新日暮里小组第一版

competitions/getting-started/house-price.md

@@ -0,0 +1,54 @@
+## **房价预测**
+
+***
+
+[房价预测](https://www.kaggle.com/c/house-prices-advanced-regression-techniques):Predict sales prices and practice feature engineering
+
+### **内容说明**
+
+- Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
+
+- With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
+
+### **开发流程**
+
+>收集数据:[数据集](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)
+
+
+>准备数据:
+```python
+def dataProcess(df_train, df_test):
+    trainLabel = df_train['SalePrice']
+    df = pd.concat((df_train,df_test), axis=0, ignore_index=True)
+    df.dropna(axis=1, inplace=True)
+    df = pd.get_dummies(df)
+    trainData = df[:df_train.shape[0]]
+    test = df[df_train.shape[0]:]
+    return trainData, trainLabel, test 
+```
+
+>模型训练：产生训练模型:
+```
+暂时没写
+```
+
+>模型评估:RMSE
+```
+暂时没写
+```
+
+>结果预测：
+```python
+def ridgeRegression(trainData, trainLabel, df_test):
+    ridge = Ridge(alpha=10.0)   # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
+    ridge.fit(trainData, trainLabel)
+    predict = ridge.predict(df_test)
+    pred_df = pd.DataFrame(predict, index=df_test["Id"], columns=["SalePrice"])
+    return pred_df 
+```
+
+>结果导出：
+```python
+def saveResult(result):
+    result.to_csv(os.path.join(data_dir,"submission.csv" ), sep=',', encoding='utf-8')
+```