ISA_630_Final_Codebook.ipynb

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      "source": [
        "## **ISA 630 Final Project**"
      ],
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            "\r[*********************100%%**********************]  1 of 1 completed\n"
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              "                 Open       High        Low      Close  Adj Close    Volume\n",
              "Date                                                                       \n",
              "2015-01-02  36.669998  37.160000  36.110001  36.360001  28.073423  23605600\n",
              "2015-01-05  36.200001  36.450001  35.939999  35.950001  27.756859  32785000\n",
              "2015-01-06  36.080002  36.230000  35.259998  35.279999  27.239555  30847600\n",
              "2015-01-07  35.639999  36.070000  35.270000  36.020000  27.810907  27725200\n",
              "2015-01-08  36.369999  37.000000  36.259998  36.689999  28.328213  31765400"
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              "      <th>2015-01-02</th>\n",
              "      <td>36.669998</td>\n",
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              "      <th>2015-01-07</th>\n",
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          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "##  Stock Price\n",
        "## Ticker: TSLA\n",
        "import yfinance as yf\n",
        "\n",
        "df = yf.download('INTC', start = '2015-01-01', end = '2024-05-06')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## plot\n",
        "df['Close'].plot()"
      ],
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          "height": 440
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            "text/plain": [
              "<Axes: xlabel='Date'>"
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          },
          "metadata": {},
          "execution_count": 20
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Preprocessing**\n",
        "\n",
        "We need to proccess the data so it can be fit to the model with the following steps.\n",
        "\n",
        "- Train/test split; This is not a random split because this is a time series problem. Rather we need to use a historical section of data, say from the start until the end of 2024, to predict a later section of the data, say the stock price each day in 2024.\n",
        "- Scaling; We need to scale the values. Since we are using a neural network, this prevents the vanishing gradient or exploding gradient effect from occuring.\n",
        "- Feature Engineering; we need to establish our lagged variables to act as the features in the RNN."
      ],
      "metadata": {
        "id": "IVEWYzf2kVze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## train/test split\n",
        "train = df['Close'][:'2023-12-31'] ## From Start of Data to 2024\n",
        "test = df['Close']['2024-01-01':] ## 2024 Data"
      ],
      "metadata": {
        "id": "WlPYxxSDkL25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Scaling\n",
        "\n",
        "## standardize the data\n",
        "## to avoid the vanishing gradient or exploding gradient effect\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(train.values.reshape(-1,1))\n",
        "\n",
        "scaled_train = scaler.transform(train.values.reshape(-1,1))\n",
        "scaled_test = scaler.transform(test.values.reshape(-1,1))"
      ],
      "metadata": {
        "id": "aPNEZv5PkTK0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## this creates our lags (X_train, y_train)\n",
        "\n",
        "import numpy as np\n",
        "def split_sequence(sequence, n_steps):\n",
        " X, y = list(), list()\n",
        " for i in range(len(sequence)):\n",
        " # find the end of this pattern\n",
        "  end_ix = i + n_steps\n",
        " # check if we are beyond the sequence\n",
        "  if end_ix > len(sequence)-1:\n",
        "    break\n",
        " # gather input and output parts of the pattern\n",
        "  seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "  X.append(seq_x)\n",
        "  y.append(seq_y)\n",
        " return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "BbPM1BfTlDm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## X_train and y_train\n",
        "X_train, y_train = split_sequence(scaled_train, 3) ## 3 lags\n",
        "X_test, y_test = split_sequence(scaled_test, 3) ## 3 lags"
      ],
      "metadata": {
        "id": "MJc9dm5nlKRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Recurrent Neural Network**"
      ],
      "metadata": {
        "id": "8tv44xZVl8bI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## RNN Architecture\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import SimpleRNN, Dense  ## For project we can also use the LSTM, or a GRU. Code is exactly the same, just use the corresponding layer\n",
        "\n",
        "rnn = Sequential()\n",
        "rnn.add(SimpleRNN(units = 60, input_shape = (3,1), activation = 'relu', return_sequences = True))  ## input shape is (lags, 1). In this example its (5,1)\n",
        "rnn.add(SimpleRNN(units = 30, activation = 'relu', return_sequences = True))\n",
        "rnn.add(SimpleRNN(units = 20, activation = 'relu', return_sequences = True))\n",
        "rnn.add(SimpleRNN(units = 15, activation = 'relu')) ## we are not returning sequences (default is False), because it is the last RNN layer\n",
        "rnn.add(Dense(60, activation ='relu'))\n",
        "rnn.add(Dense(30, activation = 'relu'))\n",
        "rnn.add(Dense(1)) ## default is linear, which is nessecary for this problem."
