Dharaneish V C

  DATA PREPROCESSING The objective of data pre-processing is to analyze, filter, transform, and encode data so that a machine learning algorithm can understand and work with the processed output. The phrase "garbage in, garbage out" is very much apt to data mining and machine learning projects. The presence of any unclean data like missing attributes, attribute values, containing noise or outliers, and duplicate or wrong data will degrade the quality of the ML results. So, It is important to manipulate or transform the raw data in a useful and efficient format before it is used in Machine learning to ensure or enhance performance. Important Libraries for Data Preprocessing: To do data preprocessing in Python, we need to import some predefined Python libraries. These libraries are used to perform some specific tasks. There are three specific libraries that we will use for data preprocessing. ·        Numpy : The Numpy Python library is used to i...

  Simpson’s paradox An article in the British Medical Journal [“Comparison of treatment of renal calculi by operative surgery, percutaneous nephrolithotomy, and extracorporeal shock wave lithotripsy” (1986, Vol. 82, pp. 879–892)] provided the following discussion of success rates in kidney stone removals. Open surgery had a success rate of 78% (273/350) and a newer method, percutaneous nephrolithotomy (PN), had a success rate of 83% (289/350). This newer method looked better, but the results changed when stone diameter was considered. For stones with diameters less than 2 centimeters, 93% (81/87) of cases of open surgery were successful compared with only 83% (234/270) of cases of PN. For stones greater than or equal to 2 centimeters, the success rates were 73% (192/263) and 69% (55/80) for open surgery and PN, respectively. Open surgery is better for both stone sizes, but less successful in total. In 1951, E. H. Simpson pointed out this apparent contradiction (known as Simpson’s...