{"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":"# House Price\n### reference\n\n* [Comprehensive data exploration with Python](https://www.kaggle.com/code/pmarcelino/comprehensive-data-exploration-with-python)\n\n* [Handling Missing Values](https://www.kaggle.com/code/dansbecker/handling-missing-values)\n\n* [XGBoost](https://www.kaggle.com/code/dansbecker/xgboost)\n","metadata":{"_uuid":"d507f816cc74a88c9afefd02cf225d1a7dd6461f","_cell_guid":"e3bc4854-2787-eae1-950d-2742ad3d7db2"}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\n#시각화 \nimport matplotlib.pyplot as plt\nimport seaborn as sns \n#확률 분포 분석 \nfrom scipy.stats import norm\nfrom scipy import stats\n\n#스케일링 \nfrom sklearn.preprocessing import StandardScaler\n\n#경고 메세지 무시 \nimport warnings\nwarnings.filterwarnings('ignore')\n\n#브라우저에서 바로 도표, 그래프, 음성등을 볼 수 있게 설정\n%matplotlib inline","metadata":{"_uuid":"d581f6797b9fde1580271358d484df67bf6b14a1","_cell_guid":"2df621e0-e03c-7aaa-6e08-40ed1d7dfecc","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T09:57:47.881952Z","iopub.execute_input":"2022-07-25T09:57:47.882389Z","iopub.status.idle":"2022-07-25T09:57:49.250606Z","shell.execute_reply.started":"2022-07-25T09:57:47.882349Z","shell.execute_reply":"2022-07-25T09:57:49.249603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = pd.read_csv('../input/train.csv')\ndf_test = pd.read_csv('../input/test.csv')\ndf_train.head()","metadata":{"_uuid":"827a72128cd211cf6af16b003e7c09951e3f2b1e","_cell_guid":"d56d5e71-4277-7a74-5306-7d5af4c7f263","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:09:31.866018Z","iopub.execute_input":"2022-07-25T10:09:31.866487Z","iopub.status.idle":"2022-07-25T10:09:31.936072Z","shell.execute_reply.started":"2022-07-25T10:09:31.866442Z","shell.execute_reply":"2022-07-25T10:09:31.935234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:07:29.322308Z","iopub.execute_input":"2022-07-25T10:07:29.322723Z","iopub.status.idle":"2022-07-25T10:07:29.340876Z","shell.execute_reply.started":"2022-07-25T10:07:29.322681Z","shell.execute_reply":"2022-07-25T10:07:29.340019Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"obj_cols = df_train.dtypes[df_train.dtypes == 'object'].index.tolist()\nobj_cols","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:07:29.725463Z","iopub.execute_input":"2022-07-25T10:07:29.725776Z","iopub.status.idle":"2022-07-25T10:07:29.733410Z","shell.execute_reply.started":"2022-07-25T10:07:29.725725Z","shell.execute_reply":"2022-07-25T10:07:29.732137Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Variable \n\n* <b>Variable</b> - Variable name.\n* <b>Type</b> - Variables' type. → 수치형 변수, 범주형 변수 \n* <b>Segment</b> - Variables' segment. → building, space, location\n    * building : 건물과 관련된 변수들. (e.g. 'OverallQual')\n    * space :  집의 공간적 특성과 관련된 변수들. (e.g. 'TotalBsmtSF')\n    * location : 집의 위칭와 관련된 변수들. (e.g. 'Neighborhood')\n* <b>Expectation</b> - Our expectation about the variable influence in 'SalePrice'. We can use a categorical scale with 'High', 'Medium' and 'Low' as possible values.\n* <b>Conclusion</b> - Our conclusions about the importance of the variable, after we give a quick look at the data. We can keep with the same categorical scale as in 'Expectation'.