文章目錄

• • 1. Baseline
  • • 1. 特征選擇
    • 2. 異常值剔除
    • 3. 建模預測
  • 2. 待優化特征工程

房價預測 kaggle 地址

參考文章：kaggle比賽：房價預測（排名前4%）

1. Baseline

import numpy as np import pandas as pd %matplotlib inline import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.model_selection import StratifiedShuffleSplit from sklearn.impute import SimpleImputer from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import LabelBinarizer from sklearn.base import BaseEstimator, TransformerMixin from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.pipeline import FeatureUnion from sklearn.model_selection import GridSearchCV from sklearn.model_selection import cross_val_score train = pd.read_csv("./train.csv") test = pd.read_csv("./test.csv") # RangeIndex: 1460 entries, 0 to 1459 # Data columns (total 81 columns):

1. 特征選擇

• 數據有79個特征，我們選出相關系數最高的10個

abs(train.corr()['SalePrice']).sort_values(ascending=False).plot.bar()

most_10_important = abs(corrmat["SalePrice"]).sort_values(ascending=False)[1:11].index

最相關的特征 ['OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea', otalBsmtSF', '1stFlrSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt', 'YearRemodAdd']

2. 異常值剔除

• 部分數據異常，刪除

sns.pairplot(x_vars=most_10_important[0:5], y_vars=['SalePrice'], data=train, dropna=True) sns.pairplot(x_vars=most_10_important[5:], y_vars=['SalePrice'], data=train, dropna=True) # help(sns.pairplot)

#刪除異常值 train = train.drop(train[(train['OverallQual']<5)&(train['SalePrice']>200000)].index) train = train.drop(train[(train['GrLivArea']>4000)&(train['SalePrice']<300000)].index) train = train.drop(train[(train['YearBuilt']<1900)&(train['SalePrice']>400000)].index) train = train.drop(train[(train['TotalBsmtSF']>6000)&(train['SalePrice']<200000)].index) sns.pairplot(x_vars=most_10_important[0:5], y_vars=['SalePrice'], data=train, dropna=True) sns.pairplot(x_vars=most_10_important[5:], y_vars=['SalePrice'], data=train, dropna=True) # help(sns.pairplot)

X_train = train[most_10_important] X_test = test[most_10_important] y_train = train['SalePrice']

• 年份數據作為文字變量

X_train['YearBuilt'] = X_train['YearBuilt'].astype(str) X_train['YearRemodAdd'] = X_train['YearRemodAdd'].astype(str) X_test['YearBuilt'] = X_test['YearBuilt'].astype(str) X_test['YearRemodAdd'] = X_test['YearRemodAdd'].astype(str) def num_cat_splitor(X_train):s = (X_train.dtypes == 'object')object_cols = list(s[s].index)num_cols = list(set(X_train.columns) - set(object_cols))return num_cols, object_cols num_cols, object_cols = num_cat_splitor(X_train) class DataFrameSelector(BaseEstimator, TransformerMixin):def __init__(self, attribute_names):self.attribute_names = attribute_namesdef fit(self, X, y=None):return selfdef transform(self, X):return X[self.attribute_names].valuesnum_pipeline = Pipeline([('selector', DataFrameSelector(num_cols)),('imputer', SimpleImputer(strategy="median")),('std_scaler', StandardScaler()),]) cat_pipeline = Pipeline([('selector', DataFrameSelector(object_cols)),('cat_encoder', OneHotEncoder(sparse=False,handle_unknown='ignore')),]) full_pipeline = FeatureUnion(transformer_list=[("num_pipeline", num_pipeline),("cat_pipeline", cat_pipeline),]) X_prepared = full_pipeline.fit_transform(X_train)

3. 建模預測

prepare_select_and_predict_pipeline = Pipeline([('preparation', full_pipeline),('forst_reg', RandomForestRegressor(random_state=0)) ]) param_grid = [{'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],'forst_reg__n_estimators' : [50,100, 150, 200,250,300,330,350],'forst_reg__max_features':[45,50, 55, 65] }]grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=7,scoring='neg_mean_squared_error', verbose=2, n_jobs=-1) grid_search_prep.fit(X_train,y_train) grid_search_prep.best_params_ final_model = grid_search_prep.best_estimator_ y_pred_test = final_model.predict(X_test) result = pd.DataFrame() result['Id'] = test['Id'] result['SalePrice'] = y_pred_test result.to_csv('housing_price_10_features.csv',index=False)

得分：19154.16762

2. 待優化特征工程

待學習 My Top 1% Approach: EDA, New Models and Stacking

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