基于Lending Club的数据分析实战项目【小白记录向】【一】
本实战项目基于Lending Club的数据集【数据集地址：https://github.com/H-Freax/lendingclub_analyse/data/】
本实战项目基于Colab环境
文章目录
- 简介
- 环境准备
- 载入数据
- 数据基本信息查看/分析
- BaseLine
-
- data preprocessing
- Algorithm
- Train
- Test
- 添加衍生变量
-
- CatBoostEncoder
-
- data preprocessing
- Algorithm
- Train
- Test
- 离散化
-
- 基于聚类continuous_open_acc
-
- data preprocessing
- Algorithm
- Train
- Test
- 改用指数性区间划分continuous_loan_amnt
-
- data preprocessing
- Algorithm
- Train
- Test
- 基于业务逻辑分析的衍生变量
-
- data preprocessing
- Algorithm
- Train
- Test
简介
本数据分析实战项目分为两篇，第一篇主要介绍了基于LightGBM的Baseline方法，以及三种添加衍生变量的方法，找到了四组可以提升效果的衍生变量，第二篇主要介绍了基于机器学习方法及深度学习网络方法的数据分析，同时对机器学习方法的集成以及将深度学习网络与机器学习方法的融合进行了实践。
环境准备
本项目采用lightgbm作为baseline
首先引入相关包
import lightgbm as lgb
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
载入数据
seed = 42 # for the same data division
kf = KFold(n_splits=5, random_state=seed,shuffle=True)
df_train = pd.read_csv('train_final.csv')
df_test = pd.read_csv('test_final.csv')
数据基本信息查看/分析
查看df_train的基本信息
df_train.describe()
为了进行数据分析的时候先跳过one-hot编码的部分，先采用以下函数列出所有列名
df_train.columns.values
将one-hot编码对应的变量排除以后进行可视化，查看规律
import matplotlib.pyplot as plt
onehotlabels=['discrete_addr_state_1_one_hot',
'discrete_addr_state_2_one_hot', 'discrete_addr_state_3_one_hot',
'discrete_addr_state_4_one_hot', 'discrete_addr_state_5_one_hot',
'discrete_addr_state_6_one_hot', 'discrete_addr_state_7_one_hot',
'discrete_addr_state_8_one_hot', 'discrete_addr_state_9_one_hot',
'discrete_addr_state_10_one_hot', 'discrete_addr_state_11_one_hot',
'discrete_addr_state_12_one_hot', 'discrete_addr_state_13_one_hot',
'discrete_addr_state_14_one_hot', 'discrete_addr_state_15_one_hot',
'discrete_addr_state_16_one_hot', 'discrete_addr_state_17_one_hot',
'discrete_addr_state_18_one_hot', 'discrete_addr_state_19_one_hot',
'discrete_addr_state_20_one_hot', 'discrete_addr_state_21_one_hot',
'discrete_addr_state_22_one_hot', 'discrete_addr_state_23_one_hot',
'discrete_addr_state_24_one_hot', 'discrete_addr_state_25_one_hot',
'discrete_addr_state_26_one_hot', 'discrete_addr_state_27_one_hot',
'discrete_addr_state_28_one_hot', 'discrete_addr_state_29_one_hot',
'discrete_addr_state_30_one_hot', 'discrete_addr_state_31_one_hot',
'discrete_addr_state_32_one_hot', 'discrete_addr_state_33_one_hot',
'discrete_addr_state_34_one_hot', 'discrete_addr_state_35_one_hot',
'discrete_addr_state_36_one_hot', 'discrete_addr_state_37_one_hot',
'discrete_addr_state_38_one_hot', 'discrete_addr_state_39_one_hot',
'discrete_addr_state_40_one_hot', 'discrete_addr_state_41_one_hot',
'discrete_addr_state_42_one_hot', 'discrete_addr_state_43_one_hot',
'discrete_addr_state_44_one_hot', 'discrete_addr_state_45_one_hot',
'discrete_addr_state_46_one_hot', 'discrete_addr_state_47_one_hot',
'discrete_addr_state_48_one_hot', 'discrete_addr_state_49_one_hot',
'discrete_application_type_1_one_hot',
'discrete_application_type_2_one_hot',
'discrete_emp_length_1_one_hot', 'discrete_emp_length_2_one_hot',
'discrete_emp_length_3_one_hot', 'discrete_emp_length_4_one_hot',
'discrete_emp_length_5_one_hot', 'discrete_emp_length_6_one_hot',
'discrete_emp_length_7_one_hot', 'discrete_emp_length_8_one_hot',
'discrete_emp_length_9_one_hot', 'discrete_emp_length_10_one_hot',
'discrete_emp_length_11_one_hot', 'discrete_emp_length_12_one_hot',
'discrete_grade_1_one_hot', 'discrete_grade_2_one_hot',
'discrete_grade_3_one_hot', 'discrete_grade_4_one_hot',
'discrete_grade_5_one_hot', 'discrete_grade_6_one_hot',
'discrete_grade_7_one_hot', 'discrete_home_ownership_1_one_hot',
'discrete_home_ownership_2_one_hot',
'discrete_home_ownership_3_one_hot',
'discrete_home_ownership_4_one_hot',
