CO2_emissions_optimized.ipynb

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      "id": "9f8a4cd8",
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        "id": "9f8a4cd8"
      },
      "outputs": [],
      "source": [
        "# Import the modules\n",
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6461a0b1",
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              "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
              "0  ACURA         ILX       COMPACT             2.0          4          AS5   \n",
              "1  ACURA         ILX       COMPACT             2.4          4           M6   \n",
              "2  ACURA  ILX HYBRID       COMPACT             1.5          4          AV7   \n",
              "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6          AS6   \n",
              "4  ACURA     RDX AWD   SUV - SMALL             3.5          6          AS6   \n",
              "\n",
              "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
              "0         Z                               9.9   \n",
              "1         Z                              11.2   \n",
              "2         Z                               6.0   \n",
              "3         Z                              12.7   \n",
              "4         Z                              12.1   \n",
              "\n",
              "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
              "0                              6.7                               8.5   \n",
              "1                              7.7                               9.6   \n",
              "2                              5.8                               5.9   \n",
              "3                              9.1                              11.1   \n",
              "4                              8.7                              10.6   \n",
              "\n",
              "   Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
              "0                           33                  196  \n",
              "1                           29                  221  \n",
              "2                           48                  136  \n",
              "3                           25                  255  \n",
              "4                           27                  244  "
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Read the csv file into a pandas DataFrame\n",
        "carbon_df = pd.read_csv(\"CO2_Emissions_Canada.csv\")\n",
        "\n",
        "# Review the DataFrame\n",
        "carbon_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "43ef59fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43ef59fe",
        "outputId": "8978a62c-c110-48f9-8d4b-458047bd60e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make                                 object\n",
              "Model                                object\n",
              "Vehicle Class                        object\n",
              "Engine Size(L)                      float64\n",
              "Cylinders                             int64\n",
              "Transmission                         object\n",
              "Fuel Type                            object\n",
              "Fuel Consumption City (L/100 km)    float64\n",
              "Fuel Consumption Hwy (L/100 km)     float64\n",
              "Fuel Consumption Comb (L/100 km)    float64\n",
              "Fuel Consumption Comb (mpg)           int64\n",
              "CO2 Emissions(g/km)                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "carbon_df.dtypes"
      ]
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    {
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      "execution_count": 5,
      "id": "a71cd129",
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          "height": 520
        },
        "id": "a71cd129",
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Vehicle Class  Engine Size(L)  Cylinders Transmission Fuel Type  \\\n",
              "0            COMPACT             2.0          4          AS5         Z   \n",
              "1            COMPACT             2.4          4           M6         Z   \n",
              "2            COMPACT             1.5          4          AV7         Z   \n",
              "3        SUV - SMALL             3.5          6          AS6         Z   \n",
              "4        SUV - SMALL             3.5          6          AS6         Z   \n",
              "...              ...             ...        ...          ...       ...   \n",
              "7380     SUV - SMALL             2.0          4          AS8         Z   \n",
              "7381     SUV - SMALL             2.0          4          AS8         Z   \n",
              "7382     SUV - SMALL             2.0          4          AS8         Z   \n",
              "7383  SUV - STANDARD             2.0          4          AS8         Z   \n",
              "7384  SUV - STANDARD             2.0          4          AS8         Z   \n",
              "\n",
              "      Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
              "0                                  9.9                              6.7   \n",
              "1                                 11.2                              7.7   \n",
              "2                                  6.0                              5.8   \n",
