RandomForestRegressor_BikesDataset.ipynb

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      "id": "cfe61180",
      "metadata": {
        "id": "cfe61180"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "db0eaaab",
      "metadata": {
        "id": "db0eaaab"
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      "outputs": [],
      "source": [
        "df=pd.read_csv('day.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "634ca2da",
      "metadata": {
        "id": "634ca2da",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
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              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
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              "      <th>windspeed</th>\n",
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              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>654</td>\n",
              "      <td>985</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-02</td>\n",
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              "      <td>670</td>\n",
              "      <td>801</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2011-01-03</td>\n",
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              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1        0        6           0   \n",
              "1        2  2011-01-02       1   0     1        0        0           0   \n",
              "2        3  2011-01-03       1   0     1        0        1           1   \n",
              "3        4  2011-01-04       1   0     1        0        2           1   \n",
              "4        5  2011-01-05       1   0     1        0        3           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
              "2  1349  \n",
              "3  1562  \n",
              "4  1600  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
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      "cell_type": "code",
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              "      <th>dteday</th>\n",
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              "      <th>yr</th>\n",
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              "      <th>726</th>\n",
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              "      <td>2012-12-27</td>\n",
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              "      <td>1341</td>\n",
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              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>730</td>\n",
              "      <td>2012-12-30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0.231700</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>0.350754</td>\n",
              "      <td>364</td>\n",
              "      <td>1432</td>\n",
              "      <td>1796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>731</td>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.215833</td>\n",
              "      <td>0.223487</td>\n",
              "      <td>0.577500</td>\n",
              "      <td>0.154846</td>\n",
              "      <td>439</td>\n",
              "      <td>2290</td>\n",
              "      <td>2729</td>\n",
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            "text/plain": [
              "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "726      727  2012-12-27       1   1    12        0        4           1   \n",
              "727      728  2012-12-28       1   1    12        0        5           1   \n",
              "728      729  2012-12-29       1   1    12        0        6           0   \n",
              "729      730  2012-12-30       1   1    12        0        0           0   \n",
              "730      731  2012-12-31       1   1    12        0        1           1   \n",
              "\n",
              "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
              "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
              "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
              "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
              "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
              "\n",
              "      cnt  \n",
              "726  2114  \n",
              "727  3095  \n",
              "728  1341  \n",
              "729  1796  \n",
              "730  2729  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     731 non-null    int64  \n",
            " 1   dteday      731 non-null    object \n",
            " 2   season      731 non-null    int64  \n",
            " 3   yr          731 non-null    int64  \n",
            " 4   mnth        731 non-null    int64  \n",
            " 5   holiday     731 non-null    int64  \n",
            " 6   weekday     731 non-null    int64  \n",
            " 7   workingday  731 non-null    int64  \n",
            " 8   weathersit  731 non-null    int64  \n",
            " 9   temp        731 non-null    float64\n",
            " 10  atemp       731 non-null    float64\n",
            " 11  hum         731 non-null    float64\n",
            " 12  windspeed   731 non-null    float64\n",
            " 13  casual      731 non-null    int64  \n",
            " 14  registered  731 non-null    int64  \n",
            " 15  cnt         731 non-null    int64  \n",
            "dtypes: float64(4), int64(11), object(1)\n",
            "memory usage: 91.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a2e57760",
