Kaggle - Bike Sharing Prediction

最新推荐文章于 2022-08-22 12:01:51 发布
最新推荐文章于 2022-08-22 12:01:51 发布
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分类专栏： Machine Learning 大数据练兵场 文章标签： kaggle 机器学习 数据挖掘 回归预测 python
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.youkuaiyun.com/qq_14959801/article/details/51281714
本研究深入分析了自行车共享需求的预测模型，利用历史数据探讨了季节、天气、工作日等因素对需求的影响，并通过多种机器学习算法进行模型训练，包括线性回归、岭回归、Lasso回归和梯度提升等，旨在提高预测精度。

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import pylab
import calendar
import numpy as np
import pandas as pd
import seaborn as sn
from scipy import stats
import missingno as msno
from datetime import datetime
import matplotlib.pyplot as plt
import warnings
pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)
%matplotlib inline

In [2]:

dailyData = pd.read_csv("C:/Users/_lc/BikeSharingDemand/train.csv")
dailyData.head(2)

Out[2]:

 datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcount
02011-01-01 00:00:0010019.8414.395810.031316
12011-01-01 01:00:0010019.0213.635800.083240

 

Out[58]:

 timeday
02011-01-01 00:00:001
12011-01-01 01:00:001
22011-01-01 02:00:001
32011-01-01 03:00:001
42011-01-01 04:00:001

In [5]:

datetime - hourly date + timestamp
season - 1 = spring, 2 = summer, 3 = fall, 4 = winter
holiday - whether the day is considered a holiday
workingday - whether the day is neither a weekend nor holiday
weather -
1: Clear, Few clouds, Partly cloudy, Partly cloudy
2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
temp - temperature in Celsius
atemp - "feels like" temperature in Celsius
humidity - relative humidity
windspeed - wind speed
casual - number of non-registered user rentals initiated
registered - number of registered user rentals initiated
count - number of total rentals (Dependent Variable)

dailyData.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
datetime      10886 non-null object
season        10886 non-null int64
holiday       10886 non-null int64
workingday    10886 non-null int64
weather       10886 non-null int64
temp          10886 non-null float64
atemp         10886 non-null float64
humidity      10886 non-null int64
windspeed     10886 non-null float64
casual        10886 non-null int64
registered    10886 non-null int64
count         10886 non-null int64
dtypes: float64(3), int64(8), object(1)
memory usage: 1020.6+ KB

In [3]:

dailyData['date'] = dailyData['datetime'].map(lambda x: x.split()[0])
dailyData['datetime'] = pd.to_datetime(dailyData.datetime)
dailyData['month'] = dailyData['datetime'].map(lambda x: x.month)
dailyData['day'] = dailyData['datetime'].map(lambda x: x.day)
dailyData['hour'] = dailyData['datetime'].map(lambda x: x.hour)
dailyData['weekday'] = dailyData['datetime'].map(lambda x: x.weekday())
dailyData.head(2)

Out[3]:

 datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcountdatemonthdayhourweekday
02011-01-01 00:00:0010019.8414.395810.0313162011-01-011105
12011-01-01 01:00:0010019.0213.635800.0832402011-01-011115

In [4]:

dailyData["season"] = dailyData.season.map({1: "Spring", 2 : "Summer", 3 : "Fall", 4 :"Winter" })
dailyData["weather"] = dailyData.weather.map({1: " Clear + Few clouds + Partly cloudy + Partly cloudy",\
                                        2 : " Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist ", \
                                        3 : " Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds", \
                                        4 :" Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog " })
dailyData.head(2)

Out[4]:

 datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcountdatemonthdayhourweekday
02011-01-01 00:00:00Spring00Clear + Few clouds + Partly cloudy + Partly c...9.8414.395810.0313162011-01-011105
12011-01-01 01:00:00Spring00Clear + Few clouds + Partly cloudy + Partly c...9.0213.635800.0832402011-01-011115

In [5]:

categoryVariableList = ["hour","weekday","month","season","weather","holiday","workingday"]
for var in categoryVariableList:
    dailyData[var] = dailyData[var].astype("category")
dailyData.head(2)

