student-study-hours-linear-regression.ipynb

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    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import plotly.express as px"
   ]
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    "df = pd.read_csv('../input/student-study-hours/score.csv')"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
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       "      <td>75</td>\n",
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       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
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    "### Assign x , y"
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    "x=df.iloc[:,:-1].values\n",
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       "       [9.2],\n",
       "       [5.5],\n",
       "       [8.3],\n",
       "       [2.7],\n",
       "       [7.7],\n",
       "       [5.9],\n",
       "       [4.5],\n",
       "       [3.3],\n",
       "       [1.1],\n",
       "       [8.9],\n",
       "       [2.5],\n",
       "       [1.9],\n",
       "       [6.1],\n",
       "       [7.4],\n",
       "       [2.7],\n",
       "       [4.8],\n",
       "       [3.8],\n",
       "       [6.9],\n",
       "       [7.8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "377c6f10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:41.828944Z",
     "iopub.status.busy": "2023-03-04T11:21:41.828287Z",
     "iopub.status.idle": "2023-03-04T11:21:41.835415Z",
     "shell.execute_reply": "2023-03-04T11:21:41.834446Z"
    },
    "papermill": {
     "duration": 0.019266,
     "end_time": "2023-03-04T11:21:41.837711",
     "exception": false,
     "start_time": "2023-03-04T11:21:41.818445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
       "       24, 67, 69, 30, 54, 35, 76, 86])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee8af24",
   "metadata": {
    "papermill": {
     "duration": 0.007931,
     "end_time": "2023-03-04T11:21:41.853761",
     "exception": false,
     "start_time": "2023-03-04T11:21:41.845830",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43caad92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:41.872663Z",
     "iopub.status.busy": "2023-03-04T11:21:41.871443Z",
     "iopub.status.idle": "2023-03-04T11:21:42.110884Z",
     "shell.execute_reply": "2023-03-04T11:21:42.109200Z"
    },
    "papermill": {
     "duration": 0.252395,
     "end_time": "2023-03-04T11:21:42.114117",
     "exception": false,
     "start_time": "2023-03-04T11:21:41.861722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train ,x_test ,y_train ,y_test =train_test_split(x,y,test_size=.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ebe01b3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.133032Z",
     "iopub.status.busy": "2023-03-04T11:21:42.131895Z",
     "iopub.status.idle": "2023-03-04T11:21:42.138820Z",
     "shell.execute_reply": "2023-03-04T11:21:42.137887Z"
    },
    "papermill": {
     "duration": 0.01874,
     "end_time": "2023-03-04T11:21:42.141016",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.122276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.9],\n",
       "       [1.9],\n",
       "       [5.1],\n",
       "       [6.1],\n",
       "       [5.5],\n",
       "       [1.1],\n",
       "       [8.5],\n",
       "       [9.2],\n",
       "       [3.2],\n",
       "       [6.9],\n",
       "       [8.9],\n",
       "       [4.5],\n",
       "       [2.7],\n",
       "       [1.5],\n",
       "       [2.5],\n",
       "       [3.5],\n",
       "       [7.7],\n",