      "metadata": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>2.496580</td>\n",
              "      <td>0.500684</td>\n",
              "      <td>6.519836</td>\n",
              "      <td>0.028728</td>\n",
              "      <td>2.997264</td>\n",
              "      <td>0.683995</td>\n",
              "      <td>1.395349</td>\n",
              "      <td>0.495385</td>\n",
              "      <td>0.474354</td>\n",
              "      <td>0.627894</td>\n",
              "      <td>0.190486</td>\n",
              "      <td>848.176471</td>\n",
              "      <td>3656.172367</td>\n",
              "      <td>4504.348837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>211.165812</td>\n",
              "      <td>1.110807</td>\n",
              "      <td>0.500342</td>\n",
              "      <td>3.451913</td>\n",
              "      <td>0.167155</td>\n",
              "      <td>2.004787</td>\n",
              "      <td>0.465233</td>\n",
              "      <td>0.544894</td>\n",
              "      <td>0.183051</td>\n",
              "      <td>0.162961</td>\n",
              "      <td>0.142429</td>\n",
              "      <td>0.077498</td>\n",
              "      <td>686.622488</td>\n",
              "      <td>1560.256377</td>\n",
              "      <td>1937.211452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.059130</td>\n",
              "      <td>0.079070</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022392</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>183.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337083</td>\n",
              "      <td>0.337842</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.134950</td>\n",
              "      <td>315.500000</td>\n",
              "      <td>2497.000000</td>\n",
              "      <td>3152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.498333</td>\n",
              "      <td>0.486733</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.180975</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>3662.000000</td>\n",
              "      <td>4548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>548.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.655417</td>\n",
              "      <td>0.608602</td>\n",
              "      <td>0.730209</td>\n",
              "      <td>0.233214</td>\n",
              "      <td>1096.000000</td>\n",
              "      <td>4776.500000</td>\n",
              "      <td>5956.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.861667</td>\n",
              "      <td>0.840896</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.507463</td>\n",
              "      <td>3410.000000</td>\n",
              "      <td>6946.000000</td>\n",
              "      <td>8714.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          instant      season          yr        mnth     holiday     weekday  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
              "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
              "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
              "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
              "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
              "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
              "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
              "\n",
              "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
              "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
              "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
              "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
              "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
              "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
              "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
              "\n",
              "            casual   registered          cnt  \n",
              "count   731.000000   731.000000   731.000000  \n",
              "mean    848.176471  3656.172367  4504.348837  \n",
              "std     686.622488  1560.256377  1937.211452  \n",
              "min       2.000000    20.000000    22.000000  \n",
              "25%     315.500000  2497.000000  3152.000000  \n",
              "50%     713.000000  3662.000000  4548.000000  \n",
              "75%    1096.000000  4776.500000  5956.000000  \n",
              "max    3410.000000  6946.000000  8714.000000  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        " df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8755b05",
      "metadata": {
        "id": "b8755b05",
        "outputId": "e820a102-b1cb-4310-8c10-863b50bd7229"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "700f2ff4",
      "metadata": {
        "id": "700f2ff4"
      },
      "outputs": [],
      "source": [
        "df.drop([\"dteday\"],axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "25caffda",
      "metadata": {
        "id": "25caffda",
        "outputId": "6e05d7ea-e514-4560-c14e-98f097a72eaa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>727</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.254167</td>\n",
              "      <td>0.226642</td>\n",
              "      <td>0.652917</td>\n",
              "      <td>0.350133</td>\n",
              "      <td>247</td>\n",
              "      <td>1867</td>\n",
              "      <td>2114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>728</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.253333</td>\n",
