ANNDelayPrediction.ipynb

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      "name": "ANNDelayPrediciton.ipynb",
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      "cell_type": "code",
      "metadata": {
        "id": "MD1nb67oc9wp",
        "colab_type": "code",
        "outputId": "9d746500-91f4-4811-b9f1-4013852917d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxDA2LywdAxv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "flightDataPath = 'gdrive/My Drive/FlightDelayPrediction/FlightData/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uzkb3vjBdQsE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRclR9ARdWab",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "outputId": "ab2cb53d-23c4-4a9c-9769-22693e5e56ed"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout,LeakyReLU\n",
        "from tensorflow.keras.optimizers import *\n",
        "from tensorflow.keras import metrics\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "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"
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              "<IPython.core.display.HTML object>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "XENA3nVldeRQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pkl_file = open(flightDataPath + '/JoinedData.pkl','rb')\n",
        "df = pickle.load(pkl_file)\n",
        "pkl_file.close()\n",
        "le = LabelEncoder()\n",
        "\n",
        "df['Origin'] = le.fit_transform(df['Origin'])\n",
        "df['Dest'] = le.fit_transform(df['Dest'])\n",
        "\n",
        "df.dropna(how='any',inplace=True)\n",
        "df.drop(columns=['FlightDate','date','ArrTime','ArrDel15','CRSArrTime','NearestDepTime'],inplace=True)\n",
        "df = df[df['ArrDelayMinutes'] > 0]\n",
        "df = df.apply(pd.to_numeric)\n",
        "X = df.loc[:, df.columns != 'ArrDelayMinutes']\n",
        "y = df.loc[:, df.columns == 'ArrDelayMinutes']\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=42,test_size=0.25)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Yv1HyRodj7C",
        "colab_type": "code",
        "outputId": "b8b548e2-3d3a-4257-d697-21ac4fd25d4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units = 32, input_dim = X_train.shape[1]))\n",
        "model.add(LeakyReLU(alpha=0.0005 ))\n",
        "model.add(Dense(units = 64))\n",
        "model.add(LeakyReLU(alpha=0.0005 ))\n",
        "model.add(Dense(units = 1  , activation = 'linear'))\n",
