Prediction_Final_Food_Sales_Predictions.ipynb

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        "# imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.compose import make_column_selector, make_column_transformer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.tree import DecisionTreeRegressor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = '/content/drive/MyDrive/CODING DOJO DS BOOTCAMP/PROJECTS/Project 1 - Sales Predictions/sales_predictions.csv'\n",
        "df = pd.read_csv(filename)\n",
        "df.head()"
      ],
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              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047   \n",
              "1           DRC01         5.92          Regular         0.019278   \n",
              "2           FDN15        17.50          Low Fat         0.016760   \n",
              "3           FDX07        19.20          Regular         0.000000   \n",
              "4           NCD19         8.93          Low Fat         0.000000   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
              "0                       1999      Medium               Tier 1   \n",
              "1                       2009      Medium               Tier 3   \n",
              "2                       1999      Medium               Tier 1   \n",
              "3                       1998         NaN               Tier 3   \n",
              "4                       1987        High               Tier 3   \n",
              "\n",
              "         Outlet_Type  Item_Outlet_Sales  \n",
              "0  Supermarket Type1          3735.1380  \n",
              "1  Supermarket Type2           443.4228  \n",
              "2  Supermarket Type1          2097.2700  \n",
              "3      Grocery Store           732.3800  \n",
              "4  Supermarket Type1           994.7052  "
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              "      <td>OUT049</td>\n",
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        "Before moving forward, we have to get rid of some columns that won't help us in predicting sales. \n",
        "I personally think the following columns don't influence sales predictions;\n",
        "\n",
        "\n",
        "1.   Outlet_Establishment_Year\n",
        "1.   Item_Identifier\n",
        "2.   Outlet_Identifier\n",
        "\n",
        "\n",
        "Therefore, I'm going to first drop those 3 columns before doing any other thing."
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        "# dropping the 3 unnecessary columns \n",
        "df = df.drop(columns=['Outlet_Establishment_Year', 'Item_Identifier', 'Outlet_Identifier'])\n",
        "df.head()"
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              "   Item_Weight Item_Fat_Content  Item_Visibility              Item_Type  \\\n",
              "0         9.30          Low Fat         0.016047                  Dairy   \n",
              "1         5.92          Regular         0.019278            Soft Drinks   \n",
              "2        17.50          Low Fat         0.016760                   Meat   \n",
              "3        19.20          Regular         0.000000  Fruits and Vegetables   \n",
              "4         8.93          Low Fat         0.000000              Household   \n",
              "\n",
              "   Item_MRP Outlet_Size Outlet_Location_Type        Outlet_Type  \\\n",
              "0  249.8092      Medium               Tier 1  Supermarket Type1   \n",
              "1   48.2692      Medium               Tier 3  Supermarket Type2   \n",
              "2  141.6180      Medium               Tier 1  Supermarket Type1   \n",
              "3  182.0950         NaN               Tier 3      Grocery Store   \n",
              "4   53.8614        High               Tier 3  Supermarket Type1   \n",
              "\n",
              "   Item_Outlet_Sales  \n",
              "0          3735.1380  \n",
              "1           443.4228  \n",
              "2          2097.2700  \n",
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              "4           994.7052  "
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              "      <td>5.92</td>\n",
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              "      <td>8.93</td>\n",
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              "          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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OK cool, Now we are gonna have to Ordinal code and OneHotEncode some columns to get our dataframe in our desired format"
      ],
      "metadata": {
        "id": "igb66E5XhxaO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Outlet_Size'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mN0GziK4iM3K",
        "outputId": "33ec7217-c3d2-41c3-8802-b9f5f93a6b2c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Medium    2793\n",
              "Small     2388\n",
              "High       932\n",
              "Name: Outlet_Size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can see Outlet_Size has Ordinal values that we can represent as numbers\n",
        "replace_dict = {'Small':0, 'Medium':1, 'High':2}\n",
        "df['Outlet_Size'].replace(replace_dict, inplace=True)"
      ],
      "metadata": {
        "id": "Sy26I7znDdcE"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separating our columns by dtype\n",
        "cat_selector = make_column_selector(dtype_include='object')\n",
        "num_selector = make_column_selector(dtype_include='number')"
      ],
      "metadata": {
        "id": "IDnejIL3iSmi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate transformers\n",
        "# median simpleImputer\n",
        "median_imputer = SimpleImputer(strategy='median')\n",
        "\n",
        "# OneHotEncoder\n",
        "ohe = OneHotEncoder(sparse=False, handle_unknown='ignore')"
      ],
      "metadata": {
        "id": "cUbLyxyKio3Y"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making tuples for column transfomation\n",
