Laptop_Price_Prediction.ipynb

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      "source": [
        "# Laptop Price Prediction\n",
        "Dataset: https://www.kaggle.com/datasets/muhammetvarl/laptop-price\n",
        "\n",
        "<br/>\n",
        "\n",
        "### Steps:\n",
        "1. Loading Dataset\n",
        "2. Data Cleaning\n",
        "3. Exploratory Data Analysis\n",
        "4. Feature Engineering\n",
        "5. Model Building\n",
        "6. Model Evaluation\n",
        "7. Model Improvement\n",
        "8. Deployment"
      ],
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      "cell_type": "markdown",
      "source": [
        "## 1. Loading Dataset"
      ],
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      }
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      "execution_count": 218,
      "metadata": {
        "id": "qvw3jI5Zac6k"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/VirajPatidar/laptop-price-predictor/main/laptop_data.csv', encoding='latin-1')"
      ],
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      "source": [
        "## 2. Data Cleaning"
      ],
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      "source": [
        "# Converting Euros to Rupees\n",
        "df.rename(columns={'Price_euros':'Price'}, inplace=True)\n",
        "df['Price'] = df['Price'].apply(lambda x: x*82)\n",
        "df.head()"
      ],
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              "   laptop_ID Company      Product   TypeName  Inches  \\\n",
              "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
              "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
              "2          3      HP       250 G6   Notebook    15.6   \n",
              "3          4   Apple  MacBook Pro  Ultrabook    15.4   \n",
              "4          5   Apple  MacBook Pro  Ultrabook    13.3   \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      Price  \n",
              "0            128GB SSD  Intel Iris Plus Graphics 640  macOS  1.37kg  109854.58  \n",
              "1  128GB Flash Storage        Intel HD Graphics 6000  macOS  1.34kg   73713.08  \n",
              "2            256GB SSD         Intel HD Graphics 620  No OS  1.86kg   47150.00  \n",
              "3            512GB SSD            AMD Radeon Pro 455  macOS  1.83kg  208070.90  \n",
              "4            256GB SSD  Intel Iris Plus Graphics 650  macOS  1.37kg  147895.20  "
            ],
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              "      <th></th>\n",
              "      <th>laptop_ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Apple</td>\n",
              "      <td>MacBook Pro</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 2.3GHz</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Apple</td>\n",
              "      <td>Macbook Air</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>1440x900</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>HP</td>\n",
              "      <td>250 G6</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</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>47150.00</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>15.4</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>208070.90</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>13.3</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>147895.20</td>\n",
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            "<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             1303 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(10)\n",
            "memory usage: 132.5+ KB\n"
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            "text/plain": [
              "laptop_ID           0\n",
              "Company             0\n",
              "Product             0\n",
              "TypeName            0\n",
              "Inches              0\n",
              "ScreenResolution    0\n",
              "Cpu                 0\n",
              "Ram                 0\n",
              "Memory              0\n",
              "Gpu                 0\n",
              "OpSys               0\n",
              "Weight              0\n",
              "Price               0\n",
              "dtype: int64"
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              "  Company   TypeName  Inches                    ScreenResolution  \\\n",
              "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
              "1   Apple  Ultrabook    13.3                            1440x900   \n",
              "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
              "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
              "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
              "\n",
              "                          Cpu   Ram               Memory  \\\n",
              "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
              "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
              "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
              "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
              "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
              "\n",
              "                            Gpu  OpSys  Weight      Price  \n",
              "0  Intel Iris Plus Graphics 640  macOS  1.37kg  109854.58  \n",
              "1        Intel HD Graphics 6000  macOS  1.34kg   73713.08  \n",
              "2         Intel HD Graphics 620  No OS  1.86kg   47150.00  \n",
              "3            AMD Radeon Pro 455  macOS  1.83kg  208070.90  \n",
              "4  Intel Iris Plus Graphics 650  macOS  1.37kg  147895.20  "
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              "      <th>1</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>1440x900</td>\n",
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              "      <td>1.86kg</td>\n",
              "      <td>47150.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>15.4</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>208070.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</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>147895.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e5e5f22-e817-4a2d-8ec5-1960b04ca223')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e5e5f22-e817-4a2d-8ec5-1960b04ca223 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-6e5e5f22-e817-4a2d-8ec5-1960b04ca223');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ram'] = df['Ram'].str.replace('GB','')\n",
        "df['Weight'] = df['Weight'].str.replace('kg','')"
      ],
      "metadata": {
        "id": "Ki2mJNxPpijH"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ram'] = df['Ram'].astype('int32')\n",
        "df['Weight'] = df['Weight'].astype('float32')"
      ],
      "metadata": {
        "id": "Hjt4cLQupks8"
      },
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BJpd5bdpmB3",
        "outputId": "248e5a74-6902-4ba7-91e8-cb614ffd94dc"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1303 entries, 0 to 1302\n",
            "Data columns (total 11 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Company           1303 non-null   object \n",
            " 1   TypeName          1303 non-null   object \n",
            " 2   Inches            1303 non-null   float64\n",
            " 3   ScreenResolution  1303 non-null   object \n",
            " 4   Cpu               1303 non-null   object \n",
            " 5   Ram               1303 non-null   int32  \n",
            " 6   Memory            1303 non-null   object \n",
            " 7   Gpu               1303 non-null   object \n",
            " 8   OpSys             1303 non-null   object \n",
            " 9   Weight            1303 non-null   float32\n",
            " 10  Price             1303 non-null   float64\n",
            "dtypes: float32(1), float64(2), int32(1), object(7)\n",
            "memory usage: 101.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "0Cde1LiMwvxv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "CRz_BZ_ppnpj"
      },
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "C82ZGO3Upre6",
        "outputId": "8d0140ca-d424-417a-d71e-cbcf98eda7ef"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57dc18490>"
            ]
          },
          "metadata": {},
          "execution_count": 231
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Company'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "bljxlMBaptAj",
        "outputId": "daa3dbe7-7955-43fe-86f6-b4944745b69d"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57db02390>"
            ]
          },
          "metadata": {},
          "execution_count": 232
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['Company'],y=df['Price'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "jscpRHAcpuM0",
        "outputId": "caeaafe6-ad93-4b7b-d903-580251588715"
      },
      "execution_count": 233,
      "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": "code",
      "source": [
        "df['TypeName'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "P1Ul5wPBpvlA",
        "outputId": "f145b753-1429-41b8-8d22-dd1ac78dc69e"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57e174dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 234
