Market_Basket_Analysis_Using_Apriori_Algorithm.ipynb

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      "source": [
        "# **Market Basket Analysis**\n",
        "Market basket analysis adalah suatu metodologi untuk melakukan analisis buying habit konsumen dengan menemukan asosiasi antar beberapa item yang berbeda, yang diletakkan konsumen dalam shopping basket (keranjang belanja) yang dibeli pada suatu transaksi tertentu. Tujuan dari market basket analysis adalah untuk mengetahui produk-produk mana yang mungkin akan dibeli secara bersamaan"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## **Algoritma Apriori**\n",
        "\n",
        "Algoritma Apriori adalah suatu algoritma dasar yang diusulkan oleh Agrawal & Srikant pada tahun 1994 untuk penentuan frequent itemsets untuk aturan asosiasi boolean. Algoritma Apriori memberi kita sifat asosiatif dalam transaksi. Ini juga dikenal sebagai Aturan Asosiasi. Aturan asosiasi atau association rule adalah teknik untuk menemukan aturan asosiasi antara suatu kombinasi item. Terdapat 3 metrik untuk mengukur ketepatan aturan, yaitu :\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "### **1. Support**\n",
        "Support adalah indikasi seberapa sering kumpulan item muncul pada dataset. Berikut perhitungan nilai support :\n",
        "\n",
        "![support.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAV0AAABXCAYAAACnZJZlAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAABUJSURBVHhe7d0PbJNnfgfw792pHpURJ3eoPioyNkc7zKo6SFhs8SESKkw3YlRS1MBUHKlphgBVidRzhiBlJGoTjsVXKVEFORaCRIpKUkFYg5mIK3C44OioUYknGjMU31GjwstVWEWxSo0473ne93HsOHawE3Ds8PtIlt/3tV/bryFfP+/zPn9+EmFACCEkK34q7gkhhGQBhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hhGQRhS4hOSz09QC8d8UKycx9HwauSmIld1DoEpKzgvD02eHwBcU6yUTwai/s53zsW8wtP4kwYpkQklOCGDhQCXfxMexerRHbkrjtROO2NnjEaiJTXXR/P3oqa9GVLIWWbsdBexkKxGr2hOA5tAWNZ8XqJFa09FVAL9YUyvdiHxSrccr2nsD2lWp5OXhxPyqHTDi2qwRTfHtZR6FLSM5KM3QRRij0kN1JcB2uRbsIo4INNbBtNGHxX6uh+pmyLRz0wxl9ziITrFvLUPxKIV54Xg31POU52RYOhcA+Pca