Rock_paper Scissor

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k nearest neighbors with well defined k value

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 so let us understand how we can chooose the perfect k for our model  from the last model i had prepared a function def regression(model):     x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2)     reg_all=model     reg_all.fit(x_train,y_train)     y_predict=reg_all.predict(x_test)     rmse_value=np.sqrt(mean_squared_error(y_test,y_predict))     print("rms error={}".format(rmse_value)) i have prepared cross value squared to get the mean of rmse where k=3 denote the mean of three iterated value of rmse Lasso is a way to conterect over fitting (we can also use ridge) to check. from sklearn.model_selection import cross_val_score from sklearn.linear_model import Lasso def regression_cv(model,k=3):     scores=cross_val_score(model,x,y,scoring='neg_mean_squared_error',cv=k)     rmse=np.sqrt(-scores)     print('reg rmse:',rmse)     print('reg mean:',rmse.mean()) importi...
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list of keyword in python

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one should not use this word as avariable in python it will throw an erroe ...............................................................................................  import keyword print (keyword.kwlist) output: ['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']
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null values

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  Null Values You need to do something about the null values. There are several popular choices when dealing with null values: Eliminate the rows: A great approach if null values are a very small percentage, such as 1% of the total dataset. However, if data is limited, it is not wise to lose the precious data. Replace with a significant value, such as the median or the mean: A great approach if the rows are valuable, and the column is reasonably balanced. However, all the data with the missing values are inputted with the same value which is practically not true. Replace with the most likely value, perhaps a 0 or 1: It's preferable to option 2 when the median might be useless. The median can often work here. Other advance techniques include the estimating the data using machine learning based model such as KNN, Random Forest, and so on, and that is out of the scope of this book. This process of filling the null values with an appropriate number is known as imputation of the data. d...
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