Capstone 2 - Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f2d014c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install pandas matplotlib seaborn tensorflow scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f79a9abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b676711a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset\n",
    "df = pd.read_csv('customer_churn-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ab1c062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
      "0     7590-VHVEG  Female              0     Yes         No       1   \n",
      "1     5575-GNVDE    Male              0      No         No      34   \n",
      "2     3668-QPYBK    Male              0      No         No       2   \n",
      "3     7795-CFOCW    Male              0      No         No      45   \n",
      "4     9237-HQITU  Female              0      No         No       2   \n",
      "...          ...     ...            ...     ...        ...     ...   \n",
      "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
      "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
      "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
      "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
      "7042  3186-AJIEK    Male              0      No         No      66   \n",
      "\n",
      "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
      "0              No  No phone service             DSL             No  ...   \n",
      "1             Yes                No             DSL            Yes  ...   \n",
      "2             Yes                No             DSL            Yes  ...   \n",
      "3              No  No phone service             DSL            Yes  ...   \n",
      "4             Yes                No     Fiber optic             No  ...   \n",
      "...           ...               ...             ...            ...  ...   \n",
      "7038          Yes               Yes             DSL            Yes  ...   \n",
      "7039          Yes               Yes     Fiber optic             No  ...   \n",
      "7040           No  No phone service             DSL            Yes  ...   \n",
      "7041          Yes               Yes     Fiber optic             No  ...   \n",
      "7042          Yes                No     Fiber optic            Yes  ...   \n",
      "\n",
      "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
      "0                  No          No          No              No  Month-to-month   \n",
      "1                 Yes          No          No              No        One year   \n",
      "2                  No          No          No              No  Month-to-month   \n",
      "3                 Yes         Yes          No              No        One year   \n",
      "4                  No          No          No              No  Month-to-month   \n",
      "...               ...         ...         ...             ...             ...   \n",
      "7038              Yes         Yes         Yes             Yes        One year   \n",
      "7039              Yes          No         Yes             Yes        One year   \n",
      "7040               No          No          No              No  Month-to-month   \n",
      "7041               No          No          No              No  Month-to-month   \n",
      "7042              Yes         Yes         Yes             Yes        Two year   \n",
      "\n",
      "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
      "0                 Yes           Electronic check          29.85         29.85   \n",
      "1                  No               Mailed check          56.95        1889.5   \n",
      "2                 Yes               Mailed check          53.85        108.15   \n",
      "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
      "4                 Yes           Electronic check          70.70        151.65   \n",
      "...               ...                        ...            ...           ...   \n",
      "7038              Yes               Mailed check          84.80        1990.5   \n",
      "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
      "7040              Yes           Electronic check          29.60        346.45   \n",
      "7041              Yes               Mailed check          74.40         306.6   \n",
      "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
      "\n",
      "     Churn  \n",
      "0       No  \n",
      "1       No  \n",
      "2      Yes  \n",
      "3       No  \n",
      "4      Yes  \n",
      "...    ...  \n",
      "7038    No  \n",
      "7039    No  \n",
      "7040    No  \n",
      "7041   Yes  \n",
      "7042    No  \n",
      "\n",
      "[7043 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95fec8ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3555\n"
     ]
    }
   ],
   "source": [
    "#Data Manipulation\n",
    "#A.a\n",
    "total_male_customers = df[df['gender'] == 'Male'].shape[0]\n",
    "print(total_male_customers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4cfc0690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7043\n"
     ]
    }
   ],
   "source": [
    "#A.b\n",
    "total_DSL_customers = df['InternetService'].shape[0]\n",
    "\n",
    "print(total_DSL_customers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ad0a0569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
      "139   0390-DCFDQ  Female              1     Yes         No       1   \n",
      "176   2656-FMOKZ  Female              1      No         No      15   \n",
      "267   3197-ARFOY  Female              1      No         No      19   \n",
      "451   5760-WRAHC  Female              1      No         No      22   \n",
      "470   4933-IKULF  Female              1      No         No      17   \n",
      "694   2682-KEVRP  Female              1      No         No      22   \n",
      "747   3966-HRMZA  Female              1      No         No       3   \n",
      "947   9904-EHEVJ  Female              1     Yes        Yes      32   \n",
      "1029  4184-TJFAN  Female              1     Yes        Yes       3   \n",
      "1112  2176-LVPNX  Female              1      No         No      71   \n",
      "1513  0661-XEYAN  Female              1      No         No       1   \n",
      "1811  2070-XYMFH  Female              1      No         No      23   \n",
      "1831  3402-XRIUO  Female              1     Yes         No      22   \n",
      "1864  7105-MXJLL  Female              1     Yes         No      26   \n",
      "1891  4193-ORFCL  Female              1      No         No       1   \n",
      "2037  8309-IEYJD  Female              1      No         No       1   \n",
      "2344  9796-MVYXX  Female              1      No         No      14   \n",
      "2441  9050-IKDZA  Female              1      No         No       2   \n",
      "2674  1855-CFULU  Female              1      No         No       4   \n",
      "3005  0516-QREYC  Female              1      No         No      24   \n",
      "3185  9907-SWKKF  Female              1      No         No       1   \n",
      "3341  1125-SNVCK  Female              1      No         No      49   \n",
      "3373  2516-VQRRV  Female              1      No         No       2   \n",
      "3374  7580-UGXNC  Female              1      No         No       2   \n",
      "3454  6773-LQTVT  Female              1     Yes        Yes      29   \n",
      "3893  5816-SCGFC  Female              1      No         No       7   \n",
      "4258  9360-AHGNL  Female              1     Yes         No      43   \n",
      "4382  2277-VWCNI  Female              1      No         No       4   \n",
      "4541  9058-CBREO  Female              1      No         No       1   \n",
      "4542  9029-FEGVJ  Female              1     Yes         No      32   \n",
      "4673  6402-ZFPPI  Female              1      No         No      25   \n",
      "5183  8988-ECPJR  Female              1     Yes        Yes      34   \n",
      "5251  8485-GJCDN  Female              1      No         No       5   \n",
      "5319  9866-OCCKE  Female              1     Yes         No      72   \n",
      "5341  2982-IHMFT  Female              1      No         No       1   \n",
      "5437  7000-WCEVQ  Female              1      No         No      20   \n",
      "5476  6060-DRTNL  Female              1      No         No       5   \n",
      "5569  0013-EXCHZ  Female              1     Yes         No       3   \n",
      "5680  6982-UQZLY  Female              1     Yes         No       1   \n",
      "5743  2384-OVPSA  Female              1      No         No      38   \n",
      "5757  5539-HIVAK  Female              1     Yes         No      28   \n",
      "5792  3500-RMZLT  Female              1      No         No      15   \n",
      "6031  0282-NVSJS  Female              1     Yes        Yes      12   \n",
      "6042  4750-UKWJK  Female              1     Yes         No      37   \n",
      "6147  6383-ZTSIW  Female              1     Yes         No      39   \n",
      "6178  3719-TDVQB  Female              1     Yes         No      54   \n",
      "6219  1496-GGSUK  Female              1      No         No       1   \n",
      "6231  3296-SILRA  Female              1     Yes         No       1   \n",
      "6534  5195-KPUNQ  Female              1      No         No      53   \n",
      "6581  2453-SAFNS  Female              1      No         No      54   \n",
      "\n",
      "     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \\\n",
      "139           Yes                No     Fiber optic                   No  ...   \n",
      "176           Yes               Yes     Fiber optic                   No  ...   \n",
      "267           Yes                No     Fiber optic                  Yes  ...   \n",
      "451           Yes                No             DSL                  Yes  ...   \n",
      "470           Yes                No              No  No internet service  ...   \n",
      "694           Yes                No              No  No internet service  ...   \n",
      "747           Yes                No     Fiber optic                   No  ...   \n",
      "947           Yes               Yes     Fiber optic                   No  ...   \n",
      "1029          Yes                No     Fiber optic                  Yes  ...   \n",
      "1112          Yes               Yes             DSL                  Yes  ...   \n",
      "1513           No  No phone service             DSL                   No  ...   \n",
      "1811          Yes               Yes     Fiber optic                   No  ...   \n",
      "1831          Yes               Yes             DSL                  Yes  ...   \n",
      "1864          Yes                No             DSL                   No  ...   \n",
      "1891          Yes                No             DSL                   No  ...   \n",
      "2037          Yes                No     Fiber optic                   No  ...   \n",
      "2344           No  No phone service             DSL                  Yes  ...   \n",
      "2441          Yes                No     Fiber optic                   No  ...   \n",
      "2674          Yes                No              No  No internet service  ...   \n",
      "3005          Yes                No              No  No internet service  ...   \n",
      "3185           No  No phone service             DSL                   No  ...   \n",
      "3341          Yes                No             DSL                   No  ...   \n",
      "3373          Yes               Yes     Fiber optic                   No  ...   \n",
      "3374          Yes                No             DSL                  Yes  ...   \n",
      "3454           No  No phone service             DSL                   No  ...   \n",
      "3893          Yes                No             DSL                   No  ...   \n",
      "4258          Yes               Yes     Fiber optic                  Yes  ...   \n",
      "4382          Yes               Yes             DSL                   No  ...   \n",
      "4541          Yes               Yes             DSL                   No  ...   \n",
      "4542          Yes               Yes     Fiber optic                   No  ...   \n",
      "4673          Yes               Yes     Fiber optic                  Yes  ...   \n",
      "5183          Yes               Yes     Fiber optic                   No  ...   \n",
      "5251          Yes                No     Fiber optic                   No  ...   \n",
      "5319          Yes               Yes     Fiber optic                  Yes  ...   \n",
      "5341          Yes               Yes     Fiber optic                   No  ...   \n",
      "5437          Yes               Yes             DSL                   No  ...   \n",
      "5476          Yes                No     Fiber optic                   No  ...   \n",
      "5569          Yes                No     Fiber optic                   No  ...   \n",
      "5680          Yes                No              No  No internet service  ...   \n",
      "5743          Yes                No              No  No internet service  ...   \n",
