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