# Price Prediction with Keras Demo
使用Tensorflow Kera API进行价格预测 使用模型为简单的DNN模型
## 实现功能
1. 爬虫 使用BeautifulSoup4,爬取了全国农产品商务信息公共服务平台的价格数据 网址为 http://nc.mofcom.gov.cn/channel/jghq2017/price_list.shtml 没有使用多线程,速度很慢
2. 数据处理 所有价格数据存入mysql数据库,根据需求取出数据,使用pandas处理取出的数据
3. 预测模型 简单的DNN模型
4. 数据展示 网页部分，使用了bootstrap前端框架，后端使用flask框架和pyechart库进行数据图表的展现。
## 界面
1. 表单
2. 预测界面
使用TensorflowKerasAPI进行价格预测使用模型为简单的DNN模型.zip
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2023-10-19
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使用TensorflowKerasAPI进行价格预测使用模型为简单的DNN模型.zip （112个子文件） 
2018_M01-M06_rate.csv 1.72MB 2018_M07-M12_rate.csv 1.53MB 2017_M07-M12_rate.csv 1.2MB 2016_M07-M12_rate.csv 1.13MB 2016_M01-M06_rate.csv 1.09MB 2017_M01-M06_rate.csv 911KB 2014_M10-M12_rate.csv 579KB 2015_M07-M09_rate.csv 570KB 2014-07-01To2014-09-30_price_data.csv 564KB 2014-10-01To2014-12-31_price_data.csv 539KB 2015_M04-M06_rate.csv 533KB 2014-04-01To2014-06-30_price_data.csv 522KB 2015-07-01To2015-09-30_price_data.csv 518KB 2015-10-01To2015-12-31_price_data.csv 507KB 2016-07-01To2016-09-30_price_data.csv 504KB 2014-01-01To2014-03-31_price_data.csv 499KB 2016-04-01To2016-06-30_price_data.csv 493KB 2015-01-01To2015-03-31_price_data.csv 479KB 2016-01-01To2016-03-31_price_data.csv 472KB 2015_M01-M03_rate.csv 466KB 2017-04-01To2017-06-30_price_data.csv 464KB 2015-04-01To2015-06-30_price_data.csv 464KB 2015_M10-M12_rate.csv 463KB 2017-07-01To2017-09-30_price_data.csv 462KB 2017-01-01To2017-03-31_price_data.csv 461KB 2016-10-01To2016-12-31_price_data.csv 457KB 2017-10-01To2017-12-31_price_data.csv 449KB 2018-01-01To2018-03-31_price_data.csv 396KB 2018-04-01To2018-05-31_price_data.csv 284KB 2014_M05_rate.csv 252KB 2014_M04_rate.csv 229KB 2014_M06_rate.csv 206KB 2014_M07_rate.csv 179KB 2014_M03_rate.csv 177KB 2014_M09_rate.csv 175KB 2014_M08_rate.csv 155KB 2014_M02_rate.csv 103KB dataset_wx_final.csv 86KB dataset_wx_web.csv 83KB 2014_M01_rate.csv 76KB avg_dollor_rate.csv 51KB 2018-06-01To2018-06-05_price_data.csv 24KB date_list.csv 20KB .gitattributes 90B model.h5 21KB nn5_best.hdf5 40KB nn3_1_best.hdf5 40KB nn3_best.hdf5 40KB nn4_best.hdf5 40KB model.hdf5 40KB nn2_best_weights.hdf5 28KB
pricePredictor.html 4KB predictResult.html 1KB trainDNN-checkpoint.ipynb 6.33MB trainDNN.ipynb 6.22MB train_DNN-checkpoint.ipynb 5.35MB test_data-checkpoint.ipynb 109KB
prediction.jpg 230KB index.jpg 170KB model.json 2KB launch.json 2KB settings.json 163B readme.md 873B ss.md 0B crawler.py 8KB flask_server.py 3KB crawler2.py 2KB mysql_op.py 2KB database_price_data0.sql 13.4MB database_price_data.sql 12.78MB database_routines0.sql 1KB database_routines.sql 1KB MakeDataSet3.sql 250B MakeDataSet2.sql 228B MakeDataSet.sql 220B MakeAvgRate.sql 219B MakeOneYearBeforeTable.sql 194B load_csv_to_mysql.sql 185B MakeOneMonthBeforeTable.sql 181B MakeOneDayBeforeTable.sql 146B MakeTodayTable.sql 120B events.out.tfevents.1554465759.SURFACE-ERIC 4.13MB events.out.tfevents.1554465808.SURFACE-ERIC 4.11MB events.out.tfevents.1554465684.SURFACE-ERIC 3.95MB events.out.tfevents.1554465617.SURFACE-ERIC 3.59MB events.out.tfevents.1554465558.SURFACE-ERIC 3.43MB events.out.tfevents.1554465515.SURFACE-ERIC 3.11MB events.out.tfevents.1554465466.SURFACE-ERIC 2.87MB events.out.tfevents.1554467553.SURFACE-ERIC 2.65MB events.out.tfevents.1554462244.SURFACE-ERIC 2.63MB events.out.tfevents.1554462212.SURFACE-ERIC 2.42MB events.out.tfevents.1554467515.SURFACE-ERIC 2.37MB events.out.tfevents.1554462159.SURFACE-ERIC 2.22MB events.out.tfevents.1554467481.SURFACE-ERIC 2.08MB events.out.tfevents.1554462123.SURFACE-ERIC 2.01MB events.out.tfevents.1554462088.SURFACE-ERIC 1.77MB events.out.tfevents.1554462045.SURFACE-ERIC 1.59MB events.out.tfevents.1554467136.SURFACE-ERIC 1.52MB events.out.tfevents.1554466353.SURFACE-ERIC 1.37MB events.out.tfevents.1554462012.SURFACE-ERIC 1.32MB共 112 条
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