      "5757          Yes               Yes              No  No internet service  ...   \n",
      "5792          Yes                No     Fiber optic                  Yes  ...   \n",
      "6031           No  No phone service             DSL                   No  ...   \n",
      "6042          Yes                No              No  No internet service  ...   \n",
      "6147          Yes                No     Fiber optic                  Yes  ...   \n",
      "6178          Yes                No              No  No internet service  ...   \n",
      "6219           No  No phone service             DSL                   No  ...   \n",
      "6231          Yes                No     Fiber optic                   No  ...   \n",
      "6534          Yes                No     Fiber optic                  Yes  ...   \n",
      "6581          Yes               Yes             DSL                   No  ...   \n",
      "\n",
      "         DeviceProtection          TechSupport          StreamingTV  \\\n",
      "139                    No                   No                   No   \n",
      "176                    No                   No                   No   \n",
      "267                    No                  Yes                  Yes   \n",
      "451                   Yes                  Yes                   No   \n",
      "470   No internet service  No internet service  No internet service   \n",
      "694   No internet service  No internet service  No internet service   \n",
      "747                   Yes                   No                   No   \n",
      "947                   Yes                   No                  Yes   \n",
      "1029                   No                  Yes                  Yes   \n",
      "1112                  Yes                  Yes                  Yes   \n",
      "1513                   No                   No                   No   \n",
      "1811                  Yes                   No                   No   \n",
      "1831                   No                  Yes                   No   \n",
      "1864                  Yes                   No                   No   \n",
      "1891                   No                   No                   No   \n",
      "2037                   No                   No                   No   \n",
      "2344                  Yes                  Yes                   No   \n",
      "2441                   No                   No                  Yes   \n",
      "2674  No internet service  No internet service  No internet service   \n",
      "3005  No internet service  No internet service  No internet service   \n",
      "3185                   No                   No                   No   \n",
      "3341                   No                   No                   No   \n",
      "3373                   No                   No                   No   \n",
      "3374                   No                   No                   No   \n",
      "3454                  Yes                   No                   No   \n",
      "3893                   No                   No                   No   \n",
      "4258                  Yes                  Yes                  Yes   \n",
      "4382                   No                   No                   No   \n",
      "4541                   No                   No                   No   \n",
      "4542                  Yes                   No                   No   \n",
      "4673                   No                   No                  Yes   \n",
      "5183                   No                   No                   No   \n",
      "5251                   No                   No                   No   \n",
      "5319                  Yes                  Yes                  Yes   \n",
      "5341                   No                   No                   No   \n",
      "5437                   No                   No                   No   \n",
      "5476                  Yes                   No                  Yes   \n",
      "5569                   No                  Yes                  Yes   \n",
      "5680  No internet service  No internet service  No internet service   \n",
      "5743  No internet service  No internet service  No internet service   \n",
      "5757  No internet service  No internet service  No internet service   \n",
      "5792                  Yes                  Yes                   No   \n",
      "6031                   No                  Yes                   No   \n",
      "6042  No internet service  No internet service  No internet service   \n",
      "6147                   No                   No                  Yes   \n",
      "6178  No internet service  No internet service  No internet service   \n",
      "6219                   No                   No                   No   \n",
      "6231                   No                   No                   No   \n",
      "6534                   No                  Yes                   No   \n",
      "6581                   No                  Yes                  Yes   \n",
      "\n",
      "          StreamingMovies        Contract PaperlessBilling PaymentMethod  \\\n",
      "139                    No  Month-to-month              Yes  Mailed check   \n",
      "176                    No  Month-to-month              Yes  Mailed check   \n",
      "267                   Yes  Month-to-month              Yes  Mailed check   \n",
      "451                   Yes  Month-to-month              Yes  Mailed check   \n",
      "470   No internet service        One year               No  Mailed check   \n",
      "694   No internet service        One year              Yes  Mailed check   \n",
      "747                    No  Month-to-month               No  Mailed check   \n",
      "947                    No  Month-to-month              Yes  Mailed check   \n",
      "1029                   No  Month-to-month              Yes  Mailed check   \n",
      "1112                  Yes        Two year               No  Mailed check   \n",
      "1513                   No  Month-to-month              Yes  Mailed check   \n",
      "1811                   No  Month-to-month              Yes  Mailed check   \n",
      "1831                   No  Month-to-month              Yes  Mailed check   \n",
      "1864                  Yes        One year               No  Mailed check   \n",
      "1891                   No  Month-to-month               No  Mailed check   \n",
      "2037                   No  Month-to-month              Yes  Mailed check   \n",
      "2344                   No        Two year               No  Mailed check   \n",
      "2441                   No  Month-to-month               No  Mailed check   \n",
      "2674  No internet service  Month-to-month               No  Mailed check   \n",
      "3005  No internet service  Month-to-month              Yes  Mailed check   \n",
      "3185                   No  Month-to-month               No  Mailed check   \n",
      "3341                   No  Month-to-month               No  Mailed check   \n",
      "3373                   No  Month-to-month              Yes  Mailed check   \n",
      "3374                   No  Month-to-month               No  Mailed check   \n",
      "3454                   No  Month-to-month              Yes  Mailed check   \n",
      "3893                   No  Month-to-month              Yes  Mailed check   \n",
      "4258                  Yes        One year              Yes  Mailed check   \n",
      "4382                   No  Month-to-month               No  Mailed check   \n",
      "4541                   No  Month-to-month               No  Mailed check   \n",
      "4542                   No  Month-to-month               No  Mailed check   \n",
      "4673                  Yes  Month-to-month              Yes  Mailed check   \n",
      "5183                   No  Month-to-month              Yes  Mailed check   \n",
      "5251                   No  Month-to-month              Yes  Mailed check   \n",
      "5319                  Yes        Two year              Yes  Mailed check   \n",
      "5341                   No  Month-to-month              Yes  Mailed check   \n",
      "5437                  Yes  Month-to-month              Yes  Mailed check   \n",
      "5476                   No  Month-to-month              Yes  Mailed check   \n",
      "5569                   No  Month-to-month              Yes  Mailed check   \n",
      "5680  No internet service  Month-to-month              Yes  Mailed check   \n",
      "5743  No internet service        Two year               No  Mailed check   \n",
      "5757  No internet service  Month-to-month               No  Mailed check   \n",
      "5792                  Yes  Month-to-month              Yes  Mailed check   \n",
      "6031                   No  Month-to-month              Yes  Mailed check   \n",
      "6042  No internet service        One year               No  Mailed check   \n",
      "6147                  Yes        One year               No  Mailed check   \n",
      "6178  No internet service        Two year              Yes  Mailed check   \n",
      "6219                   No  Month-to-month              Yes  Mailed check   \n",
      "6231                   No  Month-to-month              Yes  Mailed check   \n",
      "6534                  Yes        One year              Yes  Mailed check   \n",
      "6581                   No        One year               No  Mailed check   \n",
      "\n",
      "     MonthlyCharges  TotalCharges Churn  \n",
      "139           70.45         70.45   Yes  \n",
      "176           74.45        1145.7   Yes  \n",
      "267          105.00       2007.25    No  \n",
      "451           69.75        1545.4    No  \n",
      "470           20.65         330.6    No  \n",
      "694           20.05           417    No  \n",
      "747           75.05         202.9    No  \n",
      "947           91.35       2896.55    No  \n",
      "1029          88.30        273.75   Yes  \n",
      "1112          89.85       6293.45    No  \n",
      "1513          25.80          25.8   Yes  \n",
      "1811          79.35        1835.3    No  \n",
      "1831          63.55        1381.8    No  \n",
      "1864          60.70        1597.4    No  \n",
      "1891          45.10          45.1   Yes  \n",
      "2037          70.60          70.6    No  \n",
      "2344          39.70        692.35    No  \n",
      "2441          81.50        162.55    No  \n",
      "2674          20.05         91.45    No  \n",
      "3005          20.30        459.95    No  \n",
      "3185          25.05         25.05   Yes  \n",
      "3341          43.80       2106.05    No  \n",
      "3373          75.45         158.4   Yes  \n",
      "3374          54.85         104.2   Yes  \n",
      "3454          35.65       1025.15    No  \n",
      "3893          51.30        419.35    No  \n",
      "4258         109.55       4830.25   Yes  \n",
      "4382          48.75        179.85    No  \n",
      "4541          50.55         50.55   Yes  \n",
      "4542          79.30          2570    No  \n",
      "4673         102.80        2660.2   Yes  \n",
      "5183          79.60        2718.3   Yes  \n",
      "5251          69.05         318.5   Yes  \n",
      "5319         109.75       8075.35    No  \n",
      "5341          74.45         74.45   Yes  \n",
      "5437          61.60       1174.35   Yes  \n",
      "5476          84.85        415.55   Yes  \n",
      "5569          83.90         267.4   Yes  \n",
      "5680          20.85         20.85   Yes  \n",
      "5743          20.20         735.9    No  \n",
      "5757          25.70         734.6    No  \n",
      "5792          96.30       1426.75   Yes  \n",
      "6031          29.30         355.9    No  \n",
      "6042          19.60         727.8    No  \n",
      "6147          99.10       3877.95    No  \n",
      "6178          18.95        1031.1    No  \n",
      "6219          25.70          25.7   Yes  \n",
      "6231          76.40          76.4   Yes  \n",
      "6534          96.75       5206.55    No  \n",
      "6581          72.10       3886.05    No  \n",
      "\n",
      "[50 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "#A.c\n",
    "new_customers_female_senior_citizens =  df[(df['gender'] == 'Female') & \n",
    "                                  (df['SeniorCitizen'] == 1) & \n",
    "                                  (df['PaymentMethod'] == 'Mailed check')]\n",
    "\n",
    "print(new_customers_female_senior_citizens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69bd6759",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New customer dataframe (tenure < 10 or TotalCharges < 500):\n",
      "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
      "0     7590-VHVEG  Female              0     Yes         No       1   \n",
      "2     3668-QPYBK    Male              0      No         No       2   \n",
      "4     9237-HQITU  Female              0      No         No       2   \n",
      "5     9305-CDSKC  Female              0      No         No       8   \n",
