DNN version.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        },
        "colab_type": "code",
        "id": "WDxFiwyq8Uq1",
        "outputId": "9eedcd3a-700d-4397-d44c-d282b9d718f9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ucoboWRK8jFF"
      },
      "outputs": [],
      "source": [
        "data = pd.read_excel('Data_Train.xlsx', index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "colab_type": "code",
        "id": "JbLH51e_-oGK",
        "outputId": "15d8c9bd-9359-4606-968e-2f917f23f8c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<bound method NDFrame.head of                                        Location  ... Delivery_Time\n",
            "Restaurant                                       ...              \n",
            "ID_6321     FTI College, Law College Road, Pune  ...    30 minutes\n",
            "ID_2882                    Sector 3, Marathalli  ...    30 minutes\n",
            "ID_1595                          Mumbai Central  ...    65 minutes\n",
            "ID_5929                         Sector 1, Noida  ...    30 minutes\n",
            "ID_6123      Rmz Centennial, I Gate, Whitefield  ...    65 minutes\n",
            "...                                         ...  ...           ...\n",
            "ID_8067           BTM Layout 1, Electronic City  ...    30 minutes\n",
            "ID_4620                        Sector 14, Noida  ...    30 minutes\n",
            "ID_3392                                Majestic  ...    30 minutes\n",
            "ID_4115                    Sector 3, Marathalli  ...    30 minutes\n",
            "ID_4417                      Sector 63A,Gurgaon  ...    30 minutes\n",
            "\n",
            "[11094 rows x 8 columns]>\n"
          ]
        }
      ],
      "source": [
        "print(data.head)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "7sVULELi_vIO"
      },
      "outputs": [],
      "source": [
        "data=data.values[:,2:8]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "RKG6dZPKAHrX"
      },
      "outputs": [],
      "source": [
        "for i in range(data.shape[0]):\n",
        "    data[i,0]=data[i,0].replace('₹', '')\n",
        "    data[i,1]=data[i,1].replace('₹', '')\n",
        "    data[i,2]=data[i,2].replace('-', '0')\n",
        "    data[i,3]=data[i,3].replace('-', '0')\n",
        "    data[i,4]=data[i,4].replace('-', '0')\n",
        "    data[i,2]=data[i,2].replace('NEW', '0')\n",
        "    data[i,5]=data[i,5].replace(' minutes', '')\n",
        "\n",
        "    data[i,0]=data[i,0].replace(',', '')\n",
        "    data[i,1]=data[i,1].replace(',', '')\n",
        "    data[i,2]=data[i,2].replace(',', '')\n",
        "    data[i,2]=data[i,2].replace('Opening Soon', '0')\n",
        "    data[i,0]=data[i,0].replace('for', '0')\n",
        "    data[i,2]=data[i,2].replace('Temporarily Closed', '0')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "colab_type": "code",
        "id": "bNypB7q3AtbI",
        "outputId": "8d16ec9c-8e50-4bf1-b0b1-d46ff6c78c21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['200' '50' '3.5' '12' '4' '30']\n",
            " ['100' '50' '3.5' '11' '4' '30']\n",
            " ['150' '50' '3.6' '99' '30' '65']\n",
            " ['250' '99' '3.7' '176' '95' '30']\n",
            " ['200' '99' '3.2' '521' '235' '65']\n",
            " ['150' '50' '3.8' '46' '18' '30']\n",
            " ['150' '50' '3.7' '108' '31' '30']\n",
            " ['650' '50' '4.0' '1731' '1235' '45']\n",
            " ['100' '50' '3.9' '110' '26' '30']\n",
            " ['200' '50' '3.9' '562' '294' '65']]\n"
          ]
        }
      ],
      "source": [
        "print(data[0:10,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zcjAAxJjEu5v"
      },
      "outputs": [],
      "source": [
        "for i in range(data.shape[0]):\n",
        "    for j in range(data.shape[1]):\n",
        "        data[i,j] = float(data[i,j])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "7cbfCtVAC6Tl"
      },
      "outputs": [],
      "source": [
        "X=data[:,0:5]\n",
        "Y=data[:,5]\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "X = sc.fit_transform(X)\n",
        "\n",
        "\n",
        "Y = Y.reshape((len(Y), 1)) \n",
        "\n",
        "Y = sc.fit_transform(Y)\n",
        "\n",
        "Y = Y.ravel()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "_TmI9JnsJM1P"
      },
      "outputs": [],
      "source": [
        "def larger_model():\n",
        "\t# create model\n",
        "    model = Sequential()\n",
        "    model.add(Dense(20, input_dim=5, kernel_initializer='normal', activation='relu'))\n",
        "    #model.add(Dense(20, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dense(7, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dense(1, kernel_initializer='normal'))\n",
        "\t# Compile model\n",
        "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "    return model\n",
        "# evaluate model with standardized dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "mtH9iL3UKzZx",
        "outputId": "ffcc79ab-24de-4384-c56e-f0609a5fb5fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Epoch 1/200\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "11094/11094 [==============================] - 7s 654us/step - loss: 0.8620\n",
            "Epoch 2/200\n",
            "11094/11094 [==============================] - 2s 188us/step - loss: 0.8095\n",
            "Epoch 3/200\n",
