{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# kaggle-House-Prices-Prediction\n\nYou can use our code on Github https://github.com/EvanHong99/kaggle-House-Prices-Prediction.git\n\n- KNN\n- Dummy Variable\n- PCA\n- DeepLearning\n\n## 数据预处理\n\n1. 先使用map将文本列的文本转化为数字，从而可以调用knn的包\n2. 利用knn填补数值列缺失值\n3. 利用`pandas.get_dummies()`构建文本列onehot表示，并删除原文本列\n4. PCA降维\n5. 从train中分割出valid集（0.1）用于验证模型表现\n\n\n## DeepLearning\n\n- 简单三层Linear网络\n- adam\n\n### todo\n\n- optuna\n","metadata":{}},{"cell_type":"code","source":"!cp /kaggle/input/deeplearning/my_submission.csv /kaggle/working/submission.csv","metadata":{"execution":{"iopub.status.busy":"2022-07-25T06:01:21.522685Z","iopub.execute_input":"2022-07-25T06:01:21.523154Z","iopub.status.idle":"2022-07-25T06:01:22.310556Z","shell.execute_reply.started":"2022-07-25T06:01:21.523111Z","shell.execute_reply":"2022-07-25T06:01:22.309235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}