3_model_I_LSTM_CoinMarketCap.ipynb

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      "source": [
        "Model I.\n",
        "\n",
        "LSTM on BTC Close Prices"
      ],
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      }
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      "source": [
        "import pandas as pd"
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      "source": [
        "prices=pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/DATA_1/BTC-USD.csv\", engine='python')\n",
        "prices = prices[['Date', 'Close']]"
      ]
    },
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      "source": [
        "prices"
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              "            Date         Close\n",
              "0     2014-09-17    457.334015\n",
              "1     2014-09-18    424.440002\n",
              "2     2014-09-19    394.795990\n",
              "3     2014-09-20    408.903992\n",
              "4     2014-09-21    398.821014\n",
              "...          ...           ...\n",
              "3024  2022-12-28  16552.572266\n",
              "3025  2022-12-29  16642.341797\n",
              "3026  2022-12-30  16602.585938\n",
              "3027  2022-12-31  16547.496094\n",
              "3028  2023-01-01  16548.261719\n",
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              "      <th>Close</th>\n",
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      "cell_type": "markdown",
      "source": [
        "Converting to proper datetypes"
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      "source": [
        "prices['Date'] =  pd.to_datetime(prices['Date'], format='%Y-%m-%d')"
      ],
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        "id": "1nQOwsL8yJQw"
      },
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      "cell_type": "code",
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        "prices.dtypes"
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      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date     datetime64[ns]\n",
              "Close           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('BTC Close Price in US Dollars from 2014 to 2023', fontsize=20)\n",
        "#plt.title('Wartość BTC w dolarze amerykańskim w latach 2014 - 2023', fontsize=20)\n",
        "plt.plot(prices['Date'], prices['Close'])\n",
        "plt.gcf().autofmt_xdate()\n",
        "#plt.xlabel('Rok', fontsize=10)\n",
        "plt.xlabel('Year', fontsize=10)\n",
        "plt.ylabel('USD', fontsize=10)\n",
        "plt.show()"
      ],
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      "execution_count": 34,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_rows_when=int(round(len(prices[\"Date\"])*(0.8),0))\n"
      ],
      "metadata": {
        "id": "MLUtMK8lyrH5"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "split_rows_when"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOl5YbXNzM2V",
        "outputId": "747c00fe-2b6b-49d0-f71f-94c0144cf6da"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2423"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reshaping the values with an additional dimension for the scaler to work.\n"
      ],
      "metadata": {
        "id": "oSFAxmpX1Fdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "close_price = prices.Close.values.reshape(-1, 1)\n",
        "scaled_price = scaler.fit_transform(close_price)"
      ],
      "metadata": {
        "id": "G1UYWlVTzU7a"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Array reshaped:"
      ],
      "metadata": {
        "id": "XG4ZzXH32OZ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(close_price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izYPmWWO2GzV",
        "outputId": "b37aa181-ea61-4602-f312-cba0b20371bc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  457.334015]\n",
            " [  424.440002]\n",
            " [  394.79599 ]\n",
            " ...\n",
            " [16602.585938]\n",
            " [16547.496094]\n",
            " [16548.261719]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing NaN values from the array (if any):"
      ],
      "metadata": {
        "id": "JpCvxOBi2iUQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "scaled_price = scaled_price[~np.isnan(scaled_price)]\n",
        "scaled_price = scaled_price.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "hM1qVEWh2dJc"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Array scaled with MinMaxScaler:"
      ],
      "metadata": {
        "id": "MvIFaUCw2RsV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(scaled_price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XDMRIb-2LgH",
        "outputId": "074f5cbc-5587-4d7c-cb30-4af45ed8a067"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.00414359]\n",
            " [0.00365546]\n",
            " [0.00321557]\n",
            " ...\n",
            " [0.24372746]\n",
            " [0.24290997]\n",
            " [0.24292133]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LSTM Preparation:"
      ],
      "metadata": {
        "id": "FYtSYkoK5Z7o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the 1st dim of thearray after split_rows_when, because data cannot be randomized for LSTM neural network models to have high accuracy."
