<jupyter_start><jupyter_text>Basic Libraries<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline<jupyter_output><empty_output><jupyter_text>Preprocessing<jupyter_code>df = pd.read_csv("BankCreditCard.csv")
df.describe()
# Variables having NA's
def get_na(data): 
    null_vars = data.isnull().sum()
    null_vars = null_vars[null_vars > 0]
    if(len(null_vars) > 0):
        null_vars.sort_values(inplace=True)
        return null_vars
    else:
        print("No column have NA values")
get_na(df)
# Numerical Variables
num_datatypes = ['int32','int64','float64']
df_numerics = df.select_dtypes(num_datatypes).columns
print("Total Numerical Variables",len(df_numerics))
print(df_numerics)
# Categorical Variables

df_categorical = df.select_dtypes("O").columns
print("Total Numerical Variables",len(df_categorical))
if len(df_categorical) > 0:
    print(df_categorical)
else:
    print("No categorical Variables are present")<jupyter_output>Total Numerical Variables 0
No categorical Variables are present<jupyter_text>CONCLUSIONDeviding Data into X and Y<jupyter_code>y = df.Default_Payment
x = df
x.columns
x.drop('Customer ID', axis = 1, inplace = True)
y.value_counts()<jupyter_output><empty_output><jupyter_text>EDA - Exploratory Data Analysis<jupyter_code>x['Gender'].value_counts().plot.bar()
x['Repayment_Status_March'].value_counts().plot.bar()<jupyter_output><empty_output><jupyter_text>Scaler Data<jupyter_code>from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scale_fit = scaler.fit(x)
scale_data = scaler.transform(x)
scale_data<jupyter_output><empty_output><jupyter_text>Building Model<jupyter_code>from sklearn import svm
model = svm.SVC(kernel='rbf')
model.fit(x,y)
predicted = model.predict(x)
sns.barplot(predicted)
from sklearn.metrics import classification_report,confusion_matrix 
print(confusion_matrix(y,predicted))<jupyter_output>[[23364     0]
 [ 6633     3]]
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