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFHCAYAAABaugxTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAepklEQVR4nO3de5xdVX338c9XbiIC4RJTTMCgjVKqghgpgo9FqBfACq1CtQJ5MDXWoqK21aivp2Lry1faPmrFCxpFDd4RQaKglUZQBEGCAoJAjRQkkUtECLdHFPg+f+w1cDJMMjOZPWefWfN9v17nNXuvvU/O7zDD9+yz9tpryzYREVGXx3RdQEREtC/hHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoc27LgBg55139ty5c7suIyJiSrnssst+bXvmSNsGItznzp3LypUruy4jImJKkXTjhralWyYiokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKjQQFzEtKnmLj67r693w5LD+vp6ERGbKkfuEREVSrhHRFQo4R4RUaFRw13S0yRd3vO4S9KbJe0o6VxJPy8/dyj7S9JJklZJulLSPpP/NiIioteo4W77Ott7294beDZwH3AmsBhYYXsesKKsAxwCzCuPRcDJk1F4RERs2Hi7ZQ4GfmH7RuBwYFlpXwYcUZYPB05142JghqRdWqk2IiLGZLzh/krgS2V5lu2by/ItwKyyPBu4qec5q0tbRET0yZjDXdKWwMuArw7fZtuAx/PCkhZJWilp5dq1a8fz1IiIGMV4jtwPAX5s+9ayfutQd0v5eVtpXwPs2vO8OaVtPbaX2p5ve/7MmSPeJSoiIjbReML9VTzSJQOwHFhQlhcAZ/W0H1tGzewHrOvpvomIiD4Y0/QDkrYBXgi8rqd5CXCapIXAjcBRpf0c4FBgFc3ImuNaqzYiIsZkTOFu+15gp2Ftt9OMnhm+r4HjW6kuIiI2Sa5QjYioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKjSmcJc0Q9Lpkq6VdI2k50raUdK5kn5efu5Q9pWkkyStknSlpH0m9y1ERMRwYz1y/xDwbdt7AHsB1wCLgRW25wEryjrAIcC88lgEnNxqxRERMapRw13S9sDzgVMAbP/O9p3A4cCystsy4IiyfDhwqhsXAzMk7dJ65RERsUFjOXLfHVgLfEbSTyR9StI2wCzbN5d9bgFmleXZwE09z19d2tYjaZGklZJWrl27dtPfQUREPMpYwn1zYB/gZNvPAu7lkS4YAGwb8Hhe2PZS2/Ntz585c+Z4nhoREaMYS7ivBlbbvqSsn04T9rcOdbeUn7eV7WuAXXueP6e0RUREn4wa7rZvAW6S9LTSdDDwM2A5sKC0LQDOKsvLgWPLqJn9gHU93TcREdEHm49xvzcCX5C0JXA9cBzNB8NpkhYCNwJHlX3PAQ4FVgH3lX0jIqKPxhTuti8H5o+w6eAR9jVw/ATrioiICcgVqhERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhMYW7pBsk/VTS5ZJWlrYdJZ0r6efl5w6lXZJOkrRK0pWS9pnMNxAREY82niP3F9je2/bQjbIXAytszwNWlHWAQ4B55bEIOLmtYiMiYmwm0i1zOLCsLC8DjuhpP9WNi4EZknaZwOtERMQ4jTXcDXxH0mWSFpW2WbZvLsu3ALPK8mzgpp7nri5tERHRJ5uPcb/n2V4j6QnAuZKu7d1o25I8nhcuHxKLAHbbbbfxPDUiIkYxpiN322vKz9uAM4F9gVuHulvKz9vK7muAXXuePqe0Df83l9qeb3v+zJkzN/0dRETEo4wa7pK2kbTt0DLwIuAqYDmwoOy2ADirLC8Hji2jZvYD1vV030RERB+MpVtmFnCmpKH9v2j725IuBU6TtBC4ETiq7H8OcCiwCrgPOK71qiMiYqNGDXfb1wN7jdB+O3DwCO0Gjm+luoiI2CS5QjUiokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKjQmMNd0maSfiLpm2V9d0mXSFol6SuStiztW5X1VWX73MkpPSIiNmQ8R+4nANf0rP8r8EHbfwjcASws7QuBO0r7B8t+ERHRR2MKd0lzgMOAT5V1AQcBp5ddlgFHlOXDyzpl+8Fl/4iI6JOxHrn/B/A24KGyvhNwp+0HyvpqYHZZng3cBFC2ryv7r0fSIkkrJa1cu3btJpYfEREjGTXcJb0UuM32ZW2+sO2ltufbnj9z5sw2/+mIiGlv8zHscwDwMkmHAo8FtgM+BMyQtHk5Op8DrCn7rwF2BVZL2hzYHri99cojImKDRj1yt/0O23NszwVeCXzX9quB84BXlN0WAGeV5eVlnbL9u7bdatUREbFRExnn/nbgrZJW0fSpn1LaTwF2Ku1vBRZPrMSIiBivsXTLPMz2+cD5Zfl6YN8R9vktcGQLtUVExCbKFaoRERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoVHDXdJjJf1I0hWSrpb0ntK+u6RLJK2S9BVJW5b2rcr6qrJ97uS+hYiIGG4sR+73AwfZ3gvYG3iJpP2AfwU+aPsPgTuAhWX/hcAdpf2DZb+IiOijUcPdjXvK6hblYeAg4PTSvgw4oiwfXtYp2w+WpNYqjoiIUY2pz13SZpIuB24DzgV+Adxp+4Gyy2pgdlmeDdwEULavA3Zqs+iIiNi4MYW77Qdt7w3MAfYF9pjoC0taJGmlpJVr166d6D8XERE9xjVaxvadwHnAc4EZkjYvm+YAa8ryGmBXgLJ9e+D2Ef6tpbbn254/c+bMTSw/IiJGMpbRMjMlzSjLWwMvBK6hCflXlN0WAGeV5eVlnbL9u7bdZtEREbFxm4++C7sAyyRtRvNhcJrtb0r6GfBlSe8FfgKcUvY/BficpFXAb4BXTkLdERGxEaOGu+0rgWeN0H49Tf/78PbfAke2Ul1ERGySXKEaEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUaNRwl7SrpPMk/UzS1ZJOKO07SjpX0s/Lzx1KuySdJGmVpCsl7TPZbyIiItY3liP3B4C/t70nsB9wvKQ9gcXACtvzgBVlHeAQYF55LAJObr3qiIjYqM1H28H2zcDNZfluSdcAs4HDgQPLbsuA84G3l/ZTbRu4WNIMSbuUfyfGYe7is/v6ejcsOayvrxcRk2dcfe6S5gLPAi4BZvUE9i3ArLI8G7ip52mrS9vwf2uRpJWSVq5du3acZUdExMaMeuQ+RNLjga8Bb7Z9l6SHt9m2JI/nhW0vBZYCzJ8/f1zPjTrkm0nE5BnTkbukLWiC/Qu2zyjNt0rapWzfBbittK8Bdu15+pzSFhERfTKW0TICTgGusf2Bnk3LgQVleQFwVk/7sWXUzH7AuvS3R0T011i6ZQ4AjgF+Kuny0vZOYAlwmqSFwI3AUWXbOcChwCrgPuC4ViuOiIhRjWW0zA8AbWDzwSPsb+D4CdYVERETkCtUIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqNJbb7EXEOM1dfHZfX++GJYf19fVi8OXIPSKiQqOGu6RPS7pN0lU9bTtKOlfSz8vPHUq7JJ0kaZWkKyXtM5nFR0TEyMZy5P5Z4CXD2hYDK2zPA1aUdYBDgHnlsQg4uZ0yIyJiPEYNd9vfB34zrPlwYFlZXgYc0dN+qhsXAzMk7dJWsRERMTab2uc+y/bNZfkWYFZZng3c1LPf6tIWERF9NOETqrYNeLzPk7RI0kpJK9euXTvRMiIiosemhvutQ90t5edtpX0NsGvPfnNK26PYXmp7vu35M2fO3MQyIiJiJJsa7suBBWV5AXBWT/uxZdTMfsC6nu6biIjok1EvYpL0JeBAYGdJq4F3A0uA0yQtBG4Ejiq7nwMcCqwC7gOOm4SaIyJiFKOGu+1XbWDTwSPsa+D4iRYVERETkytUIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokKjzuceETHc3MVn9/X1blhyWF9frwYJ94iIYWr48Eq3TEREhRLuEREVSrhHRFRoUsJd0kskXSdplaTFk/EaERGxYa2Hu6TNgI8ChwB7Aq+StGfbrxMRERs2GUfu+wKrbF9v+3fAl4HDJ+F1IiJiA2S73X9QegXwEtt/U9aPAf7E9huG7bcIWFRWnwZc12ohG7cz8Os+vl6/5f1NXTW/N8j7a9uTbM8caUNn49xtLwWWdvHaklbant/Fa/dD3t/UVfN7g7y/fpqMbpk1wK4963NKW0RE9MlkhPulwDxJu0vaEnglsHwSXiciIjag9W4Z2w9IegPwn8BmwKdtX93260xQJ91BfZT3N3XV/N4g769vWj+hGhER3csVqhERFUq4R0RUKOEe0TFJW0t6Wtd1RF2mTbhL2nGEtt27qCXGR9JWI7Q96vc5FUn6c+By4NtlfW9JGV02hQxqtkybE6qSLgQOsX1XWd8TOM3207utbOIk7TNC8zrgRtsP9Luetkk6GzjC9u/L+i7AN20/u9vKJk7SZcBBwPm2n1Xafmr7Gd1W1h5JM4HXAnPpGaFn+zVd1dSmQc2W6XQnpvcB35B0GM10B6cCr+62pNZ8DNgHuBIQ8HTgamB7Sa+3/Z0ui2vB14HTytQWu9JcN/EP3ZbUmt/bXiept622I66zgAuA/wIe7LiWyTCQ2TJtwt322ZK2AL4DbAv8he3/7ristvwKWDh0PUE5cvhn4G3AGTTvecqy/clyQdzXaY7+Xmf7om6ras3Vkv4a2EzSPOBNQC3vbcjjbL+96yImy6BmS/XdMpI+zPpHQgcDvwBuALD9pg7KapWkq4Z/BRxqk3S57b27qm0iJL21dxU4lubbyU8AbH+gi7raJOlxwLuAF9G8x/8E/sX2bzstrEWS3gtcZPucrmtp06Bny3Q4cl85bP2yTqqYXFdLOplmemWAvwJ+Vk5E/r67siZs22HrZ2ygfcqyfR9NuL+r61om0QnAOyX9jkf+Hm17uw5rasNAZ0v1R+69ylf7p5bV64ZO0E11krYG/g54Xmm6kKYf/rc0X4nv6aq2Nkl6PEAN70fSN9hI37rtl/WxnJigQcyWaRPukg4EltF8ZRLNibkFtr/fYVkxBpKeDnwOGBpy9mvg2AGcs2jMJP3pxrbb/l6/aukHSS8Dnl9Wz7f9zS7radOgZst0CvfLgL+2fV1ZfyrwpUqG0x0AnAg8ifWHmj25q5raJOki4F22zyvrBwLvs71/p4W1pBz17UFzJH9duYNZNSQtAZ4DfKE0vQpYafsd3VXVnkHNlunQ5z5ki6H/+AC2/7uc4a7BKcBbaPr8ahxqts1QsAPYPl/SNl0W1JYyfO7jNCfiBOwu6XW2v9VtZa06FNjb9kMAkpbRnBSvItwZ0GyZTuG+UtKngM+X9Vfz6BMiU9W6ysJguOsl/R+arhmAo4HrO6ynTe8HXmB7FYCkpwBnA7X9PmcAvynL23dZyCQYyGyZTt0yWwHH88hJxwuAj9m+v7uq2lG+9m5GM5rk4fdj+8edFdUiSTsA72H9392Jtu/orqp2SLrU9nN61gX8qLdtqpP0KmAJcB7Nt5PnA4ttf6XTwloyqNkybcIdHu7bfBqP9G12fka7DZLOG6HZtg/qezGTSNK2NO+rhtEyf1kWX0hzruQ0mr/LI4Ff2v67rmqbDGXKiKEPrB/ZvqXLeto2iNkybcJ9UM9ox+gkPYPmku7e0TILbF/VXVUTI+kzG9nsGuZdkbSH7Ws3MPdRTd8sD2QAs2U6hftAntGeCElH2/78sCs5H1bDFZxQ/2iZWklaantR7d8sBzVbptMJ1YE8oz1BQyNGqrlicwOqGy0j6W22/22ES9iB7i9db4PtRWXxkOHTKUh6bAclTZaBzJbpFO4DeUZ7Imx/ovx8T9e1TLIaR8tcU35O6b/BMbqIZtbS0dqmqoHMlukU7q+nOaM9dER0Ac0l+lNeuTHAG3n0fNm1XML+GprRMkNzy1xQ2qYs298oi/fZ/mrvNklHdlBS6yT9ATAb2FrSs2j6owG2Ax7XWWHtG8hsmTZ97vDwGe0/Ah6ioisBJV1BcyHTT2neG1DlJezbAw/ZvrvrWtoi6ce29xmtbSqStAD438B81j+SvRv4rO0zRnreVDSI2TJtwn2kKwFp5gWf8heLSLrE9p90XcdkkfQc4NM8cm5hHfAa2wM1C994SDqE5srNo4De8d7bAXva3reTwiaBpJfb/lrXdUyWQc2W6RTu1wIvHX4loO09uq1s4srNHubR3CygxouYrgSOt31BWX8ezUUiz+y2sk0naS9gb5qbqvxTz6a7gfNquECrVwnAPwYePpFq+5+7q6g9g5ot06nP/e6h//jF9TT/I9XgGcAxNPfiHOqWcVmvwYNDwQ5g+weSpvS9YW1fIekq4MW2l3Vdz2SS9HGaPvYXAJ8CXgH8qNOi2jWQ2VJ9uPdcCbhS0jmsfyXgpZ0V1q4jgScPQj9fm3oufvmepE8AX6L53f0VcH5XdbXF9oOSdpW0ZW2/u2H2t/1MSVfafo+k91PB3DmDni3Vhzvw5z3LtwJD82ivpecr4hR3Fc3ETLd1XUjL3j9s/d09y7X0J/4PcKGk5cC9Q421XIBW/L/y8z5JTwRuB3bpsJ62DHS2VB/uto/ruoY+mAFcK+lS1u9zn9JDIW2/oOsa+uAX5fEY6r0Y7ZuSZgD/DvyY5oP5U92WNHFD2SLpANsX9m4r91jo1HQ6ofpU4GRglpsbRz8TeJnt93Zc2oRt6K4+NQ2FrPmEHDQ3ynZzP9XqSNpqaIbEMoPiY4Hfdj1rYlsGdThr9UfuPT4J/CMwdFXnlZK+CEz5cK8pxEdS8wk5Sc+luUbh8cBuZRTN6yqbFfKHlKtRS6DfL+nHTPErVMvvbn9g5rD5nbajmYK7U4/puoA+epzt4YEwpUdcDJG0n6RLJd0j6XeSHpR0V9d1tWh/28cCd5SpFp7LIzcjnur+A3gxTT80tq/gkXuNTmmS/kDSsylXqErapzwOpI4rVLek+VDenKZLbehxF80BSKem05H7r8v4UwNIegVwc7clteYjwCuBr9JcDXgs9YQfwNCkU7WdkAPA9k3NPToeVsutEl9Mc4XqHJqT40Nv8m7gnR3V1Jryjfl7kj5r+8ZB61qbTuF+PLAU2EPSGppRCq/utqT22F4laTPbDwKfkVTTPSq/McIJuU92W1JrbpK0P+Ayk+AJPDKp2JRWxu8vq/0KVeCJkr7FgHWtTadwt+0/K1PFPsb23WXCrRrcV+a2uELSv9F8I6miy03SY4AVtu8Evibpm8Bjba/ruLS2/C3wIZoJttbQXGV8fKcVtW+OpO1ojtg/SdPXvtj2d7otqzVDXWvL4eEL1DrvWqsiAMboawC27+2ZeOr0Dutp0zE0v8vjacZKzwFe3mlFLbH9EPDRnvX7Kwp2aEasvdr2LNtPsH207du7Lqplr7F9F/AiYCeav9cl3ZbULts3DWvqvGut+iN3SXvQDKHbvueKMmjOaHd+ocFESDocmGP7o2X9e8ATaLotfgis2sjTp5IVkl4OnOH6xu5eKOkGmsnDvla+odRmqK/9UOBU21dr2EmGKW4gu9aqH+deAvAI4GWUr03F3cCXbV/USWEtkHQh8MqhowZJl9PMJ/N44DO2D+6yvrZIupvmrlMP0JxcFU0323adFtYSSfvSnBA/AvgZzd/l5zf+rKlDzf1iZ9PMlrgXzTDB87u+DV1bJO1M07X2ZzR/m98BTuj6G1j14T5E0nNt/7DrOtok6VLbz+lZ/4jtN5Tli23v1111MV4lJD4AvNp25+Ok21LOm+wNXG/7Tkk7AbNtX9lxaVWrvlumx02SzgSGLgu+gObTdXWHNU3UDr0rQ8FezOxzLZNG0orh30JGapuKyonGv6A5cn8KcCZQzVzuxYm2e6c1vhM4iSk+Wk3SP21ks23/S9+KGcF0OqH6GZpumSeWxzdK21R2iaTXDm+U9DoquIJT0mMl7QjsLGkHSTuWx1yar/k1uIIyr7vtp9p++1S+CckG7CrpHfDw9ANnAD/vtqRW3DvCA2Ah8PauihoynbplrrC917C2y23v3VVNEyXpCcDXaSYLG7oxx7OBrYAjbN/aVW1tkHQC8GaaD+Nf9Wy6C/ik7Y90UliLJKnCk8TrKSdPv0BzG8gXAN+y/cFuq2qXpG1pTqQupJn69/22O52ldTqF+wqaI/UvlaZXAcdV8tX+IJoRQQBX2/5ul/W0TdIbbX+46zomQ5nQ7h949M3Np/yNVnrm4wfYgmZepwtp5tKp4k5h5ZvlW2m6mJYBHxqUu2hNp3B/EvBhmnlJDFwEvMn2LzstLDZo2NDVR3EFN1hWc3PzjwOX0TM2uoauGUnnbWSzp/oHmKR/B/6S5sr3j9q+p+OS1jNtwj2mnjKEbugPdPi4aNt+TZ9Lap2ky2oZEjiSMlLmSNtfGXXnKUbSQzRdog+w/s1jBmKobvXhPuhntGPDJP39sCbT3OXmB7b/p4OSWifpRJo7aJ3J+jda+U1XNbVN0krb87uuY7qZDuE+PCCguSBmIbCT7cf3uaQYI0nvHqF5R5p5PE60/eU+l9Q6SSN9SNn2k/tezCSRtAT4Nc1VuL23EqzmA2wQVR/uvQbxjHaMXzmJ9V9d3+kmxmY6fIANomlxEdMIZ7T3GZQz2jF+tn9Ty9wkZS6S1/PIDTrOBz5h+/edFdUy27XMvjqlVB/uw85oP2PQzmjH+El6AVDLh/PJNMMEP1bWjyltf9NZRS2bDh9gg6j6bplBP6MdGybpp6z/O4Omz/1XwLG2r+1/Ve3awMV1j2qbyiR9iuYDbFlpOgZ40HY1H2CDqPojd9vTaYqF2rx02LqB223fO9LOU9SDkp5i+xcAkp7MAMwF3rLnDPuw+m4Z3x+TqPpwj6nL9o1d19AH/wicJ+l6mm+TTwKO67ak1k2HD7CBU323TMSgK5NpPa2sXmf7/o3tP1VIejPNleAzaG6vNzRqZi7N3ZmqmiZj0CTcIzog6Wia//8+N6x9qD/6i91U1h5J/xfYH/gjmlkgVwPn0dxx6lcbe25MXMI9ogOSLgEOHj56q9zA/fs1TUlQbt4+nybon1sed9res9PCKpc+94hubDHSsFzb95ahgzXZmuaexduXx69opv+NSZRwj+jG1pK2GT7yp1xFvWVHNbVK0lKaqajvBi6h6X//QC4g7I8ME4zoxinA6WUqagDKHaa+XLbVYDeaG8fcAqyh6XO/s9OKppH0uUd0RNLfAu8AhiavuwdYYvvk7qpqV5km4o9p+tv3B54O/Ab4oe2RJoaLliTcIzpWumKwfXfXtUwWSXNobk6/P83FaTvZntFtVXVLuEfEpJD0Jh45Yv89TZ/70OOnth/qsLzq5YRqREyWucBXgbfYvrnjWqadHLlHRFQoo2UiBoykF3ZdQ0x9OXKPGDCSfml7t67riKktfe4RHZC0fEObgJ36WUvUKeEe0Y3/BRxNM7a9l4B9+19O1CbhHtGNi4H7bH9v+AZJ13VQT1Qmfe4RERXKaJmIiAol3CMiKpRwj4ioUMI9IqJCGS0T0SFJBwAnAk+i+f9RgG0/ucu6YurLaJmIDkm6FngLcBnw4FC77ds7KyqqkCP3iG6ts/2trouI+uTIPaJDkpYAmwFnAPcPtdv+cWdFRRUS7hEdknTeCM22fVDfi4mqJNwjIiqUPveIDkg62vbnJb11pO22P9DvmqIuCfeIbmxTfm7baRVRrXTLRERUKFeoRkRUKOEeEVGhhHtERIUS7hEdkbSHpIMlPX5Y+0u6qinqkXCP6ICkNwFnAW8ErpJ0eM/m93VTVdQkQyEjuvFa4Nm275E0Fzhd0lzbH6KZGTJiQhLuEd14jO17AGzfIOlAmoB/Egn3aEG6ZSK6caukvYdWStC/FNgZeEZnVUU1chFTRAckzQEesH3LCNsOsH1hB2VFRRLuEREVSrdMRESFEu4RERVKuEdEVCjhHhFRoYR7RESF/j+IAFk/zcv2uQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['TypeName'],y=df['Price'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "4zBsunglpxLj",
        "outputId": "b8b0a794-254f-4c69-ce1b-e5863f38efd5"
      },
      "execution_count": 235,
      "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": "code",
      "source": [
        "sns.distplot(df['Inches'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "A1meWIg9py0N",
        "outputId": "5e8207f8-afb3-40cb-86fc-9ab32d2cc74a"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57e711610>"
            ]
          },
          "metadata": {},
          "execution_count": 236
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=df['Inches'],y=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "cvrp6ZGup0To",
        "outputId": "49b5887c-4bdd-4e9b-9750-a2cb7a83220f"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57e2d0610>"
            ]
          },
          "metadata": {},
          "execution_count": 237
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ScreenResolution'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viBOSx0_p1sa",
        "outputId": "6c9697c6-7cf2-4977-bc6d-f009fb4676cb"
      },
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Full HD 1920x1080                                507\n",
              "1366x768                                         281\n",
              "IPS Panel Full HD 1920x1080                      230\n",
              "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
              "Full HD / Touchscreen 1920x1080                   47\n",
              "1600x900                                          23\n",
              "Touchscreen 1366x768                              16\n",
              "Quad HD+ / Touchscreen 3200x1800                  15\n",
              "IPS Panel 4K Ultra HD 3840x2160                   12\n",
              "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
              "4K Ultra HD / Touchscreen 3840x2160               10\n",
              "4K Ultra HD 3840x2160                              7\n",
              "Touchscreen 2560x1440                              7\n",