        "model.compile(loss='mean_squared_error', optimizer=Nadam(), metrics=['mean_squared_error'])\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 32)                768       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 2,945\n",
            "Trainable params: 2,945\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fsrzxivdoq0",
        "colab_type": "code",
        "outputId": "edfeb792-e17d-4c18-c96c-f993a4b4cd6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "h1 = model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=20,batch_size=128,verbose=2,use_multiprocessing=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 525213 samples, validate on 175071 samples\n",
            "Epoch 1/20\n",
            "525213/525213 - 7s - loss: 587.9713 - mean_squared_error: 587.9701 - val_loss: 333.9588 - val_mean_squared_error: 333.9587\n",
            "Epoch 2/20\n",
            "525213/525213 - 7s - loss: 262.2938 - mean_squared_error: 262.2935 - val_loss: 264.6373 - val_mean_squared_error: 264.6373\n",
            "Epoch 3/20\n",
            "525213/525213 - 7s - loss: 248.8462 - mean_squared_error: 248.8461 - val_loss: 244.1014 - val_mean_squared_error: 244.1013\n",
            "Epoch 4/20\n",
            "525213/525213 - 7s - loss: 243.5789 - mean_squared_error: 243.5793 - val_loss: 268.1866 - val_mean_squared_error: 268.1866\n",
            "Epoch 5/20\n",
            "525213/525213 - 7s - loss: 241.0083 - mean_squared_error: 241.0079 - val_loss: 252.1685 - val_mean_squared_error: 252.1685\n",
            "Epoch 6/20\n",
            "525213/525213 - 7s - loss: 239.8216 - mean_squared_error: 239.8220 - val_loss: 240.3525 - val_mean_squared_error: 240.3525\n",
            "Epoch 7/20\n",
            "525213/525213 - 7s - loss: 239.0974 - mean_squared_error: 239.0975 - val_loss: 249.5292 - val_mean_squared_error: 249.5293\n",
            "Epoch 8/20\n",
            "525213/525213 - 7s - loss: 238.3209 - mean_squared_error: 238.3209 - val_loss: 244.5150 - val_mean_squared_error: 244.5152\n",
            "Epoch 9/20\n",
            "525213/525213 - 7s - loss: 237.8219 - mean_squared_error: 237.8217 - val_loss: 241.4273 - val_mean_squared_error: 241.4276\n",
            "Epoch 10/20\n",
            "525213/525213 - 7s - loss: 237.4575 - mean_squared_error: 237.4574 - val_loss: 241.5540 - val_mean_squared_error: 241.5539\n",
            "Epoch 11/20\n",
            "525213/525213 - 7s - loss: 237.1530 - mean_squared_error: 237.1528 - val_loss: 238.8785 - val_mean_squared_error: 238.8785\n",
            "Epoch 12/20\n",
