NN for Regression for diamonds.ipynb

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    "# load the needed libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics"
   ]
  },
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       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
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       "      <td>SI1</td>\n",
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       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
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       "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "\n",
       "      z  \n",
       "0  2.43  \n",
       "1  2.31  \n",
       "2  2.31  \n",
       "3  2.63  \n",
       "4  2.75  "
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   "source": [
    "# read the dataset\n",
    "data = pd.read_csv('diamonds.csv')\n",
    "# data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/diamonds.gsheet')\n",
    "data.head()"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  53940 non-null  int64  \n",
      " 1   carat       53940 non-null  float64\n",
      " 2   cut         53940 non-null  object \n",
      " 3   color       53940 non-null  object \n",
      " 4   clarity     53940 non-null  object \n",
      " 5   depth       53940 non-null  float64\n",
      " 6   table       53940 non-null  float64\n",
      " 7   price       53940 non-null  int64  \n",
      " 8   x           53940 non-null  float64\n",
      " 9   y           53940 non-null  float64\n",
      " 10  z           53940 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# print information about the dataset\n",
    "data.info()"
   ]
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       "      <th>carat</th>\n",
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       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
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       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26970.500000</td>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
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       "      <th>std</th>\n",
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       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
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       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13485.750000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26970.500000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40455.250000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
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       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
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       "  "
      ],
      "text/plain": [
       "         Unnamed: 0         carat         depth         table         price  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean   26970.500000      0.797940     61.749405     57.457184   3932.799722   \n",
       "std    15571.281097      0.474011      1.432621      2.234491   3989.439738   \n",
       "min        1.000000      0.200000     43.000000     43.000000    326.000000   \n",
       "25%    13485.750000      0.400000     61.000000     56.000000    950.000000   \n",
       "50%    26970.500000      0.700000     61.800000     57.000000   2401.000000   \n",
       "75%    40455.250000      1.040000     62.500000     59.000000   5324.250000   \n",
       "max    53940.000000      5.010000     79.000000     95.000000  18823.000000   \n",
       "\n",
       "                  x             y             z  \n",
       "count  53940.000000  53940.000000  53940.000000  \n",
       "mean       5.731157      5.734526      3.538734  \n",
       "std        1.121761      1.142135      0.705699  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        4.710000      4.720000      2.910000  \n",
       "50%        5.700000      5.710000      3.530000  \n",
       "75%        6.540000      6.540000      4.040000  \n",
       "max       10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# used for calculating some statistical data like percentile, and mean of the numerical values of the DataFrame\n",
    "data.describe()"
   ]
  },
  {
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       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
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   "source": [
    "# drop the first column which is used for numbering the rows and is not useful for the model\n",