'discrete_policy_code_1_one_hot', 'discrete_purpose_1_one_hot',
'discrete_purpose_2_one_hot', 'discrete_purpose_3_one_hot',
'discrete_purpose_4_one_hot', 'discrete_purpose_5_one_hot',
'discrete_purpose_6_one_hot', 'discrete_purpose_7_one_hot',
'discrete_purpose_8_one_hot', 'discrete_purpose_9_one_hot',
'discrete_purpose_10_one_hot', 'discrete_purpose_11_one_hot',
'discrete_purpose_12_one_hot', 'discrete_pymnt_plan_1_one_hot',
'discrete_sub_grade_1_one_hot', 'discrete_sub_grade_2_one_hot',
'discrete_sub_grade_3_one_hot', 'discrete_sub_grade_4_one_hot',
'discrete_sub_grade_5_one_hot', 'discrete_sub_grade_6_one_hot',
'discrete_sub_grade_7_one_hot', 'discrete_sub_grade_8_one_hot',
'discrete_sub_grade_9_one_hot', 'discrete_sub_grade_10_one_hot',
'discrete_sub_grade_11_one_hot', 'discrete_sub_grade_12_one_hot',
'discrete_sub_grade_13_one_hot', 'discrete_sub_grade_14_one_hot',
'discrete_sub_grade_15_one_hot', 'discrete_sub_grade_16_one_hot',
'discrete_sub_grade_17_one_hot', 'discrete_sub_grade_18_one_hot',
'discrete_sub_grade_19_one_hot', 'discrete_sub_grade_20_one_hot',
'discrete_sub_grade_21_one_hot', 'discrete_sub_grade_22_one_hot',
'discrete_sub_grade_23_one_hot', 'discrete_sub_grade_24_one_hot',
'discrete_sub_grade_25_one_hot', 'discrete_sub_grade_26_one_hot',
'discrete_sub_grade_27_one_hot', 'discrete_sub_grade_28_one_hot',
'discrete_sub_grade_29_one_hot', 'discrete_sub_grade_30_one_hot',
'discrete_sub_grade_31_one_hot', 'discrete_sub_grade_32_one_hot',
'discrete_sub_grade_33_one_hot', 'discrete_sub_grade_34_one_hot',
'discrete_sub_grade_35_one_hot', 'discrete_term_1_one_hot',
'discrete_term_2_one_hot','loan_status']
showdf_train=df_train.drop(columns=onehotlabels)
showdf_train.hist(bins=50,figsize=(20,15))
plt.show()
由于continuous_fico_range与continuous_last_fico_range存在上下界且有高相关性，故删去high的部分进行进一步可视化分析
from pandas.plotting import scatter_matrix
scatter_matrix(showdf_train.drop(columns=['continuous_fico_range_high','continuous_last_fico_range_high']),figsize=(40,35))
BaseLine
data preprocessing
X_train = df_train.drop(columns=['loan_status']).values
Y_train = df_train['loan_status'].values.astype(int)
X_test = df_test.drop(columns=['loan_status']).values
Y_test = df_test['loan_status'].values.astype(int)
# split data for five fold
five_fold_data = []
for train_index, eval_index in kf.split(X_train):
x_train, x_eval = X_train[train_index], X_train[eval_index]
y_train, y_eval = Y_train[train_index], Y_train[eval_index]
five_fold_data.append([(x_train, y_train), (x_eval, y_eval)])
X_train.shape, Y_train.shape
Algorithm
def get_model(param):
model_list = []
for idx, [(x_train, y_train), (x_eval, y_eval)] in enumerate(five_fold_data):
print('{}-th model is training:'.format(idx))
train_data = lgb.Dataset(x_train, label=y_train)
validation_data = lgb.Dataset(x_eval, label=y_eval)
bst = lgb.train(param, train_data, valid_sets=[validation_data])
model_list.append(bst)
return model_list
Train
param_base = {
'num_leaves': 31, 'objective': 'binary', 'metric': 'binary', 'num_round':1000}
param_fine_tuning = {
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 1000,
'learning_rate': 3e-3, 'feature_fraction': 0.6, 'bagging_fraction': 0.8}
# base param train
param_base_model = get_model(param_base)
# param fine tuning
param_fine_tuning_model = get_model(param_fine_tuning)
Test
def test_model(model_list):
data = X_test
five_fold_pred = np.zeros((5, len(X_test)))
for i, bst in enumerate(model_list):
ypred = bst.predict(data, num_iteration=bst.best_iteration)
five_fold_pred[i] = ypred
ypred_mean = (five_fold_pred.mean(axis=-2)>0.5).astype(int)
return accuracy_score(ypred_mean, Y_test)
