4.ipynb

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        "<ul><font size = 5 color='Orange'>\n",
        "Learning Goals</font>\n",
        "<font color = 'Orange'>\n",
        "<ul>We are using the same dataset as the previous laboratory exercise, now you need to build Neural networks</ul>\n",
        "\n",
        "<li>Build three different networks with using:</li>\n",
        "different layers, activation functions, neurans per layers, number of layers\n",
        "<li>train your network and set the optimal number of epochs and batch size</li>\n",
        "<li>Evaluate the three networks on the same test dataset</li>\n",
        "<li>Compare the results</li>\n",
        "</ul>"
      ],
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        "id": "wsPEypIyjSq-"
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      "source": [
        "The Dataset for this exercise depends on your index number i.e number_of_data_set = index % 10:\n",
        "\n",
        "\n",
        "0.  https://www.kaggle.com/tunguz/us-elections-dataset\n",
        "1.  https://www.kaggle.com/andrewmvd/heart-failure-clinical-data\n",
        "2.  https://www.kaggle.com/sootersaalu/amazon-top-50-bestselling-books-2009-2019\n",
        "3.  https://www.kaggle.com/anmolkumar/health-insurance-cross-sell-prediction\n",
        "4.  https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009\n",
        "5.  https://www.kaggle.com/anmolkumar/house-price-prediction-challenge\n",
        "6.  https://www.kaggle.com/datasets/tadhgfitzgerald/fifa-international-soccer-mens-ranking-1993now\n",
        "7.  https://www.kaggle.com/arslanali4343/real-estate-dataset\n",
        "8.  https://www.kaggle.com/terenceshin/covid19s-impact-on-airport-traffic\n",
        "9.  https://www.kaggle.com/arslanali4343/top-personality-dataset\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "qO5SWztjhay0"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score, mean_absolute_error\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n"
      ],
      "metadata": {
        "id": "WORtycj8iCA8"
      },
      "execution_count": null,
      "outputs": []
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      "metadata": {
        "id": "tNi3STyahTOf"
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      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/train.csv')\n"
      ]
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      "source": [
        "df\n"
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              "      POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  \\\n",
              "0         Owner                   0     0        2       BHK  1300.236407   \n",
              "1        Dealer                   0     0        2       BHK  1275.000000   \n",
              "2         Owner                   0     0        2       BHK   933.159722   \n",
              "3         Owner                   0     1        2       BHK   929.921143   \n",
              "4        Dealer                   1     0        2       BHK   999.009247   \n",
              "...         ...                 ...   ...      ...       ...          ...   \n",
              "29446     Owner                   0     0        3       BHK  2500.000000   \n",
              "29447     Owner                   0     0        2       BHK   769.230769   \n",
              "29448    Dealer                   0     0        2       BHK  1022.641509   \n",
              "29449     Owner                   0     0        2       BHK   927.079009   \n",
              "29450    Dealer                   0     1        2       BHK   896.774194   \n",
              "\n",
              "       READY_TO_MOVE  RESALE                            ADDRESS  LONGITUDE  \\\n",
              "0                  1       1              Ksfc Layout,Bangalore  12.969910   \n",
              "1                  1       1          Vishweshwara Nagar,Mysore  12.274538   \n",
              "2                  1       1                   Jigani,Bangalore  12.778033   \n",
              "3                  1       1        Sector-1 Vaishali,Ghaziabad  28.642300   \n",
              "4                  0       1                   New Town,Kolkata  22.592200   \n",
              "...              ...     ...                                ...        ...   \n",
              "29446              1       1               Shamshabad Road,Agra  27.140626   \n",
              "29447              1       1  E3-108, Lake View Recidency,,Vapi  39.945409   \n",
              "29448              1       1                  Ajmer Road,Jaipur  26.928785   \n",
              "29449              1       1             Sholinganallur,Chennai  12.900150   \n",
              "29450              1       1                   Jagatpura,Jaipur  26.832353   \n",
              "\n",
