campusx-official/100-days-of-machine-learning

100-days-of-machine-learning's Issues

Day 17 Notebook has security issue - API key hardcoded

day17-api-to-dataframe/day17.ipynb has API key hardcoded. Suggest removing it.

df.append is not working in Standardization

6200 and name not in self._accessors
6201 and self._info_axis._can_hold_identifiers_and_holds_name(name)
6202 ):
6203 return self[name]
-> 6204 return object.getattribute(self, name)

AttributeError: 'DataFrame' object has no attribute 'append'

having this error. So i got a solution for it.
`data_to_append = {'Age': [5, 90, 95], 'EstimatedSalary': [1000, 250000, 350000], 'Purchased': [0, 1, 1]}

df_to_append = pd.DataFrame(data_to_append)

df = pd.concat([df, df_to_append], ignore_index=True)

print(df.describe())`

You can update the file, so others can't face this issue.

Please once check the Gradient descent code(from scratch)

Hey, I follow your gradient descent lecture and try to execute code with placement(consist of cgp and package features) dataset. The code is not working with dataset. It's really helpful for me and others who have same doubt.
Btw your lectures are amazing.
Thanks a lot.

https://colab.research.google.com/drive/1sMchVVSbO7T3bN4v7mJ5afe7neirQETV?usp=sharing

Cmpusx

Adding video link in readme file.

Video Link: https://www.youtube.com/watch?v=HLF4bFbBgwk&list=PLKnIA16_Rmvbr7zKYQuBfsVkjoLcJgxHH&index=66

Issue in code

There is an issue in code at line number 4
as it must be temp_df.head() instead of df.head()
please consider this and change the code

Batch size not mentioned in Mini-Batch GD

For Mini-batch GD, the coef_der = -2 * (value) according to the code.

** It should be coef_der = -2/self.batch_size * (value) **

n=1 in SGD and n = X_train.shape[1] in Batch. So, it should be n = self.batch_size in Mini-batch GD.

Machine learning

Missing csv file

Missing "ushape.csv "file for day60-logistic-regression-contd

Machine learning

Issue on day 45 while Feature Splitting

in the day 45 while doing groupby of 'Title and Survived' Error is occuring because of datatype.

Error can be solved by using this code.

`df['Survived'] = pd.to_numeric(df['Survived'], errors='coerce') # Convert to numeric, coerce errors to NaN

result = df.groupby('Title')['Survived'].mean().sort_values(ascending=False)

result.head()`