Out[5]:

 datetimeseasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcountdatemonthdayhourweekday
02011-01-01 00:00:00Spring00Clear + Few clouds + Partly cloudy + Partly c...9.8414.395810.0313162011-01-011105
12011-01-01 01:00:00Spring00Clear + Few clouds + Partly cloudy + Partly c...9.0213.635800.0832402011-01-011115

In [6]:

dailyData.drop('datetime', axis=1, inplace=True)
dailyData.head(2)

Out[6]:

 seasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredcountdatemonthdayhourweekday
0Spring00Clear + Few clouds + Partly cloudy + Partly c...9.8414.395810.0313162011-01-011105
1Spring00Clear + Few clouds + Partly cloudy + Partly c...9.0213.635800.0832402011-01-011115

In [7]:

dataTypeDf = dailyData.dtypes.value_counts().to_frame().reset_index().rename(columns={'index':'variableType', 0:'count'})
sn.set_style('darkgrid')
ax = plt.figure(figsize=(10,4)).add_subplot(111)
dataTypeDf.plot(kind='bar',x="variableType",y="count",ax=ax)
ax.set(xlabel='variableTypeariable Type', ylabel='Count',title="Variables DataType Count")

Out[7]:

[Text(0, 0.5, 'Count'),
 Text(0.5, 0, 'variableTypeariable Type'),
 Text(0.5, 1.0, 'Variables DataType Count')]

In [8]:

msno.matrix(dailyData,figsize=(12,5))

Out[8]:

In [8]:

fig, axes = plt.subplots(nrows=2,ncols=2)
fig.set_size_inches(12, 10)
sn.boxplot(data=dailyData,y="count",orient="v",ax=axes[0][0])
sn.boxplot(data=dailyData,y="count",x="season",orient="v",ax=axes[0][1])
sn.boxplot(data=dailyData,y="count",x="hour",orient="v",ax=axes[1][0])
sn.boxplot(data=dailyData,y="count",x="workingday",orient="v",ax=axes[1][1])

axes[0][0].set(ylabel='Count',title="Box Plot On Count")
axes[0][1].set(xlabel='Season', ylabel='Count',title="Box Plot On Count Across Season")
axes[1][0].set(xlabel='Hour Of The Day', ylabel='Count',title="Box Plot On Count Across Hour Of The Day")
axes[1][1].set(xlabel='Working Day', ylabel='Count',title="Box Plot On Count Across Working Day")

Out[8]:

[Text(0, 0.5, 'Count'),
 Text(0.5, 0, 'Working Day'),
 Text(0.5, 1.0, 'Box Plot On Count Across Working Day')]

In [9]:

dailyDataWithoutOutliers = dailyData[np.abs(dailyData["count"]-dailyData["count"].mean())<=(3*dailyData["count"].std())]
len(dailyDataWithoutOutliers)

Out[9]:

10739

In [10]:

corrMatt = dailyData[["temp","atemp","casual","registered","humidity","windspeed","count"]].corr()
mask = np.array(corrMatt)
mask[np.tril_indices_from(mask)] = False
fig,ax= plt.subplots()
fig.set_size_inches(20,10)
sn.heatmap(corrMatt, mask=mask,vmax=.8, square=True,annot=True)

Out[10]:

<matplotlib.axes._subplots.AxesSubplot at 0xc355dd8>

In [11]:

fig,(ax1,ax2,ax3) = plt.subplots(ncols=3)
fig.set_size_inches(12, 5)
sn.regplot(x="temp", y="count", data=dailyData,ax=ax1)
sn.regplot(x="windspeed", y="count", data=dailyData,ax=ax2)
sn.regplot(x="humidity", y="count", data=dailyData,ax=ax3)

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0xa761cf8>

In [12]:

fig,axes = plt.subplots(ncols=2,nrows=2)
fig.set_size_inches(12, 10)
sn.distplot(dailyData["count"],ax=axes[0][0])
stats.probplot(dailyData["count"], dist='norm', fit=True, plot=axes[0][1])
sn.distplot(np.log(dailyDataWithoutOutliers["count"]),ax=axes[1][0])
stats.probplot(np.log1p(dailyDataWithoutOutliers["count"]), dist='norm', fit=True, plot=axes[1][1])

