kaggle自行车租赁预测

kaggle自行车租赁预测

1.数据

  • 为自行车租赁系统提供的数据,提供数据为2年内华盛顿按小时记录的自行车租赁数据。数据来源:

    Kaggle自行车租赁预测比赛数据

字段介绍:
datetime    日期(年月日时分秒)
season      季节。1为春季,2为夏季,3为秋季,4为冬季
hodliday    是否为假期。1代表是,0代表不是
workingday  是否为工作日。1代表是,0代表不是。
weather     天气。1天气晴朗或多云,2有雾和云/峰等,3小雪/小雨,闪电及多云。4大雨/冰雹/闪电和大雾/大雪。
temp        摄氏温度
atemp       人们感觉的温度
humidity    湿度
windspeed   风速
casual      没有注册的预定自行车的人数
registered  注册了的预定自行车的人数
count       总租车人数
#最后三个字段3个不属于特征

2.数据预处理

  • 通过pandas导入数据

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib inline
    #pandas数据读入
    df_train = pd.read_csv("kaggle_bike_competition_train.csv",header=0)
  • 瞅一眼看看数据格式,这里打印前5行:

    df_train.head(5)

    kaggle自行车租赁预测_第1张图片

  • 查看一下数据有没有缺省值

    print(df_train.shape)
    # 看看有没有缺省值
    print(df_train.count())
    """
    (10886, 12)
    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
    """
  • 把月,日和小时单独拎出来放到df_train中:

    df_train['month'] = pd.DatetimeIndex(df_train.datetime).month
    df_train['day'] = pd.DatetimeIndex(df_train.datetime).dayofweek
    df_train['hour'] = pd.DatetimeIndex(df_train.datetime).hour
  • 将不属于特征的字段去掉,这里是datetime,casual,registered

    #datetime 通过上一步拆分月,日,时更加形象
    #casual,registered为目标预测数据
    df_train = df_train.drop(['datetime','casual','registered'],axis=1)
  • 再查看数据

    df_train.head(5)

    kaggle自行车租赁预测_第2张图片

  • 将数据分为2个部分:
    • 1.df_train_target:目标,也就是count字段
    • 2.df_train_data:用于产生特征的数据

3.特征工程

  • 下面的过程会让你看到,其实应用机器学习算法的过程,多半是在调参,各种不同的参数会带来不同的结果(比如正则化系数,比如决策树类的算法的树深和颗树,比如距离判定准则等等等)

  • 我们使用交叉验证的方式(交叉验证集约占全部数据的20%)来看看模型效果,使用以上三个模型,都跑3趟,看看它们平均值评分结果:

    • 向量回归Suport Vector Regression
    • 岭回归Ridge Regression
    • 随机森林回归Random Forest Regressor
    from sklearn import linear_model#岭回归
    from sklearn import model_selection
    from sklearn import svm#向量回归
    from sklearn.ensemble import RandomForestRegressor#随机森林回归包
    from sklearn.model_selection import learning_curve
    from sklearn.model_selection import GridSearchCV
    from sklearn.metrics import explained_variance_score
    # 切分数据(训练集和测试集)
    cv = model_selection.ShuffleSplit(n_splits=3,test_size=0.2,random_state=0)
    cv_split = cv.split(df_train_data)
    print("岭回归")
    for train,test in cv_split:
        svc = linear_model.Ridge().fit(df_train_data[train], df_train_target[train])
        print("train score: {0:.3f}, test score: {1:.3f}\n".format(
            svc.score(df_train_data[train], df_train_target[train]), 
            svc.score(df_train_data[test], df_train_target[test])))
    print("支持向量回归/SVR(kernel='rbf',C=10,gamma=.001)")
    for train,test in cv.split(df_train_data):
        svc = svm.SVR(kernel ='rbf', C = 10, gamma = .001).fit(df_train_data[train], df_train_target[train])
        print("train score: {0:.3f}, test score: {1:.3f}\n".format(
            svc.score(df_train_data[train], df_train_target[train]),
            svc.score(df_train_data[test], df_train_target[test])))
    print("随机森林回归/Random Forest(n_estimators = 100)")   
    for train, test in cv.split(df_train_data):    
        svc = RandomForestRegressor(n_estimators = 100).fit(df_train_data[train], df_train_target[train])
        print("train score: {0:.3f}, test score: {1:.3f}\n".format(
            svc.score(df_train_data[train], df_train_target[train]), 
            svc.score(df_train_data[test], df_train_target[test])))
    • 结果展示:
    岭回归
    train score: 0.339, test score: 0.332
    
    train score: 0.330, test score: 0.370
    
    train score: 0.342, test score: 0.320
    
    支持向量回归/SVR(kernel='rbf',C=10,gamma=.001)
    train score: 0.417, test score: 0.408
    
    train score: 0.406, test score: 0.452
    
    train score: 0.419, test score: 0.390
    
    随机森林回归/Random Forest(n_estimators = 100)
    train score: 0.982, test score: 0.864
    
