Petrol_price_forecasting.ipynb

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      "name": "Petrol price forecasting.ipynb",
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        "## Petrol Prices are going up continously these days and it has become very hard to predict what will be the price range in the next few months or even days.\n",
        "\n",
        "### In this project we will try to forecast the different petrol prices for the upcoming dates given to us. \n",
        "\n",
        "### We will use LSTM and ARIMA forecasting models to predict the prices then we will see how we can implement the same using Auto Keras, which is an Auto ML Library."
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "import matplotlib "
      ],
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        "df1 = pd.read_csv(\"train_data.csv\")\n",
        "df1.head()"
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              "        Date  Petrol (USD)\n",
              "0   6/9/2003         74.59\n",
              "1  6/16/2003         74.47\n",
              "2  6/23/2003         74.42\n",
              "3  6/30/2003         74.35\n",
              "4   7/7/2003         74.28"
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              "Date            0\n",
              "Petrol (USD)    2\n",
              "dtype: int64"
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              "        Date  Petrol\n",
              "0   6/9/2003   74.59\n",
              "1  6/16/2003   74.47\n",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3=df2[df2.Petrol<160]\n",
        "plt.scatter(df3.Date, df3.Petrol,color='blue',label='Prices', s=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "eys41DSPeJg8",
        "outputId": "99ae4166-5f3b-4e95-9045-9de9a064ae8e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe1e91783d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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fbUSale7u/rWqqM9WZkCgguBlbeS//rX3a8MA1qyxP7ayUt84qK9YDLj0Uvv9UWj2SNbSooPA974HvP66rvVt3arfH320ewLDlhbdgbx+vf/Prq/vP2z5ySedz7nrLv+fEybXgCAiD4rIbhF5LWnbT0XkdRH5s4j8RkQ+mbRvgYhsF5E3RGRypgpO0dHeDsycqVNHz5yp32fbL3/pfszgwb1fx+PuOf6jMC48is47L9rNHibD0Av7HD5svf/IEWDCBPsmruTEh0HWEbP6GbktSbp4cW6b3LzUEFYCmJKy7TkA45VSnwWwDcACABCRUwBcBmBc4pxlIsKlNQrYsmX6CWrVKmDjRv06cqTzDOCwGQawaZP7cW+91fvH1tbmnKFy+nROSrMT9WYPU2Oj+zoFhw8Dy5db74vH3c93ErTJMZdNbq4BQSn1IoAPUrY1K6XM/vaXAZijbesB/EopdUgp9RaA7QC+EGJ5KULa2/VsXitz5uhRPtng5w/IPNZpHdyKCv0UTNbyIUkbAKxe7e24hx7qv629HbjnHl2LCKKszLrJ0W0N7+7u3Da5hdGH8A0Av018PRJA8vSVnYlt/YjIbBFpFZHWPXv2hFAMyraZM533z5uXnXJs3qz/kNwkt2/v3m2fauHQoeg85UZRPiRpA3TaaS9SR5uZtd6tW4N/tlLWTY4zZzrnhMp1TqO0AoKIfB9AF4CH/Z6rlFqulKpVStUOHTo0nWJQDhiG86LrAPDyy9kpi9ex72b7dns78P3v2x9XURGdp9woyockbYC3dY4BHTja2vTXTrVeP447zrrJMRYDnn7a/rySktz2XQUOCCLydQAXArhCqb93ubwDIDkhwKjENiowXlIUuC0rGJZPfcrbcQcPAp//vHvN5tCh6DzlRpFTkrZc39BMhgG8+qq3Y5UC/uEfdM3gG98I5/OdguKkScC6dTpgmUN4Bw3qzXWUy74rhzWQ7InIFAA3A/iiUiq5a24NgEdE5B4AIwDUANiYdikpctxGSwDA1VdnvhwAMH68/uPy0gH4uc+5HyMSnafcKDJvXNOm6XQL+/frG1pJSe5vaKZ43H9ywjBqBqb6euf9kyYBe/bocm7frn/fGhpy/7NzDQgi8iiAcwEMEZGdAP4NelRRBYDnRP/UX1ZKXauU2iwijwHYAt2UNEcp5aF1l/KNW5u9CDB7dnbK0tCgU1OkMyIkWVRSEUdZXZ1uXonaDc20eXPu0nFXVrrXQgH9s5o1K/Pl8cM1ICilLrfY/IDD8bcDuD2dQlH0uXX7fPGL2bs5xGLAnXeG94T3rW9F58YWZX5vaOaM37Y23Q/R0KD/7zIh0zmVysqs8xqVlQHPPZe/vz+cqUyBuCX5+v3v3WeBhsUwgFtuCe96d94Z3rUKmWEAK1bon/2KFfapINrb9TDeT3wCuOYaPRv32mt1NtpM/I60twcf0OC1memGG3TN6MwzdRrskSOBhQuBvXt17SlvKaVy/u/zn/+8ovzx8cdKVVQopbvj7P/FYkoZRubLc/PN7mXx+m/ixMyXtxC89JL+/x00SP/cBg3S7196qe9xt9/u/PMuLw/3d2TRovB+F5z+TZ0aXpnTAaBVhXgvZg2BfPM6ESwb+d0NQ2cyDctnPxvetQpVckoHM5Xz/v29283Z4AsXOg/vBfRM4fnzwynXU09lb+5LoWJAIN+8dthlI9FZkNEkdkpLmdDOi3hcB3sr5kOA21yPZPffn37+HsNwTroXtkKduMiAQL557bDLxiSltjb75GV+cXSRN14Wefnud71fL4yaZDzubbZ6GLyOIspHDAjkm9eJYD09mb/Buq2L7AcT2nnjZabyCy/4u+aWLemVqa0tWEZSv/J9FJEbBgTyzS4pXKqwmnKyobRUp0omd24zladNA9591981012dLswHAyvl5QUyisgFAwL5Yhj+UltnulN5xw7n/X5u8mwu8sacqRyL9dYUklMvPPus/4eBTM1HsJK8LoZX118PLFhQuDUDEwMC+eJnKcHOTv9NB365NV80NADnnON+HbtkZGTNnKn8k58AU6fqn/HChcCpp+pBB3adzna8Lmdpx+3BINmxx/q79oABxTPYgAGBfHHqULTyxBOZXQGqocE+QJkpiL0k2WPuIv9eeUU/Nb/4IrB2rR4+OnIk8Ic/+L9WNtevnjULqKryd06x1B4ZEMgXpydyK6WlmW82cgoIgPuiJIB7MjLqy2kuwsYA6SzNrJ9By+K1GXPAAOCb3wSam92XUDWNHVs8tUcGBPLF6YncSqbnIsTjzh2c8bgeIuj0RFhVVbjDCDMlHg++mpiVpUuD1yT9NGOOGqVv7nV1wIwZ3s7xW5vIZwwI5Jvf4X1hZSG14mVMfCymnwit/rCrqvS+YnkCDMtrr4X7/6pU8Jqkn2bM5M5ur30JxdJ/AARcD4GKl9MTuZ2f/Ux3PE6aFG5ZDMO5vTp5YlxdnV42s7GxdzW0Cy7QNQMGA//27g33en5rkoah/y+feQbYuVM3BXmpsZx1Vu/X48bp32W3DvBiSnbIgEC++O1UNn35y8B774V3821pAc4913l2aurM4+pqnSI7zIVQilWQoZuA/Q3Yz6z2lhb9cHHggP/PX7So9+uGBuDGG51/nxcuBIYN8/85+YpNRuSL305lU1dXeJ3LhgFMnOieqmDOHD79Z8q4cd7XLE5mNz/B69KbhqHnlgQJBkuX9r25x2J6dJTdovcLF+pRVMWEAYF8cZql6qS7O7zO5cZGb8n1wuz0pL4aGoKNDJo+3Xr7v/+7t+A9a5b/votTTgE6OoDrruu/r65Oz5JetEgvxXrCCcBXv6qPL7ZgADAgkE/m6mR+lZWFN9bfy3rOQPrpEMhe0N+Dpibr7fPm6c59J+3twOOP+//MM85wbvaprtZLsL76KvDmm8DDDxdXM1EyBgTyJejqZF1d4aUM9tqhmc3JTsXGMPyvY3D88c4duBdc4Dz01E8G1WT8PfCOAYF8ue22YOPFKyt7R/ekwzD0DFk35eXFNVww25zWRLBTU6PTmdjp6gKWL7ffHzQNCn8PvHMNCCLyoIjsFpHXkrZdKiKbRaRHRGpTjl8gIttF5A0RmZyJQlNupLM62cGD4fQhxOO6+clNRUXxpBvIBb+jzcrK9Gxwt6R3Dz1kvd0w/GdQBbx3VpPmpYawEsCUlG2vAbgYwIvJG0XkFACXARiXOGeZiHicIE5R5zYj1K2z2UtHsJu2NuenzNLS3qybHGGUOX5Hm91wg57z4Tap0W7xpcbGYOsdPPIIfw/8cA0ISqkXAXyQsm2rUuoNi8PrAfxKKXVIKfUWgO0AvhBKSSnnNm/W1fqgli1LP9Hd6NH2o1tKSoCvfU13PhZyzvoo8Dva7MQTdaCeONH5uHfftf4d8TqQINmpp7J24FfYfQgjASQnot2Z2NaPiMwWkVYRad2zZ0/IxaBMcFs68/jjnfenu1Ric7MeDWI3nLSnR4984RNh5pm1MK95fnbu1K+Njc7HdXfrfiqr7X7dcIP/c4pdzjqVlVLLlVK1SqnaoUOH5qoY5IPb0pknneTcvn/kSPB+hOZmYPJk57kFYXVckzennuotY2hFRe+Q4xEj3NenuOee/rUEv81FXB87mLADwjsAkhezG5XYRgXAbR7B5MnONwg/6QmSGYa3FNZhdVyTN16zjJrrUpjcmh27u/vWJA3D/wijMWNYUwwi7ICwBsBlIlIhIp8GUAMgQHZ0ykeVlcCaNfb7g474aGz0Nus4zMlv5M7rSKPUFCKHDzsf39PTN7Dfeqv/Ia7XX+/veNK8DDt9FMAGAGNFZKeIzBKRi0RkJ4CzATwrIusAQCm1GcBjALYAWAtgjlIqQOsfRdFLLznv37lTJx1bt043E5i1hfJyfUMIOvLHa4diTw+bCbLJ60ijE0/s+37cOPdzzCU1DQNYvNhfuSoqgNmz/Z1DmuuIbqXU5Ta7fmNz/O0Abk+nUBQ9zc3AY4/Z709uDho4sHckUHe3/wXXkxkG8Pbb3o5N53PIv4YG3cnvxuxQNt1xB7BqlfM5S5fqEUnvvee9/6CsTAeDtWvZXBQUZyqTKy9t+F1d+gZhLq24b1/vvINDh/R7c7tXLS26E3LrVm/HZ2O5TurlNZ/RqFF9348YoTOWurnoIl2rdAsIX/2qTqPxX/8F7NrFIcfp4HoI5MpLG/5pp+mnshUr7Nt7zWGns2a5f6Zh+M95f/gwO5WzKUg+I9OMGcBzz7kft3q18/7SUuDnP2eNICysIZArtz9KoHfBFC9LWnrR2Og/533y8EbKvHjc2/yA1CYjwHtfj1tncn09g0GYGBDIlZebuJnJ1Kmj0c+w0yAzUwcMYKdyNrmlEQH0gAKr//NYDJgwIf0yxGLpX4N6MSCQIy+duiUlOk8N4JzSwM+wU78pMgYOBH77Wz4tZlNNjfsxZt+SFbeOZS+Y2jpcDAjkKB53H73zne/03ojNlAaxWG9NYdAg/wnnvIwYEtGdk0uW6Bw47EzMLi/Bfdgw+//zESP6rnHsV2UlU1uHjZ3K5GjzZud24pIS4Ec/6rutrk4nmIvHdXPTSSfpm4fXYOB1ZmppKfDkk6wV5IrZ7LN+vf0x557rfI25c/U8henT+/+elZfroO+UJZdNhOFiQCBHbW3O+7/0JesbcnW1t9FEVhobvXVWdnXpY+fMCfY5lL5Vq4CRlukrNS81gPp64MMP9WihlSv14IOzz9a5s5YutT9v+nQ+DISNTUZk6/rr3ZPFZWIymJ8OZSazy60RI+xv2kuXel+b+JVXgP/4D+Ctt/S/1av1EGYnZ5/tr6zkjgGBLD36qPPTmcnL6mV+tLcDf/pTuNekzLruOqCjQw8sOOss/drRobd7YU5mNIzeIcv794ezoBL5wyYj6scwgCuu8HasOdw0DMuW+W/+CfPzKbhhw3RzTxBB1mcGrOc3UHoYEKifxYu95Y8pLe0dbprKMPQfelubHp7Y0OA8Zry93X8wqKqy/3zKH37XZzalpsSg9DEgUB/LlgE/+IG3Y8eOte7Ua2nRTQA9PfoPfdAgPZqkqcl+aOiCBf7KWVWlE+6xUzH/mZMZgwQFChf7EOjv/D6ln3FG/2127cHJSe+sbN7s7TNF9MiV3bs576BQNDSwySgqGBDo7/w+pVtlunRqD3ZaU/ngQW+fOWOGrm2wZlBY/I5WY96qzGBAoL/z+pQO2A8pDJLczjCA11/39rmVld7LSPkhSKdy6rKcFA4GBPo7r0/pixbZDykMktwuHve2WDvA3DWFqK3N+++eKXVZTgoHAwIB8P6U7rY8YZDkdm1t7uvsArpZgblrCo+XJHmpUpflpHAwIBAAb0nsAGDNGucnsyDJ7WpqvDUFlZezmaAQOT1E2GGHcmYwIBAA3X/glnL6qaf0KmZuzOR2992nV9S67z793m5U0LRp3poM3IIR5adYDPjv//Z3DjuUM8N1HoKIPAjgQgC7lVLjE9sGA4gDGAPgrwBmKKX2iogAuA/ANACdAL6ulPq/zBSdwvTBB877J07USci88pPcrqlJ1xDsgkJZmc5Z5CUYUX66/HLg/vuBl15yP9bPuhrkj5cawkoAU1K2zQewXilVA2B94j0ATAVQk/g3G8D94RSTMu1Tn3Le/9nPZu6z3ToVv/1tBoNi8OyzetU7N488wppiprgGBKXUiwBSnx/rATQmvm4E8JWk7auU9jKAT4rI8LAKS5kzfrxuo7dSXp67ztxBg9iRXCxiMeB3v3NOmDhnDmsHmRS0D+FYpVRH4utdAI5NfD0SwI6k43YmtvUjIrNFpFVEWvfs2ROwGBSWadPsR/ocPhwsiZxh6BTGt9yiXw3D+pj//E/7a4jwBlBM6uqAvXuBhQuBIUP0cOSyMuD00/UcliVLcl3CwpZ2p7JSSgHwkAqt33nLlVK1SqnaoUOHplsMSlNTkx5SaqWy0v+6Ay0teuGUG28E7rpLv44cqbcna2wEDhywv84117B5oNhUV+tZ83v26IEOR44AmzZxqGk2BA0I75pNQYnX3Ynt7wAYnXTcqMQ2CsG2bcCZZwJHHaVvrrffbv3UHcQLL9jnnz940HqGsR0/+YzcFsPxOoOZiNIXNCCsAWAmHp4JYHXS9qtEO75H2J4AABDSSURBVAvAR0lNS5SGuXN1dtGNG4GPP9bDOH/wAz1zN/Wp2y/DAH79a/v9djOM7QTNZ0REueUaEETkUQAbAIwVkZ0iMgvAnQDOF5E2ABMT7wGgCcBfAGwH8AsAHtdMIifbtgH33mu978gRYMoU+yyiXjQ2Os8U7ury147vJ5/RhRc6X4sL4BBlj+s8BKXU5Ta7JlgcqwBwyfOQff3rzvsPHNBP3UEXtV+2zHn/aaf5a8cfPdp5f/LCJhdfDNxwg/2x06d7/1wiSg9nKkdYe7teEWzjRufjenq8tfEbBnDbbcDw4XqBmeHD9QigrVudzxs82HuZ/Qq7M5uIguOKaRF1zz3AvHneji0pcW/jb2kBzjuvb3qKXbv0CCA3fpttduxw3p+chybMzmwiSg9rCBH01FPegwGgn/ad2vgNQ6eecMtVZEXE/7rFTimwBw7sDV6GATzxhP11/HZmE1F6GBAixjCASy7xd85ddzm38Tc22j+Fuxk3zv88AKfslZ2dvX0MbusgdHdzUhpRNjEgZImXWbuAvkl2d/u79vz5zqOMVq+23+fm3HP9nxOLOQ9jveQSXd62Nh0g7EyfzklpRNnEgJAFXmftAv6WsTS5je0P2g5fXq5HGAXx9tv2zUZmed1GI519drDPJqJgGBAyzGnW7tSp/Z/s3dJQW7Fbq9j8/Lff9n9NQI/+Cdpk42UugtsaCEGbuYgoGAaEDIvH9eQxK/v26SakZG5pqO0om2xSXldCs/LLXwZvsvEyF+Hxx52Pef75YJ9NRMEwIGTY5s3OT8LLlukhpia7Mfluli617kfYvNl/n4TpsceCnefFwYPA//5v5q5PRP4xIGSYlyagefP0nADDcJ81bEcp636EIE1QppdfDn6u21yEX/3KvlZjYtoKouxiQMgwr01AV1+tb+huN0k7dv0ITqml3QwcGPxct7kImzY5n19S4n/+AxGlhwEhw8aPdx5rb2puBrZsse+IdWM1icsw0htyevXVwc91movQ3e3er3HGGRxySpRtDAgZ1tDgvCRgsvff1/l7gujp6T8iyC2LqVOgKi8HZs8OVhZAz0W4807rff/yL/bpsU1XXhn8s4koGAaEDIvFvGXs7OnRxzp1QM+ebb/usdUTt9viM7W1wLp1+prm+aWluraxfn16T+iGoSfMWXnxRefmqPJyNhcR5QIDQhacd579jTzZ3r32NYTKSh0sBgyw3i/if+GZwYOBSZN0zeQXv9A38J//XHdw19X5u1Yqp0VySkudawhPP83mIqJcYEDIgoYG+xt5st277WsIBw8Cb7zhfeEZQCe0c2Lur67WaynccYd+DeNm7DQxrbOzNy2FGShLSvSQ23XrdJAiouxjQMiCP/3Jvc3cixNPdK5BpHYqu/VHBJ3z4IXTKCMrZWU6aKYzsomI0sOAkGFmigq34Z9eboSnnOJcg0gdt79hg/P1ktclCFtDg/NIoocf1hPpzE7vw4f1+2nT0lsOlIiCY0DIsGuv9XaD6+zUi8U4+f3vnWsIyauL5XqtgVgMuC7AitpuifqIKHMYEDKovR145BHvxzulmKio0JPWnGoIyX0IbjfVqK414JSoj4gyK62AICLfEZHXRGSziNyY2DZYRJ4TkbbEa8B0bflvwYLwrtXTo5uEvKxEBugcRk7ZQuvrMz+Sp6bG/7wKq74QIsqOwAFBRMYDuAbAFwCcCuBCETkJwHwA65VSNQDWJ94XpddfD+9aEybosfleViID3HMYBZ0A58e0ae4prlNZ9YUQUXakU0P4DIA/KqU6lVJdAP4HwMUA6gE0Jo5pBPCV9IqYvz7xifCuddJJ3lciA9w7sY8+Oryy2WlqChZ4kvtCiCh70gkIrwE4R0SOFpGBAKYBGA3gWKVUR+KYXQCOtTpZRGaLSKuItO7ZsyeNYkSTYbh3Evu9HuBtJTK3HEaVlXrEUqa1tfmvIQDsQyDKlcABQSm1FcBPADQDWAvgFQDdKccoAJb5O5VSy5VStUqp2qFDhwYtRmTdemvwdQismE/0XlYic8thpFR2OpTdFsmxUlrKPgSiXPGYds2aUuoBAA8AgIgsBLATwLsiMlwp1SEiwwHsTr+Y+cUwgMWLw7teSUnvE73ZUWv15F1eDvzP/7ivy/yP/xjd1BBVVdEc/URUDNIdZXRM4vU46P6DRwCsAWCmJpsJII0EzPkp7HH0Awb03iSdOmoPH9aT0T7+2Pl6QZpxgnBbJCdVRQXw299GN1gRFbp05yE8ISJbADwNYI5S6kMAdwI4X0TaAExMvC8qbW3hpKowrVnTe5MM2lGbbPjw9MvkRU2N91QUF18MvPde+kn1iCi4dJuMzrHY9j6ACelcN9+F1SVSVqZH3CQnewvaUZusvj69871qaACuv97bsWPGsGZAlGucqZwBa9eGc50dO/pn/gwy2StZNtcaiMWAL3/Z27Hvv5/ZshCROwaEkBkG8LvfpX+dKVOAYcP6bw8y2SvZRRdl90m8qsrbcdmYF0FEzhgQQhaP288m9uOhh6y3NzWll7bay/rOYRo82P2YbM2LICJnDAghMQzgttuA73zHff6BW8CorbWuHQC6D8EpR5GbbM8BHDfOPYCVlXGoKVEUMCCEoKVFPwn/8Ic6p5CTGTPcm1Geftp+X01Nek/5ZWkNI/DPy43+mmvYoUwUBQwIaTIMYPJkoKvL2/H33ac7ne3WWF661L52ALgvPOMm24njYjFg/HjnY157LTtlISJnDAgeGQawYgVwyy361cwtFI+7J5IzlZfrYaR1dXpUzaJF+mZ5wgnAV78KdHS4LyoTi+kx+0GUlmZvhFEyt07wjg7n/USUHVluQMhPLS16GcxDh4AjR/S22bOBb31L3+SVZbam/g4fBrZs0V9XVwNz5+p/fp1/PvDYY/7Pu+SS3DTNDBvmnE4jWxPliMgZawguzCahfft6gwGgg8CyZcCSJf6acMIYb9/Q4L8voKxMB5Jc+IpLAvRsTZQjImcMCC4aG507iru6vNcQgHDG28diwOOP+zunsjJ3I3lmzrTvSK+qyk0zFhH1x4DgYuXK8K5VWhreePuvfEX3QXgxcGBuk8bFYkBzs/58szO9vFy/N7cTUe6J8vN4myG1tbWqtbU118XoxzCAo47yVwNwUlICfPRRuDfAXbt0R/Rzz+n5DyefrNNFbNyo919wgX4Cj8JNd98+3Qm/fbte86ChIRrlIspXIrJJKVUb1vXYqZzQ3g4sWKDXQT75ZOCOO4AnnwwvGAA6L1HYN8Bhw3Q580F1NTBrVq5LQUR2GBAA3HMPMG9e7/uNG4FVq4DPfCbcz7FLR0FEFAVF34fw1FN9g0GyrVvD+xy3CWdERLlW1AHBMIBLLw1+/lFHOe8fPFi333uZcEZElGtF3WQUj3tPOWHlo4+c9//oR8CcOcGvT0SUTUVdQ3BbjJ6IqJgUdUD44IPMXn/nzsxen4goTEUdELwmpQuiokKPtSciyhdpBQQRuUlENovIayLyqIhUisinReSPIrJdROIiYpPoObcMA1izJnPX7+nhoi9ElF8CBwQRGQng2wBqlVLjAZQCuAzATwDcq5Q6CcBeAJGcihSPZ3Y5yS9+kbNwiSi/pNtkVAagSkTKAAwE0AHgSwB+ndjfCMAl12VutLW5r26WjnQWsSEiyoXAAUEp9Q6AuwG8DR0IPgKwCcCHSilzMOdOACOtzheR2SLSKiKte7K90C+A0aP9n3P++d5v9NleqpKIKF3pNBl9CkA9gE8DGAFgEIApXs9XSi1XStUqpWqHDh0atBhZM2MG8LnPec9tlO2lKomI0pVOk9FEAG8ppfYopY4AeBLAPwP4ZKIJCQBGAXgnzTJmxI4d3o+tqAAeeEAvcO+lhjBgAHP8E1H+SScgvA3gLBEZKCICYAKALQBeAHBJ4piZAFanV8TM2LDB/RgzZ//zz+vXhgZvNYRnnmGHMhHln3T6EP4I3Xn8fwBeTVxrOYBbAMwVke0AjgbwQAjlDNWDDwIvveR8zPnn6yUyOzqAujq9LRbTy2k6mThRp7kmIso3RbdATnOz+00dAObP12sipGpvB0ZadpNrHR3MakpE2RH2AjkFOVPZMIAVK4BbbtGvhqG3b9sGTPHY7W03y3jECJ3K2gpTXBNRPiuoGkJ7u27nb2np3VZVpYeAXnWV/Y08lZelLnft0rWIN94Axo4F7ryTwYCIsotLaNpIXfXMZOYr8hoMAOCRR9w7hYcNA1au9H5NIqKoK4gmI6dVz/w65xzmICKi4pT3AcEwgOnTw7teU1N41yIiyid5HxDq63Vm0TCceSbnDxBR8crbgGAYwM03Ay+8EN41r7wyvGsREeWbvOxUbmkBJkwADh8O75pVVUw3QUTFLe9qCIah5xKEHQyam9lcRETFLe8CQjwOHDwYzrVOOQVYsgTYvbs3PQURUbHKuyajtjagu9vbsbEYsG9fb0K6khJdG5g8mbOKiYhS5V1AqKnRS1+6BYWqKuDjj7NTJiKiQpB3TUYNDUBlpftxn/tc5stCRFRI8i4gxGLA2rW6luBk1arslIeIqFDkXUAAdAfwhx/qNBNWbroJOPHE7JaJiCjf5V0fgqm6GnjxReDNN3Um07/+FRgzRtcMGAyIiPzL24BgOvFE4Pe/z3UpiIjyX142GRERUfgYEIiICAADAhERJTAgEBERgIisqSwiewD8LeDpQwDEAFQk3h8K4eswrhGlz+H3lv+fWcjfWy4+M0rf23uJf0Ecr5QaGvDcfiIxyiidb0hEWgEMBWDOX5YQvg7jGlH6HH5v+f+Zhfy95eIzo/S9vaeUqkUEsMmIiIgAMCAQEVFCJJqM0rQcwDkAahLv20L4OoxrROlz+L3l/2cW8veWi8+M0vf2ICIiEp3KRESUe2wyIiIiAAwIRESUkJU+BBE5HsArAD6Zjc8jIqI+PgRwplJqm9NB2aohrAfwPoAfA1AADmfpc4mIiknq4sK7AXQBKIcegOMo4wFBREYDOB5AKYDnoSdksKmKiCjzSgH0JP7tcDs446OMRKQBwEPQQWAAdECQjH4oEREl+xjAEKXUEaeDsvGkXg6gCjoY3AwGAyKiTDkIwLzpK+i+g27o+2+T28nZCAivQBdoB/REDE58ICIKh0p5NW/+PYn326Hv8yUATnW7WMYDglLqVQD7AeyDzmgqYFAgIgqDpLyaCfNKEtuOQ28fwtuuF8vGTOVEP8JK6HSvbDIiIsquD6CHnW53OignqStE5DkAgwCcAOBTAD6C7vSIAfgzdOEHAfhn6IhXBt1bDugqkTl/wix8T8p2U0liX0nS8WLxtXkNc1vqMd3o7a0vtTg3VU/S1wp9a2JdiWscgQ6Qqdcy3ye/Jn+f5ueb35u5vcxDuRR6fx7m95RcXnN4GlLKkHqN5G3dieuZ35e57wh01dW81kHo/0uVtC35Wj3o+3NP3mf+H5RB1zSr0ffnA1j/n6U+PZnf+5HEa5nF+XbfZ3KZzOuY/x/JP4MBSeck/+4l607abj69lSW974L+3TgA3f+WXB67/xNzu/m9mf8XXYnPq0g6rsTmOsnM3wmzrOYT55Gksib/bbj9v6b+7Zk/K/P/9TD0757Vzyz13OSfX2qLQ/LflVV57H5+ZlnNn435N1ICvVbBEPT/PTIdTHzmHwGclvhe9gIwAJwIfU97Cvpp/UoAGwBMANAJ4GCY6xmki7mMiIgIAOcDEBFRAgMCEREBYEAgIqIEBgQiIgLAgEBERAkMCEREBAD4f9xnuLzP4azVAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4=df3.reset_index()['Petrol']\n",
        "df4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geUWXLsjeXks",
        "outputId": "4f538c10-7ce6-47c2-b80d-bc234e8b14ed"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    74.59\n",
              "1    74.47\n",
              "2    74.42\n",
              "3    74.35\n",
              "4    74.28\n",
              "Name: Petrol, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "df4=scaler.fit_transform(np.array(df4).reshape(-1,1))"
      ],
      "metadata": {
        "id": "Jxs4srAfeax2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "og8NF-5KefIt",
        "outputId": "acd5a1d9-5595-429c-8f18-df3aa66400ef"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [0.67642731]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_size=int(len(df4)*0.65)\n",
        "test_size=len(df4)-training_size\n",
        "train_data,test_data=df4[0:training_size,:],df4[training_size:len(df4),:1]"
      ],
      "metadata": {
        "id": "-FwQWLvkeh1F"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.shape\n",
        "test_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WR-IE6hwej-A",
        "outputId": "a48718b0-1c93-47b8-fd75-576913e0062a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " import numpy\n",
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, time_step=1):\n",
        "\tdataX, dataY = [], []\n",
        "\tfor i in range(len(dataset)-time_step-1):\n",
        "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
        "\t\tdataX.append(a)\n",
        "\t\tdataY.append(dataset[i + time_step, 0])\n",
        "\treturn numpy.array(dataX), numpy.array(dataY)"
      ],
      "metadata": {
        "id": "ZPv7NwxHenDf"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
        "time_step = 100\n",
        "X_train, y_train = create_dataset(train_data, time_step)\n",
        "X_test, ytest = create_dataset(test_data, time_step)"
      ],
      "metadata": {
        "id": "iLyrLJTweqbV"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
      ],
      "metadata": {
        "id": "mezfQhVeevcd"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM\n",
        "model=Sequential()\n",
        "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))\n",
        "model.add(LSTM(50,return_sequences=True))\n",
        "model.add(LSTM(50))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error',optimizer='adam')"
      ],
      "metadata": {
        "id": "vT1xZqgoeyyU"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4ELUW44e3j9",
        "outputId": "6d895e29-3246-4c5b-a4bb-6256b99d5762"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "7/7 [==============================] - 10s 485ms/step - loss: 0.1650 - val_loss: 0.0714\n",
            "Epoch 2/100\n",
            "7/7 [==============================] - 2s 232ms/step - loss: 0.0323 - val_loss: 0.0158\n",
            "Epoch 3/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 0.0191 - val_loss: 0.0039\n",
            "Epoch 4/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 0.0119 - val_loss: 0.0063\n",
            "Epoch 5/100\n",
            "7/7 [==============================] - 2s 215ms/step - loss: 0.0082 - val_loss: 0.0044\n",
            "Epoch 6/100\n",
            "7/7 [==============================] - 1s 213ms/step - loss: 0.0072 - val_loss: 0.0053\n",
            "Epoch 7/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 0.0064 - val_loss: 0.0031\n",
            "Epoch 8/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 0.0058 - val_loss: 0.0033\n",
            "Epoch 9/100\n",
            "7/7 [==============================] - 2s 234ms/step - loss: 0.0056 - val_loss: 0.0029\n",
            "Epoch 10/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 0.0054 - val_loss: 0.0029\n",
            "Epoch 11/100\n",
            "7/7 [==============================] - 1s 214ms/step - loss: 0.0053 - val_loss: 0.0028\n",
            "Epoch 12/100\n",
            "7/7 [==============================] - 1s 217ms/step - loss: 0.0052 - val_loss: 0.0028\n",
            "Epoch 13/100\n",
            "7/7 [==============================] - 2s 229ms/step - loss: 0.0051 - val_loss: 0.0027\n",
            "Epoch 14/100\n",
            "7/7 [==============================] - 2s 221ms/step - loss: 0.0050 - val_loss: 0.0029\n",
            "Epoch 15/100\n",
            "7/7 [==============================] - 2s 223ms/step - loss: 0.0050 - val_loss: 0.0027\n",
            "Epoch 16/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 0.0049 - val_loss: 0.0031\n",
            "Epoch 17/100\n",
            "7/7 [==============================] - 2s 223ms/step - loss: 0.0048 - val_loss: 0.0026\n",
            "Epoch 18/100\n",
            "7/7 [==============================] - 1s 217ms/step - loss: 0.0045 - val_loss: 0.0027\n",
            "Epoch 19/100\n",
            "7/7 [==============================] - 2s 231ms/step - loss: 0.0046 - val_loss: 0.0024\n",
            "Epoch 20/100\n",
            "7/7 [==============================] - 2s 234ms/step - loss: 0.0042 - val_loss: 0.0023\n",