      ],
      "metadata": {
        "id": "HE5QWiDq23Jf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "treningowe = scaled_price[0:split_rows_when, :]"
      ],
      "metadata": {
        "id": "nc_yieYh2Uxj"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "treningowe.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1okrhJK9lnA",
        "outputId": "ecfdf2e3-a292-4b6a-c12d-24ddfa6ba5e9"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2423, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = []\n",
        "y_train = []"
      ],
      "metadata": {
        "id": "NNgQoUcV5b0d"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Picking the value of previous days for which the model would take the prices into account when calculating predictions:"
      ],
      "metadata": {
        "id": "TK4dHMut8KoI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prev_days=60"
      ],
      "metadata": {
        "id": "H1guVY-88d1n"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(prev_days, len(treningowe)):\n",
        "  X_train.append(treningowe[i-prev_days: i, 0])\n",
        "  y_train.append(treningowe[i, 0])\n",
        "\n",
        "  if i <= prev_days:\n",
        "    print(X_train)\n",
        "    print(y_train)\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlGOuyYH5kQp",
        "outputId": "599bc1b1-7578-4b5b-dcc6-ca9fa012d0f6"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.00414359, 0.00365546, 0.00321557, 0.00342492, 0.0032753 ,\n",
            "       0.00332473, 0.0038239 , 0.00363714, 0.00346454, 0.00335845,\n",
            "       0.00328567, 0.00295417, 0.00292874, 0.00309905, 0.00304965,\n",
            "       0.00292288, 0.00269198, 0.00223721, 0.00211322, 0.00225521,\n",
            "       0.00234585, 0.00259445, 0.0027738 , 0.0027224 , 0.00273334,\n",
            "       0.00297447, 0.00315054, 0.0033057 , 0.00321523, 0.00303393,\n",
            "       0.00305177, 0.0031658 , 0.00313766, 0.00303822, 0.00309209,\n",
            "       0.00304287, 0.00267573, 0.00267466, 0.00251033, 0.00262063,\n",
            "       0.00259518, 0.00266387, 0.00233701, 0.00248116, 0.00237752,\n",
            "       0.00219096, 0.00219308, 0.00221774, 0.00226134, 0.00239481,\n",
            "       0.00254029, 0.00243827, 0.00248387, 0.00274766, 0.00280197,\n",
            "       0.00281341, 0.00364242, 0.00360048, 0.00326041, 0.00293862])]\n",
            "[0.0031129686991635463]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "X_train -> 2363 array with prices for any 60 days from the input dates.\n",
        "\n",
        "y_train -> 2363 values of last price for those 60 days"
      ],
      "metadata": {
        "id": "pF4Oeefs9L6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IpskCPd788QQ",
        "outputId": "2050507d-2e61-46a6-d70a-4625b22bab73"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2363"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-COEen-K9DP_",
        "outputId": "23bd7685-eff6-44fb-e0eb-64eac710612b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2363"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array(X_train)\n",
        "\n",
        "y_train=np.array(y_train)"
      ],
      "metadata": {
        "id": "qMhYLYIB-VdJ"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGbPa7Rn-d9h",
        "outputId": "63c1268c-1caf-42a8-f08e-4aac7ec20170"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2363, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8ncPnCE-g_f",
        "outputId": "2679513d-f1b9-40d1-db50-180e5bc4b9cd"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2363,)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LSTM network needs the input data to be 3-dimensional - adding a dummy dimension"
      ],
      "metadata": {
        "id": "K8jFJ51a-laW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEZQ3RSg-ps_",
        "outputId": "6abf1868-ee2f-435e-e80d-c2e9be86b49a"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2363, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Same workflow for test data"
      ],
      "metadata": {
        "id": "AzEFnZEz-ymZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting scaled_price array with the number of days threshold"
      ],
      "metadata": {
        "id": "V4MnGMpJlzEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testowe = scaled_price[split_rows_when-prev_days: , :]"
      ],
      "metadata": {
        "id": "fFujuxxY-tQP"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = []\n",
        "y_test = scaled_price[split_rows_when: , :]\n",
        "\n",
        "#appending same kind of values to X_test list -> 60 days prices\n",
        "for i in range(prev_days, len(testowe)):\n",
        "  X_test.append(testowe[i-prev_days : i, 0])"
      ],
      "metadata": {
        "id": "aArw_b79-5dP"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#converting datatype to an array\n",
        "\n",
        "X_test = np.array(X_test)"
      ],
      "metadata": {
        "id": "N4XEVBIolw4N"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reshaping to a 3-dimensional array for LSTM to work with dummy dimension 1\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
      ],
      "metadata": {
        "id": "cPjY3ZtMmNVz"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining and designing the model"
      ],
      "metadata": {
        "id": "Dtshp2KdmgUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM"
      ],
      "metadata": {
        "id": "ymeDzkASmWs4"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setting up the global random seed"
      ],
      "metadata": {
        "id": "jjoXQTvonqEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(23)"
      ],
      "metadata": {
        "id": "NEDNqLHUm1DM"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model will consist of 4 layers:\n",
        "\n",
        "\n",
        "*   1st LSTM layer that will receive the input data from the sequence of prices for any 60 days and will return sequential data in the output\n",
        "*   2nd LSTM layer that will receive the 1st output and then return the last output in the output sequence\n",
        "*   3rd layer - Dense with an output of 25 units and bias set to true\n",
        "*   4th layer - Dense with an output of only one unit - one predicted Bitcoin price"
      ],
      "metadata": {
        "id": "0U2CfxsZoGPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input1 = (X_train.shape[1], 1)"
      ],
      "metadata": {
        "id": "waPZxwzABBzi"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Imodel = Sequential()\n",
        "Imodel.add(LSTM(50, return_sequences=True, input_shape=input1))\n",
        "Imodel.add(LSTM(50, return_sequences=False))\n",
        "Imodel.add(Dense(25, use_bias=True))\n",
        "Imodel.add(Dense(1, use_bias=True))"
      ],
      "metadata": {
        "id": "2Gb47vsPnsOM"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Imodel.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "to9UnYzMaaVN",
        "outputId": "5a992970-a990-4948-90dd-f45b09e4c05a"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 60, 50)            10400     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 50)                20200     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 25)                1275      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31,901\n",
            "Trainable params: 31,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compiling the Ist model.\n",
        "\n",
        "\n",
        "Optimizing with Adam - stochastic gradient descent (based on adaptive estimation of first-order and second-order moments).\n",
        "\n",
        "Loss as MSE."
      ],
      "metadata": {
        "id": "jcS9X1zJEc5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras"
      ],
      "metadata": {
        "id": "nWiCWiqIVP0K"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = keras.losses.MeanSquaredError()"
      ],
      "metadata": {
        "id": "4e4eFjW6FTkZ"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Imodel.compile(loss=loss_fn, optimizer='adam')"
      ],
      "metadata": {
        "id": "undFVXeuELZG"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preparing a callback for Early Stopping of the model if loss is increasing (monitor parameter as loss)."
