how can you improve the reliability of your results

I have selected all the features except gender as its effect on ‘charges’ is very less(concluded from the viz charts above). Next, we will select features that affect ‘charges’ the most. #One hot encoding region=pd.get_dummies(df.region, prefix='location') df = pd.concat(,axis=1) df.drop(columns='region', inplace=True) df.sex.replace(to_replace=,value=, inplace=True) df.smoker.replace(to_replace=, value=, inplace=True) One can also use label encoding for this column, however, one hot encoding gave me a better result. In here, I have used pandas’ one hot encoding function ( get_dummies) on Region and split it into 4 columns - location_NE, location_SE, location_NW, and location_SW. I prefer to use one-hot encoding over label encoding if there aren’t many unique values in the categorical feature.

One-hot Encoding - Represent categorical data as binary values - 0s,1s only.(For example representing high, medium, low as 1,2,3) OrdinalEncoding - Used for representing rank-based categorical data values as numbers.LabelEncoding - Represent categorical values as numbers (For example, a feature such as Region with values Italy, India, USA, UK can be represented as 1, 2, 3, 4).This process is called encoding and there are many ways to do this : In this step, we convert categorical variables - smoker, sex, and region - to numeric format(0, 1,2, 3, etc.) as most of the algorithms cannot handle non-numeric data. We will now go over the steps of model preparation and model development one by one.

We see that age and BMI have an average +ve correlation with charges.

How can you improve the reliability of your results