\n* <b>Comments</b> - Any general comments that occured to us.","metadata":{"_uuid":"ba13c267e3aacbef23dfbcc0ab05f002d7835e0c","_cell_guid":"79d22981-dfd7-a25f-9a78-5436213207e2"}},{"cell_type":"markdown","source":"# 2. First things first: analysing 'SalePrice'","metadata":{"_uuid":"39d104c7e40b3f66a0f6e2330119332b301be7bf","_cell_guid":"3ef87d93-0ea6-8cb2-aa2d-90d5b56a1ca1"}},{"cell_type":"markdown","source":"### 1) 분포","metadata":{}},{"cell_type":"code","source":"#descriptive statistics summary\ndf_train['SalePrice'].describe()","metadata":{"_uuid":"5c15e1bd10b8e71c0b1d62bdb260882585a35579","_cell_guid":"54452e23-f4d3-919f-c734-80a35dc9ae08","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:30.092067Z","iopub.execute_input":"2022-07-25T10:07:30.092495Z","iopub.status.idle":"2022-07-25T10:07:30.105895Z","shell.execute_reply.started":"2022-07-25T10:07:30.092455Z","shell.execute_reply":"2022-07-25T10:07:30.105077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#histogram\nsns.distplot(df_train['SalePrice']);","metadata":{"_uuid":"2f78c77caa7290298138caf167672e62d3bc5a67","_cell_guid":"6bbea362-77b6-5385-f0a8-fb53afd088b7","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:31.836058Z","iopub.execute_input":"2022-07-25T10:07:31.836637Z","iopub.status.idle":"2022-07-25T10:07:32.257736Z","shell.execute_reply.started":"2022-07-25T10:07:31.836590Z","shell.execute_reply":"2022-07-25T10:07:32.256642Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* <b>Deviate from the normal distribution.</b>\n* <b>Have appreciable positive skewness.</b>\n* <b>Show peakedness.</b>\n","metadata":{"_uuid":"f84e60c8b934615e53af10823558fe42753ac25d","_cell_guid":"f4e257f0-1dfd-0774-b346-f2a1b2a068cc"}},{"cell_type":"code","source":"#skewness and kurtosis\nprint(\"Skewness: %f\" % df_train['SalePrice'].skew())\nprint(\"Kurtosis: %f\" % df_train['SalePrice'].kurt())","metadata":{"_uuid":"2cb253768dcd75b9a450ee264626ce69c808096a","_cell_guid":"36766737-f1a3-fe40-dbec-63c31be4d5e0","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:32.372008Z","iopub.execute_input":"2022-07-25T10:07:32.372312Z","iopub.status.idle":"2022-07-25T10:07:32.378236Z","shell.execute_reply.started":"2022-07-25T10:07:32.372267Z","shell.execute_reply":"2022-07-25T10:07:32.377427Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<b>Skewness</b> : 왜도. 정규분포에 비해 얼마나 비대칭인지를 나타내는 척도.\n* |skewness| <= 0.5 : 데이터가 대칭적이다\n* 0.5 < |skewness| <= 1 : 데이터가 적당히 치우쳐져있다\n* 1 < |skewness| : 데이터가 상당히 치우쳐져있다\n\n![image.png](attachment:ca64a341-124b-435f-8cca-f65cdcea91d9.png)\n\n<b>Kurtosis</b> : 첨도. 확률분포의 꼬리가 두꺼운 정도를 나타내는 척도로 outlier가 많을 수록 큰 값을 갖는다. \n* |Kurtosis| = 0 : 정규분포를 따르는 데이터의 첨도\n* Kurtosis > 0 : 정규분포보다 뾰족, outlier 적음\n* Kurtosis < 0  : 정규분포보다 완만\n\n![image.png](attachment:152b4998-73fe-4a47-9b90-87979a0e36ac.png)","metadata":{"_uuid":"c984e32d4d10b1e6766c1b79ed8ade9a57529ffb","_cell_guid":"7a3e43cc-5b75-0b49-1ad2-426d10d7fb42"},"attachments":{"152b4998-73fe-4a47-9b90-87979a0e36ac.png":{"image/png":"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"},"ca64a341-124b-435f-8cca-f65cdcea91d9.png":{"image/png":"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"}}},{"cell_type":"code","source":"sns.distplot(np.log(df_train['SalePrice']));","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:07:32.631961Z","iopub.execute_input":"2022-07-25T10:07:32.632316Z","iopub.status.idle":"2022-07-25T10:07:32.999687Z","shell.execute_reply.started":"2022-07-25T10:07:32.632253Z","shell.execute_reply":"2022-07-25T10:07:32.998619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 