StockPrediction.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "StockPrediction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/burhanwani/AI_homework/blob/master/StockPrediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc-r4342WaCp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwEMf31fXBBJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "d33b5fd5-5f9b-4f46-8fb8-acccb63319f7"
      },
      "source": [
        "!pip install yfinance --upgrade --no-cache-dir\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: yfinance in /usr/local/lib/python3.6/dist-packages (0.1.45)\n",
            "Requirement already satisfied, skipping upgrade: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.16.5)\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.24.2)\n",
            "Requirement already satisfied, skipping upgrade: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.21.0)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.5.3)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.8)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2019.6.16)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.5.0->pandas>=0.24->yfinance) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVTUuDapXWMg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "a105305b-4a5e-4be2-cf08-fb320ce5d096"
      },
      "source": [
        "import numpy as np\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive',force_remount=True)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJowSoYXmQHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Modeling\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "#Modeling Metrics\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyiGuo2MbG_I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "878661e7-d1c2-4322-967c-93bcf11a21df"
      },
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()\n",
        "df_full = pdr.get_data_yahoo(\"JPM\", start=\"2018-01-01\").reset_index()\n",
        "df_full.to_csv('JPM.csv',index=False)\n",
        "df_full.head()\n",
        "\n",
        "\n",
        "df_full.describe()\n",
        "df_full[\"Adj Close\"].plot()\n",
        "\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c0242f5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HJ0owqikmglF4XYULHmaFWEr14ZylzTPsmZlZb+hlgasnu41FXchuMFKSSZ0ShqIJdKnh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\nGgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2g7uzzYfhL9K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_full.set_index(\"Date\", inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52J_2IWBi4rr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_size=32\n",
