several-solutions.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "import xgboost as xgb\n",
    "from collections import Counter"
   ]
  },
  {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</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>575.00</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>2537.45</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>1803.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Aspire 3</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>AMD A9-Series 9420 3GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R5</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</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 2.2GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB Flash Storage</td>\n",
       "      <td>Intel Iris Pro Graphics</td>\n",
       "      <td>Mac OS X</td>\n",
       "      <td>2.04kg</td>\n",
       "      <td>2139.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>1158.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Asus</td>\n",
       "      <td>ZenBook UX430UN</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce MX150</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>1495.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Swift 3</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.6kg</td>\n",
       "      <td>770.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "5          6    Acer         Aspire 3   Notebook    15.6   \n",
       "6          7   Apple      MacBook Pro  Ultrabook    15.4   \n",
       "7          8   Apple      Macbook Air  Ultrabook    13.3   \n",
       "8          9    Asus  ZenBook UX430UN  Ultrabook    14.0   \n",
       "9         10    Acer          Swift 3  Ultrabook    14.0   \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",
       "5                            1366x768     AMD A9-Series 9420 3GHz   4GB   \n",
       "6  IPS Panel Retina Display 2880x1800        Intel Core i7 2.2GHz  16GB   \n",
       "7                            1440x900        Intel Core i5 1.8GHz   8GB   \n",
       "8                   Full HD 1920x1080  Intel Core i7 8550U 1.8GHz  16GB   \n",
       "9         IPS Panel Full HD 1920x1080  Intel Core i5 8250U 1.6GHz   8GB   \n",
       "\n",
       "                Memory                           Gpu       OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640       macOS  1.37kg   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000       macOS  1.34kg   \n",
       "2            256GB SSD         Intel HD Graphics 620       No OS  1.86kg   \n",
       "3            512GB SSD            AMD Radeon Pro 455       macOS  1.83kg   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650       macOS  1.37kg   \n",
       "5            500GB HDD                 AMD Radeon R5  Windows 10   2.1kg   \n",
       "6  256GB Flash Storage       Intel Iris Pro Graphics    Mac OS X  2.04kg   \n",
       "7  256GB Flash Storage        Intel HD Graphics 6000       macOS  1.34kg   \n",
       "8            512GB SSD          Nvidia GeForce MX150  Windows 10   1.3kg   \n",
       "9            256GB SSD        Intel UHD Graphics 620  Windows 10   1.6kg   \n",
       "\n",
       "   Price_euros  \n",
       "0      1339.69  \n",
       "1       898.94  \n",
       "2       575.00  \n",
       "3      2537.45  \n",
       "4      1803.60  \n",
       "5       400.00  \n",
       "6      2139.97  \n",
       "7      1158.70  \n",
       "8      1495.00  \n",
       "9       770.00  "
      ]
     },
     "execution_count": 2,
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   ],
   "source": [
    "data = pd.read_csv('/kaggle/input/laptop-price/laptop_price.csv', encoding = 'latin-1')\n",
    "data.head(10)"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   laptop_ID         1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   Product           1303 non-null   object \n",
      " 3   TypeName          1303 non-null   object \n",
      " 4   Inches            1303 non-null   float64\n",
      " 5   ScreenResolution  1303 non-null   object \n",
      " 6   Cpu               1303 non-null   object \n",
      " 7   Ram               1303 non-null   object \n",
      " 8   Memory            1303 non-null   object \n",
