CodeSamples

My main motto is to repair the windows with my data saved. literally, I have tried the possible to solve the above error but I couldn't at initial phases. I have opened cmd from troubleshooting option->advanced option->cmd(you can come to troubleshooting option by pressing  F1  or F8 button simultaneously with the power button). you can check by pressing F2 with power(boot manager and system check) and F12  button (one-time boot menu). after entering cmd typed  bootrec /rebuildbcd, The  bootrec  command will search for Windows installations not included in the BCD and then ask you if you'd like to add one or more to it. then you may get a result of either installation 0 or 1  if 1 press yes to add and  Since the BCD store exists and lists a Windows installation, you'll first have to remove it manually and then try to rebuild it again. At the prompt, execute the  bcdedit  command as shown and then press  Enter :    bcdedit /export c:\bcdbac k if 0 the

Major tasks of preprocessing are: Data cleaning filling missing values smoothing of noisy data identifying and removing outliers resolving inconsistencies Data Integration integrating data from multiple databases, data file, cubes Data transformation normalization  aggregation Data reduction obtain a reduced representation of data but same results Data discretization part of data reduction but with particular importance, especially for numeric data Normalization :  The goal of normalization is to make an entire set of values have a particular property. There are 3 different ways to perform normalization : min-max normalization X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min here max, min is the new ranges  z-score normalization z = (x - u) / s where u is the mean of the training samples, s is the standard deviation normalization by decimal scaling v_new = v/pow(10,j) here j is th

Binning method:(one of the method) first sort data and partition into (equi-depth) bins then one can smooth by bin means,  smooth by bin median, smooth by bin boundaries, etc. Equal-width (distance) partitioning: It divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. The most straightforward But outliers may dominate the presentation Skewed data is not handled well. Equal-depth (frequency) partitioning: It divides the range into N intervals, each containing approximately same number of samples Good data scaling Managing categorical attributes can be tricky. Code for binning (if needed we can edit for user input instead of random) : Feel free to comment about mistakes and doubts.