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README.md

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 Mathew
 
 ## Model Description
-This is a Linear Regression model trained on the UCI Automobile dataset to predict the 'symboling' insurance risk rating from 17 car features including price, horsepower, bore, and curb-weight, amongst other continous variables.
+This is a Linear Regression model trained on the UCI Automobile dataset to predict the 'symboling' insurance risk rating from 17 car features including price, horsepower, bore, and curb-weight, amongst other continous variables. Symboling is defined as an integer value (whole number), ranging from -3 to +3.
 
 ## Intended Uses & Limitations
-This model is for educational purposes only. It is not suitable for production use because the dataset is small (only ~200 or so entries), outdated (~1980s), and contained a lot of missing values (41 missing normalized-losses, around 20% of all rows had a missing normalized-losses entry). Predictions should not be used for real insurance predictions.
+This model is for educational purposes only. It is not suitable for production use because the dataset is small (only 200 or so entries), outdated (1980s), and contained a lot of missing values (41 missing normalized-losses, around 20% of all rows had a missing normalized-losses entry). Predictions should not be used for real insurance predictions.
 
 ## Training Data
 Data source: UCI Automobile dataset (https://archive.ics.uci.edu/dataset/10/automobile). Contains ~200 cars with mixed numeric and categorical features. Missing values were imputed using MICE.