simple-linear-regression-model-by-parisanahmadi.ipynb

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   "source": [
    "# This simple linear regression model wrote by Parisan_Ahmadi\n",
    "\n",
    "import pandas as pd   #library written for the Python programming language for data manipulation.\n",
    "import numpy as np #library used for working with arrays.\n",
    "import matplotlib.pyplot as plt #data visualization and graphical plotting library\n",
    "import seaborn as sb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.47</td>\n",
       "      <td>52.21</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.50</td>\n",
       "      <td>53.12</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.52</td>\n",
       "      <td>54.48</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.55</td>\n",
       "      <td>55.84</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.57</td>\n",
       "      <td>57.20</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.60</td>\n",
       "      <td>58.57</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.63</td>\n",
       "      <td>59.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.65</td>\n",
       "      <td>61.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.68</td>\n",
       "      <td>63.11</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.70</td>\n",
       "      <td>64.47</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.73</td>\n",
       "      <td>66.28</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.75</td>\n",
       "      <td>68.10</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.78</td>\n",
       "      <td>69.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.80</td>\n",
       "      <td>72.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.83</td>\n",
       "      <td>74.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "    Height  Weight\n",
       "0     1.47   52.21\n",
       "1     1.50   53.12\n",
       "2     1.52   54.48\n",
       "3     1.55   55.84\n",
       "4     1.57   57.20\n",
       "5     1.60   58.57\n",
       "6     1.63   59.93\n",
       "7     1.65   61.29\n",
       "8     1.68   63.11\n",
       "9     1.70   64.47\n",
       "10    1.73   66.28\n",
       "11    1.75   68.10\n",
       "12    1.78   69.92\n",
       "13    1.80   72.19\n",
       "14    1.83   74.46"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = pd.read_csv(\"../input/height-and-weight-simple-linear-regression/data.csv\")\n",
    "data"
   ]
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       "      <th>Weight</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.47</td>\n",
       "      <td>52.21</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.50</td>\n",
       "      <td>53.12</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.52</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.57</td>\n",
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       "      <th>6</th>\n",
       "      <td>1.63</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.65</td>\n",
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       "      <th>8</th>\n",
       "      <td>1.68</td>\n",
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       "      <th>9</th>\n",
       "      <td>1.70</td>\n",
       "      <td>64.47</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.73</td>\n",
       "      <td>66.28</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.75</td>\n",
       "      <td>68.10</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.78</td>\n",
       "      <td>69.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.80</td>\n",
       "      <td>72.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.83</td>\n",
       "      <td>74.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Height  Weight\n",
       "0     1.47   52.21\n",
       "1     1.50   53.12\n",
       "2     1.52   54.48\n",
       "3     1.55   55.84\n",
       "4     1.57   57.20\n",
       "5     1.60   58.57\n",
       "6     1.63   59.93\n",
       "7     1.65   61.29\n",
       "8     1.68   63.11\n",
       "9     1.70   64.47\n",
       "10    1.73   66.28\n",
       "11    1.75   68.10\n",
       "12    1.78   69.92\n",
       "13    1.80   72.19\n",
       "14    1.83   74.46"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.650667</td>\n",
       "      <td>62.078000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.114235</td>\n",
       "      <td>7.037515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.470000</td>\n",
       "      <td>52.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.560000</td>\n",
       "      <td>56.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.650000</td>\n",
       "      <td>61.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.740000</td>\n",
       "      <td>67.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.830000</td>\n",
       "      <td>74.460000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          Height     Weight\n",
       "count  15.000000  15.000000\n",
       "mean    1.650667  62.078000\n",
       "std     0.114235   7.037515\n",
       "min     1.470000  52.210000\n",