Out[12]:

((array([-3.82819677, -3.60401975, -3.48099008, ...,  3.48099008,
          3.60401975,  3.82819677]),
  array([0.69314718, 0.69314718, 0.69314718, ..., 6.5971457 , 6.59850903,
         6.5998705 ])),
 (1.3486990121229765, 4.562423868087808, 0.9581176780909608))

In [13]:

fig,(ax1,ax2,ax3,ax4)= plt.subplots(nrows=4)
fig.set_size_inches(12,20)
sortOrder = ["January","February","March","April","May","June","July","August","September","October","November","December"]
hueOrder = ["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]

monthAggregated = pd.DataFrame(dailyData.groupby("month")["count"].mean()).reset_index()
sn.barplot(data=monthAggregated,x="month",y="count",ax=ax1)
ax1.set(xlabel='Month', ylabel='Avearage Count',title="Average Count By Month")

hourAggregated = pd.DataFrame(dailyData.groupby(["hour","season"],sort=True)["count"].mean()).reset_index()
sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["count"],hue=hourAggregated["season"], data=hourAggregated, join=True,ax=ax2)
ax2.set(xlabel='Hour Of The Day', ylabel='Users Count',title="Average Users Count By Hour Of The Day Across Season",label='big')

hourAggregated = pd.DataFrame(dailyData.groupby(["hour","weekday"],sort=True)["count"].mean()).reset_index()
sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["count"],hue=hourAggregated["weekday"], data=hourAggregated, join=True,ax=ax3)
ax3.set(xlabel='Hour Of The Day', ylabel='Users Count',title="Average Users Count By Hour Of The Day Across Weekdays",label='big')

hourTransformed = pd.melt(dailyData[["hour","casual","registered"]], id_vars=['hour'], value_vars=['casual', 'registered'])
hourAggregated = pd.DataFrame(hourTransformed.groupby(["hour","variable"],sort=True)["value"].mean()).reset_index()
sn.pointplot(x=hourAggregated["hour"], y=hourAggregated["value"],hue=hourAggregated["variable"],hue_order=["casual","registered"], data=hourAggregated, join=True,ax=ax4)
ax4.set(xlabel='Hour Of The Day', ylabel='Users Count',title="Average Users Count By Hour Of The Day Across User Type",label='big')

Out[13]:

[Text(0, 0.5, 'Users Count'),
 Text(0.5, 0, 'Hour Of The Day'),
 Text(0.5, 1.0, 'Average Users Count By Hour Of The Day Across User Type'),
 None]

In [14]:

dataTrain = pd.read_csv("C:/Users/_lc/BikeSharingDemand/train.csv")
dataTest = pd.read_csv("C:/Users/_lc/BikeSharingDemand/test.csv")
dataTest.head(2)

Out[14]:

 datetimeseasonholidayworkingdayweathertempatemphumiditywindspeed
02011-01-20 00:00:00101110.6611.3655626.0027
12011-01-20 01:00:00101110.6613.635560.0000

In [35]:

data = dataTrain.append(dataTest)
data.reset_index(inplace=True)
data.drop('index',inplace=True,axis=1)
data.head()

Out[35]:

 atempcasualcountdatetimeholidayhumidityregisteredseasontempweatherwindspeedworkingday
014.3953.016.02011-01-01 00:00:0008113.019.8410.00
113.6358.040.02011-01-01 01:00:0008032.019.0210.00
213.6355.032.02011-01-01 02:00:0008027.019.0210.00
314.3953.013.02011-01-01 03:00:0007510.019.8410.00
414.3950.01.02011-01-01 04:00:000751.019.8410.00

In [36]:

data["datetime"] = pd.to_datetime(data.datetime)
data["year"] = data.datetime.map(lambda x : x.year)
data["month"] = data.datetime.apply(lambda x: x.month)
data["day"] = data.datetime.apply(lambda x : x.day)
data["hour"] = data.datetime.apply(lambda x : x.hour)
data['weekday'] = data.datetime.map(lambda x: x.weekday())
data.head()