       "       [2.5],\n",
       "       [4.8],\n",
       "       [3.3]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37a716cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.159970Z",
     "iopub.status.busy": "2023-03-04T11:21:42.159463Z",
     "iopub.status.idle": "2023-03-04T11:21:42.167725Z",
     "shell.execute_reply": "2023-03-04T11:21:42.166369Z"
    },
    "papermill": {
     "duration": 0.020969,
     "end_time": "2023-03-04T11:21:42.170206",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.149237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.3],\n",
       "       [7.8],\n",
       "       [2.7],\n",
       "       [7.4],\n",
       "       [3.8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8754f9",
   "metadata": {
    "papermill": {
     "duration": 0.008004,
     "end_time": "2023-03-04T11:21:42.187034",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.179030",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "476566d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.206129Z",
     "iopub.status.busy": "2023-03-04T11:21:42.205393Z",
     "iopub.status.idle": "2023-03-04T11:21:42.308927Z",
     "shell.execute_reply": "2023-03-04T11:21:42.307609Z"
    },
    "papermill": {
     "duration": 0.116605,
     "end_time": "2023-03-04T11:21:42.311998",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.195393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82e39f42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.331487Z",
     "iopub.status.busy": "2023-03-04T11:21:42.330276Z",
     "iopub.status.idle": "2023-03-04T11:21:42.359707Z",
     "shell.execute_reply": "2023-03-04T11:21:42.358299Z"
    },
    "papermill": {
     "duration": 0.041959,
     "end_time": "2023-03-04T11:21:42.362322",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.320363",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "129a9668",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.384088Z",
     "iopub.status.busy": "2023-03-04T11:21:42.383471Z",
     "iopub.status.idle": "2023-03-04T11:21:42.391438Z",
     "shell.execute_reply": "2023-03-04T11:21:42.390327Z"
    },
    "papermill": {
     "duration": 0.020844,
     "end_time": "2023-03-04T11:21:42.393866",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.373022",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([81, 86, 30, 69, 35])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81a99fc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.414024Z",
     "iopub.status.busy": "2023-03-04T11:21:42.412756Z",
     "iopub.status.idle": "2023-03-04T11:21:42.421771Z",
     "shell.execute_reply": "2023-03-04T11:21:42.420527Z"
    },
    "papermill": {
     "duration": 0.022003,
     "end_time": "2023-03-04T11:21:42.424456",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.402453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([83.8696704 , 78.98004233, 29.10583611, 75.06833989, 39.86301784])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predect=model.predict(x_test)\n",
    "y_predect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f386d3",
   "metadata": {
    "papermill": {
     "duration": 0.008542,
     "end_time": "2023-03-04T11:21:42.441961",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.433419",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3744ca7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.461962Z",
     "iopub.status.busy": "2023-03-04T11:21:42.460956Z",
     "iopub.status.idle": "2023-03-04T11:21:42.750888Z",
     "shell.execute_reply": "2023-03-04T11:21:42.749535Z"
    },
    "papermill": {