              "IPS Panel 1366x768                                 7\n",
              "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
              "IPS Panel Retina Display 2560x1600                 6\n",
              "IPS Panel Retina Display 2304x1440                 6\n",
              "Touchscreen 2256x1504                              6\n",
              "IPS Panel Touchscreen 2560x1440                    5\n",
              "IPS Panel Retina Display 2880x1800                 4\n",
              "IPS Panel Touchscreen 1920x1200                    4\n",
              "1440x900                                           4\n",
              "IPS Panel 2560x1440                                4\n",
              "IPS Panel Quad HD+ 2560x1440                       3\n",
              "Quad HD+ 3200x1800                                 3\n",
              "1920x1080                                          3\n",
              "Touchscreen 2400x1600                              3\n",
              "2560x1440                                          3\n",
              "IPS Panel Touchscreen 1366x768                     3\n",
              "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
              "IPS Panel Full HD 2160x1440                        2\n",
              "IPS Panel Quad HD+ 3200x1800                       2\n",
              "IPS Panel Retina Display 2736x1824                 1\n",
              "IPS Panel Full HD 1920x1200                        1\n",
              "IPS Panel Full HD 2560x1440                        1\n",
              "IPS Panel Full HD 1366x768                         1\n",
              "Touchscreen / Full HD 1920x1080                    1\n",
              "Touchscreen / Quad HD+ 3200x1800                   1\n",
              "Touchscreen / 4K Ultra HD 3840x2160                1\n",
              "IPS Panel Touchscreen 2400x1600                    1\n",
              "Name: ScreenResolution, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Feature Engineering + EDA"
      ],
      "metadata": {
        "id": "NGwUJomAyIdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
      ],
      "metadata": {
        "id": "d-AOFfqFp3Uq"
      },
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "_aLoeaX9p4oZ",
        "outputId": "e4b61ac6-7df4-46fa-dce7-c70ac1f85641"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Company  TypeName  Inches             ScreenResolution  \\\n",
              "571       HP  Notebook    14.0                     1366x768   \n",
              "402   Lenovo  Notebook    15.6  IPS Panel Full HD 1920x1080   \n",
              "1185    Asus    Gaming    15.6  IPS Panel Full HD 1920x1080   \n",
              "171       HP  Notebook    17.3  IPS Panel Full HD 1920x1080   \n",
              "827     Dell  Notebook    14.0                     1366x768   \n",
              "\n",
              "                                       Cpu  Ram                Memory  \\\n",
              "571   Intel Pentium Quad Core N3710 1.6GHz    8               2TB HDD   \n",
              "402             Intel Core i5 8250U 1.6GHz    8  256GB SSD +  1TB HDD   \n",
              "1185           Intel Core i7 6700HQ 2.6GHz    8  256GB SSD +  1TB HDD   \n",
              "171             Intel Core i5 7200U 2.5GHz    6               2TB HDD   \n",
              "827               Intel Core i3 6006U 2GHz    4             500GB HDD   \n",
              "\n",
              "                          Gpu       OpSys  Weight    Price  Touchscreen  \n",
              "571     Intel HD Graphics 405  Windows 10   1.940  31898.0            0  \n",
              "402         AMD Radeon RX 550  Windows 10   2.100  93849.0            0  \n",
              "1185  Nvidia GeForce GTX 960M  Windows 10   2.591  74538.0            0  \n",
              "171            AMD Radeon 520  Windows 10   2.710  57318.0            0  \n",
              "827     Intel HD Graphics 520  Windows 10   1.760  47970.0            0  "
            ],
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              "      <th>571</th>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Pentium Quad Core N3710 1.6GHz</td>\n",
              "      <td>8</td>\n",
              "      <td>2TB HDD</td>\n",
              "      <td>Intel HD Graphics 405</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.940</td>\n",
              "      <td>31898.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Notebook</td>\n",
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              "      <td>IPS Panel Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
              "      <td>8</td>\n",
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              "      <td>Windows 10</td>\n",
              "      <td>2.100</td>\n",
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              "      <th>1185</th>\n",
              "      <td>Asus</td>\n",
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              "      <td>15.6</td>\n",
              "      <td>IPS Panel Full HD 1920x1080</td>\n",
              "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
              "      <td>8</td>\n",
              "      <td>256GB SSD +  1TB HDD</td>\n",
              "      <td>Nvidia GeForce GTX 960M</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.591</td>\n",
              "      <td>74538.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>17.3</td>\n",
              "      <td>IPS Panel Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
              "      <td>6</td>\n",
              "      <td>2TB HDD</td>\n",
              "      <td>AMD Radeon 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.710</td>\n",
              "      <td>57318.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>827</th>\n",
              "      <td>Dell</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>Intel Core i3 6006U 2GHz</td>\n",
              "      <td>4</td>\n",
              "      <td>500GB HDD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>1.760</td>\n",
              "      <td>47970.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a87809f-7a4c-49e4-94b5-3be708ed5311')\"\n",
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              "    .colab-df-convert:hover {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-7a87809f-7a4c-49e4-94b5-3be708ed5311 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-7a87809f-7a4c-49e4-94b5-3be708ed5311');\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",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df['Touchscreen'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "-EG7vMQyp6mJ",
        "outputId": "8e958335-8457-44b6-89f2-d90d710501ee"
      },
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57da09050>"
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          },
          "metadata": {},
          "execution_count": 241
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['Touchscreen'],y=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "vYVWGyWRp8Lf",
        "outputId": "493ff674-e8de-47e9-c58b-332a7a68335c"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d978810>"
            ]
          },
          "metadata": {},
          "execution_count": 242
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
      ],
      "metadata": {
        "id": "QSYOPbuTp-KB"
      },
      "execution_count": 243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wc6ap6fMp_NQ",
        "outputId": "cfc75cf1-05c5-47ec-dfa7-f2c561d9e544"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company   TypeName  Inches                    ScreenResolution  \\\n",
              "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
              "1   Apple  Ultrabook    13.3                            1440x900   \n",
              "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
              "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
              "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
              "\n",
              "                          Cpu  Ram               Memory  \\\n",
              "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
              "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
              "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
              "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
              "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
              "\n",
              "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \n",
              "0  Intel Iris Plus Graphics 640  macOS    1.37  109854.58            0    1  \n",
              "1        Intel HD Graphics 6000  macOS    1.34   73713.08            0    0  \n",
              "2         Intel HD Graphics 620  No OS    1.86   47150.00            0    0  \n",
              "3            AMD Radeon Pro 455  macOS    1.83  208070.90            0    1  \n",
              "4  Intel Iris Plus Graphics 650  macOS    1.37  147895.20            0    1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e4dae05-2f59-4130-ad95-a5a87b4ce2c9\">\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>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</th>\n",
              "      <th>Touchscreen</th>\n",
              "      <th>Ips</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 2.3GHz</td>\n",
              "      <td>8</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 640</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>109854.58</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>1440x900</td>\n",
              "      <td>Intel Core i5 1.8GHz</td>\n",
              "      <td>8</td>\n",
              "      <td>128GB Flash Storage</td>\n",
              "      <td>Intel HD Graphics 6000</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.34</td>\n",
              "      <td>73713.08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
              "      <td>8</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel HD Graphics 620</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86</td>\n",
              "      <td>47150.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>15.4</td>\n",
              "      <td>IPS Panel Retina Display 2880x1800</td>\n",
              "      <td>Intel Core i7 2.7GHz</td>\n",
              "      <td>16</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83</td>\n",
              "      <td>208070.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>13.3</td>\n",
              "      <td>IPS Panel Retina Display 2560x1600</td>\n",
              "      <td>Intel Core i5 3.1GHz</td>\n",
