000			02059cam a22002895i 4500
001			19109225
003			KCU
005			20240905103725.0
008			160526s2016 nyu 000 0 eng
020			_a9781484259818
040			_aDLC _beng _cDLC _eKCU _dAACR2
042			_apcc
082			_223 _a006.3 _bZHO
100			_aZhou, Hong _eAuthor
245	0	0	_aLearn Data Mining Through Excel : _bA Step-by-Step Approach for Understanding Machine Learning Methods / _cHong Zhou
263			_a1606
264		1	_aNew York, NY : _bApress, _c2020.
300			_axvi, 219 P. ; _bill. : _c26 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aThis book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data.
520			_aUse popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages.
906			_a0 _bibc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc _cBK _n0
999			_c36746 _d36746