       "25%     1.560000  56.520000\n",
       "50%     1.650000  61.290000\n",
       "75%     1.740000  67.190000\n",
       "max     1.830000  74.460000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe()"
   ]
  },
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     "data": {
      "text/plain": [
       "Height  Weight\n",
       "1.47    52.21     1\n",
       "1.50    53.12     1\n",
       "1.52    54.48     1\n",
       "1.55    55.84     1\n",
       "1.57    57.20     1\n",
       "1.60    58.57     1\n",
       "1.63    59.93     1\n",
       "1.65    61.29     1\n",
       "1.68    63.11     1\n",
       "1.70    64.47     1\n",
       "1.73    66.28     1\n",
       "1.75    68.10     1\n",
       "1.78    69.92     1\n",
       "1.80    72.19     1\n",
       "1.83    74.46     1\n",
       "dtype: int64"
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     "execution_count": 5,
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   "source": [
    "df.value_counts()"
   ]
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    {
     "data": {
      "text/plain": [
       "Height    0\n",
       "Weight    0\n",
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     "execution_count": 6,
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   "source": [
    "df.isnull().sum()"
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   "source": [
    "df.dropna(inplace=True)"
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    {
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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8), dpi=80)\n",
    "\n",
    "plt.scatter(df[\"Height\"],df[\"Weight\"]) \n",
    "\n",
    "plt.title(\"Height vs Weight\", color=\"green\", size=30)\n",
    "\n",
    "plt.xlabel(\"Height\", c=\"blue\", size=25)\n",
    "\n",
    "plt.ylabel(\"Weight\", c=\"red\", size=25)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "401df228",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:55.704054Z",
     "iopub.status.busy": "2022-04-21T11:14:55.703228Z",
     "iopub.status.idle": "2022-04-21T11:14:55.708407Z",
     "shell.execute_reply": "2022-04-21T11:14:55.707541Z"
    },
    "papermill": {
     "duration": 0.047588,
     "end_time": "2022-04-21T11:14:55.710250",
     "exception": false,
     "start_time": "2022-04-21T11:14:55.662662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = df[\"Height\"].values.reshape(-1,1)\n",
    "y = df[\"Weight\"].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d9860f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:55.790550Z",
     "iopub.status.busy": "2022-04-21T11:14:55.790047Z",
     "iopub.status.idle": "2022-04-21T11:14:55.795494Z",
     "shell.execute_reply": "2022-04-21T11:14:55.794377Z"
    },
    "papermill": {
     "duration": 0.047891,
     "end_time": "2022-04-21T11:14:55.797705",
     "exception": false,
     "start_time": "2022-04-21T11:14:55.749814",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a648466",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:55.877906Z",
     "iopub.status.busy": "2022-04-21T11:14:55.877605Z",
     "iopub.status.idle": "2022-04-21T11:14:55.899820Z",
     "shell.execute_reply": "2022-04-21T11:14:55.898950Z"
    },
    "papermill": {
     "duration": 0.064529,
     "end_time": "2022-04-21T11:14:55.901657",
     "exception": false,
     "start_time": "2022-04-21T11:14:55.837128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1589dc78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:55.983439Z",
     "iopub.status.busy": "2022-04-21T11:14:55.982880Z",
     "iopub.status.idle": "2022-04-21T11:14:55.987678Z",
     "shell.execute_reply": "2022-04-21T11:14:55.986763Z"
    },
    "papermill": {
     "duration": 0.048437,
     "end_time": "2022-04-21T11:14:55.989529",
     "exception": false,
     "start_time": "2022-04-21T11:14:55.941092",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "280cf657",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:56.071685Z",
     "iopub.status.busy": "2022-04-21T11:14:56.071370Z",
     "iopub.status.idle": "2022-04-21T11:14:56.077875Z",
     "shell.execute_reply": "2022-04-21T11:14:56.076482Z"
    },
    "papermill": {
     "duration": 0.050755,
     "end_time": "2022-04-21T11:14:56.079883",
     "exception": false,
     "start_time": "2022-04-21T11:14:56.029128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-39.51113063]\n",
      "[[61.63764899]]\n"
     ]
    }
   ],
   "source": [
    "#Intercept\n",
    "print(regressor.intercept_)\n",
    "\n",
    "#slope\n",
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35869cb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:56.161408Z",
     "iopub.status.busy": "2022-04-21T11:14:56.161104Z",
     "iopub.status.idle": "2022-04-21T11:14:56.338656Z",
     "shell.execute_reply": "2022-04-21T11:14:56.337699Z"
    },
    "papermill": {
     "duration": 0.221243,
     "end_time": "2022-04-21T11:14:56.341086",
     "exception": false,