        "num_samples=len(df_full)-window_size\n",
        "\n",
        "# Get indices of access for the data\n",
        "indices=np.arange(num_samples).astype(np.int)[:,None]+np.arange(window_size+1).astype(np.int)\n",
        "\n",
        "\n",
        "data = df_full['Adj Close'].values[indices] # Create the 2D matrix of training samples\n",
        "x = data[:,:-1] # Each row represents 32 days in the past\n",
        "y = data[:,-1] # Each output value represents the 33rd day"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWoSEU07jAUf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "split_fraction=0.8\n",
        "ind_split=int(split_fraction*num_samples)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7yy1M0_jCSv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x[:ind_split]\n",
        "y_train = y[:ind_split]\n",
        "x_test = x[ind_split:]\n",
        "y_test = y[ind_split:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dm96WUhEjIUU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "088f9170-93e4-4ae5-a667-c04977d1563d"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ntq2aixRkQ0a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Help Functions\n",
        "def get_performance (model_pred):\n",
        "  #Function returns standard performance metrics\n",
        "  print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, model_pred).round(4))  \n",
        "  print('Mean Squared Error:', metrics.mean_squared_error(y_test, model_pred).round(4))  \n",
        "  print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, model_pred)).round(4))\n",
        "  \n",
        "  \n",
        "  \n",
        "def get_plot (model_pred):\n",
        "  plt.scatter(model_pred, y_test, color=\"gray\")\n",
        "  plt.plot(y_test, y_test, color='red', linewidth=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9NL3ekHkSNd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_lag=np.roll(y_test,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6twu8W5VkWhe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "e71a621b-ed61-4662-dfd2-087d1574867c"
      },
      "source": [
        "get_performance(y_pred_lag)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 1.2771\n",
            "Mean Squared Error: 3.4519\n",
            "Root Mean Squared Error: 1.8579\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKfrgghpka1i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "outputId": "2c43a5e3-a8ce-4a6c-f190-f0b508cf8705"
      },
      "source": [
        "\n",