      "7     6713-OKOMC  Female              0      No         No      10   \n",
      "...          ...     ...            ...     ...        ...     ...   \n",
      "7029  2235-DWLJU  Female              1      No         No       6   \n",
      "7030  0871-OPBXW  Female              0      No         No       2   \n",
      "7032  6894-LFHLY    Male              1      No         No       1   \n",
      "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
      "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
      "\n",
      "     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \\\n",
      "0              No  No phone service             DSL                   No  ...   \n",
      "2             Yes                No             DSL                  Yes  ...   \n",
      "4             Yes                No     Fiber optic                   No  ...   \n",
      "5             Yes               Yes     Fiber optic                   No  ...   \n",
      "7              No  No phone service             DSL                  Yes  ...   \n",
      "...           ...               ...             ...                  ...  ...   \n",
      "7029           No  No phone service             DSL                   No  ...   \n",
      "7030          Yes                No              No  No internet service  ...   \n",
      "7032          Yes               Yes     Fiber optic                   No  ...   \n",
      "7040           No  No phone service             DSL                  Yes  ...   \n",
      "7041          Yes               Yes     Fiber optic                   No  ...   \n",
      "\n",
      "         DeviceProtection          TechSupport          StreamingTV  \\\n",
      "0                      No                   No                   No   \n",
      "2                      No                   No                   No   \n",
      "4                      No                   No                   No   \n",
      "5                     Yes                   No                  Yes   \n",
      "7                      No                   No                   No   \n",
      "...                   ...                  ...                  ...   \n",
      "7029                   No                   No                  Yes   \n",
      "7030  No internet service  No internet service  No internet service   \n",
      "7032                   No                   No                   No   \n",
      "7040                   No                   No                   No   \n",
      "7041                   No                   No                   No   \n",
      "\n",
      "          StreamingMovies        Contract PaperlessBilling     PaymentMethod  \\\n",
      "0                      No  Month-to-month              Yes  Electronic check   \n",
      "2                      No  Month-to-month              Yes      Mailed check   \n",
      "4                      No  Month-to-month              Yes  Electronic check   \n",
      "5                     Yes  Month-to-month              Yes  Electronic check   \n",
      "7                      No  Month-to-month               No      Mailed check   \n",
      "...                   ...             ...              ...               ...   \n",
      "7029                  Yes  Month-to-month              Yes  Electronic check   \n",
      "7030  No internet service  Month-to-month              Yes      Mailed check   \n",
      "7032                   No  Month-to-month              Yes  Electronic check   \n",
      "7040                   No  Month-to-month              Yes  Electronic check   \n",
      "7041                   No  Month-to-month              Yes      Mailed check   \n",
      "\n",
      "     MonthlyCharges  TotalCharges  Churn  \n",
      "0             29.85         29.85     No  \n",
      "2             53.85        108.15    Yes  \n",
      "4             70.70        151.65    Yes  \n",
      "5             99.65        820.50    Yes  \n",
      "7             29.75        301.90     No  \n",
      "...             ...           ...    ...  \n",
      "7029          44.40        263.05     No  \n",
      "7030          20.05         39.25     No  \n",
      "7032          75.75         75.75    Yes  \n",
      "7040          29.60        346.45     No  \n",
      "7041          74.40        306.60    Yes  \n",
      "\n",
      "[2233 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "#A.d\n",
    "# Convert 'TotalCharges' to numeric, setting errors='coerce' to handle non-numeric values\n",
    "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n",
    "\n",
    "# NaN values in 'TotalCharges' with 0 \n",
    "df['TotalCharges'].fillna(0, inplace=True)\n",
    "\n",
    "# Extraction based on the conditions\n",
    "new_customer_tenure_charges = df[(df['tenure'] < 10) | (df['TotalCharges'] < 500)]\n",
    "\n",
    "print(\"New customer dataframe (tenure < 10 or TotalCharges < 500):\")\n",
    "print(new_customer_tenure_charges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "18531ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visualization\n",
    "# B.a\n",
    "churn_counts = df['Churn'].value_counts()\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.pie(churn_counts, labels = churn_counts.index, autopct = '%1.1f%%', startangle = 140)\n",
    "plt.title('Distribution of Churning customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "93814ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# B.b\n",
    "internet_service_counts = df['InternetService'].value_counts()\n",
    "plt.figure(figsize=(9,7))\n",
    "sns.barplot(x=internet_service_counts, y = internet_service_counts.values)\n",
    "plt.title('Distribution of Internet Service')\n",
    "plt.xlabel('Internet Service')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d0b07835",
   "metadata": {},
   "outputs": [],
   "source": [
    "#C. Model Building\n",
    "# C.a\n",
    "#Convert to binary\n",
    "df['Churn'] = df['Churn'].apply(lambda x:1 if x == 'Yes' else 0)\n",
    "\n",
    "#Extract features and target\n",
    "X = df[['tenure']]\n",
    "y = df['Churn']\n",
    "\n",
    "#Split data into training & testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b7bf4feb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7289 - loss: 0.5615 - val_accuracy: 0.7471 - val_loss: 0.4973\n",
      "Epoch 2/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 718us/step - accuracy: 0.7295 - loss: 0.5331 - val_accuracy: 0.7569 - val_loss: 0.5019\n",
      "Epoch 3/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 798us/step - accuracy: 0.7407 - loss: 0.5197 - val_accuracy: 0.7471 - val_loss: 0.5015\n",
      "Epoch 4/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 745us/step - accuracy: 0.7446 - loss: 0.5301 - val_accuracy: 0.7471 - val_loss: 0.4947\n",
      "Epoch 5/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 747us/step - accuracy: 0.7327 - loss: 0.5282 - val_accuracy: 0.7569 - val_loss: 0.5071\n",
      "Epoch 6/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7364 - loss: 0.5264 - val_accuracy: 0.7569 - val_loss: 0.4948\n",
      "Epoch 7/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 724us/step - accuracy: 0.7392 - loss: 0.5218 - val_accuracy: 0.7569 - val_loss: 0.4947\n",
      "Epoch 8/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 758us/step - accuracy: 0.7463 - loss: 0.5268 - val_accuracy: 0.7507 - val_loss: 0.4954\n",
      "Epoch 9/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 764us/step - accuracy: 0.7305 - loss: 0.5251 - val_accuracy: 0.7507 - val_loss: 0.4945\n",
      "Epoch 10/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 756us/step - accuracy: 0.7333 - loss: 0.5200 - val_accuracy: 0.7569 - val_loss: 0.4963\n",
      "Epoch 11/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.7367 - loss: 0.5230 - val_accuracy: 0.7604 - val_loss: 0.4985\n",
      "Epoch 12/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 769us/step - accuracy: 0.7461 - loss: 0.5211 - val_accuracy: 0.7507 - val_loss: 0.5025\n",
      "Epoch 13/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 769us/step - accuracy: 0.7312 - loss: 0.5298 - val_accuracy: 0.7507 - val_loss: 0.5049\n",
      "Epoch 14/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 714us/step - accuracy: 0.7469 - loss: 0.5092 - val_accuracy: 0.7569 - val_loss: 0.4985\n",
      "Epoch 15/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 704us/step - accuracy: 0.7495 - loss: 0.5128 - val_accuracy: 0.7507 - val_loss: 0.4948\n",
      "Epoch 16/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 736us/step - accuracy: 0.7470 - loss: 0.5071 - val_accuracy: 0.7604 - val_loss: 0.5055\n",
      "Epoch 17/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 711us/step - accuracy: 0.7419 - loss: 0.5223 - val_accuracy: 0.7604 - val_loss: 0.4950\n",
      "Epoch 18/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 684us/step - accuracy: 0.7449 - loss: 0.5126 - val_accuracy: 0.7604 - val_loss: 0.4946\n",
      "Epoch 19/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 695us/step - accuracy: 0.7377 - loss: 0.5302 - val_accuracy: 0.7604 - val_loss: 0.4960\n",
      "Epoch 20/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 704us/step - accuracy: 0.7409 - loss: 0.5231 - val_accuracy: 0.7507 - val_loss: 0.4957\n",
      "Epoch 21/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 708us/step - accuracy: 0.7506 - loss: 0.5142 - val_accuracy: 0.7507 - val_loss: 0.4950\n",
      "Epoch 22/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 775us/step - accuracy: 0.7319 - loss: 0.5263 - val_accuracy: 0.7507 - val_loss: 0.4972\n",
      "Epoch 23/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7351 - loss: 0.5274 - val_accuracy: 0.7507 - val_loss: 0.4946\n",
      "Epoch 24/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 715us/step - accuracy: 0.7371 - loss: 0.5249 - val_accuracy: 0.7507 - val_loss: 0.4979\n",
      "Epoch 25/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 726us/step - accuracy: 0.7273 - loss: 0.5350 - val_accuracy: 0.7507 - val_loss: 0.4977\n",
      "Epoch 26/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7359 - loss: 0.5172 - val_accuracy: 0.7604 - val_loss: 0.5031\n",
      "Epoch 27/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 627us/step - accuracy: 0.7317 - loss: 0.5274 - val_accuracy: 0.7507 - val_loss: 0.4949\n",
      "Epoch 28/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 725us/step - accuracy: 0.7375 - loss: 0.5175 - val_accuracy: 0.7507 - val_loss: 0.4950\n",
      "Epoch 29/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 698us/step - accuracy: 0.7449 - loss: 0.5149 - val_accuracy: 0.7569 - val_loss: 0.4951\n",
      "Epoch 30/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 683us/step - accuracy: 0.7426 - loss: 0.5242 - val_accuracy: 0.7569 - val_loss: 0.4939\n",
      "Epoch 31/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 723us/step - accuracy: 0.7489 - loss: 0.5228 - val_accuracy: 0.7569 - val_loss: 0.4940\n",
      "Epoch 32/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 714us/step - accuracy: 0.7462 - loss: 0.5265 - val_accuracy: 0.7649 - val_loss: 0.4994\n",
      "Epoch 33/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 688us/step - accuracy: 0.7549 - loss: 0.5085 - val_accuracy: 0.7649 - val_loss: 0.4931\n",
      "Epoch 34/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 747us/step - accuracy: 0.7438 - loss: 0.5274 - val_accuracy: 0.7649 - val_loss: 0.4950\n",
      "Epoch 35/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7486 - loss: 0.5177 - val_accuracy: 0.7649 - val_loss: 0.4940\n",
      "Epoch 36/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7505 - loss: 0.5211 - val_accuracy: 0.7595 - val_loss: 0.4938\n",
      "Epoch 37/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 713us/step - accuracy: 0.7432 - loss: 0.5288 - val_accuracy: 0.7595 - val_loss: 0.4928\n",
      "Epoch 38/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 800us/step - accuracy: 0.7512 - loss: 0.5166 - val_accuracy: 0.7587 - val_loss: 0.5156\n",
      "Epoch 39/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 722us/step - accuracy: 0.7520 - loss: 0.5134 - val_accuracy: 0.7595 - val_loss: 0.4960\n",
      "Epoch 40/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7496 - loss: 0.5192 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 41/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.7551 - loss: 0.5115 - val_accuracy: 0.7595 - val_loss: 0.4941\n",
      "Epoch 42/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7520 - loss: 0.5210 - val_accuracy: 0.7595 - val_loss: 0.5067\n",
      "Epoch 43/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 696us/step - accuracy: 0.7526 - loss: 0.5219 - val_accuracy: 0.7595 - val_loss: 0.5000\n",
      "Epoch 44/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 707us/step - accuracy: 0.7547 - loss: 0.5126 - val_accuracy: 0.7595 - val_loss: 0.4948\n",
      "Epoch 45/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 679us/step - accuracy: 0.7613 - loss: 0.5033 - val_accuracy: 0.7595 - val_loss: 0.4962\n",