     "start_time": "2022-04-21T11:14:56.119843",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbTUlEQVR4nO3deZRU9Zn/8fejNCpObFxwiYrgJOBCixBUNHGtmBgVddwSM24Yx6OOuPziGhPjJBlHxw3MUSOCxkFcEYOtgmhjBBWRnWYRBRRoEGlUWlCWpn1+f3yrLLrppgu6q27dqs/rHI79vVVNPfbp+pwvT937XHN3REQkfraJugAREdk6CnARkZhSgIuIxJQCXEQkphTgIiIx1SaXL7bbbrt5p06dcvmSIiKxN3ny5BXu3qHh8ZwGeKdOnZg0aVIuX1JEJPbMbGFjx9VCERGJKQW4iEhMKcBFRGJKAS4iElMKcBGRmFKAi4jElAJcRCSmFOAiItlUXQ3XXgs1Na3+VyvARUSy4dtv4ZFHoGtXePBBGDu21V9CAS4i0tqmTIEjj4TLL4dDDoHp06FPn1Z/GQW4iEhrWbkS+vWDww6DhQvhySfhzTfhoIOy8nI5nYUiIlKQ3GHoULj++tDzvvJK+POfoX37rL6sAlxEpCVmzw6B/dZbcPjh8Oqr0LNnTl5aLRQRka3x9ddw883QvTvMmBE+sBw/PmfhDdqBi4hsGXf4xz/gmmtg8WLo2xfuugs6bDKuO+u0AxcRydSCBXDqqXDmmaG//fbb8NhjkYQ3KMBFRJq3dm34UPLgg8P53PfdB5Mnw49/HGlZaqGIiGzO6NHwn/8J8+bBueeG8N5776irArQDFxFp3JIlIbB//nMwC0H+7LN5E96gABcRqa+2NuyyDzgAystD66SyEk48MerKNqEWiohIyttvwxVXwMyZcMop8MADsP/+UVfVJO3ARUSqq8PpgEcfDV99FU4TLC/P6/AGBbiIFLO6uvTEwCefDBfmzJ4Np58e+t55Ti0UESlOkyeHdsnEiXDccWHka5aGTmWLduAiUlxWroSrrgoTAxctCkOoxoyJXXiDduAiUizcQ5vk+uthxYoQ4n/+M5SWZvVlKysrqaiooKamhtLSUhKJBGVlZa3ydyvARaTwzZoVLsZJTQwcOTInQ6cqKyspLy+ntrYWgJqaGsrLywFaJcTVQhGRwrV6Ndx0Exx6aJgYOHBgTicGVlRUfBfeKbW1tVRUVLTK368duIgUHnd48cUwMbCqCi65BO68M+dDp2qauJFxU8e3lHbgIlJY5s8PF+GcdRbssku4OGfw4EgmBpY20V9v6viWUoCLSGFYuxb+67/CxMBx4+D++yOfGJhIJCgpKal3rKSkhEQi0Sp/f0YBbmbtzWyYmX1gZnPM7Mjk8X7JY7PM7H9bpSIRkS312mvQrRvcfjuccQZ88AFcey20ibZLXFZWRp8+fb7bcZeWltKnT5+cn4UyABjl7mebWVugnZkdD5wOdHf3dWa2e6tUJCKSqaoquO46GDYMunSB11+Hn/406qrqKSsra7XAbqjZHbiZlQLHAIMB3H29u68ErgDudPd1yePLs1KhiEhDtbVwzz1hYuDLL8Nf/hLOMsmz8M62TFoonYFq4HEzm2pmg8xsR6ALcLSZTTCzt8zssMa+2cwuM7NJZjapurq6FUsXkaI0bhz06AE33BAugZ89G269FbbbLurKci6TAG8D9AQedvcewNfAzcnjuwC9gRuA58w2nf7i7gPdvZe79+oQ0X3jRKQALF8OF18MxxwDq1alJwZ27hx1ZZHJJMCrgCp3n5BcDyMEehUw3IP3gW+B3bJTpogUrbo6ePjhMDHwqafglltiNTEwm5oNcHdfBiw2s67JQwlgNvAP4HgAM+sCtAVWZKdMESlKkyZB795w5ZXh6skZM+COO2DHHaOuLC9kehZKP2Bo8gyUBUBfQivlMTObCawHLnJ3z06ZIlJUvvwSfv/7sPPeY4+w8/7Vr4p+x91QRgHu7tOAXo08dH6rViMixc0dhgwJH1CuWAH9+sGf/pT1iYFxpVkoIpIfZs4MEwPHjg1tk1Gjwtkm0iRdSi8i0Vq9Gm68MYT1zJnw6KPwzjsK7wxoBy4i0XCH4cPDJe9VVfCb34SJgbvpZLZMaQcuIrk3bx6cfDKcfTbsumvYcQ8apPDeQgpwEcmd1MTAbt1CaPfvH04VPOqoqCuLJbVQRCQ3Ro0K96GcPz+cEnjvvfD970ddVaxpBy4i2bV4cWiV/OIXYbzrG2/A008rvFuBAlxEsiM1MfDAA+GVV+C//xumT4dWupmBqIUiItkwdmy4/H3WLOjTBwYMKOqhU9miHbiItJ7PPoOLLoJjjw3nd48YAS+9pPDOEgW4iLRcXR089FCYGPj00/C734WJgaedFnVlBU0tFBFpmYkT4Yorwg2ETzgBHnww3ClHsk47cBHZOl9+GfrcRxwBS5aEnfcbbyi8c0gBLiJbxh2eeCK0Sx55BK6+OtwFXuNec04tFBHJ3MyZYdc9bhwceSSMHg2HHhp1VUVLO3ARad7q1WFG96GHhlMDBw2Ct99WeEdMO3ARaZo7vPBCmBi4ZAlcein8z/9o6FSe0A5cRBo3b164/P2cc0Jgv/tumNWt8M4bCnARqW/NGrj99jAx8N13w1WUkyaFnrfkFbVQRCRt5MgwMXDBAjjvvDAxcK+9oq5KmqAduIiEiYFnnRVuslBSAhUV4U7wCu+8pgAXKWa1tXD33WFi4MiRcMcdYWLgCSdEXZlkQC0UkWL11lvhnO7UzJIBA6BTp6irki2gHbhIsfnsM7jwQjjuOPjmmzAtcMQIhXcMKcBFikVdXRg01bUrPPMM3Hprel63xJJaKCLF4P33Q7tk8uRwR5xUkEusaQcuUsi++CKMeu3dG5YuDTvv119XeBcIBbhIIXKHv/89BPXAgXDNNWFi4C9/qYmBBUQBLlJoKivhmGOgb1/44Q9D2+T++2GnnaKuTFqZAlykUKxaBb/9LfToAXPmwODBmhhY4PQhpkjcucOwYWFi4NKl8B//ESYG7rpr1JVJlmkHLhJnH30EJ50E554Lu+8O48eHnrfCuygowEXiaM0auO22MDHwvffggQfCzYV79466MskhtVBE4ubVV8PEwI8/hl//Gu65R0OnipR24CJxsWgRnHkmnHIKbLddmBg4dKjCu4gpwEXy3fr1cNddYWLgqFHhA0pNDBTUQhHJb//8Z7gEfs4cOOMM6N8f9tsv4qIkX2S0Azez9mY2zMw+MLM5ZnbkRo/91szczHSjPJHWsmwZXHABHH98+MCyvBxefFHhLfVkugMfAIxy97PNrC3QDsDM9gV+BizKUn0ixWX9evjZz2DqVFi7Fn7/e7jlFmjXLurKJA81G+BmVgocA1wM4O7rgfXJh+8HbgRGZKk+keLx17/C1Ven13PnQpcu0dUjeS+THXhnoBp43My6A5OBa4CfAkvcfbptZjiOmV0GXAbQsWPHFhcsUnDmzQszS1LOOguef15Dp6RZmfTA2wA9gYfdvQfwNXA78Dvgtua+2d0Hunsvd+/VoUOHltQqUljq6sLQqY3Du6oqXBav8JYMZBLgVUCVu09IrocRAr0zMN3MPgH2AaaY2Z5ZqVKk0AwdCm3awLhxYT1kSJhpsvfe0dYlsdJsC8Xdl5nZYjPr6u5zgQQwxd0TqeckQ7yXu6/IXqkiBWDp0vohffTR8OabsO220dUksZXpWSj9gKHJM1AWAH2zV5JIAXKHc86BF15IH/voI/jBD6KrSWIvowB392lAr8083qmV6hEpPK+8Aqeeml4PGFD/bBORraQrMUWy5Ysv6o917doVZsyAtm2jq0kKimahiGTDlVfWD++pU8M9KRXe0ooU4CKtady4cArgww+H9W23hf63bmsmWaAWikhrWL0a9tkHamrCeuedYfFi2HHHaOuSgqYduEhL/fGP8L3vpcN73LjQ/1Z4S5ZpBy6ytaZNC3eAT7niCnjoocjKkeKjABfZUuvWQVlZOI875fPPYZddoqtJipJaKCJb4oEHYPvt0+H9yivhQ0qFt0RAO3CRTHz0Uf3RruecA88+q6FTEikFuMjm1NXBscfCO++kjy1ZAt//fnQ1iSSphSLSlCFDwsTAVHgPHRraJQpvyRPagYs0tGRJOKc75dhjoaJCEwMl7yjARVLcw91wXnwxfSyHEwMrKyupqKigpqaG0tJSEokEZWVlOXltiSe1UEQg3PV9m23S4f3Xv4ZAz2F4l5eXU5O8GKimpoby8nIqKytz8voST9qBS3H7/HPYbbf0+sADwwU6OR46VVFRQW1tbb1jtbW1VFRUaBcuTdIOXIrX5ZfXD+9p02D27EgmBqZ23pkeFwEFuBSjsWPD+duPPBLWt98e2iXdu0dWUmlp6RYdFwG1UKSYrF4dTgFctSqsd90VFi7Mi6FTiUSC8vLyem2UkpISEonEZr5Lip124FIc/vCHMDEwFd7jxsGKFXkR3gBlZWX06dPnux13aWkpffr0Uf9bNks7cClsU6dCz57p9VVXhTNM8lBZWZkCW7aIAlwK07p1cPDBMH9++tgXX4QbLYgUCLVQpPD07x8mBqbCOzUxUOEtBUY7cCkcH34Y7vye8stfwtNPa2KgFCwFuMRfXR0cfTSMH58+tnQp7LVXdDWJ5IBaKBJv//d/YWJgKryfeiq0SxTeUgS0A5d4qqqCffdNr48/Ht54I8wzESkS+m2XeHGHM86oH97z58OYMQpvKTr6jZf4eOmlENIjRoT1gw+GQN9//2jrEomIWiiS/1asgA4d0uuDD4YpUyIZOiWST7QDl/x22WX1w3v6dJg5U+EtggJc8tVbb4Xztx99NKz/9KfQLjnkkGjrEskjaqFIflm1KkwMXL06rDt0gE8+gXbtIi1LJB9pBy7549ZbYaed0uH9zjuwfLnCW6QJ2oFL9KZMgR/9KL2++moYMCC6ekRiQgEu0Vm3Dg46CBYsSB/TxECRjKmFItG4774wMTAV3iNHamKgyBbSDlxya+5cOOCA9Pq882DoUE0MFNkKGQW4mbUHBgHdAAcuAc4E+gDrgflAX3dfmZUqJf42bICf/AQmTEgf+/RT2HPP6GoSiblMWygDgFHufgDQHZgDvA50c/dDgA+BW7JTosTe3/8OJSXp8H7mmdAuUXiLtEizO3AzKwWOAS4GcPf1hF336I2e9h5wdhbqkzhbvBg6dkyvEwkYPVpDp0RaSSbvpM5ANfC4mU01s0Fm1vBW3pcAI1u9OokndzjttPrhPX++xr2KtLJM3k1tgJ7Aw+7eA/gauDn1oJndCmwAhjb2zWZ2mZlNMrNJ1dXVrVCy5LURI0JIl5eH9UMPaWKgSJZk8iFmFVDl7qlPn4aRDHAzuxg4FUi4uzf2ze4+EBgI0KtXr0afIwWg4cTAsjKYPDn0vkUkK5rdgbv7MmCxmaXuFpsAZpvZScCNwGnu/k0Wa5R8d+ml9cO7shJmzFB4i2RZpueB9wOGmllbYAHQF5gIbAe8buEc3vfc/fKsVCn56c034YQT0uu//