In [41]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [11]:
train = pd.read_csv('200811-201811.csv')
train.head()
#Danger分類點說明
#對敏感族群不健康為PM2.5數值在35.5以上
Out[11]:
| SO2 | CO | O3 | PM25 | Nox | NO | NO2 | THC | NMHC | CH4 | WindSpeed | TEMP | Humidity | Danger | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4.4 | 0.47 | 32.2 | 31 | 24 | 3.46 | 20.84 | 2.309 | 0.231 | 2.078 | 1.91 | 24.86 | 77.11 | 0 |
| 1 | 6.4 | 0.52 | 30.2 | 32 | 32 | 5.64 | 26.30 | 2.186 | 0.227 | 1.959 | 1.72 | 26.58 | 71.93 | 0 |
| 2 | 3.2 | 0.45 | 30.5 | 46 | 20 | 2.36 | 18.05 | 0.000 | 0.000 | 0.000 | 2.08 | 24.75 | 76.33 | 1 |
| 3 | 5.2 | 0.47 | 32.5 | 38 | 24 | 3.18 | 20.64 | 2.374 | 0.225 | 2.150 | 1.66 | 24.97 | 79.97 | 1 |
| 4 | 4.5 | 0.72 | 29.9 | 32 | 29 | 5.43 | 23.82 | 2.290 | 0.287 | 2.004 | 1.03 | 26.18 | 73.78 | 0 |
用heatmap(.isnull())來找出缺失的資料在哪些欄位
In [12]:
sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x139462b0>
用countplot來看Nox是否影響健康
In [13]:
sns.countplot(x='Danger',hue='Nox',data=train,palette='RdBu_r')
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x112d9080>
用直方圖看年齡分佈。缺失資料在此不計。
In [15]:
sns.distplot(train['PM25'].dropna(),kde=False,bins=30)
Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0x14668b70>
用直方圖看Nox的分佈
In [16]:
train['Nox'].hist(bins=40,figsize=(10,4))
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x555ffd0>
In [18]:
X = train.drop('Danger', axis=1)
y = train['Danger']
In [19]:
from sklearn.model_selection import train_test_split
In [32]:
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.75,random_state=42)
In [33]:
from sklearn.linear_model import LogisticRegression
In [34]:
logmodel = LogisticRegression(solver='liblinear')
logmodel.fit(X_train,y_train)
Out[34]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='warn',
n_jobs=None, penalty='l2', random_state=None, solver='liblinear',
tol=0.0001, verbose=0, warm_start=False)
In [35]:
predictions = logmodel.predict(X_test)
In [36]:
from sklearn.metrics import classification_report
print(classification_report(y_test,predictions))
precision recall f1-score support
0 0.96 0.98 0.97 560
1 0.98 0.96 0.97 526
micro avg 0.97 0.97 0.97 1086
macro avg 0.97 0.97 0.97 1086
weighted avg 0.97 0.97 0.97 1086
In [40]:
from sklearn.metrics import confusion_matrix
In [39]:
confusion_matrix(y_test,predictions)
Out[39]:
array([[549, 11],
[ 20, 506]], dtype=int64)
In [ ]: