Overview

Dataset statistics

Number of variables11
Number of observations40768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory88.0 B

Variable types

CAT10
NUM1

Reproduction

Analysis started2020-08-24 23:54:55.574734
Analysis finished2020-08-24 23:54:59.317724
Duration3.74 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

x1
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
20549
-1
20219
ValueCountFrequency (%) 
12054950.4%
 
-12021949.6%
 
2020-08-24T23:54:59.573806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.495952708
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
14076828.6%
 
.4076828.6%
 
04076828.6%
 
-2021914.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153657.2%
 
Other Punctuation4076828.6%
 
Dash Punctuation2021914.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14076850.0%
 
04076850.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-20219100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common142523100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
14076828.6%
 
.4076828.6%
 
04076828.6%
 
-2021914.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII142523100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
14076828.6%
 
.4076828.6%
 
04076828.6%
 
-2021914.2%
 

x2
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
0
13719
-1
13639
1
13410
ValueCountFrequency (%) 
01371933.7%
 
-11363933.5%
 
11341032.9%
 
2020-08-24T23:54:59.897263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.334551609
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05448740.1%
 
.4076830.0%
 
12704919.9%
 
-1363910.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1363910.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05448766.8%
 
12704933.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13639100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135943100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05448740.1%
 
.4076830.0%
 
12704919.9%
 
-1363910.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135943100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05448740.1%
 
.4076830.0%
 
12704919.9%
 
-1363910.0%
 

x3
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
13667
-1
13636
0
13465
ValueCountFrequency (%) 
11366733.5%
 
-11363633.4%
 
01346533.0%
 
2020-08-24T23:55:00.233160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.334478022
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05423339.9%
 
.4076830.0%
 
12730320.1%
 
-1363610.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1363610.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05423366.5%
 
12730333.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13636100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135940100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05423339.9%
 
.4076830.0%
 
12730320.1%
 
-1363610.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135940100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05423339.9%
 
.4076830.0%
 
12730320.1%
 
-1363610.0%
 

x4
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
13670
0
13644
-1
13454
ValueCountFrequency (%) 
11367033.5%
 
01364433.5%
 
-11345433.0%
 
2020-08-24T23:55:00.556185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.330013736
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05441240.1%
 
.4076830.0%
 
12712420.0%
 
-134549.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.1%
 
Other Punctuation4076830.0%
 
Dash Punctuation134549.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05441266.7%
 
12712433.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13454100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135758100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05441240.1%
 
.4076830.0%
 
12712420.0%
 
-134549.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135758100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05441240.1%
 
.4076830.0%
 
12712420.0%
 
-134549.9%
 

x5
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
13686
-1
13566
0
13516
ValueCountFrequency (%) 
11368633.6%
 
-11356633.3%
 
01351633.2%
 
2020-08-24T23:55:00.881175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.332760989
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05428440.0%
 
.4076830.0%
 
12725220.1%
 
-1356610.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1356610.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13566100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05428466.6%
 
12725233.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135870100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05428440.0%
 
.4076830.0%
 
12725220.1%
 
-1356610.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135870100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05428440.0%
 
.4076830.0%
 
12725220.1%
 
-1356610.0%
 

x6
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
13627
-1
13618
0
13523
ValueCountFrequency (%) 
11362733.4%
 
-11361833.4%
 
01352333.2%
 
2020-08-24T23:55:01.202520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.334036499
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05429139.9%
 
.4076830.0%
 
12724520.0%
 
-1361810.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1361810.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05429166.6%
 
12724533.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13618100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135922100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05429139.9%
 
.4076830.0%
 
12724520.0%
 
-1361810.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135922100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05429139.9%
 
.4076830.0%
 
12724520.0%
 
-1361810.0%
 

x7
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
0
13664
1
13586
-1
13518
ValueCountFrequency (%) 
01366433.5%
 
11358633.3%
 
-11351833.2%
 
2020-08-24T23:55:01.523226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.331583595
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05443240.1%
 
.4076830.0%
 
12710420.0%
 
-1351810.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1351810.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05443266.8%
 
12710433.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13518100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135822100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05443240.1%
 
.4076830.0%
 
12710420.0%
 
-1351810.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135822100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05443240.1%
 
.4076830.0%
 
12710420.0%
 
-1351810.0%
 

x8
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
-1
13627
0
13574
1
13567
ValueCountFrequency (%) 
-11362733.4%
 
01357433.3%
 
11356733.3%
 
2020-08-24T23:55:01.848770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.334257261
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05434240.0%
 
.4076830.0%
 
12719420.0%
 
-1362710.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1362710.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05434266.6%
 
12719433.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13627100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135931100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05434240.0%
 
.4076830.0%
 
12719420.0%
 
-1362710.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135931100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05434240.0%
 
.4076830.0%
 
12719420.0%
 
-1362710.0%
 

x9
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
13647
0
13573
-1
13548
ValueCountFrequency (%) 
11364733.5%
 
01357333.3%
 
-11354833.2%
 
2020-08-24T23:55:02.183897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.332319466
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05434140.0%
 
.4076830.0%
 
12719520.0%
 
-1354810.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation1354810.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13548100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05434166.6%
 
12719533.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135852100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05434140.0%
 
.4076830.0%
 
12719520.0%
 
-1354810.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135852100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05434140.0%
 