              "      <td>0.255046</td>\n",
              "      <td>0.590000</td>\n",
              "      <td>0.155471</td>\n",
              "      <td>644</td>\n",
              "      <td>2451</td>\n",
              "      <td>3095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>728</th>\n",
              "      <td>729</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.253333</td>\n",
              "      <td>0.242400</td>\n",
              "      <td>0.752917</td>\n",
              "      <td>0.124383</td>\n",
              "      <td>159</td>\n",
              "      <td>1182</td>\n",
              "      <td>1341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>730</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.255833</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>0.350754</td>\n",
              "      <td>364</td>\n",
              "      <td>1432</td>\n",
              "      <td>1796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>731</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.215833</td>\n",
              "      <td>0.223487</td>\n",
              "      <td>0.577500</td>\n",
              "      <td>0.154846</td>\n",
              "      <td>439</td>\n",
              "      <td>2290</td>\n",
              "      <td>2729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>731 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     instant  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
              "0          1       1   0     1        0        6           0           2   \n",
              "1          2       1   0     1        0        0           0           2   \n",
              "2          3       1   0     1        0        1           1           1   \n",
              "3          4       1   0     1        0        2           1           1   \n",
              "4          5       1   0     1        0        3           1           1   \n",
              "..       ...     ...  ..   ...      ...      ...         ...         ...   \n",
              "726      727       1   1    12        0        4           1           2   \n",
              "727      728       1   1    12        0        5           1           2   \n",
              "728      729       1   1    12        0        6           0           2   \n",
              "729      730       1   1    12        0        0           0           1   \n",
              "730      731       1   1    12        0        1           1           2   \n",
              "\n",
              "         temp     atemp       hum  windspeed  casual  registered   cnt  \n",
              "0    0.344167  0.363625  0.805833   0.160446     331         654   985  \n",
              "1    0.363478  0.353739  0.696087   0.248539     131         670   801  \n",
              "2    0.196364  0.189405  0.437273   0.248309     120        1229  1349  \n",
              "3    0.200000  0.212122  0.590435   0.160296     108        1454  1562  \n",
              "4    0.226957  0.229270  0.436957   0.186900      82        1518  1600  \n",
              "..        ...       ...       ...        ...     ...         ...   ...  \n",
              "726  0.254167  0.226642  0.652917   0.350133     247        1867  2114  \n",
              "727  0.253333  0.255046  0.590000   0.155471     644        2451  3095  \n",
              "728  0.253333  0.242400  0.752917   0.124383     159        1182  1341  \n",
              "729  0.255833  0.231700  0.483333   0.350754     364        1432  1796  \n",
              "730  0.215833  0.223487  0.577500   0.154846     439        2290  2729  \n",
              "\n",
              "[731 rows x 15 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "83ca7201",
      "metadata": {
        "id": "83ca7201"
      },
      "outputs": [],
      "source": [
        "#df = (df - np.min(df)) / (np.max(df) - np.min(df)).values\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "933307c0",
      "metadata": {
        "id": "933307c0"
      },
      "outputs": [],
      "source": [
        "Y=df[\"cnt\"]\n",
        "X=df.drop(labels = [\"cnt\"], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d465df69",
      "metadata": {
        "id": "d465df69"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cbe83f66",
      "metadata": {
        "id": "cbe83f66",
        "outputId": "4fa165c3-ece5-413a-e816-b94d5b5560f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(584, 14)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "070a645e",
      "metadata": {
        "id": "070a645e",
        "outputId": "bbcfed33-4853-4e98-b9a2-5eca74dbbf2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(584,)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        " y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fb4cfa9f",
      "metadata": {
        "id": "fb4cfa9f"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "074e88f2",
      "metadata": {
        "id": "074e88f2"
      },
      "outputs": [],
      "source": [
        "rf=RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32218e74",
      "metadata": {
        "id": "32218e74",
        "outputId": "9eaea35b-ad00-44fa-d792-dcc61273ed4c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b2c85448",
      "metadata": {
        "id": "b2c85448",
        "outputId": "e571940c-c5fa-49d6-809a-8b7f249c2622"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([4577.93, 6171.01, 5345.1 , 4100.87, 3015.83, 4981.79, 4192.67,\n",