2) Saleprice와 수치형 설명변수와의 관계","metadata":{"_uuid":"08e034d70ed14ef0c4fb8cc1e9dd9e15f0d1c996","_cell_guid":"73c1a953-aafd-96f6-c7bd-e2c79b3e5a0b"}},{"cell_type":"code","source":"#scatter plot grlivarea/saleprice'\ndata = pd.concat([df_train['SalePrice'], df_train['GrLivArea']], axis=1) \ndata.plot.scatter(x='GrLivArea', y='SalePrice', ylim=(0,800000));","metadata":{"_uuid":"91160363898f5caeee965a1aa81eb3abb7dcd760","_cell_guid":"db040973-0adc-e126-e657-1d8934b5a5c8","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:34.132233Z","iopub.execute_input":"2022-07-25T10:07:34.132722Z","iopub.status.idle":"2022-07-25T10:07:34.430666Z","shell.execute_reply.started":"2022-07-25T10:07:34.132659Z","shell.execute_reply":"2022-07-25T10:07:34.429821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#scatter plot totalbsmtsf/saleprice\nvar = 'TotalBsmtSF'\ndata = pd.concat([df_train['SalePrice'], df_train[var]], axis=1)\ndata.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));","metadata":{"_uuid":"3ac3db51311338fcdc16014a7c506cf3d5315af7","_cell_guid":"353def35-0f26-998d-b9a4-7356f95e80ad","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:34.610079Z","iopub.execute_input":"2022-07-25T10:07:34.610553Z","iopub.status.idle":"2022-07-25T10:07:34.935772Z","shell.execute_reply.started":"2022-07-25T10:07:34.610510Z","shell.execute_reply":"2022-07-25T10:07:34.934775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 3) Saleprice와 번주형 설명변수와의 관계","metadata":{"_uuid":"5550d4df3c0ad48d8b2b9b0905f17daa3fc8d244","_cell_guid":"b31bc890-46bf-618c-e668-17879763ad23"}},{"cell_type":"markdown","source":"<b> boxplot </b> : 자료의 five number summary를 나타낸 그림\n<br>five number summary : 최솟값, 제 1사분위(25%),  제 2사분위(50%),  제 3사분위(75%), 최댓값</br>\n\n![image.png](attachment:61162568-5fe3-40d2-a5e9-30f06e91817b.png)","metadata":{},"attachments":{"61162568-5fe3-40d2-a5e9-30f06e91817b.png":{"image/png":"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"}}},{"cell_type":"code","source":"#box plot overallqual/saleprice\nvar = 'OverallQual'\ndata = pd.concat([df_train['SalePrice'], df_train[var]], axis=1)\nf, ax = plt.subplots(figsize=(8, 6))\nfig = sns.boxplot(x=var, y=\"SalePrice\", data=data)\nfig.axis(ymin=0, ymax=800000);","metadata":{"_uuid":"e2b7aaccc3486a74a09996289a833ffb6acd0764","_cell_guid":"26d0fddc-cb09-af7d-9f03-a07233fa6c9e","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:37.308112Z","iopub.execute_input":"2022-07-25T10:07:37.308576Z","iopub.status.idle":"2022-07-25T10:07:37.779168Z","shell.execute_reply.started":"2022-07-25T10:07:37.308533Z","shell.execute_reply":"2022-07-25T10:07:37.778077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#scatter plot totalbsmtsf/saleprice\nvar = 'OverallQual'\ndata = pd.concat([df_train['SalePrice'], df_train[var]], axis=1)\ndata.