     "duration": 0.303081,
     "end_time": "2023-03-04T11:21:42.753759",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.450678",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1b3db4ce50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_train , y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d1ec6f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:42.774000Z",
     "iopub.status.busy": "2023-03-04T11:21:42.773523Z",
     "iopub.status.idle": "2023-03-04T11:21:43.060880Z",
     "shell.execute_reply": "2023-03-04T11:21:43.059674Z"
    },
    "papermill": {
     "duration": 0.300576,
     "end_time": "2023-03-04T11:21:43.063561",
     "exception": false,
     "start_time": "2023-03-04T11:21:42.762985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1b3417b450>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_train,y_train)\n",
    "plt.plot(x_train,model.predict(x_train),color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f185aaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:43.084783Z",
     "iopub.status.busy": "2023-03-04T11:21:43.084327Z",
     "iopub.status.idle": "2023-03-04T11:21:43.284153Z",
     "shell.execute_reply": "2023-03-04T11:21:43.282686Z"
    },
    "papermill": {
     "duration": 0.213802,
     "end_time": "2023-03-04T11:21:43.287032",
     "exception": false,
     "start_time": "2023-03-04T11:21:43.073230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1b34110e90>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9UjgAIAwslsDgYRgED/SqkMJHdXW16uvrfVtlZaUk6a677pIklZWVqby8XBUVFaqurpbT6VR+fr6amprCXzkAoPvefDNwzPLNbzJmQURYDKP7/6cVFRXptdde04F/HhXtcrlUVFSkxYsXS5JaW1vlcDi0fPlyzZs376Je0+v1ym63y+PxKDU1tbulAQC6Euyg0mPHJLs94qUgfoTy/d3tA07b2tq0Zs0azZ07VxaLRbW1tXK73SooKPDtY7VaNWnSJG3fvr27bwMACBfD6PpsFoIHIqjb4WPDhg06duyYZs+eLUlyu92SJIfD4befw+HwPRdMa2urvF6v3wYACLPNm6U+5/zK/+53e33M0t5h6K33P9HG3R/rrfc/UXsHYx304GyXX/7yl5oyZYpc51x0xnJOqjYMI2DtbKWlpXrssce6WwYA4EKC/Q4+flwaOLBX33bz3no99uo+1XtafGsZ9gFaNm20bs3J6NX3RnTrVufjww8/1Ouvv67777/ft+Z0OiUpoMvR0NAQ0A0525IlS+TxeHxbXV1dd0oCAJzrfGOWCASPB9fs8gsekuT2tOjBNbu0eW99r74/olu3wseqVauUnp6uqVOn+taysrLkdDp9Z8BInceFVFVVKS8vr8vXslqtSk1N9dsAAD308suBY5aysoiczdLeYeixV/cp2DudWXvs1X2MYBJYyGOXjo4OrVq1SrNmzVLfvp/+uMViUVFRkUpKSpSdna3s7GyVlJQoJSVFM2bMCGvRAIDzCNbtaGmRrNaIvP2O2qMBHY+zGZLqPS3aUXtUuSMGRaQmRJeQw8frr7+uv/3tb5o7d27Ac4sWLVJzc7MKCwvV2Nio8ePHa+vWrbLZbGEpFgBwHu3tUt8gv9YjfO2Ohqaug0d39kP8CXnsUlBQIMMwNHLkyIDnLBaLiouLVV9fr5aWFlVVVSknJycshQIAzuP55wODx4oVplw0LN02IKz7If5wbxcAiHXBxiynTgXvgkTAuKw0ZdgHyO1pCXrch0WS0z5A47LSIl0aogR3tQWAWHXqVNdns5gUPCQpqY9Fy6aNltQZNM525vGyaaOV1KfryzAgvhE+ACAW/eQnUv/+/msvvBA192a5NSdDK2beIKfdf7TitA/Qipk3cJ2PBMfYBQBiTbBuR3t74Km1Jrs1J0P5o53aUXtUDU0tSrd1jlroeIDwAQCxoqVFSk4OXI+SbkcwSX0snE6LANEVkwEAwZWWBgaP9eujOngAXaHzAQDRLtiYpaMj+DoQA+h8AEC0On6867NZCB6IYYQPAIhGS5dK514deutWxiyIC4xdACDaMGZBnKPzAQDR4tixwIDRty9jFsQdwgcARIP586XLLvNf27at8yqmQJxh7AIAZuvqoFIgTtH5AACzNDQEBo/0dIIH4h7hAwDMMHOm5HD4r737rnTkiDn1ABHE2AUAIo0xCxIcnQ8AiJSPPgoMHlddRfBAwiF8AEAk3H67lJnpv1ZTI+3bZ049gIkYuwBAb2PMAvih8wEAveX99wODx4QJBA8kPDofANAb8vKkt97yX3v/fWn4cHPqAaII4QMAwo0xC3BejF0AIFxqagKDx9SpBA/gHHQ+ACAcrrxS2r/ff+2jj6QrrjCnHiCKET4AoKcYswAhYewCAN31+uuBwWPmTIIHcAF0PgCgO4J1O44c6bwxHIDzInwAQKgYswA9wtgFAC7Whg2BwSM3l+ABhIjOBwBcDMYsQNgQPgDgfAxD6hOkSUy3A+g2xi4A0JUXXggMHv/2bwQPoIdCDh8ff/yxZs6cqUGDBiklJUXXXXed3n33Xd/zhmGouLhYLpdLycnJmjx5smpqasJaNAD0OotFuu8+/7Vjx6SNG00pB4gnIYWPxsZGTZgwQf369dOmTZu0b98+Pf3007r00kt9+5SVlam8vFwVFRWqrq6W0+lUfn6+mpqawl07AISfYXR9NovdHvl6gDgUUvhYvny5MjMztWrVKo0bN07Dhg3TzTffrBEjRkjq7Ho888wzWrp0qaZPn66cnBytXr1aJ0+e1Nq1a3vlAwBA2JSWBo5ZZs9mzAKEWUjh45VXXtGNN96ou+66S+np6br++uv17LPP+p6vra2