        "get_plot(y_pred_lag)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zsU+edD6u9XjZhtO44d57nQ8oCEgBIMffrtx+r8PJOTZA3vHJOTYASEtLQ1dX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\nohiTABFRFPsfZK8fquodj84AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VROITBKblJec",
        "colab_type": "text"
      },
      "source": [
        "# **Linear regression Modelling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gOtpGpbkeHW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "3dab60f5-906b-4aea-be95-b229864c8e2d"
      },
      "source": [
        "model_lr=LinearRegression()\n",
        "model_lr.fit(x_train, y_train)\n",
        "y_pred_lr=model_lr.predict(x_test)\n",
        "get_performance(y_pred_lr)\n",
        "get_plot(y_pred_lr)\n",
        "get_performance(y_pred_lr)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 1.1876\n",
            "Mean Squared Error: 2.1871\n",
            "Root Mean Squared Error: 1.4789\n",
            "Mean Absolute Error: 1.1876\n",
            "Mean Squared Error: 2.1871\n",
            "Root Mean Squared Error: 1.4789\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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01VAoFDbvBICfpnwO7AJytv/BtYxM+4/s7saq556z2nbd2rWA8qc5CXKrfnri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\ngIgohDEJEBGFMCYBIqIQ9v8BrWmUnTAz2FMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sI9s--88nDX3",
        "colab_type": "text"
      },
      "source": [
        "# Nearest Neighbour Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpB7MofulTDG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "66a05c16-50ca-4c79-8bc7-ecaddfe9756b"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "neigh = KNeighborsRegressor(n_neighbors=2)\n",
        "neigh.fit(x_train, y_train)\n",
        "y_pred_KNN=neigh.predict(x_test)\n",
        "get_performance(y_pred_KNN)\n",
        "get_plot(y_pred_KNN)\n",
        "get_performance(y_pred_KNN)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 3.3344\n",
            "Mean Squared Error: 14.1347\n",
            "Root Mean Squared Error: 3.7596\n",
            "Mean Absolute Error: 3.3344\n",
            "Mean Squared Error: 14.1347\n",
            "Root Mean Squared Error: 