Upload 6 files

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import ast
+
+st.title('Heart Failure Prediction')
+
+with open('model_scaler.pkl', 'rb') as file_1:
+  model_scaler = joblib.load(file_1)
+
+with open('rf_best_model.pkl', 'rb') as file_2:
+  rf_random_best = joblib.load(file_2)
+
+with open('list_num_col.txt', 'r') as file_3:
+  num_col = file_3.read()
+
+with open('list_cat_col.txt', 'r') as file_4:
+  cat_col = file_4.read()
+
+with open('boxcox.pkl', 'rb') as file_5:
+  box = joblib.load(file_5)
+
+num_col = ast.literal_eval(num_col)
+cat_col = ast.literal_eval(cat_col)
+
+
+# time, ejection_fraction, age, serum creatinine, serum_sodium, creatinine_phosphokinase, diabetes, high_blood_pressure, smoking
+
+time = st.slider('Follow up perriod (days) : ',min_value=1,max_value=365,step=1)
+age = st.slider('Age : ', 30,120,step=1)
+ejection_fraction = st.number_input('Ejection Fraction : ',min_value=1,max_value=100,step=1)
+serum_creatinine = st.number_input('Serum Creatinine : ',min_value=0.0,max_value=10.0,step=0.01)
+serum_sodium = st.number_input('Serum Sodium : ',min_value=100,max_value=150,step=1)
+anaemia = st.radio('Anemia (1=Yes, 0=NO) : ',(1, 0))
+diabetes = st.radio('Diabetes (1=Yes, 0=NO) : ',(1, 0))
+smoking = st.radio('Smoking (1=Yes, 0=NO) : ',(1, 0))
+
+if st.button('Predict'):
+
+    data_inf = pd.DataFrame({'time':time,
+                         'ejection_fraction':ejection_fraction,
+                         'age':age,
+                         'serum_creatinine':serum_creatinine,
+                         'serum_sodium':serum_sodium,
+                         'anaemia':anaemia,
+                         'diabetes':diabetes,
+                         'smoking':smoking
+    },index=[0])
+
+    data_inf_t = box.transform(data_inf)
+    data_inf_num = data_inf_t[num_col]
+    data_inf_cat = data_inf_t[cat_col]
+
+    data_inf_num_scaled = data_inf_num.copy()
+    data_inf_num_scaled[num_col] = model_scaler.transform(data_inf_num[num_col])
+    data_inf_cat_encoded = data_inf_cat.copy()
+    data_inf_num_scaled
+
+    data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
+
+    result = rf_random_best.predict(data_inf_final)[0]
+    st.write('Predicted Result:')
+    st.header(result)

boxcox.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28ee56a50a1c2e09651cb183c69b021af913aba505784c6d89e57a19ea8ad14a
+size 474

list_cat_col.txt

@@ -0,0 +1 @@
+['anaemia', 'diabetes', 'smoking']

list_num_col.txt

@@ -0,0 +1 @@
+['age', 'ejection_fraction', 'serum_creatinine', 'serum_sodium', 'time']

model_scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9dc460a36f578c03d24eaaab347fc54ad89ba41ff63175f6b4316d5b5b709748
+size 723

rf_best_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:595d9f36c1f14f41bffac6bd0e4c9fe807df305f43fd218b964020aaf6babb36
+size 400462