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correcting convert to numeric function
continuous-integration/drone/push Build is passing Details

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Dorian HODIN 5 months ago
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88
blood-transfusion/blood-transfusion/transfusion.names
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@ -0,0 +1,88 @@
Title: Blood Transfusion Service Center Data Set
Abstract: Data taken from the Blood Transfusion Service Center in Hsin-Chu City
in Taiwan -- this is a classification problem.
-----------------------------------------------------
Data Set Characteristics: Multivariate
Number of Instances: 748
Area: Business
Attribute Characteristics: Real
Number of Attributes: 5
Date Donated: 2008-10-03
Associated Tasks: Classification
Missing Values? N/A
-----------------------------------------------------
Source:
Original Owner and Donor
Prof. I-Cheng Yeh
Department of Information Management
Chung-Hua University,
Hsin Chu, Taiwan 30067, R.O.C.
e-mail:icyeh 'at' chu.edu.tw
TEL:886-3-5186511
Date Donated: October 3, 2008
-----------------------------------------------------
Data Set Information:
To demonstrate the RFMTC marketing model (a modified version of RFM), this study
adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City
in Taiwan. The center passes their blood transfusion service bus to one
university in Hsin-Chu City to gather blood donated about every three months. To
build a FRMTC model, we selected 748 donors at random from the donor database.
These 748 donor data, each one included R (Recency - months since last
donation), F (Frequency - total number of donation), M (Monetary - total blood
donated in c.c.), T (Time - months since first donation), and a binary variable
representing whether he/she donated blood in March 2007 (1 stand for donating
blood; 0 stands for not donating blood).
-----------------------------------------------------
Attribute Information:
Given is the variable name, variable type, the measurement unit and a brief
description. The "Blood Transfusion Service Center" is a classification problem.
The order of this listing corresponds to the order of numerals along the rows of
the database.
R (Recency - months since last donation),
F (Frequency - total number of donation),
M (Monetary - total blood donated in c.c.),
T (Time - months since first donation), and
a binary variable representing whether he/she donated blood in March 2007 (1
stand for donating blood; 0 stands for not donating blood).
Table 1 shows the descriptive statistics of the data. We selected 500 data at
random as the training set, and the rest 248 as the testing set.
Table 1. Descriptive statistics of the data
Variable Data Type Measurement Description min max mean std
Recency quantitative Months Input 0.03 74.4 9.74 8.07
Frequency quantitative Times Input 1 50 5.51 5.84
Monetary quantitative c.c. blood Input 250 12500 1378.68 1459.83
Time quantitative Months Input 2.27 98.3 34.42 24.32
Whether he/she donated blood in March 2007 binary 1=yes 0=no Output 0 1 1 (24%) 0 (76%)
-----------------------------------------------------
Citation Request:
NOTE: Reuse of this database is unlimited with retention of copyright notice for
Prof. I-Cheng Yeh and the following published paper:
Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM
model using Bernoulli sequence, "Expert Systems with Applications, 2008
(doi:10.1016/j.eswa.2008.07.018).

10
src/back/managing_missing_values.py
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@ -6,7 +6,9 @@ import load_csv as l