            "Epoch 21/100\n",
            "7/7 [==============================] - 2s 223ms/step - loss: 0.0041 - val_loss: 0.0025\n",
            "Epoch 22/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 0.0042 - val_loss: 0.0023\n",
            "Epoch 23/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 0.0038 - val_loss: 0.0020\n",
            "Epoch 24/100\n",
            "7/7 [==============================] - 2s 227ms/step - loss: 0.0037 - val_loss: 0.0022\n",
            "Epoch 25/100\n",
            "7/7 [==============================] - 1s 215ms/step - loss: 0.0036 - val_loss: 0.0020\n",
            "Epoch 26/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 0.0035 - val_loss: 0.0022\n",
            "Epoch 27/100\n",
            "7/7 [==============================] - 2s 237ms/step - loss: 0.0034 - val_loss: 0.0018\n",
            "Epoch 28/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 0.0032 - val_loss: 0.0017\n",
            "Epoch 29/100\n",
            "7/7 [==============================] - 1s 215ms/step - loss: 0.0031 - val_loss: 0.0016\n",
            "Epoch 30/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 0.0030 - val_loss: 0.0022\n",
            "Epoch 31/100\n",
            "7/7 [==============================] - 2s 229ms/step - loss: 0.0030 - val_loss: 0.0016\n",
            "Epoch 32/100\n",
            "7/7 [==============================] - 2s 220ms/step - loss: 0.0028 - val_loss: 0.0015\n",
            "Epoch 33/100\n",
            "7/7 [==============================] - 2s 235ms/step - loss: 0.0027 - val_loss: 0.0016\n",
            "Epoch 34/100\n",
            "7/7 [==============================] - 2s 237ms/step - loss: 0.0027 - val_loss: 0.0015\n",
            "Epoch 35/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 0.0027 - val_loss: 0.0014\n",
            "Epoch 36/100\n",
            "7/7 [==============================] - 2s 235ms/step - loss: 0.0025 - val_loss: 0.0016\n",
            "Epoch 37/100\n",
            "7/7 [==============================] - 2s 220ms/step - loss: 0.0027 - val_loss: 0.0016\n",
            "Epoch 38/100\n",
            "7/7 [==============================] - 2s 236ms/step - loss: 0.0024 - val_loss: 0.0013\n",
            "Epoch 39/100\n",
            "7/7 [==============================] - 2s 233ms/step - loss: 0.0023 - val_loss: 0.0014\n",
            "Epoch 40/100\n",
            "7/7 [==============================] - 2s 235ms/step - loss: 0.0022 - val_loss: 0.0014\n",
            "Epoch 41/100\n",
            "7/7 [==============================] - 2s 234ms/step - loss: 0.0024 - val_loss: 0.0013\n",
            "Epoch 42/100\n",
            "7/7 [==============================] - 2s 227ms/step - loss: 0.0025 - val_loss: 0.0012\n",
            "Epoch 43/100\n",
            "7/7 [==============================] - 2s 221ms/step - loss: 0.0028 - val_loss: 0.0013\n",
            "Epoch 44/100\n",
            "7/7 [==============================] - 2s 219ms/step - loss: 0.0025 - val_loss: 0.0014\n",
            "Epoch 45/100\n",
            "7/7 [==============================] - 2s 238ms/step - loss: 0.0021 - val_loss: 0.0012\n",
            "Epoch 46/100\n",
            "7/7 [==============================] - 2s 221ms/step - loss: 0.0020 - val_loss: 0.0013\n",
            "Epoch 47/100\n",
            "7/7 [==============================] - 2s 232ms/step - loss: 0.0020 - val_loss: 0.0012\n",
            "Epoch 48/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 0.0018 - val_loss: 0.0017\n",
            "Epoch 49/100\n",
            "7/7 [==============================] - 2s 307ms/step - loss: 0.0018 - val_loss: 0.0011\n",
            "Epoch 50/100\n",
            "7/7 [==============================] - 2s 272ms/step - loss: 0.0017 - val_loss: 0.0011\n",
            "Epoch 51/100\n",
            "7/7 [==============================] - 2s 226ms/step - loss: 0.0016 - val_loss: 0.0011\n",
            "Epoch 52/100\n",
            "7/7 [==============================] - 2s 217ms/step - loss: 0.0017 - val_loss: 0.0012\n",
            "Epoch 53/100\n",
            "7/7 [==============================] - 2s 219ms/step - loss: 0.0015 - val_loss: 0.0010\n",
            "Epoch 54/100\n",
            "7/7 [==============================] - 1s 210ms/step - loss: 0.0017 - val_loss: 0.0011\n",
            "Epoch 55/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 0.0018 - val_loss: 0.0014\n",
            "Epoch 56/100\n",
            "7/7 [==============================] - 2s 219ms/step - loss: 0.0017 - val_loss: 0.0014\n",
            "Epoch 57/100\n",
            "7/7 [==============================] - 2s 227ms/step - loss: 0.0018 - val_loss: 0.0013\n",
            "Epoch 58/100\n",
            "7/7 [==============================] - 2s 223ms/step - loss: 0.0016 - val_loss: 0.0010\n",
            "Epoch 59/100\n",
            "7/7 [==============================] - 1s 213ms/step - loss: 0.0015 - val_loss: 0.0011\n",
            "Epoch 60/100\n",
            "7/7 [==============================] - 2s 232ms/step - loss: 0.0014 - val_loss: 9.7493e-04\n",
            "Epoch 61/100\n",
            "7/7 [==============================] - 2s 218ms/step - loss: 0.0013 - val_loss: 0.0011\n",
            "Epoch 62/100\n",
            "7/7 [==============================] - 2s 228ms/step - loss: 0.0013 - val_loss: 9.6347e-04\n",
            "Epoch 63/100\n",
            "7/7 [==============================] - 2s 233ms/step - loss: 0.0012 - val_loss: 9.6281e-04\n",
            "Epoch 64/100\n",
            "7/7 [==============================] - 2s 227ms/step - loss: 0.0013 - val_loss: 0.0012\n",
            "Epoch 65/100\n",
            "7/7 [==============================] - 2s 234ms/step - loss: 0.0015 - val_loss: 9.4352e-04\n",
            "Epoch 66/100\n",
            "7/7 [==============================] - 2s 219ms/step - loss: 0.0011 - val_loss: 0.0010\n",
            "Epoch 67/100\n",
            "7/7 [==============================] - 2s 220ms/step - loss: 0.0011 - val_loss: 9.2321e-04\n",
            "Epoch 68/100\n",
            "7/7 [==============================] - 2s 218ms/step - loss: 0.0011 - val_loss: 9.1827e-04\n",
            "Epoch 69/100\n",
            "7/7 [==============================] - 1s 212ms/step - loss: 0.0011 - val_loss: 9.1043e-04\n",