+6kLdAYcomZphO/IOjOrnoFar5C3xwiEJQ0erWUkW0BjM2GyxwGjQQqNmxyqek6uhS9ULhOQ9FQsmFpoaHcz/UjEeMIFvAfWLscDlVM+HcY9t1661oePQblSsNqBA87jA5SXkSvTcEKtp8n9WicrP/GItNRX/7OymXbUO5TqxEaO4N8a3Tw5cTvUzCaOXAf2bTTjUVIOyYh20cYGbyyh0CZlDVIZfxYLrigve22KZeyRh4GAznEtsaKotgTYujKcWZsVGVv78i1hNU/hREMFHbN+06VBUEv3wfrj+N1Vg8+Oww7XcBlulAUplQv6g0CVkTtHBtMEolr04MxQNLhZUrXWwf2vBnp0scMXWXKNbXsqOQOE/P8yiN1EYvhP2nD+OqVDoEjLHaFlwmcSy//QleMPRoDJh379XQD9Ldbdp0RWhdDx1XRhOSF3pYhuaz2phy/XjmAKFLiFzzUIjStaK5WAPHB9Hg2o7jC+K7TlLh6JX46oYrsZSN/R1D+wtEiz1NSjJ+eNIjUKXkDlHDeOa2AU194X8CirdK3FVDANKFUP4eg8adp2Bts6GiqX5cLksNQpdQuYglb4I5vF2UvOhWZBHQaUzwbJcLPt7cenCANqauoC39qBmdT7W4k5EoUvIHBS+KyHWAdYDlzf3usOmpoVhlUEsB9HzkR1e3lJhiz4vmoQ9DoUuIXPNXVYy3NMGaYUZ4+0YzrqTtATIXVpDKaKxi6XWvG2pkAyFLiFzyQMfev7TDmnVPthq34E5ekGNn6Z7M2kzO8t+GMOYWDSYS/K2pUIyFLqEzBWi88OZl9ip+DYjKxmqUVRcJh5kp+lXfLybQ16QbvpEyVyDIt1cKeMqKHQJmROSd35QLzehInpB7ZQLnvtiOSMaaJYDd+6FxHo6giw4WSlVM51RD0II+NxiuRT6JWJxjqDQJSTvTdH5QWXArzZGG2A54bw8nQtqWhhKjHCe7E+7Xjh83Ykzg0aUGqZTSg3APyQW1+hRMBeunsWh0CUkr4Xg+6wRzcdfSNn5Ib5rradvehfUtKutqPqrXjS3OhF4IDamELrpVJp4VVthXiQ2ZsI/gkti+EnDy4U5NULYk0ChS0i+CYcQCknwX3Gis6EWdce8COp4DW5ocp3tgxCkh8AvxCq/oNY/KLH9+WtkUMOr0qF8bxOsahfqaxrRzsP7Lnu/R+Jx/j5+Nxwf12PHXhdeqD6IptejUZ8OPjwl/0xBeIdc4z8MBQtUyvbHBH0+ofF0syoE/wU3xl4xw7BQbCJPziP2B3vBi/nFJdDl29BTSSUbT3eqQb9LsPuYDabxomEADttOtF8Xq4lW2aY11mz4Ox+GLrrg/moUo1d9SnvgRXoYCgthLC5F6T/pocmwSiB8tROb9vaKtSTeaMLJtw0ZtdOlQcyfeSF4jzXA/p0FLe/NnTaHuUa6aEfd5/NRkxfjDDxOuoOYk2QodJ9p/EJHPerOLkPT4SoYUrY5lOBsqEbbFbGaKK5kwgeIrj2WbN4VPbZ/3IKyPLji6//vWtR2pKphNMF2dDdKEs4IfCcqUXd88nHrqlvRKp/OhuE9Wo36a+zH7cP8HYlKQaE7ExS6z7Dw112o2+VE0QcdqFo+9QmSMnVJGNJAO2oPiWYzi8tQU1cO00va2Aj/D4Lwn48+RwvTW1aUrTKgkM8CkGK0/ZzzIITQI3bMt/rRZusUc3wZUHXAhnVLUhwHO27fWVaaPeqdMN2MdkHcDAkPvOjcVg/XmiZ0ZHhKmlsodGeCput5VoV9OP27HvhXsFB8TOByytQlGujWlsXaV96SgOfjApebp0Y4eIcFD59LqgO7t5TAsFiTP4HLsWPg07Rolq6L9ZyCF9IYn34mxXHMCyPg9UKzpgYH22LTzcRPSYN5BpS9ZUTwVBdO38ijXljkmUCh+5SFrvSji51Bm18zZVaPO6F95eQBS6QLbWg+VwDbh/k9tqhCjaKVZrEMOK6MIHkzfKU9atsY74tvRsEUVQfaYjPM8KHrrCfFaxEyOyh0nyoJ7nNOdl8GkyHzy+m6YkvSAUvki0UfzcIYqf5e1G7Yj4HvxPoTpH7ZyEJSYEE5kiQpM5o1YEERTOvZ/RdOuOPnCSNkllHoPk23vXDzi2KrlqFwOk2YFhlRukosiwFL+GDOfPR8095ZGMxZtw7W9SM40udlZc4njIWkcbyKwQHPtYmpGz3u9H9o1Ch8mU9a44H7Wj4NaxhPA+OWJmxdTvW506FZvhVNW4w5VZ/L5Vbo3vfBcagR1Rs2YAO7Vb5rR+/XiUWeMAIXO9G4jT+nGo1H3ZCiDbSjHgTgPtGO9i/8CeEgwdNRj0r++tv2o+cq/2MMKa/3bmXK95Qud6K+Unm//Se8crvE0M0BdH6wU3mtyp2wn/JNOo0NBUbli0M6fcE0/+HZH93qaPkviJ7/aUNbkzJ6/vaVs9HoTA3j2zaUXmCn+BefdJDFD87CYndoOPZ98qEKxXFn8kOjWayXe2J5bgTytopBvcSAggVihWRmQQEMS3KvwXbuhO59D9ptdej+wYQ9x/vQ13cCTRuA3l12OONOD6Uvfov6z59D2YGTaNnCQvTUfrRfmBgAwcufYP9xBxwDoyyqYoI8XG+Z0HSyBRW33ejaa0djQy3qB+ajvKFDfs+aFV507mpAz3UR19/xcA3A1Ky8n/t4PewNjajd68L815vQ0deHE7VGeI/WoeHExFGcAn6HfL9s0fQDUr2iNHZBbXAA0vpZHj1/ngFb6y0IdtjRM+kHcWZ4FcN47H7hwTAfnIUFrt1mn95xawuwjN8P+ZUG/ITkgBwJ3TC8p9vguG2E9V/N0Mm/7GMI+HwsND2spCKi874b3a13UL7TCqPmIcbEiEmjwfg//hBGr4mmVqyEGfsz9cN10o2y9aVxA2j4MLrAipY9FTAs5BtZSe4fecnSh65PWcCxJf/FU3CvL0Pp4lgJy+dXw2rfhwqDRm6OpF5hkusjfce7MTD+A6GMssS98PMZ/Nqq9ChaGysnz1+ovOfM+NErnylM77bJ1gVvkH1H7MepfegJxpnaANMbYhlODLAfGT427LRnDVDPxwv8PvgN7iRr0pwK+3/WluS4M7tVsx8lajlBkuDtdGfdj8ORIxZLxGI5FhkRmyL3LkVa+DZrc8T1rdg00Byx/MYVucdXvr8UaZX3qYmcGpUfFkYix+TtlsihP4yJbczoqUiN5VDkS77p2/7InqT7Mr5ueV+L/NzRyKma6OvcifS/r7xuzenEnUYi3ZPeU9nXYmGfXxKbpuOHbyL9v7WKz8Ru7/ezT5IjpC8jh35tidh+54p8E/dVz8SPXx2JHSu//bo7MvKDeDBjdyKuJv46eyL94v8QIbMtNzpHsFP4/W/bITfzX1WOzatN0Ot10D7/EA9/qka0yWb4Lh/YWAf9iyql4XML20NXhdbW8vFRlOQr7LWd7HkG1Bxuio1ydD8A720V9Eu1eHi5HVs+4Kf+FWjps0KvPEMWHLSj8sAAW+I9omqg+zYA1S/10D7yoH1LI+S9DvTB+g/y0xVBN+yV+yHvVRdtyO5Dz4Y6dKXoWZUeCQMf8TFSjTDPd8Ip91TToaq1FeWZjCXyND0IwPm7RrRd0cL85mZYXzNk3O9+grAXnZvqIffC15TAZrfNoIWG0rnAPgh2ZtKHiqVic47iJWTy9PX19Yml2ZEb1QsL9TCuUBalwV60NddhZ+UmVH9wCqNxowupXtTLgStfEDuvVCHoXi2KBS4zPuK8pgiF8cPK8Up1FrgqhDF6TalrxfplKFCWxkk3eXRyi6FdqEaBgQXuPJYFN7xy4PLmX8sSu9jeHpUDl1vMTv+fjPgxUmvwzmvRC2p+9A4+hdYD0zWvAObag2hleeE8XI/KTXVwiGqVaXkYjh3bmnUozvs2yOnjHUXo9vRvs04u7+YCdirff7AhssMad2rJbtaDX0Ymnbn+2RVplh9PrB4Yi3x5UOzbORz5UWydKHrab4k0n088UY89Nl6NIYyerlG2N7kmnd6PP8arEv4sNo5XOcRvS9+d8y0Rq7UlVjUxxk7l5dfjtyOR4eQHl30/fBNx/deeiPXfGiJHnCORezP9XNeOiWO0RFp+H/8vMB33Iq7fKK/V7RObCJlls1/SDUnwefnwcDqYd+zDwWN9OHm8FbYNykl/8KwfAXkpJuQfkasiACOWTTjNjo04byoskEu10nUvAvFTlNwNwCcXhXXQL0m4Gs5KrB7RA8G8Or59n4TANeUB3cvxF+c4CaPRndaWwDhejaBMcSL7i7hPU9LOD/wi05vRT9QL15UcaAR114P29+vh+vlmtB7ah6q1mQ/pl8h/QxmBgY/BUFQ407MGVmaWmxMaoKFmVyRHzG7ohr3o2lGNuvo6VB+LdddULdChZKMFvGk7lrPwkrfGhB+IS9Gr/mZiAN4OYFh+iAXqYrZXyIte2xF44npQhf4UDewiFC6WF8ZJXje8fGFpFSzFcachoQBGRJgXLUnYiXeAuMoX9KjaYEJsLxXU8gd3Q8rgynnqzg8qGFbF6q6d59jriuXMzaz1wvjtnW6otx7CvjdZqMWPfTBtQQSUX8TJ1UPTEoQk/7vNh+o5eUN6qPUCeZpEiXd2jLcUsE46lRz7w6GIlT3WPDD5FPPe71uU/SZUAfwYGfnUJl5PaQUhXwmvORWJr4EY+TTaEqA1cul7sZH7np2+y1Ubtkj3/yWcI7PPyT8L36/VHV/ZwaszlNez9YxOqs4Y7VEem9CKYgpj17oje9hnmFztERVX/ZGs5UWW/Mi+jz1W9j35nnAdR3wVSsrqoQyMv153rFUMIbNsdku6f6eXG/4XrN8Oq1GUZx+FWYmTlfY+dkC1djeqkgxppzGUooxvHnTDc5fdPwrBf64NzeeDouQbwth9CQPnemFkpc9YDQSvJogWO53oPSt6kQU96DrQBgc7DbUeaEDF3088R5b+NMzKTArnSQd8fKdHQXiON6PtLDt5rWxBw5u6Se1ItUvksjpGbqcqkypTlEh+D5xHG1G7i7d/1UHLisuTZlJh30voLtv4kljnpVVe2pWnOEkyTcvT8sCLT5rOQFP95Loh8+Es+TEEhpziYiX7ThfOx0N5+wyOTGJnKPx+vW7SBVNCZsvsNxkLeuE40QvHkAcBOdm00K8yonRtOUpX8HmfkgvfGsAnx8/APcjrg7UwvmFFVUUJVDd60P1JP5zXwzC8XoOaamOsCmK8aZoGFe9tx8OBTvReYYGoKUDJ+q3YzAK6YNIbxpodabbYsD3sQucpD3tPDQpWW7C1ogymVF0NbzvRuK0NnhTTooTGm65NVrLrGGyrYnsE+uqw87BPrCWaSbO0TPCpYnagbZ7tyY1Ty76jevYdydU6ySyvQccH5onVSGmKNis01nZg39rpvAIhT94zNYh5bB6mMuw7sR3GVIkeL67daNneE9i+Mp2doqIzQWTwfrlMbgPtwxtZCfiZis4lZkTN4X3Tm5WWkKcgR7oBZ8etGy5loXhZkhJtCjd9UPYyYdnfZpqaWpjk9rUOuL050NpgpnTlaO3Lh8Bl7g/DzSdvXGuGiQKX5JBnKHQljHqVmtnJzb5Sk/yiPlenR8E0GuqrV6yDVTfT1gYkU9KQE07oYP1nY8oqKkJmwzMRuvKFGr9HNO0CCheolYtPiUNCxgvzizh+eIZEbaPuBaj562Q6/75Kj41vl0NzpQuOq9SEKCseeOE47oFuyw5szPaYw4Q8xjNQp+tHT2UtuqLND8ZNPYZBqtl2Y7POZiLd2YDJzM2l2YDJXESzAWeNBM9hPr8XC4P3SqZ1NZ48ntyb73MtbP9hhYF6oZEcRKGbVSH4L7gx9ooZhny4GJVvHgXhveDF/OIS6Kgil+QoCl1CSBrYmdrRdrTzNuqLjCiv3o6qlVqEbrrhON2L/i94e3kNClaWYvPbW1ESN+g/mYhClxDyWNK5RlQ7DWhtLIRrC2+3bkTFWxoMnA+h7N3tKOOzqNxyoHFHOzyaCjR1WGGg3E3qmWqnSwiZhrAX/cc9MG9aB51ahfnyRg96zoZh/XA3ysW0VVhcqMxJF+yB7498gSRDoUsImdoffXAGy2AyqIG7EkbljRqUv5cwHX44jDGxSFKj0CWETG1RMWwfW+Vu7ONDo2osKF2eUH9waxTD8gKNXzwVCl1CyNT4VFdiUKeAXxmNGmv0SBhZGtINjzJVls74BMZCnrsodAkhaYoNjarMzBJPgndQ6b2pK5k4byGZiEKXEJKe4CiG5a70YmaWeLe9cInHSpcrkRu6mTBVFpFR6BJC0hK+6ROzXifOTQj4L59RxkTWlaKIPxbyoOvdiVNlEQWFLiEkLbduKpfJsH5ZwkwcsbntdK8qVQvSoAOOFRaYqJ5hEgpdQkgaYjNiG3QFCcNlqqFZpFQ3/IKP4HfTia7jY7C+RWOMJEOhSwhJQxhj99jdIjPKViRGqQqGjXtQtboAIx9VY0fLMJbtnTzXIFFQN2BCCMkiKukSQkgWUegSQkgWUegSQkjWAP8PnsDqcaRVkZkAAAAASUVORK5CYII=)"
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      "source": [
        "### **2. Confidence**\n",
        "Confidence adalah suatu ukuran yang menunjukkan hubungan antar dua item secara conditional (berdasarkan suatu kondisi tertentu). Berikut perhitungan nilai confidence :\n",
        "\n",
        "![confidence.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "B8mKLfOrgc2j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3. Lift**\n",
        "Lift mengacu pada bagaimana peluang kedua item dibeli ketika item pertama dibeli. Berikut perhitungan nilai confidence :\n",
        "![lift.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "IKR4peUfgcmo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Jj6gV75smy9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bakery Market Basket Analysis**"
      ],
      "metadata": {
        "id": "RO6yz03Im1i2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Import Library**\n",
        "Pertama-tama mengimpor library yang akan kita gunakan untuk analisis eksplorasi data dan visualisasi data.\n"
      ],
      "metadata": {
        "id": "LWqZv3hwm_-7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UuwanqKPYdPs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Import Dataset**\n",
        "Kita akan menggunakan data BreadBasket_DMS.csv yang bisa temen-temen download [disini](https://github.com/viktree/curly-octo-chainsaw/blob/ea4fbb632382c44af0d0b53c90f4e04496926b94/BreadBasket_DMS.csv)\n",
        "\n",
        "Sekarang import data ke DataFrame dengan library pandas."
