Built a predictive machine learning model using a Streamlit application to predict weekly sales. Model achieved 97.4% accuracy and analyzed trends, patterns, and data insights using EDA. Compared various features and identified key contributors with a significant impact on prices. , stars:0, update:2024-05-09 07:04:53 (2024-05-11, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1715424399963892.htmlThe Single Character Recognition code is designed for recognizing individual characters from images using convolutional neural networks (CNNs). It achieves an accuracy of 97.2% on the provided dataset. This code utilizes PyTorch for model building and training. (2024-05-05, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1714885730178727.html
探索分类器（XGBoost、CatBoost、LightGBM、Bagging、ExtraTrees）预测中风的功效。ExtraTrees:99.8%，打包：98.5%，CatBoost:97.7%，XGBoost:96.3%，LightGBM:91.8%。
Exploring efficacy of classifiers (XGBoost, CatBoost, LightGBM, Bagging, ExtraTrees) for predicting stroke. ExtraTrees: 99.8%, Bagging: 98.5%, CatBoost: 97.7%, XGBoost: 96.3%, LightGBM: 91.8%. (2024-04-06, Jupyter Notebook, 0KB, 下载0次)
使用TensorFlow和Keras的神经网络从MNIST数据集中分类手写数字，实现97%的准确性。
A neural network with TensorFlow and Keras to classify hand-written digits from the MNIST dataset, achieving 97% accuracy. (2024-04-02, Jupyter Notebook, 0KB, 下载0次)
建立了一个基于TensorFlow的CNN，在RSI-CB256卫星图像分类上实现97%的准确性！这表明了深度学习从卫星图像中提取有意义信息的能力。
Built a TensorFlow-based CNN achieving 97% accuracy on RSI-CB256 satellite image classification! This demonstrates the power of deep learning for extracting meaningful information from satellite imagery. (2024-02-02, Jupyter Notebook, 0KB, 下载0次)
使用Streamlit应用程序构建预测机器学习模型来预测每周销售。模型实现了97.4%的准确性，并使用EDA分析了趋势、模式和数据见解。比较各种功能，并确定对价格有重大影响的关键因素。
Built a predictive machine learning model using a Streamlit application to predict weekly sales. Model achieved 97.4% accuracy and analyzed trends, patterns, and data insights using EDA. Compared various features and identified key contributors with a significant impact on prices. (2024-01-22, Jupyter Notebook, 0KB, 下载0次)
钻石价格预测器-Web应用程序：使用各种回归模型预测钻石价格：线性回归、Lasso、Ridge、ElasticNet、决策树回归、随机森林回归和KNeighbors回归。所选的随机森林回归器以97%的卓越精度部署在用户友好的烧瓶应用程序中
Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app (2024-01-10, Jupyter Notebook, 0KB, 下载0次)
该项目使用TensorFlow框架构建的深度学习模型来检测X射线图像中的肺炎。模型架构基于EfficientNetB7模型，该模型在我们的测试数据上实现了大约97%（96.96%）的准确性。这种高准确率是我们人工智能模型的优势之一。
This project uses a deep learning model built with the TensorFlow framework to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97% (96.96%) on our test data. This high accuracy rate is one of the strengths of our AI model. (2023-12-22, Jupyter Notebook, 0KB, 下载0次)
利用Python进行数据分析([https: read.douban.com reader ebook 15249337 )源代码整理版](https: read.douban.com reader ebook 15249337 %EF%BC%89%E6%BA%90%E4%BB%A3%E7%A0%81%E6%95%B4%E7%90%86%E7%89%88)
Data analysis using Python ([https: read.douban.com reader ebook 15249337) source code collation) (https: read.douban.com reader ebook 15249337% EF% BC% 89% E6% BA% 90% E4% BB% A3% E7% A0% 81% E6% 95% B4% E7% 90% 86% E7% 89% 88) (2018-12-31, Jupyter Notebook, 35601KB, 下载0次)
道路裂缝检测神经网络-，一种使用keras构建的卷积神经网络，用于检测道路裂缝，准确率为97.5%。
Road-Cracks-Detection-Neural-Network-,A convolutional neural network built using keras to detect cracks on road with 97.5% accuracy. (2021-01-07, Jupyter Notebook, 408KB, 下载0次)
这个notebook是根据去年o2o比赛冠军的代码（[https://github.com/wepe/O2O-Coupon-Usage-
Forecast）整理注释的，使用notebook（安装了插件方便整理）,加了一些自己的注释，调了bug，实测可以跑通。用xgboost应该在0.795左右。](https://github.com/wepe/O2O-Coupon-
Usage-
Forecast%EF%BC%89%E6%95%B4%E7%90%86%E6%B3%A8%E9%87%8A%E7%9A%84%EF%BC%8C%E4%BD%BF%E7%94%A8notebook%EF%BC%88%E5%AE%89%E8%A3%85%E4%BA%86%E6%8F%92%E4%BB%B6%E6%96%B9%E4%BE%BF%E6%95%B4%E7%90%86%EF%BC%89,%E5%8A%A0%E4%BA%86%E4%B8%80%E4%BA%9B%E8%87%AA%E5%B7%B1%E7%9A%84%E6%B3%A8%E9%87%8A%EF%BC%8C%E8%B0%83%E4%BA%86bug%EF%BC%8C%E5%AE%9E%E6%B5%8B%E5%8F%AF%E4%BB%A5%E8%B7%91%E9%80%9A%E3%80%82%E7%94%A8xgboost%E5%BA%94%E8%AF%A5%E5%9C%A80.795%E5%B7%A6%E5%8F%B3%E3%80%82)
,
This notebook was compiled based on the code from last year s O2O competition champion (https: github. com wepe O2O Coupon Usage - Forecast). It was annotated using the notebook (with plugins installed for ease of compilation), added some own comments, fixed bugs, and tested to run smoothly. Using xgboost should be around 0.795 (https: github.com wepe O2O-Coupon- Usage- Forecast%EF%BC%89%E6%95%B4%E7%90%86%E6%B3%A8%E9%87%8A%E7%9A%84%EF%BC%8C%E4%BD%BF%E7%94%A8notebook%EF%BC%88%E5%AE%89%E8%A3%85%E4%BA%86%E6%8F%92%E4%BB%B6%E6%96%B9%E4%BE%BF%E6%95%B4%E7%90%86%EF%BC%89,%E5%8A%A0%E4%BA%86%E4%B8%80%E4%BA%9B%E8%87%AA%E5%B7%B1%E7%9A%84%E6%B3%A8%E9%87%8A%EF%BC%8C%E8%B0%83%E4%BA%86bug%EF%BC%8C%E5%AE%9E%E6%B5%8B%E5%8F%AF%E4%BB%A5%E 8%B7%91%E9%80%9A%E3%80%82%E7%94%A8xgboost%E5%BA%94%E8%AF%A5%E5%9C%A80.795%E5%B7%A6%E5%8F%B3%E3%80%82) (2018-08-09, Jupyter Notebook, 15KB, 下载0次)
A1关于网络抓取Pandas数据科学，在python中使用Pandas学习网络抓取。-数据科学，
Learning WebScraping using Pandas in python. - Data Science
, (2021-12-02, Jupyter Notebook, 795KB, 下载0次)