3.7596\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gX9mfAS+Ptl2991273nN6LND371ctJUAim5BKAoE8Og+103pvy1JKX/Vr4677\nR+rRtth7t+2ke5Pj+/X0WdfW1qCs7G9oaWlhkiC/C6kkEMijc3+d1qvlKNsVxctgFy7AODTZabhn\n+6ccR9uBKtv0/r6HDh2GI0eqFU+ypB0hlQQCeXTuj9N6tRxlu6NkGSzpqhHQNzU6jJ3d9j46x93g\n9Ni+TrgHomwj9n1//fU/nB4XrHNNFBxCKgkEetJN7s4exY+yvaBUGSzQN38B/N+5JfZ9uxKMc00U\nHEIqCQT7pFswTDYr0d2iRAIA/F+ak/K99rzgjEhOIZUEAHX03fsqMjJSdP2ZyMhIBaIRF8hEG7m5\nDHEP3e8w1nX1SJz54GPZX8um546/J3+U5twtNd2bIARmIUXSnpBLAsFMp9NJGpeL1CPeQCRa0aP/\nE81AuP9+sr1r9L3JXZoTO6tyxd3idER9wSSgImLrzrgbl4MaJ6OVKv94U6OXszQndlbV1dUp+n0H\na+sxqR+TgIooMemqpsloXVsrBg53vme1v27+3vvsx5sdvNzfRe+zqn37dol2CA0dOkzW1yWy8biU\nNAVOwJZa7kEtk9ED01KcEkDLxs1+SQBVVVVOy0fv3v2+x+cFYnmHo0frJY0T9RXPBFREie4mNVz5\nHOjyz+7du0Vv/u5OoDrN1JKUSTuYBFQm0N1NSi9opkT939WtI12ZP/8xP0XiTA1JmbSFSUDjlLq2\nIuK9csT/550OYx0/z8O5v/yPX18X6L7Ju7eJQI6dr5TuK6WTMmmPxyRgMplQUVGB48ePY/v27UhP\n774/U319PRYtWoSzZ8/CYDDAZDIhLS0NAPDBBx/gxRdfhCAIEAQBCxYswNSpU/36Rsh3gT77cLX6\nJ8LCAvL6OTk52LZtm8OOVqfTQafTOdwQxpedb1/XArKNHTzIBeQoMDwmgZycHBQVFWHWrFkO48XF\nxSgsLERBQQHeeecdLFmyBBs3boQgCHjyySdRVlaG9PR0fPPNN7jzzjsxefJk6PWch9Y6pdo/exo5\nciTOnbvgdHQO9O2MSK61gNLTMzF+/Bg0NnIegPzPYxLIyspyGjObzaiursaGDRsAAHl5eVi+fDma\nm5uRkJAAvV6P1tbuH3BraysGDRrEBKB1Xqz+GUjuzn5sieDAgf32x3rDn2sBqX11WQpePs0JNDQ0\nIDk5GWH/On0PCwvDoEGD0NDQgMTERKxatQrz589HdHQ02tvbsX79ep+CS0qK8el5vjIa1T/5FpQx\nTpkC7NrlOPbZZ8Do0TAGLiwHYp9jVVUV9u3bhc7OTgDdO+p9+3YhLq4/Ro4c6XGbUnbs8fHxHr9L\n27/3NS5/CsrfowopGaPsE8N3A1aRAAAQfklEQVRdXV1Yt24d1q5di9GjR+Pzzz/HI488gvLycgwY\nMEDStszmNlitgVkzxWiMVf3pdzDG6Lb8o9B7scXY8+hap9OJtol2dnaisnInUlLS7GOujsqjoqK8\nuro7PDwc119/g9vvsufnWFm5054A3MUVaMH4e1QjOWPU63WSD559SgKpqak4deoULBYLwsLCYLFY\ncPr0aaSmpqKmpganT5/G6NGjAQCjR49G//79UVdXh2uvvdaXl6MgpYb6v03PHXd8fDyGDBnqMGHr\n7jqBnkf47pbZcLWNsLAw9O8f7XMph9cOkD/5lASSkpKQmZmJHTt2oKCgADt27EBmZiYSExNhsVhw\n8uRJfPfddxg+fDjq6upgNptxxRVXyB07qVT4Pw4hYfJEh7H2p57B+UefUCSe3jvulpYWtLQ4T9i6\n0rNN1N0yG64WebNYLCgqmudD5D++Pq8dIH/xmARKS0tRWVmJpqYmzJ49GwaDAeXl5SgpKcGiRYuw\ndu1axMXFwWQyAQCMRiNKSkrw8MMP21e/fPbZZ2EwGPz7TkgddDok9BpqPHkWULAxQMqEbW+920Td\nHZX7a2fNawfIn3SCihcq55yAI7XHqKbyT09r16706XlipZuNG19xuaMfOnSYaEvo1Vdfi5tumizp\ntXt/12rsDlL77xHQXowBmxMgctDRAePljn0+XRk/wZmP/q5QQI6k3LwF6D7KnjRpiuhO1t1Rua2l\ntDepi7/V1tagrMz5YjGld/oUmti8T30yYHmxUwIwf1almgQAuF6d9eqrr7WXamyly5iYWJcJAOi+\nZmDSpCn25/V8vBwTuLb5C9uyFraJ59raGq+3QSQFzwTIZ2LlHwgCrCo7/e69PlJ8fDyuv/4Gn4+s\nXR2VyzEnoKb7O5A2MAmQT1zV/5W6+MuTnjtuf9WJ5ZjAZTsoBRqTAEkSVnsEiROudxhr+fPruJQ3\nXaGI5CHHxKscK7KyHZQCjUmAvJYw6QaEVx92GGs81QL8q54erOS8z3JfJ3DZDkqBFrJJQI0tdcFM\nre2fclBTHZ5LSVOghVQS6Lnj76kvR3aa19kJ4+Akh6GLBbeh9ZX/ViYeP1BbHZ5LSVMghUyLqO2U\n3tV/uLYjO/Je1J9edkoATd/Uh1QCAFzX21mHJy0ImTMBb5YGYIeF90K5/NMb6/CkZSGTBLzZwfPI\nzjtaSgCAcvdZJlKDkEkCnpYG4JGdZ2FHvkHijWMcxlo2lOHSLfkKRRQ4XJaBtCpkkoDYKb0Nj+w8\nS5g4FuHfOC5NEArtn0TkXsgkAZ7S+05r5R9fse2YQlHIJAGAp/SSiaz+efHW29C6/r+ViUfF5Lyg\njEhNQqZFlKTpv36tUwJoqvqWCcAFdxeUEQUzj2cCJpMJFRUVOH78OLZv34709HQAQH19PRYtWoSz\nZ8/CYDDAZDIhLS0NANDR0YFnn30Wn3zyCSIjIzFq1CgsX77cr2+EvMfyj3Rqu6CMSC4ek0BOTg6K\nioowa9Ysh/Hi4mIUFhaioKAA77zzDpYsWYKNGzcCAJ577jlERkaioqICOp0OTU1N/oleYcFYI2YC\n8A0XdqNQ5bEclJWVhdTUVIcxs9mM6upq5OXlAQDy8vJQXV2N5uZmtLe3Y+vWrQ73GB44cKAfQldW\n7yuU1X7zD/13dU4JoOUvm5gAvOTqxjRsO6Zg59PEcENDA5KTkxEWFgYACAsLw6BBg9DQ0ICwsDAY\nDAasXr0an376KQYMGICHH34YWVlZsgauNDUtOuZJ7IP3IeqN/3EYY/unNOw+o1Ale3eQxWLBsWPH\ncNVVV2HhwoX46quvcN9992Hnzp2IiZF2A2SpN0zuqaqqCrt370ZLSwvi4+ORk5ODkSNHun2O0ej9\nqb27GrGU7UgledtiO3pB8OvNX/z5/uXiS4xG4xiMHz/G8wNlEqqfY6AxRvd8SgKpqak4deoULBYL\nwsLCYLFYcPr0aXvZKDw83F4quu6665CQkID6+nqPO+HezOY2WK2C5Ph6t/O1tLRg27ZtOHfugssj\nN6l3m3JXI/bX6o/uYuw9P/GzrHG4YdJ4h8e0L3wa5//vQsCPq1P6665dcmKM8mCM8pAzRr1eJ/ng\n2acW0aSkJGRmZmLHjh0AgB07diAzMxOJiYlITEzE2LFj8fHHHwPo7iIym80YOnSoLy/lk0C086mp\nRtx7fsJQU+2UAJqOfN+dAIiIevB4JlBaWorKyko0NTVh9uzZMBgMKC8vR0lJCRYtWoS1a9ciLi4O\nJpPJ/pylS5fiN7/5DUwmE8LDw7FixQrExYnclNxPAtHOp6Yacc+k9x+bNyPzm28c/p2Tv0Tkisck\nsHjxYixevNhpfMSIEdiyZYvocy6//HK89tprfY/OR4Fq51PLFcq291pcUuIw/uVPf4ohFfsUiIiI\ngkVIXjGsplJNIBgF5wSwesEC7Jl1lzIBEVHQCKm1g2zUVKrxt6g/vYz5S0scxpYWFyO8Xz9MCtGk\nR0TyCckkAKinVONPvS/+ah44EC8tWBDSSU8uUq72DsYrw4m8FbJJIKR1dcF4WaLDUMurr8GSX4D5\nCoUUTKqqqrxeEZSrh1KoC8k5gVCmP/6DUwJo/O4ELuUXKBRR8Nm9e7fXLcRcPZRCHZNAMNm0CUk/\nvcphqPH0OUDildha19LSIjou1lHG1UMp1DEJBIm4wl8ChYX2v89ufov9/z6Kj48XHRdrIXbVVszV\nQylUcE5A7draYBx+mcNQU009hKQkhQIKfjk5Odi2bZtDmadnC3HPieCoqCjodDoIgiD6WKJgxzMB\nFQv//KBjAhg1Co2nWpgA+mjkyJGYNGmK/Wg+JiYWkyZNQXp6ptMSHBcvXoROp0NkZKTTY4lCAc8E\nVCrs21ok/DzH/nfrsysQ+9QTfl38TUtctRCLTQRbrVb06xeBuXMfCFR4RAHDJKBS4f84ZP//zfsP\nwpKeAVah/Y8TwaQ1TAIq1XHbHTD/nyxYh6YBelbtAoW3kSSt4d5FrXQ6WIcNZwIIMK2tO0XEMwGi\nHrS07hQRwCRA5EQL604R2bDWQESkYUwCREQa5lUSMJlMyM7ORkZGBmpra+3j9fX1mDFjBnJzczFj\nxgx8//33Ts9dvXq10/OIiEgdvEoCOTk5KCsrw+DBgx3Gi4uLUVhYiIqKChQWFmLJkiUO//7111/j\n0KFDTs8jIiJ18GpiOCsry2nMbDajuroaGzZsAADk5eVh+fLlaG5uRmJiIi5duoRly5bhhRdeQFFR\nkU/B6fU6n57nq0C/ni8YozwYozwYozzkitGX7fjcHdTQ0IDk5GSEhYUBAMLCwjBo0CA0NDQgMTER\nL774IqZPn44hQ4b4+hJISBjg83N9kZSk/iWZGaM8GKM8GKM8lIzRLxPDX375JQ4fPozCHksfExGR\n+vicBFJTU3Hq1ClYLBYAgMViwenTp5GamoqDBw+irq4OOTk5yM7OxsmTJzF37lzs38+7MRERqYnP\nSSApKQmZmZnYsWMHAGDHjh3IzMxEYmIi7r33Xuzfvx979uzBnj17kJKSgldffRUTJvDSeyIiNdEJ\nPe+W4UJpaSkqKyvR1NSEhIQEGAwGlJeXo66uDosWLcK5c+cQFxcHk8mE4cOHOz0/OzsbL7/8MtLT\n0/3yJoiIyDdeJQEiIgpNvGKYiEjDmASIiDSMSYCISMOYBIiINCykkoDUhe7OnDmDefPmITc3F/n5\n+ViwYAGam5tFt71o0SJMnDgRBQUFKCgowB//+MeAxAh0d1dNmzbN/tofffSR6LYvXLiARx55BFOm\nTMG0adPwwQcfBCTGH374wR5bQUEBsrOzMWbMGNFtv/TSS/jZz35mf+zSpUt9itFdnK7G3b2H3iwW\nC5YuXYrJkydjypQp2LJlS0BiVNNv0t3nqJbfpKtxJX6TYrF4+j4PHTqE6dOnIzc3F3PmzIHZbBbd\ntlyfoyghhBw8eFA4ceKEcPPNNwtHjhyxj999993C1q1bBUEQhK1btwp33323IAiCcObMGeHAgQP2\nx/3+978XnnrqKdFtL1y4UHjttdcCHqMgCE6PdeWll