RNN_Youtube.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RNN_Youtube.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRSlxT4q5EHJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fRF6mEW5QbK"
      },
      "source": [
        "df = pd.read_csv('/content/monthly_milk_production.csv',index_col='Date',parse_dates=True)\n",
        "df.index.freq='MS'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agtLk7dO5SsM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "c10fc828-0a87-49c6-a562-4811d8c01f20"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Production</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1962-01-01</th>\n",
              "      <td>589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-02-01</th>\n",
              "      <td>561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-03-01</th>\n",
              "      <td>640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-04-01</th>\n",
              "      <td>656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-05-01</th>\n",
              "      <td>727</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Production\n",
              "Date                  \n",
              "1962-01-01         589\n",
              "1962-02-01         561\n",
              "1962-03-01         640\n",
              "1962-04-01         656\n",
              "1962-05-01         727"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "b1h7L_TG5tJK",
        "outputId": "908b0648-4938-4d40-d153-daf89de15aa4"
      },
      "source": [
        "df.plot(figsize=(12,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f21deb910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcew1g4f6L_X"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "zfTZaeEH6Wpn",
        "outputId": "3f6120dd-ff89-4c21-bce1-32dc4d01b02c"
      },
      "source": [
        "results = seasonal_decompose(df['Production'])\n",
        "results.plot();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzkqCHh_6lfn",
        "outputId": "79c7a98c-acaf-4ad7-a807-70898e3b83e1"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NoWcZEK6rPb"
      },
      "source": [
        "train = df.iloc[:156]\n",
        "test = df.iloc[156:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtAgIrE66u6x"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDQODrt4D5NK",
        "outputId": "add8aaaf-9916-4484-af9c-3f5967dad1cd"
      },
      "source": [
        "df.head(),df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(            Production\n",
              " Date                  \n",
              " 1962-01-01         589\n",
              " 1962-02-01         561\n",
              " 1962-03-01         640\n",
              " 1962-04-01         656\n",
              " 1962-05-01         727,             Production\n",
              " Date                  \n",
              " 1975-08-01         858\n",
              " 1975-09-01         817\n",
              " 1975-10-01         827\n",
              " 1975-11-01         797\n",
              " 1975-12-01         843)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijgmVYgC6zBz"
      },
      "source": [
        "scaler.fit(train)\n",
        "scaled_train = scaler.transform(train)\n",
        "scaled_test = scaler.transform(test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrdKbJ3g6-Et",
        "outputId": "ec956dda-aab6-433c-d846-523769fa65e7"
      },
      "source": [
        "scaled_train[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.08653846],\n",
              "       [0.01923077],\n",
              "       [0.20913462],\n",
              "       [0.24759615],\n",
              "       [0.41826923],\n",
              "       [0.34615385],\n",
              "       [0.20913462],\n",
              "       [0.11057692],\n",
              "       [0.03605769],\n",
              "       [0.05769231]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xryZ7rR62y5"
      },
      "source": [
        "from keras.preprocessing.sequence import TimeseriesGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqyTKCVt69Z9"
      },
      "source": [
        "# define generator\n",
        "n_input = 3\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ip6y5Ene7OM7",
        "outputId": "f8405e12-f285-4394-b2a4-d84b6e6dfec4"
      },
      "source": [
        "X,y = generator[0]\n",
        "print(f'Given the Array: \\n{X.flatten()}')\n",
        "print(f'Predict this y: \\n {y}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Given the Array: \n",
            "[0.08653846 0.01923077 0.20913462]\n",
            "Predict this y: \n",
            " [[0.24759615]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQyGmvsi8HyR",
        "outputId": "2124feb3-263f-46ce-cb56-7b564bc3298c"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 3, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm-T4H4I7UaV"
      },
      "source": [
        "# We do the same thing, but now instead for 12 months\n",
        "n_input = 12\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzIs_l_k7Zqg"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJa-wFQf7jw6"
      },
      "source": [
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7i4ucUNv7lbk",
        "outputId": "7f66fb4d-f3af-4d9c-ae3a-9eb97a722ea6"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 100)               40800     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 40,901\n",
            "Trainable params: 40,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8yqUZaz7nwp",
        "outputId": "94c9f117-53be-438c-ba31-d7c7fa546f42"
      },
      "source": [
        "# fit model\n",
        "model.fit(generator,epochs=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "144/144 [==============================] - 2s 5ms/step - loss: 0.0784\n",
            "Epoch 2/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0248\n",
            "Epoch 3/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0169\n",
            "Epoch 4/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0134\n",
            "Epoch 5/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0128\n",
            "Epoch 6/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0073\n",
            "Epoch 7/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0053\n",
            "Epoch 8/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0056\n",
            "Epoch 9/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0047\n",
            "Epoch 10/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0040\n",
            "Epoch 11/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0034\n",
            "Epoch 12/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0035\n",
            "Epoch 13/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0038\n",
            "Epoch 14/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0038\n",
            "Epoch 15/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0048\n",
            "Epoch 16/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0029\n",
            "Epoch 17/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0029\n",
            "Epoch 18/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0059\n",
            "Epoch 19/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0029\n",
            "Epoch 20/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0027\n",
