GRU.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3CjSpDDJaRjy"
      },
      "outputs": [],
      "source": [
        "from numpy import concatenate\n",
        "from pandas import read_excel\n",
        "from pandas import DataFrame\n",
        "import pandas as pd\n",
        "from pandas import concat\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import GRU\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "import plotly.graph_objects as go\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Accessing drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ozIbJSgCbU-S",
        "outputId": "e005caf7-1687-4eeb-afbd-e94c05791ee2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Reading data from the excel file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MqKto7rZbmRR"
      },
      "outputs": [],
      "source": [
        "dfr = read_excel(r'/content/drive/My Drive/CMIE_PROJECT/panel.xlsx', sheet_name='mainpanel')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Dropping irrelevant variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KzUwqfCRcLz2"
      },
      "outputs": [],
      "source": [
        "dfr = dfr.drop(columns=['Total assets','Net fixed assets','Export / Sales (%)','IT/ITES & other professional services','Additions to computers and IT systems during the year','Gross software','Net profit margin','Net cash inflow or (outflow) from investing activities','Size','Total forex earnings / Total income (%)','Raw material imports / Raw material purchases (%)', 'Gross computers and IT systems'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ckYxZTQzZiI"
      },
      "outputs": [],
      "source": [
        "df=dfr.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Filling missing values, linearly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ra2VqosSe8_I"
      },
      "outputs": [],
      "source": [
        "def fill_nan_linearly(column):\n",
        "    return column.fillna(method='ffill').fillna(method='bfill')\n",
        "filled_df = df.groupby('Companies').apply(lambda group: group.apply(fill_nan_linearly, axis=0))\n",
        "filled_df.reset_index(drop=True, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e8w5lIYPe_IZ"
      },
      "outputs": [],
      "source": [
        "df=filled_df.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Mathematical manipulation to avoid explosion in mape value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for i in df.columns:\n",
        "  df.loc[df[i] == 0, i] = 0.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qDJmIhmMfo4B"
      },
      "outputs": [],
      "source": [
        "train = df[df['Year'] < 2020]\n",
        "test = df[df['Year'] >= 2021]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y95ZYbaCfqDw"
      },
      "outputs": [],
      "source": [
        "X_train = train.drop(['Total income'], axis=1)\n",
        "Y_train = DataFrame(train['Total income'])\n",
        "X_test = test.drop(['Total income'], axis=1)\n",
        "Y_test = DataFrame(test['Total income'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Converting Data to supervised format"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eH_B0B4mgz-8"
      },
      "outputs": [],
      "source": [
        "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
        "    n_vars = 1 if type(data) is list else data.shape[1]\n",
        "    df = DataFrame(data)\n",
        "    cols, names = list(), list()\n",
        "    for i in range(n_in, 0, -1):\n",
        "        cols.append(df.shift(i))\n",
        "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "    for i in range(0, n_out):\n",
        "        cols.append(df.shift(-i))\n",
        "        if i == 0:\n",
        "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
        "        else:\n",
        "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\n",
        "    agg = concat(cols, axis=1)\n",
        "    agg.columns = names\n",
        "\n",
        "    if dropnan:\n",
        "        agg.dropna(inplace=True)\n",
        "    return agg\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Reshaping the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def reshape_data(train,test):\n",
        "    reframed_train = series_to_supervised(train, 1, 1)\n",
        "    reframed_test = series_to_supervised(test, 1, 1)\n",
        "\n",
        "    train= reframed_train.values\n",
        "    test=reframed_test.values\n",
        "\n",
        "    train_X, y_train = train[:, :-1], train[:, -1]\n",
        "    test_X, y_test = test[:, :-1], test[:, -1]\n",
        "\n",
        "    x_train = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
        "    x_test = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
        "    return x_train,x_test,y_train,y_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Converting categorical labels into numeric values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JBoCSS5Eg6Fx"
      },
      "outputs": [],
      "source": [
        "encoder = LabelEncoder()\n",
        "\n",
        "train_data=pd.DataFrame()\n",
        "train_data[X_train.columns]=X_train\n",
        "train_data[Y_train.columns]=Y_train\n",
        "train_data['Companies']= encoder.fit_transform(train_data['Companies'])\n",
        "\n",
        "test_data=pd.DataFrame()\n",
        "test_data[X_test.columns]=X_test\n",
        "test_data[Y_test.columns]=Y_test\n",
        "test_data['Companies']= encoder.fit_transform(test_data['Companies'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2xdWpKbYhcQk"
      },
      "outputs": [],
