Pokemon Attributes :
- Name: Name of each pokemon
- base_experience : Base Experience of Pokemon
- height : Height Of pokemon
- weight : Weight Of pokemon
- Type 1: Each pokemon has a type, this determines weakness/resistance to attacks
- Type 2: Some pokemon are dual type and have 2
- Abilities : (1,2,Hidden) Abilities of pokemon determines behavior or responding to certain conditions such as terrain,stat change etc.
- Total: sum of all stats that come after this, a general guide to how strong a pokemon is
- HP: hit points, or health, defines how much damage a pokemon can withstand before fainting
- Attack: the base modifier for normal attacks (eg. Scratch, Punch)
- Defense: the base damage resistance against normal attacks
- SP Atk: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam)
- SP Def: the base damage resistance against special attacks
- Speed: determines which pokemon attacks first each round
Importing Modules :
# Ignore warnings
import warnings
warnings.filterwarnings('ignore')
# Handle table-like data and matrices
import numpy as np
import pandas as pd
# Modelling Algorithms
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
# Modelling Helpers
from sklearn.preprocessing import Imputer , Normalizer , scale
from sklearn.cross_validation import train_test_split , StratifiedKFold
from sklearn.feature_selection import RFECV
# Visualisation
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
# Configure visualisations
%matplotlib inline
mpl.style.use( 'ggplot' )
sns.set_style( 'white' )
pylab.rcParams[ 'figure.figsize' ] = 8 , 6
Dataset Description :
pokemons.describe()
|
id |
species_id |
height |
weight |
base_experience |
| count |
721.00000 |
721.00000 |
721.000000 |
721.000000 |
721.000000 |
| mean |
361.00000 |
361.00000 |
11.399445 |
568.753121 |
142.081831 |
| std |
208.27906 |
208.27906 |
10.304594 |
901.054114 |
74.391851 |
| min |
1.00000 |
1.00000 |
1.000000 |
1.000000 |
36.000000 |
| 25% |
181.00000 |
181.00000 |
6.000000 |
94.000000 |
66.000000 |
| 50% |
361.00000 |
361.00000 |
10.000000 |
280.000000 |
147.000000 |
| 75% |
541.00000 |
541.00000 |
14.000000 |
610.000000 |
178.000000 |
| max |
721.00000 |
721.00000 |
145.000000 |
9500.000000 |
608.000000 |
|
attack |
defense |
hp |
special_attack |
special_defense |
| count |
721.000000 |
721.000000 |
721.000000 |
721.000000 |
721.000000 |
| mean |
74.986130 |
70.836338 |
68.380028 |
68.710125 |
69.319001 |
| std |
28.950913 |
29.333681 |
25.848272 |
28.748150 |
27.057671 |
| min |
5.000000 |
5.000000 |
1.000000 |
10.000000 |
20.000000 |
| 25% |
53.000000 |
50.000000 |
50.000000 |
45.000000 |
50.000000 |
| 50% |
74.000000 |
65.000000 |
65.000000 |
65.000000 |
65.000000 |
| 75% |
95.000000 |
85.000000 |
80.000000 |
90.000000 |
85.000000 |
| max |
165.000000 |
230.000000 |
255.000000 |
154.000000 |
230.000000 |
|
speed |
generation_id |
evolves_from_species_id |
evolution_chain_id |
shape_id |
| count |
721.000000 |
721.000000 |
355.000000 |
721.000000 |
721.000000 |
| mean |
65.714286 |
3.323162 |
333.819718 |
175.660194 |
7.604716 |
| std |
27.277920 |
1.669873 |
211.629743 |
110.757190 |
3.435587 |
| min |
5.000000 |
1.000000 |
1.000000 |
1.000000 |
1.000000 |
| 25% |
45.000000 |
2.000000 |
139.000000 |
76.000000 |
6.000000 |
| 50% |
65.000000 |
3.000000 |
315.000000 |
168.000000 |
8.000000 |
| 75% |
85.000000 |
5.000000 |
526.000000 |
276.000000 |
10.000000 |
| max |
160.000000 |
6.000000 |
714.000000 |
373.000000 |
14.000000 |
Correlation Heat map :
plot_correlation_map( pokemons )
Demography of Pokemons :
Primary type :
pokemons['type_1'].value_counts()
labels = ('Water', 'Normal', 'Grass', 'Bug', 'Psychic', 'Fire', 'Electric', 'Rock','Ground','Dark'
,'Dragon','Poison','Fighting','Ice','Steel','Ghost','Fairy','Flying')
sizes = [105, 93, 66, 63, 47, 47, 41, 36, 30,28,28,25,24,23,23,22,17,3]
colors = ['#2196f3', '#ffc849', '#9deb3a', '#fbff00', '#e000ff', '#ff6800', '#fffb00', '#ad7102','#fecb6a','#5a5a5a', '#ec0000', '#9e00ff', '#c11e1e', '#1eaac1', '#ccc5c5' , '#6468ff', '#fd6cff', '#6cbcff']
explode = (0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2 ,0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2) # only "explode" the 3rd slice
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
autopct='%1.1f%%', shadow=True, startangle=90)
plt.axis('equal')
plt.title("Percentage of Different Types of Pokemon")
plt.plot()
fig=plt.gcf()
fig.set_size_inches(7,7)
plt.show()
water 105
normal 93
grass 66
bug 63
fire 47
psychic 47
rock 41
electric 36
ground 30
poison 28
dark 28
fighting 25
dragon 24
ice 23
ghost 23
steel 22
fairy 17
flying 3
Secondary Type :
flying 87 
poison 31
ground 30
psychic 27
fighting 19
steel 19
fairy 18
grass 18
dark 16
dragon 14
rock 14
water 13
ghost 12
ice 10
fire 9
electric 6
normal 4
bug 3
Stats Comparison between Legendary and Non-Legendary Pokemons :
plot_distribution( pokemons , var = 'Attack' , target = 'Generation' , row = 'Legendary' )
plot_distribution( pokemons , var = 'Defense' , target = 'Generation' , row = 'Legendary' )
plot_distribution( pokemons , var = 'Sp. Atk' , target = 'Generation' , row = 'Legendary' )
plot_distribution( pokemons , var = 'Sp. Def' , target = 'Generation' , row = 'Legendary' )
plot_distribution( pokemons , var = 'Speed' , target = 'Generation' , row = 'Legendary' )
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Aditya Vichare 