[{"title":"( 42 个子文件 105.86MB ) 预测抑郁症:使用CDC NHANES网站上的医疗数据通过机器学习预测抑郁症的项目。 使用Streamlit创建了一个供用户浏览此项目中数据的配套仪表板。 使用Jupyter Notebook用python编写的主要项目流程分析和Visual Studio代码,用于编写自定义功能和创建仪表板-源码","children":[{"title":"Predicting-Depression-main","children":[{"title":"first_notebook.ipynb <span style='color:#111;'> 2.52MB </span>","children":null,"spread":false},{"title":"Images","children":[{"title":"Tuned SGD Linear Model.png <span style='color:#111;'> 27.89KB </span>","children":null,"spread":false},{"title":"Depression.jpg <span style='color:#111;'> 1.76MB </span>","children":null,"spread":false},{"title":"Most Important Features.png <span style='color:#111;'> 54.70KB </span>","children":null,"spread":false}],"spread":true},{"title":"Dashboard","children":[{"title":"depression_app.py <span style='color:#111;'> 27.70KB </span>","children":null,"spread":false},{"title":"app_cleaning.ipynb <span style='color:#111;'> 776.58KB </span>","children":null,"spread":false},{"title":"FullData.csv <span style='color:#111;'> 60.03MB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"app_cleaning-checkpoint.ipynb <span style='color:#111;'> 776.58KB </span>","children":null,"spread":false},{"title":"plotly_figures-checkpoint.ipynb <span style='color:#111;'> 352.52KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"project_functions","children":[{"title":"__pycache__","children":[{"title":"oi.cpython-36.pyc <span style='color:#111;'> 417B </span>","children":null,"spread":false},{"title":"custom_functions.cpython-36.pyc <span style='color:#111;'> 190B </span>","children":null,"spread":false},{"title":"__init__.cpython-36.pyc <span style='color:#111;'> 12.15KB </span>","children":null,"spread":false}],"spread":true},{"title":"__init__.py <span style='color:#111;'> 12.94KB </span>","children":null,"spread":false}],"spread":true},{"title":"scratch","children":[{"title":".ipynb_checkpoints","children":[{"title":"first_notebook-Copy1-checkpoint.ipynb <span style='color:#111;'> 1.65MB </span>","children":null,"spread":false},{"title":"main_notebook-checkpoint.ipynb <span style='color:#111;'> 18.36MB </span>","children":null,"spread":false},{"title":"first_notebook_copy-checkpoint.ipynb <span style='color:#111;'> 1.85MB </span>","children":null,"spread":false}],"spread":true},{"title":"main_notebook.ipynb <span style='color:#111;'> 18.36MB </span>","children":null,"spread":false},{"title":"second_notebook-copy.ipynb <span style='color:#111;'> 16.32MB </span>","children":null,"spread":false},{"title":"first_notebook_copy.ipynb <span style='color:#111;'> 1.85MB </span>","children":null,"spread":false}],"spread":true},{"title":"PredictingDepressionSlides.pdf <span style='color:#111;'> 833.89KB </span>","children":null,"spread":false},{"title":"second_notebook.ipynb <span style='color:#111;'> 16.63MB </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 47B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 6B </span>","children":null,"spread":false},{"title":"CSVFiles","children":[{"title":"yTrain.csv <span style='color:#111;'> 227.85KB </span>","children":null,"spread":false},{"title":"XTrainFinal.csv <span style='color:#111;'> 92.27MB </span>","children":null,"spread":false},{"title":"yTrainResample.csv <span style='color:#111;'> 147.53KB </span>","children":null,"spread":false},{"title":"XTestFinal.csv <span style='color:#111;'> 23.08MB </span>","children":null,"spread":false},{"title":"XTrainResample.csv <span style='color:#111;'> 39.14MB </span>","children":null,"spread":false},{"title":"yTest.csv <span style='color:#111;'> 56.99KB </span>","children":null,"spread":false},{"title":"FullData.csv <span style='color:#111;'> 60.03MB </span>","children":null,"spread":false}],"spread":true},{"title":".ipynb_checkpoints","children":[{"title":"first_notebook-Copy1-checkpoint.ipynb <span style='color:#111;'> 1.65MB </span>","children":null,"spread":false},{"title":"plotly_figures-checkpoint.ipynb <span style='color:#111;'> 352.53KB </span>","children":null,"spread":false},{"title":"first_notebook-checkpoint.ipynb <span style='color:#111;'> 2.52MB </span>","children":null,"spread":false},{"title":"Covid-checkpoint.ipynb <span style='color:#111;'> 16.86KB </span>","children":null,"spread":false},{"title":"second_notebook-checkpoint.ipynb <span style='color:#111;'> 16.63MB </span>","children":null,"spread":false},{"title":"main_notebook-checkpoint.ipynb <span style='color:#111;'> 18.36MB </span>","children":null,"spread":false},{"title":"main_notebook1-checkpoint.ipynb <span style='color:#111;'> 231.34KB </span>","children":null,"spread":false},{"title":"old_notebook-checkpoint.ipynb <span style='color:#111;'> 3.78MB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 14.41KB </span>","children":null,"spread":false},{"title":"StreamlitData.csv <span style='color:#111;'> 65.24MB </span>","children":null,"spread":false},{"title":".gitattributes <span style='color:#111;'> 66B </span>","children":null,"spread":false},{"title":"stethoscope.jpg <span style='color:#111;'> 1.00MB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]