      "Epoch 46/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717us/step - accuracy: 0.7603 - loss: 0.5068 - val_accuracy: 0.7595 - val_loss: 0.4950\n",
      "Epoch 47/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 642us/step - accuracy: 0.7586 - loss: 0.5167 - val_accuracy: 0.7595 - val_loss: 0.4932\n",
      "Epoch 48/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720us/step - accuracy: 0.7525 - loss: 0.5120 - val_accuracy: 0.7595 - val_loss: 0.4945\n",
      "Epoch 49/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step - accuracy: 0.7446 - loss: 0.5246 - val_accuracy: 0.7595 - val_loss: 0.4936\n",
      "Epoch 50/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7497 - loss: 0.5136 - val_accuracy: 0.7595 - val_loss: 0.4982\n",
      "Epoch 51/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7438 - loss: 0.5212 - val_accuracy: 0.7595 - val_loss: 0.4961\n",
      "Epoch 52/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 620us/step - accuracy: 0.7515 - loss: 0.5212 - val_accuracy: 0.7595 - val_loss: 0.4945\n",
      "Epoch 53/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 0.7435 - loss: 0.5230 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 54/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 724us/step - accuracy: 0.7577 - loss: 0.5169 - val_accuracy: 0.7595 - val_loss: 0.4972\n",
      "Epoch 55/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 660us/step - accuracy: 0.7453 - loss: 0.5199 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 56/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 646us/step - accuracy: 0.7595 - loss: 0.5056 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 57/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7414 - loss: 0.5278 - val_accuracy: 0.7595 - val_loss: 0.4935\n",
      "Epoch 58/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 654us/step - accuracy: 0.7421 - loss: 0.5196 - val_accuracy: 0.7595 - val_loss: 0.4932\n",
      "Epoch 59/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 684us/step - accuracy: 0.7456 - loss: 0.5261 - val_accuracy: 0.7595 - val_loss: 0.4932\n",
      "Epoch 60/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 657us/step - accuracy: 0.7551 - loss: 0.5093 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 61/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 668us/step - accuracy: 0.7470 - loss: 0.5236 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 62/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 796us/step - accuracy: 0.7592 - loss: 0.5060 - val_accuracy: 0.7595 - val_loss: 0.5000\n",
      "Epoch 63/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 635us/step - accuracy: 0.7572 - loss: 0.5097 - val_accuracy: 0.7595 - val_loss: 0.4942\n",
      "Epoch 64/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 643us/step - accuracy: 0.7533 - loss: 0.5107 - val_accuracy: 0.7595 - val_loss: 0.4943\n",
      "Epoch 65/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 637us/step - accuracy: 0.7508 - loss: 0.5119 - val_accuracy: 0.7595 - val_loss: 0.4944\n",
      "Epoch 66/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7470 - loss: 0.5136 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 67/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 769us/step - accuracy: 0.7557 - loss: 0.5014 - val_accuracy: 0.7595 - val_loss: 0.4940\n",
      "Epoch 68/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7513 - loss: 0.5152 - val_accuracy: 0.7595 - val_loss: 0.4958\n",
      "Epoch 69/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step - accuracy: 0.7541 - loss: 0.5116 - val_accuracy: 0.7595 - val_loss: 0.4940\n",
      "Epoch 70/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 711us/step - accuracy: 0.7523 - loss: 0.5138 - val_accuracy: 0.7649 - val_loss: 0.4941\n",
      "Epoch 71/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 0.7493 - loss: 0.5192 - val_accuracy: 0.7595 - val_loss: 0.4936\n",
      "Epoch 72/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step - accuracy: 0.7491 - loss: 0.5229 - val_accuracy: 0.7595 - val_loss: 0.4932\n",
      "Epoch 73/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7568 - loss: 0.5034 - val_accuracy: 0.7595 - val_loss: 0.4949\n",
      "Epoch 74/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 657us/step - accuracy: 0.7449 - loss: 0.5255 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 75/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 688us/step - accuracy: 0.7583 - loss: 0.5114 - val_accuracy: 0.7595 - val_loss: 0.4937\n",
      "Epoch 76/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 749us/step - accuracy: 0.7386 - loss: 0.5320 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 77/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 705us/step - accuracy: 0.7518 - loss: 0.5189 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 78/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step - accuracy: 0.7491 - loss: 0.5144 - val_accuracy: 0.7595 - val_loss: 0.4935\n",
      "Epoch 79/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7635 - loss: 0.5046 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 80/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7445 - loss: 0.5232 - val_accuracy: 0.7595 - val_loss: 0.4936\n",
      "Epoch 81/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 0.7629 - loss: 0.5030 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 82/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7392 - loss: 0.5206 - val_accuracy: 0.7595 - val_loss: 0.4935\n",
      "Epoch 83/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 761us/step - accuracy: 0.7510 - loss: 0.5171 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 84/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 690us/step - accuracy: 0.7431 - loss: 0.5231 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 85/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 725us/step - accuracy: 0.7546 - loss: 0.5047 - val_accuracy: 0.7595 - val_loss: 0.4947\n",
      "Epoch 86/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 715us/step - accuracy: 0.7584 - loss: 0.5100 - val_accuracy: 0.7595 - val_loss: 0.4936\n",
      "Epoch 87/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7567 - loss: 0.5141 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 88/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step - accuracy: 0.7450 - loss: 0.5156 - val_accuracy: 0.7595 - val_loss: 0.4940\n",
      "Epoch 89/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step - accuracy: 0.7436 - loss: 0.5132 - val_accuracy: 0.7595 - val_loss: 0.4949\n",
      "Epoch 90/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step - accuracy: 0.7585 - loss: 0.5101 - val_accuracy: 0.7595 - val_loss: 0.4990\n",
      "Epoch 91/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 691us/step - accuracy: 0.7501 - loss: 0.5150 - val_accuracy: 0.7595 - val_loss: 0.4938\n",
      "Epoch 92/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 760us/step - accuracy: 0.7528 - loss: 0.5162 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 93/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step - accuracy: 0.7445 - loss: 0.5261 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 94/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 694us/step - accuracy: 0.7453 - loss: 0.5228 - val_accuracy: 0.7595 - val_loss: 0.4951\n",
      "Epoch 95/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653us/step - accuracy: 0.7557 - loss: 0.5064 - val_accuracy: 0.7595 - val_loss: 0.4971\n",
      "Epoch 96/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7546 - loss: 0.5111 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 97/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 636us/step - accuracy: 0.7565 - loss: 0.5074 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 98/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step - accuracy: 0.7566 - loss: 0.5122 - val_accuracy: 0.7595 - val_loss: 0.4942\n",
      "Epoch 99/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7622 - loss: 0.5089 - val_accuracy: 0.7595 - val_loss: 0.4948\n",
      "Epoch 100/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 639us/step - accuracy: 0.7454 - loss: 0.5190 - val_accuracy: 0.7595 - val_loss: 0.4949\n",
      "Epoch 101/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 647us/step - accuracy: 0.7544 - loss: 0.5088 - val_accuracy: 0.7649 - val_loss: 0.4934\n",
      "Epoch 102/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 699us/step - accuracy: 0.7480 - loss: 0.5140 - val_accuracy: 0.7595 - val_loss: 0.4960\n",
      "Epoch 103/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7403 - loss: 0.5246 - val_accuracy: 0.7595 - val_loss: 0.4937\n",
      "Epoch 104/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7563 - loss: 0.5068 - val_accuracy: 0.7595 - val_loss: 0.4955\n",
      "Epoch 105/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 649us/step - accuracy: 0.7552 - loss: 0.5040 - val_accuracy: 0.7595 - val_loss: 0.4956\n",
      "Epoch 106/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 0.7423 - loss: 0.5225 - val_accuracy: 0.7595 - val_loss: 0.4933\n",
      "Epoch 107/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 640us/step - accuracy: 0.7531 - loss: 0.4991 - val_accuracy: 0.7595 - val_loss: 0.4939\n",
      "Epoch 108/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 689us/step - accuracy: 0.7486 - loss: 0.5241 - val_accuracy: 0.7595 - val_loss: 0.4942\n",
      "Epoch 109/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7496 - loss: 0.5110 - val_accuracy: 0.7595 - val_loss: 0.4947\n",
      "Epoch 110/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 683us/step - accuracy: 0.7533 - loss: 0.5151 - val_accuracy: 0.7595 - val_loss: 0.4934\n",
      "Epoch 111/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 654us/step - accuracy: 0.7526 - loss: 0.5097 - val_accuracy: 0.7595 - val_loss: 0.4951\n",
      "Epoch 112/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step - accuracy: 0.7507 - loss: 0.5154 - val_accuracy: 0.7595 - val_loss: 0.4944\n",
      "Epoch 113/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 651us/step - accuracy: 0.7498 - loss: 0.5169 - val_accuracy: 0.7595 - val_loss: 0.4935\n",
      "Epoch 114/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653us/step - accuracy: 0.7545 - loss: 0.4975 - val_accuracy: 0.7595 - val_loss: 0.4944\n",
      "Epoch 115/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 689us/step - accuracy: 0.7563 - loss: 0.5048 - val_accuracy: 0.7595 - val_loss: 0.4956\n",
      "Epoch 116/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 668us/step - accuracy: 0.7482 - loss: 0.5156 - val_accuracy: 0.7649 - val_loss: 0.4936\n",
      "Epoch 117/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 654us/step - accuracy: 0.7470 - loss: 0.5197 - val_accuracy: 0.7595 - val_loss: 0.4937\n",
      "Epoch 118/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 683us/step - accuracy: 0.7460 - loss: 0.5263 - val_accuracy: 0.7595 - val_loss: 0.4945\n",
      "Epoch 119/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 0.7592 - loss: 0.5076 - val_accuracy: 0.7595 - val_loss: 0.4940\n",
      "Epoch 120/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 671us/step - accuracy: 0.7374 - loss: 0.5255 - val_accuracy: 0.7649 - val_loss: 0.4942\n",
      "Epoch 121/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 745us/step - accuracy: 0.7520 - loss: 0.5012 - val_accuracy: 0.7595 - val_loss: 0.4951\n",
      "Epoch 122/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 742us/step - accuracy: 0.7504 - loss: 0.5093 - val_accuracy: 0.7649 - val_loss: 0.4937\n",
      "Epoch 123/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739us/step - accuracy: 0.7521 - loss: 0.5178 - val_accuracy: 0.7595 - val_loss: 0.4936\n",
      "Epoch 124/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 695us/step - accuracy: 0.7562 - loss: 0.5098 - val_accuracy: 0.7595 - val_loss: 0.4998\n",
      "Epoch 125/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 761us/step - accuracy: 0.7548 - loss: 0.5064 - val_accuracy: 0.7595 - val_loss: 0.4938\n",
      "Epoch 126/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7569 - loss: 0.5021 - val_accuracy: 0.7595 - val_loss: 0.4969\n",
      "Epoch 127/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step - accuracy: 0.7537 - loss: 0.5104 - val_accuracy: 0.7595 - val_loss: 0.4966\n",
      "Epoch 128/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 711us/step - accuracy: 0.7592 - loss: 0.5111 - val_accuracy: 0.7595 - val_loss: 0.4956\n",
      "Epoch 129/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step - accuracy: 0.7545 - loss: 0.5158 - val_accuracy: 0.7595 - val_loss: 0.4948\n",
      "Epoch 130/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7521 - loss: 0.5171 - val_accuracy: 0.7595 - val_loss: 0.4940\n",
      "Epoch 131/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 651us/step - accuracy: 0.7576 - loss: 0.5066 - val_accuracy: 0.7595 - val_loss: 0.4937\n",
      "Epoch 132/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 649us/step - accuracy: 0.7505 - loss: 0.5144 - val_accuracy: 0.7595 - val_loss: 0.4948\n",