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:07:38.636241Z","iopub.execute_input":"2022-07-25T10:07:38.637078Z","iopub.status.idle":"2022-07-25T10:07:38.936678Z","shell.execute_reply.started":"2022-07-25T10:07:38.637005Z","shell.execute_reply":"2022-07-25T10:07:38.935565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#box plot overallqual/saleprice\nvar = 'YearBuilt'\ndata = pd.concat([df_train['SalePrice'], df_train[var]], axis=1)\nf, ax = plt.subplots(figsize=(8, 6))\nfig = sns.boxplot(x=var, y=\"SalePrice\", data=data)\nfig.axis(ymin=0, ymax=800000);","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:07:39.111345Z","iopub.execute_input":"2022-07-25T10:07:39.111953Z","iopub.status.idle":"2022-07-25T10:07:42.251641Z","shell.execute_reply.started":"2022-07-25T10:07:39.111907Z","shell.execute_reply":"2022-07-25T10:07:42.250664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 결론\n\n간단한 분석 결과\n* 'GrLivArea'와 'TotalBsmtSF'은 target인 SalesPrice와 Linear하다. 양의 관계를 가져 GrLivArea/TotalBsmtSF가 증가할 때 SalesPrice도 증가한다.\n\n* 'OverallQual'과 'YearBuilt' 마찬가지로 비슷한 관계를 갖는것도 알 수 있었다.\n\n80개의 설명변수를 다 쓰기보다는 이렇게 SalesPrice와 관계있는 변수를 찾아 사용하는 것이 좋다(feature selection)","metadata":{"_uuid":"1dc93d788b3cd2446afa41e7c06d05c6bbadb480","_cell_guid":"9d2f4940-ee0c-edef-769d-65fead4f06f3"}},{"cell_type":"markdown","source":"#### Correlation matrix (heatmap style)\n모든 변수 끼리의 relation을 쉽게 보는 방법 ","metadata":{"_uuid":"bf469f1030a8768f73a18e5ad59db43c4241c603","_cell_guid":"06f8d02c-d779-f8fd-7f48-ba3c5166eda8"}},{"cell_type":"markdown","source":"<b>Correlation</b> : 상관계수\n\nPearson 상관계수의 두 변수간의 선형 관계 정도와 방향을 나타내는 척도로 [-1, 1] 사이에 값을 갖는다\n* p = 0 : 아무 상관관계가 없다\n* p > 0 : 1에 가까울 수록 완전한 양의 선형관계를 갖는다\n* p < 0 : -1에 가까울 수록 완전한 음의 선형관계를 갖는다","metadata":{}},{"cell_type":"code","source":"df_train.corr() #deafult : pearson","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:07:42.253991Z","iopub.execute_input":"2022-07-25T10:07:42.254354Z","iopub.status.idle":"2022-07-25T10:07:42.302604Z","shell.execute_reply.started":"2022-07-25T10:07:42.254287Z","shell.execute_reply":"2022-07-25T10:07:42.301670Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#correlation matrix\ncorrmat = df_train.corr()\nf, ax = plt.subplots(figsize=(12, 9))\nsns.heatmap(corrmat, vmax=.8, square=True);","metadata":{"_uuid":"5dfee22210f5a126ea34ca6475bb4f365d41317b","_cell_guid":"4eb7a6ef-adf5-6abf-947d-c95afdc477b8","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:42.514282Z","iopub.execute_input":"2022-07-25T10:07:42.514594Z","iopub.status.idle":"2022-07-25T10:07:43.709041Z","shell.execute_reply.started":"2022-07-25T10:07:42.514547Z","shell.execute_reply":"2022-07-25T10:07:43.707958Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 'SalePrice' correlation matrix (zoomed heatmap style)","metadata":{"_uuid":"6ef7d7b7747807431aa9827046c76bfa3d34ffe1","_cell_guid":"9b557956-df91-bab3-0e8b-d8ffc054470f"}},{"cell_type":"code","source":"#SalePrice와 상관계수가 큰 10개의 변수만 선택k = 10\nk = 10\ncols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index\n\ncm = np.corrcoef(df_train[cols].values.T)\nsns.set(font_scale=1.25)\n# hm = sns.heatmap(cm, cbar=True, square=True, fmt='.2f', yticklabels=cols.values, xticklabels=cols.values)\nhm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)                 \nplt.show()","metadata":{"_uuid":"a6ee47c540ce9f3f1d2af6efe0b030e76e3a3f7f","_cell_guid":"bc33db9e-9ee3-6cfe-7643-a2aff5a9234d","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:45.978418Z","iopub.execute_input":"2022-07-25T10:07:45.979027Z","iopub.status.idle":"2022-07-25T10:07:46.590592Z","shell.execute_reply.started":"2022-07-25T10:07:45.978975Z","shell.execute_reply":"2022-07-25T10:07:46.589996Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"According to our crystal ball, these are the variables most correlated with 'SalePrice'. My thoughts on this:\n\n* 'OverallQual', 'GrLivArea' 'TotalBsmtSF'가 연관성이 높다 \n* 'GarageCars', 'GarageArea'도 연관성이 높지만, 두 변수의 상관관계 자체가 높다. 이러한 경우 둘 중 SalePrice와 상관관계가 더 큰 CasrageCars만 남겨둔다.\n* 'TotalBsmtSF'과 '1stFloor'도 같은 이유로 'TotalBsmtSF'만 남긴다.\n* 'TotRmsAbvGrd'의 경우 'GrLivArea'와의 상관계수가 높으므로 사용하지 않는다. ","metadata":{"_uuid":"c10a0a0bd55e55822726c78a59ab2bb6d9763f68","_cell_guid":"f5c23b8a-aad9-809f-0fdf-f758f926f5c9"}},{"cell_type":"code","source":"#scatterplot\nsns.set()\ncols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']\nsns.pairplot(df_train[cols], size = 2.5)\nplt.show();","metadata":{"_uuid":"cdafd230216fd04cc4ecf635967925da0bce9195","_cell_guid":"5a8db5de-d3f9-9a28-f220-bb05d51c53d0","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:07:47.956185Z","iopub.execute_input":"2022-07-25T10:07:47.956473Z","iopub.status.idle":"2022-07-25T10:07:56.627862Z","shell.execute_reply.started":"2022-07-25T10:07:47.956434Z","shell.execute_reply":"2022-07-25T10:07:56.626887Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = df_train[['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt'] + obj_cols]\ndf_test = df_test[['OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt'] + obj_cols]","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:09:49.288766Z","iopub.execute_input":"2022-07-25T10:09:49.289241Z","iopub.status.idle":"2022-07-25T10:09:49.297648Z","shell.execute_reply.started":"2022-07-25T10:09:49.289194Z","shell.execute_reply":"2022-07-25T10:09:49.296952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:09:50.072833Z","iopub.execute_input":"2022-07-25T10:09:50.073117Z","iopub.status.idle":"2022-07-25T10:09:50.078778Z","shell.execute_reply.started":"2022-07-25T10:09:50.073074Z","shell.execute_reply":"2022-07-25T10:09:50.077875Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. 결측치","metadata":{"_uuid":"726efbb348d1022cabb171f622d7b4e01fe8c778","_cell_guid":"9ce00498-d5e6-9e35-debc-8d507002d461"}},{"cell_type":"code","source":"df_train.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:09:52.050660Z","iopub.execute_input":"2022-07-25T10:09:52.051097Z","iopub.status.idle":"2022-07-25T10:09:52.062047Z","shell.execute_reply.started":"2022-07-25T10:09:52.051058Z","shell.execute_reply":"2022-07-25T10:09:52.060944Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"percent = (df_train.isnull().sum()/df_train.isnull().count()).sort_values(ascending=False) # ascending=False : 내림차순으로 정렬 \ntrain_missing = percent[percent > 