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app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+# Stores loaded model in cache so that we don't need to reload model repeatedly for each input
+@st.cache(allow_output_mutation=True) 
+def load_model():
+	model = pickle.load(open('random_forest_model.sav', 'rb'))
+	country_dict = pickle.load(open('country_dict.pickle', 'rb'))
+	scaler = pickle.load(open('standardScaler.pickle', 'rb'))
+	return model, scaler, country_dict
+
+def featurize(time, country, scaler, country_dict):
+	arr = np.array([country_dict[country], time]).reshape(1,-1)
+	vector = scaler.transform(arr)
+	return vector
+
+def main():
+	model, scaler, country_dict = load_model()
+	st.title("\'365 data science\' : free-to-paid user conversion predictor")
+	list_of_countries = list(country_dict.keys())
+	st.write("\'365 data science\' is a ed-tech company that creates data science courses comprising of video lectures and \
+		exercises in the form of quizzes and exams. Some of the courses offered are free and majority of the other courses \
+		need the user to buy paid subscription. Students mostly register on this platform as 'free-tier user' as the registration is free of cost. \
+		They enroll for free courses and then if they like the content of the platform, they proceed to buy paid-subscription \
+		which offers lot of perks as compared to free tier. Paid student get access to large library of courses along with certificates, \
+		quizzes and exams.")
+	st.write("This application predicts how likely the student is to buy the paid subscription based on the number of minutes \
+		he spent engaging with the free course content and the country he comes from. In the exploratory data analysis done, it was found that \
+		total time spent by user and nationality of user are two major and most significant factor for determining how likely the user is \
+		to buy the course. Typical range for total time watched for students is mostly 0.1 to 100 minutes")
+	
+	with st.form("my_form"):
+		total_time = st.number_input('Time spent on platform watching tutorials')
+		student_country = st.selectbox('country', list_of_countries)
+		st.write('Total time spent : ', total_time)
+		st.write('Student country :', student_country)
+
+		# Every form must have a submit button.
+		submitted = st.form_submit_button("Submit")
+	
+	if submitted: 
+		vector = featurize(total_time, student_country, scaler, country_dict)
+		prediction = model.predict(vector)[0]
+		predicted_proba = model.predict_proba(vector)
+		if prediction == 0 : 
+			st.write('Student is ', str(round(predicted_proba[0][0]*100)), '%  likely to NOT buy the paid subscription')
+		else : 
+			st.write('Student is ', str(round(predicted_proba[0][1]*100)), '%  likely to buy the paid subscription')
+
+if __name__ == '__main__' : 
+	main()

requirements.txt

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+pickle
+numpy
+sklearn
+scikit-learn