14Snn76aUEQBKG+vl644YYbhLa2toDE2FNp\naamwdOlS0X/7wx/+IPz+97+XHJOUOF2NS3kPb7/9tjBnzhzBYrEIZrNZuPHGG4Vjx475PUY1/Sbd\nfY5q+U26i7GnQPwmxWJx931aLBZh8uTJwsGDBwVBEIQ1a9YIixYtEt22XJ+jmJA6E8jKykJqaqrD\nmG2hu7y8PADdC91VV1ejubkZBoMBY8eOtT921KhROHHihKpilOK9997DjBkzAABpaWm45ppr8OGH\nHwY0xkuXLmH79u24/fbbJb+uHHG6G5fyOb/77ru44447oNfrkZiYiMmTJ+P999/3e4xq+U26G5fC\nn79Jb2MM1G9SLBZ33+fhw4cRGRlpX6Bz5syZLn9jcn2OYkIqCYhxt9BdT1arFZs2bUJ2drbLbW3Y\nsAH5+fmYP38+6urqAhrj448/jvz8fJSUlODcuXOi2zlx4oTDst2pqak4efJkwGIEgD179iA5ORlX\nX321y22Vl5cjPz8fc+bMwZdffilLfN7w9j3YHnvZZZfZ/5bzs/SWkr9Jbyj9m/SWWn6Tvb/P3r+x\nxMREWK1WnD171um5/vwcQz4JeGv58uWIjo7GXXfdJfrvjz76KHbu3Int27dj6tSpuOeee+zrJvlb\nWVkZtm3bhjfffBOCIGDZsmUBeV1fvPnmm26PuGbOnIndu3dj+/btmDt3LubPn48zZ84EMMLgwd+k\nPNTym/T0fSol5JOAu4XubEwmE44ePYpVq1ZBrxf/SJKTk+3/duutt+L8+fOyZWJPMdr+NyIiAoWF\nhfjiiy9Et3PZZZfh+PHj9r8bGhqQkpISkBgB4NSpUzh48CDy8/NdbsdoNKJfv34AgPHjxyM1NRXf\nfvutLDF64s176PnYnmUYOT9Lbyj9m/REDb9Jb6jlNyn2ffb+jTU3N0Ov18NgMDg935+fY8gnAXcL\n3QHAypUrcfjwYaxZswYREREut3Pq1Cn7///oo4+g1+uRnJzs9xjPnz+P1tZWAIAgCHj33XeRmZkp\nup1p06bhjTfeAAB8//33qKqqwo033uj3GG3efvtt3HTTTUhISHC5nZ6fY01NDY4fP45hw4bJEqMn\n3rwHm2nTpmHLli2wWq1obm7Grl27kJubG5A41fCbdEctv0lvqOE36er7vOaaa3Dx4kV89tlnAIDN\nmzdj2rRpotvw5+cYUmsHSV3o7ttvv0VeXh7S0tIQFRUFABgyZAjWrFkDACgoKMD69euRnJyMX/3q\nVzCbzdDpdIiJicGTTz6JUaNG+T3GY8eO4cEHH4TFYoHVasWIESOwePFiDBo0yCnG8+fPY9GiRaip\nqYFer8cTTzyByZMn+z1Gm9zcXDz99NOYOHGiw/bmzZuHhx56CCNHjsTChQvx9ddfQ6/Xo1+/fnjo\noYdw0003SY7RXZyuxgG4fQ8947RYLFi2bBk+/vhj+7/ZJub8GaOafpOuxtX0m3T3XQOB/U2KxbJq\n1Sq33+cXX3yB4uJidHR0YPDgwXjuuecwcOBAv32OYkIqCRARkTQhXw4iIiLXmASIiDSMSYCISMOY\nBIiINIxJgIhIw5gEiIg0jEmAiEjDmASIiDTs/wMJl58KdA4RxgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klKZGm5Worn7",
        "colab_type": "text"
      },
      "source": [
        "# Comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOh2wHuyotyI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_comp=pd.DataFrame({\"lag\":np.absolute(y_test-y_pred_lag), \n",
        "              \"lr\":np.absolute(y_test-y_pred_lr), \n",
        "              \"KNN\":np.absolute(y_test-y_pred_KNN)})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8nY6tLLppmL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "ce3569d9-c34f-49c4-fd5a-61382b5d4c79"
      },
      "source": [
        "\n",
        "df_comp.plot.bar(figsize=(16, 6))\n",
        "plt.ylim(0,10)\n",
        "plt.xlim(9,20)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JAioAAABJEFABAABIgoAKAABAEgRUAAAAkiCgAgAAkAQBFQAAgCQIqAAAACRBQAUAACAJ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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}