              "        LATITUDE  TARGET(PRICE_IN_LACS)  \n",
              "0      77.597960                   55.0  \n",
              "1      76.644605                   51.0  \n",
              "2      77.632191                   43.0  \n",
              "3      77.344500                   62.5  \n",
              "4      88.484911                   60.5  \n",
              "...          ...                    ...  \n",
              "29446  78.043277                   45.0  \n",
              "29447 -86.150721                   16.0  \n",
              "29448  75.828002                   27.1  \n",
              "29449  80.227910                   67.0  \n",
              "29450  75.841749                   27.8  \n",
              "\n",
              "[29451 rows x 12 columns]"
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              "      <th>POSTED_BY</th>\n",
              "      <th>UNDER_CONSTRUCTION</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Jigani,Bangalore</td>\n",
              "      <td>12.778033</td>\n",
              "      <td>77.632191</td>\n",
              "      <td>43.0</td>\n",
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              "      <td>62.5</td>\n",
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              "      <th>4</th>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>999.009247</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>New Town,Kolkata</td>\n",
              "      <td>22.592200</td>\n",
              "      <td>88.484911</td>\n",
              "      <td>60.5</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>E3-108, Lake View Recidency,,Vapi</td>\n",
              "      <td>39.945409</td>\n",
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            "text/plain": [
              "       UNDER_CONSTRUCTION          RERA       BHK_NO.     SQUARE_FT  \\\n",
              "count        29451.000000  29451.000000  29451.000000  2.945100e+04   \n",
              "mean             0.179756      0.317918      2.392279  1.980217e+04   \n",
              "std              0.383991      0.465675      0.879091  1.901335e+06   \n",
              "min              0.000000      0.000000      1.000000  3.000000e+00   \n",
              "25%              0.000000      0.000000      2.000000  9.000211e+02   \n",
              "50%              0.000000      0.000000      2.000000  1.175057e+03   \n",
              "75%              0.000000      1.000000      3.000000  1.550688e+03   \n",
              "max              1.000000      1.000000     20.000000  2.545455e+08   \n",
              "\n",
              "       READY_TO_MOVE        RESALE     LONGITUDE      LATITUDE  \\\n",
              "count   29451.000000  29451.000000  29451.000000  29451.000000   \n",
              "mean        0.820244      0.929578     21.300255     76.837695   \n",
              "std         0.383991      0.255861      6.205306     10.557747   \n",
              "min         0.000000      0.000000    -37.713008   -121.761248   \n",
              "25%         1.000000      1.000000     18.452663     73.798100   \n",
              "50%         1.000000      1.000000     20.750000     77.324137   \n",
              "75%         1.000000      1.000000     26.900926     77.828740   \n",
              "max         1.000000      1.000000     59.912884    152.962676   \n",
              "\n",
              "       TARGET(PRICE_IN_LACS)  \n",
              "count           29451.000000  \n",
              "mean              142.898746  \n",
              "std               656.880713  \n",
              "min                 0.250000  \n",
              "25%                38.000000  \n",
              "50%                62.000000  \n",
              "75%               100.000000  \n",
              "max             30000.000000  "
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              "      <th>UNDER_CONSTRUCTION</th>\n",
              "      <th>RERA</th>\n",
              "      <th>BHK_NO.</th>\n",
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              "      <th>LONGITUDE</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>TARGET(PRICE_IN_LACS)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29451.000000</td>\n",
              "      <td>29451.000000</td>\n",
              "      <td>29451.000000</td>\n",
              "      <td>2.945100e+04</td>\n",
              "      <td>29451.000000</td>\n",
              "      <td>29451.000000</td>\n",
              "      <td>29451.000000</td>\n",
              "      <td>29451.000000</td>\n",
              "      <td>29451.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.179756</td>\n",
              "      <td>0.317918</td>\n",
              "      <td>2.392279</td>\n",
              "      <td>1.980217e+04</td>\n",
