probstat_laptop_price.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMqMqu9DtEak"
      },
      "source": [
        "## Import Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C7A5pfDos4fP"
      },
      "outputs": [],
      "source": [
        "#import some necessary librairies\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "color = sns.color_palette()\n",
        "sns.set_style('darkgrid')\n",
        "import warnings\n",
        "def ignore_warn(*args, **kwargs):\n",
        "    pass\n",
        "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)\n",
        "\n",
        "import plotly.express as px\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew #for some statistics\n",
        "\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points\n",
        "\n",
        "\n",
        "from subprocess import check_output\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfAenq1ftC-I"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f34ByV0stHbV",
        "outputId": "e9c06ffb-b6a3-4bd4-94c5-7209d949c240",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WeQ8vOpHtKs7",
        "outputId": "32cd2eb4-f9d7-42dc-b2c1-3d0479b79777",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Probabilitas Statistika\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Probabilitas Statistika"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ayGSy5kDs4fU",
        "outputId": "75825734-5061-44af-882e-8bfa54551b09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   laptop_ID Company      Product   TypeName  Inches  \\\n",
              "0          1   Apple  MacBook Pro  Ultrabook  13.300   \n",
              "1          2   Apple  Macbook Air  Ultrabook  13.300   \n",
              "2          3      HP       250 G6   Notebook  15.600   \n",
              "3          4   Apple  MacBook Pro  Ultrabook  15.400   \n",
              "4          5   Apple  MacBook Pro  Ultrabook  13.300   \n",
              "\n",
              "                     ScreenResolution                         Cpu   Ram  \\\n",
              "0  IPS Panel Retina Display 2560x1600        Intel Core i5 2.3GHz   8GB   \n",
              "1                            1440x900        Intel Core i5 1.8GHz   8GB   \n",
              "2                   Full HD 1920x1080  Intel Core i5 7200U 2.5GHz   8GB   \n",
              "3  IPS Panel Retina Display 2880x1800        Intel Core i7 2.7GHz  16GB   \n",
              "4  IPS Panel Retina Display 2560x1600        Intel Core i5 3.1GHz   8GB   \n",
              "\n",
              "                Memory                           Gpu  OpSys  Weight  \\\n",
              "0            128GB SSD  Intel Iris Plus Graphics 640  macOS  1.37kg   \n",
              "1  128GB Flash Storage        Intel HD Graphics 6000  macOS  1.34kg   \n",
              "2            256GB SSD         Intel HD Graphics 620  No OS  1.86kg   \n",
              "3            512GB SSD            AMD Radeon Pro 455  macOS  1.83kg   \n",
              "4            256GB SSD  Intel Iris Plus Graphics 650  macOS  1.37kg   \n",
              "\n",
              "   Price_euros  \n",
              "0     1339.690  \n",
              "1      898.940  \n",
              "2      575.000  \n",
              "3     2537.450  \n",
              "4     1803.600  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>laptop_ID</th>\n",
              "      <th>Company</th>\n",
              "      <th>Product</th>\n",
              "      <th>TypeName</th>\n",
              "      <th>Inches</th>\n",
              "      <th>ScreenResolution</th>\n",
              "      <th>Cpu</th>\n",
              "      <th>Ram</th>\n",
              "      <th>Memory</th>\n",
              "      <th>Gpu</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Price_euros</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.300</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 2.3GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 640</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37kg</td>\n",
              "      <td>1339.690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Apple</td>\n",
              "      <td>Macbook Air</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.300</td>\n",
              "      <td>1440x900</td>\n",
              "      <td>Intel Core i5 1.8GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>128GB Flash Storage</td>\n",
              "      <td>Intel HD Graphics 6000</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.34kg</td>\n",
              "      <td>898.940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>HP</td>\n",
              "      <td>250 G6</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.600</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel HD Graphics 620</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86kg</td>\n",
              "      <td>575.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>15.400</td>\n",
              "      <td>IPS Panel Retina Display 2880x1800</td>\n",
              "      <td>Intel Core i7 2.7GHz</td>\n",
              "      <td>16GB</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83kg</td>\n",
              "      <td>2537.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.300</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 3.1GHz</td>\n",
              "      <td>8GB</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37kg</td>\n",