    "data.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ef54aee6-beaa-4433-90c3-9d9862050a4b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   carat        cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "5   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n",
       "6   0.24  Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47\n",
       "7   0.26  Very Good     H     SI1   61.9   55.0    337  4.07  4.11  2.53\n",
       "8   0.22       Fair     E     VS2   65.1   61.0    337  3.87  3.78  2.49\n",
       "9   0.23  Very Good     H     VS1   59.4   61.0    338  4.00  4.05  2.39"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop duplicates rows\n",
    "list = ['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y', 'z']\n",
    "data.drop_duplicates(subset=list, keep='first', inplace=True)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1653313855247,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "zNMuPZgmUCpk",
    "outputId": "ba14674b-6727-40b7-a402-98e5702acfbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 53794 entries, 0 to 53939\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   carat    53794 non-null  float64\n",
      " 1   cut      53794 non-null  object \n",
      " 2   color    53794 non-null  object \n",
      " 3   clarity  53794 non-null  object \n",
      " 4   depth    53794 non-null  float64\n",
      " 5   table    53794 non-null  float64\n",
      " 6   price    53794 non-null  int64  \n",
      " 7   x        53794 non-null  float64\n",
      " 8   y        53794 non-null  float64\n",
      " 9   z        53794 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
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    },
    "id": "nwur-t_hUCpl",
    "outputId": "2d38e51b-ad60-4c3e-a51a-dddf8cd98174"
   },
   "outputs": [
    {
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       "  "
      ],
      "text/plain": [
       "         missing\n",
       "carat          0\n",
       "cut            0\n",
       "color          0\n",
       "clarity        0\n",
       "depth          0\n",
       "table          0\n",
       "price          0\n",
       "x              0\n",
       "y              0\n",
       "z              0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the number of missing data in every column\n",
    "missing = pd.DataFrame({'missing': data.isnull().sum()})\n",
    "missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
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      "userId": "09631177959643063939"
     },
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    },
    "id": "iTZ4AAIOUCpm"
   },
   "outputs": [],
   "source": [
    "# changed the categorical string data of cut column into numeric\n",
    "le = LabelEncoder()\n",
    "cut = le.fit_transform(data['cut'])\n",
    "data.drop('cut', axis=1, inplace=True)\n",
    "data['cut'] = cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1653313855248,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "M48toaK6UCpm"
   },
   "outputs": [],
   "source": [
    "# changed the categorical string data of color column into numeric\n",
    "color = le.fit_transform(data['color'])\n",
    "data.drop('color', axis=1, inplace=True)\n",
    "data['color'] = color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1653313855248,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "HPZcofqqUCpn"
   },
   "outputs": [],
   "source": [
    "# changed the categorical string data of clarity column into numeric\n",
    "clarity = le.fit_transform(data['clarity'])\n",
    "data.drop('clarity', axis=1, inplace=True)\n",
    "data['clarity'] = clarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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     "height": 363
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    "executionInfo": {
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     "status": "ok",
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    "id": "NvAk1Re_rS03",
    "outputId": "a5c067c9-d4ed-4b9c-8936-3f3894d2ea57"
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       "      <td>0.267825</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.000393</td>\n",
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       "\n",
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       "\n",
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       "\n",