      ],
      "metadata": {
        "id": "88EHa1wLGOXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=2)\n",
        "epochs = Imodel.fit(X_train, y_train, batch_size=1, shuffle=False, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "181q7Lh-GCOn",
        "outputId": "45bab462-69aa-4c8c-cd84-99afb8062c89"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2363/2363 [==============================] - 75s 30ms/step - loss: 0.0017\n",
            "Epoch 2/10\n",
            "2363/2363 [==============================] - 69s 29ms/step - loss: 6.8387e-04\n",
            "Epoch 3/10\n",
            "2363/2363 [==============================] - 71s 30ms/step - loss: 4.9632e-04\n",
            "Epoch 4/10\n",
            "2363/2363 [==============================] - 69s 29ms/step - loss: 4.2155e-04\n",
            "Epoch 5/10\n",
            "2363/2363 [==============================] - 68s 29ms/step - loss: 2.4003e-04\n",
            "Epoch 6/10\n",
            "2363/2363 [==============================] - 69s 29ms/step - loss: 3.2718e-04\n",
            "Epoch 7/10\n",
            "2363/2363 [==============================] - 68s 29ms/step - loss: 2.1518e-04\n",
            "Epoch 8/10\n",
            "2363/2363 [==============================] - 67s 29ms/step - loss: 3.3853e-04\n",
            "Epoch 9/10\n",
            "2363/2363 [==============================] - 69s 29ms/step - loss: 2.2903e-04\n",
            "Epoch 10/10\n",
            "2363/2363 [==============================] - 68s 29ms/step - loss: 2.0023e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making predictions from X_test"
      ],
      "metadata": {
        "id": "eL3vOB2gGtbA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = Imodel.predict(X_test)\n",
        "\n",
        "#unscalling the predictions\n",
        "pred = scaler.inverse_transform(pred)\n",
        "\n",
        "#number of pred\n",
        "len(pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPoOynszGNd1",
        "outputId": "ab361a06-9ab2-4811-ab5d-b1216726206a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 1s 19ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "606"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the function for calculating the root mean square error of the model"
      ],
      "metadata": {
        "id": "NSVNmbgoHfjr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rmse(prediction,testing):\n",
        "  return np.sqrt(np.mean(prediction - testing)**2)"
      ],
      "metadata": {
        "id": "jzDqVKbhHNf-"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "RMSE for Imodel"
      ],
      "metadata": {
        "id": "30hCA6uEHxQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rmse(pred,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4OUIFuJHGmj",
        "outputId": "ff66c2f2-24be-4d54-ca51-93c719073711"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36122.513193061575"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RMSE is 36122,5."