      ],
      "metadata": {
        "id": "qlcuzs3XZ_K4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "!wget https://raw.githubusercontent.com/viktree/curly-octo-chainsaw/ea4fbb632382c44af0d0b53c90f4e04496926b94/BreadBasket_DMS.csv -O dataset"
      ],
      "metadata": {
        "id": "0jRwoQSSmWYl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec8773cc-6f9d-4980-8525-e3fba47156ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-08-02 13:38:35--  https://raw.githubusercontent.com/viktree/curly-octo-chainsaw/ea4fbb632382c44af0d0b53c90f4e04496926b94/BreadBasket_DMS.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 710518 (694K) [text/plain]\n",
            "Saving to: ‘dataset’\n",
            "\n",
            "\rdataset               0%[                    ]       0  --.-KB/s               \rdataset             100%[===================>] 693.87K  --.-KB/s    in 0.009s  \n",
            "\n",
            "2023-08-02 13:38:35 (75.0 MB/s) - ‘dataset’ saved [710518/710518]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan isi Datasets:\n",
        "dataset = pd.read_csv('dataset')\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "0OeyXre_dhxR",
        "outputId": "ca76b27c-2f20-4411-c4d3-19c282cc7b8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date      Time  Transaction           Item\n",
              "0      2016-10-30  09:58:11            1          Bread\n",
              "1      2016-10-30  10:05:34            2   Scandinavian\n",
              "2      2016-10-30  10:05:34            2   Scandinavian\n",
              "3      2016-10-30  10:07:57            3  Hot chocolate\n",
              "4      2016-10-30  10:07:57            3            Jam\n",
              "...           ...       ...          ...            ...\n",
              "21288  2017-04-09  14:32:58         9682         Coffee\n",
              "21289  2017-04-09  14:32:58         9682            Tea\n",
              "21290  2017-04-09  14:57:06         9683         Coffee\n",
              "21291  2017-04-09  14:57:06         9683         Pastry\n",
              "21292  2017-04-09  15:04:24         9684      Smoothies\n",
              "\n",
              "[21293 rows x 4 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari tabel diatas, menunjukkan jumlah data pada dataset terdapat 21293 baris dan 4 kolom"
      ],
      "metadata": {
        "id": "0A0xY-Pbrxl8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Data Preparation**\n",
        "Pada tahapan ini kita akan memastikan tipe data yang digunakan sesuai, tidak ada nilai kosong"
      ],
      "metadata": {
        "id": "IOJ1fl8ipEJn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# cek tipe data dari setiap kolom\n",
        "df = pd.read_csv('dataset')\n",
        "print(df.dtypes)"
      ],
      "metadata": {
        "id": "EnD2MM1oYrge",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96c15ae3-3d4f-4395-c58d-38d366163841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date           object\n",
            "Time           object\n",
            "Transaction     int64\n",
            "Item           object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mencetak 10 data pertama\n",
        "print(df.head(10))"
      ],
      "metadata": {
        "id": "ZUvFxXVYYrdn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a898d1a-4936-4798-defa-2c6fc1f9eaee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Date      Time  Transaction           Item\n",
            "0  2016-10-30  09:58:11            1          Bread\n",
            "1  2016-10-30  10:05:34            2   Scandinavian\n",
            "2  2016-10-30  10:05:34            2   Scandinavian\n",
            "3  2016-10-30  10:07:57            3  Hot chocolate\n",
            "4  2016-10-30  10:07:57            3            Jam\n",
            "5  2016-10-30  10:07:57            3        Cookies\n",
            "6  2016-10-30  10:08:41            4         Muffin\n",
            "7  2016-10-30  10:13:03            5         Coffee\n",
            "8  2016-10-30  10:13:03            5         Pastry\n",
            "9  2016-10-30  10:13:03            5          Bread\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mencetak jumlah data item yang unik (memiliki nilai item berbeda)\n",
        "jumlah_item_unik = df['Item'].nunique()\n",
        "print(\"Jumlah data item yang unik:\", jumlah_item_unik)"
      ],
      "metadata": {
        "id": "SlSaoS7YJvDu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad7c2021-f298-4488-8014-de9c4eb92a20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah data item yang unik: 95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mencetak data item yang unik (memiliki nilai item berbeda)\n",
        "data_item_unik = df['Item'].drop_duplicates()\n",
        "\n",
        "print(\"Data item yang unik:\")\n",
        "for item in data_item_unik:\n",
        "    print(item)"
      ],
