아이스크림_구매에_대한_선형회귀분석.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/alsh02/Mathematical-Explorations/blob/main/%EC%95%84%EC%9D%B4%EC%8A%A4%ED%81%AC%EB%A6%BC_%EA%B5%AC%EB%A7%A4%EC%97%90_%EB%8C%80%ED%95%9C_%EC%84%A0%ED%98%95%ED%9A%8C%EA%B7%80%EB%B6%84%EC%84%9D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# 아이스크림 구매에 대한 선형회귀분석\n",
        "평균 기온에 따른 아이스크림 쇼핑 클릭량 평균 데이터를 계급으로 나누고, 그 데이터들을 상관분석 해봅시다. 그런 다음 데이터가 선형성을 가진다는 걸 눈으로 직접 확인하고 데이터들을 선형회귀분석하여 미래를 예측해봅시다."
      ],
      "metadata": {
        "id": "j-j8O7FwQsIv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. 데이터 계급화\n",
        "temp_ice.csv 파일을 불러온 다음 평균 기온의 도수분포표를 만들고, 날짜별 아이스크림 쇼핑 클릭량 합계와 평균 기온에 따른 아이스크림 쇼핑 클릭량 평균을 구해봅시다."
      ],
      "metadata": {
        "id": "QPeSKrOCSIcy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gnGG6gOwQY_N"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 구글 드라이브 마운트하기\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "pCzWi2nWS8bs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b9659e1-2476-4b20-c6d2-da8cd247e15f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - pandas 이용해서 평균 기온과 아이스크림 쇼핑 클릭량 데이터 추출하기\n",
        "pandas 라이브러리를 이용하면 책에 있는 코드의 csv 라이브러리를 이용하지 않고 간단하게 csv 파일을 다룰 수 있습니다. 반복문을 이용하지 않고 단 두 줄이면 원하는 열의 데이터를 추출할 수 있습니다."
      ],
      "metadata": {
        "id": "ketKSE7WWm_7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/colab_csv/temp_ice.csv', encoding='cp949')\n",
        "header = data.columns\n",
        "avg_temp = data[header[1]] # 평균기온 데이터\n",
        "ice_click = data[header[4]] # 날짜별 아이스크림 쇼핑 클릭수 데이터"
      ],
      "metadata": {
        "id": "40egt1gLSr3J"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 평균 기온 데이터 기준으로 도수 분포 구간 설정하기"
      ],
      "metadata": {
        "id": "ES2Me9UiZQqH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # 도수 분포 구간 설정을 위해 라이브러리 불러오기"
      ],
      "metadata": {
        "id": "gXdiYrhSXJic"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 아래 코드로 간격이 5인 7개의 계급을 만들 수 있다.\n",
        "bins = np.arange(min(avg_temp), max(avg_temp) + 5, 5)\n",
        "hist, bins = np.histogram(avg_temp, bins)"
      ],
      "metadata": {
        "id": "QZYOTg52XTA_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 평균 기온 데이터 기준으로 아이스크림 쇼핑 클릭수 구분하여 저장하기"
      ],
      "metadata": {
        "id": "nO8KofwGaV5j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ice_buy = np.zeros(7) # 계급별 아이스크림 쇼핑 클릭수 합계를 저장할 array 선언\n",
        "\n",
        "for i in range(len(avg_temp)) :              # 평균 기온 데이터 전체 탐색하기\n",
        "  if bins[0] <= avg_temp[i] and avg_temp[i] < bins[1] :       # 첫 번째 계급에 해당한다면 \n",
        "    ice_buy[0] = ice_buy[0] + ice_click[i]                # 첫 번째 배열 값에 해당 날짜 아이스크림 쇼핑 클릭량 저장하기\n",
        "        \n",
        "  elif bins[1] <= avg_temp[i] and avg_temp[i] < bins[2] :     # 두 번째 계급에 해당한다면     \n",
        "    ice_buy[1] = ice_buy[1] + ice_click[i]                # 두 번째 배열 값에 해당 날짜 아이스크림 쇼핑 클릭량 저장하기\n",
        "        \n",
        "  elif bins[2] <= avg_temp[i] and avg_temp[i] < bins[3] :     # 세 번째 계급에 해당한다면\n",
        "    ice_buy[2] = ice_buy[2] + ice_click[i]                # 세 번째 배열 값에 해당 날짜 아이스크림 클릭량 저장하기\n",
        "\n",
        "  elif bins[3] <= avg_temp[i] and avg_temp[i] < bins[4] :     # 네 번째 계급에 해당한다면\n",
        "    ice_buy[3] = ice_buy[3] + ice_click[i]                # 네 번째 배열 값에 해당 날짜 아이스크림 클릭량 저장하기 \n",
        "        \n",
        "  elif bins[4] <= avg_temp[i] and avg_temp[i] < bins[5] :     # 다섯 번째 계급에 해당한다면\n",