      "source": [
        "x_train,x_test,y_train,y_test=reshape_data(train_data,test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uzl5Ob89hfJh",
        "outputId": "f381a7f7-5e15-4fe3-bc55-9d7746aa8f6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "802/802 [==============================] - 6s 5ms/step - loss: 1620.1708\n",
            "Epoch 2/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 700.3201\n",
            "Epoch 3/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 493.9768\n",
            "Epoch 4/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 425.7130\n",
            "Epoch 5/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 389.3691\n",
            "Epoch 6/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 354.6677\n",
            "Epoch 7/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 362.3148\n",
            "Epoch 8/50\n",
            "802/802 [==============================] - 2s 2ms/step - loss: 366.4866\n",
            "Epoch 9/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 362.3647\n",
            "Epoch 10/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 331.9199\n",
            "Epoch 11/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 332.3635\n",
            "Epoch 12/50\n",
            "802/802 [==============================] - 4s 4ms/step - loss: 339.2112\n",
            "Epoch 13/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 356.2285\n",
            "Epoch 14/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 326.6402\n",
            "Epoch 15/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 331.6216\n",
            "Epoch 16/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 314.9782\n",
            "Epoch 17/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 335.2502\n",
            "Epoch 18/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 315.3161\n",
            "Epoch 19/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 340.0487\n",
            "Epoch 20/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 311.5841\n",
            "Epoch 21/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 328.1699\n",
            "Epoch 22/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 303.1156\n",
            "Epoch 23/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 310.3746\n",
            "Epoch 24/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 308.9505\n",
            "Epoch 25/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 303.9477\n",
            "Epoch 26/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 300.6047\n",
            "Epoch 27/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 297.4865\n",
            "Epoch 28/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 307.9904\n",
            "Epoch 29/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 309.1168\n",
            "Epoch 30/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 302.9641\n",
            "Epoch 31/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 321.3698\n",
            "Epoch 32/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 300.4870\n",
            "Epoch 33/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 295.5453\n",
            "Epoch 34/50\n",
            "802/802 [==============================] - 4s 4ms/step - loss: 309.7475\n",
            "Epoch 35/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 289.7310\n",
            "Epoch 36/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 306.3066\n",
            "Epoch 37/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 295.0598\n",
            "Epoch 38/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 304.0137\n",
            "Epoch 39/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 288.8779\n",
            "Epoch 40/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 301.2249\n",
            "Epoch 41/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 305.0357\n",
            "Epoch 42/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 295.1938\n",
            "Epoch 43/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 275.6384\n",
            "Epoch 44/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 298.6013\n",
            "Epoch 45/50\n",
            "802/802 [==============================] - 4s 6ms/step - loss: 291.7741\n",
            "Epoch 46/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 287.4616\n",
            "Epoch 47/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 301.4044\n",
            "Epoch 48/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 281.5354\n",
            "Epoch 49/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 292.0728\n",
            "Epoch 50/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 301.4151\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(GRU(60, activation='relu',input_shape=(x_train.shape[1], x_train.shape[2])))\n",
        "\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mae', optimizer='adam')\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs=50, batch_size=50,  verbose=1, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "5nWLZWIyiLuI",
        "outputId": "7358b6a8-cb0e-443e-8234-316b2130434c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'], label='Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wcf2L6lliSPR",
        "outputId": "09830ddb-375c-4a8d-e667-1855de1cbbe7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "376/376 [==============================] - 1s 2ms/step\n",
            "1253/1253 [==============================] - 2s 2ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "((40089, 1), (40089,))"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test_pre=model.predict(x_test)\n",