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       "          const element = document.querySelector('#df-544bd6e1-88a9-4eea-9875-0604478ad577');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "            carat     depth     table     price         x         y         z  \\\n",
       "carat    1.000000  0.027861  0.181091  0.921548  0.975380  0.951908  0.953542   \n",
       "depth    0.027861  1.000000 -0.297669 -0.011048 -0.025348 -0.029389  0.094757   \n",
       "table    0.181091 -0.297669  1.000000  0.126566  0.194855  0.183231  0.150270   \n",
       "price    0.921548 -0.011048  0.126566  1.000000  0.884504  0.865395  0.861208   \n",
       "x        0.975380 -0.025348  0.194855  0.884504  1.000000  0.974592  0.970686   \n",
       "y        0.951908 -0.029389  0.183231  0.865395  0.974592  1.000000  0.951844   \n",
       "z        0.953542  0.094757  0.150270  0.861208  0.970686  0.951844  1.000000   \n",
       "cut      0.017785 -0.193184  0.150366  0.040196  0.022592  0.027805  0.002442   \n",
       "color    0.291019  0.047572  0.026102  0.171825  0.269876  0.263153  0.267825   \n",
       "clarity -0.214068 -0.053165 -0.088074 -0.071218 -0.225575 -0.217459 -0.224117   \n",
       "\n",
       "              cut     color   clarity  \n",
       "carat    0.017785  0.291019 -0.214068  \n",
       "depth   -0.193184  0.047572 -0.053165  \n",
       "table    0.150366  0.026102 -0.088074  \n",
       "price    0.040196  0.171825 -0.071218  \n",
       "x        0.022592  0.269876 -0.225575  \n",
       "y        0.027805  0.263153 -0.217459  \n",
       "z        0.002442  0.267825 -0.224117  \n",
       "cut      1.000000  0.000393  0.028141  \n",
       "color    0.000393  1.000000 -0.028002  \n",
       "clarity  0.028141 -0.028002  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1653313855249,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "c9yLckfgUCpn"
   },
   "outputs": [],
   "source": [
    "# drop the label column and store it in target, and create a new data frame with no label\n",
    "attributes = data.drop('price', axis = 1)\n",
    "target = data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1653313855249,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
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    },
    "id": "wj8geXQ_UCpo",
    "outputId": "bd77dd83-ec85-48d2-da6f-a3a90c03f1df"
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   "outputs": [
    {
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       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
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       "      <th>clarity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53794.00000</td>\n",
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       "      <td>53794.000000</td>\n",
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       "      <td>53794.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.79778</td>\n",
       "      <td>61.748080</td>\n",
       "      <td>57.458109</td>\n",
       "      <td>5.731214</td>\n",
       "      <td>5.734653</td>\n",
       "      <td>3.538714</td>\n",
       "      <td>2.553947</td>\n",
       "      <td>2.593914</td>\n",
       "      <td>3.835762</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>2.233679</td>\n",
       "      <td>1.120695</td>\n",
       "      <td>1.141209</td>\n",
       "      <td>0.705037</td>\n",
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       "      <td>1.724872</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.20000</td>\n",
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       "      <td>43.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.40000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>0.70000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>1.04000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.030000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>5.01000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
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      "text/plain": [
       "             carat         depth         table             x             y  \\\n",
       "count  53794.00000  53794.000000  53794.000000  53794.000000  53794.000000   \n",
       "mean       0.79778     61.748080     57.458109      5.731214      5.734653   \n",
       "std        0.47339      1.429909      2.233679      1.120695      1.141209   \n",
       "min        0.20000     43.000000     43.000000      0.000000      0.000000   \n",