def convert_categorical_to_numeric(data):
for column in data.columns:
if data[column].nunique() <= 15:
if pd.api.types.is_numeric_dtype(data[column]):
continue
elif data[column].nunique() <= 15:
data[column] = data[column].astype('category')
data[column] = data[column].cat.codes.replace(-1, np.nan) + 1
else:
@ -71,3 +73,9 @@ def handle_missing_values(data, method, n_neighbors=5):
return impute_with_regression(data)
else:
raise ValueError("Unknown method")
df = l.return_csv('./transfusion.data')
print(df)
print(handle_missing_values(df,'mean'))

749
transfusion.data
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@ -0,0 +1,749 @@
Recency (months),Frequency (times),Monetary (c.c. blood),Time (months),"whether he/she donated blood in March 2007"
2 ,50,12500,98 ,1
0 ,13,3250,28 ,1
1 ,16,4000,35 ,1
2 ,20,5000,45 ,1
1 ,24,6000,77 ,0
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2 ,9,2250,22 ,1
5 ,46,11500,98 ,1
4 ,23,5750,58 ,0
0 ,3,750,4 ,0
2 ,10,2500,28 ,1
1 ,13,3250,47 ,0
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4 ,12,3000,34 ,1
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4 ,9,2250,26 ,1
2 ,16,4000,64 ,0
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4 ,19,4750,69 ,1
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4 ,2,500,4 ,0
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4 ,8,2000,34 ,1
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4 ,9,2250,40 ,0
2 ,5,1250,26 ,0
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3 ,5,1250,26 ,0
2 ,3,750,14 ,0
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4 ,5,1250,23 ,1
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4 ,8,2000,40 ,1
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2 ,11,2750,79 ,1
2 ,3,750,16 ,1
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4 ,8,2000,46 ,1
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2 ,12,3000,95 ,0
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6 ,3,750,14 ,0
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2 ,7,1750,58 ,1
4 ,6,1500,39 ,0
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2 ,1,250,2 ,1
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11 ,10,2500,35 ,0
11 ,4,1000,16 ,1
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4 ,5,1250,35 ,1
4 ,7,1750,52 ,0
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2 ,4,1000,35 ,0
11 ,11,2750,42 ,0
2 ,2,500,14 ,0
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9 ,8,2000,38 ,1
4 ,6,1500,47 ,0
11 ,7,1750,29 ,0
9 ,9,2250,45 ,0
4 ,6,1500,52 ,0
4 ,7,1750,58 ,0
6 ,2,500,11 ,1
4 ,7,1750,58 ,0
11 ,9,2250,38 ,0
11 ,6,1500,26 ,0
2 ,2,500,16 ,0
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4 ,1,250,4 ,0
4 ,1,250,4 ,0
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4 ,1,250,4 ,1
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,3,750,24 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,0
10 ,8,2000,39 ,0
14 ,7,1750,26 ,0
8 ,10,2500,63 ,0
11 ,3,750,15 ,0
4 ,2,500,14 ,0
2 ,4,1000,43 ,0
8 ,9,2250,58 ,0
8 ,8,2000,52 ,1
11 ,22,5500,98 ,0
4 ,3,750,25 ,1
11 ,17,4250,79 ,1
9 ,2,500,11 ,0
4 ,5,1250,46 ,0
11 ,12,3000,58 ,0
7 ,12,3000,86 ,0
11 ,2,500,11 ,0
11 ,2,500,11 ,0
11 ,2,500,11 ,0
2 ,6,1500,75 ,0
11 ,8,2000,41 ,1
11 ,3,750,16 ,1
12 ,13,3250,59 ,0
2 ,3,750,35 ,0
16 ,8,2000,28 ,0
11 ,7,1750,37 ,0
4 ,3,750,28 ,0
12 ,12,3000,58 ,0
4 ,4,1000,41 ,0
11 ,14,3500,73 ,1
2 ,2,500,23 ,0
2 ,3,750,38 ,1
4 ,5,1250,58 ,0
4 ,4,1000,43 ,1
3 ,2,500,23 ,0
11 ,8,2000,46 ,0
4 ,7,1750,82 ,0
13 ,4,1000,21 ,0
16 ,11,2750,40 ,0
16 ,7,1750,28 ,0
7 ,2,500,16 ,0