              "      <td>0.820244</td>\n",
              "      <td>0.929578</td>\n",
              "      <td>21.300255</td>\n",
              "      <td>76.837695</td>\n",
              "      <td>142.898746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.383991</td>\n",
              "      <td>0.465675</td>\n",
              "      <td>0.879091</td>\n",
              "      <td>1.901335e+06</td>\n",
              "      <td>0.383991</td>\n",
              "      <td>0.255861</td>\n",
              "      <td>6.205306</td>\n",
              "      <td>10.557747</td>\n",
              "      <td>656.880713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-37.713008</td>\n",
              "      <td>-121.761248</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000211e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.452663</td>\n",
              "      <td>73.798100</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.175057e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.750000</td>\n",
              "      <td>77.324137</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.550688e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>26.900926</td>\n",
              "      <td>77.828740</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>2.545455e+08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>59.912884</td>\n",
              "      <td>152.962676</td>\n",
              "      <td>30000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86ff878b-0d2c-44fe-a062-42936f608967')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86ff878b-0d2c-44fe-a062-42936f608967 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86ff878b-0d2c-44fe-a062-42936f608967');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['TARGET(PRICE_IN_LACS)'].plot()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "FClwaO1Y8ZHr",
        "outputId": "ae94b262-433e-4285-dd1b-2fe8d79ed156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a8e94bcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['LONGITUDE', 'LATITUDE', 'ADDRESS'], axis=1, inplace=True)\n"
      ],
      "metadata": {
        "id": "hebii6yQ7rBL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SPsz03J70gc",
        "outputId": "47ccc7b8-7372-4b9d-a546-96f6900f214c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "POSTED_BY                0\n",
              "UNDER_CONSTRUCTION       0\n",
              "RERA                     0\n",
              "BHK_NO.                  0\n",
              "BHK_OR_RK                0\n",
              "SQUARE_FT                0\n",
              "READY_TO_MOVE            0\n",
              "RESALE                   0\n",
              "TARGET(PRICE_IN_LACS)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drop_columns = ['LONGITUDE', 'LATITUDE', 'ADDRESS']\n",
        "label_columns = ['BHK_OR_RK', 'POSTED_BY']\n"
      ],
      "metadata": {
        "id": "Cr2H_Kia8lvV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = LabelEncoder()\n",
        "\n",
        "df.drop(drop_columns, axis=1, inplace=True, errors='ignore')\n",
        "\n",
        "for column in label_columns:\n",
        "  df[column] = label_encoder.fit_transform(df[column].to_numpy().reshape(-1))\n"
      ],
      "metadata": {
        "id": "ny26E5zn8sNo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('TARGET(PRICE_IN_LACS)', axis=1)\n",
        "y = df['TARGET(PRICE_IN_LACS)']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n"
      ],
      "metadata": {
        "id": "PgQgNqpY8v3w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)\n"
      ],
      "metadata": {
        "id": "JH7mKUUMD4uT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [Sequential(), Sequential(), Sequential()]\n"
      ],
      "metadata": {
        "id": "og-PdcNFHtqI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models[0] = Sequential()\n",
        "\n",
        "models[0].add(Dense(16, activation='relu'))\n",
        "models[0].add(Dense(256, activation='relu'))\n",
        "models[0].add(Dense(128, activation='relu'))\n",
        "models[0].add(Dense(64, activation='relu'))\n",
        "models[0].add(Dense(1, activation='linear'))\n",
        "\n",
        "models[0].compile(optimizer='adam', loss='mse', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "WT6zUq7x9ZdI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models[1] = Sequential()\n",
        "\n",
        "models[1].add(Dense(8, activation='relu'))\n",
        "models[1].add(Dense(128, activation='relu'))\n",
        "models[1].add(Dense(64, activation='relu'))\n",
        "models[1].add(Dense(32, activation='relu'))\n",
        "models[1].add(Dense(1, activation='linear'))\n",
        "\n",
        "models[1].compile(optimizer='adam', loss='mse', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "8GdyGHsRFLqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models[2] = Sequential()\n",
