Skip to content

This project contains the the information about how frequently a man uses the different drugs. Trained a Machine Learning model that can predict the drug uses pattern of a person using different personality measurement attributes

Notifications You must be signed in to change notification settings

deepak525/Drug-Consumption

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Drug-Consumption

Database contains records for 1885 respondents. For each respondent 12 attributes are known: Personality measurements which include NEO-FFI-R (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness), BIS-11 (impulsivity), and ImpSS (sensation seeking), level of education, age, gender, country of residence and ethnicity. All input attributes are originally categorical and are quantified. After quantification values of all input features can be considered as real-valued. In addition, participants were questioned concerning their use of 18 legal and illegal drugs (alcohol, amphetamines, amyl nitrite, benzodiazepine, cannabis, chocolate, cocaine, caffeine, crack, ecstasy, heroin, ketamine, legal highs, LSD, methadone, mushrooms, nicotine and volatile substance abuse and one fictitious drug (Semeron) which was introduced to identify over-claimers. For each drug they have to select one of the answers: never used the drug, used it over a decade ago, or in the last decade, year, month, week, or day. Database contains 18 classification problems. Each of independent label variables contains seven classes: "Never Used", "Used over a Decade Ago", "Used in Last Decade", "Used in Last Year", "Used in Last Month", "Used in Last Week", and "Used in Last Day".

Problem which can be solved:

  • Seven class classifications for each drug separately.
  • Problem can be transformed to binary classification by union of part of classes into one new class. For example, "Never Used", "Used over a Decade Ago" form class "Non-user" and all other classes form class "User".
  • The best binarization of classes for each attribute.
  • Evaluation of risk to be drug consumer for each drug.

Data Set Information:

Database contains records for 1885 respondents. For each respondent 12 attributes are known: Personality measurements which include NEO-FFI-R (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness), BIS-11 (impulsivity), and ImpSS (sensation seeking), level of education, age, gender, country of residence and ethnicity.

All input attributes are originally categorical and are quantified. After quantification values of all input features can be considered as real-valued. In addition, participants were questioned concerning their use of 18 legal and illegal drugs (alcohol, amphetamines, amyl nitrite, benzodiazepine, cannabis, chocolate, cocaine, caffeine, crack, ecstasy, heroin, ketamine, legal highs, LSD, methadone, mushrooms, nicotine and volatile substance abuse and one fictitious drug (Semeron) which was introduced to identify over-claimers.

For each drug they have to select one of the answers: never used the drug, used it over a decade ago, or in the last decade, year, month, week, or day. Database contains 18 classification problems. Each of independent label variables contains seven classes: "Never Used", "Used over a Decade Ago", "Used in Last Decade", "Used in Last Year", "Used in Last Month", "Used in Last Week", and "Used in Last Day".

Attribute Information:

  1. ID: ID is number of record in original database. Cannot be related to participant. It can be used for reference only.
  1. Age: Age is the age of participant and has one of the values:
Value Meaning Cases Fraction
-0.95197 18 - 24 643 34.11%
-0.07854 25 - 34 481 25.52%
0.49788 35 - 44 356 18.89%
1.09449 45 - 54 294 15.60%
1.82213 55 - 64 93 4.93%
2.59171 65+ 18 0.95%

3.Gender: Gender is gender of participant:

Value Meaning Cases Fraction
0.48246 Female 942 49.97%
-0.48246 Male 943 50.03%
  1. Education: Education is level of education of participant and has one of the values:
Value Meaning Cases Fraction
-2.43591 Left School Before 16 years 28 1.49%
-1.73790 Left School at 16 years 99 5.25%
-1.43719 Left School at 17 years 30 1.59%
-1.22751 Left School at 18 years 100 5.31%
-0.61113 Some College,No Certificate Or Degree 506 26.84%
-0.05921 Professional Certificate/ Diploma 270 14.32%
0.45468 University Degree 480 25.46%
1.16365 Masters Degree 283 15.01%
1.98437 Doctorate Degree 89 4.72%
  1. Country: Country is country of current residence of participant and has one of the values:
Value Meaning Cases Fraction
-0.09765 Australia 54 2.86%
0.24923 Canada 87 4.62%
-0.46841 New Zealand 5 0.27%
-0.28519 Other 118 6.26%
0.21128 Republic of Ireland 20 1.06%
0.96082 UK 1044 55.38%
-0.57009 USA 557 29.55%

6.Ethnicity: Ethnicity is ethnicity of participant and has one of the values:

Value Meaning Cases Fraction
-0.50212 Asian 26 1.38%
-1.10702 Black 33 1.75%
1.90725 Mixed-Black/Asian 3 0.16%
0.12600 Mixed-White/Asian 20 1.06%
-0.22166 Mixed-White/Black 20 1.06%
0.11440 Other 63 3.34%
-0.31685 White 1720 91.25%
  1. Nscore: Nscore is NEO-FFI-R Neuroticism. Neuroticism is one of the Big Five higher-order personality traits in the study of psychology. Individuals who score high on neuroticism are more likely than average to be moody and to experience such feelings as anxiety, worry, fear, anger, frustration, envy, jealousy, guilt, depressed mood, and loneliness. Possible values are presented in table below:
Nscore Value Nscore Value Nscore Value Nscore Value
12 -3.46436 24 -1.32828 36 0.04257 48 1.23461
13 -3.15735 25 -1.19430 37 0.13606 49 1.37297
14 -2.75696 26 -1.05308 38 0.22393 50 1.49158
15 -2.52197 27 -0.92104 39 0.31287 51 1.60383
16 -2.42317 28 -0.79151 40 0.41667 52 1.72012
17 -2.34360 29 -0.67825 41 0.52135 53 1.83990
18 -2.21844 30 -0.58016 42 0.62967 54 1.98437
19 -2.05048 31 -0.46725 43 0.73545 55 2.12700
20 -1.86962 32 -0.34799 44 0.82562 56 2.28554
21 -1.69163 33 -0.24649 45 0.91093 57 2.46262
22 -1.55078 34 -0.14882 46 1.02119 58 2.61139
23 -1.43907 35 -0.05188 47 1.13281 59 2.82196
- - - - - - 60 3.27393
  1. EScore: Escore (Real) is NEO-FFI-R Extraversion. Extraversion is one of the five personality traits of the Big Five personality theory. It indicates how outgoing and social a person is. A person who scores high in extraversion on a personality test is the life of the party. They enjoy being with people, participating in social gatherings, and are full of energy. Possible values are presented in table below:
Escore Value Escore Value Escore Value Escore Value
16 -3.27393 27 -1.76250 38 -0.30033 49 1.45421
17 -3.00537 28 -1.63340 39 -0.15487 50 1.58487
18 -3.00537 29 -1.50796 40 0.00332 51 1.74091
19 -2.72827 30 -1.37639 41 0.16767 52 1.93886
20 -2.53830 31 -1.23177 42 0.32197 53 2.12700
21 -2.44904 32 -1.09207 43 0.47617 54 2.32338
22 -2.32338 33 -0.94779 44 0.63779 55 2.57309
23 -2.21069 34 -0.80615 45 0.80523 56 2.85950
24 -2.11437 35 -0.69509 46 0.96248 57 2.85950
25 -2.03972 36 -0.57545 47 1.11406 58 3.00537
26 -1.92173 37 -0.43999 48 1.28610 59 3.27393
  1. Oscore: Oscore (Real) is NEO-FFI-R Openness to experience. Openness is one of the five personality traits of the Big Five personality theory. It indicates how open-minded a person is. A person with a high level of openness to experience in a personality test enjoys trying new things. They are imaginative, curious, and open-minded. Individuals who are low in openness to experience would rather not try new things. They are close-minded, literal and enjoy having a routine. Possible values are presented in table below:
Oscore Value Oscore Value Oscore Value
24 -3.27393 38 -1.11902 50 0.58331
26 -2.85950 39 -0.97631 51 0.72330
28 -2.63199 40 -0.84732 52 0.88309
29 -2.39883 41 -0.71727 53 1.06238
30 -2.21069 42 -0.58331 54 1.24033
31 -2.09015 43 -0.45174 55 1.43533
32 -1.97495 44 -0.31776 56 1.65653
33 -1.82919 45 -0.17779 57 1.88511
34 -1.68062 46 -0.01928 58 1.15324
35 -1.55521 47 0.14143 59 2.44904
36 -1.42424 48 0.29338 60 2.90161
37 -1.27553 49 0.44585 NaN NaN
  1. Ascore: Ascore(Real) is NEO-FFI-R Agreeableness. Agreeableness is one of the five personality traits of the Big Five personality theory. A person with a high level of agreeableness in a personality test is usually warm, friendly, and tactful. They generally have an optimistic view of human nature and get along well with others. Possible values are presented in table below:
Ascore Value Ascore Value Ascore Value
12 -3.46436 34 -1.34289 48 0.76096
16 -3.15735 35 -1.21213 49 0.94156
18 -3.00537 36 -1.07533 50 1.11406