              "3                                 12.7                              9.1   \n",
              "4                                 12.1                              8.7   \n",
              "...                                ...                              ...   \n",
              "7380                              10.7                              7.7   \n",
              "7381                              11.2                              8.3   \n",
              "7382                              11.7                              8.6   \n",
              "7383                              11.2                              8.3   \n",
              "7384                              12.2                              8.7   \n",
              "\n",
              "      CO2 Emissions(g/km)  \n",
              "0                     196  \n",
              "1                     221  \n",
              "2                     136  \n",
              "3                     255  \n",
              "4                     244  \n",
              "...                   ...  \n",
              "7380                  219  \n",
              "7381                  232  \n",
              "7382                  240  \n",
              "7383                  232  \n",
              "7384                  248  \n",
              "\n",
              "[7385 rows x 8 columns]"
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              "      <th>Vehicle Class</th>\n",
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              "      <td>4</td>\n",
              "      <td>AS8</td>\n",
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              "      <td>11.2</td>\n",
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              "    <tr>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "co2_df=carbon_df.drop(columns=['Make', 'Model', 'Fuel Consumption Comb (L/100 km)', 'Fuel Consumption Comb (mpg)'])\n",
        "co2_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95bb2002",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95bb2002",
        "outputId": "7d352a3f-0d37-48d3-94eb-ca507c499762"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SUV - SMALL                 1217\n",
              "MID-SIZE                    1133\n",
              "COMPACT                     1022\n",
              "SUV - STANDARD               735\n",
              "FULL-SIZE                    639\n",
              "SUBCOMPACT                   606\n",
              "PICKUP TRUCK - STANDARD      538\n",
              "TWO-SEATER                   460\n",
              "MINICOMPACT                  326\n",
              "STATION WAGON - SMALL        252\n",
              "PICKUP TRUCK - SMALL         159\n",
              "MINIVAN                       80\n",
              "SPECIAL PURPOSE VEHICLE       77\n",
              "VAN - PASSENGER               66\n",
              "STATION WAGON - MID-SIZE      53\n",
              "VAN - CARGO                   22\n",
              "Name: Vehicle Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "#Evaluating composition of the column by value counts\n",
        "co2_df[\"Vehicle Class\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating composition of the column by value counts\n",
        "co2_df[\"Transmission\"].value_counts()"
      ],
      "metadata": {
        "id": "sxS5oiEVSC4C",
        "outputId": "b9fbeb5e-0d41-4b57-cfcd-ad4326a0b09b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "sxS5oiEVSC4C",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AS6     1324\n",
              "AS8     1211\n",
              "M6       901\n",
              "A6       789\n",
              "A8       490\n",
              "AM7      445\n",
              "A9       339\n",
              "AS7      319\n",
              "AV       295\n",
              "M5       193\n",
              "AS10     168\n",
              "AM6      132\n",
              "AV7      118\n",
              "AV6      113\n",
              "M7        91\n",
              "A5        84\n",
              "AS9       77\n",
              "A4        65\n",
              "AM8       62\n",
              "A7        53\n",
              "AV8       39\n",
              "A10       31\n",
              "AS5       26\n",
              "AV10      11\n",
              "AM5        4\n",
              "AM9        3\n",
              "AS4        2\n",
              "Name: Transmission, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Binning of the column \"Transmission\" for values below 20\n",
        "# use the variable name `classifications_to_replace`\n",
        "Transmission_to_replace =co2_df[\"Transmission\"].value_counts().loc[lambda x: x<20].index\n",
        "\n",
        "# Replace in dataframe\n",
        "for t in Transmission_to_replace:\n",
        "    co2_df['Transmission'] = co2_df['Transmission'].replace(t,\"Other\")\n",
        "\n",
        "# Check to make sure binning was successful\n",
        "co2_df['Transmission'].value_counts()"
      ],
      "metadata": {
        "id": "ZCeDCq9ZO0_v",
        "outputId": "a1a9d72d-b595-4708-c23a-a4bf97bc7314",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "ZCeDCq9ZO0_v",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AS6      1324\n",
              "AS8      1211\n",