.4076830.0%
 
12719520.0%
 
-1354810.0%
 

x10
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.6 KiB
1
13720
0
13562
-1
13486
ValueCountFrequency (%) 
11372033.7%
 
01356233.3%
 
-11348633.1%
 
2020-08-24T23:55:02.511675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.330798666
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)3
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05433040.0%
 
.4076830.0%
 
12720620.0%
 
-134869.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8153660.0%
 
Other Punctuation4076830.0%
 
Dash Punctuation134869.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05433066.6%
 
12720633.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.40768100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13486100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135790100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05433040.0%
 
.4076830.0%
 
12720620.0%
 
-134869.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135790100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05433040.0%
 
.4076830.0%
 
12720620.0%
 
-134869.9%
 

target
Real number (ℝ)

Distinct count40368
Unique (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009558489707427686
Minimum-12.694299697875975
Maximum12.20259952545166
Zeros0
Zeros (%)0.0%
Memory size318.6 KiB
2020-08-24T23:55:02.656543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-12.6942997
5-th percentile-7.283281398
Q1-3.17632246
median0.02114815079
Q33.213844955
95-th percentile7.318846703
Maximum12.20259953
Range24.89689922
Interquartile range (IQR)6.390167415

Descriptive statistics

Standard deviation4.39261068
Coefficient of variation (CV)459.5507046
Kurtosis-0.606878549
Mean0.009558489707
Median Absolute Deviation (MAD)3.195055008
Skewness-0.007671203875
Sum389.6805084
Variance19.29502859
2020-08-24T23:55:02.777059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.0349899533< 0.1%
 
2.0452699663< 0.1%
 
1.8172899483< 0.1%
 
-1.499699952< 0.1%
 
-1.3065799472< 0.1%
 
-0.1037269982< 0.1%
 
4.4696297652< 0.1%
 
-1.9400899412< 0.1%
 
4.7258400922< 0.1%
 
-2.1735200882< 0.1%
 
-1.7268500332< 0.1%
 
4.8172001842< 0.1%
 
-3.0517699722< 0.1%
 
2.8756101132< 0.1%
 
1.5653699642< 0.1%
 
10.277299882< 0.1%
 
-3.4998900892< 0.1%
 
-3.6331000332< 0.1%
 
-2.3291299342< 0.1%
 
-5.4984397892< 0.1%
 
-2.7656199932< 0.1%
 
-4.1025199892< 0.1%
 
-5.3589901922< 0.1%
 
2.922760012< 0.1%
 
4.2218799592< 0.1%
 
Other values (40343)4071599.9%
 
ValueCountFrequency (%) 
-12.69429971< 0.1%
 
-12.045900341< 0.1%
 
-11.574799541< 0.1%
 
-11.557900431< 0.1%
 
-11.460900311< 0.1%
 
-11.317700391< 0.1%
 
-11.267800331< 0.1%
 
-11.242099761< 0.1%
 
-11.226799961< 0.1%
 
-11.223999981< 0.1%
 
ValueCountFrequency (%) 
12.202599531< 0.1%
 
11.903800011< 0.1%
 
11.889499661< 0.1%
 
11.419599531< 0.1%
 
11.406800271< 0.1%
 
11.271300321< 0.1%
 
11.237099651< 0.1%
 
11.235799791< 0.1%
 
11.135600091< 0.1%
 
11.082200051< 0.1%
 

Interactions

2020-08-24T23:54:58.382660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:55:02.927329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-24T23:55:03.159245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-24T23:55:03.385062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-24T23:55:03.627691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-24T23:55:03.835946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-24T23:54:58.856658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:59.157922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

x1x2x3x4x5x6x7x8x9x10target
01.01.01.01.0-1.00.01.00.0-1.01.07.73906
1-1.00.0-1.0-1.01.01.01.00.0-1.00.03.95676
21.00.01.01.0-1.0-1.00.01.01.01.04.71592
31.00.00.01.00.0-1.01.01.01.0-1.05.02863
4-1.00.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-11.57480
51.01.01.0-1.0-1.0-1.0-1.01.0-1.0-1.06.87817
61.0-1.00.00.0-1.00.00.00.00.00.0-1.94303
71.0-1.00.0-1.01.01.01.0-1.01.00.0-1.42592
8-1.00.0-1.0-1.0-1.01.00.01.0-1.01.0-3.65451
9-1.01.0-1.00.01.00.0-1.00.0-1.01.0-1.49176

Last rows

x1x2x3x4x5x6x7x8x9x10target
40758-1.01.01.01.0-1.0-1.0-1.00.00.00.0-7.460250
40759-1.00.00.01.01.01.01.01.01.00.03.244770
40760-1.01.01.01.01.0-1.00.01.00.00.0-1.015610
40761-1.00.0-1.0-1.0-1.00.01.0-1.00.0-1.0-4.294800
40762-1.00.0-1.01.0-1.00.00.0-1.0-1.01.0-6.565690
407631.01.00.01.00.00.00.01.0-1.00.06.764770
407641.01.00.01.01.01.0-1.01.00.01.05.538390
407651.00.00.01.00.01.01.0-1.0-1.00.03.978300
40766-1.00.00.0-1.01.00.01.00.0-1.00.0-0.609818
407671.0-1.00.00.0-1.01.00.00.00.00.0-0.813671