tps-september2024-s4e9-dl.ipynb

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     "text": [
      "Requirement already satisfied: category_encoders in /opt/conda/lib/python3.10/site-packages (2.6.3)\r\n",
      "Requirement already satisfied: numpy>=1.14.0 in /opt/conda/lib/python3.10/site-packages (from category_encoders) (1.26.4)\r\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in /opt/conda/lib/python3.10/site-packages (from category_encoders) (1.2.2)\r\n",
      "Requirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from category_encoders) (1.14.1)\r\n",
      "Requirement already satisfied: statsmodels>=0.9.0 in /opt/conda/lib/python3.10/site-packages (from category_encoders) (0.14.2)\r\n",
      "Requirement already satisfied: pandas>=1.0.5 in /opt/conda/lib/python3.10/site-packages (from category_encoders) (2.2.2)\r\n",
      "Requirement already satisfied: patsy>=0.5.1 in /opt/conda/lib/python3.10/site-packages (from category_encoders) (0.5.6)\r\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0.5->category_encoders) (2.9.0.post0)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0.5->category_encoders) (2024.1)\r\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0.5->category_encoders) (2024.1)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.20.0->category_encoders) (1.4.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.20.0->category_encoders) (3.5.0)\r\n",
      "Requirement already satisfied: packaging>=21.3 in /opt/conda/lib/python3.10/site-packages (from statsmodels>=0.9.0->category_encoders) (21.3)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=21.3->statsmodels>=0.9.0->category_encoders) (3.1.2)\r\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import sklearn\n",
    "import os\n",
    "import re \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "%pip install category_encoders\n",
    "import category_encoders as ce\n",
    "\n",
    "\n",
    "import math \n",
    "from torch.nn import Module\n",
    "from torch.nn import ModuleList\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n"
   ]
  },
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     "text": [
      "Using device: cuda\n"
     ]
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   "source": [
    "## setting up the device \n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188533 entries, 0 to 188532\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            188533 non-null  int64 \n",
      " 1   brand         188533 non-null  object\n",
      " 2   model         188533 non-null  object\n",
      " 3   model_year    188533 non-null  int64 \n",
      " 4   milage        188533 non-null  int64 \n",
      " 5   fuel_type     183450 non-null  object\n",
      " 6   engine        188533 non-null  object\n",
      " 7   transmission  188533 non-null  object\n",
      " 8   ext_col       188533 non-null  object\n",
      " 9   int_col       188533 non-null  object\n",
      " 10  accident      186081 non-null  object\n",
      " 11  clean_title   167114 non-null  object\n",
      " 12  price         188533 non-null  int64 \n",
      "dtypes: int64(4), object(9)\n",
      "memory usage: 18.7+ MB\n",
      "None\n"
     ]
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>model_year</th>\n",
       "      <th>milage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>engine</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ext_col</th>\n",
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       "      <th>clean_title</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>MINI</td>\n",
       "      <td>Cooper S Base</td>\n",
       "      <td>2007</td>\n",
       "      <td>213000</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>172.0HP 1.6L 4 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Gray</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>LS V8</td>\n",
       "      <td>2002</td>\n",
       "      <td>143250</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>252.0HP 3.9L 8 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Beige</td>\n",
       "      <td>At least 1 accident or damage reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 2500 LT</td>\n",
       "      <td>2002</td>\n",
       "      <td>136731</td>\n",
       "      <td>E85 Flex Fuel</td>\n",
       "      <td>320.0HP 5.3L 8 Cylinder Engine Flex Fuel Capab...</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Gray</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Genesis</td>\n",
       "      <td>G90 5.0 Ultimate</td>\n",
       "      <td>2017</td>\n",
       "      <td>19500</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>420.0HP 5.0L 8 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>Transmission w/Dual Shift Mode</td>\n",
       "      <td>Black</td>\n",
       "      <td>Black</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>Metris Base</td>\n",
       "      <td>2021</td>\n",
       "      <td>7388</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>208.0HP 2.0L 4 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>7-Speed A/T</td>\n",
       "      <td>Black</td>\n",
       "      <td>Beige</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>97500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id          brand              model  model_year  milage      fuel_type  \\\n",
       "0   0           MINI      Cooper S Base        2007  213000       Gasoline   \n",
