Category : data-modeling

I have a file users.csv containing username, email n role as follows: admin,[email protected],admin, user_1,[email protected],default, user_2,[email protected],default There is another file devices.csv containing device_id, description, device_type, manufacturer as follows: DT001,Temperature Sensor,Temperature,Acme, DT002,Temperature Sensor,Temperature,Acme, DT003,Temperature Sensor,Temperature,Acme, DT004,Temperature Sensor,Temperature,Acme, DT005,Temperature Sensor,Temperature,Acme, DH001,Humidity Sensor,Humidity,Krab, DH002,Humidity Sensor,Humidity,Krab, DH003,Humidity Sensor,Humidity,Krab, DH004,Humidity Sensor,Humidity,Krab, DH005,Humidity Sensor,Humidity,Krab. I use these files to load data into ..

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I’m trying to build a linear regression model but getting the following error on the last line: "endog and exog matrices are different sizes" My code is below: # import data df_nyc = pd.read_csv(‘AB_NYC_2019.csv’) # View data frame df_nyc.head(10) # Checking for null values print(df_nyc.info()) #Checking for outliers print(df_nyc.describe()) #Categorical variables to be mapped with ..

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I am trying to split my dataframe into training and test sets but get the following error on the last line: "not enough values to unpack (expected 4, got 2)" Below is my code: # import data df_nyc = pd.read_csv(‘AB_NYC_2019.csv’) # View data frame df_nyc.head(10) # Checking for null values print(df_nyc.info()) #Checking for outliers print(df_nyc.describe()) ..

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!! This is my First Question over Stack Overflow so I apologize in advance for any ambiguous statement !! PROBLEM: Inconsistent & Unorganised Data in every column, due to missing information in input data Terminologies I will Use: Input Data: Data in a column before applying the "Separate by Delimiter" feature in Power BI Output ..

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I need help with this section of the MongoDB docs to maybe update it https://docs.mongodb.com/manual/tutorial/model-tree-structures-with-materialized-paths/ Im using python and it seems like it’s not possible to use this feature with python. Can that be? If i for example save nodes like this: [{ "_id": "Programming", "path": ",Books," }, { "_id": "Databases", "path": ",Books,Programming," }] and ..

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I am currently building a system that will aggregate posts across multiple sources. These sources have very few properties in common — for example, the only thing in common across all the sources is that a post contains text: class Post: def __init__(self, text=None): self.text = text def persist(self): row_id = FoobarDB.insert(‘post’, text=self.text).row_id id = ..

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After importing Pycaret I called setup(mydf, ‘mytarget’) and run compare_models(). Then, I wanted to save a model from the comparison list and use it on another dataset. What I did was something like: lr = create_model(‘lr’). However, when I try lr.predict(mynewdfwithouttarget) I got the size mismatch error: X has 11 features per sample; expecting 37 ..

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