        "num_tuple = (median_imputer, num_selector)\n",
        "cat_tuple = (ohe, cat_selector)"
      ],
      "metadata": {
        "id": "oO5dqXyOjOnT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating col_transformer\n",
        "ct = make_column_transformer(num_tuple, cat_tuple, remainder='passthrough')\n"
      ],
      "metadata": {
        "id": "Uqc4WIC6jOZB"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transformed_names = ct.get_feature_names_out()\n",
        "actual_names = [i.split('__')[-1] for i in transformed_names]\n",
        "\n",
        "# creating a new transformer df\n",
        "new_df = pd.DataFrame(ct.fit_transform(df), columns=actual_names)\n",
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "1f5OuXkOjaOB",
        "outputId": "5a0858af-7e24-428e-b021-514d60fb0de4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Size  Item_Outlet_Sales  \\\n",
              "0         9.30         0.016047  249.8092          1.0          3735.1380   \n",
              "1         5.92         0.019278   48.2692          1.0           443.4228   \n",
              "2        17.50         0.016760  141.6180          1.0          2097.2700   \n",
              "3        19.20         0.000000  182.0950          1.0           732.3800   \n",
              "4         8.93         0.000000   53.8614          2.0           994.7052   \n",
              "\n",
              "   Item_Fat_Content_LF  Item_Fat_Content_Low Fat  Item_Fat_Content_Regular  \\\n",
              "0                  0.0                       1.0                       0.0   \n",
              "1                  0.0                       0.0                       1.0   \n",
              "2                  0.0                       1.0                       0.0   \n",
              "3                  0.0                       0.0                       1.0   \n",
              "4                  0.0                       1.0                       0.0   \n",
              "\n",
              "   Item_Fat_Content_low fat  Item_Fat_Content_reg  ...  Item_Type_Snack Foods  \\\n",
              "0                       0.0                   0.0  ...                    0.0   \n",
              "1                       0.0                   0.0  ...                    0.0   \n",
              "2                       0.0                   0.0  ...                    0.0   \n",
              "3                       0.0                   0.0  ...                    0.0   \n",
              "4                       0.0                   0.0  ...                    0.0   \n",
              "\n",
              "   Item_Type_Soft Drinks  Item_Type_Starchy Foods  \\\n",
              "0                    0.0                      0.0   \n",
              "1                    1.0                      0.0   \n",
              "2                    0.0                      0.0   \n",
              "3                    0.0                      0.0   \n",
              "4                    0.0                      0.0   \n",
              "\n",
              "   Outlet_Location_Type_Tier 1  Outlet_Location_Type_Tier 2  \\\n",
              "0                          1.0                          0.0   \n",
              "1                          0.0                          0.0   \n",
              "2                          1.0                          0.0   \n",
              "3                          0.0                          0.0   \n",
              "4                          0.0                          0.0   \n",
              "\n",
              "   Outlet_Location_Type_Tier 3  Outlet_Type_Grocery Store  \\\n",
              "0                          0.0                        0.0   \n",
              "1                          1.0                        0.0   \n",
              "2                          0.0                        0.0   \n",
              "3                          1.0                        1.0   \n",
              "4                          1.0                        0.0   \n",
              "\n",
              "   Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
              "0                            1.0                            0.0   \n",
              "1                            0.0                            1.0   \n",
              "2                            1.0                            0.0   \n",
              "3                            0.0                            0.0   \n",
              "4                            1.0                            0.0   \n",
              "\n",
              "   Outlet_Type_Supermarket Type3  \n",
              "0                            0.0  \n",
              "1                            0.0  \n",
              "2                            0.0  \n",
              "3                            0.0  \n",
              "4                            0.0  \n",
              "\n",
              "[5 rows x 33 columns]"
            ],
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              "\n",
              "  <div id=\"df-3b08ce21-bb86-4db6-b038-e54aaaddcc49\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
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              "      <th>Item_Fat_Content_Low Fat</th>\n",
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              "      <th>Item_Fat_Content_low fat</th>\n",
              "      <th>Item_Fat_Content_reg</th>\n",
              "      <th>...</th>\n",
              "      <th>Item_Type_Snack Foods</th>\n",
              "      <th>Item_Type_Soft Drinks</th>\n",
              "      <th>Item_Type_Starchy Foods</th>\n",
              "      <th>Outlet_Location_Type_Tier 1</th>\n",
              "      <th>Outlet_Location_Type_Tier 2</th>\n",
              "      <th>Outlet_Location_Type_Tier 3</th>\n",
              "      <th>Outlet_Type_Grocery Store</th>\n",
              "      <th>Outlet_Type_Supermarket Type1</th>\n",
              "      <th>Outlet_Type_Supermarket Type2</th>\n",
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              "      <th>0</th>\n",
