{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qmBKOQx4783m"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/electricity.csv\"\n",
        "data = pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "uPLyiFpw-Mq3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oL-xXlvy-ZLl",
        "outputId": "dda52986-4081-490e-bbe7-f114103ef28a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45312 entries, 0 to 45311\n",
            "Data columns (total 9 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   date       45312 non-null  float64\n",
            " 1   day        45312 non-null  object \n",
            " 2   period     45312 non-null  float64\n",
            " 3   nswprice   45312 non-null  float64\n",
            " 4   nswdemand  45312 non-null  float64\n",
            " 5   vicprice   45312 non-null  float64\n",
            " 6   vicdemand  45312 non-null  float64\n",
            " 7   transfer   45312 non-null  float64\n",
            " 8   class      45312 non-null  object \n",
            "dtypes: float64(7), object(2)\n",
            "memory usage: 3.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(), data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-91jmy1g-lL9",
        "outputId": "885c6254-53cd-473c-a77e-5abfc6b43ddf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(   date   day    period  nswprice  nswdemand  vicprice  vicdemand  transfer  \\\n",
              " 0   0.0  b'2'  0.000000  0.056443   0.439155  0.003467   0.422915  0.414912   \n",
              " 1   0.0  b'2'  0.021277  0.051699   0.415055  0.003467   0.422915  0.414912   \n",
              " 2   0.0  b'2'  0.042553  0.051489   0.385004  0.003467   0.422915  0.414912   \n",
              " 3   0.0  b'2'  0.063830  0.045485   0.314639  0.003467   0.422915  0.414912   \n",
              " 4   0.0  b'2'  0.085106  0.042482   0.251116  0.003467   0.422915  0.414912   \n",
              " \n",
              "      class  \n",
              " 0    b'UP'  \n",
              " 1    b'UP'  \n",
              " 2    b'UP'  \n",
              " 3    b'UP'  \n",
              " 4  b'DOWN'  ,\n",
              "          date   day    period  nswprice  nswdemand  vicprice  vicdemand  \\\n",
              " 45307  0.9158  b'7'  0.914894  0.044224   0.340672  0.003033   0.255049   \n",
              " 45308  0.9158  b'7'  0.936170  0.044884   0.355549  0.003072   0.241326   \n",
              " 45309  0.9158  b'7'  0.957447  0.043593   0.340970  0.002983   0.247799   \n",
              " 45310  0.9158  b'7'  0.978723  0.066651   0.329366  0.004630   0.345417   \n",
              " 45311  0.9158  b'7'  1.000000  0.050679   0.288753  0.003542   0.355256   \n",
              " \n",
              "        transfer    class  \n",
              " 45307  0.405263  b'DOWN'  \n",
              " 45308  0.420614  b'DOWN'  \n",
              " 45309  0.362281  b'DOWN'  \n",
              " 45310  0.206579    b'UP'  \n",
              " 45311  0.231140  b'DOWN'  )"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(columns=['date'])"
      ],
      "metadata": {
        "id": "T1FZym90-oI8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.get_dummies(data, columns=['day'], prefix='day')"
      ],
      "metadata": {
        "id": "4J2DpzhT-tBC"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(columns=['class'])\n",
        "y = data['class']"
      ],
      "metadata": {
        "id": "NrgeoBNd-xLy"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y_encoded = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "LEfwdL5Z-1ki"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)"
      ],
      "metadata": {
        "id": "n1rbHlbz_Isl"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwUeKsVD_bPM",
        "outputId": "38350735-dfc7-496f-ea69-00ee207bade7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "2SLxRDDd_iFH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Model Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred, target_names=le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMUwJ1x__kw6",
        "outputId": "038415e4-74ee-49bb-bea9-2fc5649bcb57"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy: 0.85\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     b'DOWN'       0.86      0.89      0.88      5215\n",
            "       b'UP'       0.85      0.80      0.82      3848\n",
            "\n",
            "    accuracy                           0.85      9063\n",
            "   macro avg       0.85      0.85      0.85      9063\n",
            "weighted avg       0.85      0.85      0.85      9063\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Model Accuracy on Test Set: {accuracy:.2f}\")\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred, target_names=le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H414Ttnf_1zh",
        "outputId": "1958ff35-21cb-4367-f99c-e9ff752f99ef"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy on Test Set: 0.85\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     b'DOWN'       0.86      0.89      0.88      5215\n",
            "       b'UP'       0.85      0.80      0.82      3848\n",
            "\n",
            "    accuracy                           0.85      9063\n",
            "   macro avg       0.85      0.85      0.85      9063\n",
            "weighted avg       0.85      0.85      0.85      9063\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "5hpdaFd8AG_I"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn==1.0.2\n",
        "!pip install xgboost --upgrade\n",
        "\n",
        "model = XGBClassifier(n_estimators=500, random_state=42)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RyBCF_sAMti",
        "outputId": "072aa23a-7b25-4b6a-e00b-4b053fe16f32"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn==1.0.2 in /usr/local/lib/python3.10/dist-packages (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (1.13.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (3.5.0)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.1.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.26.4)\n",
            "Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.23.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.13.1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=500, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=42, ...)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Model Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred, target_names=le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80phZNiEAVZ5",
        "outputId": "65e22213-91f5-4c27-c1a0-b4eaf04db020"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy: 0.84\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     b'DOWN'       0.86      0.88      0.87      5215\n",
            "       b'UP'       0.83      0.80      0.81      3848\n",
            "\n",
            "    accuracy                           0.84      9063\n",
            "   macro avg       0.84      0.84      0.84      9063\n",
            "weighted avg       0.84      0.84      0.84      9063\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(penalty='l2', C=1.0, solver='liblinear', random_state=42) # Changed penalty to 'l2'\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TazBj_1AlgB",
        "outputId": "bedb4784-c6fb-4dab-9d72-a041073daa2c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42, solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Model Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "print(\"Classificatoin Report:\")\n",
        "print(classification_report(y_test, y_pred, target_names=le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fA57l0S0A4hB",
        "outputId": "1814a73e-5f7d-44ef-e5d6-5d0763f5f2a9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy: 0.76\n",
            "Classificatoin Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     b'DOWN'       0.75      0.87      0.81      5215\n",
            "       b'UP'       0.78      0.61      0.68      3848\n",
            "\n",
            "    accuracy                           0.76      9063\n",
            "   macro avg       0.76      0.74      0.75      9063\n",
            "weighted avg       0.76      0.76      0.75      9063\n",
            "\n"
          ]
        }
      ]
    }
  ]
}