       "1   1        Lincoln              LS V8        2002  143250       Gasoline   \n",
       "2   2      Chevrolet  Silverado 2500 LT        2002  136731  E85 Flex Fuel   \n",
       "3   3        Genesis   G90 5.0 Ultimate        2017   19500       Gasoline   \n",
       "4   4  Mercedes-Benz        Metris Base        2021    7388       Gasoline   \n",
       "\n",
       "                                              engine  \\\n",
       "0       172.0HP 1.6L 4 Cylinder Engine Gasoline Fuel   \n",
       "1       252.0HP 3.9L 8 Cylinder Engine Gasoline Fuel   \n",
       "2  320.0HP 5.3L 8 Cylinder Engine Flex Fuel Capab...   \n",
       "3       420.0HP 5.0L 8 Cylinder Engine Gasoline Fuel   \n",
       "4       208.0HP 2.0L 4 Cylinder Engine Gasoline Fuel   \n",
       "\n",
       "                     transmission ext_col int_col  \\\n",
       "0                             A/T  Yellow    Gray   \n",
       "1                             A/T  Silver   Beige   \n",
       "2                             A/T    Blue    Gray   \n",
       "3  Transmission w/Dual Shift Mode   Black   Black   \n",
       "4                     7-Speed A/T   Black   Beige   \n",
       "\n",
       "                                 accident clean_title  price  \n",
       "0                           None reported         Yes   4200  \n",
       "1  At least 1 accident or damage reported         Yes   4999  \n",
       "2                           None reported         Yes  13900  \n",
       "3                           None reported         Yes  45000  \n",
       "4                           None reported         Yes  97500  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('/kaggle/input/playground-series-s4e9/train.csv')\n",
    "test = pd.read_csv('/kaggle/input/playground-series-s4e9/test.csv')\n",
    "submission = pd.read_csv('/kaggle/input/playground-series-s4e9/sample_submission.csv')\n",
    "print(train.info())\n",
    "train.head()"
   ]
  },
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   "source": [
    "## Feature engineering\n",
    "Data exploration has been conducted in the previous [ML based notebook](https://www.kaggle.com/code/sabyasachi96/tps-september2024-s4e9-ml). Anova supported target encoding of the categorical variable, helped in reducing the feature space making the DL training less cumbersome and easy. \n",
    "[All different encoding types](https://medium.com/anolytics/all-you-need-to-know-about-encoding-techniques-b3a0af68338b#:~:text=Count%20encoding%20or%20frequency%20encoding,category%20and%20the%20target%20variable)"
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     "text": [
      "Before filling the null values: 128297\n",
      "After filling the null values: 0\n",
      "Before filling the null values: 85306\n",
      "After filling the null values: 0\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188533 entries, 0 to 188532\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   id                     188533 non-null  int64  \n",
      " 1   brand                  188533 non-null  object \n",
      " 2   model                  188533 non-null  object \n",
      " 3   milage                 188533 non-null  int64  \n",
      " 4   fuel_type              188533 non-null  object \n",
      " 5   transmission           188533 non-null  object \n",
      " 6   ext_col                188533 non-null  object \n",
      " 7   int_col                188533 non-null  object \n",
      " 8   accident               188533 non-null  int64  \n",
      " 9   clean_title            188533 non-null  int64  \n",
      " 10  price                  188533 non-null  int64  \n",
      " 11  horse_power            188533 non-null  float64\n",
      " 12  liters                 188533 non-null  float64\n",
      " 13  cylinders              188533 non-null  float64\n",
      " 14  turbo                  188533 non-null  int64  \n",
      " 15  car_age                188533 non-null  int64  \n",
      " 16  Power_to_Weight_Ratio  188533 non-null  float64\n",
      " 17  milage_per_year        188533 non-null  float64\n",
      "dtypes: float64(5), int64(7), object(6)\n",
      "memory usage: 25.9+ MB\n",
      "None\n"
     ]
    },
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>MINI</td>\n",
       "      <td>Cooper S Base</td>\n",
       "      <td>213000</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Yellow</td>\n",
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       "      <td>107.500000</td>\n",
       "      <td>12529.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>LS V8</td>\n",
       "      <td>143250</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Beige</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4999</td>\n",
       "      <td>252.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>64.615385</td>\n",
       "      <td>6511.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 2500 LT</td>\n",
       "      <td>136731</td>\n",
       "      <td>E85 Flex Fuel</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Gray</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13900</td>\n",
       "      <td>320.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>60.377358</td>\n",
       "      <td>6215.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Genesis</td>\n",
       "      <td>G90 5.0 Ultimate</td>\n",
       "      <td>19500</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Transmission w/Dual Shift Mode</td>\n",