        "\n",
        "models[2].add(Dense(16, activation='relu'))\n",
        "models[2].add(Dense(256, activation='relu'))\n",
        "models[2].add(Dropout(0.25))\n",
        "models[2].add(Dense(128, activation='relu'))\n",
        "models[2].add(Dense(64, activation='relu'))\n",
        "models[2].add(Dropout(0.25))\n",
        "models[2].add(Dense(1, activation='linear'))\n",
        "\n",
        "models[2].compile(optimizer='adam', loss='mse', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "kB-O20_uFSNM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores = []\n",
        "errors = []\n",
        "\n",
        "for i, model in enumerate(models):\n",
        "  model.fit(x_train, y_train, epochs=50)\n",
        "  y_pred = model.predict(x_test)\n",
        "\n",
        "  scores.append(r2_score(y_test, y_pred))\n",
        "  errors.append(mean_absolute_error(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKJG4x2GG77x",
        "outputId": "5af8deac-b71c-490c-f64b-42b0291d8e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 60325.2148 - accuracy: 4.2445e-05\n",
            "Epoch 2/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 65256.7461 - accuracy: 4.2445e-05\n",
            "Epoch 3/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 53273.8555 - accuracy: 4.2445e-05\n",
            "Epoch 4/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 58497.5938 - accuracy: 4.2445e-05\n",
            "Epoch 5/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 66231.9141 - accuracy: 4.2445e-05\n",
            "Epoch 6/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 51710.9023 - accuracy: 4.2445e-05\n",
            "Epoch 7/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 65110.5039 - accuracy: 4.2445e-05\n",
            "Epoch 8/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 52889.7578 - accuracy: 4.2445e-05\n",
            "Epoch 9/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 57114.1445 - accuracy: 4.2445e-05\n",
            "Epoch 10/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 70823.2344 - accuracy: 4.2445e-05\n",
            "Epoch 11/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 70848.0938 - accuracy: 4.2445e-05\n",
            "Epoch 12/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 59067.1797 - accuracy: 4.2445e-05\n",
            "Epoch 13/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 56912.4414 - accuracy: 4.2445e-05\n",
            "Epoch 14/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 57307.4453 - accuracy: 4.2445e-05\n",
            "Epoch 15/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 64092.3281 - accuracy: 4.2445e-05\n",
            "Epoch 16/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 54810.5547 - accuracy: 4.2445e-05\n",
            "Epoch 17/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 55423.2227 - accuracy: 4.2445e-05\n",
            "Epoch 18/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 50224.9883 - accuracy: 4.2445e-05\n",
            "Epoch 19/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 64600.8828 - accuracy: 4.2445e-05\n",
            "Epoch 20/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 95518.1641 - accuracy: 4.2445e-05\n",
            "Epoch 21/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 53635.8203 - accuracy: 4.2445e-05\n",
            "Epoch 22/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 55944.0195 - accuracy: 4.2445e-05\n",
            "Epoch 23/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 70193.3594 - accuracy: 4.2445e-05\n",
            "Epoch 24/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 42249.4453 - accuracy: 4.2445e-05\n",
            "Epoch 25/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 59951.3008 - accuracy: 4.2445e-05\n",
            "Epoch 26/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 56684.8984 - accuracy: 4.2445e-05\n",
            "Epoch 27/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 58649.0586 - accuracy: 4.2445e-05\n",
            "Epoch 28/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 45427.0156 - accuracy: 4.2445e-05\n",
            "Epoch 29/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 47905.2227 - accuracy: 4.2445e-05\n",
            "Epoch 30/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 60868.7109 - accuracy: 4.2445e-05\n",
            "Epoch 31/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 59967.0586 - accuracy: 4.2445e-05\n",
            "Epoch 32/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 48628.8750 - accuracy: 4.2445e-05\n",
            "Epoch 33/50\n",
            "737/737 [==============================] - 3s 3ms/step - loss: 58614.7852 - accuracy: 4.2445e-05\n",