              "      <td>9.30</td>\n",
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              "      <td>3735.1380</td>\n",
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              "      <th>1</th>\n",
              "      <td>5.92</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>1.0</td>\n",
              "      <td>443.4228</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.50</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2097.2700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.20</td>\n",
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              "      <td>182.0950</td>\n",
              "      <td>1.0</td>\n",
              "      <td>732.3800</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>2.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b08ce21-bb86-4db6-b038-e54aaaddcc49')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3b08ce21-bb86-4db6-b038-e54aaaddcc49 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-3b08ce21-bb86-4db6-b038-e54aaaddcc49');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can move forward to make predictions using different models"
      ],
      "metadata": {
        "id": "KIUuTyRinnvj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploring our new dataframe**"
      ],
      "metadata": {
        "id": "ksKWJzvZpI6C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for duplicates\n",
        "new_df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xR1OYqfwpNRi",
        "outputId": "197ad6fd-bbac-4c1c-a156-36629cfa604e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "new_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zI6SEfDSo95o",
        "outputId": "660b7403-687d-49b0-e299-e2f26909f96c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Weight                        0\n",
              "Item_Visibility                    0\n",
              "Item_MRP                           0\n",
              "Outlet_Size                        0\n",
              "Item_Outlet_Sales                  0\n",
              "Item_Fat_Content_LF                0\n",
              "Item_Fat_Content_Low Fat           0\n",
              "Item_Fat_Content_Regular           0\n",
              "Item_Fat_Content_low fat           0\n",
              "Item_Fat_Content_reg               0\n",
              "Item_Type_Baking Goods             0\n",
              "Item_Type_Breads                   0\n",
              "Item_Type_Breakfast                0\n",
              "Item_Type_Canned                   0\n",
              "Item_Type_Dairy                    0\n",
              "Item_Type_Frozen Foods             0\n",
              "Item_Type_Fruits and Vegetables    0\n",
              "Item_Type_Hard Drinks              0\n",
              "Item_Type_Health and Hygiene       0\n",
              "Item_Type_Household                0\n",
              "Item_Type_Meat                     0\n",
              "Item_Type_Others                   0\n",
              "Item_Type_Seafood                  0\n",
              "Item_Type_Snack Foods              0\n",
              "Item_Type_Soft Drinks              0\n",
              "Item_Type_Starchy Foods            0\n",
              "Outlet_Location_Type_Tier 1        0\n",
              "Outlet_Location_Type_Tier 2        0\n",
              "Outlet_Location_Type_Tier 3        0\n",
              "Outlet_Type_Grocery Store          0\n",
              "Outlet_Type_Supermarket Type1      0\n",
              "Outlet_Type_Supermarket Type2      0\n",
              "Outlet_Type_Supermarket Type3      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Validation split**"
      ],
      "metadata": {
        "id": "F9PBc8wzoCg3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a target vector & a matrix feature\n",
        "y = new_df['Item_Outlet_Sales']\n",
        "X = new_df.drop(columns='Item_Outlet_Sales')\n",
        "\n",
        "# train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
      ],
      "metadata": {
        "id": "e0eBbF3djaJ1"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Linear Regression Model**"
      ],
      "metadata": {
        "id": "vsHZuW1_pjzs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Instantiate and fit the Model**"
      ],
      "metadata": {
        "id": "2YGAhCLSpqxA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate the models\n",
        "scaler = StandardScaler()\n",
        "lin_reg = LinearRegression()\n",
        "\n",
        "# create a pipeline\n",
        "lin_reg_pipe = make_pipeline(scaler, lin_reg)\n",
        "\n",
        "# fit the model using training data\n",
        "lin_reg_pipe.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "se6j-OM7jaH4",
        "outputId": "3f134918-ae96-4a76-e979-f9e199e84616"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearregression', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create Model Predictions for Training and Testing Data**"
      ],
      "metadata": {
        "id": "kWf-3KTpqGGb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model predictions\n",
        "train_pred = lin_reg_pipe.predict(X_train)\n",
        "test_pred = lin_reg_pipe.predict(X_test)"
      ],
      "metadata": {
        "id": "zy_gz8QzqDCI"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluate the Model using Regression Metrics**"
      ],
      "metadata": {
        "id": "ysqPAHuhqS_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the R2 score\n",
        "train_r2 = r2_score(y_train, train_pred)\n",
        "test_r2 = r2_score(y_test, test_pred)\n",
        "print(train_r2)\n",
        "print(test_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opL5HYXGqX9l",
        "outputId": "32b55176-8802-472a-c6f0-f1f914026459"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5606877051548549\n",
            "0.5656972162428142\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our R2 score, our model explains 56.07% of the variance in the training set and explains 56.57% of variance in the test set."
