1-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 마켓과 머신러닝"
   ]
  },
  {
   "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/1-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 생선 분류 문제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 도미 데이터 준비하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AhUYjyga-mAw"
   },
   "outputs": [],
   "source": [
    "bream_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0]\n",
    "bream_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1110,
     "status": "ok",
     "timestamp": 1586625581959,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "mSj-Xx1gENO_",
    "outputId": "618cd92c-52df-43dd-e99e-9180279379be"
   },
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 빙어 데이터 준비하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hj_L_F9RH5B0"
   },
   "outputs": [],
   "source": [
    "smelt_length = [9.8, 10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
    "smelt_weight = [6.7, 7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1102,
     "status": "ok",
     "timestamp": 1586625581961,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "z0ywO_s4S98S",
    "outputId": "7bcc2865-b732-4ff7-acfc-58a160aca56c"
   },
   "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": [
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.scatter(smelt_length, smelt_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 첫 번째 머신러닝 프로그램"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "djyDIbjmTDag"
   },
   "outputs": [],
   "source": [
    "length = bream_length+smelt_length\n",
    "weight = bream_weight+smelt_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 55
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1096,
     "status": "ok",
     "timestamp": 1586625581962,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "zMNfgqb62z7W",
    "outputId": "028f8bec-7319-472f-bb27-45de2ef20ba8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0], [29.7, 450.0], [29.7, 500.0], [30.0, 390.0], [30.0, 450.0], [30.7, 500.0], [31.0, 475.0], [31.0, 500.0], [31.5, 500.0], [32.0, 340.0], [32.0, 600.0], [32.0, 600.0], [33.0, 700.0], [33.0, 700.0], [33.5, 610.0], [33.5, 650.0], [34.0, 575.0], [34.0, 685.0], [34.5, 620.0], [35.0, 680.0], [35.0, 700.0], [35.0, 725.0], [35.0, 720.0], [36.0, 714.0], [36.0, 850.0], [37.0, 1000.0], [38.5, 920.0], [38.5, 955.0], [39.5, 925.0], [41.0, 975.0], [41.0, 950.0], [9.8, 6.7], [10.5, 7.5], [10.6, 7.0], [11.0, 9.7], [11.2, 9.8], [11.3, 8.7], [11.8, 10.0], [11.8, 9.9], [12.0, 9.8], [12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]\n"
     ]
    }
   ],
   "source": [
    "fish_data = [[l, w] for l, w in zip(length, weight)]\n",
    "\n",
    "print(fish_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 55
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1091,
     "status": "ok",
     "timestamp": 1586625581962,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "tSSIOoXz3Nm9",
    "outputId": "01ae4620-2e3f-415a-b5db-d73a003ee547"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "fish_target = [1]*35 + [0]*14\n",
    "print(fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MU_19M2Elp65"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PCYut6Q2lpgQ"
   },
   "outputs": [],
   "source": [
    "kn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s1Ip0gXK35SK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.fit(fish_data, fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1729,
     "status": "ok",
     "timestamp": 1586625582614,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "_Nqdxnci3-yw",
    "outputId": "1acb532c-6831-48b0-b894-5f3cce3af07b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.score(fish_data, fish_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-최근접 이웃 알고리즘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2003,
     "status": "ok",
     "timestamp": 1586625582893,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "mVZ6Ikqj4D7q",
    "outputId": "79a91186-474c-4855-a57f-e6697734927a"
   },
   "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": [
    "plt.scatter(bream_length, bream_weight)\n",
    "plt.scatter(smelt_length, smelt_weight)\n",