      ],
      "metadata": {
        "id": "M-1klghNZyA5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a df from dictionary of the epochs for the Ist model"
      ],
      "metadata": {
        "id": "01axdf_pUu9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import History\n",
        "plot_of_epochs = pd.DataFrame(epochs.history)"
      ],
      "metadata": {
        "id": "UaayTysaUsQf"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_of_epochs.index += 0\n",
        "plot_of_epochs.plot()\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_TOw5i7UHnzz",
        "outputId": "0cfab9b8-e0f8-4279-b701-aff7c7139023"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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7ukn7TDOrMLNVzbbV28zeMLO/BP/2SuS+JUJxYYRD1fWs3lEVdikiIqctYYFiZqnAo8A1wAhgupmNaNbtNmB/8Dz4h4EfBOuOAKYBI4HJwGPB9gCeCdqauw/4k7sPA/4UvG5Tjt/Xa6GmD4tIG5TII5RxQJm7b3T3WmAWMLVZn6nAs8HyC8AkM7OgfZa717j7JqAs2B7uPg/YF+Pzmm7rWeDT8dyZ1tCneyaFOV11PYqItEmJDJSBwLYmr8uDtph93L0eqAIiLVy3ub7uvjNY3gX0jdXJzO4ws1IzK92zZ09L9qNVTSjMZtnmfdQ1NIZdiojIaWmXg/IevctizJFtd3/C3YvcvSgnJ6eVKzu14sIIR2sbeK9c4ygi0rYkMlC2A7lNXg8K2mL2MbM0oAdQ2cJ1m9ttZv2DbfUHKs648hCN//D5KJo+LCJtS4sCxcy+ZmbdLeopM3vXzP7uFKstA4aZWb6ZdSI6yF7SrE8JMCNYvh6YGxxdlADTgllg+cAwYOkpPq/ptmYAv23JviWb3l07Mbxfli5wFJE2p6VHKLe6+0Hg74BewE3A90+2QjAmchfwGrAWmOPuq83sQTObEnR7CoiYWRlwL8HMLHdfDcwB1gCvAne6ewOAmT0PLALONbNyM7st2Nb3gU+Y2V+Aj5+qvmRWXBihdPN+auobwi5FRKTF0lrYz4J/rwV+HgSDnWwFAHd/GXi5Wdu3myxXAzecYN3vAt+N0T79BP0rgUmnqqktKC6I8PTbm1m+9cCHp8BERJJdS49Q3jGz14kGymtmlgVoGlKCXFIQIcX0nHkRaVtaGii3ET0dNdbdjwLpwC0Jq6qD69E5nZEDemgcRUTalJYGSjGw3t0PmNkXgX8jes2IJEhxYYTlW/dzrFbjKCLSNrQ0UB4HjprZaOCfgQ3AcwmrSiguiFDX4LyzZX/YpYiItEhLA6U+mM47Ffixuz8KZCWuLBmb35vUFGPRRl2PIiJtQ0tneR0ys/uJTheeaGYpRMdRJEG6ZaRxwaAeulGkiLQZLT1CuRGoIXo9yi6iV64/lLCqBIie9nqvvIrDNfVhlyIickotCpQgRH4J9DCzTwHV7q4xlASbUJhNQ6OzbHOsmyuLiCSXlt565XNEb31yA/A5YImZXZ/IwgQuHtKL9FTT9Sgi0ia0dAzlW0SvQakAMLMc4I9En2EiCdK5UypjcnspUESkTWjpGErK8TAJVJ7GunIWigsjrN5RRdWxurBLERE5qZaGwqtm9pqZ3WxmNwN/oNk9uiQxigsjNDos3aRxFBFJbi0dlP8G8ARwQfDfE+7+L4ksTKLGDO5JRloKC/V8FBFJci0dQ8Hdfw38OoG1SAwZaalcPETjKCKS/E56hGJmh8zsYIz/DpnZwdYqsqObUBhh3a5D7DtSG3YpIiIndNJAcfcsd+8e478sd+/eWkV2dMWF0WeiLNbdh0UkiWmmVhtwwaCedOmUqtNeIpLUFChtQHpqCmPzeuv5KCKS1BIaKGY22czWm1mZmd0X4/0MM5sdvL/EzPKavHd/0L7ezK4+1TbNbJKZvWtmK8xsgZkNTeS+tbbiwghlFYepOFgddikiIjElLFDMLBV4FLgGGAFMN7MRzbrdBux396HAw8APgnVHANOAkcBk4DEzSz3FNh8HvuDuFwK/IvoQsHajOHi2vI5SRCRZJfIIZRxQ5u4b3b0WmEX0eSpNTQWeDZZfACaZmQXts9y9xt03AWXB9k62TQeOTxToAexI0H6FYuSA7mRlpmlgXkSSVouvQzkDA4FtTV6XA5ecqI+715tZFRAJ2hc3W3dgsHyibd4OvGxmx4CDwPhYRZnZHcAdAIMHDz69PQpRWmoKl+T31vNRRCRptadB+a8D17r7IOBp4L9jdXL3J9y9yN2LcnJyWrXAszW+IMKWyqPsOHAs7FJERP5GIgNlO5Db5PWgoC1mHzNLI3qqqvIk68ZsD+5+PNrdlwTts4EJ8dmN5DGhMBtA04dFJCklMlCWAcPMLN/MOhEdZC9p1qcEmBEsXw/MDZ5dXwJMC2aB5QPDiD6P5UTb3E/04V/nBNv6BLA2gfsWiuH9sujVJV2nvUQkKSVsDCUYE7kLeA1IBWa6+2ozexAodfcS4Cng52ZWBuwjGhAE/eYAa4B64E53bwCItc2g/cvAr82skWjA3JqofQtLSopxSX6ExRsrcXei8xdERJKDRQ8IOqaioiIvLS0Nu4zT8uzCzfx7yWrmfeNKBke6