InsurancePredictorDL.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6833ff73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d5310bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/admin/Documents/datasets/insurance_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f893c21c",
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>affordibility</th>\n",
       "      <th>bought_insurance</th>\n",
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      "text/plain": [
       "    age  affordibility  bought_insurance\n",
       "0    22              1                 0\n",
       "1    25              0                 0\n",
       "2    47              1                 1\n",
       "3    52              0                 0\n",
       "4    46              1                 1\n",
       "5    56              1                 1\n",
       "6    55              0                 0\n",
       "7    60              0                 1\n",
       "8    62              1                 1\n",
       "9    61              1                 1\n",
       "10   18              1                 0\n",
       "11   28              1                 0\n",
       "12   27              0                 0\n",
       "13   29              0                 0\n",
       "14   49              1                 1\n",
       "15   55              1                 1\n",
       "16   25              0                 1\n",
       "17   58              1                 1\n",
       "18   19              0                 0\n",
       "19   18              1                 0\n",
       "20   21              1                 0\n",
       "21   26              0                 0\n",
       "22   40              1                 1\n",
       "23   45              1                 1\n",
       "24   50              1                 1\n",
       "25   54              1                 1\n",
       "26   23              1                 0\n",
       "27   46              1                 0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "835445e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 3)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5c614bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train , x_test , y_train , y_test = train_test_split(df[['age','affordibility']],df.bought_insurance,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "61d8b821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9479f8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled = x_train.copy()\n",
    "x_train_scaled[\"age\"] = x_train_scaled[\"age\"] / 100\n",
    "\n",
    "x_test_scaled = x_test.copy()\n",
    "x_test_scaled[\"age\"] = x_test_scaled[\"age\"] / 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "20aee0ee",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "  </thead>\n",
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      "text/plain": [
       "     age  affordibility\n",
       "9   0.61              1\n",
       "25  0.54              1\n",
       "8   0.62              1\n",
       "21  0.26              0\n",
       "0   0.22              1\n",
       "12  0.27              0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
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    }
   ],
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    "x_test_scaled"
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   "cell_type": "code",
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    },
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 12ms/step - loss: 0.6220 - accuracy: 0.6818\n",
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     ]
    },
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     "text": [
      "1/1 [==============================] - 0s 9ms/step - loss: 0.6186 - accuracy: 0.6818\n",
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     ]
    },
    {
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     ]
    },
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      "1/1 [==============================] - 0s 10ms/step - loss: 0.6119 - accuracy: 0.6818\n",
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     ]
    },
    {
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     ]
    },
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      "1/1 [==============================] - 0s 10ms/step - loss: 0.5952 - accuracy: 0.6818\n",
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     ]
    },
    {
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     "text": [
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    },
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    },
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      "Epoch 2015/5000\n",
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      "Epoch 2016/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 5ms/step - loss: 0.5789 - accuracy: 0.8182\n",
      "Epoch 2017/5000\n",
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      "1/1 [==============================] - 0s 11ms/step - loss: 0.5788 - accuracy: 0.8182\n",
      "Epoch 2020/5000\n",
      "1/1 [==============================] - 0s 52ms/step - loss: 0.5788 - accuracy: 0.8182\n",
      "Epoch 2021/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.5787 - accuracy: 0.8182\n",
      "Epoch 2022/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.5787 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 14ms/step - loss: 0.5786 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 0.5785 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 9ms/step - loss: 0.5785 - accuracy: 0.8182\n",
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      "Epoch 2030/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.5784 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 9ms/step - loss: 0.5783 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 9ms/step - loss: 0.5781 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 11ms/step - loss: 0.5781 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 9ms/step - loss: 0.5780 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 8ms/step - loss: 0.5780 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 12ms/step - loss: 0.5779 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 14ms/step - loss: 0.5779 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 11ms/step - loss: 0.5778 - accuracy: 0.8182\n",
      "Epoch 2045/5000\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.5778 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 10ms/step - loss: 0.5778 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 0.5777 - accuracy: 0.8182\n",
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      "Epoch 2096/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5758 - accuracy: 0.8182\n",
      "Epoch 2097/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.5758 - accuracy: 0.8182\n",
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      "Epoch 2099/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.5757 - accuracy: 0.8182\n",
      "Epoch 2100/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.5756 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 10ms/step - loss: 0.5756 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.5755 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 9ms/step - loss: 0.5755 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 0.5754 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 8ms/step - loss: 0.5753 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 13ms/step - loss: 0.5753 - accuracy: 0.8182\n",