            "525213/525213 - 7s - loss: 236.7413 - mean_squared_error: 236.7413 - val_loss: 239.4718 - val_mean_squared_error: 239.4718\n",
            "Epoch 13/20\n",
            "525213/525213 - 7s - loss: 236.5426 - mean_squared_error: 236.5425 - val_loss: 239.4276 - val_mean_squared_error: 239.4276\n",
            "Epoch 14/20\n",
            "525213/525213 - 7s - loss: 236.1793 - mean_squared_error: 236.1793 - val_loss: 238.3850 - val_mean_squared_error: 238.3849\n",
            "Epoch 15/20\n",
            "525213/525213 - 7s - loss: 236.0828 - mean_squared_error: 236.0829 - val_loss: 239.3014 - val_mean_squared_error: 239.3014\n",
            "Epoch 16/20\n",
            "525213/525213 - 7s - loss: 236.0480 - mean_squared_error: 236.0478 - val_loss: 241.3951 - val_mean_squared_error: 241.3951\n",
            "Epoch 17/20\n",
            "525213/525213 - 7s - loss: 235.8109 - mean_squared_error: 235.8109 - val_loss: 242.8504 - val_mean_squared_error: 242.8504\n",
            "Epoch 18/20\n",
            "525213/525213 - 7s - loss: 235.6804 - mean_squared_error: 235.6803 - val_loss: 237.2155 - val_mean_squared_error: 237.2156\n",
            "Epoch 19/20\n",
            "525213/525213 - 7s - loss: 235.6002 - mean_squared_error: 235.6004 - val_loss: 237.9964 - val_mean_squared_error: 237.9962\n",
            "Epoch 20/20\n",
            "525213/525213 - 7s - loss: 235.4691 - mean_squared_error: 235.4688 - val_loss: 237.7180 - val_mean_squared_error: 237.7180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-Xlu1s8Bx-n",
        "colab_type": "code",
        "outputId": "2c080555-26c5-4b15-8dbc-a7934a7e0e9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "\n",
        "fig,ax0 = plt.subplots(figsize=(13,7))\n",
        "ax0.plot(h1.history['mean_squared_error'])\n",
        "ax0.plot(h1.history['val_mean_squared_error'])\n",
        "ax0.set_title('Model accuracy')\n",
        "ax0.set_ylabel('Mean Squared Error')\n",
        "ax0.set_xlabel('Epoch')\n",
        "ax0.legend(['Train M1', 'Test M1'], loc='upper left',fontsize=15)\n",
        "plt.savefig('gdrive/My Drive/FlightDelayPrediction/ANNClassifierCharacteristics-V1.png')\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G7DV/M65/IXAapcDR3znZo0YXRERTtm0SMKffMU9n29YSESdGxIyImLFo0aLN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\nRKSUwoKIiIiIiJT6fzUGZaQlQNwpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 936x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUw-8LtldshQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix,mean_squared_error,r2_score,mean_absolute_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm7l41PS1dk9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(X_test,batch_size=64)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1Zi-h3u1xTl",