       "      <td>Black</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "      <td>420.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>2785.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>Metris Base</td>\n",
       "      <td>7388</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>7-Speed A/T</td>\n",
       "      <td>Black</td>\n",
       "      <td>Beige</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>97500</td>\n",
       "      <td>208.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>2462.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id          brand              model  milage      fuel_type  \\\n",
       "0   0           MINI      Cooper S Base  213000       Gasoline   \n",
       "1   1        Lincoln              LS V8  143250       Gasoline   \n",
       "2   2      Chevrolet  Silverado 2500 LT  136731  E85 Flex Fuel   \n",
       "3   3        Genesis   G90 5.0 Ultimate   19500       Gasoline   \n",
       "4   4  Mercedes-Benz        Metris Base    7388       Gasoline   \n",
       "\n",
       "                     transmission ext_col int_col  accident  clean_title  \\\n",
       "0                             A/T  Yellow    Gray         0            1   \n",
       "1                             A/T  Silver   Beige         1            1   \n",
       "2                             A/T    Blue    Gray         0            1   \n",
       "3  Transmission w/Dual Shift Mode   Black   Black         0            1   \n",
       "4                     7-Speed A/T   Black   Beige         0            1   \n",
       "\n",
       "   price  horse_power  liters  cylinders  turbo  car_age  \\\n",
       "0   4200        172.0     1.6        4.0      0       17   \n",
       "1   4999        252.0     3.9        8.0      0       22   \n",
       "2  13900        320.0     5.3        8.0      0       22   \n",
       "3  45000        420.0     5.0        8.0      0        7   \n",
       "4  97500        208.0     2.0        4.0      0        3   \n",
       "\n",
       "   Power_to_Weight_Ratio  milage_per_year  \n",
       "0             107.500000     12529.411765  \n",
       "1              64.615385      6511.363636  \n",
       "2              60.377358      6215.045455  \n",
       "3              84.000000      2785.714286  \n",
       "4             104.000000      2462.666667  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def nullValuesCols(df):\n",
    "    columns_with_null = df.columns[df.isnull().any()]\n",
    "    print(f'Total null values in the dataset {df.isnull().sum().sum()}')\n",
    "    print(f'columns {columns_with_null}')\n",
    "    for col in columns_with_null:\n",
    "        print(f' -- Null value for column {col}: {df[col].isnull().sum()}')\n",
    "    \n",
    "    print(f'Total null values in the dataset {df.isnull().sum().sum()}')\n",
    "    print('\\n\\n')\n",
    "    \n",
    "def fill_null_with_median_or_mode(df):\n",
    "    print('Before filling the null values:', df.isnull().sum().sum())\n",
    "    \n",
    "    # Loop through each column in the DataFrame\n",
    "    for column in df.columns:\n",
    "        if df[column].dtype.name in ['float64', 'int64']:\n",
    "            # For numerical columns, use median\n",
    "            median_value = df[column].median()\n",
    "            df[column] = df[column].fillna(median_value)\n",
    "        elif df[column].dtype == 'object':\n",
    "            # For object (categorical) columns, use mode\n",
    "            mode_value = df[column].mode().iloc[0]  # Get the first mode if there are multiple\n",
    "            df[column] = df[column].fillna(mode_value)\n",
    "    \n",
    "    print('After filling the null values:', df.isnull().sum().sum())\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def feature_engineering(dataframe):\n",
    "    df = dataframe.copy()\n",
    "    ## extract important features \n",
    "    df['horse_power'] = df['engine'].str.extract(r'(\\d+\\.\\d+)HP')\n",
    "    df['horse_power'] = df['horse_power'].astype(float)\n",
    "    \n",
    "    df['liters'] = df['engine'].str.extract(r'(\\d+\\.\\d+)L')\n",
    "    df['liters'] = df['liters'].astype(float)\n",
    "    \n",
    "    df['cylinders'] = df['engine'].str.extract(r'(\\d+) Cylinder')\n",
    "    df['cylinders'] = df['cylinders'].astype(float)\n",
    "    \n",
    "    df['turbo'] = df['engine'].str.extract(r'(turbo\\w*)' , flags=re.IGNORECASE)\n",
    "    df['turbo'] = np.where(df['turbo'] == 'Turbo', 1, 0)\n",
    "    \n",
    "    df['clean_title'] = np.where(df['clean_title'] == 'Yes', 1, 0)\n",
    "    df['accident'] = np.where(df['accident'] == 'At least 1 accident or damage reported', 1, 0)\n",
    "    \n",
    "    ## prepare new features indicating car features\n",
    "    df['car_age'] = 2024 - df['model_year']\n",
    "    df['Power_to_Weight_Ratio'] = df['horse_power'] / df['liters']\n",
    "    df['milage_per_year'] = np.where(df['car_age'] == 0, df['milage'], df['milage'] / df['car_age'])\n",
    "    \n",
    "    df.drop(columns = ['engine', 'model_year'], inplace = True)\n",
    "    \n",
    "    df = fill_null_with_median_or_mode(df)\n",
    "    \n",
    "    return df\n",
    "    \n",
    "    \n",
    "train_df = feature_engineering(train)\n",
    "test_df = feature_engineering(test)\n",
    "\n",