Out[36]:

 atempcasualcountdatetimeholidayhumidityregisteredseasontempweatherwindspeedworkingdayyearmonthdayhourweekday
014.3953.016.02011-01-01 00:00:0008113.019.8410.0020111105
113.6358.040.02011-01-01 01:00:0008032.019.0210.0020111115
213.6355.032.02011-01-01 02:00:0008027.019.0210.0020111125
314.3953.013.02011-01-01 03:00:0007510.019.8410.0020111135
414.3950.01.02011-01-01 04:00:000751.019.8410.0020111145

In [37]:

# ---------预测风速为零的---------
from sklearn.ensemble import RandomForestRegressor
dataWind0 = data[data["windspeed"]==0]
dataWindNot0 = data[data["windspeed"]!=0]
rfModel_wind = RandomForestRegressor()
windColumns = ["season","weather","humidity","month","temp","year","atemp"]
rfModel_wind.fit(dataWindNot0[windColumns], dataWindNot0["windspeed"])

wind0Values = rfModel_wind.predict(X= dataWind0[windColumns])
dataWind0["windspeed"] = wind0Values
data = dataWindNot0.append(dataWind0)
data.reset_index(inplace=True)
data.drop('index',inplace=True,axis=1)
data.head()

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)

Out[37]:

 atempcasualcountdatetimeholidayhumidityregisteredseasontempweatherwindspeedworkingdayyearmonthdayhourweekday
012.8800.01.02011-01-01 05:00:000751.019.8426.0032020111155
119.69512.036.02011-01-01 10:00:0007624.0115.58116.99790201111105
216.66526.056.02011-01-01 11:00:0008130.0114.76119.00120201111115
321.21029.084.02011-01-01 12:00:0007755.0117.22119.00120201111125
422.72547.094.02011-01-01 13:00:0007247.0118.86219.99950201111135

In [39]:

categoricalFeatureNames = ["season","holiday","workingday","weather","weekday","month","year","hour"]
numericalFeatureNames = ["temp","humidity","windspeed","atemp"]
dropFeatures = ['casual',"count","datetime","day","registered"]
for var in categoricalFeatureNames:
    data[var] = data[var].astype("category")

In [40]:

dataTrain = data[pd.notnull(data['count'])].sort_values(by=["datetime"])
dataTest = data[~pd.notnull(data['count'])].sort_values(by=["datetime"])
datetimecol = dataTest["datetime"]
yLabels = dataTrain["count"]
yLablesRegistered = dataTrain["registered"]
yLablesCasual = dataTrain["casual"]

dataTrain  = dataTrain.drop(dropFeatures,axis=1)
dataTest  = dataTest.drop(dropFeatures,axis=1)

In [47]:

# 对数均方误差
def rmsle(y, y_,convertExp=True):
    if convertExp:
        y = np.exp(y),
        y_ = np.exp(y_)
    log1 = np.nan_to_num(np.array([np.log(v + 1) for v in y]))
    log2 = np.nan_to_num(np.array([np.log(v + 1) for v in y_]))
    calc = (log1 - log2) ** 2
    return np.sqrt(np.mean(calc))

In [48]:

from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
import warnings
pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)

lModel = LinearRegression()

yLabelsLog = np.log1p(yLabels)
lModel.fit(X = dataTrain,y = yLabelsLog)

preds = lModel.predict(X= dataTrain)
print ("RMSLE Value For Linear Regression: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