            "Epoch 34/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 57320.6758 - accuracy: 4.2445e-05\n",
            "Epoch 35/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 62227.4102 - accuracy: 4.2445e-05\n",
            "Epoch 36/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 53746.7812 - accuracy: 4.2445e-05\n",
            "Epoch 37/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 52533.2539 - accuracy: 4.2445e-05\n",
            "Epoch 38/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 44058.0938 - accuracy: 4.2445e-05\n",
            "Epoch 39/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 75805.7031 - accuracy: 4.2445e-05\n",
            "Epoch 40/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 48186.6719 - accuracy: 4.2445e-05\n",
            "Epoch 41/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 48455.3555 - accuracy: 4.2445e-05\n",
            "Epoch 42/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 46638.0117 - accuracy: 4.2445e-05\n",
            "Epoch 43/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 43180.5898 - accuracy: 4.2445e-05\n",
            "Epoch 44/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 50016.8359 - accuracy: 4.2445e-05\n",
            "Epoch 45/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 89351.0000 - accuracy: 4.2445e-05\n",
            "Epoch 46/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 60646.5938 - accuracy: 4.2445e-05\n",
            "Epoch 47/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 44471.4922 - accuracy: 4.2445e-05\n",
            "Epoch 48/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 50967.1602 - accuracy: 4.2445e-05\n",
            "Epoch 49/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 49345.8398 - accuracy: 4.2445e-05\n",
            "Epoch 50/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 43981.3633 - accuracy: 4.2445e-05\n",
            "185/185 [==============================] - 0s 977us/step\n",
            "Epoch 1/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 358718.6875 - accuracy: 4.2445e-05\n",
            "Epoch 2/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 368677.0312 - accuracy: 4.2445e-05\n",
            "Epoch 3/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 356946.5000 - accuracy: 4.2445e-05\n",
            "Epoch 4/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 389808.3750 - accuracy: 4.2445e-05\n",
            "Epoch 5/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 360377.1562 - accuracy: 4.2445e-05\n",
            "Epoch 6/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 365253.6250 - accuracy: 4.2445e-05\n",
            "Epoch 7/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 362171.5312 - accuracy: 4.2445e-05\n",
            "Epoch 8/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 361463.0938 - accuracy: 4.2445e-05\n",
            "Epoch 9/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 371974.9688 - accuracy: 4.2445e-05\n",
            "Epoch 10/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 367753.1562 - accuracy: 4.2445e-05\n",
            "Epoch 11/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 366368.5938 - accuracy: 4.2445e-05\n",
            "Epoch 12/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 359329.8125 - accuracy: 4.2445e-05\n",
            "Epoch 13/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 366182.7500 - accuracy: 4.2445e-05\n",
            "Epoch 14/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 363348.6562 - accuracy: 4.2445e-05\n",
            "Epoch 15/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 357788.5625 - accuracy: 4.2445e-05\n",
            "Epoch 16/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 391077.7812 - accuracy: 4.2445e-05\n",
            "Epoch 17/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 358133.3438 - accuracy: 4.2445e-05\n",
            "Epoch 18/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 364492.9688 - accuracy: 4.2445e-05\n",
            "Epoch 19/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 363149.7500 - accuracy: 4.2445e-05\n",
            "Epoch 20/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 361586.5938 - accuracy: 4.2445e-05\n",
            "Epoch 21/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 357517.8438 - accuracy: 4.2445e-05\n",
            "Epoch 22/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 364360.5000 - accuracy: 4.2445e-05\n",
            "Epoch 23/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 355395.8125 - accuracy: 4.2445e-05\n",
            "Epoch 24/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 372591.7500 - accuracy: 4.2445e-05\n",
            "Epoch 25/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 366687.0625 - accuracy: 4.2445e-05\n",