        "colab_type": "code",
        "outputId": "2fd1141d-ba34-4010-e205-73806aa335de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "fig,ax1 = plt.subplots(figsize=(15,10))\n",
        "\n",
        "ax1.scatter(x=[i for i in range(50)],y=y_pred[:50],c='red',label='Predicted')\n",
        "ax1.scatter(x=[i for i in range(50)],y=y_test[:50],c='green',label='Actual')\n",
        "ax1.legend(fontsize=15)\n",
        "plt.show()\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AANBiQhsAAECLCW0AAAAtJrQBAAC0mNAGAADQYkIbAABAiwltAAAALSa0AQAA\ntJjQBgAA0GJCGwAAQIsJbQAAAC0mtAEAALSY0AYAANBiQhsAAECLCW0AAAAtJrQBAAC0mNAGAADQ\nYkIbAABAiwltAAAALSa0AQAAtJjQBgAA0GJCGwAAQIvtN7SVUj5SSrmrlHLTHmUXl1JuL6Xc0D2d\nvcdtby+l3FpK+adSym8vVsUBAABWggNpabsiyVlzlF9aa31q9/S5JCml/FKS85L8cvc+l5dSDlmo\nygIAAKw0+w1ttda/TfKjA3y8Fyb5eK31p7XW7ya5Ncmv9VA/AACAFa2XMW1vLKXc2O0++ehu2clJ\nvrfHMt/vlgEAAHAQDja0fTDJv0ry1CR3JHn/fB+glDJWStlcStl89913H2Q1AAAAlreDCm211jtr\nrTtrrbuSfCg/7wJ5e5LH7bHoKd2yuR5jstY6UmsdOe644w6mGgAAAMveQYW2UspJe1x9cZKHZpa8\nNsl5pZTDSimPT/KEJNf3VkUAAICVa/X+FiilXJ3kjCTHllK+n+SiJGeUUp6apCbZmuR1SVJrvbmU\nck2SbyZ5MMnv11p3Lk7VAQAAlr9Sa226DhkZGambN29uuhoAAACNKKV8tdY6MtdtvcweCQAAwCIT\n2gAAAFpMaAMAAGgxoQ0AAKDFhDYAAIAWE9oAAABaTGgDAABoMaENAACgxYQ2AACAFhPaAAAAWkxo\nAwAAaDGhDQAAoMWENgAAgBYT2gAAAFpMaAMAAGgxoQ0AAKDFhDYAAIAWE9oAAABaTGgDAABoMaEN\nAACgxYQ2AACAFhPaAAAAWkxoAwAAaDGhDQAAoMWENgAAgBYT2gAAAFpMaAMAAGgxoQ0AAKDFhDYA\nAIAWE9oAAABaTGgDAABoMaENAACgxYQ2AACAFhPaAAAAWkxoAwAAaDGhDQAAoMWENgAAgBYT2gAA\nAFpMaAMAAGgxoQ0AAKDFhDYAAIAWE9oAAABaTGgDAABoMaENAACgxYQ2AACAFhPaAAAAWkxoAwAA\naDGhDQAAoMWENljJOp1keDhZtWrmvNNpukYAADzM6qYrADSk00nGxpLp6ZnrU1Mz15NkdLS5egEA\nMIuWNlipxsd/HtgeMj09U74EOls6Gb5sOKsuWZXhy4bT2aKVDwBgLlraYKXatm1+5Quos6WTsY1j\nmd4xExqntk9lbONMK9/oOq18AAB70tIGK9XatfMrX0Djm8Z3B7aHTO+YzvimpWnlAwDoJ0IbrFQT\nE8ng4OyywcGZ8kW2bfvcrXl7KwcAWMmENlipRkeTyclkaCgpZeZ8cnJJJiFZu2bu1ry9lQMArGRC\nG6xko6PJ1q3Jrl0z50s0a/Ct6LgAABtvSURBVOTE+okMDsxu5RscGMzE+sVv5QMA6DdCG7DkRteN\nZvLR52fo/kNSajJ0/yGZfPT5JiEBAJiD2SOBpdfpZPStV2Z0eme3YGcyeGVy1LMcIw4A4GG0tAFL\nr+FjxAEA9BOhDVh6DR4jDgCg3+w3tJVSPlJKuauUctMeZY8ppXyhlPLt7vmju+WllPInpZRbSyk3\nllKevpiVB3rT2dLJ8GXDWXXJqgxfNpzOls7SrLjBY8QBAPSbA2lpuyLJWQ8r+6Mkm2qtT0iyqXs9\nSZ6X5And01iSDy5MNYGF1tnSydjGsUxtn0pNzdT2qYxtHFua4NbgMeIAAPrNfkNbrfVvk/zoYcUv\nTHJl9/KVSV60R/nH6oy/T3J0KeWkhaossHDGN41nesfscWXTO6YzvmkJxpU1eIw4AIB+c7CzR55Q\na72je/kHSU7oXj45yff2