Hello all i just want to know how can i connect my first assignment jupyter notebook file here
how can i connect my jupyter notebook here for submission
Hello, you need to commit your jupyter notebook file in Jovian.
- Install jovian library using
!pip install jovian
import jovianwithin the notebook
You will be prompted to enter an API key, you can get the API key after you login to your account in jovian.ai
Thank you for response
inputs_df[encoded_cols] = encoder.transform(inputs_df[categorical_cols])
error- # We (temporarily) allow for some missing keys with .loc, except in
KeyError: "None of [Index([‘MSZoning_C (all)’, ‘MSZoning_FV’, ‘MSZoning_RH’, ‘MSZoning_RL’,\n ‘MSZoning_RM’, ‘Street_Grvl’, ‘Street_Pave’, ‘Alley_Grvl’, ‘Alley_Pave’,\n …
I am getting error while trying to transform encoder with this.
I think first one is a good option. we should not manipulate data based on frequency of most occurring feature. Rathe we should create a new category. I think so. Make me correct if i am wrong.
Hey, can you restart the runtime and run all cells again?
How to remove nan values from the dictionary given in the end?
In the section ‘Making Predictions’, I have had to run the following function in a line-by-line manner to get it to work, adding in a few things. I hope that this is ok.
def predict_input(single_input): input_df = pd.DataFrame([single_input]) input_df[numeric_cols] = imputer.transform(input_df[numeric_cols]) input_df[numeric_cols] = scaler.transform(input_df[numeric_cols]) input_df[encoded_cols] = encoder.transform(input_df[categorical_cols].values) X_input = input_df[numeric_cols + encoded_cols] return model.predict(X_input)
Best to use the ‘fillna’ method, e.g.
Input_df[Categorical_cols] = Input_df[Categorical_cols].fillna(value="Unknown")
You can choose any other expression instead of ‘Unknown’.
Also, you may have to later convert the encoded array to a pd DataFrame before
model.predict will accept it.
What is the expected RMSE value in this case for Train & Val both respectively ?
I think my value is really high even after changing Ridge Model parameters.
I am trying to assign different values to weights = ??? cell block for the assignment. Do i need to use any specific formula to assign values there?
weights = model.coef_.flatten().tolist()
Try converting into a single list
use in inputs_df instead of prices_df because you have filled missing values in inputs_df and not in prices_df
It’s been 5 days I have submitted my first assignment and still not verified…!!
Is there any problem with others or it’s only for mine?
It does not appear to be a code issue.
The assignment evaluation will be done soon.
Hey @rajgt40, please check this post