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     ]
    },
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     ]
    },
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     ]
    },
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      "1/1 [==============================] - 0s 8ms/step - loss: 0.5636 - accuracy: 0.8636\n",
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    },
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     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 10ms/step - loss: 0.5522 - accuracy: 0.8636\n",
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     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 11ms/step - loss: 0.5494 - accuracy: 0.8636\n",
      "Epoch 2818/5000\n",
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     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 10ms/step - loss: 0.5468 - accuracy: 0.8636\n",
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     ]
    },
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     "text": [
      "Epoch 2978/5000\n",
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     ]
    },
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     ]
    },
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     "text": [
      "Epoch 3220/5000\n",
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    },
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     ]
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      "Epoch 3621/5000\n",
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    },
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      "Epoch 4023/5000\n",
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     ]
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     ]
    },
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      "1/1 [==============================] - 0s 8ms/step - loss: 0.4946 - accuracy: 0.9091\n",
      "Epoch 4882/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4946 - accuracy: 0.9091\n",
      "Epoch 4883/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4946 - accuracy: 0.9091\n",
      "Epoch 4884/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4946 - accuracy: 0.9091\n",
      "Epoch 4885/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4946 - accuracy: 0.9091\n",
      "Epoch 4886/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4945 - accuracy: 0.9091\n",
      "Epoch 4887/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4945 - accuracy: 0.9091\n",
      "Epoch 4888/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4945 - accuracy: 0.9091\n",
      "Epoch 4889/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4945 - accuracy: 0.9091\n",
      "Epoch 4890/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4945 - accuracy: 0.9091\n",
      "Epoch 4891/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.4944 - accuracy: 0.9091\n",
      "Epoch 4892/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4944 - accuracy: 0.9091\n",
      "Epoch 4893/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4944 - accuracy: 0.9091\n",
      "Epoch 4894/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.4944 - accuracy: 0.9091\n",
      "Epoch 4895/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4944 - accuracy: 0.9091\n",
      "Epoch 4896/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4943 - accuracy: 0.9091\n",
      "Epoch 4897/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4943 - accuracy: 0.9091\n",
      "Epoch 4898/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4943 - accuracy: 0.9091\n",
      "Epoch 4899/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4943 - accuracy: 0.9091\n",
      "Epoch 4900/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4943 - accuracy: 0.9091\n",
      "Epoch 4901/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4942 - accuracy: 0.9091\n",
      "Epoch 4902/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4942 - accuracy: 0.9091\n",
      "Epoch 4903/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4942 - accuracy: 0.9091\n",
      "Epoch 4904/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4942 - accuracy: 0.9091\n",
      "Epoch 4905/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4942 - accuracy: 0.9091\n",
      "Epoch 4906/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4941 - accuracy: 0.9091\n",
      "Epoch 4907/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4941 - accuracy: 0.9091\n",
      "Epoch 4908/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4941 - accuracy: 0.9091\n",
      "Epoch 4909/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4941 - accuracy: 0.9091\n",
      "Epoch 4910/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4941 - accuracy: 0.9091\n",
      "Epoch 4911/5000\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.4940 - accuracy: 0.9091\n",
      "Epoch 4912/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4940 - accuracy: 0.9091\n",
      "Epoch 4913/5000\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.4940 - accuracy: 0.9091\n",
      "Epoch 4914/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4940 - accuracy: 0.9091\n",
      "Epoch 4915/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4940 - accuracy: 0.9091\n",
      "Epoch 4916/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4939 - accuracy: 0.9091\n",
      "Epoch 4917/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.9091\n",
      "Epoch 4918/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4939 - accuracy: 0.9091\n",
      "Epoch 4919/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4939 - accuracy: 0.9091\n",
      "Epoch 4920/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4939 - accuracy: 0.9091\n",
      "Epoch 4921/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4938 - accuracy: 0.9091\n",
      "Epoch 4922/5000\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4938 - accuracy: 0.9091\n",
      "Epoch 4923/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4938 - accuracy: 0.9091\n",
      "Epoch 4924/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4938 - accuracy: 0.9091\n",
      "Epoch 4925/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4938 - accuracy: 0.9091\n",
      "Epoch 4926/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4937 - accuracy: 0.9091\n",
      "Epoch 4927/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4937 - accuracy: 0.9091\n",
      "Epoch 4928/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4937 - accuracy: 0.9091\n",
      "Epoch 4929/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4937 - accuracy: 0.9091\n",
      "Epoch 4930/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4937 - accuracy: 0.9091\n",
      "Epoch 4931/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4936 - accuracy: 0.9091\n",
      "Epoch 4932/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4936 - accuracy: 0.9091\n",
      "Epoch 4933/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4936 - accuracy: 0.9091\n",
      "Epoch 4934/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4936 - accuracy: 0.9091\n",
      "Epoch 4935/5000\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4936 - accuracy: 0.9091\n",
      "Epoch 4936/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4935 - accuracy: 0.9091\n",