CXMMxGRnMgowN19GtCrweEftHo1Eg+rVsEee8CaNWG9557hQ0oNnRLJKZ0SIFvmllvCxMBUeI8fHy7IUXiL5JwupZfMTJ4MvTb6R9g110D//pGVIyIKcGnO2rVhdsnChWG9zTbw+efQvn2kZYmIWiiyOffeCzvskA7v116DujqFt0ie0A5cNtVwYuC//zsMGaKJgSJ5RgEuaRs2wFFHwcSJ6WOaGCiSt9RCkeDxx8OFN6nwfvZZTQwUyXPagRe7RYtgv/3S6xNPhFGjNHRKJAb0Li1W7nDqqfXDe8ECjXsViRG9U4vRiy+GkH7llbD+299CoHfuHG1dIrJF1EIpJtXVsPvu6XX37qHnraFTIrGkHXgxcIdLLqkf3pWVMG2awlskxhTghW7MmNAuefzxsL7jjhDo3bpFW5eItJhaKIXqq6/CjnvdurDea68wMXCHHaKtS0RajXbgheimm6C0NB3e48fD0qUKb5ECox14IZk4EQ4/PL2+7jq4777o6hGRrFKAF4K1a6FLF1i8OKy33TZMDCwtjbYuEckqtVDi7u67Q2skFd6jR4eZJgpvkYKnHXhczZkDBx2UXl9wATzxhCYGihQRBXjcbNgAvXuHO+SkLFsW7lEpIkVFLZQ4GTw4XHiTCu/nngvndCu8RYqSduBxsHAhdOqUXv/85/Dqqxo6JVLklAD57Ntv4eST64f3xx9r3KuIAArw/DV8eDgdcOTIsB44MLRLNg5zESlqaqHkm+XL6/e0e/SACRM0dEpENqEdeL5wh4svrh/es2bBlCkKbxFplAI8H1RUhJ72E0+E9Z13hkDf+DxvEZEG1EKJ0ldfQYcOsH59WO+9N3z0kYZOiUhGtAOPyg03hMvdU+E9YQJUVSm8RSRj2oHnWsOJgddfH+aZiIhsIQV4rqxZEyYGVlWFdUlJuEelhk6JyFZSCyUX7roL2rVLh/frr4fWicJbRFpAO/Bsajgx8KKLwr0pNTFQRFqBAjwbNmyAI44I53CnfPZZ/bvCi4i0kFoore3RR0N/OxXew4aFc7oV3iLSyrQDby0NJwb+4hfw8ssaOiUiWaMAb6nUxMDXXksf++QT2G+/yEoSkeKg7WFLDBsWJgamwvvRR0O7ROEtIjmQ0Q7czNoDg4BugAOXAHOBZ4FOwCfAue7+ZTaKzDuffQZ77ple9+oF48dDG/2DRkRyJ9Md+ABglLsfAHQH5gA3AxXu/kOgIrkubO5w4YX1w3v27HB1pcJbRHKs2QA3s1LgGGAwgLuvd/eVwOlAcnweTwBnZKfEPPHGG+EDySFDwvquu0KgH3hgtHWJSNHKZNvYGagGHjez7sBk4BpgD3f/NPmcZUCjd9Y1s8uAywA6duzY4oJzrqYGdt0V6urCumNHmDsXtt8+2rpEpOhl0kJpA/QEHnb3HsDXNGiXuLsTeuObcPeB7t7L3Xt16NChpfXm1vXXQ/v26fB+//1wuqDCW0TyQCYBXgVUufuE5HoYIdA/M7O9AJL/XZ6dEiMwYUK43P3ee8P6hhtCu+Sww6KtS0RkI822UNx9mZktNrOu7j4XSACzk38uAu5M/ndEVivNhTVr4F//FT5Ndoa23z6ccbLTTtHWJSLSiEzPQukHDDWzGcChwB2E4D7RzD4Cfppcx9edd4aJganwrqgIga7wFpE8ldG5b+4+DejVyEOJVq0mCrNmQbdu6XXfvjB4sCYGikjeK96Tl2trQ097+vT0seXLwz0qRURioDgvpR84ENq2TYf38OHhQ0qFt4jESHHtwD/5BDp3Tq9POQXKy9UuEZFYKo4A//ZbOOmkcCuzlIULw0U5IiIxVfgtlOefDxMDU+E9eHBolyi8RSTmCncH3nBi4GGHwbvvauiUiBSMwtuBu8P55286MfD99xXeIlJQCivAR48OEwOHDg3ru+/WxEARKViFsSVduRJ22SWENYQ74nzwgYZOiUhBi/8O/LrrYOed0+E9cWI4XVDhLSIFLr4BPn58OH+7f/+wvummEOK9GrviX0Sk8MSvhfLNN7D//uEsE4AddoBlyzR0SkSKTrx24HfcATvumA7vMWNCoCu8RaQIxWMHvnhx/QtvfvMbGDQounpERPJAPAL8uefSX2tioIgIEJcWylVXQXW1JgaKiGwkHjvw7bYLf0RE5Dvx2IGLiMgmFOAiIjGlABcRiSkFuIhITCnARURiSgEuIhJTCnARkZjK+/PAKysrqaiooKamhtLSUhKJBGVlZVGXJSISubwO8MrKSsrLy6mtrQWgpqaG8vJyAIW4iBS9vG6hVFRUfBfeKbW1tVRUVERUkYhI/sjrAK+pqdmi4yIixSSvA7y0tHSLjouIFJO8DvBEIkFJSUm9YyUlJSQSiYgqEhHJH3n9IWbqg0qdhSIisqm8DnAIIa7AFhHZVF63UEREpGkKcBGRmFKAi4jElAJcRCSmFOAiIjFl7p67FzOrBhZu5bfvBqxoxXJySbVHI661x7VuUO3Zsp+7d2h4MKcB3hJmNsnde0Vdx9ZQ7dGIa+1xrRtUe66phSIiElMKcBGRmIpTgA+MuoAWUO3RiGvtca0bVHtOxaYHLiIi9cVpBy4iIhtRgIuIxFTkAW5mj5nZcjObuZnnHGdm08xslpm9tdHxk8xsrpnNM7Obc1Nxvbq2qnYz29fM3jSz2cnj1+Su6u/q2uqfe/Kxbc1sqpm9nP1qN6mrJb8z7c1smJl9YGZzzOzI3FTd4rqvSx6baWZPm9n2uan6u9ffbO1mdkOy7mnJGuvMbJfkY3n9Pm2q9nx4nzbL3SP9AxwD9ARmNvF4e2A20DG53j35322B+cD+QFtgOnBQTGrfC+iZ/Pp7wIdxqX2jx/8f8BTwclx+Z5JfPwFcmvy6LdA+3+sG9gY+BnZIrp8DLs6nn3mD5/YBxiS/zvv36WZqj/x92tyfyHfg7j4W+GIzT/k1MNzdFyWfvzx5/HBgnrsvcPf1wDPA6VkttoGtrd3dP3X3KcmvVwFzCG/SnGnBzx0z2wc4BRiU1SKbsLW1m1kp4c08OHl8vbuvzG61aS35mRNm9+9gZm2AdsDSrBXaiAxq39h5wNPJr+PwPt3Yd7Xnw/u0OZEHeAa6ADub2T/NbLKZXZg8vjeweKPnVZFnP1yarv07ZtYJ6AFMyHVxzdhc7f2BG4FvI6mseU3V3hmoBh5Ptn8GmdmO0ZW5iUbrdvclwD3AIuBToMbdR0dYZ5PMrB1wEvBC8lAc3qdAo7Vv/Fgn8vB9mvd35CHU+CMgAewAjDez96ItKWON1u7uHwKY2b8QflmudfevoiuzUU393LsAy919spkdF115m9VU7W0I/5Tu5+4TzGwAcDPwh8gqra+puqsJu9bOwErgeTM7392fjKrQzegDvOPume5480mjtefz+zQOAV4FfO7uXwNfm9lYoHvy+L4bPW8fYEkE9W1OU7V/aGYlhF+Koe4+PMoim9BU7T2B08zsZGB7YCcze9Ldz4+w1oaaqn0cUOXuqV3UMEKA54um6gb42N2rAcxsOHAUkI8B/ivS7RMI78l8f5+mNKydfH+fxqGFMgL4iZm1Sf4T5whCL2oi8EMz62xmbQk//JcirLMxjdZuZkbow85x9/sirbBpjdbu7re4+z7u3onwMx+TZ+ENTde+DFhsZl2Tz0sQPjTMF039ri8CeptZu+TvTiJ5PK8kP2M4lvD/kRKH92mjtcfhfRr5DtzMngaOA3Yzsyrgj0AJgLv/zd3nmNkoYAah5zrI3Wcmv/cq4DXCJ92PufusONRuZj8BLgAqzWxa8q/7nbu/mu+156q+zWlh7f2AockwWQD0jUPdZjYMmAJsAKaS48u+m6s9+bR/A0Yn/wVB8rEN+f4+bap24MdE/D5tji6lFxGJqTi0UEREpBEKcBGRmFKAi4jElAJcRCSmFOAiIjGlABcRiSkFuIhITP1/i3pKBoPuXL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test, y_test, color = \"gray\")\n",
    "\n",
    "plt.plot(x_test, y_pred, color = \"red\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f95d573",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:56.424842Z",
     "iopub.status.busy": "2022-04-21T11:14:56.424291Z",
     "iopub.status.idle": "2022-04-21T11:14:56.432993Z",
     "shell.execute_reply": "2022-04-21T11:14:56.432186Z"
    },
    "papermill": {
     "duration": 0.052743,
     "end_time": "2022-04-21T11:14:56.434921",
     "exception": false,
     "start_time": "2022-04-21T11:14:56.382178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  0.770409850568619\n",
      "Mean Squared Error:  0.6215757847560875\n",
      "Root Mean Squared Error:  0.7884007767348327\n",
      "R2 Score:  0.9379145785808406\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error: \", metrics.mean_absolute_error(y_test, y_pred))\n",
    "print(\"Mean Squared Error: \", metrics.mean_squared_error(y_test, y_pred))\n",
    "print(\"Root Mean Squared Error: \", np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print(\"R2 Score: \", metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42ddaaf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:56.518674Z",