              "M6        901\n",
              "A6        789\n",
              "A8        490\n",
              "AM7       445\n",
              "A9        339\n",
              "AS7       319\n",
              "AV        295\n",
              "M5        193\n",
              "AS10      168\n",
              "AM6       132\n",
              "AV7       118\n",
              "AV6       113\n",
              "M7         91\n",
              "A5         84\n",
              "AS9        77\n",
              "A4         65\n",
              "AM8        62\n",
              "A7         53\n",
              "AV8        39\n",
              "A10        31\n",
              "AS5        26\n",
              "Other      20\n",
              "Name: Transmission, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating composition of the column by value counts\n",
        "co2_df[\"Engine Size(L)\"].value_counts()"
      ],
      "metadata": {
        "id": "HchFPslwSO0f",
        "outputId": "f2d92bd5-d6e4-4f2a-9254-f8653f7c61fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "HchFPslwSO0f",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0    1460\n",
              "3.0     804\n",
              "3.6     536\n",
              "3.5     529\n",
              "2.5     423\n",
              "2.4     346\n",
              "1.6     302\n",
              "5.3     290\n",
              "1.8     216\n",
              "1.4     211\n",
              "5.0     207\n",
              "1.5     207\n",
              "4.0     198\n",
              "6.2     162\n",
              "4.4     131\n",
              "5.7     116\n",
              "3.8     114\n",
              "3.7     112\n",
              "2.7      99\n",
              "6.0      94\n",
              "3.3      85\n",
              "4.7      78\n",
              "3.4      52\n",
              "5.5      52\n",
              "4.3      52\n",
              "2.3      51\n",
              "5.2      48\n",
              "6.4      46\n",
              "5.6      36\n",
              "3.2      35\n",
              "4.8      32\n",
              "4.6      32\n",
              "6.6      29\n",
              "1.2      25\n",
              "6.7      25\n",
              "2.8      24\n",
              "2.9      23\n",
              "1.0      18\n",
              "6.5      18\n",
              "4.2      14\n",
              "1.3      11\n",
              "5.4       8\n",
              "6.8       8\n",
              "2.1       5\n",
              "8.4       5\n",
              "5.9       5\n",
              "6.3       3\n",
              "0.9       3\n",
              "8.0       3\n",
              "5.8       1\n",
              "2.2       1\n",
              "Name: Engine Size(L), dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ed33d92",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ed33d92",
        "outputId": "967034d7-575b-4ec1-bb50-c6ce522c81ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0    4182\n",
            "0.0    3203\n",
            "Name: CO2, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "co2_df.loc[co2_df[\"CO2 Emissions(g/km)\"] <= 255, \"CO2\"] = 1\n",
        "co2_df.loc[co2_df[\"CO2 Emissions(g/km)\"] > 255, \"CO2\"] = 0\n",
        "co2_df\n",
        "print(co2_df[\"CO2\"].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a StandardScaler instances\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the StandardScaler\n",
        "# Scaling the numeric columns\n",
        "co2_scaled_df= StandardScaler().fit_transform(co2_df[[\"Engine Size(L)\", \"Cylinders\", \"Fuel Consumption City (L/100 km)\", \"Fuel Consumption Hwy (L/100 km)\"]])\n",
        "# Creating a DataFrame with with the scaled data\n",
        "co2_transformed_df= pd.DataFrame(co2_scaled_df, columns= [\"Engine Size(L)\", \"Cylinders\", \"Fuel Consumption City (L/100 km)\", \"Fuel Consumption Hwy (L/100 km)\"])\n",
        "\n",
        "co2_transformed_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "P1fUFkWUxPzw",
        "outputId": "a26ea2d8-2018-4f14-c34e-cb55142c4960"
      },
      "id": "P1fUFkWUxPzw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Engine Size(L)  Cylinders  Fuel Consumption City (L/100 km)  \\\n",
              "0          -0.856721  -0.883408                         -0.759002   \n",
              "1          -0.561317  -0.883408                         -0.387577   \n",
              "2          -1.225976  -0.883408                         -1.873275   \n",
              "3           0.251043   0.210575                          0.040990   \n",
              "4           0.251043   0.210575                         -0.130437   \n",
              "...              ...        ...                               ...   \n",
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              "7383       -0.856721  -0.883408                         -0.387577   \n",
              "7384       -0.856721  -0.883408                         -0.101866   \n",
              "\n",
              "      Fuel Consumption Hwy (L/100 km)  \n",
              "0                           -1.052781  \n",
              "1                           -0.603202  \n",
              "2                           -1.457401  \n",
              "3                            0.026208  \n",
              "4                           -0.153624  \n",
              "...                               ...  \n",