      ],
      "metadata": {
        "id": "3YF_Jh6VlMtw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## compile\n",
        "rnn.compile(optimizer = 'adam', loss = 'mse')\n",
        "\n",
        "## callback\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stop = EarlyStopping(monitor = 'val_loss', patience = 10)\n",
        "\n",
        "## fit\n",
        "rnn.fit(X_train, y_train, epochs = 100, batch_size = 100, validation_data = [X_test, y_test], callbacks = early_stop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Un6oA0imAS4",
        "outputId": "1857e7cf-7648-4854-cdfb-9ca5dda3c871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "23/23 [==============================] - 11s 34ms/step - loss: 0.3418 - val_loss: 0.0500\n",
            "Epoch 2/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0282 - val_loss: 0.0289\n",
            "Epoch 3/100\n",
            "23/23 [==============================] - 1s 32ms/step - loss: 0.0170 - val_loss: 0.0215\n",
            "Epoch 4/100\n",
            "23/23 [==============================] - 1s 42ms/step - loss: 0.0137 - val_loss: 0.0192\n",
            "Epoch 5/100\n",
            "23/23 [==============================] - 1s 33ms/step - loss: 0.0128 - val_loss: 0.0167\n",
            "Epoch 6/100\n",
            "23/23 [==============================] - 1s 35ms/step - loss: 0.0120 - val_loss: 0.0163\n",
            "Epoch 7/100\n",
            "23/23 [==============================] - 1s 34ms/step - loss: 0.0114 - val_loss: 0.0158\n",
            "Epoch 8/100\n",
            "23/23 [==============================] - 1s 34ms/step - loss: 0.0110 - val_loss: 0.0158\n",
            "Epoch 9/100\n",
            "23/23 [==============================] - 1s 42ms/step - loss: 0.0106 - val_loss: 0.0136\n",
            "Epoch 10/100\n",
            "23/23 [==============================] - 1s 31ms/step - loss: 0.0099 - val_loss: 0.0147\n",
            "Epoch 11/100\n",
            "23/23 [==============================] - 1s 32ms/step - loss: 0.0098 - val_loss: 0.0139\n",
            "Epoch 12/100\n",
            "23/23 [==============================] - 1s 30ms/step - loss: 0.0091 - val_loss: 0.0141\n",
            "Epoch 13/100\n",
            "23/23 [==============================] - 1s 31ms/step - loss: 0.0094 - val_loss: 0.0148\n",
            "Epoch 14/100\n",
            "23/23 [==============================] - 1s 28ms/step - loss: 0.0098 - val_loss: 0.0137\n",
            "Epoch 15/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0091 - val_loss: 0.0151\n",
            "Epoch 16/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0092 - val_loss: 0.0136\n",
            "Epoch 17/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0091 - val_loss: 0.0132\n",
            "Epoch 18/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0090 - val_loss: 0.0142\n",
            "Epoch 19/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0088 - val_loss: 0.0140\n",
            "Epoch 20/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0088 - val_loss: 0.0131\n",
            "Epoch 21/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0084 - val_loss: 0.0124\n",
            "Epoch 22/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0095 - val_loss: 0.0138\n",
            "Epoch 23/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0090 - val_loss: 0.0144\n",
            "Epoch 24/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0088 - val_loss: 0.0137\n",
            "Epoch 25/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0084 - val_loss: 0.0139\n",
            "Epoch 26/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0087 - val_loss: 0.0138\n",
            "Epoch 27/100\n",
            "23/23 [==============================] - 0s 16ms/step - loss: 0.0092 - val_loss: 0.0134\n",
            "Epoch 28/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0088 - val_loss: 0.0134\n",
            "Epoch 29/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0084 - val_loss: 0.0128\n",
            "Epoch 30/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0091 - val_loss: 0.0146\n",
            "Epoch 31/100\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 0.0091 - val_loss: 0.0129\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ea2503c28f0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## predictions\n",
        "predictions = scaler.inverse_transform(rnn.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8alYuUMmRnX",