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            "Epoch 4/200\n",
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            "Epoch 5/200\n",
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            "Epoch 6/200\n",
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            "Epoch 7/200\n",
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            "Epoch 8/200\n",
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            "Epoch 9/200\n",
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            "Epoch 10/200\n",
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            "Epoch 11/200\n",
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            "Epoch 12/200\n",
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            "Epoch 13/200\n",
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            "Epoch 14/200\n",
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            "Epoch 15/200\n",
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            "Epoch 16/200\n",
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            "Epoch 17/200\n",
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            "Epoch 18/200\n",
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            "Epoch 19/200\n",
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            "Epoch 20/200\n",
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            "Epoch 21/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7686\n",
            "Epoch 22/200\n",
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            "Epoch 23/200\n",
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            "Epoch 24/200\n",
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            "Epoch 25/200\n",
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            "Epoch 26/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7678\n",
            "Epoch 27/200\n",
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            "Epoch 28/200\n",
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            "Epoch 29/200\n",
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            "Epoch 30/200\n",
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            "Epoch 31/200\n",
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            "Epoch 32/200\n",
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            "Epoch 33/200\n",
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            "Epoch 34/200\n",
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            "Epoch 35/200\n",
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            "Epoch 36/200\n",
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            "Epoch 39/200\n",
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            "Epoch 40/200\n",
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            "Epoch 59/200\n",
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            "Epoch 91/200\n",
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            "Epoch 92/200\n",
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            "Epoch 93/200\n",
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            "Epoch 94/200\n",
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            "Epoch 95/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7596\n",
            "Epoch 96/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7602\n",
            "Epoch 97/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7601\n",
            "Epoch 98/200\n",
            "11094/11094 [==============================] - 2s 188us/step - loss: 0.7585\n",
            "Epoch 99/200\n",
            "11094/11094 [==============================] - 2s 188us/step - loss: 0.7603\n",
            "Epoch 100/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7600\n",
            "Epoch 101/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7596\n",
            "Epoch 102/200\n",
            "11094/11094 [==============================] - 2s 178us/step - loss: 0.7589\n",
            "Epoch 103/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7593\n",
            "Epoch 104/200\n",
            "11094/11094 [==============================] - 2s 187us/step - loss: 0.7591\n",
            "Epoch 105/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7601\n",
            "Epoch 106/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7599\n",
            "Epoch 107/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7593\n",
            "Epoch 108/200\n",
            "11094/11094 [==============================] - 2s 179us/step - loss: 0.7598\n",
            "Epoch 109/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7589\n",
            "Epoch 110/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7592\n",
            "Epoch 111/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7584\n",
            "Epoch 112/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7583\n",
            "Epoch 113/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7594\n",
            "Epoch 114/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7594\n",
            "Epoch 115/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7586\n",
            "Epoch 116/200\n",
            "11094/11094 [==============================] - 2s 188us/step - loss: 0.7587\n",
            "Epoch 117/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7610\n",
