sudip-mondal-2002/Stock-price-predictor.ipynb

## Stock-price-predictor.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "stock-price-predictor.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpAuMVCwfWs8"
      },
      "source": [
        "# Stock Price Predictor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxJfRe4bfYVA"
      },
      "source": [
        "## Part 1 - Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnMTeoVilFov",
        "outputId": "d3f6656f-bdd6-459e-a309-47231b97991e"
      },
      "source": [
        "!pip install --upgrade yfinance"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.63)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.6.3)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.5.30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir9zwETrfbrp"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZT1f24vHffuf"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQ47JAxrgmaL"
      },
      "source": [
        "### Importing the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xiv3pJOgqY3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "ea2efc77-4b65-45ea-a40c-9abcd07262f1"
      },
      "source": [
        "import yfinance as yf\n",
        "data = yf.download('BHARTIARTL.NS','2012-01-01')\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title(\"Opening Price History\")\n",
        "plt.plot(data[\"Open\"])\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Open Price INR($)\")\n",
        "plt.show()\n",
        "dataset=data.filter([\"Open\"]).values\n",
        "trainingDataLength = int(len(dataset)*(0.95))\n",
        "training_set = dataset[0:trainingDataLength, :]\n",
        "training_set"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[316.31243896],\n",
              "       [319.06698608],\n",
              "       [326.09103394],\n",
              "       ...,\n",
              "       [595.        ],\n",
              "       [596.95001221],\n",
              "       [597.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HT8_2UJegtG5"
      },
      "source": [
        "### Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTrF2kR7gx9x"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyYgYocqhNUg"
      },
      "source": [
        "### Creating a data structure with 60 timesteps and 1 output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iofU21B0i6ST"
      },
      "source": [
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(60, len(training_set_scaled)):\n",
        "    X_train.append(training_set_scaled[i-60:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8yaN7Zvi95l"
      },
      "source": [
        "### Reshaping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOXqJHmNjBkz"
      },
      "source": [
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRRSOJeVjEWV"
      },
      "source": [
        "## Part 2 - Building and Training the RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4XV88JMjHXG"
      },
      "source": [
        "### Importing the Keras libraries and packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JRnqsxEjKsD"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Dropout"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEIE-1s9jNzC"
      },
      "source": [
        "### Initialising the RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1338dJ0UjRKH"
      },
      "source": [
        "regressor = Sequential()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62eg1OPGjT8z"
      },
      "source": [
        "### Adding the first LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hIinyXUjbVU"
      },
      "source": [
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XBIYLyOjlMx"
      },
      "source": [
        "### Adding a second LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UG7nrVaSjuZ2"
      },
      "source": [
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey3fHVnGj1cu"
      },
      "source": [
        "### Adding a third LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuNi6PgFj7jO"
      },
      "source": [
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYTrtfTmj933"
      },
      "source": [
        "### Adding a fourth LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp4Ty8fRkBYV"
      },
      "source": [
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ABI6rOIkHhk"
      },
      "source": [
        "### Adding the output layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aM6R1z4WkME8"
      },
      "source": [
        "regressor.add(Dense(units = 1))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLx4K7uUkPSh"
      },
      "source": [
        "### Compiling the RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTrhVN-tkbko"
      },
      "source": [
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQdR9SOhmPjz",
        "outputId": "41127e3e-5513-4915-cae8-c0163afc36c8"
      },
      "source": [
        "regressor.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 60, 50)            10400     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 60, 50)            20200     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 60, 50)            20200     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 50)                20200     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mPhwKGkkebi"
      },
      "source": [
        "### Fitting the RNN to the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I06Nkrz5kkb-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "724822c4-584e-4a5b-96d2-f0439fb32602"
      },
      "source": [
        "history = regressor.fit(X_train, y_train, epochs = 40, batch_size = 32)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "69/69 [==============================] - 14s 112ms/step - loss: 0.0139\n",
            "Epoch 2/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0063\n",
            "Epoch 3/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0046\n",
            "Epoch 4/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0043\n",
            "Epoch 5/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0037\n",
            "Epoch 6/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0034\n",
            "Epoch 7/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0033\n",
