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PricePredict.py

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+# 导入需要的模块
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import preprocessing
+from sklearn import linear_model, svm, gaussian_process
+from sklearn.ensemble import RandomForestRegressor
+from  sklearn.model_selection import train_test_split
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+# 用来绘图的，封装了matplot
+# 要注意的是一旦导入了seaborn，
+# matplotlib的默认作图风格就会被覆盖成seaborn的格式
+import seaborn as sns       
+
+from scipy import stats
+from scipy.stats import  norm
+from sklearn.preprocessing import StandardScaler
+import warnings
+warnings.filterwarnings('ignore')
+
+data_train = pd.read_csv("train.csv")
+print(data_train['SalePrice'].describe())
+sns.distplot(data_train['SalePrice'])
+
+#skewness and kurtosis 峰度和偏度
+print("Skewness: %f" % data_train['SalePrice'].skew())
+print("Kurtosis: %f" % data_train['SalePrice'].kurt())
+corrmat = data_train.corr()
+f, ax = plt.subplots(figsize=(20, 9))
+sns.heatmap(corrmat, vmax=0.8, square=True)
+k  = 10 # 关系矩阵中将显示10个特征,由此我们可以知道相关性高的数据类别，便于后续的分析
+cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
+cm = np.corrcoef(data_train[cols].values.T)
+sns.set(font_scale=1.25)
+hm = sns.heatmap(cm, cbar=True, annot=True, \
+                 square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
+sns.set()
+cols = ['SalePrice','OverallQual','GrLivArea', 'GarageCars','TotalBsmtSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt']
+sns.pairplot(data_train[cols], size = 2.5)
+
+# 获取数据
+cols = ['OverallQual','GrLivArea', 'GarageCars','TotalBsmtSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt']
+x = data_train[cols].values
+y = data_train['SalePrice'].values
+x_scaled = preprocessing.StandardScaler().fit_transform(x)
+y_scaled = preprocessing.StandardScaler().fit_transform(y.reshape(-1,1))
+X_train,X_test, y_train, y_test = train_test_split(x_scaled, y_scaled, test_size=0.33, random_state=42)
+clfs = {
+        'svm':svm.SVR(),
+        'RandomForestRegressor':RandomForestRegressor(n_estimators=400),
+        'BayesianRidge':linear_model.BayesianRidge()
+       }
+for clf in clfs:
+    try:
+        clfs[clf].fit(X_train, y_train)
+        y_pred = clfs[clf].predict(X_test)
+        print(clf + " cost:" + str(np.sum(y_pred-y_test)/len(y_pred)) )
+    except Exception as e:
+        print(clf + " Error:")
+        print(str(e))
+cols = ['OverallQual','GrLivArea', 'GarageCars','TotalBsmtSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt']
+x = data_train[cols].values
+y = data_train['SalePrice'].values
+X_train,X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
+
+clf = RandomForestRegressor(n_estimators=400)
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+
+rfr = clf
+data_test = pd.read_csv("test.csv")
+data_test[cols].isnull().sum()
+cols2 = ['OverallQual','GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt']
+cars = data_test['GarageCars'].fillna(1.766118)
+bsmt = data_test['TotalBsmtSF'].fillna(1046.117970)
+data_test_x = pd.concat( [data_test[cols2], cars, bsmt] ,axis=1)
+data_test_x.isnull().sum()
+x = data_test_x.values
+y_te_pred = rfr.predict(x)
+prediction = pd.DataFrame(y_te_pred, columns=['SalePrice'])
+result = pd.concat([ data_test['Id'], prediction], axis=1)
+# result = result.drop(resultlt.columns[0], 1)
+result.columns
+result.to_csv('./Predictions.csv', index=False)
+
+plt.show()