Analyzing models for pets 🐈 🐕 🐇 🐁 🦎 🦮 🐕‍🦺

Model 1: Pet TOPSIS Model (PTM)
Model 2: Multiple Pets TOPSIS Model (MPTM)
Model 3: Pets' future living trend predicting
Weight settings

Model 1: Pet TOPSIS Model (PTM) 🐾

A TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model for calculating whether multiple households are suitable for keeping different pets. Whether these family are suitable for keeping pets is calculated as a number for 0 to 1.

Sample input: 8 factors of certain amount of families to compare.

Input to the code as: the specie of the pet (Cat, Dog, Rabbit, Hamster, Lizard) + number of rows of the table + each number in the table (from column to row)

Cat 6 1 7 4 7 3 7 78 2 0 1 10 3 7 10 27 1 1 8 7 5 5 4 62 4 0 9 20 3 18 5 32 2 1 10 100 4 22 18 45 0 0 4 50 8 15 9 38 3

After forwardization:

1 7 4 7 3 7 0.536585 1 0 1 10 3 7 10 0.962963 1 1 8 7 5 5 4 0.926829 0.333333 0 9 20 3 18 5 1 1 1 10 100 4 22 18 1 1 0 4 50 8 15 9 1 0.666667

Sample output: A number from 0 to 1.

0.63818 0.0880036 0.642584 0.305568 0.879334 0.26832

The larger the result value is, the more suitable the family is for this pet.

Model 2: Multiple Pets TOPSIS Model (MPTM) 🐾

The only difference is an extra factor => number of cats and dogs (if they have had cats or/and dogs).

Sample input:

Cat 6 1 7 4 7 3 7 78 2 0 0 1 10 3 7 10 27 1 1 1 8 7 5 5 4 62 4 0 0 9 20 3 18 5 32 2 2 1 10 100 4 22 18 45 0 3 0 4 50 8 15 9 38 3 2

Sample output:

0.662219 0.206854 0.667652 0.310214 0.720904 0.269207

Model 3: Pets' future retention trend predicting (for 🐈 🐕 🐇 🦎) 🐾

Using a Gaussian distribution model and adjust the standard deviation for known mortality rates to obtain the predicted number of pets per year in the future.
Factors such as the species' lifetime are already set in the code.
The average lifetime of hamsters🐁(21 months) is too short to be culculated in this model.

Sample input:

Enter the specie of pets: Cat
Enter the number of pets: 1000000
Enter the age of the pets: 6

Sample output:

Year 1: 999711 pets are predicted to survive.
Year 2: 997483 pets are predicted to survive.
Year 3: 985603 pets are predicted to survive.
Year 4: 941050 pets are predicted to survive.
Year 5: 825527 pets are predicted to survive.
Year 6: 621802 pets are predicted to survive.
Year 7: 376317 pets are predicted to survive.
Year 8: 173729 pets are predicted to survive.
Year 9: 58856 pets are predicted to survive.
Year 10: 14252 pets are predicted to survive.
Year 11: 2437 pets are predicted to survive.
Year 12: 287 pets are predicted to survive.
Year 13: 21 pets are predicted to survive.
Year 14: 0 pets are predicted to survive.
Year 15: 0 pets are predicted to survive.

Weights setting:

The weights of the eight factors for different species are already calculated.

PTM

    weightsC = {0.3453, 0.2018, 0.1249, 0.1144, 0.0679, 0.0679, 0.0389, 0.0389};
    weightsD = {0.2864, 0.1835, 0.1861, 0.1100, 0.0758, 0.0802, 0.0464, 0.0316};
    weightsH = {0.3395, 0.1432, 0.2034, 0.0830, 0.0552, 0.1107, 0.0382, 0.0267};
    weightsR = {0.3142, 0.2229, 0.1415, 0.0607, 0.1032, 0.0863, 0.0395, 0.0316};
    weightsL = {0.2542, 0.2955, 0.1453, 0.1162, 0.0791, 0.0570, 0.0313, 0.0214};

Cat:

Dog:

Hamster:

Rabbit:

Lizard:

MPTM

    weightsC = {0.3044, 0.1806, 0.1084, 0.0941, 0.0564, 0.0564, 0.0332, 0.0332, 0.1333};
    weightsD = {0.2488, 0.1544, 0.1566, 0.0908, 0.0626, 0.0659, 0.0392, 0.0271, 0.1544};
    weightsH = {0.3107, 0.1337, 0.1825, 0.0713, 0.0492, 0.0930, 0.0339, 0.0239, 0.1019};
    weightsR = {0.2787, 0.2053, 0.1310, 0.0510, 0.0852, 0.0708, 0.0337, 0.0276, 0.1168};
    weightsL = {0.2367, 0.2778, 0.1397, 0.1087, 0.0777, 0.0501, 0.0280, 0.0194, 0.0618};

Cat:

Dog:

Hamster:

Rabbit:

Lizard:

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analyzing-models-for-pets-1.0's Introduction

Analyzing models for pets 🐈 🐕 🐇 🐁 🦎 🦮 🐕‍🦺

Model 1: Pet TOPSIS Model (PTM) 🐾

Sample input: 8 factors of certain amount of families to compare.

After forwardization:

Sample output: A number from 0 to 1.

Model 2: Multiple Pets TOPSIS Model (MPTM) 🐾

Sample input:

Sample output:

Model 3: Pets' future retention trend predicting (for 🐈 🐕 🐇 🦎) 🐾

Sample input:

Sample output:

Weights setting:

PTM

MPTM

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