RMSLE Value For Linear Regression:  0.9779688131592883

In [53]:

ridge_m_ = Ridge()
ridge_params_ = { 'max_iter':[3000],'alpha':[0.1, 1, 2, 3, 4, 10, 30,100,200,300,400,800,900,1000]}
rmsle_scorer = metrics.make_scorer(rmsle, greater_is_better=False)
grid_ridge_m = GridSearchCV( ridge_m_,
                          ridge_params_,
                          scoring = rmsle_scorer,
                          cv=5)
yLabelsLog = np.log1p(yLabels)
grid_ridge_m.fit( dataTrain, yLabelsLog )
preds = grid_ridge_m.predict(X= dataTrain)
print (grid_ridge_m.best_params_)
print ("RMSLE Value For Ridge Regression: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

fig,ax= plt.subplots()
fig.set_size_inches(12,5)
df = pd.DataFrame(grid_ridge_m.cv_results_)
df["alpha"] = df["params"].apply(lambda x:x["alpha"])
df["rmsle"] = df["mean_test_score"].apply(lambda x:-x)
sn.pointplot(data=df,x="alpha",y="rmsle",ax=ax)

{'alpha': 0.1, 'max_iter': 3000}
RMSLE Value For Ridge Regression:  0.9779687981095982

Out[53]:

<matplotlib.axes._subplots.AxesSubplot at 0x12c49cc0>

In [55]:

lasso_m_ = Lasso()

alpha  = 1/np.array([0.1, 1, 2, 3, 4, 10, 30,100,200,300,400,800,900,1000])
lasso_params_ = { 'max_iter':[3000],'alpha':alpha}

grid_lasso_m = GridSearchCV( lasso_m_,lasso_params_,scoring = rmsle_scorer,cv=5)
yLabelsLog = np.log1p(yLabels)
grid_lasso_m.fit( dataTrain, yLabelsLog )
preds = grid_lasso_m.predict(X= dataTrain)
print (grid_lasso_m.best_params_)
print ("RMSLE Value For Lasso Regression: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

fig,ax= plt.subplots()
fig.set_size_inches(12,5)
df = pd.DataFrame(grid_lasso_m.cv_results_)
df["alpha"] = df["params"].apply(lambda x:x["alpha"])
df["rmsle"] = df["mean_test_score"].apply(lambda x:-x)
sn.pointplot(data=df,x="alpha",y="rmsle",ax=ax)

{'alpha': 0.005, 'max_iter': 3000}
RMSLE Value For Lasso Regression:  0.9781068303163186

Out[55]:

<matplotlib.axes._subplots.AxesSubplot at 0x12dbd080>

In [56]:

from sklearn.ensemble import GradientBoostingRegressor
gbm = GradientBoostingRegressor(n_estimators=4000,alpha=0.01); ### Test 0.41
yLabelsLog = np.log1p(yLabels)
gbm.fit(dataTrain,yLabelsLog)
preds = gbm.predict(X= dataTrain)
print ("RMSLE Value For Gradient Boost: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False))

predsTest = gbm.predict(X= dataTest)
fig,(ax1,ax2)= plt.subplots(ncols=2)
fig.set_size_inches(12,5)
sn.distplot(yLabels,ax=ax1,bins=50)
sn.distplot(np.exp(predsTest),ax=ax2,bins=50)

RMSLE Value For Gradient Boost:  0.1896091619650034

Out[56]:

<matplotlib.axes._subplots.AxesSubplot at 0x14104278>

 