              "      <td>1803.600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "#Dataset: https://www.kaggle.com/muhammetvarl/laptop-price\n",
        "df = pd.read_csv('./laptop_price.csv',encoding='latin-1')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QF2hUjiss4fV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8131eaf4-e3ff-4de0-f8ca-3b0cb00d7581"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1303 entries, 0 to 1302\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   laptop_ID         1303 non-null   int64  \n",
            " 1   Company           1303 non-null   object \n",
            " 2   Product           1303 non-null   object \n",
            " 3   TypeName          1303 non-null   object \n",
            " 4   Inches            1303 non-null   float64\n",
            " 5   ScreenResolution  1303 non-null   object \n",
            " 6   Cpu               1303 non-null   object \n",
            " 7   Ram               1303 non-null   object \n",
            " 8   Memory            1303 non-null   object \n",
            " 9   Gpu               1303 non-null   object \n",
            " 10  OpSys             1303 non-null   object \n",
            " 11  Weight            1303 non-null   object \n",
            " 12  Price_euros       1303 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(10)\n",
            "memory usage: 132.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PMaqQDYndZMV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7307e24-a7a4-4ed7-b8b2-4c35f40fcdce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      13.300\n",
              "1      13.300\n",
              "2      15.600\n",
              "3      15.400\n",
              "4      13.300\n",
              "        ...  \n",
              "1298   14.000\n",
              "1299   13.300\n",
              "1300   14.000\n",
              "1301   15.600\n",
              "1302   15.600\n",
              "Name: Inches, Length: 1303, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "df['Inches']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_T0H3CTs4fW"
      },
      "source": [
        "# Data Visualization and Feature(\"independent variable\") Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCOYl8XNs4fY"
      },
      "outputs": [],
      "source": [
        "df['Inches'] = df['Inches'].apply(str)\n",
        "df['Weight'] = df['Weight'].apply(lambda x: str(x).split('k')[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9-axksDzs4fZ"
      },
      "outputs": [],
      "source": [
        "df['Weight'] = df['Weight'].apply(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "13Zh2KCos4fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "9a615d83-3e58-45e8-b01c-b7612e074f4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average Laptop Price Per Company')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "temp = df.groupby(['Company']).mean().reset_index().sort_values(by='Price_euros',ascending=False)\n",
        "plt.figure(figsize = (12,6))\n",
        "splot = sns.barplot(data = temp,x = 'Company', y = 'Price_euros',palette = 'RdBu')\n",
        "for p in splot.patches:\n",
        "    splot.annotate(format(p.get_height(), '.1f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, 9), \n",
        "                   textcoords = 'offset points')\n",
        "plt.xlabel(\"Company\", size=14)\n",
        "plt.ylabel(\"Price\", size=14)\n",
        "plt.title('Average Laptop Price Per Company')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W1FxhsBts4fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "4f3f2ec8-c0a6-4676-f361-56e90e43052a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "temp = df.groupby(['Company','Ram']).mean().reset_index().sort_values(by='Price_euros',ascending=False)\n",
        "px.scatter(data_frame = temp, x = 'Company', y = 'Price_euros',color = 'Ram',title = 'Average Laptop Price for Each Company/RAM amount')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4BAaWTZXs4fc"
      },
      "source": [
        "**Checking some outliers**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FvCEiQlns4fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "54d9ae60-d960-4cc2-e9a0-fb054ec1a6e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "px.scatter(data_frame = df,x = 'Inches',y = 'Price_euros')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BKk7Bkz5s4fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "9cdf5235-354d-4e80-95df-437cd3131226"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ],
      "source": [
        "px.scatter(data_frame = df,x = 'Ram',y = 'Price_euros')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6L1GQ4l-s4fe"
      },
      "outputs": [],
      "source": [
        "#Removing Outliers after checking them using scatter plot\n",
        "df = df[~((df['Price_euros'] > 3500) &(df['Ram']== '8GB'))]\n",
        "df = df[~((df['Price_euros'] > 3500) &(df['Ram']== '16GB'))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NMj4JSJqs4ff"
      },
      "outputs": [],
      "source": [
        "df = df[~((df['Price_euros'] == 4899) &(df['Inches']== 15.6))]\n",
        "df = df[~((df['Price_euros'] >= 5000) &(df['Inches']== 17.3))]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rutfi3lLs4fg"
      },
      "source": [
        "> We can easily compare laptops prices with respect to ram with this amazing graph above"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O4K_YmLPs4fg",