        "outputId": "db80cd66-4775-47c4-e47a-734e040e51ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "true = scaler.inverse_transform(y_test)\n",
        "y_previous = df['Close']['2024-01-01':].shift(-1).dropna()\n",
        "y_test_trimmed = true[:len(y_previous)]\n",
        "\n",
        "# Now calculate the MSE and R-squared\n",
        "mse = mean_squared_error(y_test_trimmed, y_previous, squared=False)\n",
        "r_squared = r2_score(y_test_trimmed, y_previous)\n",
        "\n",
        "# Print the results\n",
        "print(f'The MSE is: {mse}')\n",
        "print(f'The R-squared is: {r_squared}')"
      ],
      "metadata": {
        "id": "PVbUUpS67BhC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03baf2e7-7a1b-4740-a3b7-0b4cd9a2de65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The MSE is: 1.164062870146414\n",
            "The R-squared is: 0.938664452945072\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " ## PERFORMANCE METRICS\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "print(f'The MSE is: {mean_squared_error(true, predictions, squared = False)}')\n",
        "print(f'The R-squared is: {r2_score(true, predictions)}')\n",
        "\n",
        "plt.figure(figsize = (10,6))\n",
        "plt.plot(true)\n",
        "plt.plot(pd.DataFrame(predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "Bnt-zdEymwNL",
        "outputId": "d301a2f4-f76f-401d-e292-6edd1b7c76af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The MSE is: 1.1863041762706832\n",
            "The R-squared is: 0.9390840768714258\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ea23a1c3820>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **RandomForest**"
      ],
      "metadata": {
        "id": "9P09fpp3BH6D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Lags for multiple predictors\n",
        "## suppose you want to create lags for the variable close\n",
        "import numpy as np\n",
        "vars = ['Close']\n",
        "l =5\n",
        "\n",
        "for col in vars:\n",
        "  lags = np.arange(1,l,1) ## middle value is number of lags\n",
        "  for lag in lags:\n",
        "    df[\"{}_Lag{}\".format(col, lag)] = df[col].transform(lambda x: x.shift(lag))"
      ],
      "metadata": {
        "id": "NLXAP12CBLH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## drop all variables not lagged except for the y_variable (CLOSE)\n",
        "rf_data = df.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis = 1) ## inplace = True"
      ],
      "metadata": {
        "id": "aVaXM-dIBPOb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_data.dropna(inplace = True)\n",
        "\n",
        "train = rf_data[:'2023-12-31']\n",
        "test = rf_data['2024-01-01':]\n",
        "\n",
        "X_train = train.drop(['Close'], axis = 1)\n",
        "y_train = train.Close\n",
        "X_test = test.drop(['Close'], axis =1)\n",
        "y_test = test.Close"
      ],
      "metadata": {
        "id": "RDfntVp3BROD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf = RandomForestRegressor()\n",
        "\n",
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "w2ACcnyeBdSD",
        "outputId": "71585850-852c-4725-e4e3-c0ae3f32fe89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = rf.predict(X_test)"
      ],
      "metadata": {
        "id": "dBaiKaNgCT7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'The MSE is: {mean_squared_error(y_test, predictions, squared = False)}')\n",
        "print(f'The R-squared is: {r2_score(y_test, predictions)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnvzOukiCalK",
        "outputId": "d38c893d-c039-44e2-9611-51c897f2ba54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The MSE is: 1.3616922870479322\n",
            "The R-squared is: 0.9203826551504419\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## PREDICTION USING DAY BEFORE\n",
        "\n",
        "y_test_trimmed = y_test[:len(y_previous)]\n",
        "\n",
        "# Now calculate the MSE and R-squared\n",
        "mse = mean_squared_error(y_test_trimmed, y_previous, squared=False)\n",
        "r_squared = r2_score(y_test_trimmed, y_previous)\n",
        "\n",
        "# Print the results\n",
        "print(f'The MSE is: {mse}')\n",
        "print(f'The R-squared is: {r_squared}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5TBogQU3b5e",
        "outputId": "84da85fb-e0e3-427b-89bc-d028b94857eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The MSE is: 1.164062870146414\n",
            "The R-squared is: 0.938664452945072\n"
          ]
        }
      ]
    }
  ]
}