diff --git a/ann1.h5 b/ann1.h5
deleted file mode 100644
index 3eb337c..0000000
Binary files a/ann1.h5 and /dev/null differ
diff --git a/app.py b/app.py
index 5e901cd..93d474c 100644
--- a/app.py
+++ b/app.py
@@ -3,10 +3,10 @@
 import pickle
 import tensorflow
 
-model = tensorflow.keras.models.load_model("ann1.h5")
-ohe_geo = pickle.load(open("ohe_geo.pkl", "rb"))
-le_gender = pickle.load(open("le_gender.pkl", "rb"))
-sc = pickle.load(open("sc.pkl", "rb"))
+model = tensorflow.keras.models.load_model("models/ann1.h5")
+ohe_geo = pickle.load(open("preprocessing/ohe_geo.pkl", "rb"))
+le_gender = pickle.load(open("preprocessing/le_gender.pkl", "rb"))
+sc = pickle.load(open("preprocessing/sc.pkl", "rb"))
 
 st.title("Customer Churn Predictor developed by Akashvarma26")
 
@@ -48,9 +48,9 @@
     st.subheader(f"Churn Prediction Result of the customer:")
     st.progress(pred_percent)
     if pred_prob>0.5:
-        st.error(f'⚠️ {name}, the customer is likely to churn with a probability of {pred_prob:.3f}.')
+        st.error(f'⚠️ {name}, the customer is likely to churn. The churn probability is {pred_prob:.3f}.')
     else:
-        st.success(f'✅ {name}, the customer is not likely to churn with a probability of {pred_prob:.3f}.')
+        st.success(f'✅ {name}, the customer is not likely to churn. The churn probability is {pred_prob:.3f}.')
     if credit_score<500:
         st.warning(f"{name}, the credit score is quite low. Customers with low credit scores might have higher churn probabilities.")
     if balance==0:
diff --git a/Churn_Modelling.csv b/dataset/Churn_Modelling.csv
similarity index 100%
rename from Churn_Modelling.csv
rename to dataset/Churn_Modelling.csv
diff --git a/logs/fit/20241019-115828/train/events.out.tfevents.1729319308.ASUS.7224.0.v2 b/logs/fit/20241019-115828/train/events.out.tfevents.1729319308.ASUS.7224.0.v2
new file mode 100644
index 0000000..f7b27f5
Binary files /dev/null and b/logs/fit/20241019-115828/train/events.out.tfevents.1729319308.ASUS.7224.0.v2 differ
diff --git a/logs/fit/20241019-115828/validation/events.out.tfevents.1729319312.ASUS.7224.1.v2 b/logs/fit/20241019-115828/validation/events.out.tfevents.1729319312.ASUS.7224.1.v2
new file mode 100644
index 0000000..9bf8bd4
Binary files /dev/null and b/logs/fit/20241019-115828/validation/events.out.tfevents.1729319312.ASUS.7224.1.v2 differ
diff --git a/models/ann1.h5 b/models/ann1.h5
new file mode 100644
index 0000000..2e30b4c
Binary files /dev/null and b/models/ann1.h5 differ
diff --git a/notebook.ipynb b/notebook.ipynb
index 01cb52e..4adc8b7 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,9 +15,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:1: SyntaxWarning: invalid escape sequence '\\C'\n",
+      "<>:1: SyntaxWarning: invalid escape sequence '\\C'\n",
+      "C:\\Users\\rkdat\\AppData\\Local\\Temp\\ipykernel_7224\\184311580.py:1: SyntaxWarning: invalid escape sequence '\\C'\n",
+      "  df=pd.read_csv(\"dataset\\Churn_Modelling.csv\")\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -168,19 +178,19 @@
        "4         79084.10       0  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df=pd.read_csv(\"Churn_Modelling.csv\")\n",
+    "df=pd.read_csv(\"dataset\\Churn_Modelling.csv\")\n",
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +208,7 @@
        "array(['France', 'Spain', 'Germany'], dtype=object)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +334,7 @@
        "4               0.0                0.0              1.0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -493,7 +503,7 @@
        "4                0.0              1.0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -504,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -519,18 +529,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pickle.dump(le_gender,open(\"le_gender.pkl\",\"wb\"))\n",
-    "pickle.dump(ohe_geo,open(\"ohe_geo.pkl\",\"wb\"))\n",
-    "pickle.dump(sc,open(\"sc.pkl\",\"wb\"))"
+    "pickle.dump(le_gender,open(\"preprocessing/le_gender.pkl\",\"wb\"))\n",
+    "pickle.dump(ohe_geo,open(\"preprocessing/ohe_geo.pkl\",\"wb\"))\n",