hF2OiHRAZvaOuxc1b29Pg/IdwoTC49ej6Hb2IpJcFChtzNA+3cjulqFxFBFJOgqUNsbMGF/Qm0UbouMoIiLJQoHSBk0ozKbiUA0b9x4JuxQRkQ8pUNqg489H0WkvEUkmCpQ2KC/ShX7dM1msQBGRJKJAaYPMjAmF0etRGhs1jiIiyUGB0kaNL4xQeaSWDyoOhV2KiAigQGmzPnw+ik57iUiSUKC0Ubm9u5DbuzNzSsv1FEcRSQoKlDbsW9eex+a9R7j2kfnM/8uesMsRkQ5OgdKGTT6/PyV3XUrvrp340syl/Ndr66lvaAy7LBHpoBQobdywvln89s7L+NzFufz4zTI+/7Ml7KrSKTARaX0KlHagc6dUfnD9BTx842hW7aji2kfm89b6irDLEpEORoHSjnxmzCBK7rqMPlkZ3Pz0Mn7w6jqdAhORVqNAaWeG9unGb+68lOnjBvP4WxuY9sRiPYNeRFqFAqUdykxP5Xt/P4pHpo9h7c6DXPvIfOau2x12WSLSzilQ2rEpowfw+3sm0r9HZ259ppT/eHktdToFJiIJktBAMbPJZrbezMrM7L4Y72eY2ezg/SVmltfkvfuD9vVmdvWptmlR3zWzD8xsrZndk8h9ayvys7vy0lcn8MXxg3li3kY+99NFlO8/GnZZItIOJSxQzCwVeBS4BhgBTDezEc263Qbsd/ehwMPAD4J1RwDTgJHAZOAxM0s9xTZvBnKB4e5+HjArUfvW1mSmp/KdT4/ix58fw192H+aTjyzgjTU6BSYi8ZXII5RxQJm7b3T3WqK/4Kc26zMVeDZYfgGYZGYWtM9y9xp33wSUBds72Tb/EXjQ3RsB3F3zZpv51AUD+MM9l5HbuzNffq6U/+/3a6it1ykwEYmPRAbKQGBbk9flQVvMPu5eD1QBkZOse7JtFgI3mlmpmb1iZsNiFWVmdwR9Svfs6Xi3KxkS6cqv/3ECN0/I46kFm7jhp4vYtk+nwETk7LWnQfkMoNrdi4CfATNjdXL3J9y9yN2LcnJyWrXAZJGRlsoDU0by+BcuYuOew1z7yHxeXbUr7LJEpI1LZKBsJzqmcdygoC1mHzNLA3oAlSdZ92TbLAdeDJZfAi446z1o564Z1Z8/3D2Rguyu/MMv3uGBktXU1DeEXZaItFGJDJRlwDAzyzezTkQH2Uua9SkBZgTL1wNz3d2D9mnBLLB8YBiw9BTb/A1wZbB8BfBBgvarXRkc6cL//sMEbr00n2cWbub6xxextVKnwETk9CUsUIIxkbuA14C1wBx3X21mD5rZlKDbU0DEzMqAe4H7gnVXA3OANcCrwJ3u3nCibQbb+j7wWTN7H/gecHui9q296ZSWwrevG8ETN13MlsojfPKR+bz8/s6wyxKRNsaiBwQdU1FRkZeWloZdRlLZtu8odz+/nBXbDnDT+CF865PnkZmeGnZZIpJEzOydYLz6I9rToLzEQW7vLsz5SjFfnpjPzxdv4bOPL2TT3iNhlyUibYACRf5Gp7QUvvXJETz5pSK2HzjGdT9awO9W7gi7LBFJcgoUOaGPj+jLH+6ZyLn9srj7+eX860vvU12nWWAiEpsCRU5qYM/OzLpjPF+5ooBfLdnKpx99mw17DoddlogkIQWKnFJ6agr3X3MeT988lt0Hq7nuRwv4zfLmlxSJSEenQJEWu3J4H17+2kRGDujOP81ewX2/fo9jtToFJiJRChQ5Lf17dOb5L4/nqx8rZNaybXz60bcpq9ApMBFRoMgZSEtN4ZuTh/PsrePYe7iG6360gO+/so53tuyjobHjXtck0tHpwkZd2HhWdh+s5lsvvc9b6/dQ3+j06pLOlef24arz+nD5OTl0z0wPu0QRibMTXdiYFkYx0n707Z7JkzPGUnWsjvl/2cPctRW8ub6CF5dvJy3FGJffm6uG92HSeX3Jz+4adrkikkA6QtERStw1NDrLt+7nT+sqmLu2gvW7DwFQkN31w3ApyutFeqrOuIq0RSc6QlGgKFASbtu+o8xdV8Gf1lWweEMltQ2NZGWmccU5OUw6rw8fO6cPvbp2CrtMEWkhBUoMCpTWd6Smnvl/2cvcdbuZu24Pew/XkGJw8ZBeXDW8L5PO68OwPt2IPglaRJKRAiUGBUq4Ghud97dX8ad1Ffxp7W5W7zgIQG7vzkwa3perhvfhkoLeZKTpbsciyUSBEoMCJbnsqqpm7roK5q7bzYKyvVTXNdKlUyoTh2UzaXhfrhzeh5ysjLDLFOnwFCgxKFCSV3VdAws37OVPayuYu66CnVXVAIzO7cmk4X24angfRg7orlNjIiFQoMSgQGkb3J21Ow8xd91u/ri2gpXlB3CHft0zuXJ4HyYN78OlQ7Pp3EmnxkRagwIlBgVK27TnUA1vrY8eucz7YA9HahvISEvh0qHZfP3j5zBqUI+wSxRp10J5YqOZTTaz9WZWZmb3xXg/w8xmB+8vMbO8Ju/dH7SvN7OrT2Obj5iZbi7VjuVkZXBDUS6Pf/Fi3v32J/j5beOYPm4w75VX8fePv81P/7yBRt0CRqTVJSxQzCwVeBS4BhgBTDezEc263Qbsd/ehwMPAD4J1RwDTgJHAZOAxM0s91TbNrAjolah9kuSTkZbKxGE5PDBlJH+893KuGt6H772yjhlPL6XiYHXY5Yl0KIk8QhkHlLn7RnevBWYBU5v1mQo8Gyy/AEyy6CjrVGCWu9e4+yagLNjeCbcZhM1DwDcTuE+SxHp26cRPvngx3/3M+SzbvI9rfjifuet2h12WSIeRyEAZCGxr8ro8aIvZx93rgSogcpJ1T7bNu4ASd995sqLM7A4zKzWz0j179pzWDknyMzO+cMkQfnfXZeRkZXDrM6U8ULJajy4WaQXt4mZKZjYAuAH40an6uvsT7l7k7kU5OTmJL05CMaxvFr+581JunpDHMws385nHFlJWcSjsskTatUQGynYgt8nrQUFbzD5mlgb0ACpPsu6J2scAQ4EyM9sMdDGzsnjtiLRNmempPDBlJE