0]\ntrain_missing","metadata":{"_uuid":"664e03dc1434fa2c4eb730ea36ab60e37f13cd3f","_cell_guid":"ca2f89e7-1c16-c3ae-6fe0-ab4eaf7e52a1","_execution_state":"idle","execution":{"iopub.status.busy":"2022-07-25T10:09:54.212264Z","iopub.execute_input":"2022-07-25T10:09:54.212735Z","iopub.status.idle":"2022-07-25T10:09:54.230100Z","shell.execute_reply.started":"2022-07-25T10:09:54.212674Z","shell.execute_reply":"2022-07-25T10:09:54.229433Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* 결측치가 15%보다 많은 컬럼은 드랍\n* 나머지 impute","metadata":{}},{"cell_type":"code","source":"drop_cols = train_missing[train_missing > 0.15].index.tolist()\nprint(\"before drop:\", df_train.shape, df_test.shape)\ndf_train = df_train.drop(drop_cols, 1)\ndf_test = df_test.drop(drop_cols, 1)\nprint(\"after drop:\", df_train.shape, df_test.shape)","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:10:02.342086Z","iopub.execute_input":"2022-07-25T10:10:02.342659Z","iopub.status.idle":"2022-07-25T10:10:02.352828Z","shell.execute_reply.started":"2022-07-25T10:10:02.342607Z","shell.execute_reply":"2022-07-25T10:10:02.351878Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#imputation\n# M1. OneHotEncoding (<- dimension이 많이 늘어남)\n# M2. Sklearn LabelEncoder \n# M1. Sklearn SimpleImputer \n\nX_train = df_train.drop('SalePrice', 1)\ny_train = np.log(df_train.SalePrice)\nX_test = df_test\n\nfrom sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\nobj_cols = X_train.dtypes[X_train.dtypes == 'object'].index.tolist()\nX_train_le = X_train.copy()\nX_test_le = X_test.copy()\ndata_all = pd.concat([X_train_le, X_test_le])\nfor c in obj_cols:\n    data_all[c] = data_all[c].astype(str)\n    le.fit(data_all[c])\n    X_train_le[c] = le.transform(X_train_le[c].astype(str))\n    X_test_le[c] = le.transform(X_test_le[c].astype(str))\n    \n\nfrom sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy = 'most_frequent')\nX_train_impute = imputer.fit_transform(X_train_le)\nX_test_impute = imputer.transform(X_test_le)","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:17:28.807525Z","iopub.execute_input":"2022-07-25T10:17:28.808099Z","iopub.status.idle":"2022-07-25T10:17:28.925819Z","shell.execute_reply.started":"2022-07-25T10:17:28.808046Z","shell.execute_reply":"2022-07-25T10:17:28.925130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from xgboost import XGBRegressor\nregressor = XGBRegressor(n_estimators=1000, learning_rate=0.05)\nregressor.fit(X_train_impute, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:21:12.787653Z","iopub.execute_input":"2022-07-25T10:21:12.788219Z","iopub.status.idle":"2022-07-25T10:21:14.617452Z","shell.execute_reply.started":"2022-07-25T10:21:12.788153Z","shell.execute_reply":"2022-07-25T10:21:14.616428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = np.exp(regressor.predict(X_test_impute))\nsubmit = pd.read_csv('../input/sample_submission.csv')\nsubmit.SalePrice = y_pred\nsubmit.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-07-25T10:24:08.589678Z","iopub.execute_input":"2022-07-25T10:24:08.590013Z","iopub.status.idle":"2022-07-25T10:24:10.690448Z","shell.execute_reply.started":"2022-07-25T10:24:08.589961Z","shell.execute_reply":"2022-07-25T10:24:10.689357Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}