V2+1WQUGBb81qtWrSpEnavn170NdsbW2V1+v12wAg4iwW6dFH/deamqRVq8ypB4hjIYWPQ4cOacWKFcrOztaWLVv0wAMP6Jvf/Kaef/55SZLb7ZYkORwOv59zOBy+585VWloqu93u2zIzM7vzOQCge06f7nrMcsklka8HSAAhhY+Ojg7dcMMNKikp0fXXX6958+bp61//ulasWOG3n+WcP8iGYQSsnbFkyRJ5PB7fVldXF+JHAIBuWrRI6tfPf40xC9DrQrrOR0ZGhkaPHu23dtVVV+nll1+WJDmdTkmdHZCMjAzfPg0NDQHdkDOsVqusVmtIRQNAjwX7C1FLi8TvI6DXhdT5mDBhgvbv3++39t5772no0KGSpKysLDmdTlVWVvqeb2trU1VVlfLy8sJQLgD0UFtb12MWggcQESGFj0ceeURvv/22SkpKdPDgQa1du1YrV67U/PnzJXWOW4qKilRSUqL169dr7969mj17tlJSUjRjxoxe+QAAcNG+9rXAgPEf/8GYBYiwkMYuY8eO1fr167VkyRI9/vjjysrK0jPPPKN7773Xt8+iRYvU3NyswsJCNTY2avz48dq6datsNlvYiweAixas23HqlNSXu0wAkWYxjOiK/F6vV3a7XR6PR6mpqWaXAyDWnTgR/KyV6PrVB8S8UL6/ubcLgPh1552BwePJJwkegMnoNwKIT8HGLO3twe9QCyCi+FMIIL40NnZ9NgvBA4gK/EkEED8mTJDS0vzXfvITxixAlGHsAiA+BOt2dHQEXwdgKjofAGLbkSNdj1kIHkBUInwAiF0jR0r/vK2Dz5o1jFmAKMfYBUBs6qrbASDq0fkAEFs+/JDgAcQ4wgeA2JGaKg0b5r/2yisEDyDGMHYBEBvodgBxg84HgOj2v/9L8ADiDOEDQPSyWKSrrvJf+8MfCB5AjGPsAiA60e0A4hadDwDRZdcuggcQ5wgfAKKHxSJ97nP+azt2EDyAOMPYBUB0oNsBJAw6HwDM9f/+H8EDSDB0PgCYJ1joqKmRRo+OfC0AIobwAcAcdDuAhMXYBUBk/fd/BwYPq5XgASQQOh8AIidYt+PQISkrK/K1ADAN4QNAZDBmAfBPjF0A9K6XXgoMHkOGEDyABEbnA0DvCdbtOHxYysiIfC0AogbhA0D4GYbUJ0hjlW4HADF2ARBuK1cGBo8bbyR4APCh8wEgfIKNWT75REpLi3wtAKIW4QNAzzFmARACxi4AeqasLDB4TJlC8ADQJTofALov2JjF65VstsjXAiBmhNT5KC4ulsVi8ducTqfvecMwVFxcLJfLpeTkZE2ePFk1NTVhLxqAydrbu75oGMEDwAWEPHYZM2aM6uvrfduePXt8z5WVlam8vFwVFRWqrq6W0+lUfn6+mpqawlo0ABMtWSL1Padp+tWvMmYBcNFCHrv07dvXr9txhmEYeuaZZ7R06VJNnz5dkrR69Wo5HA6tXbtW8+bN63m1AMwVrNvR3CwNGBD5WgDErJA7HwcOHJDL5VJWVpbuvvtuHTp0SJJUW1srt9utgoIC375Wq1WTJk3S9u3bu3y91tZWeb1evw1AlGlr63rMQvAAEKKQwsf48eP1/PPPa8uWLXr22WfldruVl5enTz75RG63W5LkcDj8fsbhcPieC6a0tFR2u923ZWZmduNjAOg18+Z13vL+bEVFjFkAdFtIY5cpU6b4/vnqq69Wbm6uRowYodWrV+umm26SJFnO+duRYRgBa2dbsmSJFi5c6Hvs9XoJIEC0CPZn99SpwGM+ACAEPbrOx8CBA3X11VfrwIEDvuNAzu1yNDQ0BHRDzma1WpWamuq3ATDZ8eNdj1kIHgB6qEfho7W1VX/961+VkZGhrKwsOZ1OVVZW+p5va2tTVVWV8vLyelwogAi5+urA02W/8Q3GLADCJqS/wnzrW9/StGnTNGTIEDU0NOjJJ5+U1+vVrFmzZLFYVFRUpJKSEmVnZys7O1slJSVKSUnRjBkzeqt+AOEUrNvR3h780ukA0E0hhY+PPvpI99xzj/7xj3/oM5/5jG666Sa9/fbbGjp0qCRp0aJFam5uVmFhoRobGzV+/Hht3bpVNi46BES3Tz6RLr88cJ1uB4BeYDGM6Prt4vV6Zbfb5fF4OP4DiIRBg6SjR/3XHn1Ueuopc+oBEJNC+f7myDEgkQUbs3R0BF8HgDBhkAskosOHuz6bheABoJcRPoBEY7FIV1zhv/b00xzfASBiGLsAiaSrbgcARBCdDyARHDxI8AAQNQgfQLyzWKTsbP+1X/6S4AHANIxdgHhGtwNAFKLzAcSjP/+Z4AEgahE+gHhjsUjXXee/9tvfEjwARA3GLkA8odsBIAbQ+QDiwf/8D8EDQMwgfACxzmKRJk70X3v9dYIHgKjF2AWIZXQ7AMQgOh9ALNq8meABIGbR+QBiTbDQ8c470rhxka8FALqB8AHEErodAOIAYxcgFqxbR/AAEDfofADRLljoqKmRRo+OfC0AEAaEDyCa0e0AEIcYuwDR6Oc/J3gAiFt0PoBoEyx01NZKw4ZFvBQA6A2EDyCa0O0AkAAYuwDRoLQ0MHgkJRE8AMQlOh+A2YJ1O+rrJacz8rUAQAQQPgCzGIbUJ0jzkW4HgDjH2AUww3/8R2DwyMwkeABICHQ+gEgLNmZpbJQuvTTipQCAGQgfQKS0t0t9g/yRo9sBIMEwdgEiYdaswOAxdizBA0BCovMB9LZgY5YTJ6SUlMjXAgBRgPAB9Ja2NslqDVyn2wEgwTF2AXrDlCmBwWPaNIIHAKiH4aO0tFQWi0VFRUW+NcMwVFxcLJfLpeTkZE2ePFk1NTU9rROIHRaLtHmz/1prq/TKK+bUAwBRptvho7q6WitXrtQ111zjt15WVqby8nJVVFSourpaTqdT+fn5ampq6nGxQFQ7caLre7P07x/5egAgSnUrfBw/flz33nuvnn32WV122WW+dcMw9Mwzz2jp0qWaPn26cnJytHr1ap08eVJr164NW9FA1Ln+eumSS/zXvvY1xiwAEES3wsf8+fM1depU3XLLLX7rtbW1crvdKigo8K1ZrVZNmjRJ27dvD/para2t8nq9fhsQUywWafdu/7X2dukXvzClHACIdiGHj3Xr1mnXrl0qLS0NeM7tdkuSHA6H37rD4fA9d67S0lLZ7XbflpmZGWpJgDkaG7seswS7ZwsAQFKI4aOurk4LFizQmjVrNGDAgC73s5zzC9kwjIC1M5YsWSKPx+Pb6urqQikJMEdGhpSW5r+2eDFjFgC4CCFd5+Pdd99VQ0ODPve5z/nW2tvb9eabb6qiokL79++X1NkBycjI8O3T0NAQ0A05w2q1yhrsWghAtAoWpDs6gq8DAAKE1Pm4+eabtWfPHu3evdu33Xjjjbr33nu1e/duDR8+XE6nU5WVlb6faWtrU1VVlfLy8sJePBBR9fVdj1kIHgBw0ULqfNhsNuXk5PitDRw4UIMGDfKtFxUVqaSkRNnZ2crOzlZJSYlSUlI0Y8aM8FUNRFqwcLF8ubRoUeRrAYAYF/bLqy9atEjNzc0qLCxUY2Ojxo8fr61bt8pms4X7rYDI6KrbAQDoFothRNdvUa/XK7vdLo/Ho9TUVLPLQSI7dEgaMSJwPbr+yABAVAjl+5vzAYFgLJbA4PHsswQPAAgD7moLnIsxCwD0KjofwBl79xI8ACACCB+A1Bk6rr7af+03vyF4AEAvYOwC0O0AgIii84HE9fbbBA8AMAHhA4nJYpFyc/3XtmwheABABDB2QeKh2wEApqLzgcSxdSvBAwCiAJ0PJIZgoeOtt6SbburRy7Z3GNpRe1QNTS1Ktw3QuKw0JfXhJnMAcD6ED8S/Xup2bN5br8de3ad6T4tvLcM+QMumjdatORk9fn0AiFeMXRC//uu/ejV4PLhml1/wkCS3p0UPrtmlzXvre/weABCvCB+ITxaLdNdd/mt79oQleLR3GHrs1X0K9kpn1h57dZ/aOziWBACCIXwg/nTV7cjJCcvL76g9GtDx8HsrSfWeFu2oPRqW9wOAeEP4QPz41a8icjZLQ1PXwaM7+wFAouGAU8SHYKHj/fel4cPD/lbptgFh3Q8AEg3hA7EvwtfuGJeVpgz7ALk9LUGP+7BIcto7T7sFAARi7ILY9etfBwaP9PRev2hYUh+Llk0bLakzaJztzONl00ZzvQ8A6ALhA7HJYpG+8hX/NbdbOnIkIm9/a06GVsy8QU67/2jFaR+gFTNv4DofAHAejF0QWwxD6hMkM5twifRbczKUP9rJFU4BIER0PhA7fvnLwODxla+Yem+WpD4W5Y4YpDuuu0K5IwYRPADgItD5QGwIdlBpU5N0ySWRrwUA0COED0S3jg4pKSlwnTvRAkDMYuyC6FVeHhg8HnyQ4AEAMY7OB6JTsDFLc7M0gAt3AUCsI3wgupw+LfXrF7hOtwMA4gZjF0SP738/MHgsXUrwAIA4Q+cD0SHYmKWtLXgXBAAQ0+h8wFytrV3fm4XgAQBxifAB8zz8cOABpGVljFkAIM4xdkHYtHcYF3+p8WDdjvb24JdOBwDElZB+069YsULXXHONUlNTlZqaqtzcXG3atMn3vGEYKi4ulsvlUnJysiZPnqyampqwF43os3lvvSYu/4PuefZtLVi3W/c8+7YmLv+DNu+t99/x+PGuxywEDwBICCH9th88eLB+8IMfaOfOndq5c6f+9V//VXfccYcvYJSVlam8vFwVFRWqrq6W0+lUfn6+mpqaeqV4RIfNe+v14Jpdqve0+K27PS16cM2uTwPIzJmSzeb/wytXMmYBgARjMYye/eZPS0vTD3/4Q82dO1cul0tFRUVavHixJKm1tVUOh0PLly/XvHnzLur1vF6v7Ha7PB6PUlNTe1IaIqC9w9DE5X8ICB5nWNR5m/m3Hr0l8MmOjuBdEABAzAnl+7vbfe729natW7dOJ06cUG5urmpra+V2u1VQUODbx2q1atKkSdq+fXuXr9Pa2iqv1+u3IXbsqD3aZfCQJFvL8eDBwzAIHgCQoEIOH3v27NEll1wiq9WqBx54QOvXr9fo0aPldrslSQ6Hw29/h8Phey6Y0tJS2e1235aZmRlqSTBRQ1PXweO5Xy/TX350t//iiy8yZgGABBfy2S6jRo3S7t27dezYMb388suaNWuWqqqqfM9bzvnbrGEYAWtnW7JkiRYuXOh77PV6CSAxJN0W/F4rHyy/PXCR0AEAUDfCR//+/fXZz35WknTjjTequrpaP/rRj3zHebjdbmVkZPj2b2hoCOiGnM1qtcpqtYZaBqLEuKw0ZdgHyO1pkSHp8hON2lnx1YD92ts7lBT44wCABNTjcxsNw1Bra6uysrLkdDpVWVnpe66trU1VVVXKy8vr6dsgSiX1sWjZtNGSpI2rHwkIHnP/fZk27znc9fU+AAAJJ6TOx6OPPqopU6YoMzNTTU1NWrdund544w1t3rxZFotFRUVFKikpUXZ2trKzs1VSUqKUlBTNmDGjt+pHFLg1J0O1QcYsuSWva9m00bo1JyPITwEAElVI4ePIkSP66le/qvr6etntdl1zzTXavHmz8vPzJUmLFi1Sc3OzCgsL1djYqPHjx2vr1q2ynXttB8SPDz+Uhg0LWH7r4D+07XxXOAUAJKweX+cj3LjORwwZNqwzfJztjTekSZPMqAYAYKJQvr+5twu6p6tLpAMAcAHcTAOh2b+f4AEA6BHCBy5eSop05ZX+a9XVBA8AQEgYu+Di0O0AAIQJnQ+c34EDgcGjTx+CBwCg2wgf6NrYsdLIkf5rNTVSe7s59QAA4gJjFwTHmAUA0EvofMDfnj2BwWP6dIIHACBs6HzgU5/9rPT++/5rhw9LGVweHQAQPoQPdGLMAgCIEMYuia66OjB4zJlD8AAA9Bo6H4nsssukY8f81/7+d+nyy00pBwCQGAgfiYoxCwDAJIxdEk1VVWDwKCoieAAAIobORyIJ1u3weKQL3PoYAIBwInwkAsPovCR6sHUAACKMsUu827QpMHgsW0bwAACYhs5HPAs2ZjlxQkpJiXwtAAD8E+EjHjFmAQBEMcYu8ea//isweDz9NMEDABA16HzEk2BjlpYWyWqNfC0AAHSB8BEP2tulvkH+U9LtAABEIcYuse655wKDx89/TvAAAEQtOh+xLNiY5dSp4F0QAACiBJ2PWHTqVNf3ZiF4AACiHOEj1lRUSP37+6/96leMWQAAMYO/JseSYN2O9vbg1/QAACBK8a0VC5qbux6zEDwAADGGb65oV1ISeDn0jRsZswAAYhZjl2gWrNvR0RF8HQCAGEHnIxq1tHQ9ZiF4AABiHOEj2vzqV1Jysv/atm2MWQAAcSOk8FFaWqqxY8fKZrMpPT1dd955p/bv3++3j2EYKi4ulsvlUnJysiZPnqyampqwFh23LBZp5kz/NcOQJkwwpx4AAHpBSOGjqqpK8+fP19tvv63KykqdPn1aBQUFOnHihG+fsrIylZeXq6KiQtXV1XI6ncrPz1dTU1PYi48bJ04EjlNmzKDbAQCISxbD6P433N///nelp6erqqpKn//852UYhlwul4qKirR48WJJUmtrqxwOh5YvX6558+Zd8DW9Xq/sdrs8Ho9SU1O7W1rs+MUvpK9/3X9t/35p5Ehz6gEAoBtC+f7u0dkuHo9HkpSWliZJqq2tldvtVkFBgW8fq9WqSZMmafv27UHDR2trq1pbW/2KTxhdHVQKAEAc6/YBp4ZhaOHChZo4caJycnIkSW63W5LkcDj89nU4HL7nzlVaWiq73e7bMjMzu1tS7PB4AoPHvHkEDwBAQuh2+HjooYf0l7/8RS+++GLAc5ZzvlgNwwhYO2PJkiXyeDy+ra6urrslxYYf/Ui69FL/tUOHpJ/9zJRyAACItG6NXR5++GG98sorevPNNzV48GDfutPplNTZAcnIyPCtNzQ0BHRDzrBarbJard0pI/YwZgEAILTOh2EYeuihh/Tb3/5Wf/jDH5SVleX3fFZWlpxOpyorK31rbW1tqqqqUl5eXngqjkWffBIYPBYuJHgAABJSSJ2P+fPna+3atdq4caNsNpvvOA673a7k5GRZLBYVFRWppKRE2dnZys7OVklJiVJSUjRjxoxe+QBRr7RUevRR/7WPPpKuuMKcegAAMFlI4WPFihWSpMmTJ/utr1q1SrNnz5YkLVq0SM3NzSosLFRjY6PGjx+vrVu3ymazhaXgmMKYBQCAAD26zkdviIvrfLjd0lnHvEiSli2TiotNKQcAgN4Wset8IIilS6WSEv+1I0ek9HRz6gEAIMoQPsKJMQsAABfEXW3Doa4uMHiUlRE8AAAIgs5HT23aJN12m//aJ59I/7zkPAAA8Ef46Ik77pBeecV/jW4HAADnRfjojqNHpUGD/NfeeUcaN86cegAAiCEc8xGqDRsCg0dLC8EDAICLRPgIRX6+9MUvfvp40aLOMUui3JsGAIAwYOxyMf7+98DrdOzaJV1/vTn1AAAQw+h8XMivf+0fPPr2ldraCB4AAHQT4aMrhiFNnCh95Sufri1bJp06JfXrZ15dAADEOMYuwdTXSy6X/9qePVJOjjn1AAAQR+h8nOuFF/yDh83W2e0geAAAEBaEjzMMQ7rhBum++z5dKy2VvN7O4zwAAEBY8K0qSR99JGVm+q/97/9Ko0aZUw8AAHGMzscvfuEfPJxO6fRpggcAAL0kccOHYUhXXil9/eufrpWXdx5smpRkXl0AAMS5xBy7fPCBlJXlv3bwoDRihCnlAACQSBKv8/GTn/gHj6wsqb2d4AEAQIQkVvi4/XbpoYc+ffzTn0qHDkl9EutfAwAAZkqcsYthSL/73aePP/hAGjrUtHIAAEhUifNXfotF2rxZqqiQOjoIHgAAmCRxOh+S9H/+T+cGAABMkzidDwAAEBUIHwAAIKIIHwAAIKIIHwAAIKIIHwAAIKIIHwAAIKIIHwAAIKIIHwAAIKJCDh9vvvmmpk2bJpfLJYvFog0bNvg9bxiGiouL5XK5lJycrMmTJ6umpiZc9QIAgBgXcvg4ceKErr32WlVUVAR9vqysTOXl5aqoqFB1dbWcTqfy8/PV1NTU42IBAEDsC/ny6lOmTNGUKVOCPmcYhp555hktXbpU06dPlyStXr1aDodDa9eu1bx583pWLQAAiHlhPeajtrZWbrdbBQUFvjWr1apJkyZp+/btQX+mtbVVXq/XbwMAAPErrOHD7XZLkhwOh9+6w+HwPXeu0tJS2e1235aZmRnOkgAAQJTplbvaWiwWv8eGYQSsnbFkyRItXLjQ99jj8WjIkCF0QAAAiCFnvrcNw7jgvmENH06nU1JnByQjI8O33tDQENANOcNqtcpqtfoenymeDggAALGnqalJdrv9vPuENXxkZWXJ6XSqsrJS119/vSSpra1NVVVVWr58+UW9hsvlUl1dnWw2W5fdkgvxer3KzMxUXV2dUlNTu/Ua0YzPF7vi+bNJfL5YFs+fTeLzRYJhGGpqapLL5brgviGHj+PHj+vgwYO+x7W1tdq9e7fS0tI0ZMgQFRUVqaSkRNnZ2crOzlZJSYlSUlI0Y8aMi3r9Pn36aPDgwaGWFVRqampc/k92Bp8vdsXzZ5P4fLEsnj+bxOfrbRfqeJwRcvjYuXOnvvCFL/genzleY9asWXruuee0aNEiNTc3q7CwUI2NjRo/fry2bt0qm80W6lsBAIA4FHL4mDx58nkPJrFYLCouLlZxcXFP6gIAAHEqLu/tYrVatWzZMr8DWeMJny92xfNnk/h8sSyeP5vE54s2FuNizokBAAAIk7jsfAAAgOhF+AAAABFF+AAAABFF+AAAABEVd+HjzTff1LRp0+RyuWSxWLRhwwazSwqb0tJSjR07VjabTenp6brzzju1f/9+s8sKixUrVuiaa67xXSAnNzdXmzZtMrusXlNaWiqLxaKioiKzSwmL4uJiWSwWv+3M7Rbiwccff6yZM2dq0KBBSklJ0XXXXad3333X7LLCYtiwYQH/7SwWi+bPn292aWFx+vRpffe731VWVpaSk5M1fPhwPf744+ro6DC7tLBoampSUVGRhg4dquTkZOXl5am6utrssi6oV24sZ6YTJ07o2muv1Zw5c/SlL33J7HLCqqqqSvPnz9fYsWN1+vRpLV26VAUFBdq3b58GDhxodnk9MnjwYP3gBz/QZz/7WUnS6tWrdccdd+hPf/qTxowZY3J14VVdXa2VK1fqmmuuMbuUsBozZoxef/113+OkpCQTqwmfxsZGTZgwQV/4whe0adMmpaen6/3339ell15qdmlhUV1drfb2dt/jvXv3Kj8/X3fddZeJVYXP8uXL9bOf/UyrV6/WmDFjtHPnTs2ZM0d2u10LFiwwu7weu//++7V371698MILcrlcWrNmjW655Rbt27dPV1xxhdnldc2IY5KM9evXm11Gr2loaDAkGVVVVWaX0isuu+wy4xe/+IXZZYRVU1OTkZ2dbVRWVhqTJk0yFixYYHZJYbFs2TLj2muvNbuMXrF48WJj4sSJZpcRMQsWLDBGjBhhdHR0mF1KWEydOtWYO3eu39r06dONmTNnmlRR+Jw8edJISkoyXnvtNb/1a6+91li6dKlJVV2cuBu7JBKPxyNJSktLM7mS8Gpvb9e6det04sQJ5ebmml1OWM2fP19Tp07VLbfcYnYpYXfgwAG5XC5lZWXp7rvv1qFDh8wuKSxeeeUV3XjjjbrrrruUnp6u66+/Xs8++6zZZfWKtrY2rVmzRnPnzu32jT2jzcSJE/X73/9e7733niTpz3/+s7Zt26bbbrvN5Mp67vTp02pvb9eAAQP81pOTk7Vt2zaTqro4cTd2SRSGYWjhwoWaOHGicnJyzC4nLPbs2aPc3Fy1tLTokksu0fr16zV69GizywqbdevWadeuXTExjw3V+PHj9fzzz2vkyJE6cuSInnzySeXl5ammpkaDBg0yu7weOXTokFasWKGFCxfq0Ucf1Y4dO/TNb35TVqtV9913n9nlhdWGDRt07NgxzZ492+xSwmbx4sXyeDy68sorlZSUpPb2dj311FO65557zC6tx2w2m3Jzc/XEE0/oqquuksPh0Isvvqh33nlH2dnZZpd3fma3XnqT4njsUlhYaAwdOtSoq6szu5SwaW1tNQ4cOGBUV1cb3/nOd4zLL7/cqKmpMbussPjb3/5mpKenG7t37/atxdPY5VzHjx83HA6H8fTTT5tdSo/169fPyM3N9Vt7+OGHjZtuusmkinpPQUGBcfvtt5tdRli9+OKLxuDBg40XX3zR+Mtf/mI8//zzRlpamvHcc8+ZXVpYHDx40Pj85z9vSDKSkpKMsWPHGvfee69x1VVXmV3aeRE+YtBDDz1kDB482Dh06JDZpfSqm2++2fjGN75hdhlhsX79et8vhzObJMNisRhJSUnG6dOnzS4x7G655RbjgQceMLuMHhsyZIjxta99zW/tpz/9qeFyuUyqqHd88MEHRp8+fYwNGzaYXUpYDR482KioqPBbe+KJJ4xRo0aZVFHvOH78uHH48GHDMAzjy1/+snHbbbeZXNH5MXaJIYZh6OGHH9b69ev1xhtvKCsry+ySepVhGGptbTW7jLC4+eabtWfPHr+1OXPm6Morr9TixYvj5syQM1pbW/XXv/5V//Iv/2J2KT02YcKEgFPa33vvPQ0dOtSkinrHqlWrlJ6erqlTp5pdSlidPHlSffr4H96YlJQUN6fanjFw4EANHDhQjY2N2rJli8rKyswu6bziLnwcP35cBw8e9D2ura3V7t27lZaWpiFDhphYWc/Nnz9fa9eu1caNG2Wz2eR2uyVJdrtdycnJJlfXM48++qimTJmizMxMNTU1ad26dXrjjTe0efNms0sLC5vNFnBszsCBAzVo0KC4OGbnW9/6lqZNm6YhQ4aooaFBTz75pLxer2bNmmV2aT32yCOPKC8vTyUlJfryl7+sHTt2aOXKlVq5cqXZpYVNR0eHVq1apVmzZqlv3/j6Wpg2bZqeeuopDRkyRGPGjNGf/vQnlZeXa+7cuWaXFhZbtmyRYRgaNWqUDh48qG9/+9saNWqU5syZY3Zp52dy5yXs/vjHPxqSArZZs2aZXVqPBftckoxVq1aZXVqPzZ071xg6dKjRv39/4zOf+Yxx8803G1u3bjW7rF4VT8d8fOUrXzEyMjKMfv36GS6Xy5g+fXrcHK9jGIbx6quvGjk5OYbVajWuvPJKY+XKlWaXFFZbtmwxJBn79+83u5Sw83q9xoIFC4whQ4YYAwYMMIYPH24sXbrUaG1tNbu0sHjppZeM4cOHG/379zecTqcxf/5849ixY2aXdUEWwzAMc2IPAABIRFznAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARBThAwAARNT/B00BBRGDkoc3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test)\n",
    "plt.plot(x_train,model.predict(x_train),color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "985106b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:43.310667Z",
     "iopub.status.busy": "2023-03-04T11:21:43.309445Z",
     "iopub.status.idle": "2023-03-04T11:21:43.316750Z",
     "shell.execute_reply": "2023-03-04T11:21:43.315742Z"
    },
    "papermill": {
     "duration": 0.022098,
     "end_time": "2023-03-04T11:21:43.319258",
     "exception": false,
     "start_time": "2023-03-04T11:21:43.297160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.77925612])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "197f91ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:43.342866Z",
     "iopub.status.busy": "2023-03-04T11:21:43.341827Z",
     "iopub.status.idle": "2023-03-04T11:21:43.349205Z",
     "shell.execute_reply": "2023-03-04T11:21:43.348069Z"
    },
    "papermill": {
     "duration": 0.021693,
     "end_time": "2023-03-04T11:21:43.351776",
     "exception": false,
     "start_time": "2023-03-04T11:21:43.330083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.701844572119761"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23204a7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:43.374743Z",
     "iopub.status.busy": "2023-03-04T11:21:43.374258Z",
     "iopub.status.idle": "2023-03-04T11:21:43.386623Z",
     "shell.execute_reply": "2023-03-04T11:21:43.385293Z"
    },
    "papermill": {
     "duration": 0.026575,
     "end_time": "2023-03-04T11:21:43.388955",
     "exception": false,
     "start_time": "2023-03-04T11:21:43.362380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actuall</th>\n",
       "      <th>predected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>83.869670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>86</td>\n",
       "      <td>78.980042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>29.105836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>75.068340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>39.863018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actuall  predected\n",
       "0       81  83.869670\n",
       "1       86  78.980042\n",
       "2       30  29.105836\n",
       "3       69  75.068340\n",
       "4       35  39.863018"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame({'Actuall':y_test.flatten(),'predected':y_predect.flatten()})\n",
    "df2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fcd8d7c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:43.414355Z",
     "iopub.status.busy": "2023-03-04T11:21:43.413648Z",
     "iopub.status.idle": "2023-03-04T11:21:43.604676Z",
     "shell.execute_reply": "2023-03-04T11:21:43.603687Z"
    },
    "papermill": {
     "duration": 0.207102,
     "end_time": "2023-03-04T11:21:43.607343",
     "exception": false,
     "start_time": "2023-03-04T11:21:43.400241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db825ba6",