+    "pickle.dump(sc,open(\"preprocessing/sc.pkl\",\"wb\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -552,7 +562,7 @@
        "12"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -563,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -675,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -712,41 +722,43 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8079 - loss: 0.4441 - val_accuracy: 0.8525 - val_loss: 0.3555\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8223 - loss: 0.4436 - val_accuracy: 0.8410 - val_loss: 0.3824\n",
       "Epoch 2/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8528 - loss: 0.3645 - val_accuracy: 0.8550 - val_loss: 0.3445\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8572 - loss: 0.3554 - val_accuracy: 0.8570 - val_loss: 0.3546\n",
       "Epoch 3/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8623 - loss: 0.3388 - val_accuracy: 0.8550 - val_loss: 0.3572\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8530 - loss: 0.3646 - val_accuracy: 0.8590 - val_loss: 0.3467\n",
       "Epoch 4/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8578 - loss: 0.3388 - val_accuracy: 0.8545 - val_loss: 0.3562\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8637 - loss: 0.3447 - val_accuracy: 0.8565 - val_loss: 0.3422\n",
       "Epoch 5/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8565 - loss: 0.3460 - val_accuracy: 0.8545 - val_loss: 0.3477\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8632 - loss: 0.3358 - val_accuracy: 0.8580 - val_loss: 0.3480\n",
       "Epoch 6/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8615 - loss: 0.3417 - val_accuracy: 0.8610 - val_loss: 0.3413\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8635 - loss: 0.3426 - val_accuracy: 0.8555 - val_loss: 0.3454\n",
       "Epoch 7/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8647 - loss: 0.3323 - val_accuracy: 0.8520 - val_loss: 0.3415\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8622 - loss: 0.3367 - val_accuracy: 0.8575 - val_loss: 0.3411\n",
       "Epoch 8/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8493 - loss: 0.3438 - val_accuracy: 0.8615 - val_loss: 0.3357\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8645 - loss: 0.3419 - val_accuracy: 0.8590 - val_loss: 0.3400\n",
       "Epoch 9/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8677 - loss: 0.3299 - val_accuracy: 0.8605 - val_loss: 0.3625\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8652 - loss: 0.3289 - val_accuracy: 0.8585 - val_loss: 0.3382\n",
       "Epoch 10/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8705 - loss: 0.3301 - val_accuracy: 0.8605 - val_loss: 0.3378\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8677 - loss: 0.3254 - val_accuracy: 0.8620 - val_loss: 0.3430\n",
       "Epoch 11/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8619 - loss: 0.3361 - val_accuracy: 0.8605 - val_loss: 0.3542\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8669 - loss: 0.3286 - val_accuracy: 0.8565 - val_loss: 0.3480\n",
       "Epoch 12/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8641 - loss: 0.3350 - val_accuracy: 0.8580 - val_loss: 0.3438\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8643 - loss: 0.3345 - val_accuracy: 0.8585 - val_loss: 0.3447\n",
       "Epoch 13/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8672 - loss: 0.3216 - val_accuracy: 0.8560 - val_loss: 0.3501\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8664 - loss: 0.3295 - val_accuracy: 0.8525 - val_loss: 0.3462\n",
       "Epoch 14/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8710 - loss: 0.3159 - val_accuracy: 0.8625 - val_loss: 0.3505\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8687 - loss: 0.3174 - val_accuracy: 0.8615 - val_loss: 0.3431\n",
       "Epoch 15/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8689 - loss: 0.3254 - val_accuracy: 0.8455 - val_loss: 0.3575\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.3100 - val_accuracy: 0.8525 - val_loss: 0.3570\n",