/NKGL3wWo+9aMFPL90Kx15ZqNIIiUyUJYBw8ws38w6ER1kL2nWpwSYESxfD8z16E97CTAtmAWWDwwDlp5om+7+B3fv5+557p4HHA0G+kWYdF5fXvnaRIqG9Ob+F9/nq798l6qjdWGXJdLuJCxQgjGRu4DXgLXAHHdfbWYPmtmUoNtTQCQ4mrgXuC9YdzUwB1gDvArc6e4NJ9pmovZB2o++3TN57tZx3HfNcN5Ys5trfjiPpZv2hV2WSLuiCxt1YWOHs3LbAe6ZtZxt+45y15VDuWfSMNL0bBaRFgvlwkaRZDQ6tyd/uGcinx4zkEfmlnHjE4vZtu9o2GWJtHkKFOmQumWk8d+fu5AfTruQ9bsOce0j8/n9ezvCLksSoKHReWPNbhZu2KsJGQmmZ8pLhzb1woGMye3FPbOWc9evljPvgz08MGUkXTrpR6Otq29o5LcrdvDom2Vs3HsEgHP7ZnHLpXl8esxAMtN1M9F40xiKxlAEqGto5H/++AGPvbWB/EhXHpk+hvMHtr2bTB6qruP11bv53Xs72L7/GJ8ryuXGcbl0z0wPu7RWU1vfyEvLy3n0zQ1s3XeU8/p35+6rhnKkpp6Zb29m7c6D9OqSzucvGcxN4/Po1yMz7JLbHN1tOAYFijS3cMNevj57BfuO1PIvk4dz66X5pKQk9zNXqusaeHNdBSUrdzB3XQU19Y0M7NmZvt0zeHfrAbplpHHj2FxunpBHbu8uYZebMDX1DbzwTjmPvbmB7QeOMWpgD+6ZNIyPn9fnw+fmuDtLNu1j5oJNvLF2N6lmXDuqP7dels+FuT1D3oO2Q4ESgwJFYtl/pJZv/vo93lizm8vPyeH/3TA66Z4UWdfQyIKyvfxuxToARiYAAA6ASURBVA5eX7ObwzX1ZHfrxCdH9WfKhQMYk9uLlBTj/fIqnlqwkd+/t5NGd645vz+3TcznosHt5x6q1XUNzF62jZ/8eQM7q6q5MLcnX/v4MD52Ts5JH8C2tfIozy7azOxl2zhcU8+YwT259dJ8Jp/fj3TN+jspBUoMChQ5EXfnF0u28p3fryErM43/umE0Hzu3T6g1NTY6yzbvo2TlDl5ZtYt9R2rJykzjmvP7cd3oARQXRE44/Xln1TGeWbiZXy3ZyqHqei4a3JPbJxZw9ch+pCb5EdiJHKtt4JdLtvDEvI1UHKphbF4v7pk0jMuGZp/WkzwPVdfxwjvlPLNwM1sqj9K/RyY3FQ9h+tjB9OraKYF70HYpUGJQoMiprN91iHueX8763Ye47bJ8vjn5XDLSWm8w1915f3sVJSt28Pv3drLrYDWZ6Sl8/Ly+TBk9gCvOzTmteo7U1PO/pduY+fZmtu47Sm7vztwyIZ/Pjc2lW0bbmIhwpKaeXyzews/mb2Tv4VqKCyLcM2kY4wt6n9UjoRsanTfXVTDz7U0s3FBJZnoKf3/RIG6ZkMewvllx3IO2T4ESgwJFWqK6roH/eHktzy3awoj+3Xlk+hiG9umW0M8sqzhEyYodlKzcwebKo6SnGleck8N1owfw8fP60vUsf/kfn0r71IKNLNu8n6yMNKZfMpgZE/IY2LNznPYivg5V1/Hcoi08OX8j+4/WMXFYNvdMGsbYvN5x/6x1uw7y9ILNvLRiO7X1jUwcls2tl+ZzxTk5ST+m1hoUKDEoUOR0vLFmN998YSXVdY08MGUEnyvKPau/iJvbtu8ov3tvByUrdrBu1yHMoLggwpTRA5h8fj96dknM6ZcV2w7w5PyNvLJqFwCfHNWf2yfmc8Gg5BikrjpWxzNvb2bm25uoOlbHVcP7cPdVQxnTCuNAlYdreH7pVp5btIWKQzUUZHfl5kvz+OxFg8461NsyBUoMChQ5Xbuqqrl3zgoWbqjkk6P68x9/P4oenc98Sm7FoWpefm8nJSt38O7WAwCMGdyT6y4YwKcu6E+f7q03pbV8/1GeXbiZWUu3caimnnF5vbltYj4fP69vKOMs+4/UMvPtTTzz9mYO1dTziRF9ueeqYYwa1PrTuWvrG3ll1U5mLtjEyvIqsjLTmD5uMF8qHsKgXu135tyJKFBiUKDImWhodH46bwP//foH9O2eyQ+nXUjRaZx2qTpax6uroyGyaEMljQ7D+2Vx3egBTBk9IPSpvYeq65hTWs7MBZvYfuAYQyJduPXSfK6/uHX+Kq88XMOTCzbx3MLNHKlt4NpR/bjrymGMGNA94Z99Ku7Ou1sPMPPtTby6ahfuztUj+3HrZfkUDekV1yPWZKZAiUGBImdjxbYD3PP8csr3H+WeScO468qhJ5xldbS2nj+uraBkxQ7+/EEFdQ3OkEgXpowewHWjB3BOEg761jc08vqa3fxs/kaWbz1A98w0Pn/JEGZMGEL/HvEfZ6k4VM3P5m3kF4u3Ul3fwKcuGMBdVw7l3H7J990A7DhwjOcWbeH5pVupOlbH+QO7c+ul+Xzygv6tOnEjDAqUGBQocrYOVdfx7d+u5qXl2xmb14v/mTbmw0HtmvoG5n2wl5KVO/jjmt0cq2ugb/cMPnVB9EjkgkE92sxftO9s2c9TCzby6qpdpJhx3egB3HZZflzuJrCrqpqfztvAr5Zspa6hkU9fOJCvXjk04RMf4uVobT0vLd/OzAWb2LDnCDlZGXzxkiF8Yfxgsrsl1/VL8aJAiUGBIvHy0vJy/u2lVaSmGF//xDms23mIV1bt5GB1Pb26pHPNqP5MGT2AsXm92+x1HxCdOPD025uZvWwrR2obGF/Qm9svK+Cq4X1Oe/bT9gPH+MlbG5i9bBuN7nxmzEDuvHIoedldE1R9YjU2OvPL9vL025t4a/0eOqWmMOXCAdxyaR4jB7S92/icjAIlBgWKxNOWyiPcM2sFK7cdoGunVP5uZD+mjB7AZcOy292V1wer65i9dBtPv72JHVXVFGR35ZbL8rn+okF07nTy0z3b9h3lsbc28MI72wC4/uJcvvqxwtDHjuKprOIwzy7czAvvlHOsLhq8t1wa3gSHeFOgxKBAkXira