            "Epoch 118/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7580\n",
            "Epoch 119/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7587\n",
            "Epoch 120/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7593\n",
            "Epoch 121/200\n",
            "11094/11094 [==============================] - 2s 187us/step - loss: 0.7589\n",
            "Epoch 122/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7588\n",
            "Epoch 123/200\n",
            "11094/11094 [==============================] - 2s 190us/step - loss: 0.7579\n",
            "Epoch 124/200\n",
            "11094/11094 [==============================] - 2s 193us/step - loss: 0.7580\n",
            "Epoch 125/200\n",
            "11094/11094 [==============================] - 2s 194us/step - loss: 0.7592\n",
            "Epoch 126/200\n",
            "11094/11094 [==============================] - 2s 190us/step - loss: 0.7576\n",
            "Epoch 127/200\n",
            "11094/11094 [==============================] - 2s 199us/step - loss: 0.7579\n",
            "Epoch 128/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7591\n",
            "Epoch 129/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7581\n",
            "Epoch 130/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7588\n",
            "Epoch 131/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7577\n",
            "Epoch 132/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7587\n",
            "Epoch 133/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7559\n",
            "Epoch 134/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7575\n",
            "Epoch 135/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7573\n",
            "Epoch 136/200\n",
            "11094/11094 [==============================] - 2s 188us/step - loss: 0.7583\n",
            "Epoch 137/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7565\n",
            "Epoch 138/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7591\n",
            "Epoch 139/200\n",
            "11094/11094 [==============================] - 2s 187us/step - loss: 0.7572\n",
            "Epoch 140/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7572\n",
            "Epoch 141/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7582\n",
            "Epoch 142/200\n",
            "11094/11094 [==============================] - 2s 188us/step - loss: 0.7582\n",
            "Epoch 143/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7581\n",
            "Epoch 144/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7572\n",
            "Epoch 145/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7592\n",
            "Epoch 146/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7576\n",
            "Epoch 147/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7569\n",
            "Epoch 148/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7578\n",
            "Epoch 149/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7571\n",
            "Epoch 150/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7586\n",
            "Epoch 151/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7571\n",
            "Epoch 152/200\n",
            "11094/11094 [==============================] - 2s 189us/step - loss: 0.7571\n",
            "Epoch 153/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7579\n",
            "Epoch 154/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7573\n",
            "Epoch 155/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7576\n",
            "Epoch 156/200\n",
            "11094/11094 [==============================] - 2s 179us/step - loss: 0.7568\n",
            "Epoch 157/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7577\n",
            "Epoch 158/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7572\n",
            "Epoch 159/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7557\n",
            "Epoch 160/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7558\n",
            "Epoch 161/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7569\n",
            "Epoch 162/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7567\n",
            "Epoch 163/200\n",
            "11094/11094 [==============================] - 2s 191us/step - loss: 0.7581\n",
            "Epoch 164/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7568\n",
            "Epoch 165/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7571\n",
            "Epoch 166/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7568\n",
            "Epoch 167/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7573\n",
            "Epoch 168/200\n",
            "11094/11094 [==============================] - 2s 179us/step - loss: 0.7578\n",
            "Epoch 169/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7570\n",
            "Epoch 170/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7568\n",
            "Epoch 171/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7566\n",
            "Epoch 172/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7569\n",
            "Epoch 173/200\n",
            "11094/11094 [==============================] - 2s 187us/step - loss: 0.7552\n",
            "Epoch 174/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7550\n",
            "Epoch 175/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7553\n",