    "print(train_df.info())\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08a44ea4",
   "metadata": {
    "execution": {
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   "source": [
    "def encoding_scaling(datasets, _type = None):\n",
    "    df = datasets[0]\n",
    "    test = datasets[1].drop(['id'], axis = 1)\n",
    "    x = df.drop(['id', 'price'], axis = 1)\n",
    "    y = df['price']\n",
    "    \n",
    "    # Split into training and testing sets\n",
    "    X_train, X_val, y_train, y_val = train_test_split(x, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "    \n",
    "    object_cols = df.select_dtypes(include = ['object']).columns\n",
    "    if _type == 'target_encoding':\n",
    "        encoder = ce.TargetEncoder(cols = object_cols)\n",
    "    elif _type == 'frequency_encoding':\n",
    "        encoder = ce.CountEncoder(cols = object_cols)\n",
    "    else:\n",
    "        encoder = ce.TargetEncoder(cols = object_cols)\n",
    "        \n",
    "    encoder.fit(X_train, y_train)\n",
    "    \n",
    "    X_train = encoder.transform(X_train)\n",
    "    X_val = encoder.transform(X_val)\n",
    "    test = encoder.transform(test)\n",
    "    \n",
    "    scaler = MinMaxScaler()\n",
    "    X_train = pd.DataFrame(scaler.fit_transform(X_train), columns = x.columns)\n",
    "    X_val = pd.DataFrame(scaler.fit_transform(X_val), columns = x.columns)\n",
    "    test = pd.DataFrame(scaler.fit_transform(test), columns = test.columns)\n",
    "    \n",
    "    pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)\n",
    " \n",
    "    \n",
    "    return (X_train, X_val, y_train, y_val), test\n",
    "\n",
    "train, test = encoding_scaling([train_df, test_df])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076df915",
   "metadata": {
    "papermill": {
     "duration": 0.007011,
     "end_time": "2024-09-27T08:36:47.533084",
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     "start_time": "2024-09-27T08:36:47.526073",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Building ANN models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ff231c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:47.548897Z",
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     "exception": false,
     "start_time": "2024-09-27T08:36:47.540093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input dimension: 16\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "carPriceNN_1                             [1, 1]                    1,127,425\n",
       "├─Linear: 1-1                            [1, 2048]                 34,816\n",
       "├─ReLU: 1-2                              [1, 2048]                 --\n",
       "├─BatchNorm1d: 1-3                       [1, 2048]                 4,096\n",
       "├─Linear: 1-4                            [1, 1]                    2,049\n",
       "==========================================================================================\n",
       "Total params: 1,168,386\n",
       "Trainable params: 1,168,386\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 0.04\n",
       "==========================================================================================\n",
       "Input size (MB): 0.00\n",
       "Forward/backward pass size (MB): 0.03\n",
       "Params size (MB): 0.16\n",
       "Estimated Total Size (MB): 0.20\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class carPriceNN_1(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super().__init__()\n",
    "        self.fc0 = nn.Sequential(\n",
    "                        nn.Linear(input_dim, 4096),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm1d(4096), \n",
    "                        nn.Dropout(0.5))\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_dim, 2048)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.bn = nn.BatchNorm1d(2048)\n",
    "        \n",
    "        self.fc2 = nn.Linear(2048, 1)\n",
    "        \n",
    "        self.fc1_2 = nn.Sequential(\n",
    "                        nn.Linear(2048, 512),\n",
    "                        nn.ReLU(),\n",
    "#                         nn.BatchNorm1d(512), \n",
    "                        nn.Dropout(0.3))\n",
    "        \n",
    "        self.fc2_2 = nn.Linear(512, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "#         x = self.fc0(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.bn(x)\n",
    "        x = self.fc2(x)\n",
    "#         x = self.fc1_2(x)\n",
    "#         x = self.fc2_2(x)\n",
    "        return x\n",
    "    \n",
    "    \n",
    "# Create an instance of the model\n",
    "input_dim = train[0].shape[1] \n",
    "print(f\"Input dimension: {input_dim}\")\n",
    "# model = carPriceNN(input_dim).to(device)\n",
    "\n",
    "# Use torchinfo to get a summary of the model\n",
    "from torchinfo import summary\n",
    "summary(carPriceNN_1(input_dim).to(device), input_size=(1, input_dim)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8fc0257",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:47.949360Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input dimension: 16\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "carPriceNN_2                             [1, 1]                    --\n",
       "├─Sequential: 1-1                        [1, 256]                  --\n",
       "│    └─Linear: 2-1                       [1, 256]                  4,352\n",
       "│    └─ReLU: 2-2                         [1, 256]                  --\n",