      ],
      "metadata": {
        "id": "s69sopcjvBR8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the RMSE of our model\n",
        "\n",
        "# Finding the MSE first\n",
        "train_mse = mean_squared_error(y_train, train_pred)\n",
        "test_mse = mean_squared_error(y_test, test_pred)\n",
        "\n",
        "# Now calculating the RMSE\n",
        "train_rmse = np.sqrt(train_mse)\n",
        "test_rmse = np.sqrt(test_mse)\n",
        "print(train_rmse)\n",
        "print(test_rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwhCah9eqYHu",
        "outputId": "c755bd98-6d48-4ea8-bdd1-6b0c87deaea4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1140.2303613955025\n",
            "1094.637618765809\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our RMSE, it shows that we have an average error of 1140.23 in the training set and and average error of 1094.64 in the testing set.\n",
        "\n",
        "The RMSE values are relatively high, suggesting that the model's predictions are not very accurate."
      ],
      "metadata": {
        "id": "dmAiIRdqvsYh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Regression Tree Model**"
      ],
      "metadata": {
        "id": "7i1r-iuUvr6c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Instantiate the Tree Model**"
      ],
      "metadata": {
        "id": "yXu9Fsylwuuy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dec_tree = DecisionTreeRegressor(random_state=42)"
      ],
      "metadata": {
        "id": "Jhd0IjoerbpG"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fit the Model using the training data**"
      ],
      "metadata": {
        "id": "uqKFe33Iw9_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "dec_tree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "aLEGaPw4w4Md",
        "outputId": "7a6fbb05-2a2e-4e48-fcfe-7110372a7600"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict the target values for the training and testing set**"
      ],
      "metadata": {
        "id": "kumgvCibxYyF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# making predictions for both the train & test data\n",
        "train_tree_preds = dec_tree.predict(X_train)\n",
        "test_tree_preds = dec_tree.predict(X_test)"
      ],
      "metadata": {
        "id": "hS26xe6IxL4q"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluate the Model**"
      ],
      "metadata": {
        "id": "LUfc-srzx0Al"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating our model by getting the R2 score\n",
        "train_tree_score = dec_tree.score(X_train, y_train)\n",
        "test_tree_score = dec_tree.score(X_test, y_test)\n",
        "\n",
        "print(train_tree_score)\n",
        "print(test_tree_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mILmsIHkxuYt",
        "outputId": "28f65386-590e-4889-c835-f471e58fd338"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "0.1282199664431266\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It should be clear that the default decision tree had a much higher R^2 score on the training data than it did on the test data.  This is an indication that the model is overfitting.  "
      ],
      "metadata": {
        "id": "RaeCtSNUylsv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tune the Model**"
      ],
      "metadata": {
        "id": "Htqy6CyQywbw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at option for tuning this model\n",
        "dec_tree.get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hd18jS-syW7Z",
        "outputId": "c7aff615-97d9-4fe0-865e-2642277b02f3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'criterion': 'squared_error',\n",
              " 'max_depth': None,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'random_state': 42,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How leaf nodes\n",
        "dec_tree.get_n_leaves()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SpU6pdKN00jz",
        "outputId": "cbd6b523-0529-4f83-8c77-c8ccf682eb52"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6259"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the depth of our default tree\n",
        "dec_tree.get_depth()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViB6stPSzmOc",
        "outputId": "995d6411-0ade-40d5-c5e3-41f8d4db1ace"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding the optimal max_depth (hyperparameter tuning)**"
      ],
      "metadata": {
        "id": "oKwVqRPi1OkW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`max_depth` is  a parameter of the Decision Tree Model. Finding the best `max_depth` will improve the performance of our model, so we are going to do that.\n",
        "\n",
        "As you saw from the above cell, our tree 41 levels deep. But manually filling that for `max_depth` will take much time & effort. So we are going to be smart about it and use a for loop."