            "Epoch 176/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7564\n",
            "Epoch 177/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7569\n",
            "Epoch 178/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7564\n",
            "Epoch 179/200\n",
            "11094/11094 [==============================] - 2s 186us/step - loss: 0.7560\n",
            "Epoch 180/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7555\n",
            "Epoch 181/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7560\n",
            "Epoch 182/200\n",
            "11094/11094 [==============================] - 2s 179us/step - loss: 0.7563\n",
            "Epoch 183/200\n",
            "11094/11094 [==============================] - 2s 189us/step - loss: 0.7553\n",
            "Epoch 184/200\n",
            "11094/11094 [==============================] - 2s 191us/step - loss: 0.7542\n",
            "Epoch 185/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7558\n",
            "Epoch 186/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7561\n",
            "Epoch 187/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7568\n",
            "Epoch 188/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7546\n",
            "Epoch 189/200\n",
            "11094/11094 [==============================] - 2s 179us/step - loss: 0.7552\n",
            "Epoch 190/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7556\n",
            "Epoch 191/200\n",
            "11094/11094 [==============================] - 2s 185us/step - loss: 0.7563\n",
            "Epoch 192/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7552\n",
            "Epoch 193/200\n",
            "11094/11094 [==============================] - 2s 181us/step - loss: 0.7552\n",
            "Epoch 194/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7555\n",
            "Epoch 195/200\n",
            "11094/11094 [==============================] - 2s 184us/step - loss: 0.7552\n",
            "Epoch 196/200\n",
            "11094/11094 [==============================] - 2s 178us/step - loss: 0.7556\n",
            "Epoch 197/200\n",
            "11094/11094 [==============================] - 2s 183us/step - loss: 0.7556\n",
            "Epoch 198/200\n",
            "11094/11094 [==============================] - 2s 176us/step - loss: 0.7552\n",
            "Epoch 199/200\n",
            "11094/11094 [==============================] - 2s 182us/step - loss: 0.7555\n",
            "Epoch 200/200\n",
            "11094/11094 [==============================] - 2s 180us/step - loss: 0.7568\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7eff656bd400>"
            ]
          },
          "execution_count": 10,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = larger_model()\n",
        "model.fit(X,Y,nb_epoch=200, batch_size=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "4gTbt5yV2V4r"
      },
      "outputs": [],
      "source": [
        "data_test = pd.read_excel('Data_Test.xlsx', index_col=0)\n",
        "\n",
        "\n",
        "data_test=data_test.values[:,2:8]\n",
        "\n",
        "datat=data_test\n",
        "\n",
        "for i in range(datat.shape[0]):\n",
        "    datat[i,0]=datat[i,0].replace('₹', '')\n",
        "    datat[i,1]=datat[i,1].replace('₹', '')\n",
        "    datat[i,2]=datat[i,2].replace('-', '0')\n",
        "    datat[i,3]=datat[i,3].replace('-', '0')\n",
        "    datat[i,4]=datat[i,4].replace('-', '0')\n",
        "    datat[i,2]=datat[i,2].replace('NEW', '0')\n",
        "    #datat[i,5]=datat[i,5].replace(' minutes', '')\n",
        "\n",
        "    datat[i,0]=datat[i,0].replace(',', '')\n",
        "    datat[i,1]=datat[i,1].replace(',', '')\n",
        "    datat[i,2]=datat[i,2].replace(',', '')\n",
        "    datat[i,2]=datat[i,2].replace('Opening Soon', '0')\n",
        "    datat[i,0]=datat[i,0].replace('for', '0')\n",
        "    datat[i,2]=datat[i,2].replace('Temporarily Closed', '0')\n",
        "\n",
        "for i in range(datat.shape[0]):\n",
        "    \n",
        "    for j in range(datat.shape[1]):\n",
        "        \n",
        "        datat[i,j] = float(datat[i,j])\n",
        "\n",
        "Xt = datat[:,0:5]\n",
        "\n",
        "Xt = sc.transform(Xt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "colab_type": "code",
        "id": "ivs9IuQG3wW8",
        "outputId": "65d2b3f4-54b4-4b77-c2fd-913658eac26b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[38.974186]\n",
            " [98.81901 ]\n",
            " [37.286118]\n",
            " ...\n",
            " [22.509085]\n",
            " [48.418858]\n",
            " [37.286118]]\n"
          ]
        }
      ],
      "source": [
        "yp = model.predict(Xt)\n",
        "yp=abs(sc.inverse_transform(yp)) \n",
        "print(yp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "FPgzpPl74D7R"
      },
      "outputs": [],
      "source": [
        "yp=yp.astype(int).astype(str) \n",
        "for i in range(yp.shape[0]):\n",
        "    yp[i,0] = yp[i,0]+\" minutes\"\n",
        "df = pd.DataFrame (yp)\n",
        "df.to_excel(\"NNModel.xlsx\", index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "R8lqR2pw8kIU"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Shaurya Bhandari: Deep Neural Network Mode",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.7"
    },
    "vscode": {
      "interpreter": {
        "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}