Instacart_Customer_Segmentation_ipynb의_사본.ipynb

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    "colab": {
      "name": "Instacart Customer Segmentation.ipynb의 사본",
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        "<a href=\"https://colab.research.google.com/github/Heoquixote/Data_Scientist_Course/blob/main/Instacart_Customer_Segmentation_ipynb%EC%9D%98_%EC%82%AC%EB%B3%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH9Li7YqAS60",
        "outputId": "626c1f48-10d2-4d95-fb88-26cb5968625f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "R4zdtFSlAURu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_path = \"/content/drive/MyDrive/Colab Notebooks/data/instacart-market-basket-analysis/\"\n",
        "\n",
        "aisles = pd.read_csv(base_path + \"aisles.csv\")\n",
        "orders = pd.read_csv(base_path + \"orders.csv\")\n",
        "departments = pd.read_csv(base_path + \"departments.csv\")\n",
        "prior = pd.read_csv(base_path + \"order_products__prior.csv\")\n",
        "train = pd.read_csv(base_path + \"order_products__train.csv\")\n",
        "products = pd.read_csv(base_path + \"products.csv\")"
      ],
      "metadata": {
        "id": "l3TcLsBGA2FM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##orders#.user_id.value_counts()\n",
        "\n",
        "## user_id와 aisle을 같은 테이블에서 볼 수있도록 테이블들을 합쳐보세요!\n",
        "## user_id -> order_id -> product_id -> aisle_id -> aisle\n",
        "##                                   -> department_id -> department\n",
        "prior = prior[:3000000]  ## 현실 가능한 분석을 위해서 일부만 사용합니다."
      ],
      "metadata": {
        "id": "LEu7HfvoBs-J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.merge(orders, prior, on=\"order_id\")\n",
        "data = pd.merge(data, products, on=\"product_id\")\n",
        "data = pd.merge(data, aisles, on=\"aisle_id\")\n",
        "data = pd.merge(data, departments, on=\"department_id\")\n",
        "data"
      ],
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          "height": 800
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "         order_id  user_id eval_set  order_number  order_dow  \\\n",
              "0           94891        4    prior             4          5   \n",
              "1           31925    47329    prior             4          3   \n",
              "2          137640   156057    prior            17          5   \n",
              "3          120479      310    prior            93          3   \n",
              "4          135820     6770    prior             2          3   \n",
              "...           ...      ...      ...           ...        ...   \n",
              "2999995    173376   197832    prior            18          3   \n",
              "2999996     10531   197832    prior            19          3   \n",
              "2999997    129685   202262    prior            28          0   \n",
              "2999998    247879   204798    prior            40          2   \n",
              "2999999    114342   204887    prior            85          0   \n",
              "\n",
              "         order_hour_of_day  days_since_prior_order  product_id  \\\n",
              "0                       13                    15.0       22199   \n",
              "1                       10                    12.0       22199   \n",
              "2                       11                     3.0       22199   \n",
              "3                       15                     2.0       20704   \n",
              "4                       17                    10.0       20704   \n",
              "...                    ...                     ...         ...   \n",
              "2999995                 12                    18.0       39768   \n",
              "2999996                 12                    14.0       39768   \n",
              "2999997                 13                     7.0       10579   \n",
              "2999998                 15                     8.0       32048   \n",
              "2999999                 14                     2.0       39511   \n",
              "\n",
              "         add_to_cart_order  reordered                          product_name  \\\n",
              "0                        1          0                        Extra-Dry Cava   \n",
              "1                        1          0                        Extra-Dry Cava   \n",
              "2                        2          0                        Extra-Dry Cava   \n",
              "3                        1          0                                  Brut   \n",
              "4                        6          0                                  Brut   \n",
              "...                    ...        ...                                   ...   \n",
              "2999995                 22          1     Rescue Natural Sleep Liquid Melts   \n",
              "2999996                  8          1     Rescue Natural Sleep Liquid Melts   \n",
              "2999997                  4          0  Himalayan Pink Salt Liquid Hand Soap   \n",
              "2999998                  7          0                  Beef Chuck Eye Steak   \n",
              "2999999                  1          1             Baby Diaper Rash Ointment   \n",
              "\n",
              "         aisle_id  department_id                       aisle department  \n",
              "0             134              5  specialty wines champagnes    alcohol  \n",
              "1             134              5  specialty wines champagnes    alcohol  \n",
              "2             134              5  specialty wines champagnes    alcohol  \n",
              "3             134              5  specialty wines champagnes    alcohol  \n",
              "4             134              5  specialty wines champagnes    alcohol  \n",
              "...           ...            ...                         ...        ...  \n",
              "2999995         6              2                       other      other  \n",
              "2999996         6              2                       other      other  \n",
              "2999997         6              2                       other      other  \n",
              "2999998         6              2                       other      other  \n",
              "2999999         6              2                       other      other  \n",