      "Epoch 4937/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4935 - accuracy: 0.9091\n",
      "Epoch 4938/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4935 - accuracy: 0.9091\n",
      "Epoch 4939/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4935 - accuracy: 0.9091\n",
      "Epoch 4940/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4935 - accuracy: 0.9091\n",
      "Epoch 4941/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4934 - accuracy: 0.9091\n",
      "Epoch 4942/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4934 - accuracy: 0.9091\n",
      "Epoch 4943/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4934 - accuracy: 0.9091\n",
      "Epoch 4944/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4934 - accuracy: 0.9091\n",
      "Epoch 4945/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4934 - accuracy: 0.9091\n",
      "Epoch 4946/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4933 - accuracy: 0.9091\n",
      "Epoch 4947/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4933 - accuracy: 0.9091\n",
      "Epoch 4948/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4933 - accuracy: 0.9091\n",
      "Epoch 4949/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4933 - accuracy: 0.9091\n",
      "Epoch 4950/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4933 - accuracy: 0.9091\n",
      "Epoch 4951/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4932 - accuracy: 0.9091\n",
      "Epoch 4952/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4932 - accuracy: 0.9091\n",
      "Epoch 4953/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4932 - accuracy: 0.9091\n",
      "Epoch 4954/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4932 - accuracy: 0.9091\n",
      "Epoch 4955/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4932 - accuracy: 0.9091\n",
      "Epoch 4956/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4931 - accuracy: 0.9091\n",
      "Epoch 4957/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4931 - accuracy: 0.9091\n",
      "Epoch 4958/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4931 - accuracy: 0.9091\n",
      "Epoch 4959/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4931 - accuracy: 0.9091\n",
      "Epoch 4960/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4931 - accuracy: 0.9091\n",
      "Epoch 4961/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4930 - accuracy: 0.9091\n",
      "Epoch 4962/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4930 - accuracy: 0.9091\n",
      "Epoch 4963/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4930 - accuracy: 0.9091\n",
      "Epoch 4964/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4930 - accuracy: 0.9091\n",
      "Epoch 4965/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4930 - accuracy: 0.9091\n",
      "Epoch 4966/5000\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4929 - accuracy: 0.9091\n",
      "Epoch 4967/5000\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4929 - accuracy: 0.9091\n",
      "Epoch 4968/5000\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4929 - accuracy: 0.9091\n",
      "Epoch 4969/5000\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4929 - accuracy: 0.9091\n",
      "Epoch 4970/5000\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4929 - accuracy: 0.9091\n",
      "Epoch 4971/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4929 - accuracy: 0.9091\n",
      "Epoch 4972/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4928 - accuracy: 0.9091\n",
      "Epoch 4973/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4928 - accuracy: 0.9091\n",
      "Epoch 4974/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4928 - accuracy: 0.9091\n",
      "Epoch 4975/5000\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4928 - accuracy: 0.9091\n",
      "Epoch 4976/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4928 - accuracy: 0.9091\n",
      "Epoch 4977/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4927 - accuracy: 0.9091\n",
      "Epoch 4978/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4927 - accuracy: 0.9091\n",
      "Epoch 4979/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4927 - accuracy: 0.9091\n",
      "Epoch 4980/5000\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4927 - accuracy: 0.9091\n",
      "Epoch 4981/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4927 - accuracy: 0.9091\n",
      "Epoch 4982/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4926 - accuracy: 0.9091\n",
      "Epoch 4983/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4926 - accuracy: 0.9091\n",
      "Epoch 4984/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4926 - accuracy: 0.9091\n",
      "Epoch 4985/5000\n",
      "1/1 [==============================] - 0s 89ms/step - loss: 0.4926 - accuracy: 0.9091\n",
      "Epoch 4986/5000\n",
      "1/1 [==============================] - 0s 37ms/step - loss: 0.4926 - accuracy: 0.9091\n",
      "Epoch 4987/5000\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4925 - accuracy: 0.9091\n",
      "Epoch 4988/5000\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4925 - accuracy: 0.9091\n",
      "Epoch 4989/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4925 - accuracy: 0.9091\n",
      "Epoch 4990/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4925 - accuracy: 0.9091\n",
      "Epoch 4991/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4925 - accuracy: 0.9091\n",
      "Epoch 4992/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4924 - accuracy: 0.9091\n",
      "Epoch 4993/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4924 - accuracy: 0.9091\n",
      "Epoch 4994/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4924 - accuracy: 0.9091\n",
      "Epoch 4995/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4924 - accuracy: 0.9091\n",
      "Epoch 4996/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4924 - accuracy: 0.9091\n",
      "Epoch 4997/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4923 - accuracy: 0.9091\n",
      "Epoch 4998/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4923 - accuracy: 0.9091\n",
      "Epoch 4999/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4923 - accuracy: 0.9091\n",
      "Epoch 5000/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4923 - accuracy: 0.9091\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd3b41a7070>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(1,input_shape=(2,),activation='sigmoid',kernel_initializer='ones',bias_initializer='zeros')\n",
    "])\n",
    "\n",
    "model.compile(optimizer = 'adam',\n",
    "              loss = 'binary_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "model.fit(x_train_scaled,y_train,epochs=5000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "f9e8e7a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 22ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.81626725],\n",
       "       [0.7576756 ],\n",
       "       [0.8236743 ],\n",
       "       [0.1881953 ],\n",
       "       [0.38559118],\n",
       "       [0.19598241]], dtype=float32)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8942f749",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0b86a6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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