        "    ice_buy[4] = ice_buy[4] + ice_click[i]                # 다섯 번째 배열 값에 해당 날짜 아이스크림 클릭량 저장하기\n",
        "        \n",
        "  elif bins[5] <= avg_temp[i] and avg_temp[i] < bins[6] :     # 여섯 번째 계급에 해당한다면\n",
        "    ice_buy[5] = ice_buy[5] + ice_click[i]                # 여섯 번째 배열 값에 해당 날짜 아이스크림 클릭수 저장하기\n",
        "        \n",
        "  else:                                               # 일곱 번째 계급에 해당한다면(이외의 값에 해당한다면\n",
        "    ice_buy[6] = ice_buy[6] + ice_click[i]                # 일곱 번째 배열 값에 해당 날짜 아이스크림 클릭량 저장하기                \n"
      ],
      "metadata": {
        "id": "05z0Fpj1X8ga"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 평균 기온 데이터 계급별 아이스크림 쇼핑 클릭수의 평균 구하기"
      ],
      "metadata": {
        "id": "2uWthhqIaemt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ice_buy_avg = np.zeros(7)\n",
        "for i in range(len(ice_buy)):\n",
        "  ice_buy_avg[i] = ice_buy[i] / hist[i] # (클릭수 합계) / (데이터 개수)"
      ],
      "metadata": {
        "id": "RMS5kkRTYbhE"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 평균 기온에 따른 아이스크림 쇼핑 클릭수의 평균 막대그래프로 표현하기"
      ],
      "metadata": {
        "id": "uDk-qnGFf-1O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # 그래프를 그리기 위한 라이브러리"
      ],
      "metadata": {
        "id": "XQsd4CLkazpH"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Average temperature')\n",
        "plt.ylabel('Number of ice cream shopping click')\n",
        "plt.bar(bins[0:7], ice_buy_avg, align='edge', width=2)\n",
        "plt.xticks(bins[0:7])"
      ],
      "metadata": {
        "id": "ll95VSUxa3_N",
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          "base_uri": "https://localhost:8080/",
          "height": 522
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        "outputId": "ac68908a-10da-4d4a-cc8d-2e65d72df3ea"
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"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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. 상관분석\n",
        "데이터들의 선형성을 살펴보기 위해 상관분석을 해봅시다. "
      ],
      "metadata": {
        "id": "FVmveVmsgkNA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 데이터 시각화\n",
        "평균 기온에 따른 아이스크림 쇼핑 클릭량의 평균 데이터가 어떤 식으로 분포되어 있는지 눈으로 직접 봐봅시다."
      ],
      "metadata": {
        "id": "V_mAakq2hC51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Average temperature')\n",
        "plt.ylabel('Number of ice cream shopping click')\n",
        "plt.scatter(avg_temp, ice_click)"
      ],
      "metadata": {
        "id": "oJObPXwTglW2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "5e8e7a9b-e9ce-4374-f921-74fb7ed380d5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4f04226ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 공분산\n",
        "cov = np.cov(avg_temp, ice_click)[0, 1]\n",
        "\n",
        "# 표준편차\n",
        "avg_temp_std = np.std(avg_temp)\n",
        "ice_click_std = np.std(ice_click)\n",
        "\n",
        "# 상관계수 = 공분산 / 각 변수의 표준편차의 곱\n",
        "corrcoeff = cov / (avg_temp_std * ice_click_std)\n",
        "corrcoeff"
      ],
      "metadata": {
        "id": "13n01QYShUar",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4262100e-017e-4d38-e02a-0d3e5030927b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6691695022419083"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0.6691695022419083 정도면 강한 양의 선형 관계라고 할 수 있습니다.\n",
        "따라서 선형회귀분석을 하기 적절함을 알 수 있습니다."