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          "height": 542
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        "outputId": "2e3eff3c-f6b7-4235-b99d-85232b395d94"
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        }
      ],
      "source": [
        "temp = df.groupby(['Company','Memory']).mean().reset_index().sort_values(by='Price_euros',ascending=False)\n",
        "px.scatter(data_frame = temp, x = 'Memory', y = 'Price_euros',color = 'Company',title = 'Average Laptop Price for Each Company/Memory')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NmEta-G6s4fh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "991ceea8-ce4e-48ce-b497-82df5241a5d4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Laptop Price for Each Company/Size in Inches\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('3bbc4a99-8ccc-4b4d-b98e-efb330e87ada');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "temp = df.groupby(['Company','Inches']).mean().reset_index().sort_values(by='Price_euros',ascending=False)\n",
        "px.scatter(data_frame = temp, x = 'Inches', y = 'Price_euros',color = 'Company', title = 'Average Laptop Price for Each Company/Size in Inches')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8pwtGFMs4fh"
      },
      "source": [
        "# Checking skweness and transforming the target column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cG_tsWghs4fh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "eacbc290-8b50-46a6-b1f5-6a626b8d0989"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " mu = 1119.01 and sigma = 688.94\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.distplot(df['Price_euros'] , fit=norm)\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(df['Price_euros'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
        "            loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Price_euros distribution')\n",
        "\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(df['Price_euros'], plot=plt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34QldO2Os4fi"
      },
      "source": [
        "**Log Transformation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NyXe0K4-s4fi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "5f7111fd-16ed-46b4-e26d-4808d347de70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " mu = 6.84 and sigma = 0.62\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
        "df[\"Price_euros\"] = np.log1p(df[\"Price_euros\"])\n",
        "\n",
        "#Check the new distribution \n",
        "sns.distplot(df['Price_euros'] , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(df['Price_euros'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
        "            loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Price_euros distribution')\n",
        "\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(df['Price_euros'], plot=plt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jmSizjr1s4fj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "outputId": "d3036666-44db-45fb-cedd-e83a13d574c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f136f981070>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Correlation map to see how features are correlated with Price_euros\n",
        "corrmat = df.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, square=True,cmap = 'RdBu',annot = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXWtLDrcs4fj"
      },
      "source": [
        "**There is no Correlation between the price and other variables and there is no intercorrelation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aOhDtC_os4fj"
      },
      "outputs": [],
      "source": [
        "#Dont need ID column for the model\n",
        "df.drop(columns = 'laptop_ID',inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HSyZLvN9s4fk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed0d34ed-0791-426a-d139-0648e09d3e2f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu',\n",
              "       'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price_euros'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pLeBmEUTs4fk"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "cols = ['Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu',\n",
        "       'Ram', 'Memory', 'Gpu', 'OpSys']\n",
        "for c in cols:\n",
        "    lbl = LabelEncoder() \n",
        "    lbl.fit(list(df[c].values)) \n",
        "    df[c] = lbl.transform(list(df[c].values))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation map to see how features are correlated with Price_euros\n",
        "corrmat = df.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, square=True,cmap = 'RdBu',annot = True)"
      ],
      "metadata": {
        "id": "YMdcKzJtKPHY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "4c490468-463e-4e00-f761-9f3a6d85a36b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f136f621730>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LuSiBvNJs4fk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "25b47c1c-553b-48d9-ac9f-e70c3f536876"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Company  Product  TypeName  Inches  ScreenResolution  Cpu  Ram  Memory  \\\n",
              "0        1      300         4       7                23   65    8       4   \n",
              "1        1      301         4       7                 1   63    8       2   \n",