      "Epoch 133/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7441 - loss: 0.5243 - val_accuracy: 0.7595 - val_loss: 0.4946\n",
      "Epoch 134/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7521 - loss: 0.5073 - val_accuracy: 0.7595 - val_loss: 0.4956\n",
      "Epoch 135/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step - accuracy: 0.7472 - loss: 0.5206 - val_accuracy: 0.7595 - val_loss: 0.4952\n",
      "Epoch 136/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7468 - loss: 0.5142 - val_accuracy: 0.7595 - val_loss: 0.4938\n",
      "Epoch 137/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step - accuracy: 0.7580 - loss: 0.5097 - val_accuracy: 0.7595 - val_loss: 0.4937\n",
      "Epoch 138/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.7600 - loss: 0.5035 - val_accuracy: 0.7649 - val_loss: 0.4945\n",
      "Epoch 139/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 770us/step - accuracy: 0.7443 - loss: 0.5200 - val_accuracy: 0.7649 - val_loss: 0.4937\n",
      "Epoch 140/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7579 - loss: 0.5100 - val_accuracy: 0.7595 - val_loss: 0.4938\n",
      "Epoch 141/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7503 - loss: 0.5144 - val_accuracy: 0.7649 - val_loss: 0.4942\n",
      "Epoch 142/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 645us/step - accuracy: 0.7545 - loss: 0.5142 - val_accuracy: 0.7595 - val_loss: 0.4936\n",
      "Epoch 143/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7430 - loss: 0.5176 - val_accuracy: 0.7649 - val_loss: 0.4941\n",
      "Epoch 144/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 674us/step - accuracy: 0.7571 - loss: 0.5064 - val_accuracy: 0.7649 - val_loss: 0.4950\n",
      "Epoch 145/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7518 - loss: 0.5203 - val_accuracy: 0.7649 - val_loss: 0.4941\n",
      "Epoch 146/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 677us/step - accuracy: 0.7561 - loss: 0.5100 - val_accuracy: 0.7595 - val_loss: 0.4971\n",
      "Epoch 147/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 650us/step - accuracy: 0.7468 - loss: 0.5111 - val_accuracy: 0.7595 - val_loss: 0.4958\n",
      "Epoch 148/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 697us/step - accuracy: 0.7519 - loss: 0.5119 - val_accuracy: 0.7649 - val_loss: 0.4938\n",
      "Epoch 149/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 728us/step - accuracy: 0.7486 - loss: 0.5235 - val_accuracy: 0.7595 - val_loss: 0.4965\n",
      "Epoch 150/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 698us/step - accuracy: 0.7458 - loss: 0.5283 - val_accuracy: 0.7649 - val_loss: 0.4937\n",
      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 841us/step\n",
      "Confusion Matrix:\n",
      "[[950  86]\n",
      " [257 116]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=1, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile\n",
    "model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Fit the model\n",
    "history = model.fit(X_train, y_train, epochs=150, batch_size=10, validation_split=0.2)\n",
    "\n",
    "# Predict values\n",
    "y_pred = (model.predict(X_test) > 0.5).astype(\"int32\")\n",
    "\n",
    "# Build the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "\n",
    "# Plot the Accuracy vs Epochs graph\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f899abc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6948 - loss: 0.8030 - val_accuracy: 0.7507 - val_loss: 0.5188\n",
      "Epoch 2/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7246 - loss: 0.5841 - val_accuracy: 0.7507 - val_loss: 0.5234\n",
      "Epoch 3/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 705us/step - accuracy: 0.7201 - loss: 0.5707 - val_accuracy: 0.7507 - val_loss: 0.5314\n",
      "Epoch 4/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 737us/step - accuracy: 0.7335 - loss: 0.5578 - val_accuracy: 0.7507 - val_loss: 0.5144\n",
      "Epoch 5/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 709us/step - accuracy: 0.7340 - loss: 0.5415 - val_accuracy: 0.7507 - val_loss: 0.5171\n",
      "Epoch 6/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 742us/step - accuracy: 0.7387 - loss: 0.5378 - val_accuracy: 0.7507 - val_loss: 0.5147\n",
      "Epoch 7/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 699us/step - accuracy: 0.7403 - loss: 0.5383 - val_accuracy: 0.7507 - val_loss: 0.5156\n",
      "Epoch 8/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 727us/step - accuracy: 0.7353 - loss: 0.5424 - val_accuracy: 0.7507 - val_loss: 0.5202\n",
      "Epoch 9/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 694us/step - accuracy: 0.7212 - loss: 0.5551 - val_accuracy: 0.7507 - val_loss: 0.5120\n",
      "Epoch 10/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716us/step - accuracy: 0.7300 - loss: 0.5507 - val_accuracy: 0.7507 - val_loss: 0.5157\n",
      "Epoch 11/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672us/step - accuracy: 0.7241 - loss: 0.5533 - val_accuracy: 0.7507 - val_loss: 0.5128\n",
      "Epoch 12/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739us/step - accuracy: 0.7229 - loss: 0.5493 - val_accuracy: 0.7507 - val_loss: 0.5085\n",
      "Epoch 13/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7391 - loss: 0.5374 - val_accuracy: 0.7507 - val_loss: 0.5170\n",
      "Epoch 14/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739us/step - accuracy: 0.7277 - loss: 0.5480 - val_accuracy: 0.7507 - val_loss: 0.5179\n",
      "Epoch 15/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 698us/step - accuracy: 0.7348 - loss: 0.5424 - val_accuracy: 0.7507 - val_loss: 0.5186\n",
      "Epoch 16/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 760us/step - accuracy: 0.7248 - loss: 0.5424 - val_accuracy: 0.7507 - val_loss: 0.5187\n",
      "Epoch 17/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.7466 - loss: 0.5310 - val_accuracy: 0.7507 - val_loss: 0.5239\n",
      "Epoch 18/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 778us/step - accuracy: 0.7276 - loss: 0.5485 - val_accuracy: 0.7507 - val_loss: 0.5074\n",
      "Epoch 19/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7360 - loss: 0.5400 - val_accuracy: 0.7507 - val_loss: 0.5124\n",
      "Epoch 20/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 897us/step - accuracy: 0.7282 - loss: 0.5541 - val_accuracy: 0.7507 - val_loss: 0.5182\n",
      "Epoch 21/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 742us/step - accuracy: 0.7261 - loss: 0.5407 - val_accuracy: 0.7507 - val_loss: 0.5154\n",
      "Epoch 22/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 757us/step - accuracy: 0.7236 - loss: 0.5528 - val_accuracy: 0.7507 - val_loss: 0.5155\n",
      "Epoch 23/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 745us/step - accuracy: 0.7363 - loss: 0.5378 - val_accuracy: 0.7507 - val_loss: 0.5252\n",
      "Epoch 24/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716us/step - accuracy: 0.7301 - loss: 0.5421 - val_accuracy: 0.7507 - val_loss: 0.5127\n",
      "Epoch 25/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 715us/step - accuracy: 0.7211 - loss: 0.5590 - val_accuracy: 0.7507 - val_loss: 0.5108\n",
      "Epoch 26/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 730us/step - accuracy: 0.7253 - loss: 0.5466 - val_accuracy: 0.7507 - val_loss: 0.5159\n",
      "Epoch 27/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 696us/step - accuracy: 0.7334 - loss: 0.5452 - val_accuracy: 0.7507 - val_loss: 0.5189\n",
      "Epoch 28/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 740us/step - accuracy: 0.7354 - loss: 0.5373 - val_accuracy: 0.7507 - val_loss: 0.5096\n",
      "Epoch 29/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 902us/step - accuracy: 0.7323 - loss: 0.5399 - val_accuracy: 0.7507 - val_loss: 0.5137\n",
      "Epoch 30/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 750us/step - accuracy: 0.7236 - loss: 0.5492 - val_accuracy: 0.7507 - val_loss: 0.5154\n",
      "Epoch 31/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 761us/step - accuracy: 0.7140 - loss: 0.5581 - val_accuracy: 0.7507 - val_loss: 0.5112\n",
      "Epoch 32/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 807us/step - accuracy: 0.7309 - loss: 0.5428 - val_accuracy: 0.7507 - val_loss: 0.5115\n",
      "Epoch 33/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 684us/step - accuracy: 0.7389 - loss: 0.5342 - val_accuracy: 0.7507 - val_loss: 0.5149\n",
      "Epoch 34/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.7254 - loss: 0.5538 - val_accuracy: 0.7507 - val_loss: 0.5162\n",
      "Epoch 35/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 741us/step - accuracy: 0.7264 - loss: 0.5492 - val_accuracy: 0.7507 - val_loss: 0.5124\n",
      "Epoch 36/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 684us/step - accuracy: 0.7298 - loss: 0.5416 - val_accuracy: 0.7507 - val_loss: 0.5163\n",
      "Epoch 37/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 796us/step - accuracy: 0.7261 - loss: 0.5401 - val_accuracy: 0.7507 - val_loss: 0.5153\n",
      "Epoch 38/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 852us/step - accuracy: 0.7285 - loss: 0.5454 - val_accuracy: 0.7507 - val_loss: 0.5172\n",
      "Epoch 39/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 732us/step - accuracy: 0.7298 - loss: 0.5417 - val_accuracy: 0.7507 - val_loss: 0.5165\n",
      "Epoch 40/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 679us/step - accuracy: 0.7296 - loss: 0.5426 - val_accuracy: 0.7507 - val_loss: 0.5119\n",
      "Epoch 41/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7378 - loss: 0.5331 - val_accuracy: 0.7507 - val_loss: 0.5098\n",
      "Epoch 42/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step - accuracy: 0.7298 - loss: 0.5545 - val_accuracy: 0.7507 - val_loss: 0.5115\n",
      "Epoch 43/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 743us/step - accuracy: 0.7334 - loss: 0.5412 - val_accuracy: 0.7507 - val_loss: 0.5154\n",
      "Epoch 44/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 742us/step - accuracy: 0.7360 - loss: 0.5373 - val_accuracy: 0.7507 - val_loss: 0.5263\n",
      "Epoch 45/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7305 - loss: 0.5452 - val_accuracy: 0.7507 - val_loss: 0.5184\n",
      "Epoch 46/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7307 - loss: 0.5396 - val_accuracy: 0.7507 - val_loss: 0.5212\n",
      "Epoch 47/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 821us/step - accuracy: 0.7410 - loss: 0.5336 - val_accuracy: 0.7507 - val_loss: 0.5252\n",
      "Epoch 48/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 778us/step - accuracy: 0.7333 - loss: 0.5360 - val_accuracy: 0.7507 - val_loss: 0.5240\n",
      "Epoch 49/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 676us/step - accuracy: 0.7318 - loss: 0.5385 - val_accuracy: 0.7507 - val_loss: 0.5103\n",
      "Epoch 50/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 699us/step - accuracy: 0.7295 - loss: 0.5362 - val_accuracy: 0.7507 - val_loss: 0.5187\n",
      "Epoch 51/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7227 - loss: 0.5447 - val_accuracy: 0.7507 - val_loss: 0.5101\n",
      "Epoch 52/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step - accuracy: 0.7306 - loss: 0.5370 - val_accuracy: 0.7507 - val_loss: 0.5082\n",
      "Epoch 53/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7263 - loss: 0.5437 - val_accuracy: 0.7507 - val_loss: 0.5067\n",
      "Epoch 54/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step - accuracy: 0.7402 - loss: 0.5295 - val_accuracy: 0.7507 - val_loss: 0.5095\n",
      "Epoch 55/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step - accuracy: 0.7301 - loss: 0.5377 - val_accuracy: 0.7507 - val_loss: 0.5135\n",
      "Epoch 56/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7326 - loss: 0.5299 - val_accuracy: 0.7507 - val_loss: 0.5169\n",
      "Epoch 57/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7318 - loss: 0.5345 - val_accuracy: 0.7507 - val_loss: 0.5125\n",
      "Epoch 58/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 679us/step - accuracy: 0.7274 - loss: 0.5326 - val_accuracy: 0.7507 - val_loss: 0.5237\n",
      "Epoch 59/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7365 - loss: 0.5332 - val_accuracy: 0.7507 - val_loss: 0.5172\n",
      "Epoch 60/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 697us/step - accuracy: 0.7206 - loss: 0.5531 - val_accuracy: 0.7507 - val_loss: 0.5090\n",
      "Epoch 61/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7226 - loss: 0.5405 - val_accuracy: 0.7507 - val_loss: 0.5216\n",
      "Epoch 62/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 810us/step - accuracy: 0.7283 - loss: 0.5426 - val_accuracy: 0.7507 - val_loss: 0.5044\n",
      "Epoch 63/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 730us/step - accuracy: 0.7250 - loss: 0.5423 - val_accuracy: 0.7507 - val_loss: 0.5129\n",