     "iopub.status.busy": "2022-04-21T11:14:56.518226Z",
     "iopub.status.idle": "2022-04-21T11:14:56.695875Z",
     "shell.execute_reply": "2022-04-21T11:14:56.695304Z"
    },
    "papermill": {
     "duration": 0.221974,
     "end_time": "2022-04-21T11:14:56.697777",
     "exception": false,
     "start_time": "2022-04-21T11:14:56.475803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test, y_test, color = \"gray\")\n",
    "\n",
    "plt.scatter(x_test, y_pred, color = \"red\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "734e2846",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:56.783328Z",
     "iopub.status.busy": "2022-04-21T11:14:56.782914Z",
     "iopub.status.idle": "2022-04-21T11:14:57.021418Z",
     "shell.execute_reply": "2022-04-21T11:14:57.020724Z"
    },
    "papermill": {
     "duration": 0.284145,
     "end_time": "2022-04-21T11:14:57.023294",
     "exception": false,
     "start_time": "2022-04-21T11:14:56.739149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApEAAAIECAYAAABBgHU7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAxOAAAMTgF/d4wjAAAfDUlEQVR4nO3df4zc933f+ddbpEqGm+IsWU7lamWRbar+4VqJDMlGgyaOUPWWTa6FWwcxAvSki4Wc/Ed7ORC+q5NeUaEX4OQCMYzWQBs3EhxWKlw0DFzh0ILRMS4uTeJT5UiJEPtiGRJFL1EZpiSfW1G0GfJ9f3DY0Mouv/OJlprZ5eMBCMJ856vZ97yxP56a2Zmt7g4AAIy4ZtEDAACw/YhIAACGiUgAAIaJSAAAholIAACGiUgAAIbtXtQH3rNnT7/tbW9b1Id/033rW9/Knj17Fj3GUrOjaXY0zY6m2dE0O5pmR9N2wo5Onjz57e7e8E4sLCLf9ra3ZX19fVEf/k139OjRrK2tLXqMpWZH0+xomh1Ns6NpdjTNjqbthB1V1dc3u87T2QAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADNu96AEAALi87s6TL7yS46dezf4bVnLHLdelqhY6k4gEAFhi66+czj0PP5Gvvnw61+66JmfPnc/N1+/L4Q+9J6vX7VvYXJ7OBgBYUt2dex5+Ii+8dDpnz3VOf/tczp7rvPDS6dz78BPp7oXNJiIBAJbUky+8kvWXX8u5898Zi+fOd068fDpPvvDKgiYTkQAAS+v4qVeze9fGv/t47a5rcvzUq2/yRH9IRAIALKn9N6zk7LnzG1539tz57L9h5U2e6A+JSACAJXXHLdfl5uv3Zdc13/lo5K5rKu+4fl/uuOW6BU0mIgEAllZV5fCH3pNb3rov1+6q7PsTu3Ltrsr+t+7L4fveu9C3+Zn7LX6qak+Sn0+yluRMkt9J8tNJjl1y2r4kfybJ93T3y1s4JwDAVWn1un05duh92/p9Ih9M0klu7e6uqhu7+6Uk33/xhKr6SJL3CUgAgK1TVblz//W5c//1ix7lv5orIqtqJcl9SVZ79oZE3f3iBqfel+Rntm48AACWUc3zJpVVdVuSx5J8JsndSV5L8kB3H7vknB9I8iu5EJp/sMFtHEpy6OLllZWVm44cOfKG78B2cebMmezdu3fRYyw1O5pmR9PsaJodTbOjaXY0bSfs6ODBgye7e3Wj6+aNyHcn+UKSe7v7cFXdnuTxJO/s7q/NznkoyUvd/b/OM9Tq6mqvr6/Pex+2vaNHj2ZtbW3RYyw1O5pmR9PsaJodTbOjaXY0bSfsqKo2jch5fyfyRJLzSR5Nku5+qqqeT/KuJF+rqu9O8uNJ7tyCeQEAWHJzvcVPd5/KhVdhryVJVR1IciDJl2anfDDJ73T3/3slhgQAYLmMvDr7w0keqqqP5cKjkvd398nZdfcl+edbPRwAAMtp7ojs7ueS3LXJdT+wZRMBALD0/MUaAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAhs0dkVW1p6o+WVXPVtUzVfXI5Y4DALBz7R4498EkneTW7u6qunHiOAAAO9RcEVlVK0nuS7La3Z0k3f3iZsev1LAAACyHmrXf5U+qui3JY0k+k+TuJK8leSDJ1zc63t3HNriNQ0kOXby8srJy05EjR974Pdgmzpw5k7179y56jKVmR9PsaJodTbOjaXY0zY6m7YQdHTx48GR3r2503bwR+e4kX0hyb3cfrqrbkzye5EeTfH6D4+/s7q9d7jZXV1d7fX198K5sX0ePHs3a2tqix1hqdjTNjqbZ0TQ7mmZH0+xo2k7YUVVtGpHzvrDmRJLzSR5Nku5+KsnzSW7e5Pi73uDMAAAssbkisrtPJTmWZC1JqupAkgNJfmuT41+6EsMCALAcRl6d/eEkD1XVx3Lh0cf7u/tkVW14/ArMCgDAkpg7Irv7uSR3zXscAICdy1+sAQBgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABgmIgEAGCYiAQAYJiIBABg2NwRWVV7quqTVfVsVT1TVY/Mjh+vqt+vqqdn/3zwyo0LAMAy2D1w7oNJOsmt3d1VdeMl132wu5/e0skAAFhac0VkVa0kuS/Jand3knT3i1dyMAAAllfNmvDyJ1XdluSxJJ9JcneS15I80N3Hqup4km8mqSRPJPlod399g9s4lOTQxcsrKys3HTlyZCvuw7Zw5syZ7N27d9FjLDU7mmZH0+xomh1Ns6NpdjRtJ+zo4MGDJ7t7daPr5o3Idyf5QpJ7u/twVd2e5PEk70yyp7tPVNW1SX4uybu6+0embnN1dbXX19dH7se2dvTo0aytrS16jKVmR9PsaJodTbOjaXY0zY6m7YQdVdWmETnvC2tOJDmf5NEk6e6nkjyfC8F4YnbsbJJPJPnBNzowAADLba6I7O5TSY4lWUuSqjqQ5ECSL1XVWy459SeSPLXFMwIAsGRGXp394SQPVdXHcuFRyfuT7Enyf1bVrlz4ncjnktyz5VMCALBU5o7I7n4uyV0bXHX71o0DAMB24C/WAAAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwTEQCADBMRAIAMExEAgAwbPeiBwBgOXR3nnzhlRw/9Wr237CSO265LlW16LGAJSUiAcj6K6dzz8NP5Ksvn861u67J2XPnc/P1+3L4Q+/J6nX7Fj0esIQ8nQ1wlevu3PPwE3nhpdM5e65z+tvncvZc54WXTufeh59Idy96RGAJzR2RVbWnqj5ZVc9W1TNV9cjrrv/Jquqqev+WTwnAFfPkC69k/eXXcu78d8biufOdEy+fzpMvvLKgyYBlNvJ09oNJOsmt3d1VdePFK6pqf5KfSvL5rR0PgCvt+KlXs3tX5dvn/uh11+66JsdPvZo791//5g8GLLW5HomsqpUk9yX5ez17XqO7X5xdd02SX0zyd5J86wrNCcAVsv+GlZw9d37D686eO5/9N6y8yRMB20HN87suVXVbkseSfCbJ3UleS/JAdx+rqo8k+ZPd/Q+q6t8n+UR3f3aD2ziU5NDFyysrKzcdOXJkS+7EdnDmzJns3bt30WMsNTuaZkfT7GjaRjv6+n/51oWnsy/9kVDJ7msqN3z3njd3wCXg82iaHU3bCTs6ePDgye5e3ei6eZ/O3p3kliRf7O6PVtXtSR6vqg8k+UCSH5q6ge7+eJKPX7y8urraa2trc3747e/o0aO5mu7vH4cdTbOjaXY0baMdbfTq7Hdcvy+H73tvbnrLdy1o0sXxeTTNjqbt9B3NG5EnkpxP8miSdPdTVfV8ktuS7E/y7Oy9xG5M8qmqent3/9OtHxeAK2H1un05duh93icSmNtcEdndp6rqWJK1JP+2qg4kOZDkV7r7n1w873JPZwOw3Koqd+6/3otogLmMvDr7w0keqqqP5cKjkvd398krMxYAAMts7ojs7ueS3DVxzg+/0YEAAFh+/mINAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADDRCQAAMNEJAAAw0QkAADD5o7IqtpTVZ+sqmer6pmqemR2/Fer6ner6umq+vWquv3KjQsAwDLYPXDug0k6ya3d3VV14+z4j3f3N5Kkqv5Gkk8n+b6tHBIAgOUyV0RW1UqS+5KsdncnSXe/OPv3Ny459b/JhdAEAGAHq1kTXv6kqtuSPJbkM0nuTvJakge6+9js+sNJ7pqd/iPd/cwGt3EoyaGLl1dWVm46cuTIG74D28WZM2eyd+/eRY+x1Oxomh1Ns6NpdjTNjqbZ0bSdsKODBw+e7O7Vja6bNyLfneQLSe7t7sOz33t8PMk7u/trl5x3b5IPdvePTN3m6upqr6+vz3sftr2jR49mbW1t0WMsNTuaZkfT7GiaHU2zo2l2NG0n7KiqNo3IeV9YcyLJ+SSPJkl3P5Xk+STvuvSk7v6lJHdV1Vv/+OMCALDs5orI7j6V5FiStSSpqgNJDiT5UlX96YvnVdX7k7yU5OUtnxQAgKUx8ursDyd5qKo+lguPSt4/++//dVV91+zY15P8dz3Pc+QAAGxbc0dkdz+XP3zxzKXes3XjAACwHfiLNQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADJs7IqtqT1V9sqqerapnquqRqtpbVZ+tqi9X1e9U1eNV9b1XcmAAABZv98C5DybpJLd2d1fVjbPjn0ry72bH/naSX0zyw1s7JgAAy2SuiKyqlST3JVnt7k6S7n5xdvW/veTUzyf5yJZOCADA0qlZE17+pKrbkjyW5DNJ7k7yWpIHuvvY6877F0le7u6f3uA2DiU5dPHyysrKTUeOHHlj028jZ86cyd69exc9xlKzo2l2NM2OptnRNDuaZkfTdsKODh48eLK7Vze6bt6IfHeSLyS5t7sPV9XtSR5P8s7u/trsnJ9N8teS/OXuPj11m6urq72+vj5wN7a3o0ePZm1tbdFjLDU7mmZH0+xomh1Ns6NpdjRtJ+yoqjaNyHlfWHMiyfkkjyZJdz+V5Pkk75p9gI8k+ZtJ/uo8AQkAwPY2V0R296kkx5KsJUlVHUhyIMmXZk9T/0SSv9Ld37hCcwIAsERGXp394SQPVdXHcuFRyfuTVJKfT/Jcks9VVZJ8q7vfu9WDAgCwPOaOyO5+LsldG1xVWzcOAADbgb9YAwDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAsLkjsqr2VNUnq+rZqnqmqh6ZHf/HVXW8qrqqvv+KTQoAwNLYPXDug0k6ya3d3VV14+z4Lyf5R0n+w1YPBwDAcporIqtqJcl9SVa7u5Oku1+c/fv/np1zpWYEAGDJ1KwJL39S1W1JHkvymSR3J3ktyQPdfeySc44neX93P73JbRxKcuji5ZWVlZuOHDnyRmbfVs6cOZO9e/cueoylZkfT7GiaHU2zo2l2NM2Opu2EHR08ePBkd69udN28EfnuJF9Icm93H66q25M8nuSd3f212TnHc5mIfL3V1dVeX1+f7x7sAEePHs3a2tqix1hqdjTNjqbZ0TQ7mmZH0+xo2k7YUVVtGpHzvrDmRJLzSR5Nku5+KsnzSd61JRMCALCtzBWR3X0qybEka0lSVQeSHEjypSs3GgAAy2rkfSI/nOR/qapnknw2yf3dfbKqfqGq1pOsJjlaVV+5AnMCALBE5n6Ln+5+LsldGxy/f0snAgBg6fmLNQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAwTkQAADBORAAAME5EAAAybOyKrak9VfbKqnq2qZ6rqkdnxP1dVv1lVX66q/1hV77xy4wIAsAx2D5z7YJJOcmt3d1XdODv+C0k+1d2frqofS/LpJHdu7ZgAACyTuR6JrKqVJPcl+Xvd3UnS3S9W1fckuSPJI7NTjyS5uaq+90oMCwDAcqhZE17+pKrbkjyW5DNJ7k7yWpIHknwjyb/s7j9/yblPJPlod//a627jUJJDFy+vrKzcdOTIkTd+D7aJM2fOZO/evYseY6nZ0TQ7mmZH0+xomh1Ns6NpO2FHBw8ePNndqxtdN+/T2buT3JLki9390aq6PcnjSX503iG6++NJPn7x8urqaq+trc37n297R48ezdV0f/847GiaHU2zo2l2NM2OptnRtJ2+o3lfWHMiyfkkjyZJdz+V5PlcCMu3V9XuJKmqSvKO2fkAAOxQc0Vkd59KcizJWpJU1YEkB5L8RpLfTvK3Zqd+IMl6d39l60cFAGBZjLw6+8NJHqqqj+XCo5L3d/fJqro/yaer6meTfDPJT16BOQEAWCJzR2R3P5fkrg2O/36Sv7iVQwEAsNz8xRoAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACG7V70AFdad+fJF17J8VOvZv8NK7njlutSVYseCwBgW9vREbn+yunc8/AT+erLp3Ptrmty9tz53Hz9vhz+0Huyet2+RY8HALBt7dins7s79zz8RF546XTOnuuc/va5nD3XeeGl07n34SfS3YseEQBg29qxEfnkC69k/eXXcu78d8biufOdEy+fzpMvvLKgyQAAtr8dG5HHT72a3bs2/t3Ha3ddk+OnXn2TJwIA2Dl2bETuv2ElZ8+d3/C6s+fOZ/8NK2/yRAAAO8eOjcg7brkuN1+/L7uu+c5HI3ddU3nH9ftyxy3XLWgyAIDtb+6IrKrjVfX7VfX07J8Pzo4frKonq+p3q+rzVfV9V27c+VVVDn/oPbnlrfty7a7Kvj+xK9fuqux/674cvu+93uYHAOANGH2Lnw9299MXL1TVdUkeTfJD3f17VfWDs8t/YetG/ONbvW5fjh16n/eJBADYYm/0fSL/bJKXuvv3kqS7f72q3lFV7+7u337j471xVZU791+fO/dfv+hRAAB2jJr3/RKr6niSbyapJE8k+WiSbyd5Lslf6+7frKq/nuTfJPlAd//K6/77Q0kOXby8srJy05EjR7biPmwLZ86cyd69exc9xlKzo2l2NM2OptnRNDuaZkfTdsKODh48eLK7Vze6biQi39HdJ6rq2iQ/l+Rd3f0jVXVXkgeSfHeS30pyV5Kf6e7HLnd7q6urvb6+PnA3trejR49mbW1t0WMsNTuaZkfT7GiaHU2zo2l2NG0n7KiqNo3IuZ/O7u4Ts3+frapPJPny7PLnknxu9oH2JHkxyRff4MwAACyxuV6dXVUrVfWWSw79RJKnZte9/ZLjfz/Jr3X3V7ZsQgAAls68j0T+qSRHqmpXLvxO5HNJ7pld9w9nr8renQtPZ9+35VMCALBU5orI7n4uye2bXPdTWzoRAABLb8f+xRoAAK4cEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMAwEQkAwDARCQDAMBEJAMCw6u7FfOCqbyX5+kI++GJ8d5L/sughlpwdTbOjaXY0zY6m2dE0O5q2E3b0tu7es9EVC4vIq01VrXf36qLnWGZ2NM2OptnRNDuaZkfT7GjaTt+Rp7MBABgmIgEAGCYi3zwfX/QA24AdTbOjaXY0zY6m2dE0O5q2o3fkdyIBABjmkUgAAIaJSAAAholIAACGicgtVlXHq+r3q+rp2T8fnB0/WFVPVtXvVtXnq+r7Fj3rolTVnqr6ZFU9W1XPVNUjs+N/rqp+s6q+XFX/sareuehZF+UyO/rHs8+xrqrvX/CYC7fRnqpqb1V9dvZ59DtV9XhVfe+iZ12Uy3wu/ers+9HTVfXrVXX7omddlM12dMn1Pzn7mnv/gkZcuMt8Hm34M+9qdJkdXfbzazvbvegBdqgPdvfTFy9U1XVJHk3yQ939e1X1g7PLf2FB8y3ag0k6ya3d3VV14+z4LyT5VHd/uqp+LMmnk9y5oBkXbbMd/XKSf5TkPyxssuWy2Z4+leTfzY797SS/mOSHFzTjom22ox/v7m8kSVX9jVz4erta/+d2sx2lqvYn+akkn1/QbMti0x3ldT/zrmKb7ehyu9vWvDp7i1XV8STvf11E3pHkX3b3rZcc+2aSH+7u337Th1ygqlpJ8p+SrHb3Ny85/j1JvpLk+u7+g6qq2Xl/qbu/sphpF2OzHb3unON53efZ1WaePc3OuyPJL3f3/jdrtmUxsKP/Icn/3N3f/yaNtjQut6OquibJryb5u0l+Psknuvuzb/qQCzaxo+O5yr8XJZf92TbX1+B25ensK+Pw7CHrh6rqbUmeTfLWqvqBJKmqv57kTybZv8AZF+XPJnk5yc/Ont7/9ar6y0luTvKfuvsPkqQv/N/NiSTvWNyoC7PZjvhO8+7pp5P8mzd3tKVx2R1V1eGq+mqS/z3Jf7+oIRfscjs6lOQ3uvsLixtvKUx9rb3+Z97VaLMd7ejv5yJy6/1Qd9+W5N1JTiX5pe7+/5L8WJL/o6q+kOS/TfLFJH+wuDEXZneSW5J8sbvvSPI/JflX8asVl9pwR1X1pxY71tKZ3FNV/WyS703yM4sZceEuu6Puvqe7b07yvyX52OLGXKjNdvS+JB9I8nOLHG5JXO7z6I/8zFvcmAu12c+279ro+E75fu4H9xbr7hOzf5+tqk8k+fLs8ueSfC658Eu2SV7MhZC82pxIcj4Xfic03f1UVT2fC19kb6+q3Zc8nf2O2flXm8129K4kX1vkYEvmsnuqqo8k+ZtJ7u7u04sbc6Hm+lzq7l+qqn9WVW/t7pcWM+rCbLaj23Lh2aJnL3w7yo1JPlVVb+/uf7qgWRdl08+j7v6/Zse+42feVWizHd28yfEd8f3cI5FbqKpWquotlxz6iSRPza57+yXH/36SX7vaftcvSbr7VJJjSdaSpKoOJDmQ5DeS/HaSvzU79QNJ1u3oO3b0pUXOtWwut6eqOpQLX39/5eKLR65GEzv60xfPm73q+KVceNrtqnKZHf1Kd7+9u/fPfp/280n+x6swIKc+j95yyan/9Wfe1eYyO/qtTY7viO/nXlizharqzyQ5kmRXkkryXJKf7u7jVfXPk/xgLjz6+1tJ/s7V+sNttqeHktyQC/+H9g+7+0hV/flceIXoW5N8M8lPdvczCxt0gS6zo19I8qO58KjIS0n+c3dfzW9f80f2lOT/SfLVXPj6+8+zU7/V3e9dyJALtsmOnkzyr3PhqbbzSb6e5CNX64sjNvt6e905/z5X6Qtrkk0/j57KJj/zFjTmQl3m+/bk59d2JSIBABjm6WwAAIaJSAAAholIAACGiUgAAIaJSAAAholIAACGiUgAAIaJSAAAhv3/IXCfusRjBTYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8), dpi = 80)\n",
    "\n",
    "plt.scatter(y_test, y_pred)\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2dce6b42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:57.113037Z",
     "iopub.status.busy": "2022-04-21T11:14:57.112372Z",
     "iopub.status.idle": "2022-04-21T11:14:57.123451Z",
     "shell.execute_reply": "2022-04-21T11:14:57.122633Z"
    },
    "papermill": {
     "duration": 0.057645,
     "end_time": "2022-04-21T11:14:57.125357",
     "exception": false,
     "start_time": "2022-04-21T11:14:57.067712",