            "Epoch 21/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0030\n",
            "Epoch 22/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0031\n",
            "Epoch 23/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0034\n",
            "Epoch 24/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0034\n",
            "Epoch 25/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0041\n",
            "Epoch 26/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0039\n",
            "Epoch 27/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0026\n",
            "Epoch 28/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0042\n",
            "Epoch 29/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0025\n",
            "Epoch 30/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0035\n",
            "Epoch 31/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0026\n",
            "Epoch 32/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0029\n",
            "Epoch 33/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0024\n",
            "Epoch 34/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0026\n",
            "Epoch 35/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0030\n",
            "Epoch 36/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0035\n",
            "Epoch 37/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0029\n",
            "Epoch 38/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0022\n",
            "Epoch 39/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0021\n",
            "Epoch 40/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0031\n",
            "Epoch 41/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0025\n",
            "Epoch 42/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0017\n",
            "Epoch 43/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0019\n",
            "Epoch 44/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0021\n",
            "Epoch 45/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0027\n",
            "Epoch 46/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0019\n",
            "Epoch 47/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0019\n",
            "Epoch 48/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0021\n",
            "Epoch 49/50\n",
            "144/144 [==============================] - 1s 6ms/step - loss: 0.0021\n",
            "Epoch 50/50\n",
            "144/144 [==============================] - 1s 5ms/step - loss: 0.0020\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8f219a16d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "OjDsOKR67rs8",
        "outputId": "46a3d438-6cea-4a47-f4bf-b60f2246561d"
      },
      "source": [
        "loss_per_epoch = model.history.history['loss']\n",
        "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8f21cbb290>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6h-fell70_S"
      },
      "source": [
        "last_train_batch = scaled_train[-12:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwwv8O-p79bN"
      },
      "source": [
        "last_train_batch = last_train_batch.reshape((1, n_input, n_features))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-4WD4t78MpJ",
        "outputId": "6f5ab088-0933-41e7-cf6a-239dc4236d6d"
      },
      "source": [
        "model.predict(last_train_batch)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.6046513]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbojFADE8StA",
        "outputId": "e708a2e2-ccf1-48e7-cace-a3b7a6202de1"
      },
      "source": [
        "scaled_test[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.67548077])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MP-5Wez8jbY"
      },
      "source": [
        "test_predictions = []\n",
        "\n",
        "first_eval_batch = scaled_train[-n_input:]\n",
        "current_batch = first_eval_batch.reshape((1, n_input, n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "    \n",
        "    # get the prediction value for the first batch\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "    \n",
        "    # append the prediction into the array\n",
        "    test_predictions.append(current_pred) \n",
        "    \n",
        "    # use the prediction to update the batch and remove the first value\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mhvFuEy8sB7",
        "outputId": "e65693ce-1c66-46b1-91e2-069e239ca984"
      },
      "source": [
        "test_predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.6046513], dtype=float32),\n",
              " array([0.5854211], dtype=float32),\n",
              " array([0.7456695], dtype=float32),\n",
              " array([0.8121553], dtype=float32),\n",
              " array([0.911981], dtype=float32),\n",
              " array([0.8914289], dtype=float32),\n",
              " array([0.8224216], dtype=float32),\n",
              " array([0.7289473], dtype=float32),\n",
              " array([0.6303051], dtype=float32),\n",
              " array([0.59199744], dtype=float32),\n",
              " array([0.54971915], dtype=float32),\n",
              " array([0.5722453], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "YYSqV10E9hlO",
        "outputId": "fe8ed427-3d97-429e-9349-115ba1168bc0"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>Production</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-01-01</th>\n",
              "      <td>834</td>\n",
              "      <td>813.303572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-01</th>\n",
              "      <td>782</td>\n",
              "      <td>809.682804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-03-01</th>\n",
              "      <td>892</td>\n",
              "      <td>879.037941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-04-01</th>\n",
              "      <td>903</td>\n",
              "      <td>912.407272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-05-01</th>\n",
              "      <td>966</td>\n",
              "      <td>954.157072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Production  Predictions\n",
              "Date                               \n",
              "1975-01-01         834   813.303572\n",
              "1975-02-01         782   809.682804\n",
              "1975-03-01         892   879.037941\n",
              "1975-04-01         903   912.407272\n",
              "1975-05-01         966   954.157072"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHHzjNDK9EJZ"
      },
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dsN45zf9I9u"
      },
      "source": [
        "test['Predictions'] = true_predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "xJ7sI5xh9Lb0",
        "outputId": "6aeaec0b-9c02-4d87-ca51-987048dae0ba"
      },
      "source": [
        "test.plot(figsize=(14,5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f2020fed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCsYjzge9OVe",
        "outputId": "4771bf0b-a885-4e3f-d16e-b5b9bd255cc2"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "rmse=sqrt(mean_squared_error(test['Production'],test['Predictions']))\n",
        "print(rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "24.270639050894633\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bld3MIqDNW6I"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}