      ],
      "metadata": {
        "id": "rlf2E2dDvEsm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. 선형회귀분석\n",
        "데이터가 선형성을 띄는 경우, 그 경향을 가장 잘 표현해주는 직선의 식을 찾는 과정을 선형회귀라고 합니다. \n",
        "\n",
        "데이터와 우리가 찾은 직선 사이의 오차가 가장 작을 때 함수식을 잘 찾았다고 이야기할 수 있을 것입니다. 이를 수식으로 표현한 것이 평균제곱근오차(Root Mean Sqaure Error, RMSE)입니다.\n",
        "\n",
        "평균제곱근오차를 가장 작게 만드는 함수의 절편과 기울기를 찾는 방법을 최소제곱법(Least Square Method)라고 합니다.\n",
        "\n",
        "최소제곱법을 통해 평균 기온에 따른 아이스크림 쇼핑 클릭수 평균 데이터를 대표하는 직선을 찾아봅시다."
      ],
      "metadata": {
        "id": "rupcK2rWvl1Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 최소제곱법으로 평균제곱근오차를 최소로 만드는 기울기와 절편 찾기\n",
        "아래 식은 최소제곱법으로 구한 기울기와 절편이다.\n",
        "\n",
        "$\n",
        "\\beta_1 = {\\displaystyle \\sum^n_{i=1}(X_i - \\overline X)(Y_i - \\overline Y) \\over \\displaystyle \\sum^n_{i=1}(X_i - \\overline X)^2},\\space\n",
        "\\beta_0 = \\overline Y - \\beta_1\\overline X\n",
        "$"
      ],
      "metadata": {
        "id": "jlk2t-LH5VMG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_temp_mean = np.mean(avg_temp)\n",
        "ice_click_mean = np.mean(ice_click)\n",
        "\n",
        "n = len(avg_temp)\n",
        "\n",
        "avg_temp_and_ice_click_sum = 0\n",
        "avg_temp_sum = 0\n",
        "for i in range(n):\n",
        "  avg_temp_and_ice_click_sum += (avg_temp[i] - avg_temp_mean) * (ice_click[i] - ice_click_mean)\n",
        "  avg_temp_sum += (avg_temp[i] - avg_temp_mean) ** 2\n",
        "\n",
        "# 기울기와 절편 선언\n",
        "# 편미분을 통해 평균제곱근오차를 최소로 만드는 기울기와 절편을 구할 수 있다.\n",
        "beta1 = avg_temp_and_ice_click_sum / avg_temp_sum\n",
        "beta0 = ice_click_mean - (beta1 * avg_temp_mean)"
      ],
      "metadata": {
        "id": "pp2F8wETyGX-"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### - 학습된 선형회귀모델을 기반으로 기온이 주어지면 아이스크림 쇼핑 클릭수 예측하기"
      ],
      "metadata": {
        "id": "lWXe6aFG7A_q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def regression(beta0, beta1, x):\n",
        "  return beta0 + beta1 * x"
      ],
      "metadata": {
        "id": "6AiU_vl4yVKU"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_temp = float(input(\"오늘의 기온은 : \"))\n",
        "\n",
        "predicted_ice_click = regression(beta0, beta1, input_temp)\n",
        "print(f\"오늘의 아이스크림 쇼핑 클릭수는 100점을 기준으로 {predicted_ice_click} 만큼 예상됩니다.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ziu8yR384zYZ",
        "outputId": "1c61602d-a7a2-4503-9c77-f54530804cb5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "오늘의 기온은 : 30\n",
            "오늘의 아이스크림 쇼핑 클릭량은 100점을 기준으로 63.931663973079566 만큼 예상됩니다.\n"
          ]
        }
      ]
    }
  ]
}