This project forked from gd2016229035/car_sales_forecast
天池比赛_印象盐城·数创未来大数据竞赛 - 乘用车零售量预测
数据：赛题与数据
排名：46/2500（复赛B榜最终结果提交有误...）
思路：1. 训练数据处理：（12月滑窗，增大数据量，只取一年内12月作为label，时间久远的数据变化较大；训练集挑出label为0的，以及特征多数为0的，让模型去学习和预测较大的非0销量）
2.手工特征（12月销量、6月产量、销量平均数标准差、待预测月标签（提升很大！）、有销量的月份数；没用的特征：省份城市车型、斜率、中位数、宏观经济？、车型配置、车厢数、车价格）
3.人工规则（人工预测测试集的0销量：9月-12月为0000则为0；8月-12月为00100则为0）人工预测0值提升很大（0.1个点）
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