WO773bI7ArTKtu1zjx/bW/mCGx0V0gAADkDPE5HUWmuSOt/7lVLGSimb\nSymb77777l6rAczT2jVzjx/bWzkAAM042NB250PdHrvnd3XLb0/yuD2WO6Vb9gi11sla60itdeS4\n4447yGoAB2ti/UQGB2aPKxscGMzEeuPKAADa5GBD27VJzu9ePj/Jhj3KX9WdRfK0JNv36EYJtMjo\nutFMnjOZoTVDKSkZWjOUyXMmM7pOl0UAgDYpM70b97FAKVcnOSPJsUnuTHJRkk8nuSbJ2iRTSc6t\ntf6olFKS/D+ZmW1yOsmra62b91eJkZGRunnzfhcDAABYlkopX621jsx1234nIqm1vmIvN62fY9ma\n5PfnVz0AAAD2pueJSAAAAFg8QhsAAECLCW0AAAAtJrQBAAC0mNAGAADQYkIbAABAiwltAMCy0dnS\nyfBlw1l1yaoMXzaczpZO01UC6Nl+j9MGANAPOls6Gds4lukd00mSqe1TGds4liQZXTfaZNUAeqKl\nDQBYFsY3je8ObA+Z3jGd8U3jDdUIYGEIbQDAsrBt+7Z5lQP0C6ENAFgW1q5ZO69ygH4htAEAy8LE\n+okMDgzOKhscGMzE+omGagSwMIQ2AGBZGF03mslzJjO0ZiglJUNrhjJ5zqRJSIC+Z/ZIAGDZGL0x\nGb0sybYka5Mcl2Rds3UC6JXQBgAsD51OMjaWTHdnkJyamrmeJKNa24D+pXskALA8jI//PLA9ZHp6\nphygjwltAMDysG0vU/vvrRygTwhtAMDysHYvU/vvrRygTwhtAMDyMDGRDM6e8j+DgzPlAH1MaAMA\nlofR0WRyMhkaSkqZOZ+cNAkJ0PeENgBYhjpbOhm+bDirLlmV4cuG09nSabpKS2N0NNm6Ndm1a+Zc\nYAOWAVP+A8Ay09nSydjGsUzvmJlJcWr7VMY2zkx970DTAP1HSxsALDPjm8Z3B7aHTO+YzvgmU98D\n9COhDQCWmW3b557ifm/lALSb0AYAy8zaNXNPcb+3cgDaTWgDgGVmYv1EBgdmT30/ODCYifWmvgfo\nR0IbACwzo+tGM/no8zN0/yEpNRm6/5BMPvp8k5AA9CmzRwLActPpZPStV2Z0eme3YGcyeGVy1LNM\ngQ/Qh7S0AcByMz6eTM+ePTLT0zPlAPQdoQ0Alptte5klcm/lALSa0AYAy83avcwSubdyAFpNaAOA\n5WZiIhmcPXtkBgdnygHoO0IbACw3o6PJ5GQyNJSUMnM+OWkSEoA+ZfZIAFiORkeFNIBlQksbAABA\niwltAAAALSa0AQAAtJjQBgAA0GJCGwAAQIsJbQAAAC0mtAEAALSY0AYAANBiQhsAAECLCW0AAAAt\nJrQBAAC0mNAGAADQYkIbAABAiwltAAAALSa0AQAAtJjQBgAA0GJCGwAAQIsJbdCjzpZOhi8bzqpL\nVmX4suF0tnSarhIAAMvI6qYrAP2ss6WTsY1jmd4xnSSZ2j6VsY1jSZLRdaNNVg0AgGVCSxv0YHzT\n+O7A9pDpHdMZ3zTeUI0AAFhuhDbowbbt2+ZVDgAA8yW0QQ/Wrlk7r3IAAJgvoQ16MLF+IoMDg7PK\nBgcGM7F+oqEaAQCw3Aht0IPRdaOZPGcyQ2uGUlIytGYok+dMmoQEAIAFU2qtTdchIyMjdfPmzU1X\nAwAAoBGllK/WWkfmuk1LGwAAQIv1dJy2UsrWJP8nyc4kD9ZaR0opj0nyiSTDSbYmObfW+uPeqgkA\nALAyLURL22/VWp+6R1PeHyXZVGt9QpJN3esAAAAchMXoHvnCJFd2L1+Z5EWLsA4AAKDPdLZ0MnzZ\ncFZdsirDlw2ns6XTdJX6Qk/dI5PUJH9TSqlJ/qzWOpnkhFrrHd3bf5DkhB7XAQAA9LnOlk7GNo5l\nesd0kmRq+1TGNo4liZm396PXlrb/u9b69CTPS/L7pZTf2PPGOjM15ZzTU5ZSxkopm0spm+++++4e\nqwEAALTZ+Kbx3YHtIdM7pjO+abyhGvWPnkJbrfX27vldSf57kl9Lcmcp5aQk6Z7ftZf7TtZaR2qt\nI8cdd1wv1QAAAFpu2/Zt8yrn5w46tJVSjiylPOqhy0mem+SmJNcmOb+72PlJNvRaSQAAoL+tXbN2\nXuX8XC8tbSck+V+llG8kuT7JZ2utn0/y3iTPKaV8O8mzu9cBAIAVbGL9RAbLobPKBsuhmVg/0VCN\n+sdBh7Za62211l/tnn651jrRLf9hrXV9rfUJtdZn11p/tHDVBQBgwXU6yfBwsmrVzHnHjH4svNEb\nk8lra4buTUpNhu6duT56Y9M1a78yM1dIs0ZGRurmzZubrgYAwMrT6SRjY8n0HhNEDA4mk5PJ6AHM\n6NfpJOPjybZtydq1ycTEgd2PlWd4OJmaemT50FCydetS16Z1Silf3ePY17NvE9oAAFawXn5I9xr4\nWFlWrUrmyh6lJLt2LX19WmZfoW0xDq4NAEC/2LYtnXXJ8IXJqotmzjvrZsr3a3x8dmBLZq6Pm8Kd\nOazdy4QjeytnN6ENAGAF6/zmYzJ2TjJ1dFLLzPnYOTPl+7W3YHcggY+VZ2JipiV2T4ODM+Xsk9AG\nALCCjT87mZ49oV+mD50p3y8tJ8zH6OhM19mhoZkukUNDutIeIKENAGAF2/bg3BN97618Fi0nzNfo\n6MxYyV27Zs4FtgMitAEArGA9HfBYywksCaENAGAFm1g/kcGB2a1lgwODB37AYy0nsOiENgCAFWx0\n3Wgmz5nM0JqhlJQMrRnK5DmTGV0nfEFbOE4bAABAwxynDQAAoE8JbQAAAC0mtAEAALSY0AYAANBi\nQhsAAECLCW0AAAAtJrQBANCcTicZHk5WrZo573SarhG0zuqmKwAAwArV6SRjY8n09Mz1qamZ60ky\n6uDe8BAtbQAANGN8/OeB7SHT0zPlwG5CGwAAzdi2bX7lsEIJbQAAHLTOlk6GLxvOqktWZfiy4XS2\nzGNM2tq18yuHFUpoAwDgoHS2dDK2cSxT26dSUzO1fSpjG8cOPLhNTCSDg7PLBgdnyoHdhDYAAA7K\n+KbxTO+YPSZtesd0xjcd4Ji00dFkcjIZGkpKmTmfnDQJCTyM0AYwH6amBtht2/a5x57trXxOo6PJ\n1q3Jrl0z5yslsPk+YR6ENoAD9dDU1FNTSa0/n5raF22r9TTeBtintWvmHnu2t3K6fJ8wT0IbwIEy\nNXXf6Xm8DbBPE+snMjgwe0za4MBgJtYbk7ZPvk+YJ6EN4EA1PDV1P7cYNVX3nsfbAPs0um40k+dM\nZmjNUEpKhtYMZfKcyYyuWyFdHA+WQx0wT0IbwIFqcGrqBWkxamj8RJOtXQsy3gbYp9F1o9l64dbs\numhXtl64VWA7EA51wDwJbQAHqsGpqXtuMWpw/ESTrV3G2wCt5FAHzJPQBnCgGpyauucWowbHTzTZ\n2mW8DdBKDnXAPK1uugIAfWV0tJEv1bVr1mZq+9Sc5QekwfETPde9Bw910xrfNJ5t27dl7Zq1mVg/\nofsW0LyGvk/oT1ra2K3XiQL6eZIEaLueW4waHD8xcdjZGdwxu2xwx0z5Uujn8Tb2qwAkQhtdvU4U\nYFptWFw9z9DW4PiJ0f/8uUxemwzdm5Q6cz557Uw5e2e/ykrhzwnYv1JrbboOGRkZqZs3b266Giva\n8GXDc3ZfGlozlK0Xbl30+wNLoNOZGcO2bdtMC9vExNJ0zVm1ambyk4crJdm1a/HX36fsV1kJHvpz\nYs/JigYHBh02gBWplPLVWuvIXLdpaSNJ7xMFmFYb+sDoaLJ160xQ2rp16cZSmNr6oNivshI4liIc\nGKGNJL1Pi21abWCvTG19UOxXWQl6/nOioeNP9jtdUvuP0EaS3ic5aHpabTsfaDFTWx+UpversBR6\n+nOiweNP9jPjZfuT0EaS3ic56HmShB7Y+UAfaKprZh9rcr8KS6WnPycaPP7kQmjqD2ddUvuTiUjo\newbrA0D/6mzpHNyxFPt4kqMmJ2BZdcmq1DzydSsp2XVRu1+35c5EJH3EsdLmr/HB+vrTA9Cwfv7+\nP+hjKTY8yVEvr3mTrV3Gy/Ynoa1FHCvt4DS689GfnpXCnxPQWiv1+7/JSY56fc2b/MN5JY+X7ec/\nN4S2Fun1X5eV2ke50Z1Pn/enp880FZxW8p8TKzmsruTn3mdW6vd/k5Mc9fqaN/mH8+i60Uw++vwM\n3X9ISk2G7j8kk48+f9mPl+33PzeMaWuRXvsYr+Q+ygfdH75Xfdyfnj7zUHDa80+CwcGl+YEyPDwT\n1B5uaGhmUpHlqsnXvGkr+bn3oZX8/d+UXl/zRg8qvkK3736YA8GYtvlq6N9Fx0o7eKM3JlsvS3Zd\nMnM+euMSrdhBg1kqTbbqbttLd529lS+0plp8VnJL+kp+7n1oJX//N9XdrdfXfEFmhz3YfeMK3b4b\nnwOhR0LbwzXYDajfj5XWmCa7bjloMEulyeC0dm0665LhC5NVF82cd9Zlaf6cWIjt+2B/2DQdVpu0\nkp97H5pYP5HBcuisssFy6LL//m+yu9tC/OY66AlYkqTTSefSV2f4xVNZ9a6a4RdPpXPpqw9s/7YQ\n23cfdp/u9z83hLaHa/Dfh177GPf8r00fboBJmv3HqOmDBvfre7aSHex71mCrbucPz87YC5Kpo5Na\nZs7HXjBTvuh63b57CX0ruSV9JT/3XjS0Tx69MZm8tmbo3sz8frh35vqS9TppSJNj+Zo+jmLnz9+c\nsd/eMXu//Ns70vnzN+//zr1u3306zrnvGzdqrY2fnvGMZ9TWKKXWmY/g7FMpi7/uq66qdXBw9noH\nB2fK5/MYQ0Mz9R0aOvD7LsS6m9Lre3awr9lCWYnvWdOaes97ec8afL+HLh2quTiPOA1dOrTo6+55\n+x4amvv+Q0P7v+9K3sYafu5XXf76OvTWQ2q5KHXorYfUqy5//ZKsd2blfbhP7uVz3sfKxWXOfVO5\neAl+szVs6MJHPu9cnDp0YfZ/514/qw1/3q668ao6dOlQLReXOnTpUL3qxgPfxnq571JIsrnuJS81\nHthq20Jbkx/EXtfdy0bYzzv8Hn+UXfWMgTp0YWZ+HFyYetUzBvrjR3w/v2dNavI9X4htvIGw2egP\no15fs5X6p04fr/uqy19fB8dnf9YGx7M0wa1f98lN/uHcoEb/UGpYuWju0FYuOoDQVmtv23eDn7er\nbryqDk4Mzt4/TAweePhqep++H/sKbbpHPtxCjFFqavxEL92Imh6/0EuXkh7es566FyyEHt+zOccZ\nGXOyT42+571uZ6OjM7M17to1c75E3XAbHQfQ6z65125Avb7mvezbmu6C1NDnbfy2yUwPzC6bHpgp\nX/yV9+n36Artzrog3d163UYbGqKwduCYeZU/Qi/bd4Oft566xDa9T+2R0PZwvY5RanL8RC9fGAux\nAR7szqvXjaiH92z8qT/M9Oyx25k+dKZ8SfTwnnV+8zEZO+dh44zOmSlfEn06nq7R97xPf1g1Og6g\n131yk5MF9bpvW6kzvB25c17lC7vyPv0eXaGTYi3IWP6D3UYbDgATL/jA3JPPvOADS7Dy5j5v2+aY\nsn9f5bP0+z51b01wS3lqVffIXjU5fqLJdfdpl5Keuxf0qofnPvTuY+buFvLuYxa92k2Pd+lFo+95\n091xe9D2cQD71FR3mKa7dvapobceMve+7a2HLMHKh/rze/Sh+7e421cr9fJ+t2CIQqP75YY+bz3t\nH/pgn5p9dI90cO2F1uvBljudmcS/bdvMv3MTE/Nv5TvYgyX2su5eDr7b4AGqhyeOzdSDj2xhGVp9\nTLaO37Oo607S03vW6MFU+/hgy02+550tnYz999dkuv5sd9lgOTSTL/7Iks04xhLqdd/Wx9tZLzof\nfEPGbv/grC6SgzuSyZNfn9HXX77IK+/T71EOTi/baIO/XVayzv9VMnZOZvWYGfxZMrkxGb1xP5mm\nD7YxB9deSk2On+i1G1Ev6266S8lBarR7QdLTe9boOKOmx0D2oMn3fHzT+KzAliTT9WdLMj01Deh1\n37ZSu7y9/vJMnvz62Ye/WYrAlvTv9ygHp5dttE+7u/e70fuGMrkxsw9vsXGmfL/6fZ+6tya4pTwt\nq+6Rfdz9qSd9PK12v3b76nkGpV60oFtIL5qaLrjp6an79bPetxbi+0CXt5Wjz/erfalPD8Oyoi3z\nbsQx5f/SuerGq+rgJYfO/iF9yaHL/8dRjxtRo8fkaVhfHm9khX5Z9RqUm5yeutGQ38d63T77+ftA\nyF9iK3S/2rhefsS3PAAsW8v4dd9XaDOmbYENXzacqTlmsBlaM5StF25d+gotpYPsy9/Z0snYxrFZ\nU7gODgzObwaoPtXXz72XsRt9qtftu8n3u5/3TZ0tnYxvGs+27duyds3aTKyfWJLto9f3q99f877d\nN/WzFbhfbVpT+xeYy77GtAltC6zRySH6VD//sOnVSn7u/Wghtu+mfiD0676pn4Nuv77miX0TK4M/\nJ2gbE5EsoUYnh+hT27bPPch6b+XLyUp+7v1oIbbv0XWj2Xrh1uy6aFe2Xrh1yX4Y9Ou+qacDqfao\n1+2zX1/zZGXvmzpbOhm+bDirLlmV4cuG09ly4Mfd6uW+LL0m9y8wX0LbHHrZ6TZ6ENo+1c8/bHrV\nz899Jf446eftu1/r3mR46HX77NfXPOnvfVMvHmp5mdo+lZqaqe1TGds4dkD7t17uu+djrLT9apNW\n8p8TvfJZXXpC28P0utMdXTeayXMmM7RmKCUlQ2uGNLPvRz//sOlVvz73hfhx0o/6efvu17o3GR56\n3T779TVP+nff1KteWl56bbVZqfvVJq3UPyd65bPajEUb01ZKOSvJB5IckuTPa63v3duybRrTph9/\nM1byQOB+fO62E5ZK02NO+nH7XCgr8bn3Mg6x1zGM9qtLr+n9S7/yWV08Sz4RSSnlkCTfSvKcJN9P\n8g9JXlFr/eZcy7cptPXzwHFYKrYTltJKDA80o5cfoyt54pp+Zv8yfz6ri2dfoW31Iq3z15LcWmu9\nrVuBjyd5YZI5Q1ubrF2zds6drqZy+DnbCUtpdN2oH1EsiYn1E3O2vBxIt9Be7pvYrzbF/mX+fFab\nsVhj2k5O8r09rn+/W9Z6K7UfP8yH7QRYjnoZh9jrGEb7VfqFz2ozFqt75EuTnFVr/d3u9VcmObXW\n+sY9lhlLMpYka9eufcbU1CMTe1M0lcP+2U4AFpb9Kv3CZ3VxNDGm7deTXFxr/e3u9bcnSa31PXMt\n36YxbQAAAEutiYNr/0OSJ5RSHl9KOTTJeUmuXaR1AQAALFuLMhFJrfXBUsobk/x1Zqb8/0it9ebF\nWBcAAMBytlizR6bW+rkkn1usxwcAAFgJFqt7JAAAAAtAaAMAAGgxoQ0AAKDFhDYAAIAWE9oAAABa\nTGgDAABoMaENAACgxYQ2AACAFhPaAAAAWkxoAwAAaDGhDQAAoMWENgAAgBYT2gAAAFpMaAMAAGgx\noQ0AAKDFhDYAAIAWE9oAAABarNRam65DSil3J