       "Epoch 16/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8724 - loss: 0.3086 - val_accuracy: 0.8625 - val_loss: 0.3418\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8722 - loss: 0.3060 - val_accuracy: 0.8585 - val_loss: 0.3417\n",
       "Epoch 17/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8706 - loss: 0.3159 - val_accuracy: 0.8585 - val_loss: 0.3419\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8765 - loss: 0.3106 - val_accuracy: 0.8550 - val_loss: 0.3499\n",
       "Epoch 18/100\n",
-      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.3126 - val_accuracy: 0.8515 - val_loss: 0.3597\n"
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8724 - loss: 0.3057 - val_accuracy: 0.8615 - val_loss: 0.3477\n",
+      "Epoch 19/100\n",
+      "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8726 - loss: 0.3118 - val_accuracy: 0.8565 - val_loss: 0.3476\n"
      ]
     }
    ],
@@ -756,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -768,12 +780,12 @@
     }
    ],
    "source": [
-    "model.save('ann1.h5')"
+    "model.save('models/ann1.h5')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -782,19 +794,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Reusing TensorBoard on port 6006 (pid 19204), started 10:07:45 ago. (Use '!kill 19204' to kill it.)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
        "\n",
-       "      <iframe id=\"tensorboard-frame-f30d915702e8f4f2\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
+       "      <iframe id=\"tensorboard-frame-2bfb85ef3c42067d\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
        "      </iframe>\n",
        "      <script>\n",
        "        (function() {\n",
-       "          const frame = document.getElementById(\"tensorboard-frame-f30d915702e8f4f2\");\n",
-       "          const url = new URL(\"/\", window.location);\n",
+       "          const frame = document.getElementById(\"tensorboard-frame-2bfb85ef3c42067d\");\n",
+       "          const url = new URL(\"http://localhost\");\n",
        "          const port = 6006;\n",
        "          if (port) {\n",
        "            url.port = port;\n",
@@ -818,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -830,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -842,14 +863,14 @@
     }
    ],
    "source": [
-    "model=load_model(\"ann1.h5\")\n",
-    "ohe_geo=pickle.load(open(\"ohe_geo.pkl\",\"rb\"))\n",
-    "le_gender=pickle.load(open(\"le_gender.pkl\",\"rb\"))"
+    "model=load_model(\"models/ann1.h5\")\n",
+    "ohe_geo=pickle.load(open(\"preprocessing/ohe_geo.pkl\",\"rb\"))\n",
+    "le_gender=pickle.load(open(\"preprocessing/le_gender.pkl\",\"rb\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -869,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -878,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -897,7 +918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -906,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -915,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -984,7 +1005,7 @@
        "0              0.0  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -995,16 +1016,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sc=pickle.load(open('sc.pkl','rb'))"
+    "sc=pickle.load(open('preprocessing/sc.pkl','rb'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1015,7 +1036,7 @@
        "         1.74075464, -0.56888201]])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1027,7 +1048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1035,7 +1056,7 @@
      "output_type": "stream",
      "text": [
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
-      "Customer is not likely to Churn with probability of 0.067\n"
+      "Customer is not likely to Churn with probability of 0.061\n"
      ]
     }
    ],
diff --git a/le_gender.pkl b/preprocessing/le_gender.pkl
similarity index 100%
rename from le_gender.pkl
rename to preprocessing/le_gender.pkl
diff --git a/ohe_geo.pkl b/preprocessing/ohe_geo.pkl
similarity index 100%
rename from ohe_geo.pkl
rename to preprocessing/ohe_geo.pkl
diff --git a/sc.pkl b/preprocessing/sc.pkl
similarity index 100%
rename from sc.pkl
rename to preprocessing/sc.pkl