            "Epoch 26/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 362940.4062 - accuracy: 4.2445e-05\n",
            "Epoch 27/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 365071.5938 - accuracy: 4.2445e-05\n",
            "Epoch 28/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 357975.0938 - accuracy: 4.2445e-05\n",
            "Epoch 29/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 367002.7812 - accuracy: 4.2445e-05\n",
            "Epoch 30/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 361933.8438 - accuracy: 4.2445e-05\n",
            "Epoch 31/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 361720.2188 - accuracy: 4.2445e-05\n",
            "Epoch 32/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 366331.0938 - accuracy: 4.2445e-05\n",
            "Epoch 33/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 368022.0938 - accuracy: 4.2445e-05\n",
            "Epoch 34/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 361613.8750 - accuracy: 4.2445e-05\n",
            "Epoch 35/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 363276.2500 - accuracy: 4.2445e-05\n",
            "Epoch 36/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 362020.5000 - accuracy: 4.2445e-05\n",
            "Epoch 37/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 363205.8125 - accuracy: 4.2445e-05\n",
            "Epoch 38/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 360895.5938 - accuracy: 4.2445e-05\n",
            "Epoch 39/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 380744.2812 - accuracy: 4.2445e-05\n",
            "Epoch 40/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 359673.6875 - accuracy: 4.2445e-05\n",
            "Epoch 41/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 359678.2812 - accuracy: 4.2445e-05\n",
            "Epoch 42/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 363501.1875 - accuracy: 4.2445e-05\n",
            "Epoch 43/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 364626.5938 - accuracy: 4.2445e-05\n",
            "Epoch 44/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 362721.8125 - accuracy: 4.2445e-05\n",
            "Epoch 45/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 359453.1875 - accuracy: 4.2445e-05\n",
            "Epoch 46/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 365203.5938 - accuracy: 4.2445e-05\n",
            "Epoch 47/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 362798.2812 - accuracy: 4.2445e-05\n",
            "Epoch 48/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 373876.0625 - accuracy: 4.2445e-05\n",
            "Epoch 49/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 356103.7188 - accuracy: 4.2445e-05\n",
            "Epoch 50/50\n",
            "737/737 [==============================] - 1s 1ms/step - loss: 367013.3438 - accuracy: 4.2445e-05\n",
            "185/185 [==============================] - 0s 925us/step\n",
            "Epoch 1/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 74726.5391 - accuracy: 4.2445e-05\n",
            "Epoch 2/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 59705.9414 - accuracy: 4.2445e-05\n",
            "Epoch 3/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 73071.9531 - accuracy: 4.2445e-05\n",
            "Epoch 4/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 77445.7422 - accuracy: 4.2445e-05\n",
            "Epoch 5/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 96871.9531 - accuracy: 4.2445e-05\n",
            "Epoch 6/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 63509.5312 - accuracy: 4.2445e-05\n",
            "Epoch 7/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 66671.9375 - accuracy: 4.2445e-05\n",
            "Epoch 8/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 49206.2461 - accuracy: 4.2445e-05\n",
            "Epoch 9/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 83656.4844 - accuracy: 4.2445e-05\n",
            "Epoch 10/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 70729.5312 - accuracy: 4.2445e-05\n",
            "Epoch 11/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 75207.2969 - accuracy: 4.2445e-05\n",
            "Epoch 12/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 67262.1719 - accuracy: 4.2445e-05\n",
            "Epoch 13/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 61872.4766 - accuracy: 4.2445e-05\n",
            "Epoch 14/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 55271.4609 - accuracy: 4.2445e-05\n",
            "Epoch 15/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 48500.5273 - accuracy: 4.2445e-05\n",
            "Epoch 16/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 70886.9531 - accuracy: 4.2445e-05\n",
            "Epoch 17/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 65907.8281 - accuracy: 4.2445e-05\n",