       "25%        0.40000     61.000000     56.000000      4.710000      4.720000   \n",
       "50%        0.70000     61.800000     57.000000      5.700000      5.710000   \n",
       "75%        1.04000     62.500000     59.000000      6.540000      6.540000   \n",
       "max        5.01000     79.000000     95.000000     10.740000     58.900000   \n",
       "\n",
       "                  z           cut         color       clarity  \n",
       "count  53794.000000  53794.000000  53794.000000  53794.000000  \n",
       "mean       3.538714      2.553947      2.593914      3.835762  \n",
       "std        0.705037      1.027569      1.701117      1.724872  \n",
       "min        0.000000      0.000000      0.000000      0.000000  \n",
       "25%        2.910000      2.000000      1.000000      2.000000  \n",
       "50%        3.530000      2.000000      3.000000      4.000000  \n",
       "75%        4.030000      3.000000      4.000000      5.000000  \n",
       "max       31.800000      4.000000      6.000000      7.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "executionInfo": {
     "elapsed": 804,
     "status": "ok",
     "timestamp": 1653313856034,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
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    },
    "id": "_BBA7PqDUCpo",
    "outputId": "a6be1dc0-fea1-43ec-b510-bc3a0228d3d6"
   },
   "outputs": [
    {
     "data": {
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SRQCw9gQ/AACMpbm5uczOzmZ+fj5LS0uZn5/P7Oxs5ubm2i4NANaM4c4AAIylQwOcd+7cmeFwmE6nk16vZ7AzAFUR/AAAMLZmZmYyMzOThYWFbNu2re1yAGDNOeoFAAAAUCnBDwAAAEClBD8AAAAAlRL8AAAAAFRK8AMAsMJgMMjU1FS2b9+eqampDAaDtksCADhuPtULAGBkMBhkbm4u/X4/y8vLmZiYyOzsbJL4iG8AYEOy4wcAYKTX66Xf76fb7WZycjLdbjf9fj+9Xq/t0gAAjovgBwBgZDgcZnp6+oi16enpDIfDlipivV122WXZtGlTut1uNm3alMsuu6ztkgBgTQl+AABGOp1ObrrppiNm/Nx0003pdDptl8Y6uOyyy3Lvvfdmx44d+dznPpcdO3bk3nvvFf4AUBUzfgAARrrdbm6++ebcfPPNufDCC/Pwww9n165d2bFjR9ulsQ6+/OUv593vfnc+/OEPZ2FhIR/+8IeTJHfccUfLlQHA2rHjBwBgZH5+Prt27cpdd92Vt73tbbnrrruya9euzM/Pt10a66Bpmrz//e8/Yu39739/mqZpqSIAWHuCHwCAkeFwmBtvvDEPPvhg7rvvvjz44IO58cYbzfipVCklN9xwwxFrN9xwQ0opLVUEAGvPUS8AgJFDM34+85nPZDgcptPp5B3veIcZP5X60R/90dx+++1Jkh//8R/Pz/7sz+b222/PpZde2nJlALB2BD8AACNm/IyXL33pS7nssstyxx135Pbbb08pJZdeemm+9KUvtV0aAKwZwQ8AwMj8/Hwuv/zyvO9978u+ffuyZcuWXH755Wb8VOxQyLOwsJBt27a1WwwArAPBDwDAyMMPP5zHHnssBw4cSJIcOHAgv/M7v5Nnnnmm5coAAI6P4c4AACObNm3K3r17c/bZZ6eUkrPPPjt79+7Npk1+ZAIANiY/xQAAjCwvL7/oE51KKVleXm6pIgCAEyP4AQBYYfPmzXnyySfTNE2efPLJbN68ue2SWEeDwSBTU1PZvn17pqamMhgM2i4JANaUGT8AACu88MILmZw8+CNS0zTZv39/yxWxXgaDQebm5tLv97O8vJyJiYnMzs4mSWZmZlquDgDWhh0/AABHWVpaOuIrder1eun3++l2u5mcnEy3202/30+v12u7NABYM4IfAICjHJrzc/S8H+oyHA4zPT19xNr09HSGw2FLFQHA2hP8AAAcpWmaI75Sp06nk8XFxSPWFhcX0+l0WqoIANaeGT8AAIylubm5XHLJJS9a/8QnPtFCNQCwPuz4AQBgLP3Mz/xMkhcf7Tu0DgA1EPwAADCWlpaWsnXr1hw4cCDz8/M5cOBAtm7daqg3AFUR/AAAMLa+8pWvvOJtANjoBD8AAIytH/mRH3nF2wCw0RnuDADAWJqcnMxTTz11eLbPynUAqIUdPwAAAACVEvwAADCWDg13bpom8/PzaZrGcGcAqiP4AQBgbBnuDEDtBD8AAIwtw50BqJ3JdQAAjCXDnQEYB3b8AAAwll5ulo8ZPwDURPADAMBYWzncGQBqI/gBAGBsffSjH33F2wCw0Ql+AAAYW1dfffUr3gaAjU7wAwDAWCul5Atf+MKLhjwDQA0EPwAAjKWVM31uueWWl1wHgI1O8AMAwFhaucPn0ksvfcl1ANjoBD8AAIy1pmlyww032OkDQJUEPwAAjK13vvOdr3gbADY6wQ8AAGPr13/911/xNgBsdJNtFwAAAG0y0weAmtnxAwAAAFApwQ8AAGOtaZrMz88b7gxAlRz1AgBgbJ155pmZmprKcDhMp9PJmWeemaeffrrtsgBgzQh+AAAYW08//XR2796d5eXlTExM5JJLLmm7JABYU4IfAADGmrAHgJqZ8QMAAABQKcEPAABjzXBnAGrmqBcAAGNtfn4+y8vLmZ+fb7sUAFhzgh8AAMba9u3b0zRNSiltlwIAa07wAwBUaa1/iT/W13NsaOM41Cs9A6BGgh8AoErH80v8K4U7QgEAYCMy3BkAYOTlwh2hT90MdwagZoIfAIAVmqZJ0zQ5f9dvH/4eAGCjEvwAADDWbr311jz//PO59dZb2y4FANacGT8AAIy1X/iFX2i7BABYN4IfAAA2PJ/iBgAvzVEvAAA2vEPzmI73z8qZTsfzBwBOVYIfAAAAgEoJfgAAAAAqJfgBAAAAqJThzgDAKevNN92b7z63v7XrX3D951u57mtfszl/cuOlrVwbAKiL4AcAOGV997n9+foH3tbKtRcWFrJt27ZWrt1W4AQA1MdRLwAAAIBKCX4AAAAAKuWoFwBwyjqzc30uuvv69gq4u53LntlJknaOuAEAdRH8AACnrKeHHzDjBwDgBDjqBQAAAFApwQ8AAABApRz1AgBOaa0ee/piO9d+7Ws2t3JdAKA+gh8A4JTV1nyf5GDg1Ob1AQDWwqqOepVSziqlfKqU8m9LKcNSyt8vpZxdSvlyKeXPRl+3rnexAAAAAKzeamf83Jbki03TvCnJm5MMk1yf5L6maf5OkvtGtwEAAAA4Rbxq8FNKeW2S/yZJP0mapnmhaZrvJHl7krtHD7s7yTvWq0gAAAAAjl1pmuaVH1DKf5HkI0kezsHdPg8keW+SbzRNc9boMSXJU4duH/X8dyV5V5Kcc845P3jPPfes6RvYCPbu3Zszzjij7TI4SfR7vOj3eNHv8XL1F5/JR3/s9LbL4CTR7/Hiv+fjRb/Hy7j2u9vtPtA0zcUvdd9qhjtPJvmvkuxsmuYPSim35ahjXU3TNKWUl0yQmqb5SA4GR7n44oubbdu2HUvtVVhYWMg4vu9xpd/jRb/Hi36PmS9+Xr/HiX6PFf89Hy/6PV70+8VWM+Pn8SSPN03zB6Pbn8rBIOhbpZRzk2T09dvrUyIAAAAAx+NVg5+maZ5I8hellL87Wtqeg8e+PpvkqtHaVUl+a10qBAAAgGM0GAwyNTWV7du3Z2pqKoPBoO2SoBWrOeqVJDuTfLyU8j1JHknyzhwMjX6zlDKb5LEk/3h9SgQAOHkOji4cfX/zwa+vNhMRgFPLYDDI3Nxc+v1+lpeXMzExkdnZ2STJzMxMy9XBybWq4Kdpmj9O8lJDgravbTkAAO1ZGfocvS78WX9vvunefPe5/a1d/4LrP9/KdV/7ms35kxsvbeXaUKter5d+v59ut3t45ku/38/OnTsFP4yd1e74AQDYUF4uxDlZrycoOnbffW5/vv6Bt7Vy7TaHgbYVOEHNhsNhpqenj1ibnp7OcDhsqSJoz2qGOwMAbDhN0xzzn7V8PQDa0+l0sri4eMTa4uJiOp1OSxVBewQ/AAAAVGVubi6zs7OZn5/P0tJS5ufnMzs7m7m5ubZLg5POUS8AAACqcmiOz86dOzMcDtPpdNLr9cz3YSwJfgAAAKjOzMxMZmZmWp3hBacCR70AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAq5VO9AAA4JZzZuT4X3X19ewXc3c5lz+wkydvauTgA1RP8AABwSnh6+IF8/QPtBCBtftzzBdd/vpXrAjAeHPUCAAAAqJTgBwAAAKBSgh8AAACASpnxAwDAKaPVeTdfbOfar33N5lauC8B4EPwAAHBKaGuwc3IwcGrz+gCwXhz1AgAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AACO8u53vzuf+9zn8u53v7vtUgAATshk2wUAAJxKSim5/fbbc/vttx++3TRNy1UBABwfO34AAFY4OuQR+gAAG5ngBwBgpJRyTOsAAKc6wQ8AwMih3T1bt27Npk2bsnXr1iPWAQA2GsEPAMAKb3rTm/Lss8/mwIEDefbZZ/OmN72p7ZIAAI6b4c4AACv86Z/+aT70oQ/lwgsvzMMPP5xf/MVfbLskAIDjJvgBAFihaZr80i/9Ug4cOJBNmzY55gUAbGiOegEAHOXAgQNHfAUA2Kjs+AEAGJmcnMzExEQOHDiQ/fv3Z/Pmzdm0aVOWl5fbLg0A4LgIfgAARpaWlvK6170un/jEJ7K8vJyJiYlcccUVeeKJJ9ouDQDguDjqBQCwwtVXX52dO3fmsssuy86dO3P11Ve3XRIAwHGz4wcAYOS8887LRz/60Rft+DnvvPPaLg0A4LgIfgAARm655Za8973vzTXXXJPHHnss559/fpaXl3Prrbe2XRoAwHER/AAAjMzMzOT+++/PnXfemaZp8s1vfjPXXnttZmZm2i6NV1FKOfHXuPn4n9s0zQlfHwDWgxk/AAAjg8Ege/bsybnnnptSSs4999zs2bMng8Gg7dJ4FU3THPOfycmD/wa6devW3Hnnndm6dWuSg5/udqyvBQCnKsEPAMDIddddl4mJidx111259957c9ddd2ViYiLXXXdd26WxDpaWlrJ169Y8+eSTeeMb35gnn3wyW7duzdLSUtulAcCaEfwAAIw8/vjj+djHPpZut5vJycl0u9187GMfy+OPP952aayTr3zlK694GwA2OjN+AAAYW295y1vyhje8IcPhMJ1OJ48++mjbJQHAmrLjBwBg5LzzzstVV12V+fn5LC0tZX5+PldddZWPc6/Upk2b8uyzz+ahhx7KgQMH8tBDD+XZZ5/Npk1+RAagHv6vBgAwcsstt2RpaSnXXHNNLrvsslxzzTVZWlrKLbfc0nZprIOzzjrrmNYBYCMS/AAAjMzMzOS2227L6aefniQ5/fTTc9ttt/k490o9+eSTueWWW9I0Tebn59M0TW655ZY8+eSTbZcGAGvGjB8AgBVmZmYyMzOThYWFbNu2re1yWGdTU1OveBsANjo7fgAAGEuTk5O58sorj5jpdOWVV2Zy0r+NAlAP/1cDAGAs7dixI7/6q7+aSy655PBaKSU/93M/12JVALC27PgBAGCsTUxMHPEVAGoi+AEAYCzdeeed+dCHPnT4mNfS0lI+9KEP5c4772y7NABYM4IfAADG0r59+7Jjx44j1nbs2JF9+/a1VBEArD3BDwAAY2nLli254447jli74447smXLlpYqAoC1Z7gzAABj6dprr82uXbuSJBdeeGFuvfXW7Nq160W7gABgIxP8AAAwlnbv3p0ked/73pd9+/Zly5Yt2bFjx+F1AKiBo14AAIyt3bt35/nnn8/8/Hyef/55oQ8A1RH8AAAAAFRK8AMAAABQKcEPAAAAQKUEPwAAAACVEvwAAAAAVErwAwAAAFApwQ8AAABApQQ/AAAAAJUS/AAAAABUSvADAAAAUCnBDwAAAEClBD8AAAAAlRL8AAAAAFRK8AMAAABQKcEPAAAAQKUEPwAAAACVEvwAAAAAVErwAwAAAFApwQ8AwAqDwSBTU1PZvn17pqamMhgM2i6JdaTfANRusu0CAABOFYPBIHNzc+n3+1leXs7ExERmZ2eTJDMzMy1Xx1rTbwDGgR0/AAAjvV4v/X4/3W43k5OT6Xa76ff76fV6bZfGOtBvAMaB4AcAYGQ4HGZ6evqItenp6QyHw5YqYj3pNwDjQPADADDS6XSyuLh4xNri4mI6nU5LFbGe9BuAcWDGDwDAyNzcXN7+9rfn+eefz/79+7N58+acdtpp+bVf+7W2S2MdzM3N5ad+6qdy+umn57HHHsv555+fZ555JrfddlvbpQHAmrHjBwBg5P77788zzzyTs88+O6WUnH322XnmmWdy//33t10a66yU0nYJALAuBD8AACN33nlnPvjBD+aJJ57I7/7u7+aJJ57IBz/4wdx5551tl8Y66PV62bNnTx599NHcd999efTRR7Nnzx7DnQGoiuAHAGBk37592bFjxxFrO3bsyL59+1qqiPVkuDMA40DwAwAwsmXLltxxxx1HrN1xxx3ZsmVLSxWxngx3BmAcGO4MADBy7bXXZteuXUmSCy+8MLfeemt27dr1ol1A1GFubi6zs7Pp9/tZXl7O/Px8ZmdnHfUCoCqCHwCAkd27dydJ3ve+92Xfvn3ZsmVLduzYcXiduszMzCRJdu7cmeFwmE6nk16vd3gdAGrgqBcAwAq7d+/O888/n/n5+Tz//PNCn8rNzMzkwQcfzH333ZcHH3xQ6ANAdQQ/AAAAAJUS/AAAAABUSvADAAAAUCnBD5puOqEAABvCSURBVAAAAEClBD8AAAAAlRL8AAAAAFRK8AMAAABQKcEPAAAAQKVWHfyUUiZKKX9USvnt0e03lFL+oJTy56WUPaWU71m/MgEAAAA4Vsey4+e9SYYrbt+c5FeapnljkqeSzK5lYQAAAACcmFUFP6WU85K8Lcn/PbpdklyS5FOjh9yd5B3rUSAAAAAAx6c0TfPqDyrlU0nen+TMJL+Y5Ookvz/a7ZNSyg8k+ULTNFMv8dx3JXlXkpxzzjk/eM8996xZ8RvF3r17c8YZZ7RdBieJfo8X/R4v+j1e9Hu86Pd40e/xot/jZVz73e12H2ia5uKXum/y1Z5cSrk8ybebpnmglLLtWC/eNM1HknwkSS6++OJm27ZjfokNb2FhIeP4vseVfo8X/R4v+j1e9Hu86Pd40e/xot/jRb9f7FWDnyQ/nOS/K6X8eJLTkvxnSW5LclYpZbJpmqUk5yX5xvqVCQAAAMCxetUZP03T3NA0zXlN01yQ5KeT/G7TNFcmmU/yk6OHXZXkt9atSgAAAACO2bF8qtfRdiX5+VLKnyf5viT9tSkJAAAAgLWwmqNehzVNs5BkYfT9I0l+aO1LAgAAAGAtnMiOHwAAAABOYYIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAACozmAwyNTUVLZv356pqakMBoO2S2Id6ffLm2y7AAAAAFhLg8Egc3Nz6ff7WV5ezsTERGZnZ5MkMzMzLVfHWtPvV2bHDwAAAFXp9Xrp9/vpdruZnJxMt9tNv99Pr9druzTWgX6/MsEPAAAAVRkOh5menj5ibXp6OsPhsKWKWE/6/coEPwAAAFSl0+lkcXHxiLXFxcV0Op2WKmI96fcrE/wAAABQlbm5uczOzmZ+fj5LS0uZn5/P7Oxs5ubm2i6NdaDfr8xwZwAAAKpyaKDvzp07MxwO0+l00uv1DPqtlH6/MsEPAAAA1ZmZmcnMzEwWFhaybdu2tsthnen3y3PUCwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKvGvyUUn6glDJfSnm4lPJQKeW9o/WzSylfLqX82ejr1vUvFwAAAIDVWs2On6Ukv9A0zYVJ3pLk50opFya5Psl9TdP8nST3jW4DAAAAcIp41eCnaZpvNk3zh6Pvn04yTPL9Sd6e5O7Rw+5O8o71KhIAAACAY1eapln9g0u5IMm/TDKV5N83TXPWaL0keerQ7aOe864k70qSc8455wfvueeeE696g9m7d2/OOOOMtsvgJNHv8aLf40W/x4t+jxf9Hi/6PV70e7yMa7+73e4DTdNc/FL3rTr4KaWckeQrSXpN03y6lPKdlUFPKeWppmlecc7PxRdf3Hz1q189htLrsLCwkG3btrVdBieJfo8X/R4v+j1e9Hu86Pd40e/xot/jZVz7XUp52eBnVZ/qVUrZnOT/SfLxpmk+PVr+Vinl3NH95yb59loUCwAAAMDaWM2nepUk/STDpmluXXHXZ5NcNfr+qiS/tfblAQAAAHC8JlfxmB9O8k+S/JtSyh+P1t6X5ANJfrOUMpvksST/eH1KBAAAAOB4vGrw0zTNYpLyMndvX9tyAAAAAFgrq5rxAwAAAMDGI/gBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAACASgl+AAAAACol+AEAAAColOAHAAAAoFKCHwAAAIBKCX4AAAAAKiX4AQAAAKiU4AcAAACgUoIfAAAAgEoJfgAAAAAqJfgBAAAAqJTgBwAAAKBSgh8AAGAsDAaDTE1NZfv27ZmamspgMGi7JIB1N9l2AQAAAOttMBhkbm4u/X4/y8vLmZiYyOzsbJJkZmam5epYD4PBIL1eL8PhMJ1OJ3Nzc3rNWBL8AAAA1ev1eun3++l2u1lYWMi2bdvS7/ezc+dOYUCFBH3wnzjqBQAAVG84HOaTn/xkTjvttHS73Zx22mn55Cc/meFw2HZprINer5crrrgiO3fuzGWXXZadO3fmiiuuSK/Xa7s0OOns+AEAAKp31lln5fbbbz98e9++fbn99ttz9tlnt1gV6+Xhhx/OI488kueeey5J8tBDD+WRRx7J888/33JlcPLZ8QMAAFTvqaeeSpKcccYZR3w9tE59nnvuuWzadPBX3k2bNh0OgWDcCH4AAIDqNU2TUkr27t2bJNm7d29KKWmapuXKWA8v11f9ZhwJfgAAgLFw9C/9QoD6HThw4IivMI4EPwAAAACVEvwAAABjo5RyxFfqtnnz5iO+wjgS/AAAAGPj0PEux7zGw/79+4/4CuPIx7kDAAAbylrv1jnW1xManVz6DSdG8AMAAGwox/OL+Cv9su8X+1ObfsOJcdQLAACo3nve855jWmdju+iii45pHWom+AEAAKq3e/fuvOc978mWLVuSJFu2bMl73vOe7N69u+XKWA9f+9rXXhTyXHTRRfna177WUkWst8FgkKmpqWzfvj1TU1MZDAZtl3TKcNQLAAAYC7t3787u3btzwfWfz9c/8La2y2GdHQp59Lt+g8Egc3Nz6ff7WV5ezsTERGZnZ5MkMzMzLVfXPjt+AAAAgA2r1+ul3++n2+1mcnIy3W43/X4/vV6v7dJOCYIfAAAAYMMaDoeZnp4+Ym16ejrD4bClik4tgh8AAABgw+p0OllcXDxibXFxMZ1Op6WKTi2CHwAAAGDDmpuby+zsbObn57O0tJT5+fnMzs5mbm6u7dJOCYY7AwAAABvWzMxM7r///rz1rW/Nvn37smXLllx77bUGO4/Y8QMAAABsWIPBIHv27Mm5556bTZs25dxzz82ePXt8pPuI4AcAAADYsK677rpMTk7mrrvuype+9KXcddddmZyczHXXXdd2aacER70AGBullFav3zRNq9cHAKjR448/ngsuuCCXXHLJ4bULLrg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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "atts = list = ['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'x', 'y', 'z']\n",
    "plt.figure(figsize=(20,10))\n",
    "attributes[atts].boxplot()\n",
    "plt.title('diamonds attributes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1653313856034,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "wH6t8LAfUCpp",
    "outputId": "7010d68a-1d18-4a09-e669-2f5beffab04e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ee5758f4-bd78-4f7f-89f6-edb0f7964194\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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",
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       "      <th>carat</th>\n",
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       "      <th>depth</th>\n",
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       "      <td>-0.029389</td>\n",
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       "      <td>0.181091</td>\n",
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       "      <td>0.026102</td>\n",
       "      <td>-0.088074</td>\n",
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       "      <th>x</th>\n",
       "      <td>0.975380</td>\n",
       "      <td>-0.025348</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.225575</td>\n",
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       "      <th>y</th>\n",
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       "      <td>0.974592</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.094757</td>\n",
       "      <td>0.150270</td>\n",
       "      <td>0.970686</td>\n",
       "      <td>0.951844</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002442</td>\n",
       "      <td>0.267825</td>\n",
       "      <td>-0.224117</td>\n",
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       "      <th>cut</th>\n",
       "      <td>0.017785</td>\n",
       "      <td>-0.193184</td>\n",
       "      <td>0.150366</td>\n",
       "      <td>0.022592</td>\n",
       "      <td>0.027805</td>\n",
       "      <td>0.002442</td>\n",
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       "      <td>0.000393</td>\n",
       "      <td>0.028141</td>\n",
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       "      <th>color</th>\n",
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       "      <td>0.047572</td>\n",
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       "      <td>-0.028002</td>\n",
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       "      <th>clarity</th>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  "
      ],
      "text/plain": [
       "            carat     depth     table         x         y         z       cut  \\\n",
       "carat    1.000000  0.027861  0.181091  0.975380  0.951908  0.953542  0.017785   \n",