    "plt.scatter(30, 600, marker='^')\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2000,
     "status": "ok",
     "timestamp": 1586625582894,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "7WL14YIlr86-",
    "outputId": "5fca15c2-894c-4d5b-cb39-50cd718adb96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.predict([[30, 600]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 864
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1994,
     "status": "ok",
     "timestamp": 1586625582894,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "6WJ_METitoY1",
    "outputId": "77664b70-6dd9-43e0-c9e4-752cdca1ea5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  25.4  242. ]\n",
      " [  26.3  290. ]\n",
      " [  26.5  340. ]\n",
      " [  29.   363. ]\n",
      " [  29.   430. ]\n",
      " [  29.7  450. ]\n",
      " [  29.7  500. ]\n",
      " [  30.   390. ]\n",
      " [  30.   450. ]\n",
      " [  30.7  500. ]\n",
      " [  31.   475. ]\n",
      " [  31.   500. ]\n",
      " [  31.5  500. ]\n",
      " [  32.   340. ]\n",
      " [  32.   600. ]\n",
      " [  32.   600. ]\n",
      " [  33.   700. ]\n",
      " [  33.   700. ]\n",
      " [  33.5  610. ]\n",
      " [  33.5  650. ]\n",
      " [  34.   575. ]\n",
      " [  34.   685. ]\n",
      " [  34.5  620. ]\n",
      " [  35.   680. ]\n",
      " [  35.   700. ]\n",
      " [  35.   725. ]\n",
      " [  35.   720. ]\n",
      " [  36.   714. ]\n",
      " [  36.   850. ]\n",
      " [  37.  1000. ]\n",
      " [  38.5  920. ]\n",
      " [  38.5  955. ]\n",
      " [  39.5  925. ]\n",
      " [  41.   975. ]\n",
      " [  41.   950. ]\n",
      " [   9.8    6.7]\n",
      " [  10.5    7.5]\n",
      " [  10.6    7. ]\n",
      " [  11.     9.7]\n",
      " [  11.2    9.8]\n",
      " [  11.3    8.7]\n",
      " [  11.8   10. ]\n",
      " [  11.8    9.9]\n",
      " [  12.     9.8]\n",
      " [  12.2   12.2]\n",
      " [  12.4   13.4]\n",
      " [  13.    12.2]\n",
      " [  14.3   19.7]\n",
      " [  15.    19.9]]\n"
     ]
    }
   ],
   "source": [
    "print(kn._fit_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1989,
     "status": "ok",
     "timestamp": 1586625582894,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "OhERu04-u_Mx",
    "outputId": "c617716d-9d73-485d-c7a3-e1539aa3702b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(kn._y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "scTxBP0ZvOs0"
   },
   "outputs": [],
   "source": [
    "kn49 = KNeighborsClassifier(n_neighbors=49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 777,
     "status": "ok",
     "timestamp": 1586625641590,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "gErf9HtixCQw",
    "outputId": "4b19ea19-e38d-4b47-f9f3-2ea555197fb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn49.fit(fish_data, fish_target)\n",
    "kn49.score(fish_data, fish_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1976,
     "status": "ok",
     "timestamp": 1586625582895,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "EGEvjYGrxGfF",
    "outputId": "66c77a93-6e49-4825-c926-6cae4319a221"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "print(35/49)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mfasb9WsRoQV"
   },
   "source": [
    "### 확인 문제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 707,
     "status": "ok",
     "timestamp": 1586626289284,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "3pQ2JVooP-y5",
    "outputId": "9c462dc3-91fa-47a3-ec82-99237c303a07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 0.9795918367346939\n"
     ]
    }
   ],
   "source": [
    "kn = KNeighborsClassifier()\n",
    "kn.fit(fish_data, fish_target)\n",
    "\n",
    "for n in range(5, 50):\n",
    "    # 최근접 이웃 개수 설정\n",
    "    kn.n_neighbors = n\n",
    "    # 점수 계산\n",
    "    score = kn.score(fish_data, fish_target)\n",
    "    # 100% 정확도에 미치지 못하는 이웃 개수 출력\n",
    "    if score < 1:\n",
    "        print(n, score)\n",
    "        break"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyN+9d13XrV1XXUMVWxDDq6+",
   "collapsed_sections": [],
   "name": "1-3. 마켓과 머신러닝",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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