            "Epoch 70/100\n",
            "7/7 [==============================] - 2s 223ms/step - loss: 0.0011 - val_loss: 9.3036e-04\n",
            "Epoch 71/100\n",
            "7/7 [==============================] - 2s 229ms/step - loss: 0.0011 - val_loss: 0.0010\n",
            "Epoch 72/100\n",
            "7/7 [==============================] - 2s 236ms/step - loss: 0.0011 - val_loss: 9.1515e-04\n",
            "Epoch 73/100\n",
            "7/7 [==============================] - 2s 231ms/step - loss: 0.0011 - val_loss: 0.0011\n",
            "Epoch 74/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 0.0010 - val_loss: 0.0014\n",
            "Epoch 75/100\n",
            "7/7 [==============================] - 2s 223ms/step - loss: 9.8985e-04 - val_loss: 9.5896e-04\n",
            "Epoch 76/100\n",
            "7/7 [==============================] - 2s 226ms/step - loss: 9.5527e-04 - val_loss: 0.0012\n",
            "Epoch 77/100\n",
            "7/7 [==============================] - 2s 229ms/step - loss: 0.0010 - val_loss: 0.0015\n",
            "Epoch 78/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 0.0011 - val_loss: 9.5870e-04\n",
            "Epoch 79/100\n",
            "7/7 [==============================] - 2s 226ms/step - loss: 9.6306e-04 - val_loss: 9.1669e-04\n",
            "Epoch 80/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 9.2888e-04 - val_loss: 8.9108e-04\n",
            "Epoch 81/100\n",
            "7/7 [==============================] - 2s 226ms/step - loss: 9.1701e-04 - val_loss: 9.0571e-04\n",
            "Epoch 82/100\n",
            "7/7 [==============================] - 2s 219ms/step - loss: 9.3630e-04 - val_loss: 9.0701e-04\n",
            "Epoch 83/100\n",
            "7/7 [==============================] - 2s 229ms/step - loss: 0.0010 - val_loss: 0.0012\n",
            "Epoch 84/100\n",
            "7/7 [==============================] - 2s 236ms/step - loss: 9.3773e-04 - val_loss: 9.9974e-04\n",
            "Epoch 85/100\n",
            "7/7 [==============================] - 2s 233ms/step - loss: 9.1636e-04 - val_loss: 8.8229e-04\n",
            "Epoch 86/100\n",
            "7/7 [==============================] - 2s 227ms/step - loss: 9.3929e-04 - val_loss: 8.6825e-04\n",
            "Epoch 87/100\n",
            "7/7 [==============================] - 2s 221ms/step - loss: 9.3483e-04 - val_loss: 9.1341e-04\n",
            "Epoch 88/100\n",
            "7/7 [==============================] - 2s 231ms/step - loss: 8.8741e-04 - val_loss: 8.9391e-04\n",
            "Epoch 89/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 8.9131e-04 - val_loss: 0.0010\n",
            "Epoch 90/100\n",
            "7/7 [==============================] - 2s 233ms/step - loss: 8.4005e-04 - val_loss: 8.8831e-04\n",
            "Epoch 91/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 8.6292e-04 - val_loss: 0.0011\n",
            "Epoch 92/100\n",
            "7/7 [==============================] - 2s 225ms/step - loss: 9.6774e-04 - val_loss: 8.6872e-04\n",
            "Epoch 93/100\n",
            "7/7 [==============================] - 2s 359ms/step - loss: 8.8846e-04 - val_loss: 8.2948e-04\n",
            "Epoch 94/100\n",
            "7/7 [==============================] - 3s 436ms/step - loss: 8.0493e-04 - val_loss: 8.1244e-04\n",
            "Epoch 95/100\n",
            "7/7 [==============================] - 2s 224ms/step - loss: 8.1083e-04 - val_loss: 8.6752e-04\n",
            "Epoch 96/100\n",
            "7/7 [==============================] - 2s 242ms/step - loss: 8.0164e-04 - val_loss: 8.2857e-04\n",
            "Epoch 97/100\n",
            "7/7 [==============================] - 2s 236ms/step - loss: 8.2529e-04 - val_loss: 0.0012\n",
            "Epoch 98/100\n",
            "7/7 [==============================] - 2s 220ms/step - loss: 8.7233e-04 - val_loss: 8.5896e-04\n",
            "Epoch 99/100\n",
            "7/7 [==============================] - 2s 230ms/step - loss: 9.4718e-04 - val_loss: 9.8964e-04\n",
            "Epoch 100/100\n",
            "7/7 [==============================] - 2s 233ms/step - loss: 9.1626e-04 - val_loss: 0.0012\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe17828e650>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Lets Do the prediction and check performance metrics\n",
        "train_predict=model.predict(X_train)\n",
        "test_predict=model.predict(X_test)"
      ],
      "metadata": {
        "id": "6l21skRGfoh5"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##Transformback to original form\n",
        "train_predict=scaler.inverse_transform(train_predict)\n",
        "test_predict=scaler.inverse_transform(test_predict)"
      ],
      "metadata": {
        "id": "RQd1l6VLfsN0"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "### Plotting \n",
        "# shift train predictions for plotting\n",
        "look_back=100\n",
        "trainPredictPlot = numpy.empty_like(df4)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = numpy.empty_like(df4)\n",
        "testPredictPlot[:, :] = numpy.nan\n",
        "testPredictPlot[len(train_predict)+(look_back*2)+1:len(df4)-1, :] = test_predict\n",
        "# plot baseline and predictions\n",
        "plt.plot(scaler.inverse_transform(df4))\n",
        "plt.plot(trainPredictPlot)\n",
        "plt.plot(testPredictPlot)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "HA2B1NoUfwB9",
        "outputId": "257f0bbd-6797-4711-b790-a0d63bf7052b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test=pd.read_csv(\"test_data.csv\")\n"
      ],
      "metadata": {
        "id": "mG5OcNUWf21O"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"prtrol price prediction\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vpPZeHJMf54I",
        "outputId": "71eda95e-810b-4ece-a324-1d5458daaf41"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe17c29f9d0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe1784d3610> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe1783d2f90> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade patsy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSSWeK2Yf8rS",