              "7380                        -0.603202  \n",
              "7381                        -0.333455  \n",
              "7382                        -0.198582  \n",
              "7383                        -0.333455  \n",
              "7384                        -0.153624  \n",
              "\n",
              "[7385 rows x 4 columns]"
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              "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
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              "      <td>-0.856721</td>\n",
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              "      <td>-0.153624</td>\n",
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              "<p>7385 rows × 4 columns</p>\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "<style>\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-440ae390-a1d7-469a-ae6d-19e4ecd6cae1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      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-82e0a022-1f16-4042-9f4f-25d0c47f90b7 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-82e0a022-1f16-4042-9f4f-25d0c47f90b7');\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": [
        "# Transform the Card Type column using get_dummies()\n",
        "dummies_df=pd.get_dummies(co2_df[[\"Vehicle Class\", \"Transmission\",\"Fuel Type\"]])\n",
        "dummies_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "FhuUlF8f3K_h",
        "outputId": "8fa61a3d-99f9-4172-fcf0-6127a4558a76"
      },
      "id": "FhuUlF8f3K_h",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Vehicle Class_COMPACT  Vehicle Class_FULL-SIZE  Vehicle Class_MID-SIZE  \\\n",
              "0                      1                        0                       0   \n",
              "1                      1                        0                       0   \n",
              "2                      1                        0                       0   \n",
              "3                      0                        0                       0   \n",
              "4                      0                        0                       0   \n",
              "\n",
              "   Vehicle Class_MINICOMPACT  Vehicle Class_MINIVAN  \\\n",
              "0                          0                      0   \n",
              "1                          0                      0   \n",
              "2                          0                      0   \n",
              "3                          0                      0   \n",
              "4                          0                      0   \n",
              "\n",
              "   Vehicle Class_PICKUP TRUCK - SMALL  Vehicle Class_PICKUP TRUCK - STANDARD  \\\n",
              "0                                   0                                      0   \n",
              "1                                   0                                      0   \n",
              "2                                   0                                      0   \n",
              "3                                   0                                      0   \n",
              "4                                   0                                      0   \n",
              "\n",
              "   Vehicle Class_SPECIAL PURPOSE VEHICLE  \\\n",
              "0                                      0   \n",
              "1                                      0   \n",
              "2                                      0   \n",
              "3                                      0   \n",
              "4                                      0   \n",
              "\n",
              "   Vehicle Class_STATION WAGON - MID-SIZE  \\\n",
              "0                                       0   \n",
              "1                                       0   \n",
              "2                                       0   \n",
              "3                                       0   \n",
              "4                                       0   \n",
              "\n",
              "   Vehicle Class_STATION WAGON - SMALL  ...  Transmission_AV8  \\\n",
              "0                                    0  ...                 0   \n",
              "1                                    0  ...                 0   \n",
              "2                                    0  ...                 0   \n",
              "3                                    0  ...                 0   \n",
              "4                                    0  ...                 0   \n",
              "\n",
              "   Transmission_M5  Transmission_M6  Transmission_M7  Transmission_Other  \\\n",
              "0                0                0                0                   0   \n",
              "1                0                1                0                   0   \n",
              "2                0                0                0                   0   \n",
              "3                0                0                0                   0   \n",
              "4                0                0                0                   0   \n",
              "\n",
              "   Fuel Type_D  Fuel Type_E  Fuel Type_N  Fuel Type_X  Fuel Type_Z  \n",
              "0            0            0            0            0            1  \n",
