LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3CjSpDDJaRjy"
      },
      "outputs": [],
      "source": [
        "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 LSTM\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Accessing drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": "hbhftmn5cGz3"
      },
      "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'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2JxSRcaMcX6S"
      },
      "outputs": [],
      "source": [
        "df=dfr.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Filling missing values, linearly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmYNYzXS9vaK",
        "outputId": "5d4d9c3e-7335-44d8-92d5-ba57c57bb428"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-8-db6b5264b618>:2: FutureWarning: Series.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
            "  return column.interpolate(method='linear', limit_direction='both')\n"
          ]
        }
      ],
      "source": [
        "def fill_nan_linearly(column):\n",
        "    return column.interpolate(method='linear', limit_direction='both')\n",
        "\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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCktQuZXe15x",
        "outputId": "313a06fb-f0d5-4ccc-9565-a358127abba1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-13-d2a5495eb679>:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0.1' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  df.loc[df[i] == 0, i] = 0.1\n"
          ]
        }
      ],
      "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": {},
      "outputs": [],
      "source": [
        "train = df[df['Year'] < 2020]\n",
        "test = df[df['Year'] >= 2021]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y95ZYbaCfqDw"
      },
      "outputs": [],
      "source": [
        "\n",
        "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": "2c0685f7-e79a-455e-8858-e1031df2d88a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "802/802 [==============================] - 4s 3ms/step - loss: 1431.2318\n",
            "Epoch 2/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 662.2263\n",
            "Epoch 3/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 507.6883\n",
            "Epoch 4/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 451.2021\n",
            "Epoch 5/50\n",
            "802/802 [==============================] - 4s 4ms/step - loss: 405.0074\n",
            "Epoch 6/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 401.1196\n",
            "Epoch 7/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 362.6795\n",
            "Epoch 8/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 365.8473\n",
            "Epoch 9/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 345.3854\n",
            "Epoch 10/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 332.5894\n",
            "Epoch 11/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 333.6026\n",
            "Epoch 12/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 335.6139\n",
            "Epoch 13/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 313.0128\n",
            "Epoch 14/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 312.4037\n",
            "Epoch 15/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 310.3531\n",
            "Epoch 16/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 308.7943\n",
            "Epoch 17/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 314.9883\n",
            "Epoch 18/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 302.2535\n",
            "Epoch 19/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 294.4894\n",
            "Epoch 20/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 292.9085\n",
            "Epoch 21/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 310.1677\n",
            "Epoch 22/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 284.3866\n",
            "Epoch 23/50\n",
            "802/802 [==============================] - 4s 5ms/step - loss: 295.6024\n",
            "Epoch 24/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 293.5872\n",
            "Epoch 25/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 303.2685\n",
            "Epoch 26/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 288.4458\n",
            "Epoch 27/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 284.2109\n",
            "Epoch 28/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 282.5897\n",
            "Epoch 29/50\n",
            "802/802 [==============================] - 5s 6ms/step - loss: 283.3885\n",
            "Epoch 30/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 283.0323\n",
            "Epoch 31/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 284.6054\n",
            "Epoch 32/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 288.1869\n",
            "Epoch 33/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 298.6791\n",
            "Epoch 34/50\n",
            "802/802 [==============================] - 3s 4ms/step - loss: 287.4196\n",
            "Epoch 35/50\n",
            "802/802 [==============================] - 3s 3ms/step - loss: 284.3891\n",
            "Epoch 36/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 301.5960\n",
            "Epoch 37/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 288.6729\n",
            "Epoch 38/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 262.1246\n",
            "Epoch 39/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 284.9652\n",
            "Epoch 40/50\n",
            "802/802 [==============================] - 4s 4ms/step - loss: 283.2823\n",
            "Epoch 41/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 277.7010\n",
            "Epoch 42/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 276.6682\n",
            "Epoch 43/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 279.4724\n",
            "Epoch 44/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 265.8454\n",
            "Epoch 45/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 272.0915\n",
            "Epoch 46/50\n",
            "802/802 [==============================] - 4s 4ms/step - loss: 276.1911\n",
            "Epoch 47/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 282.4590\n",
            "Epoch 48/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 276.3682\n",
            "Epoch 49/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 269.3725\n",
            "Epoch 50/50\n",
            "802/802 [==============================] - 2s 3ms/step - loss: 266.7471\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(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",