23 -2.90161 37 -0.91699 51 1.2861
24 -2.78793 38 -0.76096 52 1.45039
25 -2.70172 39 -0.60633 53 1.61108
26 -2.53830 40 -0.45321 54 1.81866
27 -2.35413 41 -0.30172 55 2.03972
28 -2.21844 42 -0.15487 56 2.23427
29 -2.07848 43 -0.01729 57 2.46262
30 -1.92595 44 0.13136 58 2.75696
31 -1.77200 45 0.28783 59 3.15735
32 -1.62090 46 0.43852 60 3.46436
33 -1.47955 47 0.59042 NaN NaN
  1. Cscore: Cscore (Real) is NEO-FFI-R Conscientiousness. Conscientiousness is one of the five personality traits of the Big Five personality theory. A person scoring high in conscientiousness usually has a high level of self-discipline. These individuals prefer to follow a plan, rather than act spontaneously. Their methodic planning and perseverance usually makes them highly successful in their chosen occupation. Possible values are presented in table below:
Cscore Value Cscore Value Cscore Value
17 -3.46436 32 -1.25773 46 0.58489
19 -3.15735 33 -1.13788 47 0.7583
20 -2.90161 34 -1.01450 48 0.93949
21 -2.72827 35 -0.89891 49 1.13407
22 -2.57309 36 -0.78155 50 1.30612
23 -2.42317 37 -0.65253 51 1.46191
24 -2.30408 38 -0.52745 52 1.63088
25 -2.18109 39 -0.40581 53 1.81175
26 -2.04506 40 -0.27607 54 2.04506
27 -1.92173 41 -0.14277 55 2.33337
28 -1.78169 42 -0.00665 56 2.63199
29 -1.64101 43 0.12331 57 3.00537
30 -1.51840 44 0.25953 59 3.46436
31 -1.38502 45 0.41594 NaN NaN
  1. Impulsive: Impulsive (Real) is impulsiveness measured by BIS-11. In psychology, impulsivity (or impulsiveness) is a tendency to act on a whim, displaying behavior characterized by little or no forethought, reflection, or consideration of the consequences. If you describe someone as impulsive, you mean that they do things suddenly without thinking about them carefully first. Possible values are presented in table below:
Impulsiveness Cases Fraction
-2.55524 20 1.06%
-1.37983 276 14.64%
-0.71126 307 16.29%
-0.21712 355 18.83%
0.19268 257 13.63%
0.52975 216 11.46%
0.88113 195 10.34%
1.29221 148 7.85%
1.86203 104 5.52%
2.90161 7 0.37%
  1. Sensation: SS(Real) is sensation seeing measured by ImpSS. Sensation is input about the physical world obtained by our sensory receptors, and perception is the process by which the brain selects, organizes, and interprets these sensations. In other words, senses are the physiological basis of perception. Possible values are presented in table below:
SS Cases Fraction
-2.07848 71 3.77%
-1.54858 87 4.62%
-1.18084 132 7.00%
-0.84637 169 8.97%
-0.52593 211 11.19%
-0.21575 223 11.83%
0.07987 219 11.62%
0.40148 249 13.21%
0.76540 211 11.19%
1.22470 210 11.14%
1.92173 103 5.46%

The remaining columns are divided inti 7 classes:

Value Description
CL0 Never Used
CL1 Used over a Decade Ago
CL2 Used in Last Decade
CL3 Used in Last Year
CL4 Used in Last Month
CL5 Used in Last Week
CL6 Used in Last Day

References

  1. Drug, Wikipedia URL: https://en.wikipedia.org/wiki/Drug
  2. The Five Factor Model of personality Model of Personality and Evaluation of Drug Consumption risk, E.Fehrman, A.K. Muhammad, E.M. Mirkes, V. Egan, A.N Gorban. URL: https://arxiv.org/abs/1506.06297
  3. Detecting and Assessing Alcohol and Other Drug Use. URL: https://www.ncbi.nlm.nih.gov/books/NBK236259/
  4. Ibid.
  5. UCI-Machine Learning Repository URL: archive.ics.uci.edu/ml/datasets/Drug+consumption+%28quantified%29
  6. Numpy Documentation
  7. Matplotlib Documentation
  8. Seaborn Documentation
  9. Pandas Documentation.

About

This project contains the the information about how frequently a man uses the different drugs. Trained a Machine Learning model that can predict the drug uses pattern of a person using different personality measurement attributes

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published