2hk1fYqzuvfvUPczbauoZFXV+3iyfkbWVleRc8u6XzxkiF8qXjI38xQ27z3CI++WcaLy7eTasa0cbl85YrCpL3uJR6qjtYxa1l02vH2A8fI7d2ZsXm9GdCjM/17ZjKgZ+cPl9vSDTwVKDEoUETiw90p3bKfJ+dv5PU1u0lLMaaMHshtl+XTKS2FR98s47crtpOemsLnLxnMVy4v7FB3+T0+weH5pVvZuOcIuw5W09D40d+9WRlpH4ZM/x6dGdAjWO6ZyYAenenXIzNp/kgJJVDMbDLwQyAVeNLdv9/s/QzgOeBioBK40d03B+/dD9wGNAD3uPtrJ9ummf0SKALqgKXAV9y97mT1KVBE4m9L5RGefnszc0q3cbS2ATPITEvlpuIh3D4xnz5ZHSdITqS+oZE9h2vYceAYOw5Us7Mq+u+OA8fYWRX9t/JI7d+sl92tUxA4mfTv0ZmBQeAcX87JymiVU2qtHihmlgp8AHwCKAeWAdPdfU2TPl8FLnD3fzCzacBn3P1GMxsBPA+MAwYAfwTOCVaLuU0zuxZ4JejzK2Ceuz9+shoVKCKJU3W0jtmlW6mua+QLlwwm0k5nPCVKdV0DO6uq2XngGDuqjofNR4PncE39R9ZJSzH6ds9kQBAyA3p2brIcPdLp2SX9rGcXnihQEnmV0jigzN03BgXMAqYCa5r0mQo8ECy/APzYons6FZjl7jXAJjMrC7bHibbp7i8f36iZLQUGJWrHROTUenRJ547LC8Muo83KTE8lP7sr+SeZ9Xawui4aLgeq2R4EzvHlFdsO8OqqXdQ2NH5knc7pqfTvmcl/fGYU4wsica05kYEyENjW5HU5cMmJ+rh7vZlVAZGgfXGzdQcGyyfdppmlAzcBX4tVlJndAdwBMHjw4JbvjYhIkumemU73fukM7xf7LgKNjc7eIzXsDE6rbT9w/IjnGD27xH8SQHu8u9ljRE93zY/1prs/ATwB0VNerVmYiEhrSkkx+mRl0icrk9Gt8ETKRAbKdiC3yetBQVusPuVmlgb0IDo4f7J1T7hNM/t3IAf4ShzqFxGR05DIq62WAcPMLN/MOgHTgJJmfUqAGcHy9cBcj84SKAGmmVmGmeUDw4jO3DrhNs3sduBqooP0jYiISKtK2BFKMCZyF/Aa0Sm+M919tZk9CJS6ewnwFPDzYNB9H9GAIOg3h+gAfj1wp7s3AMTaZvCRPwG2AIuCGQwvuvuDido/ERH5KF3YqGnDIiKn5UTThtvXDYZERCQ0ChQREYkLBYqIiMSFAkVEROKiQw/Km9keojPDzkQ2sDeO5bR1+j7+St/FR+n7+Kj28H0Mcfec5o0dOlDOhpmVxprl0FHp+/grfRcfpe/jo9rz96FTXiIiEhcKFBERiQsFypl7IuwCkoy+j7/Sd/FR+j4+qt1+HxpDERGRuNARioiIxIUCRURE4kKBcgbMbLKZrTezMjO7L+x6wmJmuWb2ppmtMbPVZhbzKZkdjZmlmtlyM/t92LWEzcx6mtkLZrbOzNaaWXHYNYXFzL4e/JysMrPnzSwz7JriTYFymswsFXgUuAYYAUw3sxHhVhWaeuCf3X0EMB64swN/F019DVgbdhFJ4ofAq+4+HBhNB/1ezGwgcA9Q5O7nE338xrRwq4o/BcrpGweUuftGd68FZgFTQ64pFO6+093fDZYPEf1lMTDcqsJlZoOATwJPhl1L2MysB3A50ece4e617n4g3KpClQZ0Dp5O2wXYEXI9cadAOX0DgW1NXpfTwX+JAphZHjAGWBJuJaH7H+CbgJ4aCvnAHuDp4BTgk2bWNeyiwuDu24H/ArYCO4Eqd3893KriT4EiZ83MugG/Bv7J3Q+GXU9YzOxTQIW7vxN2LUkiDbgIeNzdxwBHgA455mhmvYieycgHBgBdzeyL4VYVfwqU07cdyG3yelDQ1iGZWTrRMPmlu78Ydj0huxSYYmabiZ4KvcrMfhFuSaEqB8rd/fhR6wtEA6Yj+jiwyd33uHsd8CIwIeSa4k6BcvqWAcPMLN/MOhEdWCsJuaZQmJkRPT++1t3/O+x6wubu97v7IHfPI/r/i7nu3u7+Cm0pd98FbDOzc4OmScCaEEsK01ZgvJl1CX5uJtEOJyikhV1AW+Pu9WZ2F/Aa0ZkaM919dchlheVS4CbgfTNbEbT9q7u/HGJNklzuBn4Z/PG1Ebgl5HpC4e5LzOwF4F2isyOX0w5vwaJbr4iISFzolJeIiMSFAkVEROJCgSIiInGhQBERkbhQoIiISFwoUETaKDP7mO5oLMlEgSIiInGhQBFJMDP7opktNbMVZvbT4Hkph83s4eD5GH8ys5yg74VmttjM3jOzl4J7QGFmQ83sj2a20szeNbPCYPPdmjxv5JfBVdgioVCgiCSQmZ0H3Ahc6u4XAg3AF4CuQKm7jwT+DPx7sMpzwL+4+wXA+03afwk86u6jid4DamfQPgb4J6LP5ikgevcCkVDo1isiiTUJuBhYFhw8dAYqiN7efnbQ5xfAi8HzQ3q6+5+D9meB/zWzLGCgu78E4O7VAMH2lrp7efB6BZAHLEj8bon8LQWKSGIZ8Ky73/+RRrP/06zfmd4DqabJcgP6mZYQ6ZSXSGL9CbjezPoAmFlvMxtC9Gfv+qDP54EF7l4F7DeziUH7TcCfg6dhlpvZp4NtZJhZl1bdC5EW0F8zIgnk7mvM7N+A180sBagD7iT6sKlxwXsVRMdZAGYAPwkCo+ndeW8CfmpmDwbbuKEVd0OkRXS3YZEQmNlhd+8Wdh0i8aRTXiIiEhc6QhERkbjQEYqIiMSFAkVEROJCgSIiInGhQBERkbhQoIiISFz8/wJnH/hmJY5cAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best performance of model I is observed in the last epoch (10th), so no need to create it all over again."