        "y_train_pre=model.predict(x_train)\n",
        "\n",
        "y_test_pre.shape,y_test.shape\n",
        "y_train_pre.shape,y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JQVQj7DMaMqd"
      },
      "outputs": [],
      "source": [
        "y_dat_pred=list(y_test_pre)+list(y_train_pre)\n",
        "y_dat_act=list(y_test)+list(y_train)\n",
        "year_dat=list(test_data['Year'])[1:]+list(train_data['Year'])[1:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-aJC9bmYW8KG",
        "outputId": "ccda4306-53b2-4932-8240-8a5111f67891"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pa\",\n  \"rows\": 52115,\n  \"fields\": [\n    {\n      \"column\": \"Prediction\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 52086,\n        \"samples\": [\n          359.5938415527344,\n          49.05832290649414,\n          3221.65478515625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2010,\n        \"max\": 2023,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          2019,\n          2017,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actual Values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50846.03279021042,\n        \"min\": -80.6,\n        \"max\": 2426325.6,\n        \"num_unique_values\": 39163,\n        \"samples\": [\n          7391.5,\n          820.6,\n          15957.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "pa"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-255f4b49-0000-4c7d-b52c-729bbfd35b17\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Year</th>\n",
              "      <th>Actual Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5164.920410</td>\n",
              "      <td>2022</td>\n",
              "      <td>5229.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5984.759766</td>\n",
              "      <td>2023</td>\n",
              "      <td>6044.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1181.523071</td>\n",
              "      <td>2021</td>\n",
              "      <td>1229.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1940.618042</td>\n",
              "      <td>2022</td>\n",
              "      <td>1957.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1941.694092</td>\n",
              "      <td>2023</td>\n",
              "      <td>1957.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <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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              "      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",
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              "    .colab-df-buttons div {\n",
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              "\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-255f4b49-0000-4c7d-b52c-729bbfd35b17 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-255f4b49-0000-4c7d-b52c-729bbfd35b17');\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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              "\n",
              "\n",
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              "            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",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\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-593aa8de-e465-48fa-a7d7-2fba44811f29 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": [
              "    Prediction  Year  Actual Values\n",
              "0  5164.920410  2022         5229.5\n",
              "1  5984.759766  2023         6044.2\n",
              "2  1181.523071  2021         1229.1\n",
              "3  1940.618042  2022         1957.7\n",
              "4  1941.694092  2023         1957.7"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pa=pd.DataFrame()\n",
        "pa['Prediction']=[i[0] for i in y_dat_pred]\n",
        "pa['Year']=year_dat\n",
        "pa['Actual Values']=y_dat_act\n",
        "pa.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caM5_4lJm3Ty",
        "outputId": "abade0d0-ca39-4f62-c6a9-b075c9bdd717"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Year\n",
              "2010    24728048.0\n",
              "2011    25242684.0\n",
              "2012    28490680.0\n",
              "2013    32132138.0\n",
              "2014    35889896.0\n",
              "2015    39154820.0\n",
              "2016    41555328.0\n",
              "2017    45378004.0\n",
              "2018    48746280.0\n",
              "2019    55225236.0\n",
              "2021    54394016.0\n",
              "2022    68527344.0\n",
              "2023    79834072.0\n",
              "Name: Prediction, dtype: float32"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pa.groupby('Year')['Prediction'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_Yxco81m_Kw",
        "outputId": "d4043760-cc81-43eb-8576-e2f9717c5f35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Year\n",
              "2010    2.534904e+07\n",
              "2011    2.555476e+07\n",
              "2012    2.860712e+07\n",
              "2013    3.207748e+07\n",
              "2014    3.613649e+07\n",
              "2015    3.914672e+07\n",
              "2016    4.172200e+07\n",
              "2017    4.538526e+07\n",
              "2018    4.881630e+07\n",
              "2019    5.521588e+07\n",
              "2021    5.529552e+07\n",
              "2022    6.857170e+07\n",
              "2023    7.971540e+07\n",