        "outputId": "6ca0831b-aad6-46d1-f027-24d120d4c37f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: patsy in /usr/local/lib/python3.7/dist-packages (0.5.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.4 in /usr/local/lib/python3.7/dist-packages (from patsy) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install statsmodels\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LIMdx3bgAbJ",
        "outputId": "b3649c29-f2e6-4c50-c916-ac2150347206"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (0.12.2)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.1 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (1.7.3)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (0.5.2)\n",
            "Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (1.3.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->statsmodels) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->statsmodels) (2022.2.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5->statsmodels) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-deps statsmodels\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghg6JUjjgEe9",
        "outputId": "5959e80d-eee2-4c21-9f35-9b3d0170c6d5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (0.12.2)\n",
            "Collecting statsmodels\n",
            "  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 4.5 MB/s \n",
            "\u001b[?25hInstalling collected packages: statsmodels\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.12.2\n",
            "    Uninstalling statsmodels-0.12.2:\n",
            "      Successfully uninstalled statsmodels-0.12.2\n",
            "Successfully installed statsmodels-0.13.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "from matplotlib import pyplot as plt\n",
        "from pandas import read_csv\n",
        "from pandas import datetime \n",
        "from matplotlib import pyplot\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64oCpms8gHgu",
        "outputId": "cfbae4fc-e908-45a0-feb0-ef49f1a61e55"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
            "  \"\"\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"train_data.csv\")\n",
        "df.head()"
      ],
      "metadata": {
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              "        Date  Petrol (USD)\n",
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        "df.rename(columns={\"Petrol (USD)\": \"Petrol\"}, inplace = True)\n",
        "df.head()"
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              "        Date  Petrol\n",
              "0   6/9/2003   74.59\n",
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    {
      "cell_type": "code",
      "source": [
        "df2 = df.dropna()\n",
        "df3=df2[df2.Petrol<160]\n",
        "df3.head()"
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      "execution_count": 30,
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              "        Date  Petrol\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df4=df3.reset_index()['Petrol']\n",
        "df4.head()"
      ],
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        "id": "h0RYwBuWgYXv",
        "outputId": "318aeb29-0193-437d-bb67-61a7647fb4df"
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      "execution_count": 31,
      "outputs": [
        {
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              "0    74.59\n",
              "1    74.47\n",
              "2    74.42\n",
              "3    74.35\n",
              "4    74.28\n",
              "Name: Petrol, dtype: float64"
            ]
          },
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        }
      ]
    },
    {
      "cell_type": "code",
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        "model1 = ARIMA(df4.values, order=(5,1,0))\n",
        "model_fit1 = model1.fit()\n",
        "output1= model_fit1.forecast(steps=30)\n",
        "output1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMOrff9agbHn",
        "outputId": "de5b1726-920a-498e-edd0-83d805d4d7ee"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([119.80670595, 119.52143347, 119.30634688, 119.14818591,\n",
              "       119.02925477, 118.93899198, 118.87071704, 118.81917609,\n",
              "       118.78030427, 118.75097698, 118.72884   , 118.71212989,\n",
              "       118.69951735, 118.68999809, 118.68281349, 118.67739086,\n",
              "       118.67329807, 118.67020899, 118.66787748, 118.66611776,\n",
              "       118.66478959, 118.66378715, 118.66303054, 118.66245949,\n",
              "       118.66202848, 118.66170317, 118.66145764, 118.66127233,\n",
              "       118.66113246, 118.66102689])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14UQ5EAcggMw",
        "outputId": "2aca356d-4595-45ce-f913-e9ca00269c34"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1\n",
            "  Cloning https://github.com/keras-team/keras-tuner.git (to revision 1.0.2rc1) to /tmp/pip-req-build-qki6ur2_\n",
            "  Running command git clone -q https://github.com/keras-team/keras-tuner.git /tmp/pip-req-build-qki6ur2_\n",
            "  Running command git checkout -q 0fb69434a132093518e0e53d40020145ae192629\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (21.3)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (0.16.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (1.21.6)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (0.8.10)\n",
            "Collecting terminaltables\n",
            "  Downloading terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)\n",
            "Collecting colorama\n",
            "  Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (4.64.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (2.23.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from keras-tuner==1.0.2rc1) (1.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras-tuner==1.0.2rc1) (3.0.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner==1.0.2rc1) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner==1.0.2rc1) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner==1.0.2rc1) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner==1.0.2rc1) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->keras-tuner==1.0.2rc1) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->keras-tuner==1.0.2rc1) (3.1.0)\n",