5lquh5zODbJPU1XghXD542l4rPGUvFZY6n4rLGU\nFuvzNlRrPW6uG1oR2tqqlLK51jrSdD1YGXzeWCo+aywVnzWWis8aS6mJz5vukQAAAC0mtAEAALSY\n0LZvk01XgBXF542l4rPGUvFZY6n4rLGUlvzzZkwbAABAi2lpAwAAaDGhbS9KKWeVUv6plHJrKeWP\nmq4Py0cp5SOllLtKKTftUfaYUsoXSinf7p4/usk6sjyUUh5XSvlSKeWbpZSbSylv7pb7vLHgSimH\nl1KuL6V8o/t5u6Rb/vhSyle636efKKUc2nRdWR5KKYeUUr5eSvlM97rPGguulLK1lLKllHJDKWVz\nt2zJv0eFtjmUUg5J8qdJnpfkl5K8opTyS83WimXkiiRnPazsj5JsqrU+Icmm7nXo1YNJ3lLr/9/e\n/YPKUUVxHP8ekgiiQjBqkDwliIJY6LMJEVPEB4poMBYigkIKwcZCCxG0EYQUNv7pVUjhH4IaTWnA\ngFYiUUEhKaIo+oh5hQa1iag/i7nBJTyL4M7OMHw/sOy9dxf2FIc9e2bmzuYmYCfwePsuM9/Uh7PA\nSpJbgGXg7qraCbwAvJTkeuAX4NEBY9S0PAEcn5mba+rLHUmWZ27zv/A6atO2vh3AySTfJvkDeBvY\nO3BMmogkHwM/n7e8FzjQxgeA+xcalCYpyakkn7fxb3Q/brZhvqkH6fzeppvaI8AK8E5bN980F1W1\nBNwLvNrmhbmmxVl4HbVpW9824IeZ+Y9tTerL1iSn2vgnYOuQwWh6qmo7cCvwKeabetIuV/sSWAOO\nAN8AZ5L82d5iPdW8vAw8Dfzd5lsw19SPAB9W1bGqeqytLbyObuz7AyRdmCSpKm/rqrmpqkuBd4En\nk/zaHZDumG+apyR/ActVtRk4BNw4cEiaoKraA6wlOVZVu4eOR5O3K8lqVV0FHKmqE7MvLqqOeqZt\nfavANTPzpbYm9eV0VV0N0J7XBo5HE1FVm+gatjeSvNeWzTf1KskZ4ChwG7C5qs4dJLaeah5uB+6r\nqu/otrCsAK9grqkHSVbb8xrdwagdDFBHbdrW9xlwQ7sL0UXAQ8DhgWPStB0G9rXxPuCDAWPRRLQ9\nHq8Bx5O8OPOS+aa5q6or2xk2qupi4E66fZRHgQfa28w3/W9JnkmylGQ73W+0j5I8jLmmOauqS6rq\nsnNj4C7gawaoo/659n+oqnvorpfeALyeZP/AIWkiquotYDdwBXAaeA54HzgIXAt8DzyY5PyblUgX\npKp2AZ8AX/Hvvo9n6fa1mW+aq6q6mW5D/ga6g8IHkzxfVdfRnQ25HPgCeCTJ2eEi1ZS0yyOfSrLH\nXNO8tZw61KYbgTeT7K+qLSy4jtq0SZIkSdKIeXmkJEmSJI2YTZskSZIkjZhNmyRJkiSNmE2bJEmS\nJI2YTZskSZIkjZhNmyRJkiSNmE2bJEmSJI2YTZskSZIkjdg/LqlswdBb+BoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qneXmVgJ2FI4",
        "colab_type": "code",
        "outputId": "64449dba-52f7-426b-f427-f86dda9d1f2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(mean_squared_error(y_test,y_pred))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "237.71798491030594\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3r3WIFu2oxH",
        "colab_type": "code",
        "outputId": "67d73c5b-0f8e-4aef-ebae-26e7d2764cc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(r2_score(y_test,y_pred))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9368207820138907\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rl6KWrQH3EwG",
        "colab_type": "code",
        "outputId": "b1b9a31f-f92f-4a87-d531-780fc435412b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(mean_absolute_error(y_test,y_pred))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10.376015990361564\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGhFXpnb-Bqv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}