              "1            0            0            0            0            1  \n",
              "2            0            0            0            0            1  \n",
              "3            0            0            0            0            1  \n",
              "4            0            0            0            0            1  \n",
              "\n",
              "[5 rows x 45 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Vehicle Class_COMPACT</th>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate the df_shopping_transformed and the card_dummies DataFrames\n",
        "co2_new_df=pd.concat([co2_transformed_df, dummies_df], axis=1)\n",
        "co2_new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "Sd6UBsA268YB",
        "outputId": "cce9bfbb-543f-45d5-f3e9-dfcfd37ed659"
      },
      "id": "Sd6UBsA268YB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Engine Size(L)  Cylinders  Fuel Consumption City (L/100 km)  \\\n",
              "0          -0.856721  -0.883408                         -0.759002   \n",
              "1          -0.561317  -0.883408                         -0.387577   \n",
              "2          -1.225976  -0.883408                         -1.873275   \n",
              "3           0.251043   0.210575                          0.040990   \n",
              "4           0.251043   0.210575                         -0.130437   \n",
              "...              ...        ...                               ...   \n",
              "7380       -0.856721  -0.883408                         -0.530433   \n",
              "7381       -0.856721  -0.883408                         -0.387577   \n",
              "7382       -0.856721  -0.883408                         -0.244721   \n",
              "7383       -0.856721  -0.883408                         -0.387577   \n",
              "7384       -0.856721  -0.883408                         -0.101866   \n",
              "\n",
              "      Fuel Consumption Hwy (L/100 km)  Vehicle Class_COMPACT  \\\n",
              "0                           -1.052781                      1   \n",
              "1                           -0.603202                      1   \n",
              "2                           -1.457401                      1   \n",
              "3                            0.026208                      0   \n",
              "4                           -0.153624                      0   \n",
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              "7380                        -0.603202                      0   \n",
              "7381                        -0.333455                      0   \n",
              "7382                        -0.198582                      0   \n",
              "7383                        -0.333455                      0   \n",
              "7384                        -0.153624                      0   \n",
              "\n",
              "      Vehicle Class_FULL-SIZE  Vehicle Class_MID-SIZE  \\\n",
              "0                           0                       0   \n",
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              "7381                        0                       0   \n",
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              "7384                        0                       0   \n",
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              "      Vehicle Class_MINICOMPACT  Vehicle Class_MINIVAN  \\\n",
              "0                             0                      0   \n",
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              "7381                          0                      0   \n",
              "7382                          0                      0   \n",
              "7383                          0                      0   \n",
              "7384                          0                      0   \n",
              "\n",
              "      Vehicle Class_PICKUP TRUCK - SMALL  ...  Transmission_AV8  \\\n",
              "0                                      0  ...                 0   \n",
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              "3                                      0  ...                 0   \n",
              "4                                      0  ...                 0   \n",
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              "7381                                   0  ...                 0   \n",
              "7382                                   0  ...                 0   \n",
              "7383                                   0  ...                 0   \n",
              "7384                                   0  ...                 0   \n",
              "\n",
              "      Transmission_M5  Transmission_M6  Transmission_M7  Transmission_Other  \\\n",
              "0                   0                0                0                   0   \n",
              "1                   0                1                0                   0   \n",
              "2                   0                0                0                   0   \n",
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              "\n",
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              "      <td>-1.225976</td>\n",
              "      <td>-0.883408</td>\n",
              "      <td>-1.873275</td>\n",