      "Epoch 64/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7295 - loss: 0.5364 - val_accuracy: 0.7507 - val_loss: 0.5121\n",
      "Epoch 65/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 676us/step - accuracy: 0.7349 - loss: 0.5319 - val_accuracy: 0.7507 - val_loss: 0.5187\n",
      "Epoch 66/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 691us/step - accuracy: 0.7233 - loss: 0.5425 - val_accuracy: 0.7507 - val_loss: 0.5040\n",
      "Epoch 67/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7154 - loss: 0.5464 - val_accuracy: 0.7507 - val_loss: 0.5020\n",
      "Epoch 68/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7227 - loss: 0.5459 - val_accuracy: 0.7507 - val_loss: 0.5104\n",
      "Epoch 69/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 0.7356 - loss: 0.5295 - val_accuracy: 0.7507 - val_loss: 0.5123\n",
      "Epoch 70/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7253 - loss: 0.5461 - val_accuracy: 0.7507 - val_loss: 0.5100\n",
      "Epoch 71/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7444 - loss: 0.5224 - val_accuracy: 0.7507 - val_loss: 0.5070\n",
      "Epoch 72/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7352 - loss: 0.5320 - val_accuracy: 0.7507 - val_loss: 0.5063\n",
      "Epoch 73/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 752us/step - accuracy: 0.7122 - loss: 0.5543 - val_accuracy: 0.7507 - val_loss: 0.5084\n",
      "Epoch 74/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7282 - loss: 0.5348 - val_accuracy: 0.7507 - val_loss: 0.5089\n",
      "Epoch 75/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 701us/step - accuracy: 0.7205 - loss: 0.5368 - val_accuracy: 0.7507 - val_loss: 0.5118\n",
      "Epoch 76/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 773us/step - accuracy: 0.7200 - loss: 0.5375 - val_accuracy: 0.7507 - val_loss: 0.5097\n",
      "Epoch 77/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 736us/step - accuracy: 0.7373 - loss: 0.5321 - val_accuracy: 0.7507 - val_loss: 0.5114\n",
      "Epoch 78/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 769us/step - accuracy: 0.7245 - loss: 0.5404 - val_accuracy: 0.7507 - val_loss: 0.5089\n",
      "Epoch 79/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 771us/step - accuracy: 0.7233 - loss: 0.5518 - val_accuracy: 0.7507 - val_loss: 0.5010\n",
      "Epoch 80/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 782us/step - accuracy: 0.7295 - loss: 0.5351 - val_accuracy: 0.7507 - val_loss: 0.5061\n",
      "Epoch 81/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733us/step - accuracy: 0.7258 - loss: 0.5390 - val_accuracy: 0.7507 - val_loss: 0.5090\n",
      "Epoch 82/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 760us/step - accuracy: 0.7371 - loss: 0.5338 - val_accuracy: 0.7507 - val_loss: 0.5084\n",
      "Epoch 83/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7229 - loss: 0.5360 - val_accuracy: 0.7507 - val_loss: 0.5139\n",
      "Epoch 84/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 756us/step - accuracy: 0.7257 - loss: 0.5401 - val_accuracy: 0.7507 - val_loss: 0.5181\n",
      "Epoch 85/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7349 - loss: 0.5355 - val_accuracy: 0.7507 - val_loss: 0.5056\n",
      "Epoch 86/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 756us/step - accuracy: 0.7359 - loss: 0.5321 - val_accuracy: 0.7507 - val_loss: 0.5089\n",
      "Epoch 87/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7310 - loss: 0.5332 - val_accuracy: 0.7507 - val_loss: 0.5101\n",
      "Epoch 88/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733us/step - accuracy: 0.7333 - loss: 0.5241 - val_accuracy: 0.7507 - val_loss: 0.5103\n",
      "Epoch 89/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 773us/step - accuracy: 0.7390 - loss: 0.5247 - val_accuracy: 0.7507 - val_loss: 0.5050\n",
      "Epoch 90/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7261 - loss: 0.5378 - val_accuracy: 0.7507 - val_loss: 0.5086\n",
      "Epoch 91/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 728us/step - accuracy: 0.7285 - loss: 0.5267 - val_accuracy: 0.7507 - val_loss: 0.5219\n",
      "Epoch 92/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 762us/step - accuracy: 0.7391 - loss: 0.5227 - val_accuracy: 0.7507 - val_loss: 0.5197\n",
      "Epoch 93/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 785us/step - accuracy: 0.7311 - loss: 0.5298 - val_accuracy: 0.7507 - val_loss: 0.5079\n",
      "Epoch 94/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721us/step - accuracy: 0.7466 - loss: 0.5233 - val_accuracy: 0.7507 - val_loss: 0.5063\n",
      "Epoch 95/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 775us/step - accuracy: 0.7333 - loss: 0.5336 - val_accuracy: 0.7507 - val_loss: 0.5110\n",
      "Epoch 96/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 778us/step - accuracy: 0.7244 - loss: 0.5401 - val_accuracy: 0.7507 - val_loss: 0.5011\n",
      "Epoch 97/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 813us/step - accuracy: 0.7406 - loss: 0.5215 - val_accuracy: 0.7507 - val_loss: 0.5097\n",
      "Epoch 98/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 698us/step - accuracy: 0.7420 - loss: 0.5257 - val_accuracy: 0.7507 - val_loss: 0.5055\n",
      "Epoch 99/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7226 - loss: 0.5436 - val_accuracy: 0.7507 - val_loss: 0.5075\n",
      "Epoch 100/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 777us/step - accuracy: 0.7327 - loss: 0.5298 - val_accuracy: 0.7507 - val_loss: 0.5225\n",
      "Epoch 101/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 714us/step - accuracy: 0.7356 - loss: 0.5301 - val_accuracy: 0.7507 - val_loss: 0.5179\n",
      "Epoch 102/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step - accuracy: 0.7291 - loss: 0.5437 - val_accuracy: 0.7507 - val_loss: 0.5094\n",
      "Epoch 103/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716us/step - accuracy: 0.7364 - loss: 0.5287 - val_accuracy: 0.7507 - val_loss: 0.5089\n",
      "Epoch 104/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 691us/step - accuracy: 0.7212 - loss: 0.5424 - val_accuracy: 0.7507 - val_loss: 0.5130\n",
      "Epoch 105/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7256 - loss: 0.5386 - val_accuracy: 0.7507 - val_loss: 0.5050\n",
      "Epoch 106/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 695us/step - accuracy: 0.7210 - loss: 0.5406 - val_accuracy: 0.7507 - val_loss: 0.5080\n",
      "Epoch 107/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 704us/step - accuracy: 0.7219 - loss: 0.5429 - val_accuracy: 0.7507 - val_loss: 0.5020\n",
      "Epoch 108/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717us/step - accuracy: 0.7366 - loss: 0.5339 - val_accuracy: 0.7507 - val_loss: 0.5030\n",
      "Epoch 109/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 691us/step - accuracy: 0.7341 - loss: 0.5260 - val_accuracy: 0.7507 - val_loss: 0.5190\n",
      "Epoch 110/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step - accuracy: 0.7269 - loss: 0.5420 - val_accuracy: 0.7507 - val_loss: 0.5090\n",
      "Epoch 111/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7334 - loss: 0.5333 - val_accuracy: 0.7507 - val_loss: 0.5038\n",
      "Epoch 112/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672us/step - accuracy: 0.7303 - loss: 0.5350 - val_accuracy: 0.7507 - val_loss: 0.5134\n",
      "Epoch 113/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7409 - loss: 0.5268 - val_accuracy: 0.7507 - val_loss: 0.5060\n",
      "Epoch 114/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720us/step - accuracy: 0.7316 - loss: 0.5275 - val_accuracy: 0.7507 - val_loss: 0.5083\n",
      "Epoch 115/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7325 - loss: 0.5300 - val_accuracy: 0.7507 - val_loss: 0.5099\n",
      "Epoch 116/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7330 - loss: 0.5392 - val_accuracy: 0.7507 - val_loss: 0.5095\n",
      "Epoch 117/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7273 - loss: 0.5357 - val_accuracy: 0.7507 - val_loss: 0.5068\n",
      "Epoch 118/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7293 - loss: 0.5333 - val_accuracy: 0.7507 - val_loss: 0.5079\n",
      "Epoch 119/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717us/step - accuracy: 0.7300 - loss: 0.5343 - val_accuracy: 0.7507 - val_loss: 0.5033\n",
      "Epoch 120/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7273 - loss: 0.5430 - val_accuracy: 0.7507 - val_loss: 0.5059\n",
      "Epoch 121/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 711us/step - accuracy: 0.7317 - loss: 0.5387 - val_accuracy: 0.7507 - val_loss: 0.5101\n",
      "Epoch 122/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 690us/step - accuracy: 0.7346 - loss: 0.5317 - val_accuracy: 0.7507 - val_loss: 0.5133\n",
      "Epoch 123/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 749us/step - accuracy: 0.7217 - loss: 0.5450 - val_accuracy: 0.7507 - val_loss: 0.5090\n",
      "Epoch 124/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 727us/step - accuracy: 0.7352 - loss: 0.5276 - val_accuracy: 0.7507 - val_loss: 0.5067\n",
      "Epoch 125/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 718us/step - accuracy: 0.7365 - loss: 0.5276 - val_accuracy: 0.7507 - val_loss: 0.5145\n",
      "Epoch 126/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 688us/step - accuracy: 0.7260 - loss: 0.5410 - val_accuracy: 0.7507 - val_loss: 0.5116\n",
      "Epoch 127/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 676us/step - accuracy: 0.7326 - loss: 0.5312 - val_accuracy: 0.7507 - val_loss: 0.5174\n",
      "Epoch 128/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step - accuracy: 0.7262 - loss: 0.5364 - val_accuracy: 0.7507 - val_loss: 0.5125\n",
      "Epoch 129/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step - accuracy: 0.7338 - loss: 0.5278 - val_accuracy: 0.7507 - val_loss: 0.5080\n",
      "Epoch 130/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7416 - loss: 0.5261 - val_accuracy: 0.7507 - val_loss: 0.5071\n",
      "Epoch 131/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step - accuracy: 0.7354 - loss: 0.5236 - val_accuracy: 0.7507 - val_loss: 0.5068\n",
      "Epoch 132/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 705us/step - accuracy: 0.7285 - loss: 0.5383 - val_accuracy: 0.7507 - val_loss: 0.5081\n",
      "Epoch 133/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7312 - loss: 0.5325 - val_accuracy: 0.7507 - val_loss: 0.5091\n",
      "Epoch 134/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7397 - loss: 0.5312 - val_accuracy: 0.7507 - val_loss: 0.5129\n",
      "Epoch 135/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 699us/step - accuracy: 0.7291 - loss: 0.5335 - val_accuracy: 0.7507 - val_loss: 0.5145\n",
      "Epoch 136/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 677us/step - accuracy: 0.7355 - loss: 0.5355 - val_accuracy: 0.7507 - val_loss: 0.5055\n",
      "Epoch 137/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7288 - loss: 0.5372 - val_accuracy: 0.7507 - val_loss: 0.5043\n",
      "Epoch 138/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7292 - loss: 0.5417 - val_accuracy: 0.7507 - val_loss: 0.5115\n",
      "Epoch 139/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 703us/step - accuracy: 0.7366 - loss: 0.5248 - val_accuracy: 0.7507 - val_loss: 0.5124\n",
      "Epoch 140/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7349 - loss: 0.5313 - val_accuracy: 0.7507 - val_loss: 0.5051\n",
      "Epoch 141/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step - accuracy: 0.7374 - loss: 0.5346 - val_accuracy: 0.7507 - val_loss: 0.5069\n",
      "Epoch 142/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7362 - loss: 0.5255 - val_accuracy: 0.7507 - val_loss: 0.5193\n",
      "Epoch 143/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7116 - loss: 0.5540 - val_accuracy: 0.7507 - val_loss: 0.5076\n",
      "Epoch 144/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 745us/step - accuracy: 0.7275 - loss: 0.5307 - val_accuracy: 0.7507 - val_loss: 0.5144\n",
      "Epoch 145/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7330 - loss: 0.5335 - val_accuracy: 0.7507 - val_loss: 0.5088\n",
      "Epoch 146/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7238 - loss: 0.5362 - val_accuracy: 0.7507 - val_loss: 0.5111\n",
      "Epoch 147/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7327 - loss: 0.5295 - val_accuracy: 0.7507 - val_loss: 0.5098\n",
      "Epoch 148/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7311 - loss: 0.5379 - val_accuracy: 0.7507 - val_loss: 0.5058\n",
      "Epoch 149/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7368 - loss: 0.5238 - val_accuracy: 0.7507 - val_loss: 0.5121\n",
      "Epoch 150/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7216 - loss: 0.5398 - val_accuracy: 0.7507 - val_loss: 0.5081\n",
      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 955us/step\n",
      "Confusion Matrix with Dropout:\n",
      "[[1036    0]\n",
      " [ 373    0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# C.b\n",