            "Building wheels for collected packages: keras-tuner\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.2rc1-py3-none-any.whl size=85445 sha256=036d7cdbeb1321d859446169bd50a8b8c4af254605f13ec783f3b0528df0976c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-w3z4fs65/wheels/44/e5/92/e83049ca00432aec622a4fa0200e254d88aefae9d74aa86941\n",
            "Successfully built keras-tuner\n",
            "Installing collected packages: terminaltables, colorama, keras-tuner\n",
            "Successfully installed colorama-0.4.5 keras-tuner-1.0.2rc1 terminaltables-3.1.10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install autokeras\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRjJByGVgjgg",
        "outputId": "4621ad14-c84d-4c0d-80c5-abf14da4527a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting autokeras\n",
            "  Downloading autokeras-1.0.19-py3-none-any.whl (162 kB)\n",
            "\u001b[K     |████████████████████████████████| 162 kB 5.1 MB/s \n",
            "\u001b[?25hCollecting keras-tuner>=1.1.0\n",
            "  Downloading keras_tuner-1.1.3-py3-none-any.whl (135 kB)\n",
            "\u001b[K     |████████████████████████████████| 135 kB 42.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from autokeras) (2.8.2+zzzcolab20220719082949)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from autokeras) (1.3.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from autokeras) (21.3)\n",
            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from keras-tuner>=1.1.0->autokeras) (2.8.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner>=1.1.0->autokeras) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner>=1.1.0->autokeras) (1.21.6)\n",
            "Collecting kt-legacy\n",
            "  Downloading kt_legacy-1.0.4-py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from keras-tuner>=1.1.0->autokeras) (7.9.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (0.2.0)\n",
            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (2.8.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (1.1.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (4.1.1)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (1.6.3)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (3.3.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (57.4.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (1.47.0)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (0.5.3)\n",
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            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (1.1.0)\n",
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            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (2.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (2.8.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.8.0->autokeras) (0.26.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow>=2.8.0->autokeras) (0.37.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow>=2.8.0->autokeras) (1.5.2)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner>=1.1.0->autokeras) (1.8.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner>=1.1.0->autokeras) (0.6.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner>=1.1.0->autokeras) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner>=1.1.0->autokeras) (0.4.6)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner>=1.1.0->autokeras) (1.35.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner>=1.1.0->autokeras) (3.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner>=1.1.0->autokeras) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner>=1.1.0->autokeras) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras-tuner>=1.1.0->autokeras) (4.2.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner>=1.1.0->autokeras) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->keras-tuner>=1.1.0->autokeras) (4.12.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras-tuner>=1.1.0->autokeras) (3.8.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->keras-tuner>=1.1.0->autokeras) (0.4.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner>=1.1.0->autokeras) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner>=1.1.0->autokeras) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner>=1.1.0->autokeras) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner>=1.1.0->autokeras) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner>=1.1.0->autokeras) (3.2.0)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (4.8.0)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (5.1.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (0.7.5)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (4.4.2)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (0.2.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (2.6.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner>=1.1.0->autokeras) (2.0.10)\n",
            "Collecting jedi>=0.10\n",
            "  Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 35.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.10->ipython->keras-tuner>=1.1.0->autokeras) (0.8.3)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->keras-tuner>=1.1.0->autokeras) (0.2.5)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->autokeras) (3.0.9)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->autokeras) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->autokeras) (2.8.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython->keras-tuner>=1.1.0->autokeras) (0.7.0)\n",
            "Installing collected packages: jedi, kt-legacy, keras-tuner, autokeras\n",
            "  Attempting uninstall: keras-tuner\n",
            "    Found existing installation: keras-tuner 1.0.2rc1\n",
            "    Uninstalling keras-tuner-1.0.2rc1:\n",
            "      Successfully uninstalled keras-tuner-1.0.2rc1\n",
            "Successfully installed autokeras-1.0.19 jedi-0.18.1 keras-tuner-1.1.3 kt-legacy-1.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show autokeras\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkOHaahogm_v",
        "outputId": "9adab420-9273-44a0-c452-fca32ed70683"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: autokeras\n",
            "Version: 1.0.19\n",
            "Summary: AutoML for deep learning\n",
            "Home-page: http://autokeras.com\n",
            "Author: DATA Lab, Keras Team\n",
