weather.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/seattle-weather.csv')"
      ],
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      "execution_count": null,
      "outputs": []
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              "         date  precipitation  temp_max  temp_min  wind  weather\n",
              "0  2012-01-01            0.0      12.8       5.0   4.7  drizzle\n",
              "1  2012-01-02           10.9      10.6       2.8   4.5     rain\n",
              "2  2012-01-03            0.8      11.7       7.2   2.3     rain\n",
              "3  2012-01-04           20.3      12.2       5.6   4.7     rain\n",
              "4  2012-01-05            1.3       8.9       2.8   6.1     rain"
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              "      <th>4</th>\n",
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              "      <td>1.3</td>\n",
              "      <td>8.9</td>\n",
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              "            date  precipitation  temp_max  temp_min  wind weather\n",
              "1456  2015-12-27            8.6       4.4       1.7   2.9    rain\n",
              "1457  2015-12-28            1.5       5.0       1.7   1.3    rain\n",
              "1458  2015-12-29            0.0       7.2       0.6   2.6     fog\n",
              "1459  2015-12-30            0.0       5.6      -1.0   3.4     sun\n",
              "1460  2015-12-31            0.0       5.6      -2.1   3.5     sun"
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              "(1461, 6)"
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              "       precipitation     temp_max     temp_min         wind\n",
              "count    1461.000000  1461.000000  1461.000000  1461.000000\n",
              "mean        3.029432    16.439083     8.234771     3.241136\n",
              "std         6.680194     7.349758     5.023004     1.437825\n",
              "min         0.000000    -1.600000    -7.100000     0.400000\n",
              "25%         0.000000    10.600000     4.400000     2.200000\n",
              "50%         0.000000    15.600000     8.300000     3.000000\n",
              "75%         2.800000    22.200000    12.200000     4.000000\n",
              "max        55.900000    35.600000    18.300000     9.500000"
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              "      <th>mean</th>\n",
              "      <td>3.029432</td>\n",
              "      <td>16.439083</td>\n",
              "      <td>8.234771</td>\n",
              "      <td>3.241136</td>\n",
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              "      <th>std</th>\n",
              "      <td>6.680194</td>\n",
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              "      <td>0.400000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.600000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>2.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>8.300000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.800000</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>55.900000</td>\n",
              "      <td>35.600000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>9.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ccdac2c-820c-4487-b793-ae20fae102ed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ccdac2c-820c-4487-b793-ae20fae102ed 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-1ccdac2c-820c-4487-b793-ae20fae102ed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqaLsIxcWlIF",
        "outputId": "79d64907-bb7e-439e-930b-813bb3955e40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of             date  precipitation  temp_max  temp_min  wind  weather\n",
              "0     2012-01-01            0.0      12.8       5.0   4.7  drizzle\n",
              "1     2012-01-02           10.9      10.6       2.8   4.5     rain\n",
              "2     2012-01-03            0.8      11.7       7.2   2.3     rain\n",
              "3     2012-01-04           20.3      12.2       5.6   4.7     rain\n",
              "4     2012-01-05            1.3       8.9       2.8   6.1     rain\n",
              "...          ...            ...       ...       ...   ...      ...\n",
              "1456  2015-12-27            8.6       4.4       1.7   2.9     rain\n",
              "1457  2015-12-28            1.5       5.0       1.7   1.3     rain\n",
              "1458  2015-12-29            0.0       7.2       0.6   2.6      fog\n",
              "1459  2015-12-30            0.0       5.6      -1.0   3.4      sun\n",
              "1460  2015-12-31            0.0       5.6      -2.1   3.5      sun\n",
              "\n",
              "[1461 rows x 6 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7k91Y7NWrRp",
        "outputId": "e22a2cab-cd04-47fe-b6f6-98a72192cc3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date             0\n",
              "precipitation    0\n",
              "temp_max         0\n",
              "temp_min         0\n",
              "wind             0\n",
              "weather          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "data['weather']=LabelEncoder().fit_transform(data['weather'])"
