-
Notifications
You must be signed in to change notification settings - Fork 2
/
00-Cleaning Data.py
71 lines (52 loc) · 1.84 KB
/
00-Cleaning Data.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
#**************************#
#import packages
#**************************#
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, PowerTransformer
#from sklearn.preprocessing import MinMaxScaler
#from sklearn.impute import SimpleImputer
from sklearn.feature_selection import SelectKBest , chi2, f_classif
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix , classification_report, roc_auc_score, f1_score, accuracy_score
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
import warnings
warnings.filterwarnings('ignore')
#**************************#
#import and Explore data
#**************************#
df = pd.read_excel("data/Bankruptcy_data_Final.xlsx")
df.head()
data = df.copy()
#Data Statistics
print(df.describe().transpose())
# Check missing value
print(df.isnull().sum())
# target distribution -- Class0: 99.4%, Class1 : 0.6%
print(df["BK"].value_counts())
#****************************#
#Data Cleaning and Engineering
#****************************#
#Drop rows with more than 3 missing values
df = df[df.isnull().sum(axis=1) < 3]
#Fill remaining missing value with 0
df = df.fillna(0)
#Feature Scaling
df.drop(['Data Year - Fiscal' ], axis = 1 , inplace = True)
num_features = [col for col in df.columns if col != 'BK']
scaler = PowerTransformer(method='yeo-johnson')
df[num_features] = scaler.fit_transform(df[num_features])
#Histogram
df.hist(figsize = (13,13), bins = 20)
plt.show()
#Export the cleaned data
df.to_csv('data/cleaned_data.csv')