sklearn实战：Kaggle自行车租赁预测（岭回归，支持向量回归，随机森林回归）-优快云博客

本文链接：https://blog.youkuaiyun.com/Douhh_sisy/article/details/80586478

这篇博客通过sklearn库在Kaggle自行车租赁问题上应用岭回归、支持向量回归和随机森林回归算法，并利用GridSearch寻找最佳超参数。文章探讨了学习曲线，强调了随着训练样本数量增加验证分数提升的现象，以及如何识别和避免过拟合问题。

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df_train=pd.read_csv('kaggle_bike_competition_train.csv',header = 0)

df_train.head(10)

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	datetime	season	weather	temp	atemp	humidity	windspeed	casual	registered	count
0	2011-01-01 00:00:00	1	1	9.84	14.395	81	0.0000	3	13	16
1	2011-01-01 01:00:00	1	1	9.02	13.635	80	0.0000	8	32	40
2	2011-01-01 02:00:00	1	1	9.02	13.635	80	0.0000	5	27	32
3	2011-01-01 03:00:00	1	1	9.84	14.395	75	0.0000	3	10	13
4	2011-01-01 04:00:00	1	1	9.84	14.395	75	0.0000	0	1	1
5	2011-01-01 05:00:00	1	2	9.84	12.880	75	6.0032	0	1	1
6	2011-01-01 06:00:00	1	1	9.02	13.635	80	0.0000	2	0	2
7	2011-01-01 07:00:00	1	1	8.20	12.880	86	0.0000	1	2	3
8	2011-01-01 08:00:00	1	1	9.84	14.395	75	0.0000	1	7	8
9	2011-01-01 09:00:00	1	1	13.12	17.425	76	0.0000	8	6	14

字段的名字和类型

df_train.dtypes

datetime object season int64 holiday int64 workingday int64 weather int64 temp float64 atemp float64 humidity int64 windspeed float64 casual int64 registered int64 count int64 dtype: object

df_train.shape

(10886, 12) 看看有没有缺省的字段，好吧，其实那个字段的数据都不缺！

df_train.count()

datetime 10886 season 10886 holiday 10886 workingday 10886 weather 10886 temp 10886 atemp 10886 humidity 10886 windspeed 10886 casual 10886 registered 10886 count 10886 dtype: int64

type(df_train.datetime)

pandas.core.series.Series

# 把月、日、和 小时单独拎出来，放到3列中
df_train['month']=pd.DatetimeIndex(df_train.datetime).month  #创建新的一列'month'，月份
df_train['dayofweek']=pd.DatetimeIndex(df_train.datetime).dayofweek #星期几
df_train['hour']=pd.DatetimeIndex(df_train.datetime).hour #小时

df_train.head(20)

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