【SVM】kaggle之澳大利亚天气预测

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：

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