      ],
      "metadata": {
        "id": "m-_E78hLZ3qZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model evaluation"
      ],
      "metadata": {
        "id": "DfJIU29Pe1qg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "y_test_eval = scaler.inverse_transform(y_test)\n",
        "#pred is inverted already\n",
        "\n",
        "array_conc_eval_pred = np.concatenate((y_test_eval,pred),axis=1)\n",
        "\n",
        "df_pred = pd.DataFrame(data = array_conc_eval_pred, columns=[\"actual\", \"predicted\"])\n",
        "\n",
        "df_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "i1gKXHWbaGKA",
        "outputId": "948393e9-7696-4a66-9623-cf196c54d6d9"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           actual     predicted\n",
              "0    56396.515625  57609.953125\n",
              "1    57356.402344  56344.523438\n",
              "2    58803.777344  57444.332031\n",
              "3    58232.316406  58756.574219\n",
              "4    55859.796875  58160.500000\n",
              "..            ...           ...\n",
              "601  16552.572266  16946.335938\n",
              "602  16642.341797  16782.958984\n",
              "603  16602.585938  16844.162109\n",
              "604  16547.496094  16803.640625\n",
              "605  16548.261719  16751.966797\n",
              "\n",
              "[606 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ad658f0-5fe6-4c55-a21b-7e674fa20fd0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>actual</th>\n",
              "      <th>predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56396.515625</td>\n",
              "      <td>57609.953125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57356.402344</td>\n",
              "      <td>56344.523438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>58803.777344</td>\n",
              "      <td>57444.332031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58232.316406</td>\n",
              "      <td>58756.574219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55859.796875</td>\n",
              "      <td>58160.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601</th>\n",
              "      <td>16552.572266</td>\n",
              "      <td>16946.335938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602</th>\n",
              "      <td>16642.341797</td>\n",
              "      <td>16782.958984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>16602.585938</td>\n",
              "      <td>16844.162109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>16547.496094</td>\n",
              "      <td>16803.640625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>605</th>\n",
              "      <td>16548.261719</td>\n",
              "      <td>16751.966797</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>606 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ad658f0-5fe6-4c55-a21b-7e674fa20fd0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ad658f0-5fe6-4c55-a21b-7e674fa20fd0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ad658f0-5fe6-4c55-a21b-7e674fa20fd0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "results = Imodel.evaluate(X_test, y_test)\n",
        "\n",
        "print(\"mse: %s\" % (mean_squared_error(y_test_eval, pred)))\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9fjxLKhfGML",
        "outputId": "cb2912f1-195e-45c5-8220-24a84dbb5b2b"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 0s 18ms/step - loss: 5.1095e-04\n",
            "mse: 2320339.8493948737\n",
            "0.0005109483026899397\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ar_test = np.repeat(1, len(y_test_eval))\n",
        "ar_pred = np.repeat(2, len(pred))\n",
        "\n",
        "df_test = pd.DataFrame(data = np.concatenate((y_test_eval,(np.reshape(ar_test, (-1, 1)))),axis=1), columns=[\"price\",\"act/pred\"])\n",
        "df_pred = pd.DataFrame(data = np.concatenate((pred,(np.reshape(ar_pred, (-1, 1)))),axis=1), columns=[\"price\",\"act/pred\"])\n",
        "\n",
        "df_both = [df_test, df_pred]\n",
        "df_3 = pd.concat(df_both, ignore_index=False)\n",
        "\n",
        "df_3[\"act/pred\"].replace({1: \"act\", 2: \"pred\"}, inplace=True)\n",
        "df_3.head(10)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "M9Te38A5f3KJ",
        "outputId": "a8634b3b-717d-4372-9de4-c36ce3c9f19d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          price act/pred\n",
              "0  56396.515625      act\n",
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              "3  58232.316406      act\n",
              "4  55859.796875      act\n",
              "5  56704.574219      act\n",
              "6  49150.535156      act\n",
              "7  49716.191406      act\n",
              "8  49880.535156      act\n",
              "9  46760.187500      act"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UOC1ZvPbhpXO",
        "outputId": "bf191199-41e5-496e-cbd3-f5c432e14e2a"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1212"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualization of the predicted and actual BTC prices from **May 07, 2021** to **Dec 31, 2022**"
      ],
      "metadata": {
        "id": "Eg7E5zKiiQoK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "fig = px.line(df_3, x=df_3.index.values, y=\"price\", color='act/pred', title='BTC Price from May 2021 to Jan 2023')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "iJ_ADGupe-Fb",
        "outputId": "db652089-2442-4527-9638-0bd2cb060df9"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
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