              "      <td>-1.457401</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.251043</td>\n",
              "      <td>0.210575</td>\n",
              "      <td>0.040990</td>\n",
              "      <td>0.026208</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.251043</td>\n",
              "      <td>0.210575</td>\n",
              "      <td>-0.130437</td>\n",
              "      <td>-0.153624</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7380</th>\n",
              "      <td>-0.856721</td>\n",
              "      <td>-0.883408</td>\n",
              "      <td>-0.530433</td>\n",
              "      <td>-0.603202</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7381</th>\n",
              "      <td>-0.856721</td>\n",
              "      <td>-0.883408</td>\n",
              "      <td>-0.387577</td>\n",
              "      <td>-0.333455</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7382</th>\n",
              "      <td>-0.856721</td>\n",
              "      <td>-0.883408</td>\n",
              "      <td>-0.244721</td>\n",
              "      <td>-0.198582</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7383</th>\n",
              "      <td>-0.856721</td>\n",
              "      <td>-0.883408</td>\n",
              "      <td>-0.387577</td>\n",
              "      <td>-0.333455</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7384</th>\n",
              "      <td>-0.856721</td>\n",
              "      <td>-0.883408</td>\n",
              "      <td>-0.101866</td>\n",
              "      <td>-0.153624</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7385 rows × 49 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff158eec-9fe7-4a17-8022-efa69a147f60')\"\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",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d985543-76fa-404e-9141-a05db3948c02')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-6d985543-76fa-404e-9141-a05db3948c02 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff158eec-9fe7-4a17-8022-efa69a147f60 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-ff158eec-9fe7-4a17-8022-efa69a147f60');\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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split our preprocessed data into our features and target arrays\n",
        "X=co2_new_df.values\n",
        "y=co2_df[\"CO2\"].values\n",
        "\n",
        "# Split the preprocessed data into a training and testing dataset\n",
        "X_train, X_test, y_train, y_test= train_test_split(X,y, random_state= 1)"
      ],
      "metadata": {
        "id": "1XROmxFmwgpk"
      },
      "id": "1XROmxFmwgpk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b0fe111",
      "metadata": {
        "id": "1b0fe111"
      },
      "outputs": [],
      "source": [
        "##Compile, Train and Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
        "input_features = len(X_train[0])\n",
        "\n",
        "nn = tf.keras.models.Sequential()\n",
        "\n",
        "# First hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=20, activation=\"relu\", input_dim= input_features))\n",
        "\n",
        "# Second hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=20, activation=\"relu\"))\n",
        "\n",
        "# Output layer\n",
        "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
        "\n",
        "# Check the structure of the model\n",
        "nn.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRozMk3tpuZq",
        "outputId": "d0f24528-7909-4bb8-9af0-68e6b7f24eb2"
      },
      "id": "uRozMk3tpuZq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_12 (Dense)            (None, 20)                1000      \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 20)                420       \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 1)                 21        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,441\n",
            "Trainable params: 1,441\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "9NX6W2oFrnWD"
      },
      "id": "9NX6W2oFrnWD",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "fit_model = nn.fit(X_train, y_train, epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdh89xxDrJ5i",
        "outputId": "84058b82-dd59-4bf3-b943-07c119dbe8cb"
      },
      "id": "tdh89xxDrJ5i",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "174/174 [==============================] - 2s 3ms/step - loss: 0.3223 - accuracy: 0.8953\n",
            "Epoch 2/50\n",
            "174/174 [==============================] - 1s 3ms/step - loss: 0.1484 - accuracy: 0.9426\n",
            "Epoch 3/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.1149 - accuracy: 0.9549\n",
            "Epoch 4/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.0873 - accuracy: 0.9644\n",
            "Epoch 5/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0664 - accuracy: 0.9762\n",
            "Epoch 6/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0545 - accuracy: 0.9787\n",
            "Epoch 7/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0483 - accuracy: 0.9801\n",
            "Epoch 8/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0438 - accuracy: 0.9816\n",
            "Epoch 9/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0400 - accuracy: 0.9834\n",
