csv file in excel looks like this!

%%-*- text -*-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This is a PROMISE Software Engineering Repository data set made publicly
% improvable predictive models of software engineering.
%
% follow the acknowledgment guidelines posted on the PROMISE repository
% web page http://promise.site.uottawa.ca/SERepository .
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1. Title/Topic: cocomo81/software cost estimation
@relation  cocomo81
% 2. Sources:
"%                Year =	 1981}"
%
%           then converted by Tim Menzies from
%           http://www.vuse.vanderbilt.edu/~dfisher/tech-reports/raw-TSE-95
%           to arff fort.
%
%        -- Donor: Tim Menzies [email protected]
%
%     -- Date: December 2 2004
% 3. Past Usage
%  4. Relevant Information
%
%    The COCOMO software cost model measures effort in calendar months
%    of 152 hours (and includes development and management hours).
%    COCOMO assumes that the effort grows more than linearly on
%    domain-specific parameters; "KSLOC" is estimated directly or
%    computed from a function point analysis; and "c" is the product
%    of over a dozen "effort multipliers". I.e.
%
%    months=a*(KSLOC^b)*(EM1* EM2 * EM3 * ...)
%
%    The effort multipliers are as follows:
%
%     increase | acap | analysts capability
%     these to | pcap | programmers capability
%    decrease  | aexp | application experience
%      effort  | modp | modern programing practices
%              | tool | use of  software tools
%              | vexp | virtual machine experience
%              | lexp | language experience
%    ----------+------+---------------------------
%              | sced | schedule constraint
%    ----------+------+---------------------------
%     decrease | stor | main memory constraint
%     these to | data | data base size
%     decrease | time | time constraint for cpu
%       effort | turn | turnaround time
%              | virt | machine volatility
%              | cplx | process complexity
%              | rely | required software reliability
%
%    issues such as ``have we built this kind of system before?''.  The
%    COCOMO~II effort multipliers are similar but COCOMO~II dropped one
%    of the effort multiplier parameters; renamed some others; and
%
%    The effort multipliers fall into three groups: those that are
%    positively correlated to more effort; those that are
%    negatively correlated to more effort; and a third group
%    U-shaped correlation to effort; i.e. giving programmers either
%    too much or too little time to develop a system can be
%    detrimental.
%
%    The standard numeric values of the effort multipliers are:
%
"%      	        very				very	extra	productivity"
"%       	        low	low	nominal	high	high	high	range"
%    ---------------------------------------------------------------------
"%    acap	1.46   	1.19   	1.00   	0.86   	0.71   		2.06"
"%    pcap	1.42.  	1.17   	1.00   	0.86   	0.70 		1.67"
"%    aexp   	1.29   	1.13   	1.00   	0.91   	0.82   		1.57"
"%    modp   	1.24.  	1.10 	1.00 	0.91 	0.82 		1.34"
"%    tool   	1.24 	1.10 	1.00 	0.91 	0.83 		1.49"
"%    vexp   	1.21 	1.10 	1.00 	0.90 	  		1.34"
"%    lexp   	1.14 	1.07 	1.00 	0.95 	  		1.20"
"%    sced   	1.23 	1.08 	1.00 	1.04 	1.10 	  	e"
"%    stor   	       	       	1.00   	1.06   	1.21   	1.56	-1.21"
"%    data   	    	 0.94 	1.00 	1.08 	1.16		-1.23"
"%    time   	  	    	1.00   	1.11   	1.30   	1.66	-1.30"
"%    turn   	       	0.87   	1.00   	1.07   	1.15   		-1.32"
"%    virt   	       	0.87   	1.00   	1.15   	1.30   		-1.49"
"%    cplx   	0.70 	0.85 	1.00 	1.15 	1.30 	1.65	-1.86"
"%    rely   	0.75	 0.88	 1.00 	 1.15 	 1.40		-1.87"
%
%    These were learnt by Barry Boehm after a regression analysis of the
%    projects in the COCOMO I data set.
"%        Year      =	 1981}"
%
%     The last column of the above table shows max(E)/min(EM) and shows
%     increasing "acap" (analyst experience) from very low to very
%     high will most decrease effort while increasing "rely"
%     (required reliability) from very low to very high will most
%     increase effort.
%
%     There is much more to COCOMO that the above description. The
%     COCOMO~II text is over 500 pages long and offers
%     all the details needed to implement data capture and analysis of
%     COCOMO in an industrial context.
%         Author = "Barry Boehm and Ellis Horowitz and Ray Madachy and
%                   Donald Reifer and Bradford K. Clark and Bert Steece
%           ibsn = "0130266922"}
%
%     Included in that book is not just an effort model but other
%     (?all) of the validation work on COCOMO has focused on the effort
%     model.
"%         title =	 ""Bayesian Analysis of Empirical Software Engineering"
"%         year =	 ""1999""}"
%
%     The value of an effort predictor can be reported many ways
%     including MMRE and PRED(N).MMRE and PRED are computed from the
%     difference between the actual and estimated value:
%
%     RE.i = (estimate.i - actual.i) / (actual.i)
%
%     averaged over the "T" items in the "Test" set; i.e.
%
%     MRE.i  = abs(RE.i)
%     MMRE.i = 100/T*( MRE.1 + MRE.2 + ... + MRE.T)
%
%     PRED(N) reports the average percentage of estimates that were
%     within N% of the actual values:
%
%     count = 0
%     for(i=1;i<=T;i++) do if MRE.i <= N/100 then count++ fi done
%     PRED(N) = 100/T * count
%
%     within 30% of the actual.  Shepperd and Schofield comment that
%     "MMRE is fairly conservative with a bias against overestimates
%     while Pred(25) will identify those prediction systems that are
%     generally accurate but occasionally wildly inaccurate".
%         note="Available from
%                \url{http://www.utdallas.edu/~rbanker/SE_XII.pdf}"}
%  5. Number of instances: 63
%       one for LOC and one for actual development effort.
%  7. Attribute information:
@attribute rely numeric
@attribute data numeric
@attribute cplx numeric
@attribute time numeric
@attribute stor numeric
@attribute virt numeric
@attribute turn numeric
@attribute acap numeric
@attribute aexp numeric
@attribute pcap numeric
@attribute vexp numeric
@attribute lexp numeric
@attribute modp numeric
@attribute tool numeric
@attribute sced numeric
@attribute loc numeric
@attribute actual numeric
% 8. Missing attributes: none
% 9: Class distribution: the class value (actual) is continuous.
%    distribution was found:
%    Instances     Range
%    ---------  ----------------
"%      1 .. 10	  5.9 ..    15"
"%     11 .. 20	  18  ..    47"
"%     21 .. 30	  50  ..    87"
"%     31 .. 40	  88  ..   218"
"%     41 .. 50	 230  ..   539"
"%     51 .. 60 	 605  ..  2455"
"%     61 .. 63	6400  .. 11400"
@data