              "2        7       50         3      14                 8   74    8      16   \n",
              "3        1      300         4      13                25   85    1      29   \n",
              "4        1      300         4       7                23   67    8      16   \n",
              "\n",
              "   Gpu  OpSys  Weight  Price_euros  \n",
              "0   58      8   1.370        7.201  \n",
              "1   51      8   1.340        6.802  \n",
              "2   53      4   1.860        6.356  \n",
              "3    9      8   1.830        7.839  \n",
              "4   59      8   1.370        7.498  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3ebd053-a636-41e2-9426-23ac7c35ffec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Product</th>\n",
              "      <th>TypeName</th>\n",
              "      <th>Inches</th>\n",
              "      <th>ScreenResolution</th>\n",
              "      <th>Cpu</th>\n",
              "      <th>Ram</th>\n",
              "      <th>Memory</th>\n",
              "      <th>Gpu</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Price_euros</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>300</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>23</td>\n",
              "      <td>65</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>58</td>\n",
              "      <td>8</td>\n",
              "      <td>1.370</td>\n",
              "      <td>7.201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>63</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>8</td>\n",
              "      <td>1.340</td>\n",
              "      <td>6.802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>50</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>74</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>53</td>\n",
              "      <td>4</td>\n",
              "      <td>1.860</td>\n",
              "      <td>6.356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>300</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>85</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>1.830</td>\n",
              "      <td>7.839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>300</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>23</td>\n",
              "      <td>67</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>59</td>\n",
              "      <td>8</td>\n",
              "      <td>1.370</td>\n",
              "      <td>7.498</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3ebd053-a636-41e2-9426-23ac7c35ffec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  <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",
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              "    }\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-f3ebd053-a636-41e2-9426-23ac7c35ffec 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-f3ebd053-a636-41e2-9426-23ac7c35ffec');\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",
              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZevHMcSRs4fk"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FBBdt9VRs4fl"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a-d1t13vs4fl"
      },
      "outputs": [],
      "source": [
        "X = df.iloc[:,:-1]\n",
        "y = df['Price_euros']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ev9x9o6Os4fl"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mJKzoct8s4fl"
      },
      "outputs": [],
      "source": [
        "#Validation function\n",
        "n_folds = 5\n",
        "\n",
        "def rmsle_cv(model):\n",
        "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(X_train.values)\n",
        "    rmse= np.sqrt(-cross_val_score(model, X_train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
        "    return(rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_9jse_6s4fl"
      },
      "source": [
        "**Lasso Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x-EWXeO9s4fm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7fdb79e-f401-4cda-c36e-5a5d33d20f0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Lasso score: 0.4139 (0.0149)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
        "score = rmsle_cv(lasso)\n",
        "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEvpiuRZs4fm"
      },
      "source": [
        "**Elastic Net Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LftwysLhs4fm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e37c1f23-6138-4393-c546-5840bd822da6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ElasticNet score: 0.4139 (0.0149)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
        "score = rmsle_cv(ENet)\n",
        "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6lww5Dbs4fm"
      },
      "source": [
        "**Kernel Ridge Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RB9VSd6Zs4fn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efa8a8b9-ae9d-4d46-b5b3-522cb6f46ae2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel Ridge score: 0.3032 (0.0178)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\n",
        "score = rmsle_cv(KRR)\n",
        "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFHySvFvs4fn"
      },
      "source": [
        "**Gradient Boosting Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M7YkzgBks4fn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a95f7656-8b3e-42a2-9fa0-91889efc2bc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting score: 0.1700 (0.0059)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
