3-2_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 선형 회귀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/3-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CgQlTY9VpWeb"
   },
   "source": [
    "## k-최근접 이웃의 한계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HhWInkOKqg6s"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "perch_length = np.array(\n",
    "    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0, \n",
    "     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5, \n",
    "     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5, \n",
    "     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0, \n",
    "     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0, \n",
    "     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]\n",
    "     )\n",
    "perch_weight = np.array(\n",
    "    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, \n",
    "     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, \n",
    "     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, \n",
    "     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, \n",
    "     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, \n",
    "     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, \n",
    "     1000.0, 1000.0]\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7UVMy686qhP9"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 훈련 세트와 테스트 세트로 나눕니다\n",
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    perch_length, perch_weight, random_state=42)\n",
    "# 훈련 세트와 테스트 세트를 2차원 배열로 바꿉니다\n",
    "train_input = train_input.reshape(-1, 1)\n",
    "test_input = test_input.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1686,
     "status": "ok",
     "timestamp": 1589780993181,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "k7Uocvq2quTE",
    "outputId": "19602848-587f-4336-8c6d-cbfd2aea2e0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "knr = KNeighborsRegressor(n_neighbors=3)\n",
    "# k-최근접 이웃 회귀 모델을 훈련합니다\n",
    "knr.fit(train_input, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1681,
     "status": "ok",
     "timestamp": 1589780993182,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "CFOp6T9-q0QR",
    "outputId": "22ed65b3-ba87-46f6-8752-ff4986ff84c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1033.33333333]\n"
     ]
    }
   ],
   "source": [
    "print(knr.predict([[50]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G8FR1q-IuBlU"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1672,
     "status": "ok",
     "timestamp": 1589780993183,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "wR9eDDH2q9nP",
    "outputId": "7a76bf0d-0abd-405e-e514-37de33419568"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 50cm 농어의 이웃을 구합니다\n",
    "distances, indexes = knr.kneighbors([[50]])\n",
    "\n",
    "# 훈련 세트의 산점도를 그립니다\n",
    "plt.scatter(train_input, train_target)\n",
    "# 훈련 세트 중에서 이웃 샘플만 다시 그립니다\n",
    "plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n",
    "# 50cm 농어 데이터\n",
    "plt.scatter(50, 1033, marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1664,
     "status": "ok",
     "timestamp": 1589780993184,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "-eljdQI_d0go",
    "outputId": "811fcc57-7b36-498e-ef55-6415e77db69c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1033.3333333333333\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(train_target[indexes]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1658,
     "status": "ok",
     "timestamp": 1589780993184,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "8cw87IL3t_3p",
    "outputId": "49c65839-728c-4c45-aa47-3d2f36ff4e15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1033.33333333]\n"
     ]
    }
   ],
   "source": [
    "print(knr.predict([[100]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1654,
     "status": "ok",
     "timestamp": 1589780993185,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "YIMtpZKPfQjc",
    "outputId": "6a30c6c1-2c0f-4523-b4ad-857e0d2168f7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 100cm 농어의 이웃을 구합니다\n",
    "distances, indexes = knr.kneighbors([[100]])\n",
    "\n",
    "# 훈련 세트의 산점도를 그립니다\u001f\n",
    "plt.scatter(train_input, train_target)\n",
    "# 훈련 세트 중에서 이웃 샘플만 다시 그립니다\u001f\n",
    "plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n",
    "# 100cm 농어 데이터\n",
    "plt.scatter(100, 1033, marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yFadoVqdpe93"
   },
   "source": [
    "## 선형 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AKnGV8g5uN_U"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1645,
     "status": "ok",
     "timestamp": 1589780993185,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "Ypqgx5RRuOWg",
