机器学习项目实战基于Python实现的银行客户认购产品预测源代码+数据集

import pandas as pd import numpy as np train = pd.read_csv('train.csv') test = pd.read_csv('test.csv') submission = pd.read_csv("submission.csv") data = pd.concat([train, test], axis=0, ignore_index=True) # 把所有的相同类别的特征编码为同一个值 numerical_features = [x for x in data.columns if data[x].dtype != object] category_features = [x for x in data.columns if data[x].dtype == object] def get_mapfunction(x): mapp = dict(zip(x.unique().tolist(), range(len(x.unique().tolist())))) def mapfunction(y): if y in mapp: return mapp[y] else: return -1 return mapfunction for i in category_features: data[i] = data[i].apply(get_mapfunction(data[i])) y_train_ = data["subscribe"][:train.shape[0]] data = data.drop(["id"], axis=1) data = data.drop(["subscribe"], axis=1) x_train_ = data[:train.shape[0]] x_test_ = data[train.shape[0]:] x_train = np.array(x_train_) y_train = np.array(y_train_) x_test = np.array(x_test_) # 定义 XGBoost模型 from xgboost.sklearn import XGBClassifier clf1 = XGBClassifier(colsample_bytree=0.8, learning_rate=0.1, max_depth=5, subsample=1) # 最优的 # 在训练集上训练XGBoost模型 clf1.fit(x_train, y_train) # 在训练集和测试集上分布利用训练好的模型进行预测 train_predict1 = clf1.predict(x_train) test_predict1 = clf1.predict(x_test) result_xgb = list(test_predict1) from lightgbm.sklearn import LGBMClassifier clf2 = LGBMClassifier(feature_fraction=0.5, learning_rate=0.1, max_depth=-1, num_leaves=16) clf2.fit(x_train, y_train) # 在训练集和测试集上分布利用训练好的模型进行预测 train_predict2 = clf2.predict(x_train) test_predict2 = clf2.predict(x_test) result_lgb = list(test_predict2) from catboost import CatBoostClassifier clf3 = CatBoostClassifier(depth=5, learning_rate=0.05, iterations=250) clf3.fit(x_train, y_train) # 在训练集和测试集上分布利用训练好的模型进行预测 train_predict3 = clf3.predict(x_train) test_predict3 = clf3.predict(x_test) result_ctb = list(test_predict3) from sklearn.metrics import accuracy_score print('acc_xgb=', accuracy_score(train_predict1, y_train)) print('acc_lgb=', accuracy_score(train_predict2, y_train)) print('acc_ctb=', accuracy_score(train_predict3, y_train)) result_all = list(test_predict1+test_predict2+test_predict3) submission['subscribe'] = result_all submission['subscribe'] = submission['subscribe'].map(lambda x: 'no' if x < 1.5 else 'yes') submission.to_csv("submit4.csv", index=False)