       "depth    0.027861  1.000000 -0.297669 -0.025348 -0.029389  0.094757 -0.193184   \n",
       "table    0.181091 -0.297669  1.000000  0.194855  0.183231  0.150270  0.150366   \n",
       "x        0.975380 -0.025348  0.194855  1.000000  0.974592  0.970686  0.022592   \n",
       "y        0.951908 -0.029389  0.183231  0.974592  1.000000  0.951844  0.027805   \n",
       "z        0.953542  0.094757  0.150270  0.970686  0.951844  1.000000  0.002442   \n",
       "cut      0.017785 -0.193184  0.150366  0.022592  0.027805  0.002442  1.000000   \n",
       "color    0.291019  0.047572  0.026102  0.269876  0.263153  0.267825  0.000393   \n",
       "clarity -0.214068 -0.053165 -0.088074 -0.225575 -0.217459 -0.224117  0.028141   \n",
       "\n",
       "            color   clarity  \n",
       "carat    0.291019 -0.214068  \n",
       "depth    0.047572 -0.053165  \n",
       "table    0.026102 -0.088074  \n",
       "x        0.269876 -0.225575  \n",
       "y        0.263153 -0.217459  \n",
       "z        0.267825 -0.224117  \n",
       "cut      0.000393  0.028141  \n",
       "color    1.000000 -0.028002  \n",
       "clarity -0.028002  1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 492,
     "status": "ok",
     "timestamp": 1653313856520,
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      "displayName": "Mahmoud Abusaqer",
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    },
    "id": "Lf2gdP2qUCpp",
    "outputId": "34dc2b3f-5f75-42fe-cee0-4ccfa7cd4e68"
   },
   "outputs": [
    {
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      "text/plain": [
       "   carat  depth  table     x     y     z  cut  color  clarity\n",
       "0   0.23   61.5   55.0  3.95  3.98  2.43    2      1        3\n",
       "1   0.21   59.8   61.0  3.89  3.84  2.31    3      1        2\n",
       "2   0.23   56.9   65.0  4.05  4.07  2.31    1      1        4\n",
       "3   0.29   62.4   58.0  4.20  4.23  2.63    3      5        5\n",
       "4   0.31   63.3   58.0  4.34  4.35  2.75    1      6        3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1653313856521,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "UTEvOV6rUCpp"
   },
   "outputs": [],
   "source": [
    "# Model 1\n",
    "# specify and bulid the model\n",
    "\n",
    "MLPRegressor.n_outputs_=1\n",
    "MLPRegressor.n_layers_=1\n",
    "model1 = MLPRegressor(solver='lbfgs', activation='identity', alpha=1e-5, hidden_layer_sizes=(1, 1),\n",
    "                      random_state=1, learning_rate_init=0.001, max_iter=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1653313856521,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "AIyiIcg8UCpq"
   },
   "outputs": [],
   "source": [
    "# split the data into traing and test data\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(attributes, target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2265,
     "status": "ok",
     "timestamp": 1653313858784,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "GASfLGBGUCpq",
    "outputId": "559e8272-fc88-4cbc-afe5-11d3a248054a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='identity', alpha=1e-05, hidden_layer_sizes=(1, 1),\n",
       "             max_iter=800, random_state=1, solver='lbfgs')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "model1.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1653313858784,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "j5ORY026UCpq"
   },
   "outputs": [],
   "source": [
    "# test the model\n",
    "y_pred1 = model1.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1653313858784,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "uT-gYleyUCpq",
    "outputId": "a35a82c2-596c-4add-e9a7-3c15a5ff572f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for model 1:  1826626.9687074705\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error for model 1: \", metrics.mean_squared_error(y_test1, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1653313858785,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "sMp7r9BDUCpr"
   },
   "outputs": [],
   "source": [
    "# Model 2\n",
    "# specify and bulid the model\n",
    "\n",
    "MLPRegressor.n_outputs_=1\n",
    "MLPRegressor.n_layers_=9\n",
    "model2 = MLPRegressor(solver='adam', activation='relu', alpha=1e-5, hidden_layer_sizes=(100, 2),\n",
    "                      random_state=1, learning_rate_init=0.001, max_iter= 1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1653313858785,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "2iyE2EhcUCpr"