            "Author-email: jhfjhfj1@gmail.com\n",
            "License: Apache License 2.0\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: tensorflow, pandas, packaging, keras-tuner\n",
            "Required-by: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "import autokeras as ak"
      ],
      "metadata": {
        "id": "HX3Y7qaQgp7I"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg = ak.StructuredDataRegressor(\n",
        "    overwrite=True, max_trials=3\n",
        ")\n"
      ],
      "metadata": {
        "id": "6Nv_q659gsnD"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.fit(x=X_train, y=y_train, verbose=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "M9d6xH4xgvJw",
        "outputId": "a21749a0-4ffa-47a5-a872-39be50f22be0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-668facb0735d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/tasks/structured_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, epochs, callbacks, validation_split, validation_data, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_split\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m         )\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/auto_model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, callbacks, validation_split, validation_data, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         )\n\u001b[0;32m--> 283\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_analyze_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_hyper_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/auto_model.py\u001b[0m in \u001b[0;36m_analyze_data\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0manalyser\u001b[0m \u001b[0;32min\u001b[0m \u001b[0manalysers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m             \u001b[0manalyser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manalyser\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manalysers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/analysers/input_analysers.py\u001b[0m in \u001b[0;36mfinalize\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_column_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/analysers/input_analysers.py\u001b[0m in \u001b[0;36mcheck\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    113\u001b[0m                 \u001b[0;34m\"(batch_size, num_features), but \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m                 \"got input shape {shape}.\".format(\n\u001b[0;32m--> 115\u001b[0;31m                     \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_input_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m                 )\n\u001b[1;32m    117\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: Expect the data to StructuredDataInput to have shape (batch_size, num_features), but got input shape [32, 100, 1]."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "mae, _  = reg.evaluate(X_test, ytest, verbose=0)\n",
        "#print('MAE: %.3f' % mae)\n",
        "# use the model to make a prediction\n",
        "yhat_test = reg.predict(X_test)\n",
        "\n",
        "# get the best performing model\n",
        "model = reg.export_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "aC2_FuBsg0H1",
        "outputId": "2a7375b8-eab3-417c-b191-9714bd77c8f7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-f4fd58fe0939>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# evaluate the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmae\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#print('MAE: %.3f' % mae)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# use the model to make a prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0myhat_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/tasks/structured_data.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_from_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/auto_model.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    487\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_adapt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_heads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m         \u001b[0mpipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtuner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_best_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    490\u001b[0m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtuner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_best_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/engine/tuner.py\u001b[0m in \u001b[0;36mget_best_pipeline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_best_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpipeline_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_pipeline_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_pipeline_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/pipeline.py\u001b[0m in \u001b[0;36mload_pipeline\u001b[0;34m(filepath, custom_objects)\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mcustom_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcustom_object_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mPipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/autokeras/utils/io_utils.py\u001b[0m in \u001b[0;36mload_json\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m    112\u001b[0m       \u001b[0mstring\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstring\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mregular\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \"\"\"\n\u001b[0;32m--> 114\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preread_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m       \u001b[0mlength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36m_preread_check\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     75\u001b[0m                                            \"File isn't open for reading\")\n\u001b[1;32m     76\u001b[0m       self._read_buf = _pywrap_file_io.BufferedInputStream(\n\u001b[0;32m---> 77\u001b[0;31m           compat.path_to_str(self.__name), 1024 * 512)\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_prewrite_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotFoundError\u001b[0m: ./structured_data_regressor/best_pipeline; No such file or directory"
          ]
        }
      ]
    }
  ]
}