    "outputId": "4cd87532-d4e2-40eb-d1e0-b75056c536c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "# 선형 회귀 모델 훈련\n",
    "lr.fit(train_input, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1641,
     "status": "ok",
     "timestamp": 1589780993186,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "_Dh2FgXouRLU",
    "outputId": "36431778-59e3-4005-e780-76c2cc9dfb67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1241.83860323]\n"
     ]
    }
   ],
   "source": [
    "# 50cm 농어에 대한 예측\n",
    "print(lr.predict([[50]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1635,
     "status": "ok",
     "timestamp": 1589780993186,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "Gy_CKIiIMxDu",
    "outputId": "865ebeb5-8507-4c7a-e4e0-5fe1d47507ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[39.01714496] -709.0186449535477\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_, lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2094,
     "status": "ok",
     "timestamp": 1589780993650,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "VumOdzlFuV_5",
    "outputId": "7a8d52e8-7643-4de4-bcb6-2af933a8a4f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 훈련 세트의 산점도를 그립니다\u001f\n",
    "plt.scatter(train_input, train_target)\n",
    "# 15에서 50까지 1차 방정식 그래프를 그립니다\n",
    "plt.plot([15, 50], [15*lr.coef_+lr.intercept_, 50*lr.coef_+lr.intercept_])\n",
    "# 50cm 농어 데이터\n",
    "plt.scatter(50, 1241.8, marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2087,
     "status": "ok",
     "timestamp": 1589780993651,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "myWLMckTQZzr",
    "outputId": "5432100b-ce41-4141-a31c-acaa099a5edb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.939846333997604\n",
      "0.8247503123313558\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(train_input, train_target))\n",
    "print(lr.score(test_input, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h-akkHaaQc0b"
   },
   "source": [
    "## 다항 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wcdsYFxB2q82"
   },
   "outputs": [],
   "source": [
    "train_poly = np.column_stack((train_input ** 2, train_input))\n",
    "test_poly = np.column_stack((test_input ** 2, test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2077,
     "status": "ok",
     "timestamp": 1589780993652,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "JJ6tf1F96EzF",
    "outputId": "2335267c-34a7-4ccb-985a-855cfefce38a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 2) (14, 2)\n"
     ]
    }
   ],
   "source": [
    "print(train_poly.shape, test_poly.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2073,
     "status": "ok",
     "timestamp": 1589780993653,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "LTlN32tML4Kf",
    "outputId": "4d2d6db5-5296-471c-b72a-0097786f9d4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1573.98423528]\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(train_poly, train_target)\n",
    "\n",
    "print(lr.predict([[50**2, 50]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2068,
     "status": "ok",
     "timestamp": 1589780993654,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "k_fGreBgNCAc",
    "outputId": "f26d2789-e893-4ed0-8fd2-857d14f75776"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.01433211 -21.55792498] 116.0502107827827\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_, lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2063,
     "status": "ok",
     "timestamp": 1589780993654,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "c91qVYoPLz1H",
    "outputId": "7cc80bfd-8aba-4a81-876f-ea7bbe1cbc2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 구간별 직선을 그리기 위해 15에서 49까지 정수 배열을 만듭니다\n",
    "point = np.arange(15, 50)\n",
    "# 훈련 세트의 산점도를 그립니다\n",
    "plt.scatter(train_input, train_target)\n",
    "# 15에서 49까지 2차 방정식 그래프를 그립니다\n",
    "plt.plot(point, 1.01*point**2 - 21.6*point + 116.05)\n",
    "# 50cm 농어 데이터\n",
    "plt.scatter([50], [1574], marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2056,
     "status": "ok",
     "timestamp": 1589780993655,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "oCGTrZezL3E4",
    "outputId": "eab10302-8966-4f12-e703-bb8c5805fa56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9706807451768623\n",
      "0.9775935108325122\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(train_poly, train_target))\n",
    "print(lr.score(test_poly, test_target))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyP/K+2VKm+7gJYWBdi4NH3X",
   "collapsed_sections": [],
   "name": "3-2 선형 회귀.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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