loanNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5df06ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import Library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import warnings\n",
    "from collections import Counter\n",
    "warnings.filterwarnings('ignore')\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4f036ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Y': 332, 'N': 148})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('C:/Users/Cris/Downloads/MLAPLI-master/bankloan.csv')\n",
    "df=df.dropna()\n",
    "df.isna().any()\n",
    "df=df.drop('Loan_ID', axis=1)\n",
    "df['LoanAmount'] = (df['LoanAmount'] * 1000).astype(int)\n",
    "Counter(df['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce2c78a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_y=df['Loan_Status']\n",
    "pre_X=df.drop('Loan_Status', axis=1)\n",
    "dm_X=pd.get_dummies(pre_X)\n",
    "dm_y=pre_y.map(dict(Y=1, N=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b2eb0bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X1, y = smote.fit_resample(dm_X, dm_y)\n",
    "sc = MinMaxScaler()\n",
    "X = sc.fit_transform(X1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e63dd512",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00f393b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 0s 4ms/step - loss: 0.3628 - accuracy: 0.8889\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.36280545592308044, 0.8888888955116272]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = Sequential()\n",
    "classifier.add(Dense(200, activation='relu', kernel_initializer='random_normal', input_dim=X_test.shape[1]))\n",
    "classifier.add(Dense(400, activation='relu', kernel_initializer='random_normal'))\n",
    "classifier.add(Dense(4, activation='relu', kernel_initializer='random_normal'))\n",
    "classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))\n",
    "classifier.compile(optimizer ='adam', loss='binary_crossentropy', metrics =['accuracy'])\n",
    "classifier.fit(X_train,y_train, batch_size=20, epochs=50, verbose=0)\n",
    "eval_model=classifier.evaluate(X_train, y_train)\n",
    "eval_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2f3cbff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred=classifier.predict(X_test)\n",
    "y_pred =(y_pred>0.61)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ffc4e325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ax = plt.subplot()\n",
    "sns.heatmap(cm, annot= True, ax = ax);\n",
    "\n",
    "ax.set_xlabel('Predicted');ax.set_ylabel('Actual');\n",
    "ax.set_title('Confusion Matrix');\n",
    "ax.xaxis.set_ticklabels(['No', 'Yes']); ax.yaxis.set_ticklabels(['No', 'Yes']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddb0e404",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import joblib\n",
    "filename = 'loan_model.h5'\n",
    "classifier.save(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5e57cd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 173ms/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras  # Import 'keras' from 'tensorflow'\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense\n",
    "sc = MinMaxScaler()\n",
    "mdl = keras.models.load_model('C:/Users/Cris/loan_model.h5')\n",
    "X= pd.read_excel('C:/Users/Cris/test.xlsx')\n",
    "X_test = sc.fit_transform(X)\n",
    "y_pred = mdl.predict(X_test)\n",
    "y_pred = (y_pred>0.61)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "731e5a54",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rejected</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Status\n",
       "0   Approved\n",
       "1   Rejected\n",
       "2   Rejected\n",
       "3   Rejected\n",
       "4   Approved\n",
       "5   Rejected\n",
       "6   Rejected\n",
       "7   Rejected\n",
       "8   Approved\n",
       "9   Approved\n",
       "10  Approved\n",
       "11  Rejected\n",
       "12  Rejected"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val=pd.DataFrame(y_pred, columns=['Status'])\n",
    "val=val.replace({True: 'Approved', False: 'Rejected'})\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a21c1526",
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    \"Dependents\": 1,\n",
    "    \"ApplicantIncome\": 458300,\n",
    "    \"CoapplicantIncome\": 15080,\n",
    "    \"LoanAmount\": 128000,\n",
    "    \"Loan_Amount_Term\": 360,\n",
    "    \"Credit_History\": 0,\n",
    "    \"Gender_Female\": 0,\n",
    "    \"Gender_Male\": 1,\n",
    "    \"Married_No\": 0,\n",
    "    \"Married_Yes\": 1,\n",
    "    \"Education_Graduate\": 1,\n",
    "    \"Education_Not_Graduate\": 0,\n",
    "    \"Self_Employed_No\": 0,\n",
    "    \"Self_Employed_Yes\": 1,\n",
    "    \"Property_Area_Rural\": 1,\n",
