机器学习进度05（FaceBook案例）

Facebook签到位置预测K值调优

 

 

#案例facebook
def facebook_demo():
    data = pd.read_csv("C:/Users/26301/Desktop/train.csv")
    #缩小数据范围
    data = data.query("x<2.5 & x>2 & y<1.5 & y>1")
    #处理时间特征
    #转换为年月日时分秒
    time_value = pd.to_datetime(data["time"],unit="s")
    date = pd.DatetimeIndex(time_value)
    #人工排除年和月两个信息
    data["day"] = date.day
    data["weekday"] = date.weekday
    data["hour"] = date.hour
    #print(data)
    
    #过滤掉签到次数少的地方
    #先统计每个地点被签到的次数
    place_count = data.groupby("place_id").count()[ "row_id"]
    place_count[place_count>3]
    data_final=data[data["place_id"].isin(place_count[place_count>3].index.values)]
    # 筛选特征值和目标值
    x = data_final[["x", "y", "accuracy", "day", "weekday", "hour"]]
    y = data_final["place_id"]
    # 数据集划分
    x_train, x_test, y_train, y_test = train_test_split(x, y)
    # 3）特征工程：标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)

    # 4）KNN算法预估器
    estimator = KNeighborsClassifier()

    # 加入网格搜索与交叉验证
    # 参数准备
    param_dict = {"n_neighbors": [3, 5, 7, 9]}
    estimator = GridSearchCV(estimator, param_grid=param_dict, cv=3)
    estimator.fit(x_train, y_train)
    # 5）模型评估
    # 方法1：直接比对真实值和预测值
    y_predict = estimator.predict(x_test)
    print("y_predict:\n", y_predict)
    print("直接比对真实值和预测值:\n", y_test == y_predict)

    # 方法2：计算准确率
    score = estimator.score(x_test, y_test)
    print("准确率为：\n", score)

    # 最佳参数：best_params_
    print("最佳参数：\n", estimator.best_params_)
    # 最佳结果：best_score_
    print("最佳结果：\n", estimator.best_score_)
    # 最佳估计器：best_estimator_
    print("最佳估计器:\n", estimator.best_estimator_)
    # 交叉验证结果：cv_results_
    print("交叉验证结果:\n", estimator.cv_results_)

if __name__=="__main__":
    facebook_demo()

 

 

 

 

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