   },
   "outputs": [],
   "source": [
    "# split the data into traing and test data\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(attributes, target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 288423,
     "status": "ok",
     "timestamp": 1653314147204,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "edxkAuSPUCpr",
    "outputId": "79f40e56-9f36-4b1d-bbc0-8d57c23bfcd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(alpha=1e-05, hidden_layer_sizes=(100, 2), max_iter=1500,\n",
       "             random_state=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "model2.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1653314147205,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "-dq_3m5AUCpr"
   },
   "outputs": [],
   "source": [
    "# test the model\n",
    "y_pred2 = model2.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1653314147205,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "EDIE2GiSUCps",
    "outputId": "d7af3f5f-d8bf-46ab-9456-7160be26a771"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for model 2:  558542.7968620801\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error for model 2: \", metrics.mean_squared_error(y_test2, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1653314147206,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "gtsT1jhVUCps"
   },
   "outputs": [],
   "source": [
    "# Model 3\n",
    "# specify and bulid the model\n",
    "\n",
    "MLPRegressor.n_outputs_=1\n",
    "MLPRegressor.n_layers_=5\n",
    "model3 = MLPRegressor(solver='sgd', activation='logistic', alpha=1e-5, hidden_layer_sizes=(30, 5),\n",
    "                      random_state=1, learning_rate_init=0.001, max_iter= 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1653314147206,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "YrtrzxnTUCps"
   },
   "outputs": [],
   "source": [
    "# split the data into traing and test data\n",
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(attributes, target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3369,
     "status": "ok",
     "timestamp": 1653314150562,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "9lo6hWdDUCps",
    "outputId": "a089d08c-ea29-4966-9ee7-b05e32bf893b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='logistic', alpha=1e-05, hidden_layer_sizes=(30, 5),\n",
       "             max_iter=2000, random_state=1, solver='sgd')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "model3.fit(X_train3, y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1653314150563,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "2QaEwnIrUCps"
   },
   "outputs": [],
   "source": [
    "# test the model\n",
    "y_pred3 = model3.predict(X_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1653314150563,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "3pxCQne1UCpt",
    "outputId": "56b11ae9-3b62-4a01-c43d-3682ba3160ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for model 3:  16072171.487760758\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error for model 3: \", metrics.mean_squared_error(y_test3, y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1653314150563,
     "user": {
      "displayName": "Mahmoud Abusaqer",
      "userId": "09631177959643063939"
     },
     "user_tz": -180
    },
    "id": "Xqs8wFS5UCpt",
    "outputId": "9a3b9200-3531-4272-9858-40afcd1865ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation for the predicted models\n",
      "Mean squared error for model 1:  1826626.9687074705\n",
      "Mean squared error for model 2:  558542.7968620801\n",
      "Mean squared error for model 3:  16072171.487760758\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluation for the predicted models\")\n",
    "print(\"Mean squared error for model 1: \", metrics.mean_squared_error(y_test1, y_pred1))\n",
    "print(\"Mean squared error for model 2: \", metrics.mean_squared_error(y_test2, y_pred2))\n",
    "print(\"Mean squared error for model 3: \", metrics.mean_squared_error(y_test3, y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so the best model among the above is model 2, I even get a better MSE in other runs with a value 493228.0599333643"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "NN for Regression for diamonds.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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   "pygments_lexer": "ipython3",
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