMA. Students in this course can use this dataset to practice building and evaluating different types of predictive models, as well as working with real-world financial data.