              "      <td>8</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>147895.20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e4dae05-2f59-4130-ad95-a5a87b4ce2c9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e4dae05-2f59-4130-ad95-a5a87b4ce2c9 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-1e4dae05-2f59-4130-ad95-a5a87b4ce2c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ips'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "CnRrG9aMqBUT",
        "outputId": "648169f7-1e00-426b-bd87-8b8546906a3d"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d9637d0>"
            ]
          },
          "metadata": {},
          "execution_count": 245
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['Ips'],y=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "U-TrHFztqCvT",
        "outputId": "9673d0c9-9403-4e00-a02e-812a063de7fa"
      },
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d8d6250>"
            ]
          },
          "metadata": {},
          "execution_count": 246
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
      ],
      "metadata": {
        "id": "x4Hd60K7qEgm"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['X_res'] = new[0]\n",
        "df['Y_res'] = new[1]"
      ],
      "metadata": {
        "id": "muMIRKX-qFst"
      },
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "zWiGDC0SqG3k",
        "outputId": "52799c9c-c8bc-4ca1-e9d8-eaf3e4fea253"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Company  TypeName  Inches   ScreenResolution  \\\n",
              "550  Lenovo  Notebook    15.6  Full HD 1920x1080   \n",
              "941    Asus  Notebook    17.3           1600x900   \n",
              "591  Lenovo  Notebook    15.6  Full HD 1920x1080   \n",
              "622    Dell  Notebook    15.6  Full HD 1920x1080   \n",
              "974    Asus    Gaming    17.3  Full HD 1920x1080   \n",
              "\n",
              "                                      Cpu  Ram                Memory  \\\n",
              "550              Intel Core i3 6006U 2GHz    4             128GB SSD   \n",
              "941  Intel Pentium Quad Core N3710 1.6GHz    4               1TB HDD   \n",
              "591           AMD A12-Series 9720P 3.6GHz   12             512GB SSD   \n",
              "622            Intel Core i5 8250U 1.6GHz    4               1TB HDD   \n",
              "974           Intel Core i7 7700HQ 2.8GHz   16  256GB SSD +  1TB HDD   \n",
              "\n",
              "                         Gpu       OpSys  Weight     Price  Touchscreen  Ips  \\\n",
              "550    Intel HD Graphics 520  Windows 10    2.20   40918.0            0    0   \n",
              "941     Nvidia GeForce 920MX  Windows 10    2.80   43460.0            0    0   \n",
              "591           AMD Radeon 530  Windows 10    2.20   77818.0            0    0   \n",
              "622           AMD Radeon 530  Windows 10    2.20   63632.0            0    0   \n",
              "974  Nvidia GeForce GTX 1060  Windows 10    2.73  168091.8            0    0   \n",
              "\n",
              "            X_res Y_res  \n",
              "550  Full HD 1920  1080  \n",
              "941          1600   900  \n",
              "591  Full HD 1920  1080  \n",
              "622  Full HD 1920  1080  \n",
              "974  Full HD 1920  1080  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae95b412-e3d5-4765-a762-07266900c28a\">\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>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</th>\n",
              "      <th>Touchscreen</th>\n",
              "      <th>Ips</th>\n",
              "      <th>X_res</th>\n",
              "      <th>Y_res</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>550</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i3 6006U 2GHz</td>\n",
              "      <td>4</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel HD Graphics 520</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.20</td>\n",
              "      <td>40918.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Full HD 1920</td>\n",
              "      <td>1080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>Asus</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>17.3</td>\n",
              "      <td>1600x900</td>\n",
              "      <td>Intel Pentium Quad Core N3710 1.6GHz</td>\n",
              "      <td>4</td>\n",
              "      <td>1TB HDD</td>\n",
              "      <td>Nvidia GeForce 920MX</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.80</td>\n",
              "      <td>43460.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1600</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>AMD A12-Series 9720P 3.6GHz</td>\n",
              "      <td>12</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon 530</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.20</td>\n",
              "      <td>77818.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Full HD 1920</td>\n",
              "      <td>1080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>622</th>\n",
              "      <td>Dell</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>15.6</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
              "      <td>4</td>\n",
              "      <td>1TB HDD</td>\n",
              "      <td>AMD Radeon 530</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.20</td>\n",
              "      <td>63632.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Full HD 1920</td>\n",
              "      <td>1080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>974</th>\n",
              "      <td>Asus</td>\n",
              "      <td>Gaming</td>\n",
              "      <td>17.3</td>\n",
              "      <td>Full HD 1920x1080</td>\n",
              "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
              "      <td>16</td>\n",
              "      <td>256GB SSD +  1TB HDD</td>\n",
              "      <td>Nvidia GeForce GTX 1060</td>\n",
              "      <td>Windows 10</td>\n",
              "      <td>2.73</td>\n",
              "      <td>168091.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Full HD 1920</td>\n",
              "      <td>1080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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    {
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        "df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
      ],
      "metadata": {
        "id": "fV_FiG0eqJGW"
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      "execution_count": 250,
      "outputs": []
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        "df.head()"
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      "execution_count": 251,
      "outputs": [
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          "data": {
            "text/plain": [
              "  Company   TypeName  Inches                    ScreenResolution  \\\n",
              "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
              "1   Apple  Ultrabook    13.3                            1440x900   \n",
              "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
              "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
              "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
              "\n",
              "                          Cpu  Ram               Memory  \\\n",
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              "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
              "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
              "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
              "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
              "\n",
              "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
              "0  Intel Iris Plus Graphics 640  macOS    1.37  109854.58            0    1   \n",
              "1        Intel HD Graphics 6000  macOS    1.34   73713.08            0    0   \n",
              "2         Intel HD Graphics 620  No OS    1.86   47150.00            0    0   \n",
              "3            AMD Radeon Pro 455  macOS    1.83  208070.90            0    1   \n",
              "4  Intel Iris Plus Graphics 650  macOS    1.37  147895.20            0    1   \n",
              "\n",
              "  X_res Y_res  \n",
              "0  2560  1600  \n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-3d3aab56-907e-43cf-b93a-03898c0470ea');\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",
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          "metadata": {},
          "execution_count": 251
        }
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    {
      "cell_type": "code",
      "source": [
        "df['X_res'] = df['X_res'].astype('int')\n",
        "df['Y_res'] = df['Y_res'].astype('int')"
      ],
      "metadata": {
        "id": "iLBhCzgpqLw2"
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      "execution_count": 252,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
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        "id": "Z-BKXDaOqNBa",
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      },
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1303 entries, 0 to 1302\n",
            "Data columns (total 15 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Company           1303 non-null   object \n",
            " 1   TypeName          1303 non-null   object \n",
            " 2   Inches            1303 non-null   float64\n",
            " 3   ScreenResolution  1303 non-null   object \n",
            " 4   Cpu               1303 non-null   object \n",
            " 5   Ram               1303 non-null   int32  \n",
            " 6   Memory            1303 non-null   object \n",
            " 7   Gpu               1303 non-null   object \n",
            " 8   OpSys             1303 non-null   object \n",
            " 9   Weight            1303 non-null   float32\n",
            " 10  Price             1303 non-null   float64\n",
            " 11  Touchscreen       1303 non-null   int64  \n",
            " 12  Ips               1303 non-null   int64  \n",
            " 13  X_res             1303 non-null   int64  \n",
            " 14  Y_res             1303 non-null   int64  \n",
            "dtypes: float32(1), float64(2), int32(1), int64(4), object(7)\n",
            "memory usage: 142.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()['Price']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ABOSDvkqOWG",
        "outputId": "ddfac26e-512e-47bf-b98c-fbde7d083b29"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Inches         0.068197\n",
              "Ram            0.743007\n",
              "Weight         0.210370\n",
              "Price          1.000000\n",