    "# Model with dropout\n",
    "model_dropout = Sequential()\n",
    "model_dropout.add(Dense(12, input_dim=1, activation='relu'))\n",
    "model_dropout.add(Dropout(0.3))\n",
    "model_dropout.add(Dense(8, activation='relu'))\n",
    "model_dropout.add(Dropout(0.2))\n",
    "model_dropout.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile \n",
    "model_dropout.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Fit the model\n",
    "history_dropout = model_dropout.fit(X_train, y_train, epochs=150, batch_size=10, validation_split=0.2)\n",
    "\n",
    "# Predict values on the test set\n",
    "y_pred_dropout = (model_dropout.predict(X_test) > 0.5).astype(\"int32\")\n",
    "\n",
    "# Build  confusion matrix\n",
    "cm_dropout = confusion_matrix(y_test, y_pred_dropout)\n",
    "print(\"Confusion Matrix with Dropout:\")\n",
    "print(cm_dropout)\n",
    "\n",
    "# Plot Accuracy vs Epochs graph\n",
    "plt.plot(history_dropout.history['accuracy'])\n",
    "plt.plot(history_dropout.history['val_accuracy'])\n",
    "plt.title('Model accuracy with Dropout')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "802950a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_12656\\968325397.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['TotalCharges'] = pd.to_numeric(X['TotalCharges'], errors='coerce')\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_12656\\968325397.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['TotalCharges'].fillna(X['TotalCharges'].mean(), inplace=True)\n",
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.5704 - loss: 25.5880 - val_accuracy: 0.7675 - val_loss: 0.9729\n",
      "Epoch 2/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678us/step - accuracy: 0.7380 - loss: 0.7340 - val_accuracy: 0.7870 - val_loss: 0.9938\n",
      "Epoch 3/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 754us/step - accuracy: 0.7227 - loss: 0.9575 - val_accuracy: 0.7799 - val_loss: 0.7266\n",
      "Epoch 4/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step - accuracy: 0.7251 - loss: 0.8608 - val_accuracy: 0.7853 - val_loss: 0.6448\n",
      "Epoch 5/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 651us/step - accuracy: 0.7341 - loss: 1.1789 - val_accuracy: 0.7791 - val_loss: 1.4694\n",
      "Epoch 6/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7357 - loss: 1.1084 - val_accuracy: 0.7205 - val_loss: 0.5280\n",
      "Epoch 7/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7124 - loss: 1.0755 - val_accuracy: 0.7924 - val_loss: 1.0606\n",
      "Epoch 8/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 705us/step - accuracy: 0.7545 - loss: 0.7414 - val_accuracy: 0.7853 - val_loss: 0.5678\n",
      "Epoch 9/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 642us/step - accuracy: 0.7531 - loss: 0.7039 - val_accuracy: 0.7782 - val_loss: 0.4828\n",
      "Epoch 10/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 659us/step - accuracy: 0.7296 - loss: 1.0218 - val_accuracy: 0.7826 - val_loss: 0.7795\n",
      "Epoch 11/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7435 - loss: 0.8517 - val_accuracy: 0.7853 - val_loss: 1.2738\n",
      "Epoch 12/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 668us/step - accuracy: 0.7482 - loss: 0.9681 - val_accuracy: 0.7773 - val_loss: 1.3320\n",
      "Epoch 13/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step - accuracy: 0.7496 - loss: 0.7023 - val_accuracy: 0.7551 - val_loss: 0.4575\n",
      "Epoch 14/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7272 - loss: 0.9526 - val_accuracy: 0.7445 - val_loss: 0.4832\n",
      "Epoch 15/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7347 - loss: 0.8448 - val_accuracy: 0.7888 - val_loss: 1.0686\n",
      "Epoch 16/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653us/step - accuracy: 0.7370 - loss: 0.7863 - val_accuracy: 0.7613 - val_loss: 0.4604\n",
      "Epoch 17/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 706us/step - accuracy: 0.7371 - loss: 0.9256 - val_accuracy: 0.7808 - val_loss: 0.4719\n",
      "Epoch 18/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 704us/step - accuracy: 0.7459 - loss: 0.8142 - val_accuracy: 0.7933 - val_loss: 0.7221\n",
      "Epoch 19/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 800us/step - accuracy: 0.7507 - loss: 0.7156 - val_accuracy: 0.7879 - val_loss: 0.7373\n",
      "Epoch 20/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 719us/step - accuracy: 0.7426 - loss: 0.8200 - val_accuracy: 0.7799 - val_loss: 2.0438\n",
      "Epoch 21/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 698us/step - accuracy: 0.7278 - loss: 1.0050 - val_accuracy: 0.7755 - val_loss: 0.4370\n",
      "Epoch 22/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 747us/step - accuracy: 0.7279 - loss: 1.0186 - val_accuracy: 0.7303 - val_loss: 0.4956\n",
      "Epoch 23/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 654us/step - accuracy: 0.7423 - loss: 0.7487 - val_accuracy: 0.7569 - val_loss: 0.4757\n",
      "Epoch 24/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 683us/step - accuracy: 0.7293 - loss: 0.8086 - val_accuracy: 0.7702 - val_loss: 0.4500\n",
      "Epoch 25/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step - accuracy: 0.7326 - loss: 0.7691 - val_accuracy: 0.7728 - val_loss: 3.1075\n",
      "Epoch 26/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7292 - loss: 1.2239 - val_accuracy: 0.5723 - val_loss: 0.7810\n",
      "Epoch 27/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 729us/step - accuracy: 0.7356 - loss: 0.8190 - val_accuracy: 0.7773 - val_loss: 1.8072\n",
      "Epoch 28/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 743us/step - accuracy: 0.7399 - loss: 0.8484 - val_accuracy: 0.7391 - val_loss: 0.4932\n",
      "Epoch 29/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step - accuracy: 0.7397 - loss: 0.8788 - val_accuracy: 0.5297 - val_loss: 1.1476\n",
      "Epoch 30/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716us/step - accuracy: 0.7283 - loss: 0.7391 - val_accuracy: 0.7249 - val_loss: 0.5198\n",
      "Epoch 31/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step - accuracy: 0.7232 - loss: 0.9281 - val_accuracy: 0.7551 - val_loss: 0.4737\n",
      "Epoch 32/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 0.7281 - loss: 0.6942 - val_accuracy: 0.6522 - val_loss: 0.6133\n",
      "Epoch 33/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step - accuracy: 0.7520 - loss: 0.6881 - val_accuracy: 0.7817 - val_loss: 0.9549\n",
      "Epoch 34/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step - accuracy: 0.7349 - loss: 0.7511 - val_accuracy: 0.7888 - val_loss: 0.8700\n",
      "Epoch 35/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 690us/step - accuracy: 0.7372 - loss: 0.8762 - val_accuracy: 0.7897 - val_loss: 0.8004\n",
      "Epoch 36/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 660us/step - accuracy: 0.7368 - loss: 0.7725 - val_accuracy: 0.7844 - val_loss: 0.5864\n",
      "Epoch 37/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step - accuracy: 0.7459 - loss: 0.7941 - val_accuracy: 0.7844 - val_loss: 0.5578\n",
      "Epoch 38/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 710us/step - accuracy: 0.7391 - loss: 0.8063 - val_accuracy: 0.7950 - val_loss: 0.4905\n",
      "Epoch 39/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step - accuracy: 0.7516 - loss: 0.6977 - val_accuracy: 0.7489 - val_loss: 0.4887\n",
      "Epoch 40/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7389 - loss: 0.8386 - val_accuracy: 0.7817 - val_loss: 0.5126\n",
      "Epoch 41/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 0.7430 - loss: 0.7355 - val_accuracy: 0.7915 - val_loss: 0.6642\n",
      "Epoch 42/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step - accuracy: 0.7583 - loss: 0.6944 - val_accuracy: 0.7604 - val_loss: 0.4478\n",
      "Epoch 43/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 776us/step - accuracy: 0.7388 - loss: 0.8218 - val_accuracy: 0.7613 - val_loss: 0.4459\n",
      "Epoch 44/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step - accuracy: 0.7505 - loss: 0.6770 - val_accuracy: 0.7720 - val_loss: 0.4587\n",
      "Epoch 45/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 679us/step - accuracy: 0.7460 - loss: 0.6995 - val_accuracy: 0.7888 - val_loss: 0.9092\n",
      "Epoch 46/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 725us/step - accuracy: 0.7349 - loss: 0.8351 - val_accuracy: 0.5430 - val_loss: 0.9988\n",
      "Epoch 47/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 674us/step - accuracy: 0.7260 - loss: 0.7857 - val_accuracy: 0.7808 - val_loss: 1.1690\n",
      "Epoch 48/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716us/step - accuracy: 0.7271 - loss: 1.0990 - val_accuracy: 0.7817 - val_loss: 0.5061\n",
      "Epoch 49/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step - accuracy: 0.7439 - loss: 0.7367 - val_accuracy: 0.7649 - val_loss: 0.4496\n",
      "Epoch 50/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 657us/step - accuracy: 0.7394 - loss: 0.6718 - val_accuracy: 0.7649 - val_loss: 0.4531\n",
      "Epoch 51/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 719us/step - accuracy: 0.7395 - loss: 0.7166 - val_accuracy: 0.7915 - val_loss: 0.5938\n",
      "Epoch 52/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7337 - loss: 0.6434 - val_accuracy: 0.7862 - val_loss: 0.8717\n",
      "Epoch 53/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 722us/step - accuracy: 0.7460 - loss: 0.7277 - val_accuracy: 0.7808 - val_loss: 1.6752\n",
      "Epoch 54/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7400 - loss: 0.7765 - val_accuracy: 0.7817 - val_loss: 1.1543\n",
      "Epoch 55/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step - accuracy: 0.7520 - loss: 0.7965 - val_accuracy: 0.7870 - val_loss: 0.7162\n",
      "Epoch 56/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 645us/step - accuracy: 0.7321 - loss: 0.8431 - val_accuracy: 0.7782 - val_loss: 0.4891\n",
      "Epoch 57/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7342 - loss: 0.7631 - val_accuracy: 0.7924 - val_loss: 0.6437\n",
      "Epoch 58/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 696us/step - accuracy: 0.7514 - loss: 0.6763 - val_accuracy: 0.7888 - val_loss: 0.7014\n",
      "Epoch 59/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step - accuracy: 0.7326 - loss: 0.7477 - val_accuracy: 0.7835 - val_loss: 0.9310\n",
      "Epoch 60/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7430 - loss: 0.7016 - val_accuracy: 0.7853 - val_loss: 0.9990\n",
      "Epoch 61/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.7460 - loss: 0.6990 - val_accuracy: 0.6362 - val_loss: 0.6129\n",
      "Epoch 62/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716us/step - accuracy: 0.7508 - loss: 0.6441 - val_accuracy: 0.7507 - val_loss: 0.4830\n",
      "Epoch 63/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step - accuracy: 0.7474 - loss: 0.6156 - val_accuracy: 0.7817 - val_loss: 0.4893\n",
      "Epoch 64/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 647us/step - accuracy: 0.7541 - loss: 0.6481 - val_accuracy: 0.6442 - val_loss: 0.5952\n",
      "Epoch 65/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733us/step - accuracy: 0.7466 - loss: 0.6962 - val_accuracy: 0.7853 - val_loss: 0.4649\n",
      "Epoch 66/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7382 - loss: 0.6912 - val_accuracy: 0.6007 - val_loss: 0.6768\n",
      "Epoch 67/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step - accuracy: 0.7492 - loss: 0.6625 - val_accuracy: 0.7817 - val_loss: 1.1936\n",
      "Epoch 68/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 705us/step - accuracy: 0.7442 - loss: 0.6533 - val_accuracy: 0.7666 - val_loss: 0.4586\n",
      "Epoch 69/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 730us/step - accuracy: 0.7562 - loss: 0.6420 - val_accuracy: 0.7853 - val_loss: 0.9908\n",
      "Epoch 70/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 729us/step - accuracy: 0.7505 - loss: 0.7184 - val_accuracy: 0.7657 - val_loss: 0.4422\n",
      "Epoch 71/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 694us/step - accuracy: 0.7424 - loss: 0.6182 - val_accuracy: 0.7693 - val_loss: 0.4397\n",
      "Epoch 72/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7328 - loss: 0.7800 - val_accuracy: 0.7649 - val_loss: 0.4441\n",
      "Epoch 73/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step - accuracy: 0.7472 - loss: 0.6312 - val_accuracy: 0.7675 - val_loss: 0.4361\n",
      "Epoch 74/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 831us/step - accuracy: 0.7643 - loss: 0.5882 - val_accuracy: 0.7693 - val_loss: 0.4337\n",
      "Epoch 75/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739us/step - accuracy: 0.7572 - loss: 0.6294 - val_accuracy: 0.7888 - val_loss: 0.6543\n",
      "Epoch 76/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7618 - loss: 0.6111 - val_accuracy: 0.7870 - val_loss: 0.6900\n",