    "    \"Property_Area_Semiurban\": 0,\n",
    "    \"Property_Area_Urban\": 0\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b2ded20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[     1, 458300,  15080, 128000,    360,      0,      0,      1,\n",
       "             0,      1,      1,      0,      0,      1,      1,      0,\n",
       "             0]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit=np.array(list(data.values()))\n",
    "unit=unit.reshape(1,-1)\n",
    "unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1b9a9b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Data:\n",
      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
      "Original Data:\n",
      "[[1.000e+00 4.583e+05 1.508e+04 1.280e+05 3.600e+02 0.000e+00 0.000e+00\n",
      "  1.000e+00 0.000e+00 1.000e+00 1.000e+00 0.000e+00 0.000e+00 1.000e+00\n",
      "  1.000e+00 0.000e+00 0.000e+00]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create a MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Convert data to a 2D array and then scale it\n",
    "input_data = [list(data.values())]\n",
    "scaled_data = scaler.fit_transform(input_data)\n",
    "\n",
    "# Inverse transform to get the original values\n",
    "original_data = scaler.inverse_transform(scaled_data)\n",
    "\n",
    "# Print the scaled and original data\n",
    "print(\"Scaled Data:\")\n",
    "print(scaled_data)\n",
    "\n",
    "print(\"Original Data:\")\n",
    "print(original_data)\n",
    "#This code creates a MinMaxScaler, scales your data, and then inverts the scaling to retrieve the original values. This ensures that your input data is properly scaled for use with your model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de038767",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Failed to find data adapter that can handle input: (<class 'dict'> containing {\"<class 'str'>\"} keys and {\"<class 'int'>\"} values), <class 'NoneType'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Make loan prediction using the Keras model\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m prediction \u001b[38;5;241m=\u001b[39m mdl\u001b[38;5;241m.\u001b[39mpredict(data)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prediction[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.61\u001b[39m:  \u001b[38;5;66;03m# Adjust the threshold as needed\u001b[39;00m\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLoan Application Approved\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\data_adapter.py:1105\u001b[0m, in \u001b[0;36mselect_data_adapter\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1102\u001b[0m adapter_cls \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mcls\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m ALL_ADAPTER_CLS \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mcan_handle(x, y)]\n\u001b[0;32m   1103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m adapter_cls:\n\u001b[0;32m   1104\u001b[0m     \u001b[38;5;66;03m# TODO(scottzhu): This should be a less implementation-specific error.\u001b[39;00m\n\u001b[1;32m-> 1105\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1106\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to find data adapter that can handle input: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m   1107\u001b[0m             _type_name(x), _type_name(y)\n\u001b[0;32m   1108\u001b[0m         )\n\u001b[0;32m   1109\u001b[0m     )\n\u001b[0;32m   1110\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(adapter_cls) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1111\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m   1112\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData adapters should be mutually exclusive for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1113\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhandling inputs. Found multiple adapters \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m to handle \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1114\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(adapter_cls, _type_name(x), _type_name(y))\n\u001b[0;32m   1115\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Failed to find data adapter that can handle input: (<class 'dict'> containing {\"<class 'str'>\"} keys and {\"<class 'int'>\"} values), <class 'NoneType'>"
     ]
    }
   ],
   "source": [
    "# Make loan prediction using the Keras model\n",
    "prediction = mdl.predict(data)\n",
    "if prediction[0][0] > 0.61:  # Adjust the threshold as needed\n",
    "    print('Loan Application Approved')\n",
    "else:\n",
    "    print('Loan Application Rejected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a79b65aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}