              "Touchscreen    0.191226\n",
              "Ips            0.252208\n",
              "X_res          0.556529\n",
              "Y_res          0.552809\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
      ],
      "metadata": {
        "id": "kXCvXPTfqPxA"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()['Price']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "dF2wdsWPqQg1",
        "outputId": "84f20511-e806-4c5c-a9a4-2c1bd01d7cc6"
      },
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Inches         0.068197\n",
              "Ram            0.743007\n",
              "Weight         0.210370\n",
              "Price          1.000000\n",
              "Touchscreen    0.191226\n",
              "Ips            0.252208\n",
              "X_res          0.556529\n",
              "Y_res          0.552809\n",
              "ppi            0.473487\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['ScreenResolution'],inplace=True)"
      ],
      "metadata": {
        "id": "Rju21zzDqQd8"
      },
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aSQrWMllqQaf",
        "outputId": "fb5c4ee3-ceef-48a9-905a-466bd08859e0"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company   TypeName  Inches                         Cpu  Ram  \\\n",
              "0   Apple  Ultrabook    13.3        Intel Core i5 2.3GHz    8   \n",
              "1   Apple  Ultrabook    13.3        Intel Core i5 1.8GHz    8   \n",
              "2      HP   Notebook    15.6  Intel Core i5 7200U 2.5GHz    8   \n",
              "3   Apple  Ultrabook    15.4        Intel Core i7 2.7GHz   16   \n",
              "4   Apple  Ultrabook    13.3        Intel Core i5 3.1GHz    8   \n",
              "\n",
              "                Memory                           Gpu  OpSys  Weight  \\\n",
              "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
              "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
              "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
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              "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
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              "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "cell_type": "code",
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        {
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          "data": {
            "text/plain": [
              "Intel Core i5 7200U 2.5GHz       190\n",
              "Intel Core i7 7700HQ 2.8GHz      146\n",
              "Intel Core i7 7500U 2.7GHz       134\n",
              "Intel Core i7 8550U 1.8GHz        73\n",
              "Intel Core i5 8250U 1.6GHz        72\n",
              "                                ... \n",
              "Intel Core M M3-6Y30 0.9GHz        1\n",
              "AMD A9-Series 9420 2.9GHz          1\n",
              "Intel Core i3 6006U 2.2GHz         1\n",
              "AMD A6-Series 7310 2GHz            1\n",
              "Intel Xeon E3-1535M v6 3.1GHz      1\n",
              "Name: Cpu, Length: 118, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
      ],
      "metadata": {
        "id": "Kcff469xqYnL"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pDAoFqR3qYlD",
        "outputId": "0172a11d-e0f5-443d-ab74-3479c302b124"
      },
      "execution_count": 263,
      "outputs": [
        {
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            "text/plain": [
              "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
              "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
              "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
              "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
              "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
              "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
              "\n",
              "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
              "0  Intel Iris Plus Graphics 640  macOS    1.37  109854.58            0    1   \n",
              "1        Intel HD Graphics 6000  macOS    1.34   73713.08            0    0   \n",
              "2         Intel HD Graphics 620  No OS    1.86   47150.00            0    0   \n",
              "3            AMD Radeon Pro 455  macOS    1.83  208070.90            0    1   \n",
              "4  Intel Iris Plus Graphics 650  macOS    1.37  147895.20            0    1   \n",
              "\n",
              "          ppi       Cpu Name  \n",
              "0  226.983005  Intel Core i5  \n",
              "1  127.677940  Intel Core i5  \n",
              "2  141.211998  Intel Core i5  \n",
              "3  220.534624  Intel Core i7  \n",
              "4  226.983005  Intel Core i5  "
            ],
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              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
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              "      <td>16</td>\n",
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              "      <td>147895.20</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fetch_processor(text):\n",
        "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\n",
        "        return text\n",
        "    else:\n",
        "        if text.split()[0] == 'Intel':\n",
        "            return 'Other Intel Processor'\n",
        "        else:\n",
        "            return 'AMD Processor'"
      ],
      "metadata": {
        "id": "2yj9pTtVqYh_"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
      ],
      "metadata": {
        "id": "uLyNGUD1qYfb"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cpu brand'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "DZvyw7weqYc0",
        "outputId": "511e2cf3-b42f-4267-d3dc-8077287adcc9"
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      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57e1b3810>"
            ]
          },
          "metadata": {},
          "execution_count": 266
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['Cpu brand'],y=df['Price'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "-DrxmwqIqhv7",
        "outputId": "5d4da62d-b5cb-4023-b98b-8dabfcb5730b"
      },
      "execution_count": 267,
      "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": "code",
      "source": [
        "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
      ],
      "metadata": {
        "id": "1Zveq1c0qhtD"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UTv0fYiwqhpp",
        "outputId": "57f9521f-4dfa-4b15-e7be-15ce1dde2987"
      },
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company   TypeName  Ram               Memory                           Gpu  \\\n",
              "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
              "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
              "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
              "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
              "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
              "\n",
              "   OpSys  Weight      Price  Touchscreen  Ips         ppi      Cpu brand  \n",
              "0  macOS    1.37  109854.58            0    1  226.983005  Intel Core i5  \n",
              "1  macOS    1.34   73713.08            0    0  127.677940  Intel Core i5  \n",
              "2  No OS    1.86   47150.00            0    0  141.211998  Intel Core i5  \n",
              "3  macOS    1.83  208070.90            0    1  220.534624  Intel Core i7  \n",
              "4  macOS    1.37  147895.20            0    1  226.983005  Intel Core i5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50899118-0c1c-423b-bc34-dd1a3f9a0819\">\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>TypeName</th>\n",
              "      <th>Ram</th>\n",
              "      <th>Memory</th>\n",
              "      <th>Gpu</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Price</th>\n",
              "      <th>Touchscreen</th>\n",
              "      <th>Ips</th>\n",
              "      <th>ppi</th>\n",
              "      <th>Cpu brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>128GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 640</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>109854.58</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.983005</td>\n",
              "      <td>Intel Core i5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>128GB Flash Storage</td>\n",
              "      <td>Intel HD Graphics 6000</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.34</td>\n",
              "      <td>73713.08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>127.677940</td>\n",
              "      <td>Intel Core i5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>8</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel HD Graphics 620</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86</td>\n",
              "      <td>47150.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>141.211998</td>\n",
              "      <td>Intel Core i5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>16</td>\n",
              "      <td>512GB SSD</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83</td>\n",
              "      <td>208070.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>220.534624</td>\n",
              "      <td>Intel Core i7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>256GB SSD</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>147895.20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.983005</td>\n",
              "      <td>Intel Core i5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50899118-0c1c-423b-bc34-dd1a3f9a0819')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-50899118-0c1c-423b-bc34-dd1a3f9a0819 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-50899118-0c1c-423b-bc34-dd1a3f9a0819');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ram'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "G68AJ3N3qhm3",
        "outputId": "34f77831-81b2-4055-9e6b-9628753772fe"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d759590>"
            ]
          },
          "metadata": {},
          "execution_count": 270
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['Ram'],y=df['Price'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "yK10lH-cqhjM",
        "outputId": "4931364e-16f5-468d-e1e8-8fadd53cc384"
      },
      "execution_count": 271,
      "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": "code",
      "source": [
        "df['Memory'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHvj3Yd_qhgL",