        "                                   max_depth=4, max_features='sqrt',\n",
        "                                   min_samples_leaf=15, min_samples_split=10, \n",
        "                                   loss='huber', random_state =5)\n",
        "score = rmsle_cv(GBoost)\n",
        "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMUanfXhs4fn"
      },
      "source": [
        "**XGBoost Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ic9pPJIus4fo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fce4873-c3c9-47ce-a808-aafbc9256dfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Xgboost score: 0.1759 (0.0043)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
        "                             learning_rate=0.05, max_depth=3, \n",
        "                             min_child_weight=1.7817, n_estimators=2200,\n",
        "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
        "                             subsample=0.5213, silent=1,\n",
        "                             random_state =7, nthread = -1)\n",
        "score = rmsle_cv(model_xgb)\n",
        "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdcWVhNds4fo"
      },
      "source": [
        "**LGBM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gk5QleGzs4fo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbd6d170-d0a8-41e2-cdaf-4bfa86f90ebc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LGBM score: 0.1862 (0.0084)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
        "                              learning_rate=0.05, n_estimators=720,\n",
        "                              max_bin = 55, bagging_fraction = 0.8,\n",
        "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
        "                              feature_fraction_seed=9, bagging_seed=9,\n",
        "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n",
        "score = rmsle_cv(model_lgb)\n",
        "print(\"LGBM score: {:.4f} ({:.4f})\\n\" .format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ncyPlMxGs4fp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d66de433-2180-4f4b-b567-72cf783dc992"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ElasticNet score: 0.4139 (0.0149)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "score = rmsle_cv(ENet)\n",
        "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "adgfAlODs4fp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46bf7afc-2c03-4b7a-9914-5a89f7181742"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel Ridge score: 0.3032 (0.0178)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "score = rmsle_cv(KRR)\n",
        "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lA8uzzEs4fq"
      },
      "source": [
        "# Fitting the best model and getting the predictions array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sNlNQMiys4fq"
      },
      "outputs": [],
      "source": [
        "GBoost.fit(X_train,y_train)\n",
        "gboost_pred = GBoost.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Br89LXXs4fq"
      },
      "outputs": [],
      "source": [
        "result = pd.DataFrame({'Y_TEST':y_test.values,'Y_PRED' : pd.Series(gboost_pred).values})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0sUqa6A5s4fr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "83a588d2-40eb-414a-91d4-87fdfb625213"
      },
      "outputs": [
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "px.line(data_frame = result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIWfGKCAs4fr"
      },
      "source": [
        "# Almost perfect fit on the test data!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nz8TD7r0s4fr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c39132bb-4b25-4e16-9f0d-abcaffc95207"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.11970947575019962\n"
          ]
        }
      ],
      "source": [
        "# MAE \n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "print('MAE:', mean_absolute_error(y_test, gboost_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE\n",
        "from sklearn.metrics import mean_squared_error\n",
        "print('MSE:', mean_squared_error(y_test, gboost_pred))"
      ],
      "metadata": {
        "id": "Ymwk5600xOkV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a1ca232-8d19-42f3-fdb9-73d411df8b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 0.029589429836364495\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# T Test\n",
        "from scipy import stats\n",
        "stats.ttest_rel(y_test, gboost_pred)\n",
        "print('t-statistic = %6.3f pvalue = %6.4f' % stats.ttest_rel(y_test, gboost_pred))\n",
        "\n",
        "# RMSE\n",
        "from sklearn.metrics import mean_squared_error\n",
        "print('RMSE:', np.sqrt(mean_squared_error(y_test, gboost_pred)))\n",
        "\n",
        "# R2\n",
        "from sklearn.metrics import r2_score\n",
        "print('R2:', r2_score(y_test, gboost_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRWXSzC_h7RU",
        "outputId": "42bf55e8-6833-4634-e5da-b1a5558f71ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t-statistic = -1.721 pvalue = 0.0868\n",
            "RMSE: 0.1720157836838367\n",
            "R2: 0.9162330021962233\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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