base_score = test_model(param_base_model)
fine_tuning_score = test_model(param_fine_tuning_model)
print('base: {}, fine tuning: {}'.format(base_score, fine_tuning_score))
添加衍生变量
CatBoostEncoder
导入相关环境
pip install category_encoders
import category_encoders as ce #CatBoostEncoder的相关包
#Create the encoder
target_enc = ce.CatBoostEncoder(cols='continuous_open_acc')
target_enc.fit(df_train['continuous_open_acc'], df_train['loan_status'])
#Transform the features, rename columns with _cb suffix, and join to dataframe
train_CBE = df_train.join(target_enc.transform(df_train['continuous_open_acc']).add_suffix('_cb'))
test_CBE = df_test.join(target_enc.transform(df_test['continuous_open_acc']).add_suffix('_cb'))
data preprocessing
X_train = train_CBE.drop(columns=['loan_status']).values
Y_train = train_CBE['loan_status'].values.astype(int)
X_test = test_CBE.drop(columns=['loan_status']).values
Y_test = test_CBE['loan_status'].values.astype(int)
# split data for five fold
five_fold_data = []
for train_index, eval_index in kf.split(X_train):
x_train, x_eval = X_train[train_index], X_train[eval_index]
y_train, y_eval = Y_train[train_index], Y_train[eval_index]
five_fold_data.append([(x_train, y_train), (x_eval, y_eval)])
Algorithm
def get_model(param):
model_list = []
for idx, [(x_train, y_train), (x_eval, y_eval)] in enumerate(five_fold_data):
print('{}-th model is training:'.format(idx))
train_data = lgb.Dataset(x_train, label=y_train)
validation_data = lgb.Dataset(x_eval, label=y_eval)
bst = lgb.train(param, train_data, valid_sets=[validation_data])
model_list.append(bst)
return model_list
Train
param_base = {
'num_leaves': 31, 'objective': 'binary', 'metric': 'binary', 'num_round':1000}
param_fine_tuning = {
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 1000,
'learning_rate': 3e-3, 'feature_fraction': 0.6, 'bagging_fraction': 0.8}
param_fine_tuningfinal={
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 1200,
'learning_rate': 3e-3, 'feature_fraction': 0.6, 'bagging_fraction': 0.8}
# base param train
param_base_model = get_model(param_base)
# param fine tuning
param_fine_tuning_model = get_model(param_fine_tuning)
param_fine_tuningfinal_model = get_model(param_fine_tuningfinal)
Test
def test_model(model_list):
data = X_test
five_fold_pred = np.zeros((5, len(X_test)))
for i, bst in enumerate(model_list):
ypred = bst.predict(data, num_iteration=bst.best_iteration)
five_fold_pred[i] = ypred
ypred_mean = (five_fold_pred.mean(axis=-2)>0.5).astype(int)
return accuracy_score(ypred_mean, Y_test)
base_score = test_model(param_base_model)
fine_tuning_score = test_model(param_fine_tuning_model)
fine_tuningfinal_score=test_model(param_fine_tuningfinal_model)
print('base: {}, fine tuning: {} , fine tuning final: {}'.format(base_score, fine_tuning_score, fine_tuningfinal_score))
base: 0.91568, fine tuning: 0.91774 , fine tuning final: 0.91796
离散化
基于聚类continuous_open_acc
df_train.groupby('continuous_open_acc')['continuous_open_acc'].unique()
!pip install KMeans
from sklearn.cluster import KMeans
ddtrain=df_train['continuous_open_acc']
ddtest=df_test['continuous_open_acc']
data_reshape1=ddtrain.values.reshape((ddtrain.shape[0],1))
model_kmeans=KMeans(n_clusters=5,random_state=0)
kmeans_result=model_kmeans.fit_predict(data_reshape1)
traina=kmeans_result
data_reshape2=ddtest.values.reshape((ddtest.shape[0],1))
model_kmeans=KMeans(n_clusters=5,random_state=0)
kmeans_result=model_kmeans.fit_predict(data_reshape2)
testa=kmeans_result
train_KM = df_train.copy()
test_KM = df_test.copy()
train_KM['continuous_open_acc_km']=traina
test_KM['continuous_open_acc_km']=testa
data preprocessing