      " 9   Gpu               1303 non-null   object \n",
      " 10  OpSys             1303 non-null   object \n",
      " 11  Weight            1303 non-null   object \n",
      " 12  Price_euros       1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(10)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "data = data.dropna()\n",
    "data = data.drop(columns = ['laptop_ID'])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5827936",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Task - predict laptop price \n",
    "y = np.array(data['Price_euros'])\n",
    "data = data.drop(columns = ['Price_euros'])"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Company', 'Product', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu',\n",
       "       'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
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   "id": "7c53db43",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Preprocessing\n",
    "ram1 = list(data['Ram'])\n",
    "ram2 = []\n",
    "for i in range(data.shape[0]):\n",
    "    ram2.append(float(ram1[i].replace('GB', '')))\n",
    "ram2 = np.array(ram2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff09bd7d",
   "metadata": {
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   "outputs": [],
   "source": [
    "weight1 = list(data['Weight'])\n",
    "weight2 = []\n",
    "for i in range(data.shape[0]):\n",
    "    weight2.append(float(weight1[i].replace('kg', '')))\n",
    "weight2 = np.array(weight2)"
   ]
  },
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   "execution_count": 9,
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     "iopub.execute_input": "2022-04-08T11:49:49.745087Z",
     "iopub.status.busy": "2022-04-08T11:49:49.744047Z",
     "iopub.status.idle": "2022-04-08T11:49:49.750246Z",
     "shell.execute_reply": "2022-04-08T11:49:49.750800Z",
     "shell.execute_reply.started": "2022-04-08T10:32:33.980929Z"
    },
    "papermill": {
     "duration": 0.035057,
     "end_time": "2022-04-08T11:49:49.750984",
     "exception": false,
     "start_time": "2022-04-08T11:49:49.715927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "inches2 = np.array(data['Inches'])\n",
    "data = data.drop(columns = ['Inches', 'Ram', 'Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2156c288",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:49.805650Z",
     "iopub.status.busy": "2022-04-08T11:49:49.804638Z",
     "iopub.status.idle": "2022-04-08T11:49:50.322877Z",
     "shell.execute_reply": "2022-04-08T11:49:50.323434Z",
     "shell.execute_reply.started": "2022-04-08T11:26:34.198486Z"
    },
    "papermill": {
     "duration": 0.547135,
     "end_time": "2022-04-08T11:49:50.323675",
     "exception": false,
     "start_time": "2022-04-08T11:49:49.776540",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, sharex=True,\n",
    "                                    figsize=(20, 6))\n",
    "ax0.set_title('Ram - Price_euros')\n",
    "ax0.scatter(ram2, y)\n",
    "ax0.set_xlabel('Ram')\n",
    "ax0.set_ylabel('Price_euros')\n",
    "\n",
    "ax1.set_title('Weight - Price_euros')\n",
    "ax1.scatter(weight2, y)\n",
    "ax1.set_xlabel('Weight')\n",
    "ax1.set_ylabel('Price_euros')\n",
    "\n",
    "ax2.set_title('Inches - Price_euros')\n",
    "ax2.scatter(inches2, y)\n",
    "ax2.set_xlabel('Inches')\n",
    "ax2.set_ylabel('Price_euros')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6041e0b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:50.395624Z",
     "iopub.status.busy": "2022-04-08T11:49:50.394524Z",
     "iopub.status.idle": "2022-04-08T11:49:50.411279Z",
     "shell.execute_reply": "2022-04-08T11:49:50.410673Z",
     "shell.execute_reply.started": "2022-04-08T10:32:34.659335Z"
    },
    "papermill": {
     "duration": 0.059526,
     "end_time": "2022-04-08T11:49:50.411441",
     "exception": false,
     "start_time": "2022-04-08T11:49:50.351915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Encode categorical features\n",
    "encoder = OneHotEncoder()\n",
    "data = encoder.fit_transform(data)\n",
    "data = data.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5a4adaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:50.470833Z",
