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【SVM】kaggle之澳大利亚天气预测

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项目目标

 

由于大气运动极为复杂,影响天气的因素较多,而人们认识大气本身运动的能力极为有限,因此天气预报水平较低,预报员在预报实践中,每次预报的过程都极为复杂,需要综合分析,并预报各气象要素,比如温度、降水等。本项目需要训练一个二分类模型,来预测在给定天气因素下,城市是否下雨。

 

数据说明

 

本数据包含了来自澳大利亚多个气候站的日常共15W的数据,项目随机抽取了1W条数据作为样本。特征如下:

 

特征 含义
Date 观察日期
Location 获取该信息的气象站的名称
MinTemp 以摄氏度为单位的低温度
MaxTemp 以摄氏度为单位的高温度
Rainfall 当天记录的降雨量,单位为mm
Evaporation 到早上9点之前的24小时的A级蒸发量(mm)
Sunshine 白日受到日照的完整小时
WindGustDir 在到午夜12点前的24小时中的强风的风向
WindGustSpeed 在到午夜12点前的24小时中的强风速(km/h)
WindDir9am 9点时的风向
WindDir3pm 下午3点时的风向
WindSpeed9am 上午9点之前每个十分钟的风速的平均值(km/h)
WindSpeed3pm 下午3点之前每个十分钟的风速的平均值(km/h)
Humidity9am 上午9点的湿度(百分比)
Humidity3am 下午3点的湿度(百分比)
Pressure9am 上午9点平均海平面上的大气压(hpa)
Pressure3pm 下午3点平均海平面上的大气压(hpa)
Cloud9am 上午9点的天空被云层遮蔽的程度,0表示完全晴朗的天空,而8表示它完全是阴天
Cloud3pm 下午3点的天空被云层遮蔽的程度
Temp9am 上午9点的摄氏度温度
Temp3pm 下午3点的摄氏度温度

 

项目过程

 

-处理缺失值

 

-删除与预测无关的特征

 

-随机抽样

 

-对分类变量进行编码

 

-处理异常值

 

-数据归一化

 

-训练模型

 

-模型预测

 

项目代码(Jupyter)

 

import pandas as pd
import numpy as np

 

读取数据 探索数据

 

weather = pd.read_csv("weather.csv", index_col=0)
weather.head()
weather.info()

 

 

<class 'pandas.core.frame.DataFrame'>
Int64Index: 142193 entries, 0 to 142192
Data columns (total 20 columns):
 #   Column         Non-Null Count   Dtype  
---  ------         --------------   -----  
 0   MinTemp        141556 non-null  float64
 1   MaxTemp        141871 non-null  float64
 2   Rainfall       140787 non-null  float64
 3   Evaporation    81350 non-null   float64
 4   Sunshine       74377 non-null   float64
 5   WindGustDir    132863 non-null  object 
 6   WindGustSpeed  132923 non-null  float64
 7   WindDir9am     132180 non-null  object 
 8   WindDir3pm     138415 non-null  object 
 9   WindSpeed9am   140845 non-null  float64
 10  WindSpeed3pm   139563 non-null  float64
 11  Humidity9am    140419 non-null  float64
 12  Humidity3pm    138583 non-null  float64
 13  Pressure9am    128179 non-null  float64
 14  Pressure3pm    128212 non-null  float64
 15  Cloud9am       88536 non-null   float64
 16  Cloud3pm       85099 non-null   float64
 17  Temp9am        141289 non-null  float64
 18  Temp3pm        139467 non-null  float64
 19  RainTomorrow   142193 non-null  object 
dtypes: float64(16), object(4)
memory usage: 22.8+ MB

 

删除与预测无关的特征

 

weather.drop(["Date", "Location"],inplace=True, axis=1)

 

删除缺失值,重置索引

 

weather.dropna(inplace=True)
weather.index = range(len(weather))

 

1.WindGustDir WindDir9am WindDir3pm 属于定性数据中的无序数据——OneHotEncoder

 

2.Cloud9am Cloud3pm 属于定性数据中的有序数据——OrdinalEncoder

 

3.RainTomorrow 属于标签变量——LabelEncoder

 

为了简便起见,WindGustDir WindDir9am WindDir3pm 三个风向中只保留第一个最强风向

 

weather_sample.drop(["WindDir9am", "WindDir3pm"], inplace=True, axis=1)

 

编码分类变量

 

from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder,LabelEncoder
print(np.unique(weather_sample["RainTomorrow"]))
print(np.unique(weather_sample["WindGustDir"]))
print(np.unique(weather_sample["Cloud9am"]))
print(np.unique(weather_sample["Cloud3pm"]))

 

['No' 'Yes']
['E' 'ENE' 'ESE' 'N' 'NE' 'NNE' 'NNW' 'NW' 'S' 'SE' 'SSE' 'SSW' 'SW' 'W'
 'WNW' 'WSW']
[0. 1. 2. 3. 4. 5. 6. 7. 8.]
[0. 1. 2. 3. 4. 5. 6. 7. 8.]