            "Epoch 18/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 59225.2031 - accuracy: 4.2445e-05\n",
            "Epoch 19/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 80184.2109 - accuracy: 4.2445e-05\n",
            "Epoch 20/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 87397.3359 - accuracy: 4.2445e-05\n",
            "Epoch 21/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 54896.2227 - accuracy: 4.2445e-05\n",
            "Epoch 22/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 53481.5938 - accuracy: 4.2445e-05\n",
            "Epoch 23/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 62397.2344 - accuracy: 4.2445e-05\n",
            "Epoch 24/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 56575.0547 - accuracy: 4.2445e-05\n",
            "Epoch 25/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 73580.3672 - accuracy: 4.2445e-05\n",
            "Epoch 26/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 53247.9883 - accuracy: 4.2445e-05\n",
            "Epoch 27/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 60311.2109 - accuracy: 4.2445e-05\n",
            "Epoch 28/50\n",
            "737/737 [==============================] - 2s 3ms/step - loss: 66504.1562 - accuracy: 4.2445e-05\n",
            "Epoch 29/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 77942.5391 - accuracy: 4.2445e-05\n",
            "Epoch 30/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 71871.0703 - accuracy: 4.2445e-05\n",
            "Epoch 31/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 58836.6328 - accuracy: 4.2445e-05\n",
            "Epoch 32/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 58286.8438 - accuracy: 4.2445e-05\n",
            "Epoch 33/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 69654.5938 - accuracy: 4.2445e-05\n",
            "Epoch 34/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 63564.0820 - accuracy: 4.2445e-05\n",
            "Epoch 35/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 53035.2617 - accuracy: 4.2445e-05\n",
            "Epoch 36/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 59781.5234 - accuracy: 4.2445e-05\n",
            "Epoch 37/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 67184.4922 - accuracy: 4.2445e-05\n",
            "Epoch 38/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 65648.5312 - accuracy: 4.2445e-05\n",
            "Epoch 39/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 47121.1445 - accuracy: 4.2445e-05\n",
            "Epoch 40/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 57568.0117 - accuracy: 4.2445e-05\n",
            "Epoch 41/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 67490.0781 - accuracy: 4.2445e-05\n",
            "Epoch 42/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 54514.7773 - accuracy: 4.2445e-05\n",
            "Epoch 43/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 63334.7031 - accuracy: 4.2445e-05\n",
            "Epoch 44/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 58928.6914 - accuracy: 4.2445e-05\n",
            "Epoch 45/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 50653.0273 - accuracy: 4.2445e-05\n",
            "Epoch 46/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 72383.9531 - accuracy: 4.2445e-05\n",
            "Epoch 47/50\n",
            "737/737 [==============================] - 2s 2ms/step - loss: 54005.2227 - accuracy: 4.2445e-05\n",
            "Epoch 48/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 63089.3516 - accuracy: 4.2445e-05\n",
            "Epoch 49/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 78304.5625 - accuracy: 4.2445e-05\n",
            "Epoch 50/50\n",
            "737/737 [==============================] - 1s 2ms/step - loss: 55569.6914 - accuracy: 4.2445e-05\n",
            "185/185 [==============================] - 0s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(models)):\n",
        "  print(f'model {i} r2 score: {scores[i]}')\n",
        "  print(f'model {i} mean absolute error: {errors[i]}')\n",
        "  print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZfRWYWCLbJw",
        "outputId": "30c52fd0-a498-4f55-e690-f643a9c9032a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model 0 r2 score: 0.8468015602860862\n",
            "model 0 mean absolute error: 63.9722561994147\n",
            "\n",
            "model 1 r2 score: 0.04933321295509874\n",
            "model 1 mean absolute error: 109.95457691928888\n",
            "\n",
            "model 2 r2 score: 0.8563828720144817\n",
            "model 2 mean absolute error: 67.53417150259463\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model 0 and 2 are good, while model 1 is underperforming severely. Model 3 is the most accurate model."
      ],
      "metadata": {
        "id": "rZ5xatBBMXHE"
      }
    }
  ]
}