F1nalyze_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KM9OnfT6LZQ9"
   },
   "source": [
    "**Loading the Datasets**"
   ]
  },
  {
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   "execution_count": 3,
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    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LGDrEWUGurwb",
    "outputId": "89e2edb8-c8de-4d87-8234-764313279e01"
   },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "UuHGxd97l8VR"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as plt\n",
    "import sklearn as sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "height": 377
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    "id": "rJBHdFaampKL",
    "outputId": "af673d61-7f92-468d-daab-e43583f5017c"
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      "<ipython-input-5-f93206acbd38>:1: DtypeWarning: Columns (4,13,14,16,47) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  train = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Datasets/train.csv')\n"
     ]
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       "   resultId  racerId  driverId  constructorId number  grid position_x  \\\n",
       "0     20025      833       642             51      2     1          1   \n",
       "1     20025      833       642             51      2     1          1   \n",
       "2     20025      833       642             51      2     1          1   \n",
       "3     20025      833       642             51      2     1          1   \n",
       "4     20025      833       642             51      2     1          1   \n",
       "\n",
       "  positionText_x  positionOrder  points  ...  points_y position  \\\n",
       "0              1              1     9.0  ...      3.33        3   \n",
       "1              1              1     9.0  ...      6.33        3   \n",
       "2              1              1     9.0  ...      6.33        4   \n",
       "3              1              1     9.0  ...     10.33        3   \n",
       "4              1              1     9.0  ...     10.33        4   \n",
       "\n",
       "  positionText_y wins constructorRef     company nationality_y  \\\n",
       "0              3    0           alfa  Alfa Romeo         Swiss   \n",
       "1              3    0           alfa  Alfa Romeo         Swiss   \n",
       "2              4    0           alfa  Alfa Romeo         Swiss   \n",
       "3              3    0           alfa  Alfa Romeo         Swiss   \n",
       "4              4    0           alfa  Alfa Romeo         Swiss   \n",
       "\n",
       "                                                 url    status  \\\n",
       "0  http://en.wikipedia.org/wiki/Alfa_Romeo_in_For...  Finished   \n",
       "1  http://en.wikipedia.org/wiki/Alfa_Romeo_in_For...  Finished   \n",
       "2  http://en.wikipedia.org/wiki/Alfa_Romeo_in_For...  Finished   \n",
       "3  http://en.wikipedia.org/wiki/Alfa_Romeo_in_For...  Finished   \n",
       "4  http://en.wikipedia.org/wiki/Alfa_Romeo_in_For...  Finished   \n",
       "\n",
       "   result_driver_standing  \n",
       "0               922731975  \n",
       "1               923172525  \n",
       "2               923833350  \n",
       "3               925195050  \n",
       "4               926576775  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "train = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Datasets/train.csv')\n",
    "train.head()"
   ]
  },
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    "id": "keu1XJvWwco5",
    "outputId": "d2600879-2a19-4f3c-fad6-db4d3d666727"
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     "text": [
      "<ipython-input-6-4e1af4bc4d41>:1: DtypeWarning: Columns (13,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  test = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Datasets/test.csv')\n"
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       "      <th>racerId</th>\n",
       "      <th>driverId</th>\n",
       "      <th>constructorId</th>\n",
       "      <th>number</th>\n",
       "      <th>grid</th>\n",
       "      <th>position_x</th>\n",
       "      <th>positionText_x</th>\n",
       "      <th>positionOrder</th>\n",
       "      <th>points</th>\n",
       "      <th>...</th>\n",
       "      <th>driverStandingsId</th>\n",
       "      <th>raceId_y</th>\n",
       "      <th>points_y</th>\n",
       "      <th>wins</th>\n",
       "      <th>constructorRef</th>\n",
       "      <th>company</th>\n",
       "      <th>nationality_y</th>\n",
       "      <th>url</th>\n",
       "      <th>status</th>\n",
       "      <th>result_driver_standing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23949</td>\n",
       "      <td>997</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12474</td>\n",
       "      <td>93</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>+1 Lap</td>\n",
       "      <td>298739826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23949</td>\n",
       "      <td>997</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12494</td>\n",
       "      <td>94</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>+1 Lap</td>\n",
       "      <td>299218806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23949</td>\n",
       "      <td>997</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12514</td>\n",
       "      <td>95</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>+1 Lap</td>\n",
       "      <td>299697786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23949</td>\n",
       "      <td>997</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12534</td>\n",
       "      <td>96</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>+1 Lap</td>\n",
       "      <td>300176766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23949</td>\n",
       "      <td>997</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12554</td>\n",
       "      <td>97</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>+1 Lap</td>\n",
       "      <td>300655746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
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       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
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       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "      border-radius: 50%;\n",
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       "      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",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\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-fb0d3aa9-02f4-402e-9bb9-3e1190c742fc 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-fb0d3aa9-02f4-402e-9bb9-3e1190c742fc');\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",
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       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-26e2150b-d297-4892-902f-974b71684a05\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26e2150b-d297-4892-902f-974b71684a05')\"\n",
       "            title=\"Suggest charts\"\n",
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       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-26e2150b-d297-4892-902f-974b71684a05 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   resultId  racerId  driverId  constructorId  number  grid position_x  \\\n",
       "0     23949      997         4              1      14    13          8   \n",
       "1     23949      997         4              1      14    13          8   \n",
       "2     23949      997         4              1      14    13          8   \n",
       "3     23949      997         4              1      14    13          8   \n",
       "4     23949      997         4              1      14    13          8   \n",
       "\n",
       "  positionText_x  positionOrder  points  ...  driverStandingsId raceId_y  \\\n",
       "0              8              8     4.0  ...              12474       93   \n",
       "1              8              8     4.0  ...              12494       94   \n",
       "2              8              8     4.0  ...              12514       95   \n",
       "3              8              8     4.0  ...              12534       96   \n",
       "4              8              8     4.0  ...              12554       97   \n",
       "\n",
       "  points_y wins  constructorRef  company nationality_y  \\\n",
       "0     16.0    0         mclaren  McLaren       British   \n",
       "1     21.0    0         mclaren  McLaren       British   \n",
       "2     21.0    0         mclaren  McLaren       British   \n",
       "3     25.0    0         mclaren  McLaren       British   \n",
       "4     25.0    0         mclaren  McLaren       British   \n",
       "\n",
       "                                    url  status  result_driver_standing  \n",
       "0  http://en.wikipedia.org/wiki/McLaren  +1 Lap               298739826  \n",
       "1  http://en.wikipedia.org/wiki/McLaren  +1 Lap               299218806  \n",
       "2  http://en.wikipedia.org/wiki/McLaren  +1 Lap               299697786  \n",
       "3  http://en.wikipedia.org/wiki/McLaren  +1 Lap               300176766  \n",
       "4  http://en.wikipedia.org/wiki/McLaren  +1 Lap               300655746  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Datasets/test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wVF6vMUKwj1Q",
    "outputId": "7d912733-13ee-4f96-a243-2097084e0294"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2830101, 55)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PsN6JRdsxjwm",
    "outputId": "f3a12f14-6457-4ed4-d4a9-5067e0d63607"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(352928, 53)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rtW3QXZ2xlPh",
    "outputId": "1f283f4f-8054-4773-81d9-d54020e5fbf8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns present in train but absent in test:\n",
      "{'positionText_y', 'position'}\n"
     ]
    }
   ],
   "source": [
    "# Find columns that are in train but not in test\n",
    "missing_in_test = set(train.columns) - set(test.columns)\n",
    "\n",
    "# Print the missing columns\n",
    "print(\"Columns present in train but absent in test:\")\n",
    "print(missing_in_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Aj-U2lmWyevh",
    "outputId": "a1162af7-d525-4864-b6b6-7d5594d95384"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resultId                  0\n",
       "racerId                   0\n",
       "driverId                  0\n",
       "constructorId             0\n",
       "number                    0\n",
       "grid                      0\n",
       "position_x                0\n",
       "positionText_x            0\n",
       "positionOrder             0\n",
       "points                    0\n",
       "laps                      0\n",
       "time_x                    0\n",
       "timetaken_in_millisec     0\n",
       "fastestLap                0\n",
       "rank                      0\n",
       "fastestLapTime            0\n",
       "max_speed                 0\n",
       "statusId                  0\n",
       "year                      0\n",
       "round                     0\n",
       "circuitId                 0\n",
       "grand_prix                0\n",
       "date                      0\n",
       "time_y                    0\n",
       "url_x                     0\n",
       "fp1_date                  0\n",
       "fp1_time                  0\n",
       "fp2_date                  0\n",
       "fp2_time                  0\n",
       "fp3_date                  0\n",
       "fp3_time                  0\n",
       "quali_date                0\n",
       "quali_time                0\n",
       "sprint_date               0\n",
       "sprint_time               0\n",
       "driverRef                 0\n",
       "driver_num                0\n",
       "driver_code               0\n",
       "forename                  0\n",
       "surname                   0\n",
       "dob                       0\n",
       "nationality               0\n",
       "url_y                     0\n",
       "driverStandingsId         0\n",
       "raceId_y                  0\n",
       "points_y                  0\n",
       "position                  0\n",
       "positionText_y            0\n",
       "wins                      0\n",
       "constructorRef            0\n",
       "company                   0\n",
       "nationality_y             0\n",
       "url                       0\n",
       "status                    0\n",
       "result_driver_standing    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "B5oGjzyp0eEH"
   },
   "outputs": [],
   "source": [
    "columns_with_nulls = ['status', 'result_driver_standing']\n",
    "for column in columns_with_nulls:\n",
    "    train[column].fillna(train[column].mode()[0], inplace=True)\n",
    "    test[column].fillna(test[column].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CRinCbi-0Xr3",
    "outputId": "372c1713-b911-495d-9053-33a0c017704b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resultId                  0\n",
       "racerId                   0\n",
       "driverId                  0\n",
       "constructorId             0\n",
       "number                    0\n",
       "grid                      0\n",
       "position_x                0\n",
       "positionText_x            0\n",
       "positionOrder             0\n",
       "points                    0\n",
       "laps                      0\n",
       "time_x                    0\n",
       "timetaken_in_millisec     0\n",
       "fastestLap                0\n",
       "rank                      0\n",
       "fastestLapTime            0\n",
       "max_speed                 0\n",
       "statusId                  0\n",
       "year                      0\n",
       "round                     0\n",
       "circuitId                 0\n",
       "grand_prix                0\n",
       "date                      0\n",
       "time_y                    0\n",
       "url_x                     0\n",
       "fp1_date                  0\n",
       "fp1_time                  0\n",
       "fp2_date                  0\n",
       "fp2_time                  0\n",
       "fp3_date                  0\n",
       "fp3_time                  0\n",
       "quali_date                0\n",
       "quali_time                0\n",
       "sprint_date               0\n",
       "sprint_time               0\n",
       "driverRef                 0\n",
       "driver_num                0\n",
       "driver_code               0\n",
       "forename                  0\n",
       "surname                   0\n",
       "dob                       0\n",
       "nationality               0\n",
       "url_y                     0\n",
       "driverStandingsId         0\n",
       "raceId_y                  0\n",
       "points_y                  0\n",
       "wins                      0\n",
       "constructorRef            0\n",
       "company                   0\n",
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       "url                       0\n",
       "status                    0\n",
       "result_driver_standing    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "VURcso6g22JY"
   },
   "outputs": [],
   "source": [
    "def count_backslash_n(df):\n",
    "    return (df == '\\\\N').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MxiZXSAIKZV_",
    "outputId": "0ee63b0c-c6c6-4080-cc53-66a92d233801"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occurrences of '\\N' in Train Dataset:\n",
      "resultId                        0\n",
      "racerId                         0\n",
      "driverId                        0\n",
      "constructorId                   0\n",
      "number                        160\n",
      "grid                            0\n",
      "position_x                1125357\n",
      "positionText_x                  0\n",
      "positionOrder                   0\n",
      "points                          0\n",
      "laps                            0\n",
      "time_x                    1871686\n",
      "timetaken_in_millisec     1871741\n",
      "fastestLap                2112010\n",
      "rank                      2105375\n",
      "fastestLapTime            2112010\n",
      "max_speed                 2112010\n",
      "statusId                        0\n",
      "year                            0\n",
      "round                           0\n",
      "circuitId                       0\n",
      "grand_prix                      0\n",
      "date                            0\n",
      "time_y                    2150026\n",
      "url_x                           0\n",
      "fp1_date                  2830101\n",
      "fp1_time                  2830101\n",
      "fp2_date                  2830101\n",
      "fp2_time                  2830101\n",
      "fp3_date                  2830101\n",
      "fp3_time                  2830101\n",
      "quali_date                2830101\n",
      "quali_time                2830101\n",
      "sprint_date               2830101\n",
      "sprint_time               2830101\n",
      "driverRef                       0\n",
      "driver_num                2354922\n",
      "driver_code               1731200\n",
      "forename                        0\n",
      "surname                         0\n",
      "dob                             0\n",
      "nationality                     0\n",
      "url_y                           0\n",
      "driverStandingsId               0\n",
      "raceId_y                        0\n",
      "points_y                        0\n",
      "position                        0\n",
      "positionText_y                  0\n",
      "wins                            0\n",
      "constructorRef                  0\n",
      "company                         0\n",
      "nationality_y                   0\n",
      "url                             0\n",
      "status                          0\n",
      "result_driver_standing          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train_backslash_n_counts = count_backslash_n(train)\n",
    "print(\"Occurrences of '\\\\N' in Train Dataset:\")\n",
    "print(train_backslash_n_counts)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L_JYDEXBKd5x",
    "outputId": "a9a81363-9a4b-4253-f1f2-bdc12ae1c24d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Occurrences of '\\N' in Test Dataset:\n",
      "resultId                       0\n",
      "racerId                        0\n",
      "driverId                       0\n",
      "constructorId                  0\n",
      "number                         0\n",
      "grid                           0\n",
      "position_x                 45237\n",
      "positionText_x                 0\n",
      "positionOrder                  0\n",
      "points                         0\n",
      "laps                           0\n",
      "time_x                    145425\n",
      "timetaken_in_millisec     145425\n",
      "fastestLap                 12225\n",
      "rank                           0\n",
      "fastestLapTime             12225\n",
      "max_speed                  12225\n",
      "statusId                       0\n",
      "year                           0\n",
      "round                          0\n",
      "circuitId                      0\n",
      "grand_prix                     0\n",
      "date                           0\n",
      "time_y                         0\n",
      "url_x                          0\n",
      "fp1_date                  177237\n",
      "fp1_time                  249976\n",
      "fp2_date                  177237\n",
      "fp2_time                  249976\n",
      "fp3_date                  204987\n",
      "fp3_time                  267989\n",
      "quali_date                177237\n",
      "quali_time                249976\n",
      "sprint_date               325178\n",
      "sprint_time               334915\n",
      "driverRef                      0\n",
      "driver_num                     0\n",
      "driver_code                    0\n",
      "forename                       0\n",
      "surname                        0\n",
      "dob                            0\n",
      "nationality                    0\n",
      "url_y                          0\n",
      "driverStandingsId              0\n",
      "raceId_y                       0\n",
      "points_y                       0\n",
      "wins                           0\n",
      "constructorRef                 0\n",
      "company                        0\n",
      "nationality_y                  0\n",
      "url                            0\n",
      "status                         0\n",
      "result_driver_standing         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the occurrences of '\\N' in each column of the test dataset\n",
    "test_backslash_n_counts = count_backslash_n(test)\n",
    "print(\"\\nOccurrences of '\\\\N' in Test Dataset:\")\n",
    "print(test_backslash_n_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FnacnvAYKpIS",
    "outputId": "c00a163e-ca00-40cd-f907-e864561ae64d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['resultId', 'racerId', 'driverId', 'constructorId', 'grid',\n",
       "       'positionText_x', 'positionOrder', 'points', 'laps', 'statusId', 'year',\n",
       "       'round', 'circuitId', 'grand_prix', 'date', 'url_x', 'driverRef',\n",
       "       'forename', 'surname', 'dob', 'nationality', 'url_y',\n",
       "       'driverStandingsId', 'raceId_y', 'points_y', 'position',\n",
       "       'positionText_y', 'wins', 'constructorRef', 'company', 'nationality_y',\n",
       "       'url', 'status', 'result_driver_standing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop_train = train_backslash_n_counts[train_backslash_n_counts > 100].index\n",
    "#dropping the columns having \\N greater than 100\n",
    "train_temp = train.drop(columns=columns_to_drop_train)\n",
    "train_temp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "l4mYYXJ1ZF1x",
    "outputId": "be9b87e8-c4fa-4c18-eb5a-d2098d712446"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset with specified columns:\n"
     ]
    },
    {
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       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-671d68bf-0281-4825-8f53-6f909fb528d2 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "  <div id=\"id_0b5804bf-7d79-4183-be76-66f6b7e86faf\">\n",
       "    <style>\n",
       "      .colab-df-generate {\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-generate: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-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate: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",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_cleaned')\"\n",
       "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_0b5804bf-7d79-4183-be76-66f6b7e86faf button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('train_cleaned');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "         grid positionText_x  points  laps  round nationality  points_y  \\\n",
       "0           1              1     9.0    70      1     Italian      3.33   \n",
       "1           1              1     9.0    70      1     Italian      6.33   \n",
       "2           1              1     9.0    70      1     Italian      6.33   \n",
       "3           1              1     9.0    70      1     Italian     10.33   \n",
       "4           1              1     9.0    70      1     Italian     10.33   \n",
       "...       ...            ...     ...   ...    ...         ...       ...   \n",
       "2830096     4              2    18.0    71     19  Australian     61.50   \n",
       "2830097     4              2    18.0    71     19  Australian     69.50   \n",
       "2830098     4              2    18.0    71     19  Australian      4.00   \n",
       "2830099     4              2    18.0    71     19  Australian      6.00   \n",
       "2830100     4              2    18.0    71     19  Australian     24.00   \n",
       "\n",
       "         position  wins     company    status  \n",
       "0               3     0  Alfa Romeo  Finished  \n",
       "1               3     0  Alfa Romeo  Finished  \n",
       "2               4     0  Alfa Romeo  Finished  \n",
       "3               3     0  Alfa Romeo  Finished  \n",
       "4               4     0  Alfa Romeo  Finished  \n",
       "...           ...   ...         ...       ...  \n",
       "2830096         4     2    Red Bull  Finished  \n",
       "2830097         4     2    Red Bull  Finished  \n",
       "2830098         8     0    Red Bull  Finished  \n",
       "2830099        10     0    Red Bull  Finished  \n",
       "2830100         8     0    Red Bull  Finished  \n",
       "\n",
       "[2830101 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the columns to include\n",
    "columns_to_include = [\"grid\", \"positionText_x\", \"points\", \"laps\", \"round\", \"nationality\", \"points_y\", \"position\", \"wins\", \"company\", \"status\"]\n",
    "\n",
    "# Select the specified columns from the train dataset\n",
    "train_cleaned = train[columns_to_include]\n",
    "\n",
    "# Display the cleaned train dataset\n",
    "print(\"Train Dataset with specified columns:\")\n",
    "train_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ChWIqeys12DL",
    "outputId": "4cfe24d4-0f2d-48b0-b2db-1831c510a1ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2830101 entries, 0 to 2830100\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   grid            int64  \n",
      " 1   positionText_x  object \n",
      " 2   points          float64\n",
      " 3   laps            int64  \n",
      " 4   round           int64  \n",
      " 5   nationality     object \n",
      " 6   points_y        float64\n",
      " 7   position        int64  \n",
      " 8   wins            int64  \n",
      " 9   company         object \n",
      " 10  status          object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 237.5+ MB\n"
     ]
    }
   ],
   "source": [
    "train_cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YiwnPuAT6EOa",
    "outputId": "ac7de854-ff46-4910-8c2a-62cd75998ac4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'positionText_x':\n",
      " ['1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' 'R' 'N' 'W' '12' '13' '14'\n",
      " '15' '16' '17' '18' '19' '20' '21' '22' '23' '24' 'D' 'F' '25' '26' '27'\n",
      " '28' '29' '30' '31' '32' '33' 'E'] \n",
      "\n",
      "Unique values in column 'nationality':\n",
      " ['Italian' 'British' 'French' 'Belgian' 'Argentine' 'Irish' 'Thai' 'Swiss'\n",
      " 'Monegasque' 'American' 'German' 'Brazilian' 'Spanish' 'Australian'\n",
      " 'Uruguayan' 'East German' 'Dutch' 'Swedish' 'Argentine-Italian'\n",
      " 'New Zealander' 'Portuguese' 'Venezuelan' 'American-Italian'\n",
      " 'South African' 'Mexican' 'Canadian' 'Rhodesian' 'Austrian'\n",
      " 'Liechtensteiner' 'Danish' 'Finnish' 'Japanese' 'Chilean' 'Colombian'\n",
      " 'Czech' 'Malaysian' 'Hungarian' 'Indian' 'Polish' 'Russian'] \n",
      "\n",
      "Unique values in column 'company':\n",
      " ['Alfa Romeo' 'Talbot-Lago' 'ERA' 'Maserati' 'Alta' 'Ferrari' 'Simca'\n",
      " 'Cooper' 'Kurtis Kraft' 'Deidt' 'Moore' 'Lesovsky' 'Nichels' 'Marchese'\n",
      " 'Stevens' 'Langley' 'Ewing' 'Rae' 'Olson' 'Wetteroth' 'Snowberger'\n",
      " 'Adams' 'Watson' 'Milano' 'HWM' 'Veritas' 'Sherman' 'Schroeder' 'Kuzma'\n",
      " 'Pawl' 'Hall' 'Bromme' 'Trevis' 'BRM' 'OSCA' 'Gordini' 'Frazer Nash'\n",
      " 'AFM' 'Aston Butterworth' 'Connaught' 'BMW' 'Cisitalia' 'Turner'\n",
      " 'Del Roy' 'EMW' 'Phillips' 'Pankratz' 'Mercedes' 'Vanwall' 'Klenk'\n",
      " 'Lancia' 'Epperly' 'Arzani-Volpini' 'Bugatti' 'Emeryson' 'Dunn' 'Porsche'\n",
      " 'Team Lotus' 'Cooper-Climax' 'Cooper-Maserati' 'Elder' 'Christensen'\n",
      " 'Sutton' 'Aston Martin' 'Cooper-Borgward' 'JBW' 'Fry' 'Cooper-OSCA'\n",
      " 'Tec-Mec' 'Behra-Porsche' 'Scarab' 'Cooper-Castellotti' 'Meskowski'\n",
      " 'Lotus-Climax' 'De Tomaso-Osca' 'Gilby' 'Ferguson' 'MBM' 'Lotus-Maserati'\n",
      " 'De Tomaso-Alfa Romeo' 'Lola' 'Lotus-BRM' 'ENB' 'Brabham' 'De Tomaso'\n",
      " 'Lotus-Borgward' 'LDS' 'Cooper-Alfa Romeo' 'ATS' 'Scirocco' 'BRP'\n",
      " 'De Tomaso-Ferrari' 'Stebro' 'Lotus-Ford' 'Brabham-Climax' 'Brabham-BRM'\n",
      " 'Brabham-Ford' 'Honda' 'Derrington' 'LDS-Alfa Romeo' 'Cooper-Ford'\n",
      " 'LDS-Climax' 'RE' 'Brabham-Repco' 'McLaren-Ford' 'Eagle-Climax'\n",
      " 'McLaren-Serenissima' 'Cooper-Ferrari' 'Shannon' 'Eagle-Weslake'\n",
      " 'McLaren-BRM' 'Matra' 'Cooper-ATS' 'Protos' 'Matra-Ford' 'Cooper-BRM'\n",
      " 'McLaren' 'BRM-Ford' 'March' 'McLaren-Alfa Romeo' 'Bellasi' 'Surtees'\n",
      " 'Tyrrell' 'March-Ford' 'March-Alfa Romeo' 'Lotus-Pratt &amp; Whitney'\n",
      " 'Tecno' 'Politoys' 'Connew' 'Iso Marlboro' 'Shadow' 'Ensign' 'Hesketh'\n",
      " 'Trojan' 'Amon' 'Token' 'Lyncar' 'Maki' 'Parnelli' 'Penske' 'Shadow-Ford'\n",
      " 'Williams' 'Fittipaldi' 'Embassy Hill' 'Shadow-Matra' 'Wolf'\n",
      " 'Brabham-Alfa Romeo' 'Ligier' 'Boro' 'Kojima' 'LEC' 'Renault' 'McGuire'\n",
      " 'Apollon' 'Merzario' 'Theodore' 'Arrows' 'Martini' 'Kauhsen' 'Rebaque'\n",
      " 'Osella' 'Toleman' 'RAM' 'Spirit' 'Minardi' 'Zakspeed' 'Benetton' 'AGS'\n",
      " 'Larrousse' 'Coloni' 'Rial' 'Euro Brun' 'Dallara' 'Onyx' 'Leyton House'\n",
      " 'Life' 'Lambo' 'Jordan' 'Footwork' 'Fondmetal' 'Andrea Moda' 'Sauber'\n",
      " 'Simtek' 'Pacific' 'Forti' 'Prost' 'Stewart' 'BAR' 'Jaguar' 'Toyota'\n",
      " 'Red Bull' 'Toro Rosso' 'BMW Sauber' 'MF1' 'Super Aguri' 'Spyker MF1'\n",
      " 'Spyker' 'Force India' 'Brawn' 'Lotus' 'HRT' 'Virgin' 'Lotus F1'\n",
      " 'Marussia' 'Caterham'] \n",
      "\n",
      "Unique values in column 'status':\n",
      " ['Finished' '+2 Laps' '+3 Laps' '+5 Laps' '+6 Laps' 'Oil leak'\n",
      " 'Not classified' 'Out of fuel' 'Engine' 'Transmission' 'Clutch'\n",
      " 'Oil pressure' 'Gearbox' 'Supercharger' '+1 Lap' 'Axle' 'Accident'\n",
      " 'Collision' 'Spun off' '+8 Laps' '+10 Laps' '+11 Laps' '+12 Laps'\n",
      " '+13 Laps' '+15 Laps' '+16 Laps' 'Oil line' 'Wheel bearing' 'Stalled'\n",
      " '+4 Laps' '+7 Laps' 'Suspension' 'Oil pump' 'Oil pipe' 'Overheating'\n",
      " 'Fuel pump' 'Radiator' 'Retired' 'Brakes' 'Water pipe' 'Withdrew'\n",
      " 'Magneto' 'Ignition' 'Fuel system' '+20 Laps' 'Heat shield fire'\n",
      " 'Fuel leak' '+19 Laps' '+22 Laps' '+9 Laps' 'Wheel' 'Halfshaft'\n",
      " 'Steering' '+18 Laps' 'Differential' 'Turbo' 'Exhaust' '+14 Laps'\n",
      " 'Disqualified' 'Electrical' 'Did not qualify' '+17 Laps' '+24 Laps'\n",
      " 'Throttle' 'Physical' '+46 Laps' 'Injection' 'Fuel pressure' 'Tyre'\n",
      " 'Chassis' 'Water leak' 'Water pump' 'Distributor' 'Handling' '+30 Laps'\n",
      " '+44 Laps' 'Vibrations' 'Puncture' 'Battery' '+21 Laps' '+23 Laps'\n",
      " '+25 Laps' 'Driver unwell' '+29 Laps' 'Did not prequalify' 'Alternator'\n",
      " 'Injury' 'Broken wing' 'Fuel pipe' 'Fatal accident' 'Eye injury'\n",
      " 'Injured' 'Mechanical' 'Spark plugs' 'CV joint' 'Excluded' 'Driveshaft'\n",
      " 'Hydraulics' 'Fire' 'Safety belt' '107% Rule' 'Fuel' 'Underweight'\n",
      " 'Rear wing' 'Safety concerns' 'Not restarted' 'Crankshaft' 'Fuel rig'\n",
      " 'Wheel rim' 'Power loss' 'Safety' 'Electronics' 'Drivetrain'\n",
      " 'Launch control' 'Pneumatics' 'Engine fire' '+26 Laps' 'Tyre puncture'\n",
      " 'Wheel nut' 'Technical' 'Track rod' 'Front wing' 'Water pressure'\n",
      " 'Refuelling' 'Driver Seat' '+42 Laps' 'Engine misfire' 'Collision damage'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# List of columns you are interested in\n",
    "columns_of_interest = ['positionText_x', 'nationality', 'company','status']\n",
    "\n",
    "# Find and print unique values for each specified column\n",
    "for column in columns_of_interest:\n",
    "    unique_values = train_cleaned[column].unique()\n",
    "    print(f\"Unique values in column '{column}':\\n\", unique_values, \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZH7CKL2u68rs",
    "outputId": "ff850dfc-90bc-4a2d-b5fe-07e3d1429bd6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-2eae6ffcafd6>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_cleaned[column] = label_encoder.fit_transform(train_cleaned[column])\n",
      "<ipython-input-8-2eae6ffcafd6>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_cleaned[column] = label_encoder.fit_transform(train_cleaned[column])\n",
      "<ipython-input-8-2eae6ffcafd6>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_cleaned[column] = label_encoder.fit_transform(train_cleaned[column])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   grid  positionText_x  points  laps  round  nationality  points_y  position  \\\n",
      "0     1               0     9.0    70      1           22      3.33         3   \n",
      "1     1               0     9.0    70      1           22      6.33         3   \n",
      "2     1               0     9.0    70      1           22      6.33         4   \n",
      "3     1               0     9.0    70      1           22     10.33         3   \n",
      "4     1               0     9.0    70      1           22     10.33         4   \n",
      "\n",
      "   wins  company  status  \n",
      "0     0        4      62  \n",
      "1     0        4      62  \n",
      "2     0        4      62  \n",
      "3     0        4      62  \n",
      "4     0        4      62  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-2eae6ffcafd6>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_cleaned[column] = label_encoder.fit_transform(train_cleaned[column])\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "columns_to_encode = ['positionText_x', 'nationality', 'company', 'status']\n",
    "for column in columns_to_encode:\n",
    "    train_cleaned[column] = label_encoder.fit_transform(train_cleaned[column])\n",
    "\n",
    "print(train_cleaned.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bmUTEpRfCaDk"
   },
   "source": [
    "**Testing Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
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       "\n",
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       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
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       "  }\n",
       "\n",
       "  @keyframes spin {\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "  }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a489b5a8-b2d9-4ee3-9a75-baa9b2c1c4a4 button');\n",
       "      quickchartButtonEl.style.display =\n",
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       "\n",
       "      .colab-df-generate:hover {\n",
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       "      [theme=dark] .colab-df-generate {\n",
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       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_cleaned')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "        document.querySelector('#id_852c9f3b-88a5-43eb-b7a3-98d1137d01e6 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "        grid positionText_x  points  laps  round nationality  points_y  wins  \\\n",
       "0         13              8     4.0    70      9     Spanish      16.0     0   \n",
       "1         13              8     4.0    70      9     Spanish      21.0     0   \n",
       "2         13              8     4.0    70      9     Spanish      21.0     0   \n",
       "3         13              8     4.0    70      9     Spanish      25.0     0   \n",
       "4         13              8     4.0    70      9     Spanish      25.0     0   \n",
       "...      ...            ...     ...   ...    ...         ...       ...   ...   \n",
       "352923     5              R     0.0     0     12  Australian       5.0     0   \n",
       "352924     5              R     0.0     0     12  Australian       5.0     0   \n",
       "352925     5              R     0.0     0     12  Australian      17.0     0   \n",
       "352926     5              R     0.0     0     12  Australian      27.0     0   \n",
       "352927     5              R     0.0     0     12  Australian      34.0     0   \n",
       "\n",
       "        company            status  \n",
       "0       McLaren            +1 Lap  \n",
       "1       McLaren            +1 Lap  \n",
       "2       McLaren            +1 Lap  \n",
       "3       McLaren            +1 Lap  \n",
       "4       McLaren            +1 Lap  \n",
       "...         ...               ...  \n",
       "352923  McLaren  Collision damage  \n",
       "352924  McLaren  Collision damage  \n",
       "352925  McLaren  Collision damage  \n",
       "352926  McLaren  Collision damage  \n",
       "352927  McLaren  Collision damage  \n",
       "\n",
       "[352928 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_include = [\"grid\", \"positionText_x\", \"points\", \"laps\", \"round\", \"nationality\", \"points_y\", \"wins\", \"company\", \"status\"]\n",
    "test_cleaned = test[columns_to_include]\n",
    "test_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1uSC0BoIAtwN",
    "outputId": "e8c126fd-1533-436c-fab5-2948d59d8bf8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occurrences of '\\N' in Train Dataset:\n",
      "grid              0\n",
      "positionText_x    0\n",
      "points            0\n",
      "laps              0\n",
      "round             0\n",
      "nationality       0\n",
      "points_y          0\n",
      "wins              0\n",
      "company           0\n",
      "status            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "test_backslash_n_counts = count_backslash_n(test_cleaned)\n",
    "print(\"Occurrences of '\\\\N' in Train Dataset:\")\n",
    "print(test_backslash_n_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nJSv2J-6-5G7",
    "outputId": "7e558703-b392-4e2c-a97a-bb6f3a869971"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   grid  positionText_x  points  laps  round  nationality  points_y  wins  \\\n",
      "0    13              18     4.0    70      9           19      16.0     0   \n",
      "1    13              18     4.0    70      9           19      21.0     0   \n",
      "2    13              18     4.0    70      9           19      21.0     0   \n",
      "3    13              18     4.0    70      9           19      25.0     0   \n",
      "4    13              18     4.0    70      9           19      25.0     0   \n",
      "\n",
      "   company  status  \n",
      "0        7       0  \n",
      "1        7       0  \n",
      "2        7       0  \n",
      "3        7       0  \n",
      "4        7       0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-b155a5fbebdc>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_cleaned[column] = label_encoder.fit_transform(test_cleaned[column])\n",
      "<ipython-input-10-b155a5fbebdc>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_cleaned[column] = label_encoder.fit_transform(test_cleaned[column])\n",
      "<ipython-input-10-b155a5fbebdc>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_cleaned[column] = label_encoder.fit_transform(test_cleaned[column])\n",
      "<ipython-input-10-b155a5fbebdc>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_cleaned[column] = label_encoder.fit_transform(test_cleaned[column])\n"
     ]
    }
   ],
   "source": [
    "for column in columns_to_encode:\n",
    "    test_cleaned[column] = label_encoder.fit_transform(test_cleaned[column])\n",
    "\n",
    "print(test_cleaned.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MuJS6GYHMtMY"
   },
   "source": [
    "**Working with validation dataset to check rmse**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d_-4nmNcJJnp",
    "outputId": "4ac667db-06ff-494a-af72-089acc1a1229"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-4f0f5184d2a3>:1: DtypeWarning: Columns (13,16,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  validation = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Datasets/validation.csv')\n",
      "<ipython-input-11-4f0f5184d2a3>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_cleaned[column] = label_encoder.fit_transform(validation_cleaned[column])\n",
      "<ipython-input-11-4f0f5184d2a3>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_cleaned[column] = label_encoder.fit_transform(validation_cleaned[column])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   grid  positionText_x  points  laps  round  nationality  points_y  position  \\\n",
      "0     4              11    18.0    71     19            1      28.0         8   \n",
      "1     4              11    18.0    71     19            1      53.0         4   \n",
      "2     4              11    18.0    71     19            1      78.0         1   \n",
      "3     4              11    18.0    71     19            1      93.0         1   \n",
      "4     4              11    18.0    71     19            1     103.0         3   \n",
      "\n",
      "   wins  company  status  \n",
      "0     0        9      22  \n",
      "1     1        9      22  \n",
      "2     2        9      22  \n",
      "3     2        9      22  \n",
      "4     2        9      22  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-4f0f5184d2a3>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_cleaned[column] = label_encoder.fit_transform(validation_cleaned[column])\n",
      "<ipython-input-11-4f0f5184d2a3>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_cleaned[column] = label_encoder.fit_transform(validation_cleaned[column])\n"
     ]
    }
   ],
   "source": [
    "validation = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Datasets/validation.csv')\n",
    "columns_to_include = [\"grid\", \"positionText_x\", \"points\", \"laps\", \"round\", \"nationality\", \"points_y\", \"position\", \"wins\", \"company\", \"status\"]\n",
    "validation_cleaned = validation[columns_to_include]\n",
    "validation_cleaned\n",
    "\n",
    "for column in columns_to_encode:\n",
    "    validation_cleaned[column] = label_encoder.fit_transform(validation_cleaned[column])\n",
    "\n",
    "print(validation_cleaned.head())\n",
    "\n",
    "X_val = validation_cleaned.drop('position', axis=1)\n",
    "y_val = validation_cleaned['position']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "DX9P7-r7BbGA"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "TfI2HfMXPmb8"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "SuXq8Y7AB_sZ"
   },
   "outputs": [],
   "source": [
    "X_train = train_cleaned.drop('position', axis=1)\n",
    "y_train = train_cleaned['position']\n",
    "\n",
    "X_test = test_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6jz7cPTfFMhk"
   },
   "source": [
    "Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "avKmGrnpEUMm"
   },
   "outputs": [],
   "source": [
    "# Initialize the model\n",
    "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "dt_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_dt = dt_classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "eCEjpPLJFiAw"
   },
   "outputs": [],
   "source": [
    "# Predict on validation dataset\n",
    "y_val_pred_dt = dt_classifier.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r2Qc79uOPcjd",
    "outputId": "f6f0b82a-2d6a-427a-d965-a4e36732b285"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree RMSE: 5.7278866028465805\n"
     ]
    }
   ],
   "source": [
    "# Finding RMSE for the predicted values on validation dataset\n",
    "dt_rmse = np.sqrt(mean_squared_error(y_val, y_val_pred_dt))\n",
    "print(f\"Decision Tree RMSE: {dt_rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sZrJbg8nGMfu"
   },
   "source": [
    "Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7RzzcPKgFvFe"
   },
   "outputs": [],
   "source": [
    "# Initialize the model\n",
    "rf_classifier = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_rf = rf_classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lqxlzUm3GUvH"
   },
   "outputs": [],
   "source": [
    "# Predict on validation dataset\n",
    "y_val_pred_rf = rf_classifier.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xAnbz17pP6r2",
    "outputId": "71f49edf-6b99-4009-c9da-a4ba6bb38f6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest RMSE: 4.517699181356329\n"
     ]
    }
   ],
   "source": [
    "# Finding RMSE for the predicted values on validation dataset\n",
    "rf_rmse = np.sqrt(mean_squared_error(y_val, y_val_pred_rf))\n",
    "print(f\"Random Forest RMSE: {rf_rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wxFspUhgI9m8"
   },
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MKp2-wqDIXtt"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nDSuD3s4JH2n",
    "outputId": "2982f16d-9691-4be5-c97f-1ee1a9763b59"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Initialize the model\n",
    "logreg_classifier = LogisticRegression(random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "logreg_classifier.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_logreg = logreg_classifier.predict(X_test_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uvwOAZjgQOo_"
   },
   "outputs": [],
   "source": [
    "X_val_scaled = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JNfirYWQQG9W"
   },
   "outputs": [],
   "source": [
    "# Predict on validation dataset\n",
    "y_val_pred_logreg = logreg_classifier.predict(X_val_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SkmRUHCUQLxl",
    "outputId": "beb59bb2-eca1-4aa1-f303-d462f3dbe965"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression RMSE: 3.4597168389668886\n"
     ]
    }
   ],
   "source": [
    "# Finding RMSE for the predicted values on validation dataset\n",
    "lr_rmse = np.sqrt(mean_squared_error(y_val, y_val_pred_logreg))\n",
    "print(f\"Logistic Regression RMSE: {lr_rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kMLQLwRTRwys"
   },
   "source": [
    "Exporting Predictions like sample submmission\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tI5IngQUQfAS"
   },
   "outputs": [],
   "source": [
    "result_ds = test['result_driver_standing']\n",
    "dt_pred_df = pd.DataFrame({'position': y_pred_dt})\n",
    "rf_pred_df = pd.DataFrame({'position': y_pred_rf})\n",
    "lr_pred_df = pd.DataFrame({'position': y_pred_logreg})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LGwysGieTmVS"
   },
   "outputs": [],
   "source": [
    "dt_final = pd.concat([dt_pred_df,result_ds], axis=1)\n",
    "rf_final = pd.concat([rf_pred_df,result_ds], axis=1)\n",
    "lr_final = pd.concat([lr_pred_df,result_ds], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8WcRtOS1SMLF"
   },
   "outputs": [],
   "source": [
    "dt_final.to_csv('dt_predictions.csv', index=False)\n",
    "rf_final.to_csv('rf_predictions.csv', index=False)\n",
    "lr_final.to_csv('lr_predictions.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WLO3lcjDT53i"
   },
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "TPU",
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  "kernelspec": {
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