       "│    └─BatchNorm1d: 2-3                  [1, 256]                  512\n",
       "│    └─Dropout: 2-4                      [1, 256]                  --\n",
       "├─Sequential: 1-2                        [1, 256]                  --\n",
       "│    └─Linear: 2-5                       [1, 256]                  65,792\n",
       "│    └─ReLU: 2-6                         [1, 256]                  --\n",
       "│    └─BatchNorm1d: 2-7                  [1, 256]                  512\n",
       "│    └─Dropout: 2-8                      [1, 256]                  --\n",
       "├─Sequential: 1-3                        [1, 128]                  --\n",
       "│    └─Linear: 2-9                       [1, 128]                  32,896\n",
       "│    └─ReLU: 2-10                        [1, 128]                  --\n",
       "│    └─BatchNorm1d: 2-11                 [1, 128]                  256\n",
       "│    └─Dropout: 2-12                     [1, 128]                  --\n",
       "├─Sequential: 1-4                        [1, 64]                   --\n",
       "│    └─Linear: 2-13                      [1, 64]                   8,256\n",
       "│    └─ReLU: 2-14                        [1, 64]                   --\n",
       "│    └─BatchNorm1d: 2-15                 [1, 64]                   128\n",
       "│    └─Dropout: 2-16                     [1, 64]                   --\n",
       "├─Sequential: 1-5                        [1, 32]                   --\n",
       "│    └─Linear: 2-17                      [1, 32]                   2,080\n",
       "│    └─ReLU: 2-18                        [1, 32]                   --\n",
       "│    └─BatchNorm1d: 2-19                 [1, 32]                   64\n",
       "│    └─Dropout: 2-20                     [1, 32]                   --\n",
       "├─Sequential: 1-6                        [1, 1]                    --\n",
       "│    └─Linear: 2-21                      [1, 1]                    33\n",
       "├─ReLU: 1-7                              [1, 1]                    --\n",
       "==========================================================================================\n",
       "Total params: 114,881\n",
       "Trainable params: 114,881\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 0.11\n",
       "==========================================================================================\n",
       "Input size (MB): 0.00\n",
       "Forward/backward pass size (MB): 0.01\n",
       "Params size (MB): 0.46\n",
       "Estimated Total Size (MB): 0.47\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class carPriceNN_2(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc1 = nn.Sequential(\n",
    "                        nn.Linear(input_dim, 256),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm1d(256), \n",
    "                        nn.Dropout(0.5))\n",
    "        \n",
    "        self.fc2 = nn.Sequential(\n",
    "                        nn.Linear(256, 256), \n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm1d(256),\n",
    "                        nn.Dropout(0.5))\n",
    "        \n",
    "        self.fc3 = nn.Sequential(\n",
    "                        nn.Linear(256, 128),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm1d(128),\n",
    "                        nn.Dropout(0.4))\n",
    "        \n",
    "        self.fc4 = nn.Sequential(\n",
    "                        nn.Linear(128, 64),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm1d(64),\n",
    "                        nn.Dropout(0.3))\n",
    "\n",
    "        self.fc5 = nn.Sequential(   \n",
    "                        nn.Linear(64, 32),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm1d(32),\n",
    "                        nn.Dropout(0.2))\n",
    "            \n",
    "        self.fc6 = nn.Sequential(\n",
    "                        nn.Linear(32, 1))\n",
    "        \n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "#         print('fc1', x.shape)\n",
    "        x = self.fc2(x)\n",
    "#         print('fc2', x.shape)\n",
    "        x = self.fc3(x)\n",
    "#         print('fc3', x.shape)\n",
    "        x = self.fc4(x)\n",
    "#         print('fc4', x.shape)\n",
    "        x = self.fc5(x)\n",
    "#         print('fc5', x.shape)\n",
    "        x = self.fc6(x)\n",
    "#         print('fc6', x.shape)\n",
    "        x = self.relu(x)  # Added sigmoid activation\n",
    "        return x\n",
    "\n",
    "# Create an instance of the model\n",
    "input_dim = train[0].shape[1]\n",
    "print(f\"Input dimension: {input_dim}\")\n",
    "# model = carPriceNN(input_dim).to(device)\n",
    "\n",
    "# Use torchinfo to get a summary of the model\n",
    "from torchinfo import summary\n",
    "summary(carPriceNN_2(input_dim).to(device), input_size=(1, input_dim))  # Changed batch size to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f16a383",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:47.996412Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 2048, 1024, 512)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "carPriceNN_3                             [1, 1]                    --\n",
       "├─ModuleList: 1-1                        --                        --\n",
       "│    └─Block: 2-1                        [1, 1024]                 --\n",
       "│    │    └─Sequential: 3-1              [1, 1024]                 19,456\n",
       "│    └─Block: 2-2                        [1, 512]                  --\n",
       "│    │    └─Sequential: 3-2              [1, 512]                  525,824\n",
       "│    └─Block: 2-3                        [1, 256]                  --\n",
       "│    │    └─Sequential: 3-3              [1, 256]                  131,840\n",
       "│    └─Block: 2-4                        [1, 128]                  --\n",
       "│    │    └─Sequential: 3-4              [1, 128]                  33,152\n",
       "├─Linear: 1-2                            [1, 1]                    129\n",
       "==========================================================================================\n",
       "Total params: 710,401\n",
       "Trainable params: 710,401\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 0.71\n",
       "==========================================================================================\n",
       "Input size (MB): 0.00\n",
       "Forward/backward pass size (MB): 0.03\n",
       "Params size (MB): 2.84\n",
       "Estimated Total Size (MB): 2.87\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def fcnn_Layers(startFeature, endFeature, input_dim):# = 0):\n",
    "    startPower = int(math.log2(startFeature))\n",
    "    endPower = int(math.log2(endFeature))\n",
    "    if input_dim != 0:\n",
    "        h_neuronsLayers = [input_dim]\n",
    "    else:\n",
    "        h_neuronsLayers = []\n",
    "        \n",
    "    while startFeature >= endFeature:\n",
    "        h_neuronsLayers.append(startFeature)\n",
    "        startFeature //= 2\n",
    "    return tuple(h_neuronsLayers)#, tuple(deCh)\n",
    "\n",
    "fcnn_list = fcnn_Layers(2048, 512, 1000)\n",
    "print(fcnn_list)\n",
    "\n",
    "class Block(Module):\n",
    "    def __init__(self, in_features, out_features):\n",
    "        super().__init__()\n",
    "        self.fc_block = nn.Sequential(\n",
    "        nn.Linear(in_features, out_features),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm1d(out_features),\n",
    "        nn.Dropout(0.5))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        fcnBlock = self.fc_block(x)\n",
    "        return fcnBlock\n",
    "# print(Block(64,32))\n",
    "\n",
    "class carPriceNN_3(nn.Module):\n",
    "    def __init__(self, input_dim, startFeature, endFeature, h_layers = 0):\n",
    "        super().__init__() \n",
    "        self.featureLayer_lists = fcnn_Layers(startFeature, endFeature, input_dim)\n",
    "        self.fcnBlock = ModuleList([Block(self.featureLayer_lists[i], self.featureLayer_lists[i+1]) \n",
    "                                  for i in range(len(self.featureLayer_lists) - 1)])\n",
    "        self.relu = nn.ReLU()\n",
    "        self.lastFc = nn.Linear(endFeature, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        blockOutputs = []\n",
    "        for block in self.fcnBlock:\n",
    "            # print(block)\n",
    "            x = block(x)\n",
    "            blockOutputs.append(x)\n",
    "        x = self.lastFc(x)\n",
    "        \n",
    "        return x #blockOutputs\n",
    "        \n",
    "\n",
    "from torchinfo import summary\n",
    "input_dim = train[0].shape[1]\n",
    "# model = carPriceNN(input_dim, 64, 1).to(device)\n",
    "summary(carPriceNN_3(input_dim, 1024, 128).to(device), input_size = (1, input_dim), depth = 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4bb70dc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:48.046426Z",
     "iopub.status.busy": "2024-09-27T08:36:48.046117Z",
     "iopub.status.idle": "2024-09-27T08:36:48.050316Z",
     "shell.execute_reply": "2024-09-27T08:36:48.049589Z"
    },
    "papermill": {
     "duration": 0.014836,
     "end_time": "2024-09-27T08:36:48.052186",
     "exception": false,
     "start_time": "2024-09-27T08:36:48.037350",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def rmse(y_pred, y):\n",
    "    return torch.sqrt(torch.mean((y_pred - y) ** 2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f442cdc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:48.069063Z",
     "iopub.status.busy": "2024-09-27T08:36:48.068750Z",
     "iopub.status.idle": "2024-09-27T08:36:48.085335Z",
     "shell.execute_reply": "2024-09-27T08:36:48.084536Z"
    },
    "papermill": {
     "duration": 0.027463,
     "end_time": "2024-09-27T08:36:48.087437",
     "exception": false,
     "start_time": "2024-09-27T08:36:48.059974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "def dataLoader(df, batch_size = 64, _type = None):\n",
    "    if _type == 'train':\n",
    "        x_tensor = torch.tensor(df[0].values, dtype = torch.float32)\n",
    "        y_tensor = torch.tensor(df[1].values, dtype = torch.float32)\n",
    "        dataset = TensorDataset(x_tensor, y_tensor)\n",
    "        data_loader = DataLoader(dataset, batch_size, shuffle = True)\n",
    "        return data_loader\n",
    "    elif _type == 'test':\n",
    "        df = torch.tensor(df.values, dtype = torch.float32)\n",
    "        return df\n",
    "    return \n",
    "\n",
    " \n",
    "X_train, X_val, y_train, y_val = train   \n",
    "trainLoader = dataLoader((X_train, y_train), batch_size = 1024, _type = 'train')\n",
    "valLoader = dataLoader((X_val, y_val), batch_size = 1024, _type = 'train')\n",
    "_test = dataLoader(test, _type = 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "899b9749",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:48.105701Z",
     "iopub.status.busy": "2024-09-27T08:36:48.104912Z",
     "iopub.status.idle": "2024-09-27T08:36:48.118546Z",
     "shell.execute_reply": "2024-09-27T08:36:48.117689Z"
    },
    "papermill": {
     "duration": 0.025053,
     "end_time": "2024-09-27T08:36:48.120444",
     "exception": false,
     "start_time": "2024-09-27T08:36:48.095391",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train(epoch, train_loader, criterion, model, device):\n",
    "    total_loss = 0\n",
    "    start_time = time.time()\n",
    "    model.train()\n",
    "    \n",
    "    for i, batch in enumerate(train_loader):\n",
    "        x, y = batch[0].to(device), batch[1].to(device)\n",
    "        y_pred = model(x)\n",
    "        \n",
    "        loss = torch.sqrt(criterion(y_pred.squeeze(), y))\n",
    "        total_loss += loss.item()\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    end_time = time.time()\n",
    "\n",
    "    return total_loss/len(train_loader)\n",
    "\n",
    "def val(epoch, val_loader, criterion, model, device = device, print_every = 0):\n",
    "    total_loss = 0\n",
    "    start_time = time.time()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        for i, batch in enumerate(val_loader):\n",
    "            x, y = batch[0].to(device), batch[1].to(device)\n",
    "            y_pred = model(x)\n",
    "            \n",
    "            #loss = criterion(y_pred.squeeze(), y)\n",
    "            loss = torch.sqrt(criterion(y_pred.squeeze(), y))\n",
    "            total_loss += loss.item()\n",
    "\n",
    "    return total_loss/len(val_loader)\n",
    "\n",
    "def plotResults(_train_loss, _test_loss):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.style.use(\"ggplot\")\n",
    "\n",
    "    plt.plot(_train_loss, label=\"train_loss\", marker='d')\n",
    "    plt.plot(_test_loss, label=\"test_loss\", marker='p')\n",
    "\n",
    "    plt.title(f\"Model Results\") \n",
    "    plt.xlabel(\"Epoch #\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a1b87aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:36:48.138208Z",
     "iopub.status.busy": "2024-09-27T08:36:48.137420Z",
     "iopub.status.idle": "2024-09-27T08:41:34.105357Z",
     "shell.execute_reply": "2024-09-27T08:41:34.104549Z"
    },
    "papermill": {
     "duration": 285.979281,
     "end_time": "2024-09-27T08:41:34.107702",
     "exception": false,
     "start_time": "2024-09-27T08:36:48.128421",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "Epoch [10/100], trainLoss: 82536.55420300388, valLoss: 80902.98943192829\n",
      "Epoch [20/100], trainLoss: 72347.02375545059, valLoss: 71908.86848958333\n",
      "Epoch [30/100], trainLoss: 68891.2816436531, valLoss: 68636.431640625\n",
      "Epoch [40/100], trainLoss: 68773.59600593508, valLoss: 68418.24738069283\n",
      "Epoch [50/100], trainLoss: 68518.45560804264, valLoss: 69110.0191830184\n",
      "Epoch [60/100], trainLoss: 68687.87219900679, valLoss: 67960.32757994186\n",
      "Epoch [70/100], trainLoss: 68277.85580184108, valLoss: 67505.39598473837\n",
      "Epoch [80/100], trainLoss: 68204.99565467538, valLoss: 67906.82008539244\n",
      "Epoch [90/100], trainLoss: 69042.17837027616, valLoss: 68446.71119488857\n",
      "Epoch [100/100], trainLoss: 68359.17405523256, valLoss: 68197.35130511143\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "train_loss = []\n",
    "val_loss = []\n",
    "Num_epochs = 100\n",
    "input_dim = X_train.shape[1]\n",
    "# Check if CUDA is available and set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "model = carPriceNN_1(input_dim).to(device)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor = 0.1, patience = 5)\n",
    "\n",
    "for epoch in range(Num_epochs):\n",
    "    loss = train(epoch, trainLoader, criterion, model, device = device)\n",
    "    train_loss.append(loss)\n",
    "    \n",
    "    loss = val(epoch, trainLoader, criterion, model, device = device)\n",
    "    val_loss.append(loss)\n",
    "    \n",
    "    if (epoch+1)%10 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{Num_epochs}], trainLoss: {train_loss[epoch]}, valLoss: {val_loss[epoch]}\")\n",
    "\n",
    "plotResults(train_loss, val_loss)\n",
    "# Predict on the test set\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    X_test_tensor = _test.to(device)\n",
    "    test_predictions = model(X_test_tensor).squeeze().cpu().numpy()\n",
    "\n",
    "# Prepare the submission file\n",
    "submission['price'] = test_predictions\n",
    "submission.to_csv(f'{model.__class__.__name__}_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "400d415a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:41:34.129246Z",
     "iopub.status.busy": "2024-09-27T08:41:34.128902Z",
     "iopub.status.idle": "2024-09-27T08:46:50.697295Z",
     "shell.execute_reply": "2024-09-27T08:46:50.696458Z"
    },
    "papermill": {
     "duration": 316.581562,
     "end_time": "2024-09-27T08:46:50.699718",
     "exception": false,
     "start_time": "2024-09-27T08:41:34.118156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "Epoch [10/100], trainLoss: 88275.93759084302, valLoss: 88119.60377301357\n",
      "Epoch [20/100], trainLoss: 88369.49742611434, valLoss: 88189.40634084302\n",
      "Epoch [30/100], trainLoss: 87661.92590237403, valLoss: 87728.10213783915\n",
      "Epoch [40/100], trainLoss: 87523.01635174418, valLoss: 87224.64344113372\n",
