PulkitS01 · May 25, 2023 11:20
diff --git a/app.py b/app.py
 %%writefile app.py
 
 import pickle
 import streamlit as st
 
 # loading the trained model
 pickle_in = open('classifier.pkl', 'rb') 
 classifier = pickle.load(pickle_in)
 
 @st.cache()
  
 # defining the function which will make the prediction using the data which the user inputs 
 def prediction(Gender, Married, ApplicantIncome, LoanAmount, Credit_History):   
 
    # Pre-processing user input    
    if Gender == "Male":
        Gender = 0
    else:
        Gender = 1
 
    if Married == "Unmarried":
        Married = 0
    else:
        Married = 1
 
    if Credit_History == "Unclear Debts":
        Credit_History = 0
    else:
        Credit_History = 1  
 
    LoanAmount = LoanAmount / 1000
 
    # Making predictions 
    prediction = classifier.predict( 
        [[Gender, Married, ApplicantIncome, LoanAmount, Credit_History]])
     
    if prediction == 0:
        pred = 'Rejected'
    else:
        pred = 'Approved'
    return pred
      
  
 # this is the main function in which we define our webpage  
 def main():       
    # front end elements of the web page 
    html_temp = """ 
    <div style ="background-color:yellow;padding:13px"> 
    <h1 style ="color:black;text-align:center;">Streamlit Loan Prediction ML App</h1> 
    </div> 
    """
      
    # display the front end aspect
    st.markdown(html_temp, unsafe_allow_html = True) 
      
    # following lines create boxes in which user can enter data required to make prediction 
    Gender = st.selectbox('Gender',("Male","Female"))
    Married = st.selectbox('Marital Status',("Unmarried","Married")) 
    ApplicantIncome = st.number_input("Applicants monthly income") 
    LoanAmount = st.number_input("Total loan amount")
    Credit_History = st.selectbox('Credit_History',("Unclear Debts","No Unclear Debts"))
    result =""
      
    # when 'Predict' is clicked, make the prediction and store it 
    if st.button("Predict"): 
        result = prediction(Gender, Married, ApplicantIncome, LoanAmount, Credit_History) 
        st.success('Your loan is {}'.format(result))
        print(LoanAmount)
     
 if __name__=='__main__': 
    main()
diff --git a/data_prep.py b/data_prep.py
 X = train[['Gender', 'Married', 'ApplicantIncome', 'LoanAmount', 'Credit_History']]
 y = train.Loan_Status
 X.shape, y.shape
diff --git a/installation.py b/installation.py
 !pip install -q pyngrok

 !pip install -q streamlit

 !pip install -q streamlit_ace
diff --git a/loading_data.py b/loading_data.py
 import pandas as pd
 
 train = pd.read_csv('train_ctrUa4K.csv') 
 train.head()
diff --git a/missing_value.py b/missing_value.py
 train.isnull().sum()
diff --git a/missing_value_imputation.py b/missing_value_imputation.py
 train = train.dropna()
 train.isnull().sum()
diff --git a/model_building.py b/model_building.py
 from sklearn.ensemble import RandomForestClassifier 
 model = RandomForestClassifier(max_depth=4, random_state = 10) 
 model.fit(x_train, y_train)
diff --git a/model_saving.py b/model_saving.py
 # saving the model 
 import pickle 
 pickle_out = open("classifier.pkl", mode = "wb") 
 pickle.dump(model, pickle_out) 
 pickle_out.close()
diff --git a/preprocess.py b/preprocess.py
 train['Gender']= train['Gender'].map({'Male':0, 'Female':1})
 train['Married']= train['Married'].map({'No':0, 'Yes':1})
 train['Loan_Status']= train['Loan_Status'].map({'N':0, 'Y':1})
diff --git a/public_url.py b/public_url.py
 from pyngrok import ngrok
 
 public_url = ngrok.connect('8501')
 public_url
diff --git a/running_app.py b/running_app.py
 !streamlit run app.py &>/dev/null&
diff --git a/train_score.py b/train_score.py
 pred_train = model.predict(x_train)
 accuracy_score(y_train,pred_train)
diff --git a/train_test_split.py b/train_test_split.py
 from sklearn.model_selection import train_test_split
 x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size = 0.2, random_state = 10)
diff --git a/val_score.py b/val_score.py
 from sklearn.metrics import accuracy_score
 pred_cv = model.predict(x_cv)
 accuracy_score(y_cv,pred_cv)
	%%writefile app.py

	import pickle
	import streamlit as st

	# loading the trained model
	pickle_in = open('classifier.pkl', 'rb')
	classifier = pickle.load(pickle_in)

	@st.cache()

	# defining the function which will make the prediction using the data which the user inputs
	def prediction(Gender, Married, ApplicantIncome, LoanAmount, Credit_History):

	# Pre-processing user input
	if Gender == "Male":
	Gender = 0
	else:
	Gender = 1

	if Married == "Unmarried":
	Married = 0
	else:
	Married = 1

	if Credit_History == "Unclear Debts":
	Credit_History = 0
	else:
	Credit_History = 1

	LoanAmount = LoanAmount / 1000

	# Making predictions
	prediction = classifier.predict(
	[[Gender, Married, ApplicantIncome, LoanAmount, Credit_History]])

	if prediction == 0:
	pred = 'Rejected'
	else:
	pred = 'Approved'
	return pred


	# this is the main function in which we define our webpage
	def main():
	# front end elements of the web page
	html_temp = """
	<div style ="background-color:yellow;padding:13px">
	<h1 style ="color:black;text-align:center;">Streamlit Loan Prediction ML App</h1>
	</div>
	"""

	# display the front end aspect
	st.markdown(html_temp, unsafe_allow_html = True)

	# following lines create boxes in which user can enter data required to make prediction
	Gender = st.selectbox('Gender',("Male","Female"))
	Married = st.selectbox('Marital Status',("Unmarried","Married"))
	ApplicantIncome = st.number_input("Applicants monthly income")
	LoanAmount = st.number_input("Total loan amount")
	Credit_History = st.selectbox('Credit_History',("Unclear Debts","No Unclear Debts"))
	result =""

	# when 'Predict' is clicked, make the prediction and store it
	if st.button("Predict"):
	result = prediction(Gender, Married, ApplicantIncome, LoanAmount, Credit_History)
	st.success('Your loan is {}'.format(result))
	print(LoanAmount)

	if __name__=='__main__':
	main()
	X = train[['Gender', 'Married', 'ApplicantIncome', 'LoanAmount', 'Credit_History']]
	y = train.Loan_Status
	X.shape, y.shape
	!pip install -q pyngrok

	!pip install -q streamlit

	!pip install -q streamlit_ace
	import pandas as pd

	train = pd.read_csv('train_ctrUa4K.csv')
	train.head()
	from sklearn.ensemble import RandomForestClassifier
	model = RandomForestClassifier(max_depth=4, random_state = 10)
	model.fit(x_train, y_train)
	# saving the model
	import pickle
	pickle_out = open("classifier.pkl", mode = "wb")
	pickle.dump(model, pickle_out)
	pickle_out.close()
	train['Gender']= train['Gender'].map({'Male':0, 'Female':1})
	train['Married']= train['Married'].map({'No':0, 'Yes':1})
	train['Loan_Status']= train['Loan_Status'].map({'N':0, 'Y':1})
	from pyngrok import ngrok

	public_url = ngrok.connect('8501')
	public_url
	pred_train = model.predict(x_train)
	accuracy_score(y_train,pred_train)
	from sklearn.model_selection import train_test_split
	x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size = 0.2, random_state = 10)