changed kaggle stuff

README.md

@@ -14,7 +14,7 @@ This is a project for MDS 522 Group6.
 We will use the StandardScaler(), CountVector() and other method to transform the data and build a linear regression model which can predict a Ramen restaurant's star based on its brand, variety,style, countries and other indicators. And we can also get some conclusions like What ingredients or flavors are most commonly advertised on ramen package labels? in this process. The star scales from 0 to 5, representing how tasty the Ramen will be. This question may not be of the world-changing kind, but it is a good start of figuring out a result in real-life problems with data science for us. Considering the usefulness of this model for food lovers around the world when choosing nearby ramen restaurants, we think this is a very interesting and meaningful question.
 
 ### Dataset
-The dataset we used in this project is downloaded from [kaggle](https://www.kaggle.com/residentmario/ramen-ratings), created by the data analysist called Aleksey Bilogur.  Each record in the dataset is a single ramen product review. It is updated as of September 22nd, 2021. Current as of review #3950.
+The dataset we used in this project is downloaded from an data analysist called Aleksey Bilogur.  Each record in the dataset is a single ramen product review. It is updated as of September 22nd, 2021. Current as of review #3950.
 reviews are based on personal preferences, not on sales of popularity.
 Scores are in .25 increments – rounding is NOT recommended. Think of letter
 grading; a 3.5 score out of 5 stars – (3.5 * 2) * 10 = 70 = C.
@@ -64,4 +64,4 @@ be found in virtually every corner of the country and produce countless regional
 variations of this common noodle dish.
 <https://www.japan-guide.com/e/e2042.html>.
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