X_train = train_KM.drop(columns=['loan_status']).values
Y_train = train_KM['loan_status'].values.astype(int)
X_test = test_KM.drop(columns=['loan_status']).values
Y_test = test_KM['loan_status'].values.astype(int)
# split data for five fold
five_fold_data = []
for train_index, eval_index in kf.split(X_train):
x_train, x_eval = X_train[train_index], X_train[eval_index]
y_train, y_eval = Y_train[train_index], Y_train[eval_index]
five_fold_data.append([(x_train, y_train), (x_eval, y_eval)])
Algorithm
def get_model(param):
model_list = []
for idx, [(x_train, y_train), (x_eval, y_eval)] in enumerate(five_fold_data):
print('{}-th model is training:'.format(idx))
train_data = lgb.Dataset(x_train, label=y_train)
validation_data = lgb.Dataset(x_eval, label=y_eval)
bst = lgb.train(param, train_data, valid_sets=[validation_data])
model_list.append(bst)
return model_list
Train
param_base = {
'num_leaves': 31, 'objective': 'binary', 'metric': 'binary', 'num_round':1000}
param_fine_tuning = {
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 1000,
'learning_rate': 3e-3, 'feature_fraction': 0.6, 'bagging_fraction': 0.8}
param_fine_tuningfinal={
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 800,
'learning_rate': 6e-3, 'feature_fraction': 0.8, 'bagging_fraction': 0.6,'boosting':'goss','tree_learning':'feature','max_depth':20,'min_sum_hessian_in_leaf':100}
# # base param train
param_base_model = get_model(param_base)
# # param fine tuning
param_fine_tuning_model = get_model(param_fine_tuning)
param_fine_tuningfinal_model = get_model(param_fine_tuningfinal)
Test
def test_model(model_list):
data = X_test
five_fold_pred = np.zeros((5, len(X_test)))
for i, bst in enumerate(model_list):
ypred = bst.predict(data, num_iteration=bst.best_iteration)
five_fold_pred[i] = ypred
ypred_mean = (five_fold_pred.mean(axis=-2)>0.5).astype(int)
return accuracy_score(ypred_mean, Y_test)
base_score = test_model(param_base_model)
fine_tuning_score = test_model(param_fine_tuning_model)
fine_tuningfinal_score=test_model(param_fine_tuningfinal_model)
print('base: {}, fine tuning: {} , fine tuning final: {}'.format(base_score, fine_tuning_score, fine_tuningfinal_score))
base: 0.91598, fine tuning: 0.91776 , fine tuning final: 0.91874
改用指数性区间划分continuous_loan_amnt
train_ZQ = df_train.copy()
test_ZQ = df_test.copy()
trainbins=np.floor(np.log10(train_ZQ['continuous_loan_amnt'])) #取对数之后再向下取整
testbins=np.floor(np.log10(test_ZQ['continuous_loan_amnt']))
train_ZQ['continuous_loan_amnt_km']=trainbins
test_ZQ['continuous_loan_amnt_km']=testbins
data preprocessing
X_train = train_ZQ.drop(columns=['loan_status']).values
Y_train = train_ZQ['loan_status'].values.astype(int)
X_test = test_ZQ.drop(columns=['loan_status']).values
Y_test = test_ZQ['loan_status'].values.astype(int)
# split data for five fold
five_fold_data = []
for train_index, eval_index in kf.split(X_train):
x_train, x_eval = X_train[train_index], X_train[eval_index]
y_train, y_eval = Y_train[train_index], Y_train[eval_index]
five_fold_data.append([(x_train, y_train), (x_eval, y_eval)])
Algorithm
def get_model(param):
model_list = []
for idx, [(x_train, y_train), (x_eval, y_eval)] in enumerate(five_fold_data):
print('{}-th model is training:'.format(idx))
train_data = lgb.Dataset(x_train, label=y_train)
validation_data = lgb.Dataset(x_eval, label=y_eval)
bst = lgb.train(param, train_data, valid_sets=[validation_data])
model_list.append(bst)
return model_list
Train
param_base = {
'num_leaves': 31, 'objective': 'binary', 'metric': 'binary', 'num_round':1000}
param_fine_tuning = {
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 1000,