            "Epoch 8/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0030\n",
            "Epoch 9/40\n",
            "69/69 [==============================] - 8s 114ms/step - loss: 0.0030\n",
            "Epoch 10/40\n",
            "69/69 [==============================] - 8s 114ms/step - loss: 0.0029\n",
            "Epoch 11/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0027\n",
            "Epoch 12/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0024\n",
            "Epoch 13/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0024\n",
            "Epoch 14/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0022\n",
            "Epoch 15/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0022\n",
            "Epoch 16/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0022\n",
            "Epoch 17/40\n",
            "69/69 [==============================] - 8s 114ms/step - loss: 0.0020\n",
            "Epoch 18/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0021\n",
            "Epoch 19/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0020\n",
            "Epoch 20/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0020\n",
            "Epoch 21/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0019\n",
            "Epoch 22/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0019\n",
            "Epoch 23/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0018\n",
            "Epoch 24/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0018\n",
            "Epoch 25/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0017\n",
            "Epoch 26/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0016\n",
            "Epoch 27/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0016\n",
            "Epoch 28/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0016\n",
            "Epoch 29/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0016\n",
            "Epoch 30/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0016\n",
            "Epoch 31/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0014\n",
            "Epoch 32/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0014\n",
            "Epoch 33/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0015\n",
            "Epoch 34/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0016\n",
            "Epoch 35/40\n",
            "69/69 [==============================] - 8s 114ms/step - loss: 0.0014\n",
            "Epoch 36/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0012\n",
            "Epoch 37/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0013\n",
            "Epoch 38/40\n",
            "69/69 [==============================] - 8s 112ms/step - loss: 0.0012\n",
            "Epoch 39/40\n",
            "69/69 [==============================] - 8s 114ms/step - loss: 0.0014\n",
            "Epoch 40/40\n",
            "69/69 [==============================] - 8s 113ms/step - loss: 0.0014\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "7ZuL2e1dmgFq",
        "outputId": "d2339600-c19c-4e94-8411-5dec9a7c12c0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.plot(history.history['loss'])\n",
        "plt.legend(['Training'])\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tfx8mIvsk3Kz",
        "outputId": "00a1f975-b626-4504-87f1-ef0e80af32bc"
      },
      "source": [
        "regressor.save('model')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses while saving (showing 5 of 20). These functions will not be directly callable after loading.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: model/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: model/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgr_Qy6lnLWC",
        "outputId": "03be4009-37ed-449c-b4da-553c3f361ada"
      },
      "source": [
        "!zip -r model.zip model"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "updating: model/ (stored 0%)\n",
            "updating: model/variables/ (stored 0%)\n",
            "updating: model/variables/variables.data-00000-of-00001 (deflated 7%)\n",
            "updating: model/variables/variables.index (deflated 69%)\n",
            "updating: model/keras_metadata.pb (deflated 93%)\n",
            "updating: model/saved_model.pb (deflated 90%)\n",
            "updating: model/assets/ (stored 0%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hRau_lIkrE8"
      },
      "source": [
        "## Part 3 - Making the predictions and visualising the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrvrLblxkz42"
      },
      "source": [
        "### Getting the predicted stock price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emikTvUpk3Ck"
      },
      "source": [
        "import tensorflow\n",
        "regressor = tensorflow.keras.models.load_model('model')\n",
        "dataset_total = dataset\n",
        "dataset_test = dataset[trainingDataLength:, :]\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:]\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(60, len(inputs)):\n",
        "    X_test.append(inputs[i-60:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "predicted_stock_prices = regressor.predict(X_test)\n",
        "predicted_stock_prices = sc.inverse_transform(predicted_stock_prices)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFTNs3YHk6FQ"
      },
      "source": [
        "### Visualising the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OUI8U49k9tH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "9d16deab-6df5-4a09-d124-cf11dd23fc4d"
      },
      "source": [
        "plt.plot(dataset_test, color = 'red', label = 'Real Stock Price')\n",
        "plt.plot(predicted_stock_prices, color = 'blue', label = 'Predicted Stock Price')\n",
        "plt.title('Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQ8g3EIQpGBh",
        "outputId": "44d132f4-323b-4c9a-b40b-a2266726e765"
      },
      "source": [
        "import math\n",
        "MSE = np.square(np.subtract(dataset_test,predicted_stock_prices)).mean() \n",
        "RMSE = math.sqrt(MSE)\n",
        "print(RMSE)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12.175206236174073\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}
No results found