      "Epoch 77/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 814us/step - accuracy: 0.7570 - loss: 0.7259 - val_accuracy: 0.7728 - val_loss: 0.4459\n",
      "Epoch 78/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 674us/step - accuracy: 0.7554 - loss: 0.6007 - val_accuracy: 0.7906 - val_loss: 0.6170\n",
      "Epoch 79/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step - accuracy: 0.7464 - loss: 0.7219 - val_accuracy: 0.7933 - val_loss: 0.7551\n",
      "Epoch 80/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733us/step - accuracy: 0.7411 - loss: 0.6304 - val_accuracy: 0.7888 - val_loss: 0.5240\n",
      "Epoch 81/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 639us/step - accuracy: 0.7589 - loss: 0.5515 - val_accuracy: 0.7817 - val_loss: 0.4413\n",
      "Epoch 82/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 703us/step - accuracy: 0.7587 - loss: 0.6316 - val_accuracy: 0.7835 - val_loss: 0.4567\n",
      "Epoch 83/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 671us/step - accuracy: 0.7658 - loss: 0.5919 - val_accuracy: 0.7649 - val_loss: 0.4516\n",
      "Epoch 84/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 644us/step - accuracy: 0.7621 - loss: 0.6361 - val_accuracy: 0.7746 - val_loss: 0.4323\n",
      "Epoch 85/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 654us/step - accuracy: 0.7585 - loss: 0.5573 - val_accuracy: 0.7844 - val_loss: 0.7284\n",
      "Epoch 86/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661us/step - accuracy: 0.7543 - loss: 0.6225 - val_accuracy: 0.7755 - val_loss: 0.4459\n",
      "Epoch 87/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 643us/step - accuracy: 0.7526 - loss: 0.7014 - val_accuracy: 0.7862 - val_loss: 0.4667\n",
      "Epoch 88/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 677us/step - accuracy: 0.7717 - loss: 0.5536 - val_accuracy: 0.7835 - val_loss: 0.4948\n",
      "Epoch 89/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 640us/step - accuracy: 0.7684 - loss: 0.5803 - val_accuracy: 0.7702 - val_loss: 0.4421\n",
      "Epoch 90/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7630 - loss: 0.5713 - val_accuracy: 0.6903 - val_loss: 0.5167\n",
      "Epoch 91/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7479 - loss: 0.6459 - val_accuracy: 0.7684 - val_loss: 0.4446\n",
      "Epoch 92/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 650us/step - accuracy: 0.7612 - loss: 0.5695 - val_accuracy: 0.7915 - val_loss: 0.4835\n",
      "Epoch 93/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 729us/step - accuracy: 0.7498 - loss: 0.6249 - val_accuracy: 0.7853 - val_loss: 0.5258\n",
      "Epoch 94/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 0.7554 - loss: 0.5947 - val_accuracy: 0.7844 - val_loss: 0.4581\n",
      "Epoch 95/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720us/step - accuracy: 0.7755 - loss: 0.5471 - val_accuracy: 0.7924 - val_loss: 0.6112\n",
      "Epoch 96/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 0.7503 - loss: 0.5821 - val_accuracy: 0.7631 - val_loss: 0.4465\n",
      "Epoch 97/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7640 - loss: 0.5540 - val_accuracy: 0.5581 - val_loss: 0.8828\n",
      "Epoch 98/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7426 - loss: 0.7094 - val_accuracy: 0.7666 - val_loss: 0.4509\n",
      "Epoch 99/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 741us/step - accuracy: 0.7481 - loss: 0.7091 - val_accuracy: 0.7675 - val_loss: 0.4376\n",
      "Epoch 100/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step - accuracy: 0.7553 - loss: 0.5929 - val_accuracy: 0.7684 - val_loss: 0.4414\n",
      "Epoch 101/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 668us/step - accuracy: 0.7529 - loss: 0.5968 - val_accuracy: 0.6681 - val_loss: 0.5649\n",
      "Epoch 102/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step - accuracy: 0.7558 - loss: 0.6092 - val_accuracy: 0.6477 - val_loss: 0.5915\n",
      "Epoch 103/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 706us/step - accuracy: 0.7730 - loss: 0.5383 - val_accuracy: 0.7879 - val_loss: 0.5175\n",
      "Epoch 104/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 668us/step - accuracy: 0.7475 - loss: 0.6414 - val_accuracy: 0.5475 - val_loss: 0.9155\n",
      "Epoch 105/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 743us/step - accuracy: 0.7556 - loss: 0.5854 - val_accuracy: 0.6548 - val_loss: 0.5727\n",
      "Epoch 106/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 701us/step - accuracy: 0.7461 - loss: 0.5649 - val_accuracy: 0.7684 - val_loss: 0.4554\n",
      "Epoch 107/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step - accuracy: 0.7722 - loss: 0.5323 - val_accuracy: 0.7897 - val_loss: 0.4742\n",
      "Epoch 108/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 715us/step - accuracy: 0.7552 - loss: 0.5635 - val_accuracy: 0.7711 - val_loss: 0.4520\n",
      "Epoch 109/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 816us/step - accuracy: 0.7656 - loss: 0.5904 - val_accuracy: 0.7906 - val_loss: 0.5028\n",
      "Epoch 110/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 869us/step - accuracy: 0.7572 - loss: 0.5679 - val_accuracy: 0.7826 - val_loss: 0.8047\n",
      "Epoch 111/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 784us/step - accuracy: 0.7491 - loss: 0.6663 - val_accuracy: 0.7808 - val_loss: 0.8529\n",
      "Epoch 112/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step - accuracy: 0.7649 - loss: 0.5612 - val_accuracy: 0.7888 - val_loss: 0.4436\n",
      "Epoch 113/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7603 - loss: 0.5629 - val_accuracy: 0.7933 - val_loss: 0.5186\n",
      "Epoch 114/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 758us/step - accuracy: 0.7653 - loss: 0.5703 - val_accuracy: 0.7906 - val_loss: 0.5883\n",
      "Epoch 115/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 765us/step - accuracy: 0.7678 - loss: 0.6333 - val_accuracy: 0.7853 - val_loss: 0.4602\n",
      "Epoch 116/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7494 - loss: 0.5848 - val_accuracy: 0.7311 - val_loss: 0.4920\n",
      "Epoch 117/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 691us/step - accuracy: 0.7619 - loss: 0.5877 - val_accuracy: 0.7924 - val_loss: 0.5780\n",
      "Epoch 118/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7660 - loss: 0.5366 - val_accuracy: 0.7826 - val_loss: 0.4502\n",
      "Epoch 119/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739us/step - accuracy: 0.7559 - loss: 0.5406 - val_accuracy: 0.7817 - val_loss: 0.4373\n",
      "Epoch 120/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step - accuracy: 0.7586 - loss: 0.5303 - val_accuracy: 0.7791 - val_loss: 0.4332\n",
      "Epoch 121/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.7510 - loss: 0.6525 - val_accuracy: 0.7870 - val_loss: 0.7506\n",
      "Epoch 122/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 762us/step - accuracy: 0.7637 - loss: 0.5533 - val_accuracy: 0.7613 - val_loss: 0.4640\n",
      "Epoch 123/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 785us/step - accuracy: 0.7431 - loss: 0.6143 - val_accuracy: 0.7870 - val_loss: 0.4706\n",
      "Epoch 124/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 778us/step - accuracy: 0.7492 - loss: 0.5888 - val_accuracy: 0.7870 - val_loss: 0.4581\n",
      "Epoch 125/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.7701 - loss: 0.5812 - val_accuracy: 0.7657 - val_loss: 0.4751\n",
      "Epoch 126/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 711us/step - accuracy: 0.7601 - loss: 0.5538 - val_accuracy: 0.7853 - val_loss: 0.4489\n",
      "Epoch 127/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.7663 - loss: 0.5476 - val_accuracy: 0.7560 - val_loss: 0.4812\n",
      "Epoch 128/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 688us/step - accuracy: 0.7666 - loss: 0.5777 - val_accuracy: 0.7178 - val_loss: 0.5074\n",
      "Epoch 129/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720us/step - accuracy: 0.7739 - loss: 0.5109 - val_accuracy: 0.7418 - val_loss: 0.4903\n",
      "Epoch 130/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 750us/step - accuracy: 0.7624 - loss: 0.5877 - val_accuracy: 0.7702 - val_loss: 0.4575\n",
      "Epoch 131/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 691us/step - accuracy: 0.7563 - loss: 0.6180 - val_accuracy: 0.6886 - val_loss: 0.5277\n",
      "Epoch 132/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 676us/step - accuracy: 0.7389 - loss: 0.6031 - val_accuracy: 0.7924 - val_loss: 0.4996\n",
      "Epoch 133/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739us/step - accuracy: 0.7585 - loss: 0.5884 - val_accuracy: 0.7782 - val_loss: 1.0983\n",
      "Epoch 134/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 802us/step - accuracy: 0.7643 - loss: 0.5965 - val_accuracy: 0.7844 - val_loss: 0.4325\n",
      "Epoch 135/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.7600 - loss: 0.5792 - val_accuracy: 0.7826 - val_loss: 0.9968\n",
      "Epoch 136/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 729us/step - accuracy: 0.7766 - loss: 0.5452 - val_accuracy: 0.7604 - val_loss: 0.4750\n",
      "Epoch 137/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 683us/step - accuracy: 0.7751 - loss: 0.5186 - val_accuracy: 0.7959 - val_loss: 0.5379\n",
      "Epoch 138/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 659us/step - accuracy: 0.7524 - loss: 0.5613 - val_accuracy: 0.7897 - val_loss: 0.4820\n",
      "Epoch 139/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step - accuracy: 0.7496 - loss: 0.5670 - val_accuracy: 0.7835 - val_loss: 0.7949\n",
      "Epoch 140/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step - accuracy: 0.7603 - loss: 0.6083 - val_accuracy: 0.7977 - val_loss: 0.5341\n",
      "Epoch 141/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 676us/step - accuracy: 0.7792 - loss: 0.5239 - val_accuracy: 0.7906 - val_loss: 0.4968\n",
      "Epoch 142/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 689us/step - accuracy: 0.7719 - loss: 0.5349 - val_accuracy: 0.7764 - val_loss: 0.4355\n",
      "Epoch 143/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 674us/step - accuracy: 0.7573 - loss: 0.6318 - val_accuracy: 0.7888 - val_loss: 0.6334\n",
      "Epoch 144/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step - accuracy: 0.7644 - loss: 0.5227 - val_accuracy: 0.7862 - val_loss: 0.4413\n",
      "Epoch 145/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step - accuracy: 0.7679 - loss: 0.5203 - val_accuracy: 0.7879 - val_loss: 0.4471\n",
      "Epoch 146/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 689us/step - accuracy: 0.7674 - loss: 0.5466 - val_accuracy: 0.7853 - val_loss: 0.6492\n",
      "Epoch 147/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 677us/step - accuracy: 0.7539 - loss: 0.5754 - val_accuracy: 0.6877 - val_loss: 0.5261\n",
      "Epoch 148/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 0.7653 - loss: 0.5058 - val_accuracy: 0.7125 - val_loss: 0.5076\n",
      "Epoch 149/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 751us/step - accuracy: 0.7651 - loss: 0.5644 - val_accuracy: 0.7347 - val_loss: 0.4941\n",
      "Epoch 150/150\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step - accuracy: 0.7714 - loss: 0.5562 - val_accuracy: 0.7941 - val_loss: 0.5447\n",
      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 795us/step\n",
      "Confusion Matrix with Multiple Features:\n",
      "[[979  57]\n",
      " [237 136]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# C.c\n",
    "# Extract features and target\n",
    "X = df[['tenure', 'MonthlyCharges', 'TotalCharges']]\n",
    "y = df['Churn']\n",
    "\n",
    "# Handle missing values in 'TotalCharges'\n",
    "X['TotalCharges'] = pd.to_numeric(X['TotalCharges'], errors='coerce')\n",
    "X['TotalCharges'].fillna(X['TotalCharges'].mean(), inplace=True)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define the model\n",
    "model_multi = Sequential()\n",
    "model_multi.add(Dense(12, input_dim=3, activation='relu'))\n",
    "model_multi.add(Dense(8, activation='relu'))\n",
    "model_multi.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile \n",
    "model_multi.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Fit\n",
    "history_multi = model_multi.fit(X_train, y_train, epochs=150, batch_size=10, validation_split=0.2)\n",
    "\n",
    "# Predict the values on the test set\n",
    "y_pred_multi = (model_multi.predict(X_test) > 0.5).astype(\"int32\")\n",
    "\n",
    "# confusion matrix\n",
    "cm_multi = confusion_matrix(y_test, y_pred_multi)\n",
    "print(\"Confusion Matrix with Multiple Features:\")\n",
    "print(cm_multi)\n",
    "\n",
    "# Accuracy vs Epochs graph\n",
    "plt.plot(history_multi.history['accuracy'])\n",
    "plt.plot(history_multi.history['val_accuracy'])\n",
    "plt.title('Model accuracy with Multiple Features')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ca4db72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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