X=pd.concat([X,pd.get_dummies(df['Gender']),pd.get_dummies(df['Geography'])],axis=1)

CreditScore  Age  Tenure  Balance  NumOfProducts  HasCrCard
7050          591   29       6      0.0              2          1

          IsActiveMember  EstimatedSalary  Female  Germany  Spain
7050               1        108684.65       0        0      0

prediction=pd.DataFrame({'Geography':'France','Credit Score':600,'Gender':'Male','Age':40,'Tenure':3,'Balance':60000,'Number of Products':2,'HasCrCard':1,'IsActiveMember':1,'EstimatedSalary':50000},index=[1])
prediction=pd.concat([prediction,pd.get_dummies(prediction['Gender']),pd.get_dummies(prediction['Geography'])],axis=1)
prediction.drop(['Gender','Geography','France','Male'],1,inplace=True)

Age  Balance  Credit Score  EstimatedSalary  HasCrCard  IsActiveMember
 1   40    60000           600            50000          1               1

    Number of Products  Tenure
1                   2       3

df=pd.concat([train,test],axis=0)

df=pd.concat([df,pd.get_dummies(df[['city','gender','relevent_experience','enrolled_university','education_level','major_discipline','experience','company_size','company_type','last_new_job']])],axis=1)
df.drop(['city','gender','relevent_experience','enrolled_university','education_level','major_discipline','experience','company_size','company_type','last_new_job'],axis=1,inplace=True)

train=df[:train.shape[0]]
test=df[train.shape[0]:]