        "outputId": "871965d5-8e74-40a0-905b-395ae0e35a3b"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "256GB SSD                        412\n",
              "1TB HDD                          223\n",
              "500GB HDD                        132\n",
              "512GB SSD                        118\n",
              "128GB SSD +  1TB HDD              94\n",
              "128GB SSD                         76\n",
              "256GB SSD +  1TB HDD              73\n",
              "32GB Flash Storage                38\n",
              "2TB HDD                           16\n",
              "64GB Flash Storage                15\n",
              "512GB SSD +  1TB HDD              14\n",
              "1TB SSD                           14\n",
              "256GB SSD +  2TB HDD              10\n",
              "1.0TB Hybrid                       9\n",
              "256GB Flash Storage                8\n",
              "16GB Flash Storage                 7\n",
              "32GB SSD                           6\n",
              "180GB SSD                          5\n",
              "128GB Flash Storage                4\n",
              "512GB SSD +  2TB HDD               3\n",
              "16GB SSD                           3\n",
              "512GB Flash Storage                2\n",
              "1TB SSD +  1TB HDD                 2\n",
              "256GB SSD +  500GB HDD             2\n",
              "128GB SSD +  2TB HDD               2\n",
              "256GB SSD +  256GB SSD             2\n",
              "512GB SSD +  256GB SSD             1\n",
              "512GB SSD +  512GB SSD             1\n",
              "64GB Flash Storage +  1TB HDD      1\n",
              "1TB HDD +  1TB HDD                 1\n",
              "32GB HDD                           1\n",
              "64GB SSD                           1\n",
              "128GB HDD                          1\n",
              "240GB SSD                          1\n",
              "8GB SSD                            1\n",
              "508GB Hybrid                       1\n",
              "1.0TB HDD                          1\n",
              "512GB SSD +  1.0TB Hybrid          1\n",
              "256GB SSD +  1.0TB Hybrid          1\n",
              "Name: Memory, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
        "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
        "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
        "new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
        "\n",
        "df[\"first\"]= new[0]\n",
        "df[\"first\"]=df[\"first\"].str.strip()\n",
        "\n",
        "df[\"second\"]= new[1]\n",
        "\n",
        "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
        "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
        "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
        "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
        "\n",
        "df['first'] = df['first'].str.replace(r'\\D', '')\n",
        "\n",
        "df[\"second\"].fillna(\"0\", inplace = True)\n",
        "\n",
        "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
        "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
        "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
        "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
        "\n",
        "df['second'] = df['second'].str.replace(r'\\D', '')\n",
        "\n",
        "df[\"first\"] = df[\"first\"].astype(int)\n",
        "df[\"second\"] = df[\"second\"].astype(int)\n",
        "\n",
        "df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
        "df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
        "df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
        "df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
        "\n",
        "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
        "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
        "       'Layer2Flash_Storage'],inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PB0LCkbtqq7z",
        "outputId": "dbe10c9c-c6b2-4d03-a0c6-9e5fc80d3b87"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: FutureWarning: The default value of regex will change from True to False in a future version.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(5)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
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        "id": "ay-x1141qsd5",
        "outputId": "c30f75ec-87be-4e15-8ac6-378f8a858864"
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              "     Company  TypeName  Ram               Memory  \\\n",
              "1153    Dell    Gaming    8             1000 HDD   \n",
              "1033      HP  Notebook    8              256 SSD   \n",
              "709       HP  Notebook    6             2000 HDD   \n",
              "1218  Lenovo    Gaming    8  128 SSD +  1000 HDD   \n",
              "1084    Dell  Notebook    8              256 SSD   \n",
              "\n",
              "                                 Gpu       OpSys  Weight      Price  \\\n",
              "1153       Nvidia GeForce GTX 1050Ti  Windows 10    2.62   98318.00   \n",
              "1033           Intel HD Graphics 520   Windows 7    1.95   97990.00   \n",
              "709                   AMD Radeon 530  Windows 10    2.71   53710.82   \n",
              "1218  Nvidia GeForce GTX 960<U+039C>  Windows 10    2.60  104304.00   \n",
              "1084           Intel HD Graphics 620  Windows 10    2.18   61500.00   \n",
              "\n",
              "      Touchscreen  Ips         ppi      Cpu brand   HDD  SSD  Hybrid  \\\n",
              "1153            0    0  141.211998  Intel Core i7  1000    0       0   \n",
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              "\n",
              "      Flash_Storage  \n",
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              "                                                     [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",
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        "df.drop(columns=['Memory'],inplace=True)"
      ],
      "metadata": {
        "id": "-2PnlJsQqt70"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "dG9_m7nWqvam",
        "outputId": "28d55503-ac6b-4a43-9ff8-66e1988eadfa"
      },
      "execution_count": 276,
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          "data": {
            "text/plain": [
              "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
              "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
              "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
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              "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
              "\n",
              "       Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  Hybrid  \\\n",
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              "1   73713.08            0    0  127.677940  Intel Core i5    0    0       0   \n",
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              "Ram              0.743007\n",
              "Weight           0.210370\n",
              "Price            1.000000\n",
              "Touchscreen      0.191226\n",
              "Ips              0.252208\n",
              "ppi              0.473487\n",
              "HDD             -0.096441\n",
              "SSD              0.670799\n",
              "Hybrid           0.007989\n",
              "Flash_Storage   -0.040511\n",
              "Name: Price, dtype: float64"
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              "Intel HD Graphics 620      281\n",
              "Intel HD Graphics 520      185\n",
              "Intel UHD Graphics 620      68\n",
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              "Nvidia GeForce GTX 1060     48\n",
              "                          ... \n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>16</td>\n",
              "      <td>AMD Radeon Pro 455</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83</td>\n",
              "      <td>208070.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>220.534624</td>\n",
              "      <td>Intel Core i7</td>\n",
              "      <td>0</td>\n",
              "      <td>512</td>\n",
              "      <td>AMD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>Intel Iris Plus Graphics 650</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>147895.20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.983005</td>\n",
              "      <td>Intel Core i5</td>\n",
              "      <td>0</td>\n",
              "      <td>256</td>\n",
              "      <td>Intel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22011d0c-a1bd-49cb-bb6c-6f2fa9eacd1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-22011d0c-a1bd-49cb-bb6c-6f2fa9eacd1f 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-22011d0c-a1bd-49cb-bb6c-6f2fa9eacd1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gpu brand'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFQLShZLq6K9",
        "outputId": "17b5fd86-213b-41c4-b7bf-aa32351eb94f"
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intel     722\n",
              "Nvidia    400\n",
              "AMD       180\n",
              "ARM         1\n",
              "Name: Gpu brand, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['Gpu brand'] != 'ARM']"
      ],
      "metadata": {
        "id": "wtlj_uhLq6H3"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gpu brand'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0fhJWNVq6FP",
        "outputId": "c343c3b0-a3a0-4800-a4aa-92dc0105ed15"
      },
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intel     722\n",
              "Nvidia    400\n",
              "AMD       180\n",
              "Name: Gpu brand, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['Gpu brand'],y=df['Price'],estimator=np.median)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "u3Nd4VDwrAXS",
        "outputId": "b158d413-92b7-4b1d-a50e-18b875d1fda8"
      },
      "execution_count": 286,
      "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": "code",
      "source": [
        "df.drop(columns=['Gpu'],inplace=True)"
      ],
      "metadata": {
        "id": "rtrj-S6_rBDD"
      },
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "m6q8jDrGrBAs",
        "outputId": "ad0ff5d5-1a9b-4c27-f5a0-b257397b2cd8"
      },
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company   TypeName  Ram  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
              "0   Apple  Ultrabook    8  macOS    1.37  109854.58            0    1   \n",
              "1   Apple  Ultrabook    8  macOS    1.34   73713.08            0    0   \n",
