text_plain_output_1.png image_output_1.png
from scipy import stats

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

import statsmodels.api as sm

train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')

train_data.head(10)[['damageDealt', 'winPlacePerc']]

import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='darkgrid')
data_to_plot = train_data.sample(n=200)[['damageDealt', 'winPlacePerc']]

data_to_plot = train_data[train_data.matchType.isin({'solo', 'solo-fpp'})].sample(n=200)[['damageDealt', 'winPlacePerc']]

from scipy import stats
solo_train_data = train_data[train_data.matchType.isin({'solo', 'solo-fpp'})]

import statsmodels.api as sm
sample = solo_train_data.sample(n=10)

sns.regplot(x='damageDealt', y='winPlacePerc', x_jitter=5, logistic=True, data=solo_train_data.sample(n=2000)[['damageDealt', 'winPlacePerc']])