        "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": "5fb62267-8acb-45b5-e8ef-0b5a86aa5b5c"
      },
      "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": "11c9d98e-9f87-493d-8923-634abf85c5e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "502/502 [==============================] - 1s 2ms/step\n",
            "1253/1253 [==============================] - 3s 2ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "((40089, 1), (40089,))"
            ]
          },
          "execution_count": 21,
          "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": "a6b4eb84-7182-4984-efcc-b3fe4a5a1ad4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pa\",\n  \"rows\": 56124,\n  \"fields\": [\n    {\n      \"column\": \"Prediction\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 56101,\n        \"samples\": [\n          1807.5240478515625,\n          1377.95703125,\n          25226.10546875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.031116165977256,\n        \"min\": 2010.0,\n        \"max\": 2023.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          2016.0,\n          2018.0,\n          2021.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actual Values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51254.904712344425,\n        \"min\": -80.6,\n        \"max\": 2426325.6,\n        \"num_unique_values\": 41692,\n        \"samples\": [\n          224832.1,\n          25305.9,\n          28250.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "pa"
            },
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              "      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",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-e5e4f1b2-b4bc-43ec-a0cf-1ce8657e97db 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-e5e4f1b2-b4bc-43ec-a0cf-1ce8657e97db');\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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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04bfef01-2283-482e-81bf-0fdb8bfb5aed')\"\n",
              "            title=\"Suggest charts\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      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-04bfef01-2283-482e-81bf-0fdb8bfb5aed 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  4419.878418  2021.0         4379.6\n",
              "1  5257.450195  2022.0         5229.5\n",
              "2  6078.783691  2023.0         6044.2\n",
              "3  1138.969849  2020.0         1116.2\n",
              "4  1269.174072  2021.0         1229.1"
            ]
          },
          "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": "762d7bf4-e01d-411f-8f76-b67b0da81639"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Year\n",
              "2010.0    25021092.0\n",
              "2011.0    25540144.0\n",
              "2012.0    28806486.0\n",
              "2013.0    32404152.0\n",
              "2014.0    36143764.0\n",
              "2015.0    39406340.0\n",
              "2016.0    41832272.0\n",
              "2017.0    45636184.0\n",
              "2018.0    49043204.0\n",
              "2019.0    55552340.0\n",
              "2020.0    55631784.0\n",
              "2021.0    55221980.0\n",
              "2022.0    68746136.0\n",
              "2023.0    80198456.0\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": "046298c6-2b7f-4262-f294-52ebcd4eea9e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Year\n",
              "2010.0    2.534904e+07\n",
              "2011.0    2.555476e+07\n",
              "2012.0    2.860712e+07\n",
              "2013.0    3.207748e+07\n",
              "2014.0    3.613649e+07\n",
              "2015.0    3.914672e+07\n",
              "2016.0    4.172200e+07\n",
              "2017.0    4.538526e+07\n",
              "2018.0    4.881630e+07\n",
              "2019.0    5.521588e+07\n",
              "2020.0    5.557216e+07\n",
              "2021.0    5.529990e+07\n",
              "2022.0    6.857170e+07\n",
              "2023.0    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",
        "    rmse = np.sqrt(mean_absolute_percentage_error(actual,prediction))\n",
        "    return rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z6hISpu6XNWV"
      },
      "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": "zHBFyyMVcvGb"
      },
      "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": "315d88bc-c6ec-4abd-b559-6f589cd6e496"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3046.1343141859097\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": "1HnSyXYTalvO",
        "outputId": "b2df8ef9-5695-4d70-ea3d-6d29ff4c7b7a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2091.0691118689347\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": "b22e2f2d-75f6-4ae4-cd7b-041d4931f014"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8727830148926595\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": "bqnWbBg-anYz",
        "outputId": "96801aa4-dcb5-4f63-9f03-5feed604f7f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4.8336999264217\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": "6qgSFeaBZOp0",
        "outputId": "f2fef599-147b-49f6-f8a6-d312eb8a76a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9990274606967601\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": "xnvAP7GNaoch",
        "outputId": "b70c62a8-9a0c-4ee5-d6ed-d862ea31e0f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9987540388209241\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": "dUYuvWSycxLS",
        "outputId": "83ab78cd-9940-43fa-ad15-794d9038e6b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "18.5979948704376\n"
          ]
        }
      ],
      "source": [
        "print(MAE(y_test,[i[0] for i in y_test_pre]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "MAE of train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3M0vPi3ucxfD",
        "outputId": "2940ba7e-67c7-4701-de46-d8e82e8c6c36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "15.053460204716687\n"
          ]
        }
      ],
      "source": [
        "print(MAE(y_train,[i[0] for i in y_train_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": "037d99ab-e31a-433e-d6b3-c4d9a6290128"
      },
      "outputs": [
        {
          "data": {
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              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "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='LSTM',\n",
        "    xaxis=dict(title='Year', tickfont=dict(size=14)),\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": "zVIBKDveYTNc"
      },
      "outputs": [],
      "source": []
    }
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