    train score: 0.982, test score: 0.880
    
    train score: 0.981, test score: 0.869

4.模型参数调整

  • 不用自己折腾,通过GridSearch,帮我们调节最佳参数

    X = df_train_data
    y = df_train_target
    
    X_train, X_test, y_train, y_test = model_selection.train_test_split(
        X, y, test_size=0.2, random_state=0)
    tuned_parameters = [{'n_estimators':[10,100,500]}]   
    
    scores = ['r2']
    
    for score in scores:
        print(score)
        clf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)
        clf.fit(X_train, y_train)
        #best_estimator_ returns the best estimator chosen by the search
        print(clf.best_estimator_)
        print("得分分别是:")
        #grid_scores_的返回值:
        #    * a dict of parameter settings
        #    * the mean score over the cross-validation folds 
        #    * the list of scores for each fold
    
        for mean_score in clf.cv_results_["mean_test_score"]:
            print("%0.3f"
                  % (mean_score,))
    #得到结果需要花费些时间
    • 结果展示
    r2
    RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
               max_features='auto', max_leaf_nodes=None,
               min_impurity_decrease=0.0, min_impurity_split=None,
               min_samples_leaf=1, min_samples_split=2,
               min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,
               oob_score=False, random_state=None, verbose=0, warm_start=False)
    得分分别是:
    0.846
    0.861
    0.863
  • 可视化展示,看看模型学习曲线是否过拟合或欠拟合

    
    def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                            n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
    
        plt.figure()
        plt.title(title)
        if ylim is not None:
            plt.ylim(*ylim)
        plt.xlabel("Training examples")
        plt.ylabel("Score")
        train_sizes, train_scores, test_scores = learning_curve(
            estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
        train_scores_mean = np.mean(train_scores, axis=1)
        train_scores_std = np.std(train_scores, axis=1)
        test_scores_mean = np.mean(test_scores, axis=1)
        test_scores_std = np.std(test_scores, axis=1)
        plt.grid()
    
        plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                         train_scores_mean + train_scores_std, alpha=0.1,
                         color="r")
        plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                         test_scores_mean + test_scores_std, alpha=0.1, color="g")
        plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
                 label="Training score")
        plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
                 label="Cross-validation score")
    
        plt.legend(loc="best")
        return plt
    
    
    title = "Learning Curves (Random Forest, n_estimators = 100)"
    cv = model_selection.ShuffleSplit(n_splits=3,test_size=0.2,random_state=0)
    cv_split = cv.split(df_train_data)
    estimator = RandomForestRegressor(n_estimators = 100)
    plot_learning_curve(estimator, title, X, y, (0.0, 1.01), cv=cv_split, n_jobs=4)
    
    plt.show()

    kaggle自行车租赁预测_第3张图片

  • 随机森林算法学习能力比较强,由图可以发现,训练集和测试机分差较大,过拟合很明显,尝试缓解过拟合(未必成功):

    print("随机森林回归/Random Forest(n_estimators=200, max_features=0.6, max_depth=15)")
    cv = model_selection.ShuffleSplit(n_splits=6,test_size=0.2,random_state=0)
    
    
    for train, test in cv.split(df_train_data): 
        svc = RandomForestRegressor(n_estimators = 200, max_features=0.6, max_depth=15).fit(df_train_data[train], df_train_target[train])
        print("train score: {0:.3f}, test score: {1:.3f}\n".format(
            svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))
    • 显示结果
    随机森林回归/Random Forest(n_estimators=200, max_features=0.6, max_depth=15)
    train score: 0.965, test score: 0.868
    
    train score: 0.966, test score: 0.885
    
    train score: 0.966, test score: 0.873
    
    train score: 0.965, test score: 0.876
    
    train score: 0.966, test score: 0.869
    
    train score: 0.966, test score: 0.872

5.特征项分析:

1.温度对租车影响:

df_train_origin.groupby('temp').mean().plot(y='count', marker='o')
plt.show()

kaggle自行车租赁预测_第4张图片

2.风速对租车影响:

df_train_origin.groupby('windspeed').mean().plot(y='count', marker='o')
plt.show()

kaggle自行车租赁预测_第5张图片

3.湿度对租车影响

# 湿度
df_train_origin.groupby('humidity').mean().plot(y='count', marker='o')
plt.show()

kaggle自行车租赁预测_第6张图片

4.温度与湿度变化

df_train_origin.plot(x='temp', y='humidity', kind='scatter')
plt.show()

kaggle自行车租赁预测_第7张图片


# scatter一下各个维度
fig, axs = plt.subplots(2, 3, sharey=True)
df_train_origin.plot(kind='scatter', x='temp', y='count', ax=axs[0, 0], figsize=(16, 8), color='magenta')
df_train_origin.plot(kind='scatter', x='atemp', y='count', ax=axs[0, 1], color='cyan')
df_train_origin.plot(kind='scatter', x='humidity', y='count', ax=axs[0, 2], color='red')
df_train_origin.plot(kind='scatter', x='windspeed', y='count', ax=axs[1, 0], color='yellow')
df_train_origin.plot(kind='scatter', x='month', y='count', ax=axs[1, 1], color='blue')
df_train_origin.plot(kind='scatter', x='hour', y='count', ax=axs[1, 2], color='green')

kaggle自行车租赁预测_第8张图片

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