Hobisist need to pay attention!

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Hobisist out there. move whatevere you feel bliss at!
Location: Mumbai, Maharashtra, India

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Project 2: pdf extractor using python

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let us prepare a project of few codes to extract the whole pdf    import pip install PyPDF2 code: ............................................................................................................................................. from PyPDF2 import PdfFileReader #read a pdf file ie. by rb mode file=open("Handbook.pdf",'rb') #reader ia s variable use to read file  reader=PdfFileReader(file) #lets get the info of the pdf document print("document info:",reader.getDocumentInfo()) print() #getNumPages() this comand can get you page numbers of pdf print("number pf pages are:",reader.getNumPages()) #lets take variable "pages" to take comand over get number of pages pages=reader.getNumPages() for i in range(0,pages):     print("page number=",i+1)     pageObj = reader.getPage(i)     print(pageObj.extractText()) print() print(reader.getDocumentInfo().creator) file.close() ............................................................
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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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