4 ,5,1250,58 ,0
4 ,5,1250,58 ,0
4 ,4,1000,46 ,0
14 ,13,3250,57 ,0
4 ,3,750,34 ,0
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14 ,16,4000,70 ,0
14 ,4,1000,22 ,1
14 ,5,1250,26 ,0
8 ,2,500,16 ,0
11 ,5,1250,33 ,0
11 ,2,500,14 ,0
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9 ,2,500,16 ,1
14 ,5,1250,28 ,1
14 ,3,750,19 ,1
14 ,4,1000,23 ,1
16 ,12,3000,50 ,0
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11 ,5,1250,35 ,0
11 ,5,1250,35 ,0
2 ,4,1000,70 ,0
14 ,5,1250,28 ,0
14 ,2,500,14 ,0
14 ,2,500,14 ,0
14 ,2,500,14 ,0
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14 ,2,500,14 ,0
2 ,3,750,52 ,0
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15 ,16,4000,82 ,0
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14 ,8,2000,50 ,1
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16 ,7,1750,39 ,0
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16 ,6,1500,35 ,0
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16 ,3,750,21 ,1
11 ,3,750,28 ,0
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11 ,2,500,24 ,0
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14 ,2,500,21 ,1
23 ,15,3750,57 ,0
14 ,4,1000,38 ,0
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21 ,3,750,26 ,0
16 ,8,2000,76 ,0
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18 ,2,500,23 ,0
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14 ,5,1250,64 ,0
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16 ,1,250,16 ,0
16 ,1,250,16 ,0
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16 ,4,1000,45 ,0
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16 ,1,250,16 ,0
16 ,1,250,16 ,0
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21 ,2,500,23 ,0
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16 ,3,750,40 ,0
9 ,2,500,52 ,0
9 ,2,500,52 ,0
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14 ,4,1000,64 ,0
14 ,2,500,35 ,0
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21 ,2,500,25 ,0
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11 ,3,750,76 ,0
11 ,2,500,52 ,0
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23 ,12,3000,86 ,0
21 ,3,750,35 ,0
23 ,2,500,26 ,0
23 ,2,500,26 ,0
23 ,8,2000,64 ,0
16 ,3,750,50 ,0
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21 ,3,750,38 ,0
23 ,2,500,28 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,1
21 ,1,250,21 ,0
21 ,1,250,21 ,0
21 ,5,1250,60 ,0
23 ,4,1000,45 ,0
21 ,4,1000,52 ,0
22 ,1,250,22 ,1
11 ,2,500,70 ,0
23 ,5,1250,58 ,0
23 ,3,750,40 ,0
23 ,3,750,41 ,0
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21 ,2,500,35 ,0
26 ,5,1250,49 ,1
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23 ,1,250,23 ,0
23 ,1,250,23 ,0
23 ,1,250,23 ,0
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23 ,1,250,23 ,0
23 ,1,250,23 ,0
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16 ,2,500,70 ,0
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35 ,3,750,64 ,0
38 ,1,250,38 ,0
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40 ,1,250,40 ,0
74 ,1,250,74 ,0
2 ,43,10750,86 ,1
6 ,22,5500,28 ,1
2 ,34,8500,77 ,1
2 ,44,11000,98 ,0
0 ,26,6500,76 ,1
2 ,41,10250,98 ,1
3 ,21,5250,42 ,1
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2 ,21,5250,52 ,1
2 ,13,3250,32 ,1
4 ,4,1000,4 ,1
2 ,11,2750,26 ,0
2 ,11,2750,28 ,0
3 ,14,3500,35 ,0
4 ,16,4000,38 ,1
4 ,6,1500,14 ,0
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4 ,33,8250,98 ,1
3 ,10,2500,33 ,1
4 ,10,2500,28 ,1
2 ,11,2750,40 ,1
2 ,11,2750,41 ,1
4 ,13,3250,39 ,1
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4 ,9,2250,28 ,0
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2 ,5,1250,16 ,1