     "iopub.status.busy": "2022-04-08T11:49:50.469355Z",
     "iopub.status.idle": "2022-04-08T11:49:50.476009Z",
     "shell.execute_reply": "2022-04-08T11:49:50.476516Z",
     "shell.execute_reply.started": "2022-04-08T10:32:36.725888Z"
    },
    "papermill": {
     "duration": 0.03921,
     "end_time": "2022-04-08T11:49:50.476699",
     "exception": false,
     "start_time": "2022-04-08T11:49:50.437489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = np.concatenate((data, inches2[:, np.newaxis], ram2[:, np.newaxis], weight2[:, np.newaxis]), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ceb4e7fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:50.537514Z",
     "iopub.status.busy": "2022-04-08T11:49:50.536535Z",
     "iopub.status.idle": "2022-04-08T11:49:50.539930Z",
     "shell.execute_reply": "2022-04-08T11:49:50.540435Z",
     "shell.execute_reply.started": "2022-04-08T10:32:37.575053Z"
    },
    "papermill": {
     "duration": 0.037015,
     "end_time": "2022-04-08T11:49:50.540601",
     "exception": false,
     "start_time": "2022-04-08T11:49:50.503586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 962)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9804c9c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:50.601502Z",
     "iopub.status.busy": "2022-04-08T11:49:50.599957Z",
     "iopub.status.idle": "2022-04-08T11:49:50.607865Z",
     "shell.execute_reply": "2022-04-08T11:49:50.608469Z",
     "shell.execute_reply.started": "2022-04-08T10:32:39.885238Z"
    },
    "papermill": {
     "duration": 0.041359,
     "end_time": "2022-04-08T11:49:50.608645",
     "exception": false,
     "start_time": "2022-04-08T11:49:50.567286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd075b3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:50.665679Z",
     "iopub.status.busy": "2022-04-08T11:49:50.664654Z",
     "iopub.status.idle": "2022-04-08T11:49:50.809740Z",
     "shell.execute_reply": "2022-04-08T11:49:50.810791Z",
     "shell.execute_reply.started": "2022-04-08T10:32:48.301393Z"
    },
    "papermill": {
     "duration": 0.176021,
     "end_time": "2022-04-08T11:49:50.811122",
     "exception": false,
     "start_time": "2022-04-08T11:49:50.635101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.8803367654970733\n",
      "mse: 64070.59034568524\n",
      "mae: 174.58759506713886\n"
     ]
    }
   ],
   "source": [
    "# Ridge regression\n",
    "model = Ridge()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b303f7eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:50.940511Z",
     "iopub.status.busy": "2022-04-08T11:49:50.936918Z",
     "iopub.status.idle": "2022-04-08T11:49:51.009461Z",
     "shell.execute_reply": "2022-04-08T11:49:51.010632Z",
     "shell.execute_reply.started": "2022-04-08T10:32:49.439168Z"
    },
    "papermill": {
     "duration": 0.113354,
     "end_time": "2022-04-08T11:49:51.010923",
     "exception": false,
     "start_time": "2022-04-08T11:49:50.897569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.831928136644965\n",
      "mse: 89989.74121323007\n",
      "mae: 211.43151020087257\n"
     ]
    }
   ],
   "source": [
    "# Lasso regression\n",
    "model = Lasso()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab1c898c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:51.143587Z",
     "iopub.status.busy": "2022-04-08T11:49:51.142159Z",
     "iopub.status.idle": "2022-04-08T11:49:51.706723Z",
     "shell.execute_reply": "2022-04-08T11:49:51.707517Z",
     "shell.execute_reply.started": "2022-04-08T10:32:53.359715Z"
    },
    "papermill": {
     "duration": 0.602873,
     "end_time": "2022-04-08T11:49:51.707698",
     "exception": false,
     "start_time": "2022-04-08T11:49:51.104825",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.016593876658251294\n",
      "mse: 526539.4265314346\n",
      "mae: 503.2037140030953\n"
     ]
    }
   ],
   "source": [
    "# Epsilon-Support Vector Regression\n",
    "model = SVR()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2591250f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:51.767861Z",
     "iopub.status.busy": "2022-04-08T11:49:51.766860Z",
     "iopub.status.idle": "2022-04-08T11:49:52.747752Z",
     "shell.execute_reply": "2022-04-08T11:49:52.747059Z",
     "shell.execute_reply.started": "2022-04-08T10:32:59.468565Z"
    },
    "papermill": {
     "duration": 1.012073,
     "end_time": "2022-04-08T11:49:52.747902",
     "exception": false,
     "start_time": "2022-04-08T11:49:51.735829",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.8307711179597765\n",
      "mse: 90609.23700497382\n",
      "mae: 209.62665427886384\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting\n",
    "model = GradientBoostingRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4930a296",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:52.814218Z",
     "iopub.status.busy": "2022-04-08T11:49:52.812951Z",
     "iopub.status.idle": "2022-04-08T11:49:54.743340Z",
     "shell.execute_reply": "2022-04-08T11:49:54.742731Z",
     "shell.execute_reply.started": "2022-04-08T10:33:02.275576Z"
    },
    "papermill": {
     "duration": 1.966478,
     "end_time": "2022-04-08T11:49:54.743503",
     "exception": false,
     "start_time": "2022-04-08T11:49:52.777025",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.8261261228111374\n",
      "mse: 93096.27976762688\n",
      "mae: 193.532868744186\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c69a8ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:54.808742Z",
     "iopub.status.busy": "2022-04-08T11:49:54.807416Z",
     "iopub.status.idle": "2022-04-08T11:49:55.478144Z",
     "shell.execute_reply": "2022-04-08T11:49:55.478812Z",
     "shell.execute_reply.started": "2022-04-08T10:33:06.715449Z"
    },
    "papermill": {
     "duration": 0.706543,
     "end_time": "2022-04-08T11:49:55.479067",
     "exception": false,
     "start_time": "2022-04-08T11:49:54.772524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.5742595263325125\n",
      "mse: 227951.7480472287\n",
      "mae: 396.9984864718329\n"
     ]
    }
   ],
   "source": [
    "# Ada Boost\n",
    "model = AdaBoostRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea8ae4b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:55.544108Z",
     "iopub.status.busy": "2022-04-08T11:49:55.543437Z",
     "iopub.status.idle": "2022-04-08T11:49:57.534713Z",
     "shell.execute_reply": "2022-04-08T11:49:57.535665Z",
     "shell.execute_reply.started": "2022-04-08T10:33:18.534252Z"
    },
    "papermill": {
     "duration": 2.027005,
     "end_time": "2022-04-08T11:49:57.535921",
     "exception": false,
     "start_time": "2022-04-08T11:49:55.508916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.8519125753255192\n",
      "mse: 79289.58933024763\n",
      "mae: 188.47837437261538\n"
     ]
    }
   ],
   "source": [
    "# XGBoost Regression\n",
    "model = xgb.XGBRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd039d51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T11:49:57.608682Z",
     "iopub.status.busy": "2022-04-08T11:49:57.607937Z",
     "iopub.status.idle": "2022-04-08T11:49:57.975231Z",
     "shell.execute_reply": "2022-04-08T11:49:57.975749Z",
     "shell.execute_reply.started": "2022-04-08T10:54:14.256837Z"
    },
    "papermill": {
     "duration": 0.407175,
     "end_time": "2022-04-08T11:49:57.975924",
     "exception": false,
     "start_time": "2022-04-08T11:49:57.568749",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.8803367654970733\n",
      "mse: 64070.59034568524\n",
      "mae: 174.58759506713886\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Best model - Ridge regression\n",
    "best_model = Ridge()\n",
    "best_model.fit(X_train, y_train)\n",
    "y_pred = best_model.predict(X_test)\n",
    "print('R2:', r2_score(y_test, y_pred))\n",
    "print('mse:', mean_squared_error(y_test, y_pred))\n",
    "print('mae:', mean_absolute_error(y_test, y_pred))\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.plot(np.arange(len(y_test)), y_test, label = 'y_test')\n",
    "plt.plot(np.arange(len(y_test)), y_pred, label = 'y_pred')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.title('Comparison y_test and y_train')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 21.534447,
   "end_time": "2022-04-08T11:49:58.820303",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-04-08T11:49:37.285856",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}