      ],
      "metadata": {
        "id": "rhlb2YA71bdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_depth_range = list(range(1, dec_tree.get_depth()))\n",
        "\n",
        "r2 = []\n",
        "for depth in max_depth_range:\n",
        "  dec_tree = DecisionTreeRegressor(max_depth = depth, random_state=42)\n",
        "  dec_tree.fit(X_train, y_train)\n",
        "  score = dec_tree.score(X_test, y_test)\n",
        "  r2.append(score)\n"
      ],
      "metadata": {
        "id": "76fuiuV80-Bg"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To help us see the results of trying various tree depths, the following code creates a visualization of R2 vs max_depth."
      ],
      "metadata": {
        "id": "qRJp7paS3rVv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize which max_depth leads to the highest r2 score\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(max_depth_range, r2)\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('R2');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "nPuRF3oe2UcW",
        "outputId": "87f8ac4f-c67c-4ab5-9fcc-992fce7eda9f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "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": "markdown",
      "source": [
        "From the image above, it looks like the best score for the model is when the parameter max_depth is 5.  "
      ],
      "metadata": {
        "id": "JeCFU8gI3w0W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's run the model with our optimized value for max_depth\n",
        "\n",
        "# creating a model with the best max_depth\n",
        "dec_tree_5 = DecisionTreeRegressor(max_depth = 5, random_state=42)\n",
        "dec_tree_5.fit(X_train, y_train)\n",
        "\n",
        "# making predictions using model with the best max_depth\n",
        "dec_tree_5_test_preds = dec_tree_5.predict(X_test)\n",
        "\n",
        "# R2 scores of our best model\n",
        "train_5_score = dec_tree_5.score(X_train, y_train)\n",
        "test_5_score = dec_tree_5.score(X_test, y_test)\n",
        "print(train_5_score)\n",
        "print(test_5_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRyKAPgK2nkR",
        "outputId": "722db2cc-f03f-4248-98b9-7e6aa03c90ca"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6039251094395393\n",
            "0.5947194175939473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see above, having `max_depth` of 5 massively improved the r2 test score of our model which is a good thing"
      ],
      "metadata": {
        "id": "dR4ZPu6H4Hye"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our R2 score, our model explains 60.4% of the variance in the training set and explains 59.5% of variance in the test set. \n",
        "\n",
        "This is an improvement from our previous model"
      ],
      "metadata": {
        "id": "V4tWaMNN9d__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the RMSE of our tree model\n",
        "\n",
        "# calculate model mse\n",
        "dec_tree_mse = mean_squared_error(y_test, dec_tree_5_test_preds)\n",
        "\n",
        "# Now calculating the RMSE\n",
        "dec_tree_rmse = np.sqrt(dec_tree_mse)\n",
        "print(dec_tree_rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5k7_koq36cp",
        "outputId": "5e6fedf9-d800-4c86-c06e-8a262712404d"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1057.4308119434352\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our RMSE, it shows we have an average error of 27.1 in our testing set.\n",
        "\n",
        "This is a **very big** improvement from the previous model."
      ],
      "metadata": {
        "id": "do-AIHjDABNi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Recommendation. Which Model is Best?**"
      ],
      "metadata": {
        "id": "D-1Pbv-eAlZk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Regression Tree Model is Better**"
      ],
      "metadata": {
        "id": "ncurQG5mDW9C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Its very clear that the Regression/Decision tree model is much better than the Linear Regression model as it the regression tree outperforms in both R^2 and RMSE metrics "
      ],
      "metadata": {
        "id": "R7_ZAjg4A046"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the mae of the Regression model\n",
        "reg_mae = mean_absolute_error(y_test, test_pred)\n",
        "print(reg_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEPywjBYBiGP",
        "outputId": "28000683-5d8d-4274-e2ee-9d84e683f9b0"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "805.4413140610877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the mae of the tree model\n",
        "dec_tree_mae = mean_absolute_error(y_test, dec_tree_5_test_preds)\n",
        "print(dec_tree_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tk1k12D5-XG",
        "outputId": "618a43c9-c6fa-4d5a-b363-ce4e30a945c6"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "738.1985550019423\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can again see the Regression tree model has a much lower Mean Absolute Error(MAE).\n",
        "\n",
        "This clearly indicates the Regression tree model is the better model for this data"
      ],
      "metadata": {
        "id": "baEvS5EpC28n"
      }
    }
  ]
}