2 ,15,3750,64 ,0
5 ,24,6000,79 ,0
2 ,6,1500,22 ,1
4 ,5,1250,16 ,1
2 ,4,1000,14 ,1
4 ,8,2000,28 ,0
2 ,4,1000,14 ,0
2 ,6,1500,26 ,0
4 ,5,1250,16 ,1
2 ,7,1750,32 ,1
2 ,6,1500,26 ,1
2 ,8,2000,38 ,1
2 ,2,500,4 ,1
2 ,6,1500,28 ,1
2 ,10,2500,52 ,0
4 ,16,4000,70 ,1
4 ,2,500,4 ,1
1 ,14,3500,95 ,0
4 ,2,500,4 ,1
7 ,14,3500,48 ,0
2 ,3,750,11 ,0
2 ,12,3000,70 ,1
4 ,7,1750,32 ,1
4 ,4,1000,16 ,0
2 ,6,1500,35 ,1
4 ,6,1500,28 ,1
2 ,3,750,14 ,0
2 ,4,1000,23 ,0
4 ,4,1000,18 ,0
5 ,6,1500,28 ,0
4 ,6,1500,30 ,0
14 ,5,1250,14 ,0
3 ,8,2000,50 ,0
4 ,11,2750,64 ,1
4 ,9,2250,52 ,0
4 ,16,4000,98 ,1
7 ,10,2500,47 ,0
4 ,14,3500,86 ,0
2 ,9,2250,75 ,0
4 ,6,1500,35 ,0
4 ,9,2250,55 ,0
4 ,6,1500,35 ,1
2 ,6,1500,45 ,0
2 ,6,1500,47 ,0
4 ,2,500,9 ,0
2 ,2,500,11 ,1
2 ,2,500,11 ,0
2 ,2,500,11 ,1
4 ,6,1500,38 ,1
3 ,4,1000,29 ,1
9 ,9,2250,38 ,0
11 ,5,1250,18 ,0
2 ,3,750,21 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,1
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,1
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
11 ,11,2750,38 ,0
2 ,3,750,22 ,0
9 ,11,2750,49 ,1
5 ,11,2750,75 ,0
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3 ,1,250,3 ,1
4 ,6,1500,43 ,0
2 ,3,750,24 ,0
12 ,11,2750,39 ,0
2 ,2,500,14 ,0
4 ,6,1500,46 ,0
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14 ,8,2000,26 ,0
4 ,2,500,13 ,0
4 ,11,2750,95 ,0
2 ,7,1750,77 ,0
2 ,7,1750,77 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,7,1750,62 ,0
4 ,1,250,4 ,0
4 ,4,1000,34 ,1
11 ,6,1500,28 ,0
13 ,3,750,14 ,1
7 ,5,1250,35 ,0
9 ,9,2250,54 ,0
11 ,2,500,11 ,0
2 ,5,1250,63 ,0
7 ,11,2750,89 ,0
8 ,9,2250,64 ,0
2 ,2,500,22 ,0
6 ,3,750,26 ,0
12 ,15,3750,71 ,0
13 ,3,750,16 ,0
11 ,16,4000,89 ,0
4 ,5,1250,58 ,0
14 ,7,1750,35 ,0
11 ,4,1000,27 ,0
7 ,9,2250,89 ,1
11 ,8,2000,52 ,1
7 ,5,1250,52 ,0
11 ,6,1500,41 ,0
10 ,5,1250,38 ,0
14 ,2,500,14 ,1
14 ,2,500,14 ,0
14 ,2,500,14 ,0
2 ,2,500,33 ,0
11 ,3,750,23 ,0
14 ,8,2000,46 ,0
9 ,1,250,9 ,0
16 ,5,1250,27 ,0
14 ,4,1000,26 ,0
4 ,2,500,30 ,0
14 ,3,750,21 ,0
16 ,16,4000,77 ,0
4 ,2,500,31 ,0
14 ,8,2000,50 ,0
11 ,3,750,26 ,0
14 ,7,1750,45 ,0
15 ,5,1250,33 ,0
16 ,2,500,16 ,0
16 ,3,750,21 ,0
11 ,8,2000,72 ,0
11 ,1,250,11 ,0
11 ,1,250,11 ,0
11 ,1,250,11 ,0
11 ,1,250,11 ,1
11 ,1,250,11 ,0
2 ,3,750,75 ,1
2 ,3,750,77 ,0
16 ,4,1000,28 ,0
16 ,15,3750,87 ,0
16 ,14,3500,83 ,0
16 ,10,2500,62 ,0
16 ,3,750,23 ,0
14 ,3,750,26 ,0
23 ,19,4750,62 ,0
11 ,7,1750,75 ,0
14 ,3,750,28 ,0
20 ,14,3500,69 ,1
4 ,2,500,46 ,0
11 ,2,500,25 ,0
11 ,3,750,37 ,0
16 ,4,1000,33 ,0
21 ,7,1750,38 ,0
13 ,7,1750,76 ,0
16 ,6,1500,50 ,0
14 ,3,750,33 ,0
14 ,1,250,14 ,0
14 ,1,250,14 ,0
14 ,1,250,14 ,0
14 ,1,250,14 ,0
14 ,1,250,14 ,0
14 ,1,250,14 ,0
17 ,7,1750,58 ,1
14 ,3,750,35 ,0
14 ,3,750,35 ,0
16 ,7,1750,64 ,0
21 ,2,500,21 ,0
16 ,3,750,35 ,0
16 ,1,250,16 ,0
16 ,1,250,16 ,0
16 ,1,250,16 ,0
16 ,1,250,16 ,0
16 ,1,250,16 ,0
14 ,2,500,29 ,0
11 ,4,1000,74 ,0
11 ,2,500,38 ,1
21 ,6,1500,48 ,0
23 ,2,500,23 ,0
23 ,6,1500,45 ,0
14 ,2,500,35 ,1
16 ,6,1500,81 ,0
16 ,4,1000,58 ,0
16 ,5,1250,71 ,0
21 ,2,500,26 ,0
21 ,3,750,35 ,0
21 ,3,750,35 ,0
23 ,8,2000,69 ,0
21 ,3,750,38 ,0
23 ,3,750,35 ,0
21 ,3,750,40 ,0
23 ,2,500,28 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
25 ,6,1500,50 ,0
21 ,1,250,21 ,0
21 ,1,250,21 ,0
23 ,3,750,39 ,0
21 ,2,500,33 ,0
14 ,3,750,79 ,0
23 ,1,250,23 ,1
23 ,1,250,23 ,0
23 ,1,250,23 ,0
23 ,1,250,23 ,0
23 ,1,250,23 ,0
23 ,1,250,23 ,0
23 ,1,250,23 ,0
23 ,4,1000,52 ,0
23 ,1,250,23 ,0
23 ,7,1750,88 ,0
16 ,3,750,86 ,0
23 ,2,500,38 ,0
21 ,2,500,52 ,0
23 ,3,750,62 ,0
39 ,1,250,39 ,0
72 ,1,250,72 ,0
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