Walmet Sales Prediction（updating

参考kaggle notebook：
keras

一、题目

1.项目题目：沃尔玛销量预测

预测沃尔玛未来28天的销量

2.评分标准：RMSSE

RMSSE.png

n为40341训练样本量，h为28天，Yt为实际销量值，Yt^为预测销量值

3.数据描述

数据有3049种产品，共3大类，7个部门，在3个洲的10个商场里销售

sales_train.csv:这是主要的训练集，含有每个从2011-1-29到2016-5-22的1941天每天的(不包括到2016-6-19的28天)销量,含商品的ID，部门，分类，商店，洲.
sell_prices.csv:商店的商品每周均价
calendar.csv:日期的星期、月份、年和该洲是否允许用食品券（food stamp,低收入家庭的补助）购买

二、正文

1.导入数据

#导入库
import pandas as pd
import seaborn as sns
import lightgbm as lgb
import numpy as np

#导入数据 import data
calendar = pd.read_csv('calendar.csv')
sample_submission = pd.read_csv('sample_submission.csv')
sales_train_validation = pd.read_csv('sales_train_validation.csv')
sell_prices = pd.read_csv('sell_prices.csv')

#减小内存占用 reduce the memory usage
def reduce_mem_usage(df, verbose=True):
    numerics = ["int16", "int32", "int64", "float16", "float32", "float64"]
    start_mem = df.memory_usage().sum() / 1024 ** 2
    for col in df.columns:
        col_type = df[col].dtypes
        if col_type in numerics:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == "int":
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)
            else:
                if (
                    c_min > np.finfo(np.float16).min
                    and c_max < np.finfo(np.float16).max
                ):
                    df[col] = df[col].astype(np.float16)
                elif (
                    c_min > np.finfo(np.float32).min
                    and c_max < np.finfo(np.float32).max
                ):
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
    end_mem = df.memory_usage().sum() / 1024 ** 2
    if verbose:
        print(
            "Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)".format(
                end_mem, 100 * (start_mem - end_mem) / start_mem
            )
        )
    return df

#减小dataframe占用内存
print("缩小前占用内存为:",sell_prices.memory_usage().sum() / (1024 ** 2), "MB")
calendar = reduce_mem_usage(calendar)
sample_submission = reduce_mem_usage(sample_submission)
sales_train_validation = reduce_mem_usage(sales_train_validation)
sell_prices = reduce_mem_usage(sell_prices)
print("缩小后占用内存为:",sell_prices.memory_usage().sum() / (1024 ** 2), "MB")

sales_train_validation.head()
calendar.head()

image.png