     "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>Actual</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.11</td>\n",
       "      <td>64.040120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.57</td>\n",
       "      <td>59.109108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.28</td>\n",
       "      <td>67.122002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  predicted\n",
       "0   63.11  64.040120\n",
       "1   58.57  59.109108\n",
       "2   66.28  67.122002"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare = pd.DataFrame({\"Actual\": y_test.flatten(), \"predicted\": y_pred.flatten()})\n",
    "compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b2479c51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:57.214941Z",
     "iopub.status.busy": "2022-04-21T11:14:57.214521Z",
     "iopub.status.idle": "2022-04-21T11:14:57.434873Z",
     "shell.execute_reply": "2022-04-21T11:14:57.434190Z"
    },
    "papermill": {
     "duration": 0.268149,
     "end_time": "2022-04-21T11:14:57.436846",
     "exception": false,
     "start_time": "2022-04-21T11:14:57.168697",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = x_train\n",
    "b = y_train\n",
    "c = x_test\n",
    "d = y_pred\n",
    "plt.scatter(a,b)\n",
    "plt.scatter(c,d)\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f85c788e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:57.527280Z",
     "iopub.status.busy": "2022-04-21T11:14:57.526872Z",
     "iopub.status.idle": "2022-04-21T11:14:57.537139Z",
     "shell.execute_reply": "2022-04-21T11:14:57.536346Z"
    },
    "papermill": {
     "duration": 0.058125,
     "end_time": "2022-04-21T11:14:57.539189",
     "exception": false,
     "start_time": "2022-04-21T11:14:57.481064",
     "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.38</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.66</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.95</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height  Weight\n",
       "0    1.38       6\n",
       "1    1.66       8\n",
       "2    1.95      10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2 = pd.DataFrame({\"Height\":[1.38, 1.66, 1.95], \"Weight\":[6,8,10]})\n",
    "df2"
   ]
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       "    Height  Weight\n",
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       "12    1.78   69.92\n",
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     "execution_count": 21,
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       "    Height  Weight\n",
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       "0     1.38    6.00\n",
       "1     1.66    8.00\n",
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     "execution_count": 22,
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   "source": [
    "df3 = df.append(df2)\n",
    "df3"
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  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f6a6fbbf",
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   "outputs": [],
   "source": [
    "train = df3.iloc[:15]\n",
    "test = df3.iloc[15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5beb73a3",
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       "    Height  Weight\n",
       "0     1.47   52.21\n",
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       "2     1.52   54.48\n",
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       "12    1.78   69.92\n",
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       "14    1.83   74.46"
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     "execution_count": 24,
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       "   Height  Weight\n",
       "0    1.38     6.0\n",
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   "id": "2e910508",
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   "outputs": [],
   "source": [
    "x_train = df3[\"Height\"][:15].values.reshape(-1,1)\n",
    "y_train = df3[\"Weight\"][:15].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "233cfc91",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_test =  df3[\"Height\"][15:].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "da80bb91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:58.380737Z",
     "iopub.status.busy": "2022-04-21T11:14:58.380205Z",
     "iopub.status.idle": "2022-04-21T11:14:58.383775Z",
     "shell.execute_reply": "2022-04-21T11:14:58.383018Z"
    },
    "papermill": {
     "duration": 0.053742,
     "end_time": "2022-04-21T11:14:58.385642",
     "exception": false,
     "start_time": "2022-04-21T11:14:58.331900",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "99bf43a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:58.481571Z",
     "iopub.status.busy": "2022-04-21T11:14:58.481001Z",
     "iopub.status.idle": "2022-04-21T11:14:58.487652Z",
     "shell.execute_reply": "2022-04-21T11:14:58.486900Z"
    },
    "papermill": {
     "duration": 0.057511,
     "end_time": "2022-04-21T11:14:58.489895",
     "exception": false,
     "start_time": "2022-04-21T11:14:58.432384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d67b0960",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:58.587251Z",