      ],
      "metadata": {
        "id": "hCcsglGKWy6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes=plt.subplots(figsize=(8, 6))\n",
        "sns.heatmap(data.corr(), ax=axes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "OXiSqASzW_Mo",
        "outputId": "2ed1beb0-fdea-43d6-db30-449332528abf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f618ba9d1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MACHINE LEARNING MODEL FOR PREDICTION"
      ],
      "metadata": {
        "id": "zUPZfs1hXs1y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "features=[\"precipitation\", \"temp_max\", \"temp_min\", \"wind\"]\n",
        "X=data[features]\n",
        "y=data.weather\n",
        "train_X, test_X, train_y, test_y = train_test_split(X, y,random_state = 0)"
      ],
      "metadata": {
        "id": "W4QdHeMcXIkD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bs8x0WFOXnCt",
        "outputId": "e0c8242f-2d9d-4e1c-f853-47189a72599d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      precipitation  temp_max  temp_min  wind\n",
            "1135            6.1      13.3       8.3   2.5\n",
            "1214            0.0      16.1       7.2   4.7\n",
            "1407            6.6      11.1       7.8   1.8\n",
            "811             0.0      11.1       1.1   1.8\n",
            "985             0.0      24.4      12.8   5.9\n",
            "...             ...       ...       ...   ...\n",
            "763             0.0       8.9       1.1   2.5\n",
            "835             0.5      14.4       7.8   4.0\n",
            "1216            0.0      18.3       8.9   3.7\n",
            "559             0.0      26.1      11.1   3.1\n",
            "684             3.0      10.6       7.2   6.0\n",
            "\n",
            "[1095 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVKdppK6X2Sp",
        "outputId": "370964ca-a3e2-4d8b-e780-738c76205a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      precipitation  temp_max  temp_min  wind\n",
            "530             0.0      20.0      12.2   3.7\n",
            "657             0.0      10.6       7.8   1.4\n",
            "459             8.4      14.4      10.0   3.0\n",
            "279             0.0      23.9       7.8   5.1\n",
            "656             0.0      12.8       7.2   1.2\n",
            "...             ...       ...       ...   ...\n",
            "781             3.0      10.0       5.6   6.9\n",
            "1391            0.0      12.8       7.2   2.6\n",
            "1168           17.0      13.9       9.4   3.8\n",
            "847             6.9      11.1       6.1   5.8\n",
            "1425            0.0       9.4      -1.0   4.3\n",
            "\n",
            "[366 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64XuFAf5X9JW",
        "outputId": "c5a43feb-1775-4f04-b557-2c1e105ab156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1135    2\n",
            "1214    4\n",
            "1407    2\n",
            "811     4\n",
            "985     4\n",
            "       ..\n",
            "763     4\n",
            "835     2\n",
            "1216    4\n",
            "559     4\n",
            "684     2\n",
            "Name: weather, Length: 1095, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2m02vQVnYBiN",
        "outputId": "f76ac7b2-8d2d-44bc-956d-a852cc26184c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "530     4\n",
            "657     4\n",
            "459     2\n",
            "279     4\n",
            "656     4\n",
            "       ..\n",
            "781     2\n",
            "1391    1\n",
            "1168    2\n",
            "847     2\n",
            "1425    4\n",
            "Name: weather, Length: 366, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "model1=DecisionTreeRegressor(random_state=1)\n",
        "model1.fit(train_X, train_y)\n",
        "pred1=model1.predict(test_X)\n",
        "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0C3WUD6XYI3F",
        "outputId": "d3092bb5-d010-44d9-9102-d79ce2a6dfcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.713115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "model2=RandomForestRegressor(random_state=1)\n",
        "model2.fit(train_X, train_y)\n",
        "pred2=model2.predict(test_X)\n",
        "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred2)))\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_5-go6yYRNf",
        "outputId": "9549f60e-7c72-452a-d71d-b706c1f22cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.694026\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor\n",
        "\n",
        "model3= XGBRegressor(n_estimators=100, learning_rate=0.04)\n",
        "model3.fit(train_X, train_y)\n",
        "pred3=model3.predict(test_X)\n",
        "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred3)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lfep38hYUiZ",
        "outputId": "285a8a7c-6af6-476c-8d77-920b7613dc3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[09:25:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Mean Absolute Error: 0.679224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2L_jN83VYYm3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}