'learning_rate': 3e-3, 'feature_fraction': 0.6, 'bagging_fraction': 0.8}
param_fine_tuningfinal={
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 900,
'learning_rate': 7e-3, 'feature_fraction': 0.8, 'bagging_fraction': 0.6,'max_depth':20,'min_sum_hessian_in_leaf':100}
# base param train
param_base_model = get_model(param_base)
# param fine tuning
param_fine_tuning_model = get_model(param_fine_tuning)
param_fine_tuningfinal_model = get_model(param_fine_tuningfinal)
Test
def test_model(model_list):
data = X_test
five_fold_pred = np.zeros((5, len(X_test)))
for i, bst in enumerate(model_list):
ypred = bst.predict(data, num_iteration=bst.best_iteration)
five_fold_pred[i] = ypred
ypred_mean = (five_fold_pred.mean(axis=-2)>0.5).astype(int)
return accuracy_score(ypred_mean, Y_test)
base_score = test_model(param_base_model)
fine_tuning_score = test_model(param_fine_tuning_model)
fine_tuningfinal_score=test_model(param_fine_tuningfinal_model)
print('base: {}, fine tuning: {} , fine tuning final: {}'.format(base_score, fine_tuning_score, fine_tuningfinal_score))
base: 0.91586, fine tuning: 0.91764 , fine tuning final: 0.91842
基于业务逻辑分析的衍生变量
train_YW = df_train.copy()
test_YW = df_test.copy()
train_YW['installment_feat']=train_YW['continuous_installment'] / ((train_YW['continuous_annual_inc']+1) / 12)
test_YW['installment_feat']=test_YW['continuous_installment'] / ((test_YW['continuous_annual_inc']+1) / 12)
data preprocessing
X_train = train_YW.drop(columns=['loan_status']).values
Y_train = train_YW['loan_status'].values.astype(int)
X_test = test_YW.drop(columns=['loan_status']).values
Y_test = test_YW['loan_status'].values.astype(int)
# split data for five fold
five_fold_data = []
for train_index, eval_index in kf.split(X_train):
x_train, x_eval = X_train[train_index], X_train[eval_index]
y_train, y_eval = Y_train[train_index], Y_train[eval_index]
five_fold_data.append([(x_train, y_train), (x_eval, y_eval)])
Algorithm
def get_model(param):
model_list = []
for idx, [(x_train, y_train), (x_eval, y_eval)] in enumerate(five_fold_data):
print('{}-th model is training:'.format(idx))
train_data = lgb.Dataset(x_train, label=y_train)
validation_data = lgb.Dataset(x_eval, label=y_eval)
bst = lgb.train(param, train_data, valid_sets=[validation_data])
model_list.append(bst)
return model_list
Train
param_base = {
'num_leaves': 31, 'objective': 'binary', 'metric': 'binary', 'num_round':1000}
param_fine_tuning = {
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 1000,
'learning_rate': 3e-3, 'feature_fraction': 0.6, 'bagging_fraction': 0.8}
param_fine_tuningfinal={
'num_thread': 8,'num_leaves': 128, 'metric': 'binary', 'objective': 'binary', 'num_round': 900,
'learning_rate': 7e-3, 'feature_fraction': 0.8, 'bagging_fraction': 0.6,'max_depth':20,'min_sum_hessian_in_leaf':100}
# base param train
param_base_model = get_model(param_base)
# param fine tuning
param_fine_tuning_model = get_model(param_fine_tuning)
param_fine_tuningfinal_model = get_model(param_fine_tuningfinal)
Test
def test_model(model_list):
data = X_test
five_fold_pred = np.zeros((5, len(X_test)))
for i, bst in enumerate(model_list):
ypred = bst.predict(data, num_iteration=bst.best_iteration)
five_fold_pred[i] = ypred
ypred_mean = (five_fold_pred.mean(axis=-2)>0.5).astype(int)
return accuracy_score(ypred_mean, Y_test)
base_score = test_model(param_base_model)
fine_tuning_score = test_model(param_fine_tuning_model)
fine_tuningfinal_score=test_model(param_fine_tuningfinal_model)
print('base: {}, fine tuning: {} , fine tuning final: {}'.format(base_score, fine_tuning_score, fine_tuningfinal_score))
base: 0.9162, fine tuning: 0.91758 , fine tuning final: 0.91844