城市气候与海洋的关系研究
导入包
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import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
导入数据各个海滨城市数据
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ferrara1 = pd.read_csv('./ferrara_150715.csv')
ferrara2 = pd.read_csv('./ferrara_250715.csv')
ferrara3 = pd.read_csv('./ferrara_270615.csv')
ferrara=pd.concat([ferrara1,ferrara2,ferrara3],ignore_index=True)
torino1 = pd.read_csv('./torino_150715.csv')
torino2 = pd.read_csv('./torino_250715.csv')
torino3 = pd.read_csv('./torino_270615.csv')
torino = pd.concat([torino1,torino2,torino3],ignore_index=True)
mantova1 = pd.read_csv('./mantova_150715.csv')
mantova2 = pd.read_csv('./mantova_250715.csv')
mantova3 = pd.read_csv('./mantova_270615.csv')
mantova = pd.concat([mantova1,mantova2,mantova3],ignore_index=True)
milano1 = pd.read_csv('./milano_150715.csv')
milano2 = pd.read_csv('./milano_250715.csv')
milano3 = pd.read_csv('./milano_270615.csv')
milano = pd.concat([milano1,milano2,milano3],ignore_index=True)
ravenna1 = pd.read_csv('./ravenna_150715.csv')
ravenna2 = pd.read_csv('./ravenna_250715.csv')
ravenna3 = pd.read_csv('./ravenna_270615.csv')
ravenna = pd.concat([ravenna1,ravenna2,ravenna3],ignore_index=True)
asti1 = pd.read_csv('./asti_150715.csv')
asti2 = pd.read_csv('./asti_250715.csv')
asti3 = pd.read_csv('./asti_270615.csv')
asti = pd.concat([asti1,asti2,asti3],ignore_index=True)
bologna1 = pd.read_csv('./bologna_150715.csv')
bologna2 = pd.read_csv('./bologna_250715.csv')
bologna3 = pd.read_csv('./bologna_270615.csv')
bologna = pd.concat([bologna1,bologna2,bologna3],ignore_index=True)
piacenza1 = pd.read_csv('./piacenza_150715.csv')
piacenza2 = pd.read_csv('./piacenza_250715.csv')
piacenza3 = pd.read_csv('./piacenza_270615.csv')
piacenza = pd.concat([piacenza1,piacenza2,piacenza3],ignore_index=True)
cesena1 = pd.read_csv('./cesena_150715.csv')
cesena2 = pd.read_csv('./cesena_250715.csv')
cesena3 = pd.read_csv('./cesena_270615.csv')
cesena = pd.concat([cesena1,cesena2,cesena3],ignore_index=True)
faenza1 = pd.read_csv('./faenza_150715.csv')
faenza2 = pd.read_csv('./faenza_250715.csv')
faenza3 = pd.read_csv('./faenza_270615.csv')
faenza = pd.concat([faenza1,faenza2,faenza3],ignore_index=True)
去除没用的列
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cesena.head(5)
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city_list = [ferrara,torino,mantova,milano,ravenna,asti,bologna,piacenza,cesena,faenza]
for city in city_list:
city.drop(labels='Unnamed: 0',axis=1,inplace=True)
显示最高温度于离海远近的关系（观察多个城市）
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city_max_temp = []
city_dist = []
for city in city_list:
max_temp = city['temp'].max()
city_max_temp.append(max_temp)
dist = city['dist'][0]
city_dist.append(dist)
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city_max_temp
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city_dist
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plt.scatter(city_dist,city_max_temp)
plt.xlabel('距离')
plt.ylabel('最高温度')
plt.title('距离和温度之间的关系图')
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观察发现，离海近的可以形成一条直线，离海远的也能形成一条直线。
- 分别以100公里和50公里为分界点，划分为离海近和离海远的两组数据(近海：小于100 远海：大于50)
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#找出所有的近海城市(温度和距离)
np_city_dist = np.array(city_dist)
np_city_max_temp = np.array(city_max_temp)
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near_condition = np_city_dist < 100
near_city_dist = np_city_dist[near_condition]
near_city_max_temp = np_city_max_temp[near_condition]
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plt.scatter(near_city_dist,near_city_max_temp)
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机器学习
- 算法模型对象:特殊的对象.在该对象中已经集成好个一个方程(还没有求出解的方程).
- 模型对象的作用:通过方程实现预测或者分类
- 样本数据(df,np):
- 特征数据:自变量
- 目标(标签)数据:因变量
- 模型对象的分类:
- 有监督学习:模型需要的样本数据中存在特征和目标
- 无监督学习:模型需要的样本数据中存在特征
- 半监督学习:模型需要的样本数据部分需要有特征和目标,部分只需要特征数据
- sklearn模块:封装了多种模型对象.
面积 采光率 楼层 售价 100 30% 18 33w 80 80% 3 133w
导入sklearn，建立线性回归算法模型对象
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#1.导包
from sklearn.linear_model import LinearRegression
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#2.实例化模型对象
linner = LinearRegression()
#3.提取样本数据
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#4.训练模型
linner.fit(near_city_dist.reshape(-1,1),near_city_max_temp)
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#5.预测
linner.predict(38)
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linner.score(near_city_dist.reshape(-1,1),near_city_max_temp)
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#绘制回归曲线
x = np.linspace(10,70,num=100)
y = linner.predict(x.reshape(-1,1))
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plt.scatter(near_city_dist,near_city_max_temp)
plt.scatter(x,y,marker=1)
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样本集：用于对机器学习算法模型对象进行训练。样本集通常为一个DataFrame。
- 特征数据：特征数据的变化会影响目标数据的变化。通常为多列。 - 目标数据：结果。通常为一列。