              "2      HP   Notebook    8  No OS    1.86   47150.00            0    0   \n",
              "3   Apple  Ultrabook   16  macOS    1.83  208070.90            0    1   \n",
              "4   Apple  Ultrabook    8  macOS    1.37  147895.20            0    1   \n",
              "\n",
              "          ppi      Cpu brand  HDD  SSD Gpu brand  \n",
              "0  226.983005  Intel Core i5    0  128     Intel  \n",
              "1  127.677940  Intel Core i5    0    0     Intel  \n",
              "2  141.211998  Intel Core i5    0  256     Intel  \n",
              "3  220.534624  Intel Core i7    0  512       AMD  \n",
              "4  226.983005  Intel Core i5    0  256     Intel  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-374d3d23-a20c-46bd-8277-27a4f2380c86\">\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>TypeName</th>\n",
              "      <th>Ram</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Price</th>\n",
              "      <th>Touchscreen</th>\n",
              "      <th>Ips</th>\n",
              "      <th>ppi</th>\n",
              "      <th>Cpu brand</th>\n",
              "      <th>HDD</th>\n",
              "      <th>SSD</th>\n",
              "      <th>Gpu brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>109854.58</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.983005</td>\n",
              "      <td>Intel Core i5</td>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "      <td>Intel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.34</td>\n",
              "      <td>73713.08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>127.677940</td>\n",
              "      <td>Intel Core i5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>8</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86</td>\n",
              "      <td>47150.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>141.211998</td>\n",
              "      <td>Intel Core i5</td>\n",
              "      <td>0</td>\n",
              "      <td>256</td>\n",
              "      <td>Intel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>16</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83</td>\n",
              "      <td>208070.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>220.534624</td>\n",
              "      <td>Intel Core i7</td>\n",
              "      <td>0</td>\n",
              "      <td>512</td>\n",
              "      <td>AMD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>147895.20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.983005</td>\n",
              "      <td>Intel Core i5</td>\n",
              "      <td>0</td>\n",
              "      <td>256</td>\n",
              "      <td>Intel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-374d3d23-a20c-46bd-8277-27a4f2380c86')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-374d3d23-a20c-46bd-8277-27a4f2380c86 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-374d3d23-a20c-46bd-8277-27a4f2380c86');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['OpSys'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHxoaSPTrA-T",
        "outputId": "36eda2c0-456b-40a8-f3a7-bf2a120062fe"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Windows 10      1072\n",
              "No OS             66\n",
              "Linux             62\n",
              "Windows 7         45\n",
              "Chrome OS         26\n",
              "macOS             13\n",
              "Mac OS X           8\n",
              "Windows 10 S       8\n",
              "Android            2\n",
              "Name: OpSys, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['OpSys'],y=df['Price'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "_h9EzOqTrA7h",
        "outputId": "59e35933-95c1-442f-e8d5-3c9975caa0db"
      },
      "execution_count": 290,
      "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": "code",
      "source": [
        "def cat_os(inp):\n",
        "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
        "        return 'Windows'\n",
        "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
        "        return 'Mac'\n",
        "    else:\n",
        "        return 'Others/No OS/Linux'"
      ],
      "metadata": {
        "id": "rHqhJLOzrH_T"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['os'] = df['OpSys'].apply(cat_os)"
      ],
      "metadata": {
        "id": "Aem2tpOhrH9R"
      },
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NmKxSnQnrH63",
        "outputId": "7b8733ac-cc2f-416a-a0fc-af420d51656c"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company   TypeName  Ram  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
              "0   Apple  Ultrabook    8  macOS    1.37  109854.58            0    1   \n",
              "1   Apple  Ultrabook    8  macOS    1.34   73713.08            0    0   \n",
              "2      HP   Notebook    8  No OS    1.86   47150.00            0    0   \n",
              "3   Apple  Ultrabook   16  macOS    1.83  208070.90            0    1   \n",
              "4   Apple  Ultrabook    8  macOS    1.37  147895.20            0    1   \n",
              "\n",
              "          ppi      Cpu brand  HDD  SSD Gpu brand                  os  \n",
              "0  226.983005  Intel Core i5    0  128     Intel                 Mac  \n",
              "1  127.677940  Intel Core i5    0    0     Intel                 Mac  \n",
              "2  141.211998  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
              "3  220.534624  Intel Core i7    0  512       AMD                 Mac  \n",
              "4  226.983005  Intel Core i5    0  256     Intel                 Mac  "
            ],
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>macOS</td>\n",
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              "      <td>Mac</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
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              "      <td>macOS</td>\n",
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              "      <td>Mac</td>\n",
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              "      <td>HP</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>8</td>\n",
              "      <td>No OS</td>\n",
              "      <td>1.86</td>\n",
              "      <td>47150.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>141.211998</td>\n",
              "      <td>Intel Core i5</td>\n",
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              "      <td>Intel</td>\n",
              "      <td>Others/No OS/Linux</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>16</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.83</td>\n",
              "      <td>208070.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>220.534624</td>\n",
              "      <td>Intel Core i7</td>\n",
              "      <td>0</td>\n",
              "      <td>512</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Mac</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>macOS</td>\n",
              "      <td>1.37</td>\n",
              "      <td>147895.20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.983005</td>\n",
              "      <td>Intel Core i5</td>\n",
              "      <td>0</td>\n",
              "      <td>256</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Mac</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-c99b2a19-0cea-4a9a-8115-65e628b22b6c button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c99b2a19-0cea-4a9a-8115-65e628b22b6c');\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",
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          },
          "metadata": {},
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['OpSys'],inplace=True)"
      ],
      "metadata": {
        "id": "iuk5loYLrH4N"
      },
      "execution_count": 294,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df['os'],y=df['Price'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "sZqICkWnrH1W",
        "outputId": "430f2b68-148f-4e03-d3e0-9df64ecf30ad"
      },
      "execution_count": 295,
      "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": "code",
      "source": [
        "sns.distplot(df['Weight'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Jv91R8i_rOQR",
        "outputId": "b339173f-bf48-4aab-d5b2-a781ca140831"
      },
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d3fa610>"
            ]
          },
          "metadata": {},
          "execution_count": 296
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=df['Weight'],y=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "7bzWVrkMrP8b",
        "outputId": "dc18fbc2-c2f2-461f-9678-61242f4f2e2e"
      },
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d35f5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 297
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()['Price']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHkMhGRKrRZi",
        "outputId": "6a1c9753-6eea-4148-df37-45f4d639f220"
      },
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ram            0.742905\n",
              "Weight         0.209867\n",
              "Price          1.000000\n",
              "Touchscreen    0.192917\n",
              "Ips            0.253320\n",
              "ppi            0.475368\n",
              "HDD           -0.096891\n",
              "SSD            0.670660\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "Pm2o6yporSlC",
        "outputId": "14d26573-2521-42c0-8cbf-6386e628e2c0"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d2ca350>"
            ]
          },
          "metadata": {},
          "execution_count": 299
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(np.log(df['Price']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "nuHS4tjprT8v",
        "outputId": "c13dd551-8ad8-48cb-f6e1-f47e750cac60"
      },
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa57d4a9750>"
            ]
          },
          "metadata": {},
          "execution_count": 300
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Price'])\n",
        "y = np.log(df['Price'])"
      ],
      "metadata": {
        "id": "840owFR-rVne"
      },
      "execution_count": 301,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "X34Zo8kGrW3U",
        "outputId": "63832833-0010-497c-f5dd-8c4795612b39"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
              "0      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
              "1      Apple           Ultrabook    8    1.34            0    0  127.677940   \n",
              "2         HP            Notebook    8    1.86            0    0  141.211998   \n",