              "\n",
              "[3000000 rows x 15 columns]"
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# user-department matrix\n",
        "customers = pd.crosstab(index=data.user_id, columns=data.department)\n",
        "customers"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "9D7YTVpePjr8",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "department  alcohol  babies  bakery  beverages  breakfast  bulk  canned goods  \\\n",
              "user_id                                                                         \n",
              "4                 1       0       0          1          0     0             0   \n",
              "5                 0       0       0          0          0     0             1   \n",
              "6                 0       0       0          0          0     0             2   \n",
              "7                 0       0       1          9          0     0             2   \n",
              "9                 0       5       0          1          1     0             1   \n",
              "...             ...     ...     ...        ...        ...   ...           ...   \n",
              "206203            0       0       0          3          0     0             2   \n",
              "206204            0       0       2          0          0     0             0   \n",
              "206206            0       0       1          0          0     0             0   \n",
              "206207            0       0       0          1          0     0             2   \n",
              "206208            0       0       4          0          2     0             1   \n",
              "\n",
              "department  dairy eggs  deli  dry goods pasta  ...  household  international  \\\n",
              "user_id                                        ...                             \n",
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              "user_id                                                                 \n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### data plotting\n",
        "\n",
        "\n",
        "- 원본 데이터(고차원)의 분포 또는 모양을 확인하기 위해서는 눈으로 볼 수 있는 형태(2, 3차원)로 변환을 해야합니다.\n",
        "\n",
        "\n",
        "- 원본 데이터 ------> 저차원 데이터(2차원) :  **t-SNE**\n",
        "\n",
        "\n",
        "(t-SNE : t-distributed Stochastic Nearest Embedding)"
      ],
      "metadata": {
        "id": "egBLgn78fJfr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "tsne = TSNE(n_components=2,\n",
        "            perplexity=30,\n",
        "            learning_rate='warn',\n",
        "            random_state=42,\n",
        "            n_jobs=-1)\n",
        "\n",
        "tsne_data = tsne.fit_transform(customers)\n",
        "tsne_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bguMpMjnf-Z9",
        "outputId": "2b805c70-db4e-4258-8a92-62c8899fa965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "sns.scatterplot(x=tsne_data[:, 0], y=tsne_data[:, 1])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fInhnxn_jXmt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ztzjW_Fewvhq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clustering\n",
        "\n",
        "1. K-means\n",
        "\n",
        "\n",
        "\n",
        "2. Hierarchical Agglomerative Clustering"
      ],
      "metadata": {
        "id": "Am3UqTA0c6Pj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = customers  # customers 데이터에 있는 수치값들을 사용."
      ],
      "metadata": {
        "id": "Ncur022bVF0w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_K = int(input(\"실험에 사용될 시작 클러스터 개수를 입력하세요 : \"))\n",
        "end_K = int(input(\"실험에 사용될 마지막 클러스터 개수를 입력하세요 : \"))"
      ],
      "metadata": {
        "id": "xbdbF048uNHa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## K-means에서 최적의 K(클러스터 수)를 찾기 위해서 적당한 구간에서 다 돌려보고, 가장 좋은 것을 찾습니다.\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "scores = []\n",
        "preds = []\n",
        "\n",
        "for K in range(start_K, end_K+1):\n",
        "    model = KMeans(n_clusters=K)\n",
        "    pred = model.fit_predict(X)\n",
        "    preds.append(pred)\n",
        "    score = silhouette_score(X, pred)\n",
        "    scores.append(score)\n",
        "\n",
        "optimal_K = np.argmax(scores) + 2\n",
        "print(\"Optimal K: %d with score: %.4f\" % (optimal_K, np.max(scores)))"
      ],
      "metadata": {
        "id": "Tk-opSceli-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle\n",
        "\n",
        "# with open(base_path + \"kmeans.pk\", 'wb') as f:\n",
        "#     pickle.dump(model, f)"
      ],
      "metadata": {
        "id": "f6ZGSffIwGWW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with open(base_path + \"kmeans.pk\", 'rb') as f:\n",
        "#     model = pickle.load(f)\n",
        "\n",
        "# model"
      ],
      "metadata": {
        "id": "LAVgvZKLwbt2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 클러스터 개수가 2일 때부터 10까지 다 돌린 결과 중에 가장 잘 나왔을 때의 Cluster label.\n",
        "best_pred = preds[np.argmax(scores)]"
      ],
      "metadata": {
        "id": "6nlWGfLCd_zs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tsne_df = pd.DataFrame(data=tsne_data, columns=[\"X1\", \"X2\"])\n",
        "tsne_df['cluster'] = best_pred"
      ],
      "metadata": {
        "id": "W0_1fTomkTPr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tsne_df"
      ],
      "metadata": {
        "id": "FffrUSolknWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data=tsne_df, x=\"X1\", y=\"X2\",\n",
        "                hue='cluster', palette=\"Set3\")\n",
        "plt.savefig(base_path + \"cluster_map.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2rC_PRRlexoI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customers['label'] = best_pred\n",
        "customers"
      ],
      "metadata": {
        "id": "ISB8Y76YrHWK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "group1 = customers[customers.label == 0]\n",
        "group2 = customers[customers.label == 1]"
      ],
      "metadata": {
        "id": "R9JYTnMXx1LN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(group1.sum().sort_values(ascending=False)[:10]) # 그룹별로 가장 많이 구매한 품목 top10.\n",
        "group2.sum().sort_values(ascending=False)[:10]"
      ],
      "metadata": {
        "id": "uy4Y1bTnyCX4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}