     "iopub.status.busy": "2022-04-21T11:14:58.586953Z",
     "iopub.status.idle": "2022-04-21T11:14:58.591196Z",
     "shell.execute_reply": "2022-04-21T11:14:58.590205Z"
    },
    "papermill": {
     "duration": 0.055168,
     "end_time": "2022-04-21T11:14:58.593207",
     "exception": false,
     "start_time": "2022-04-21T11:14:58.538039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "71ffae35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:58.689888Z",
     "iopub.status.busy": "2022-04-21T11:14:58.689610Z",
     "iopub.status.idle": "2022-04-21T11:14:58.694738Z",
     "shell.execute_reply": "2022-04-21T11:14:58.694099Z"
    },
    "papermill": {
     "duration": 0.055541,
     "end_time": "2022-04-21T11:14:58.696490",
     "exception": false,
     "start_time": "2022-04-21T11:14:58.640949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[45.49366151],\n",
       "       [62.64987374],\n",
       "       [80.41880784]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9002d46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:58.793346Z",
     "iopub.status.busy": "2022-04-21T11:14:58.792899Z",
     "iopub.status.idle": "2022-04-21T11:14:59.005285Z",
     "shell.execute_reply": "2022-04-21T11:14:59.004464Z"
    },
    "papermill": {
     "duration": 0.263115,
     "end_time": "2022-04-21T11:14:59.007230",
     "exception": false,
     "start_time": "2022-04-21T11:14:58.744115",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mznoBXgd8qLp9LvC56vZSIICfADYB72n2XIODg9kNo6OjXdlON/ViT5m92Zc9laOUvoBtOUOm1plCeQXw+oi4GxgBXhkR/5KZP6ie+8fAx4Gz5vYeI0lqRdMAz8z1mXlaZi4HLgS+nJm/GxFLASIigLXA7k4WKkl6prl8kOcTEfE8pqZRdgJ/2JaKJEm1tBTgmXkzcHN1+5UdqEeSVNNR/UlMSdKzq3UYYds2FvEgcE8XNnUK8FAXttNNvdgT9GZf9lSOUvp6YWY+b/pgVwO8WyJiW850zGTBerEn6M2+7KkcpfflFIokFcoAl6RC9WqAXz3fBXRAL/YEvdmXPZWj6L56cg5cko4FvboHLkk9zwCXpEIVG+ARcU1EPBARs34HS0T8YkQ8EREXdKu2I9Wsp4g4NyIeOeQsSO/pdo1Hos5rVfW2MyK+FRFf6WZ9R6LGa7XukNdpd0QciIiTu11nq2r0dWJE/FtE3Fa9Vhd3u8ZW1ejppIi4PiK+GRHfiIiXdbvGI1VsgAMbgV+bbYWIWAB8EPhiNwpqg4006Qn4Wj59FqS/7kJN7bCRWfqKiAHgQ8DrM/OlwG91p6w52cgsPWXmhoOvE7Ae+Epm/k+XapuLjcz+M/gnwO2ZeQZTXy/9dxGxuAt1zcVGZu/p3cDOzPx54C3Ald0oqh2KDfDM/CrQ7Bfi7cBngAc6X9Hc1eypODX6+h1gc2beW61/1L9eLb5WbwKu7WA5bVOjrwROqL6FtL9a94lu1HakavT0c8CXq3W/DSyPiEY3apurYgO8mYhYBpwP/ON819JmZ1d/vn4hIl4638W0yc8CJ0XEzRGxPSLeMt8FtUtEHM/U3t9n5ruWNrkKeAlwP7ALeEdmPjm/Jc3ZbcBvAkTEWcALgdPmtaKa5vJ1ske7vwfelZlPTu0s9IRbmfpOhImIOA/YApw+vyW1xUJgEFgN9AH/GRFfz8z/nt+y2uI3gP8oZPqkjjVMfX30K4GfBr4UEV/LzEfntaq5+QBwZUTsZOpNaQdwYF4rqqmXA3wVMFKF9ynAeRHxRGZumdeq5uDQX5LM/HxEfCgiTsnMEr6MZzb3AQ9n5mPAYxHxVeAMoBcC/EIKmT6p6WLgA9Vpvr4bEXcBLwa+Mb9lHbnq9+pieOoENXcBd85rUTX17BRKZr4oM5dXZxK6DvjjksMbICJeUP2AHfxT7zjg4fmtqi1uAH45IhZWUw4vB+6Y55rmLCJOBH6Vqf56xb1M/aVENU+8gkLC7tlExMAh/4j9feCrpfxFUeweeERcy9R/wU+JiPuA9wKLADLzn+axtCNWo6cLgD+KiCeASeDCLOCjtM36ysw7IuJG4JvAk8BHM/OoPkVfzZ+/84EvVn9ZFKFGX+8DNkbELqbOxvWuo/0vwBo9vQTYFBEJfAu4ZJ5KbZkfpZekQvXsFIok9ToDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXq/wFsj7pK6i1MLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test, y_pred)\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c99ace9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:59.106576Z",
     "iopub.status.busy": "2022-04-21T11:14:59.106155Z",
     "iopub.status.idle": "2022-04-21T11:14:59.340543Z",
     "shell.execute_reply": "2022-04-21T11:14:59.339851Z"
    },
    "papermill": {
     "duration": 0.286537,
     "end_time": "2022-04-21T11:14:59.342549",
     "exception": false,
     "start_time": "2022-04-21T11:14:59.056012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = x_train\n",
    "b = y_train\n",
    "c = x_test\n",
    "d = y_pred\n",
    "plt.scatter(a,b)\n",
    "plt.scatter(c,d)\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "319a9ffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:59.444236Z",
     "iopub.status.busy": "2022-04-21T11:14:59.443752Z",
     "iopub.status.idle": "2022-04-21T11:14:59.449410Z",
     "shell.execute_reply": "2022-04-21T11:14:59.448319Z"
    },
    "papermill": {
     "duration": 0.05824,
     "end_time": "2022-04-21T11:14:59.451253",
     "exception": false,
     "start_time": "2022-04-21T11:14:59.393013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.38],\n",
       "       [1.66],\n",
       "       [1.95]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fec2fe4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T11:14:59.552700Z",
     "iopub.status.busy": "2022-04-21T11:14:59.552364Z",
     "iopub.status.idle": "2022-04-21T11:14:59.557861Z",
     "shell.execute_reply": "2022-04-21T11:14:59.556978Z"
    },
    "papermill": {
     "duration": 0.059286,
     "end_time": "2022-04-21T11:14:59.560107",
     "exception": false,
     "start_time": "2022-04-21T11:14:59.500821",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[45.49366151],\n",
       "       [62.64987374],\n",
       "       [80.41880784]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35fcd7a4",
   "metadata": {
    "papermill": {
     "duration": 0.049631,
     "end_time": "2022-04-21T11:14:59.667630",
     "exception": false,
     "start_time": "2022-04-21T11:14:59.617999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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