            "Epoch 10/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0387 - accuracy: 0.9839\n",
            "Epoch 11/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0367 - accuracy: 0.9827\n",
            "Epoch 12/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0351 - accuracy: 0.9866\n",
            "Epoch 13/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0342 - accuracy: 0.9856\n",
            "Epoch 14/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0341 - accuracy: 0.9852\n",
            "Epoch 15/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0327 - accuracy: 0.9861\n",
            "Epoch 16/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0314 - accuracy: 0.9863\n",
            "Epoch 17/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0309 - accuracy: 0.9854\n",
            "Epoch 18/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0301 - accuracy: 0.9868\n",
            "Epoch 19/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0310 - accuracy: 0.9865\n",
            "Epoch 20/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0298 - accuracy: 0.9870\n",
            "Epoch 21/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0297 - accuracy: 0.9874\n",
            "Epoch 22/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0295 - accuracy: 0.9866\n",
            "Epoch 23/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0294 - accuracy: 0.9859\n",
            "Epoch 24/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0289 - accuracy: 0.9875\n",
            "Epoch 25/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0286 - accuracy: 0.9879\n",
            "Epoch 26/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0282 - accuracy: 0.9863\n",
            "Epoch 27/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0280 - accuracy: 0.9866\n",
            "Epoch 28/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0282 - accuracy: 0.9868\n",
            "Epoch 29/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0275 - accuracy: 0.9868\n",
            "Epoch 30/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0282 - accuracy: 0.9870\n",
            "Epoch 31/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0262 - accuracy: 0.9886\n",
            "Epoch 32/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0273 - accuracy: 0.9877\n",
            "Epoch 33/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0266 - accuracy: 0.9865\n",
            "Epoch 34/50\n",
            "174/174 [==============================] - 1s 3ms/step - loss: 0.0272 - accuracy: 0.9877\n",
            "Epoch 35/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.0260 - accuracy: 0.9879\n",
            "Epoch 36/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.0262 - accuracy: 0.9866\n",
            "Epoch 37/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.0265 - accuracy: 0.9879\n",
            "Epoch 38/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.0256 - accuracy: 0.9874\n",
            "Epoch 39/50\n",
            "174/174 [==============================] - 0s 3ms/step - loss: 0.0257 - accuracy: 0.9881\n",
            "Epoch 40/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0247 - accuracy: 0.9888\n",
            "Epoch 41/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0256 - accuracy: 0.9881\n",
            "Epoch 42/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0244 - accuracy: 0.9874\n",
            "Epoch 43/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0248 - accuracy: 0.9884\n",
            "Epoch 44/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0241 - accuracy: 0.9884\n",
            "Epoch 45/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0242 - accuracy: 0.9884\n",
            "Epoch 46/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0245 - accuracy: 0.9881\n",
            "Epoch 47/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0247 - accuracy: 0.9890\n",
            "Epoch 48/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0241 - accuracy: 0.9884\n",
            "Epoch 49/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0245 - accuracy: 0.9892\n",
            "Epoch 50/50\n",
            "174/174 [==============================] - 0s 2ms/step - loss: 0.0235 - accuracy: 0.9890\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model using the test data\n",
        "model_loss, model_accuracy = nn.evaluate(X_test, y_test,verbose=2)\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "206yvF-9sKub",
        "outputId": "2d9cb5dc-7e07-4ba2-8091-2a8afee7cf2b"
      },
      "id": "206yvF-9sKub",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "58/58 - 0s - loss: 0.0461 - accuracy: 0.9821 - 218ms/epoch - 4ms/step\n",
            "Loss: 0.046138204634189606, Accuracy: 0.9821332097053528\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The first model had 1 layer and without binning, had a Loss of 0.043466 an accuracy 0.981591 and . Optimization was attempted by adding layers to 2, and binning the \"Transmisssion\" column."
      ],
      "metadata": {
        "id": "tnAR0DlSfdQL"
      },
      "id": "tnAR0DlSfdQL"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "uy7fHBa-bnwZ"
      },
      "id": "uy7fHBa-bnwZ"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "3WUu8JHKbpH3"
      },
      "id": "3WUu8JHKbpH3"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}