              "Name: Actual Values, dtype: float64"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pa.groupby('Year')['Actual Values'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eml7k3nsnQa0"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "def RMSE(actual,prediction):\n",
        "    rmse = np.sqrt(mean_squared_error(actual,prediction))\n",
        "    return rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0q-zFSunyMyt"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "def MAPE(actual,prediction):\n",
        "    mape = np.sqrt(mean_absolute_percentage_error(actual,prediction))\n",
        "    return mape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "saNp1PTy37V-"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "def R2(actual,prediction):\n",
        "    r2= np.sqrt(r2_score(actual,prediction))\n",
        "    return r2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nM8KmPwhcjY3"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "def MAE(actual,prediction):\n",
        "    mae= np.sqrt(mean_absolute_error(actual,prediction))\n",
        "    return mae"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "RMSE of test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOwMUXJZnTBw",
        "outputId": "c946d30d-3e14-4284-a77c-b5bc117baefd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2721.1755180670043\n"
          ]
        }
      ],
      "source": [
        "print(RMSE(y_test,[i[0] for i in y_test_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "RMSE of train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfVHtbFydDxY",
        "outputId": "652042ba-30a8-41da-f2cf-4e1c471a1bef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2099.395540291813\n"
          ]
        }
      ],
      "source": [
        "print(RMSE(y_train,[i[0] for i in y_train_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "MAPE of test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CbDcnsGcyUKA",
        "outputId": "4e01d948-09ef-4a04-8a12-963ad9f3a49a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.2442657406291893\n"
          ]
        }
      ],
      "source": [
        "print(MAPE(y_test,[i[0] for i in y_test_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "MAPE of train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXJp4X0ldEgO",
        "outputId": "160b4d52-a963-4ba6-c4ba-312f1078bfdf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4.564541670461355\n"
          ]
        }
      ],
      "source": [
        "print(MAPE(y_train,[i[0] for i in y_train_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "R2 of test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeACwxfh35-K",
        "outputId": "08555ba4-0a9b-474c-a165-7663edc481c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9993021248731327\n"
          ]
        }
      ],
      "source": [
        "print(R2(y_test,[i[0] for i in y_test_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "R2 of train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvjvYxtAdFWE",
        "outputId": "aa8b09ca-1ee1-4809-84b6-bbfa626d0a7b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9987440902283621\n"
          ]
        }
      ],
      "source": [
        "print(R2(y_train,[i[0] for i in y_train_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "MAE of test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NfwkxFpdF2E",
        "outputId": "adc2a188-8f85-483b-d58d-c9991f11219b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "15.326698308520887\n"
          ]
        }
      ],
      "source": [
        "print(MAE(y_train,[i[0] for i in y_train_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "MAE of train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqey8LxfdGNC",
        "outputId": "de91e4e5-a719-45f8-b761-4d8c912842c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19.220265477247313\n"
          ]
        }
      ],
      "source": [
        "print(MAE(y_test,[i[0] for i in y_test_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "t5z873-0oBuC",
        "outputId": "25746e6c-e803-4c1a-b563-a7e0c9c4439e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<html>\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "grouped_data = pa.groupby('Year').agg({'Prediction': 'sum', 'Actual Values': 'sum'}).reset_index()\n",
        "\n",
        "trace_pred = go.Scatter(x=grouped_data['Year'], y=grouped_data['Prediction'], mode='lines', name='Prediction', line=dict(color='red'))\n",
        "trace_actual = go.Scatter(x=grouped_data['Year'], y=grouped_data['Actual Values'], mode='lines', name='Actual Values', line=dict(color='blue'))\n",
        "\n",
        "fig = go.Figure([trace_pred, trace_actual])\n",
        "fig.update_layout(\n",
        "    title='GRU',\n",
        "    xaxis=dict(title='Year', tickfont=dict(size=14), dtick=1),\n",
        "    yaxis=dict(title='Total Income (in Million Rs.)', tickfont=dict(size=14)),\n",
        "    legend=dict(font=dict(size=14))\n",
        ")\n",
        "\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IKhelRCfYmoS"
      },
      "outputs": [],
      "source": [
        "dataf={'model':['LSTM', 'GRU'],\n",
        "       'mse_tr':[],\n",
        "       'mse_tt':[],\n",
        "       'r2_tr':[],\n",
        "       'r2_tt':[],\n",
        "       'mape_tt':[],\n",
        "       'mape_tr':[],\n",
        "       'mae_tt':[],\n",
        "       'mae_tr':[]}"
      ]
    }
  ],
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    },
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      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
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  "nbformat_minor": 0
}