 

# 查看样本不均衡问题,较轻微
weather_sample["RainTomorrow"].value_counts()

 

No     7750
Yes    2250
Name: RainTomorrow, dtype: int64

 

# 编码标签
weather_sample["RainTomorrow"] = pd.DataFrame(LabelEncoder().fit_transform(weather_sample["RainTomorrow"]))

 

# 编码Cloud9am Cloud3pm
oe = OrdinalEncoder().fit(weather_sample["Cloud9am"].values.reshape(-1, 1))
weather_sample["Cloud9am"] = pd.DataFrame(oe.transform(weather_sample["Cloud9am"].values.reshape(-1, 1)))
weather_sample["Cloud3pm"] = pd.DataFrame(oe.transform(weather_sample["Cloud3pm"].values.reshape(-1, 1)))

 

# 编码WindGustDir
ohe = OneHotEncoder(sparse=False)
ohe.fit(weather_sample["WindGustDir"].values.reshape(-1, 1))
WindGustDir_df = pd.DataFrame(ohe.transform(weather_sample["WindGustDir"].values.reshape(-1, 1)), columns=ohe.get_feature_names())

 

WindGustDir_df.tail()

 

 

合并数据

 

weather_sample_new = pd.concat([weather_sample,WindGustDir_df],axis=1)
weather_sample_new.drop(["WindGustDir"], inplace=True, axis=1)
weather_sample_new

 

 

调整列顺序,将数值型变量与分类变量分开,便于数据归一化

 

Cloud9am = weather_sample_new.iloc[:,12]
Cloud3pm = weather_sample_new.iloc[:,13]
weather_sample_new.drop(["Cloud9am"], inplace=True, axis=1)
weather_sample_new.drop(["Cloud3pm"], inplace=True, axis=1)
weather_sample_new["Cloud9am"] = Cloud9am
weather_sample_new["Cloud3pm"] = Cloud3pm
RainTomorrow = weather_sample_new["RainTomorrow"]
weather_sample_new.drop(["RainTomorrow"], inplace=True, axis=1)
weather_sample_new["RainTomorrow"] = RainTomorrow
weather_sample_new.head()

 

 

为了防止数据归一化受到异常值影响,在此之前先处理异常值

 

# 观察数据异常情况
weather_sample_new.describe([0.01,0.99])

 

因为数据归一化只针对数值型变量,所以将两者进行分离

 

# 对数值型变量和分类变量进行切片
weather_sample_mv = weather_sample_new.iloc[:,0:14]
weather_sample_cv = weather_sample_new.iloc[:,14:33]

 

盖帽法处理异常值

 

## 盖帽法处理数值型变量的异常值
def cap(df,quantile=[0.01,0.99]):
    for col in df:
        # 生成分位数
        Q01,Q99 = df[col].quantile(quantile).values.tolist()
        
        # 替换异常值为指定的分位数
        if Q01 > df[col].min():
            df.loc[df[col] < Q01, col] = Q01
        
        if Q99 < df[col].max():
            df.loc[df[col] > Q99, col] = Q99
        
cap(weather_sample_mv)
weather_sample_mv.describe([0.01,0.99])

 

 

数据归一化

 

from sklearn.preprocessing import StandardScaler
weather_sample_mv = pd.DataFrame(StandardScaler().fit_transform(weather_sample_mv))
weather_sample_mv

 

 

重新合并数据

 

weather_sample = pd.concat([weather_sample_mv, weather_sample_cv], axis=1)
weather_sample.head()

 

 

划分特征与标签

 

X = weather_sample.iloc[:,:-1]
y = weather_sample.iloc[:,-1]

 

print(X.shape)
print(y.shape)

 

(10000, 32)
(10000,)

 

创建模型与交叉验证

 

from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_auc_score, recall_score

 

for kernel in ["linear","poly","rbf"]:
    accuracy = cross_val_score(SVC(kernel=kernel), X, y, cv=5, scoring="accuracy").mean()
    print("{}:{}".format(kernel,accuracy))

 

linear:0.8564
poly:0.8532
rbf:0.8531000000000001

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