              "       4316.21, 5210.08, 5284.98, 2212.59, 7408.86, 1978.23, 1143.68,\n",
              "       5561.47, 5947.83, 4143.45, 5170.31, 3467.93, 2122.32, 1459.95,\n",
              "       6407.53, 6674.83, 2704.3 , 4816.37, 2520.62, 5065.52, 4138.81,\n",
              "       3982.84, 4861.86, 6576.22, 4851.7 , 1261.46, 4923.36, 6871.38,\n",
              "       7475.59, 5670.24, 7636.82, 7696.52, 4578.63, 3687.54, 1638.66,\n",
              "       3821.67, 7310.67, 7563.05, 3348.07, 6273.46, 3891.95, 6759.16,\n",
              "       4058.49, 6470.75, 3514.17, 4013.69, 4483.01, 5087.86, 4681.06,\n",
              "       1378.11,  753.47, 6332.59, 3414.73, 5324.82, 4312.78, 1063.81,\n",
              "       7650.72, 3390.5 , 4163.83, 2789.99, 7255.96, 4867.96, 4964.59,\n",
              "       6987.19, 1579.78, 6288.15, 4597.64, 7467.89, 4731.77, 4456.09,\n",
              "       4168.28, 1099.41, 3532.06,  843.19, 5659.23, 5527.35, 2697.8 ,\n",
              "       7610.58, 1908.88, 4357.08, 1238.49, 4437.3 , 4645.86, 2348.5 ,\n",
              "       7461.08, 4169.69, 1779.1 , 1855.15, 5611.7 , 4400.46, 4324.3 ,\n",
              "       3759.74, 2319.78, 5607.26, 3123.11, 5394.73, 1385.53, 5513.18,\n",
              "       5022.16, 4823.38, 5636.84, 1129.87, 7589.58, 3767.92, 1529.21,\n",
              "       7549.34, 6689.28, 2430.94, 4705.52, 6197.13, 2718.75,  846.54,\n",
              "       6769.99, 4670.15, 7576.26, 4487.84, 7139.76, 6915.12, 6311.46,\n",
              "       4054.63, 5493.91, 1107.22, 6012.88, 3952.3 , 3215.55, 1850.74,\n",
              "       4797.63, 5062.54, 1111.28, 5223.1 , 6676.55, 4732.12, 4136.84,\n",
              "       4575.47, 2552.89, 5283.14, 3972.55, 2754.27, 5025.09, 4259.52])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred=rf.predict(X_test)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f6a5b1b",
      "metadata": {
        "id": "3f6a5b1b",
        "outputId": "384a7ace-8733-4e89-d30e-eb5efeea0021"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "283    4563\n",
            "453    6133\n",
            "266    5423\n",
            "269    4120\n",
            "120    3351\n",
            "       ... \n",
            "119    5312\n",
            "308    3926\n",
            "77     3117\n",
            "425    4990\n",
            "388    4339\n",
            "Name: cnt, Length: 147, dtype: int64 [4577.93 6171.01 5345.1  4100.87 3015.83 4981.79 4192.67 4316.21 5210.08\n",
            " 5284.98 2212.59 7408.86 1978.23 1143.68 5561.47 5947.83 4143.45 5170.31\n",
            " 3467.93 2122.32 1459.95 6407.53 6674.83 2704.3  4816.37 2520.62 5065.52\n",
            " 4138.81 3982.84 4861.86 6576.22 4851.7  1261.46 4923.36 6871.38 7475.59\n",
            " 5670.24 7636.82 7696.52 4578.63 3687.54 1638.66 3821.67 7310.67 7563.05\n",
            " 3348.07 6273.46 3891.95 6759.16 4058.49 6470.75 3514.17 4013.69 4483.01\n",
            " 5087.86 4681.06 1378.11  753.47 6332.59 3414.73 5324.82 4312.78 1063.81\n",
            " 7650.72 3390.5  4163.83 2789.99 7255.96 4867.96 4964.59 6987.19 1579.78\n",
            " 6288.15 4597.64 7467.89 4731.77 4456.09 4168.28 1099.41 3532.06  843.19\n",
            " 5659.23 5527.35 2697.8  7610.58 1908.88 4357.08 1238.49 4437.3  4645.86\n",
            " 2348.5  7461.08 4169.69 1779.1  1855.15 5611.7  4400.46 4324.3  3759.74\n",
            " 2319.78 5607.26 3123.11 5394.73 1385.53 5513.18 5022.16 4823.38 5636.84\n",
            " 1129.87 7589.58 3767.92 1529.21 7549.34 6689.28 2430.94 4705.52 6197.13\n",
            " 2718.75  846.54 6769.99 4670.15 7576.26 4487.84 7139.76 6915.12 6311.46\n",
            " 4054.63 5493.91 1107.22 6012.88 3952.3  3215.55 1850.74 4797.63 5062.54\n",
            " 1111.28 5223.1  6676.55 4732.12 4136.84 4575.47 2552.89 5283.14 3972.55\n",
            " 2754.27 5025.09 4259.52]\n"
          ]
        }
      ],
      "source": [
        "print(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00467570",
      "metadata": {
        "id": "00467570",
        "outputId": "b0e8072f-b175-4520-e882-eeea58977958"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 73.31360544217684\n",
            "Mean Squared Error: 13703.817544217682\n",
            "Root Mean Squared Error: 117.0633057119851\n"
          ]
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c26c7644",
      "metadata": {
        "id": "c26c7644",
        "outputId": "c2a07d27-4e83-4d38-c2a2-dd30fd44292c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "registered    0.880774\n",
            "casual        0.098032\n",
            "instant       0.015963\n",
            "temp          0.001394\n",
            "atemp         0.000753\n",
            "windspeed     0.000702\n",
            "hum           0.000600\n",
            "yr            0.000483\n",
            "weekday       0.000326\n",
            "workingday    0.000321\n",
            "mnth          0.000299\n",
            "season        0.000198\n",
            "weathersit    0.000132\n",
            "holiday       0.000024\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "feature_list = list(X.columns)\n",
        "feature_imp = pd.Series(rf.feature_importances_,index=feature_list).sort_values(ascending=False)\n",
        "print(feature_imp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "304b6777",
      "metadata": {
        "id": "304b6777"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}