   "metadata": {
    "papermill": {
     "duration": 0.01119,
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   "source": [
    "___________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "### Build model with statsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3180c0b2",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16674661",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:45.227017Z",
     "iopub.status.busy": "2023-03-04T11:21:45.226229Z",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "X_stat=sm.add_constant(x_train)\n",
    "model2=sm.OLS(y_train,X_stat).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d23a124",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-04T11:21:45.261388Z",
     "iopub.status.busy": "2023-03-04T11:21:45.260664Z",
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     "shell.execute_reply": "2023-03-04T11:21:45.277666Z"
    },
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     "end_time": "2023-03-04T11:21:45.282208",
     "exception": false,
     "start_time": "2023-03-04T11:21:45.248256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   343.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 04 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>3.56e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:21:45</td>     <th>  Log-Likelihood:    </th> <td> -62.468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>   128.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>   130.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    2.7018</td> <td>    2.828</td> <td>    0.955</td> <td> 0.352</td> <td>   -3.240</td> <td>    8.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    9.7793</td> <td>    0.528</td> <td>   18.537</td> <td> 0.000</td> <td>    8.671</td> <td>   10.888</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.184</td> <th>  Durbin-Watson:     </th> <td>   2.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.075</td> <th>  Jarque-Bera (JB):  </th> <td>   1.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.402</td> <th>  Prob(JB):          </th> <td>   0.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.680</td> <th>  Cond. No.          </th> <td>    12.0</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.950\n",
       "Model:                            OLS   Adj. R-squared:                  0.947\n",
       "Method:                 Least Squares   F-statistic:                     343.6\n",
       "Date:                Sat, 04 Mar 2023   Prob (F-statistic):           3.56e-13\n",
       "Time:                        11:21:45   Log-Likelihood:                -62.468\n",
       "No. Observations:                  20   AIC:                             128.9\n",
       "Df Residuals:                      18   BIC:                             130.9\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.7018      2.828      0.955      0.352      -3.240       8.644\n",
       "x1             9.7793      0.528     18.537      0.000       8.671      10.888\n",
       "==============================================================================\n",
       "Omnibus:                        5.184   Durbin-Watson:                   2.213\n",
       "Prob(Omnibus):                  0.075   Jarque-Bera (JB):                1.992\n",
       "Skew:                          -0.402   Prob(JB):                        0.369\n",
       "Kurtosis:                       1.680   Cond. No.                         12.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.summary()"
   ]
  }
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