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bike-share-prediction:共享单车使用量预测API
05-10
自行车份额预测 该工具允许希望获得共享单车使用量预测的人们。 目前，它可以与系统一起使用，但是可以在一分钟内扩展到任何共享单车系统，尤其是任何系统。 该Web服务的示例实例在Heroku上运行，您可以在以下位置访问它： : 。 它包含Vélib合同的站点，但不做任何预测。 安装 从获取API密钥 从获取API密钥 从获取API密钥（每天最多1000个调用） 使用以下格式创建config/api_keys.yml文件： jcdecaux: API_KEY openweathermap: API_KEY forecastio: API_KEY bundle install bundle exec rake db:create bundle exec rake db:migrate bundle exec whenever -i bundle exec rake fetch:p
kaggle-bike-sharing-demand:我的 kaggle“游乐场”比赛代码
06-28
在本项目中，我们将探索和分析“kaggle-bike-sharing-demand”数据集，这是一个源自Kaggle竞赛的真实世界问题，旨在预测共享单车的使用需求。这个数据集包含了多个变量，如天气条件、时间、日期等，这些因素可能会...
kaggle-bike-sharing:在 R 中为 Kaggle.com Bike Sharing Demand 挑战实现条件推理树模型
06-20
在这个名为“kaggle-bike-sharing”的项目中，我们关注的是使用条件推理树（CART：Classification And Regression Trees）模型来解决Kaggle上的“自行车共享需求”预测挑战。Kaggle是一个全球知名的机器学习竞赛平台...
matlabauc代码-kaggle-seizure-prediction:在墨尔本大学AES/MathWorks/NIH癫痫预测中工作201
05-24
Matlab的耳语在2016年9月至2016年12月工作 挑战概述 提供给挑战的数据是来自三个癫痫患者的10分钟颅内脑电图（iEEG）信号。 对于训练数据，iEEG剪辑被分类为发生在正常脑部活动（发作性）发作前（发作前）一小时或...
kaggle-click-through-rate-prediction:Avazu 的 Kaggle 点击率预测竞赛的机器学习代码
07-09
kaggle-点击率预测 Avazu 的 Kaggle 点击率预测竞赛的机器学习代码 比赛页面： :
kaggle-environments
05-15
pip install kaggle-environments TLDR； from kaggle_environments import make # Setup a tictactoe environment. env = make ( "tictactoe" ) # Basic agent which marks the first available cell. def my_...
kaggle平台bike-sharing竞赛数据集
08-31
kaggle平台bike-sharing竞赛的数据集。https://www.kaggle.com/c/bike-sharing-demand
【AI—pytorch学习】项目实战-单车预测器
weixin_43912994的博客
02-29 1126
Pytorch实战项目学习笔记——单车预测器 学习自集智俱乐部——《Pytorch深度学习原理与实战》 1.数据 原书中给出的地址好像有些问题或者被修改了，下载的数据与书中提到的数据不一致。查阅后可在 http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset 地址下下载。下载后包含连个文件，书中案例使用的是hour文件 共享单...
Bike sharing demand prediction 【kaggle competition】
anzhi的博客
06-28 1683
Bike sharing demand prediction
kaggle——Bike Sharing Demand
Do not mess it up
08-15 1238
个人感觉最主要是通过visualization来获取一些信息， 比如24小时变化的曲线 工作日24h变话的曲线 周末24h变换的曲线 从中就可以挖掘出一些有用的信息 挖掘出来最简单的应用就是根据时间来预测，用当前时间段的所有的平均值代替就行了， 但是这样做出来的结果是比较差的，还不如直接用random forest来的LB效果好 也许应该对不同的时间进
Kaggle共享单车需求项目详解
coffeetogether的博客
07-08 4902
Kaggle共享单车需求项目详解 导入库 %matplotlib inline import numpy as np import pandas as pd from datetime import datetime import warnings warnings.filterwarnings('ignore') import matplotlib.pyplot as plt import seaborn as sns sns.set(style='whitegrid' , palette='t
【数据分析实战】kaggle项目：bike sharing demand
SpriteNym的博客
08-22 1538
前10%，不戳！
实战四：Kaggle自行车租赁预测比赛
超级圈的博客
06-29 2840
第一步：数据读取与预分析 import numpy as np import pandas as pd import matplotlib.pyplot as plt df_train = pd.read_csv('kaggle_bike_competition_train.csv') print(df_train.head()) print(df_train.shape) print(d...
模型训练的一些坑
追求灵魂代码
02-15 8485
背景 用随机森林来实现房价预测，期间遇到一些坑。 正文 问题：RandomForestClassifier创建的随机森林模型，训练函数fit()的时候，内存报错。 报错：MemoryError: could not allocate 613351424 bytes 因为根据提示，现在n_estimators参数现在从10变成100。也就是说，你需要更大的内存来跑这个模型。 FutureWarn...
Kaggle/kaggle-api
最新发布
12-10
3. 下载数据集，如在`kaggle-Click-Through-Rate-Prediction`项目中： ```python from kaggle.api.kaggle_api_extended import KaggleApi api = KaggleApi() api.authenticate() dataset = "your_dataset_name...
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