              "3      Apple           Ultrabook   16    1.83            0    1  220.534624   \n",
              "4      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
              "...      ...                 ...  ...     ...          ...  ...         ...   \n",
              "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
              "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
              "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
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              "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
              "\n",
              "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
              "0             Intel Core i5     0  128     Intel                 Mac  \n",
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              "1301          Intel Core i7  1000    0       AMD             Windows  \n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVk2Qbi9rYSH",
        "outputId": "63ea0f63-90fc-4023-ac0c-d0ceea211567"
      },
      "execution_count": 303,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       11.606913\n",
              "1       11.207936\n",
              "2       10.761089\n",
              "3       12.245634\n",
              "4       11.904259\n",
              "          ...    \n",
              "1298    10.865058\n",
              "1299    11.719273\n",
              "1300     9.840441\n",
              "1301    11.045287\n",
              "1302    10.317516\n",
              "Name: Price, Length: 1302, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=2)"
      ],
      "metadata": {
        "id": "-2k5OfmdrZmE"
      },
      "execution_count": 304,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vM_dGXAerayN",
        "outputId": "c0292422-7f72-4b03-e0ee-e4d326c92474"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
              "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
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              "...       ...                 ...  ...     ...          ...  ...         ...   \n",
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              "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
              "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
              "\n",
              "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
              "183           Intel Core i5     0  128     Intel             Windows  \n",
              "1141          Intel Core i7  1000  128    Nvidia             Windows  \n",
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              "...                     ...   ...  ...       ...                 ...  \n",
              "466           Intel Core i3   500    0    Nvidia             Windows  \n",
              "299           Intel Core i7     0  512    Nvidia             Windows  \n",
              "493           AMD Processor  1000    0       AMD             Windows  \n",
              "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
              "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
              "\n",
              "[1106 rows x 12 columns]"
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              "      <td>4</td>\n",
              "      <td>2.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.454670</td>\n",
              "      <td>Intel Core i3</td>\n",
              "      <td>500</td>\n",
              "      <td>0</td>\n",
              "      <td>Nvidia</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>Asus</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>16</td>\n",
              "      <td>1.63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>141.211998</td>\n",
              "      <td>Intel Core i7</td>\n",
              "      <td>0</td>\n",
              "      <td>512</td>\n",
              "      <td>Nvidia</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>Acer</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>8</td>\n",
              "      <td>2.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.454670</td>\n",
              "      <td>AMD Processor</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Notebook</td>\n",
              "      <td>8</td>\n",
              "      <td>2.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.454670</td>\n",
              "      <td>Intel Core i3</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>Nvidia</td>\n",
              "      <td>Others/No OS/Linux</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1193</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Ultrabook</td>\n",
              "      <td>8</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>226.415547</td>\n",
              "      <td>Other Intel Processor</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Mac</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1106 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4221ff0c-e710-43f8-808a-292a50a0af72')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4221ff0c-e710-43f8-808a-292a50a0af72 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-4221ff0c-e710-43f8-808a-292a50a0af72');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import r2_score, mean_absolute_error"
      ],
      "metadata": {
        "id": "k4YWcNxMrc5C"
      },
      "execution_count": 306,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
        "from sklearn.svm import SVR\n",
        "from xgboost import XGBRegressor"
      ],
      "metadata": {
        "id": "jMhKrTZArc2o"
      },
      "execution_count": 307,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building and Evaluation"
      ],
      "metadata": {
        "id": "ruPlV4LC8doc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Linear Regression"
      ],
      "metadata": {
        "id": "e3yBRERCrg7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "step2 = LinearRegression()\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Zdw0wq8riy2",
        "outputId": "943d9bca-6462-42ed-96b5-9fa5f5d8578a"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.8073277448418648\n",
            "MAE 0.2101782797642873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ridge Regression"
      ],
      "metadata": {
        "id": "ehxUJ_oJroDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "step2 = Ridge(alpha=10)\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGQuVJ70rqbl",
        "outputId": "bd7f9e8f-e125-484f-cc1a-df0d31115650"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.8127331031311811\n",
            "MAE 0.20926802242582948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lasso Regression"
      ],
      "metadata": {
        "id": "7CpaGEeMrqnC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "step2 = Lasso(alpha=0.001)\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4vuPW-prs7x",
        "outputId": "ab2e9171-8e1f-4ff9-dd70-89b5c4f4bd21"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.8071853945317103\n",
            "MAE 0.2111436161347258\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "05ob5gwZrvUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "step2 = RandomForestRegressor(n_estimators=100,\n",
        "                              random_state=3,\n",
        "                              max_samples=0.5,\n",
        "                              max_features=0.75,\n",
        "                              max_depth=15)\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ven25Z-Frx8R",
        "outputId": "efcd14df-9183-4380-c6a6-5db518731b4c"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.8873960312190967\n",
            "MAE 0.1570796822667481\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gradient Boost"
      ],
      "metadata": {
        "id": "N5sbpulhvZDp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "step2 = GradientBoostingRegressor(n_estimators=500)\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXBO_mIPvRFk",
        "outputId": "ae37d3c9-75bf-4e5e-a60b-164fdee72719"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.8807321925498299\n",
            "MAE 0.15965274067094842\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XgBoost "
      ],
      "metadata": {
        "id": "Vyf4C2GVvVpy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "step2 = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YR_L8CB2vUMs",
        "outputId": "5d44eda9-cddb-4843-ec38-bafddcbe96bc"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14:15:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "R2 score 0.8788421644780057\n",
            "MAE 0.16420416772287788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Model Improvement: Stacking"
      ],
      "metadata": {
        "id": "sKlKUtjB8K5F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
        "\n",
        "step1 = ColumnTransformer(transformers=[\n",
        "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
        "],remainder='passthrough')\n",
        "\n",
        "\n",
        "estimators = [\n",
        "    ('rf', RandomForestRegressor(n_estimators=350,random_state=3,max_samples=0.5,max_features=0.75,max_depth=15)),\n",
        "    ('gbdt',GradientBoostingRegressor(n_estimators=100,max_features=0.5)),\n",
        "    ('xgb', XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5))\n",
        "]\n",
        "\n",
        "step2 = StackingRegressor(estimators=estimators, final_estimator=Ridge(alpha=100))\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('step1',step1),\n",
        "    ('step2',step2)\n",
        "])\n",
        "\n",
        "pipe.fit(X_train,y_train)\n",
        "\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "print('R2 score',r2_score(y_test,y_pred))\n",
        "print('MAE',mean_absolute_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xH8_9oQfve5p",
        "outputId": "6790406b-b8e7-4d0b-b61e-765974916e30"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14:15:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[14:15:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[14:15:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[14:15:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[14:15:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[14:15:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "R2 score 0.8788756100890851\n",
            "MAE 0.1682426124171779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Deployment: Exporting the Model"
      ],
      "metadata": {
        "id": "rcAwHjJrsPey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "pickle.dump(df,open('df.pkl','wb'))\n",
        "pickle.dump(pipe,open('pipe.pkl','wb'))"
      ],
      "metadata": {
        "id": "OTCB_F2_uuHJ"
      },
      "execution_count": 315,
      "outputs": []
    }
  ]
}