      "Epoch [50/100], trainLoss: 86872.20385174418, valLoss: 86863.42005813954\n",
      "Epoch [60/100], trainLoss: 85293.34241763566, valLoss: 85666.26671511628\n",
      "Epoch [70/100], trainLoss: 84848.45909035853, valLoss: 83910.57627785853\n",
      "Epoch [80/100], trainLoss: 84774.07670179264, valLoss: 84573.18407824612\n",
      "Epoch [90/100], trainLoss: 83020.60459060078, valLoss: 83008.39852834302\n",
      "Epoch [100/100], trainLoss: 82726.78806322675, valLoss: 82212.15161700582\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "train_loss = []\n",
    "val_loss = []\n",
    "Num_epochs = 100\n",
    "input_dim = X_train.shape[1]\n",
    "# Check if CUDA is available and set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "model = carPriceNN_2(input_dim).to(device)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "for epoch in range(Num_epochs):\n",
    "    loss = train(epoch, trainLoader, criterion, model, device = device)\n",
    "    train_loss.append(loss)\n",
    "    \n",
    "    loss = val(epoch, trainLoader, criterion, model, device = device)\n",
    "    val_loss.append(loss)\n",
    "    \n",
    "    if (epoch+1)%10 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{Num_epochs}], trainLoss: {train_loss[epoch]}, valLoss: {val_loss[epoch]}\")\n",
    "\n",
    "\n",
    "plotResults(train_loss, val_loss)\n",
    "\n",
    "# Predict on the test set\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    X_test_tensor = _test.to(device)\n",
    "    test_predictions = model(X_test_tensor).squeeze().cpu().numpy()\n",
    "\n",
    "# Prepare the submission file\n",
    "submission['price'] = test_predictions\n",
    "submission.to_csv(f'{model.__class__.__name__}_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "480b15d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-27T08:46:50.726641Z",
     "iopub.status.busy": "2024-09-27T08:46:50.725791Z",
     "iopub.status.idle": "2024-09-27T08:49:33.134233Z",
     "shell.execute_reply": "2024-09-27T08:49:33.133414Z"
    },
    "papermill": {
     "duration": 162.424172,
     "end_time": "2024-09-27T08:49:33.136561",
     "exception": false,
     "start_time": "2024-09-27T08:46:50.712389",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "Epoch [10/50], trainLoss: 88136.2300448159, valLoss: 87697.8894137597\n",
      "Epoch [20/50], trainLoss: 85083.08342417635, valLoss: 85229.14195736434\n",
      "Epoch [30/50], trainLoss: 82473.83808745156, valLoss: 134857.43883236434\n",
      "Epoch [40/50], trainLoss: 80169.52056080426, valLoss: 720598.144591812\n",
      "Epoch [50/50], trainLoss: 76024.50829699612, valLoss: 170263.32606589148\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "train_loss = []\n",
    "val_loss = []\n",
    "Num_epochs = 50\n",
    "earlyStop = 0\n",
    "input_dim = X_train.shape[1]\n",
    "# Check if CUDA is available and set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "model = carPriceNN_3(input_dim, 2048, 256).to(device)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "for epoch in range(Num_epochs):\n",
    "    loss = train(epoch, trainLoader, criterion, model, device = device)\n",
    "    train_loss.append(loss)\n",
    "    \n",
    "    loss = val(epoch, trainLoader, criterion, model, device = device)\n",
    "    val_loss.append(loss)\n",
    "    \n",
    "    if (epoch+1)%10 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{Num_epochs}], trainLoss: {train_loss[epoch]}, valLoss: {val_loss[epoch]}\")\n",
    "\n",
    "\n",
    "plotResults(train_loss, val_loss)\n",
    "# Predict on the test set\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    X_test_tensor = _test.to(device)\n",
    "    test_predictions = model(X_test_tensor).squeeze().cpu().numpy()\n",
    "\n",
    "# Prepare the submission file\n",
    "submission['price'] = test_predictions\n",
    "submission.to_csv(f'{model.__class__.__name__}_submission.csv', index=False)\n",
    "# print(submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f7167f",
   "metadata": {
    "papermill": {
     "duration": 0.013417,
     "end_time": "2024-09-27T08:49:33.163987",
     "exception": false,
     "start_time": "2024-09-27T08:49:33.150570",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "based on the results of all the ANN models, it was clearly that more complex models performed bad compared to a simple 2 layer ANN model. Probable reason is that the data is not complex and a simple 2 layer ANN models could capture most of the information in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08a3a8a4",
   "metadata": {
    "papermill": {
     "duration": 0.013431,
     "end_time": "2024-09-27T08:49:33.190668",
     "exception": false,
     "start_time": "2024-09-27T08:49:33.177237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "850c02d1",
   "metadata": {
    "papermill": {
     "duration": 0.0131,
     "end_time": "2024-09-27T08:49:33.217273",
     "exception": false,
     "start_time": "2024-09-27T08:49:33.204173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "databundleVersionId": 9057646,
     "sourceId": 76728,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30775,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 798.018341,
   "end_time": "2024-09-27T08:49:34.854210",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-09-27T08:36:16.835869",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}