models updated

Decision_Tree.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd74add73292c3c2e13ae449fccb13adff435991b047b1b36dc131193c1223ef
+size 43285

Logistic_Regression.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17fc47b03f6713cd89e45e5802a88f0cc4ee50a80fd69c00b61cade81c772872
+size 1656

Neural_Network.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08d2cfbd6226d8d4c031c51c78140dfb38c2d918fb674f7bc5840930a2e3b11e
+size 73970

Pycaret_Best.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1194ab6f7177b92d1c3573edfd069b9b6f314c74335ce94be925bb6f00441a2b
+size 16937901

Random_Forest.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0cca312143c3f831f22e4242fe9cc1deffcf915d642f912d36300626baf0eb7a
+size 6095186

Support_Vector_Machine.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99ee2cb377c604dde52a44375e39d37bbd118ec7fe587cd63055f550424a0310
+size 384281

Support_Vector_Machine_Optimized.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:968e596bb78ff711f9a98ab3d88ff75d569adfd013c4682140a973c8c2b0a3fa
+size 343378

app.py

@@ -1,38 +1,60 @@
 import streamlit as st
 from pybanking.churn_prediction import model_churn
-import pandas as pd
 import pickle
-x = st.slider('Select a value')
-st.write('This is the sample data')
+import sklearn.metrics as metrics
+from mlxtend.plotting import plot_confusion_matrix
+import matplotlib.pyplot as plt
+
+st.set_page_config(page_title="Customer Churn Prediction Model")
+
+st.title('Customer Churn Prediction Model')
+# x = st.slider('Select a value')
+
+st.subheader('This is the Sample Data')
 
 df = model_churn.get_data()
+
 st.dataframe(df.head(5))
 
 model_names = [
-            "Logistic_Regression",
-            "Support_Vector_Machine",
-            "Support_Vector_Machine_Optimized",
-            "Decision_Tree",
-            "Neural_Network",
-            "Random_Forest"
-        ]
+     "Logistic_Regression",
+     "Support_Vector_Machine",
+     "Support_Vector_Machine_Optimized",
+     "Decision_Tree",
+     "Neural_Network",
+     "Random_Forest",
+     "Pycaret_Best"
+     ]
+
 option = st.selectbox(
-     'Which model would you like to use?',
-     model_names)
+     'Select a model to be used',
+     model_names
+     )
 
-filename = 'lr.sav'
-loaded_model = pickle.load(open(filename, 'rb'))
+st.write("Model Loaded :  ", option)
 
-st.write('Model loaded')
+X, y = model_churn.preprocess_inputs(df, option)
 
+model = pickle.load(open(option+'.pkl', 'rb'))
 
 option2 = st.selectbox(
      'Which dataset would you like to use for prediction?',
-     ['Sample dataset','Upload custom'])
-     
+     ['Sample Dataset', 'Upload Custom']
+     )
+
+if option2 == 'Upload custom':
+     model = model_churn.train(df, model)
 
-X, y = model_churn.preprocess_inputs(df)
 st.dataframe(X.head(5))
-result = loaded_model.score(X, y)
 
-st.write('Result',result)
+y_pred = model.predict(X)
+
+st.write("Accuracy:",metrics.accuracy_score(y, y_pred))
+st.write("Precision:",metrics.precision_score(y, y_pred))
+st.write("Recall:",metrics.recall_score(y, y_pred))
+
+fig, ax = plot_confusion_matrix(conf_mat=metrics.confusion_matrix(y, y_pred), figsize=(6, 6), cmap=plt.cm.Reds, colorbar=True)
+plt.xlabel('Predictions', fontsize=18)
+plt.ylabel('Actuals', fontsize=18)
+plt.title('Confusion Matrix', fontsize=18)
+st.pyplot(fig)