      "metadata": {
        "id": "65doHSBGYran",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a917cca8-ddf2-49d0-f90a-f3e136fd2d20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data item yang unik:\n",
            "Bread\n",
            "Scandinavian\n",
            "Hot chocolate\n",
            "Jam\n",
            "Cookies\n",
            "Muffin\n",
            "Coffee\n",
            "Pastry\n",
            "Medialuna\n",
            "Tea\n",
            "NONE\n",
            "Tartine\n",
            "Basket\n",
            "Mineral water\n",
            "Farm House\n",
            "Fudge\n",
            "Juice\n",
            "Ella's Kitchen Pouches\n",
            "Victorian Sponge\n",
            "Frittata\n",
            "Hearty & Seasonal\n",
            "Soup\n",
            "Pick and Mix Bowls\n",
            "Smoothies\n",
            "Cake\n",
            "Mighty Protein\n",
            "Chicken sand\n",
            "Coke\n",
            "My-5 Fruit Shoot\n",
            "Focaccia\n",
            "Sandwich\n",
            "Alfajores\n",
            "Eggs\n",
            "Brownie\n",
            "Dulce de Leche\n",
            "Honey\n",
            "The BART\n",
            "Granola\n",
            "Fairy Doors\n",
            "Empanadas\n",
            "Keeping It Local\n",
            "Art Tray\n",
            "Bowl Nic Pitt\n",
            "Bread Pudding\n",
            "Adjustment\n",
            "Truffles\n",
            "Chimichurri Oil\n",
            "Bacon\n",
            "Spread\n",
            "Kids biscuit\n",
            "Siblings\n",
            "Caramel bites\n",
            "Jammie Dodgers\n",
            "Tiffin\n",
            "Olum & polenta\n",
            "Polenta\n",
            "The Nomad\n",
            "Hack the stack\n",
            "Bakewell\n",
            "Lemon and coconut\n",
            "Toast\n",
            "Scone\n",
            "Crepes\n",
            "Vegan mincepie\n",
            "Bare Popcorn\n",
            "Muesli\n",
            "Crisps\n",
            "Pintxos\n",
            "Gingerbread syrup\n",
            "Panatone\n",
            "Brioche and salami\n",
            "Afternoon with the baker\n",
            "Salad\n",
            "Chicken Stew\n",
            "Spanish Brunch\n",
            "Raspberry shortbread sandwich\n",
            "Extra Salami or Feta\n",
            "Duck egg\n",
            "Baguette\n",
            "Valentine's card\n",
            "Tshirt\n",
            "Vegan Feast\n",
            "Postcard\n",
            "Nomad bag\n",
            "Chocolates\n",
            "Coffee granules \n",
            "Drinking chocolate spoons \n",
            "Christmas common\n",
            "Argentina Night\n",
            "Half slice Monster \n",
            "Gift voucher\n",
            "Cherry me Dried fruit\n",
            "Mortimer\n",
            "Raw bars\n",
            "Tacos/Fajita\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek missing value dan zeros\n",
        "df = pd.read_csv('dataset')\n",
        "print(\"Missing values :\")\n",
        "print(df.isnull().sum())\n",
        "print(\"Zeros pada kolom :\")\n",
        "print((df == 0).sum())"
      ],
      "metadata": {
        "id": "Xv_U40nzasgl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ab13e9f-7adb-404c-90fa-3fec0bf42c3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values :\n",
            "Date           0\n",
            "Time           0\n",
            "Transaction    0\n",
            "Item           0\n",
            "dtype: int64\n",
            "Zeros pada kolom :\n",
            "Date           0\n",
            "Time           0\n",
            "Transaction    0\n",
            "Item           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terlihat bahwa pada dataset tidak terdapat missing value dan zeros"
      ],
      "metadata": {
        "id": "UGtXdEUhrGnM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# cek nilai \"NONE\" pada kolom Item\n",
        "print(\"Nilai NONE pada kolom :\", len(df[df['Item'] == 'NONE']))"
      ],
      "metadata": {
        "id": "rPSVH7n1asdz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84a0f78a-a630-4910-e1d1-3ed72fc98226"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nilai NONE pada kolom : 786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terdapat nilai 'NONE' di dataset kita. Ini bisa berarti bahwa ada item yang tidak dibeli, atau nama item tersebut tidak dicatat. Terdapat 786 data dengan nilai \"None\" dari total 21293. Karena data ini tidak berguna, maka bisa kita hapus saja."
      ],
      "metadata": {
        "id": "wVV6XvsRsMsu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# menghapus baris dengan nilai Item = None\n",
        "df = df[df['Item'] != 'NONE']"
      ],
      "metadata": {
        "id": "GkZJTOj1biZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# cek perubahan yang terjadi\n",
        "print(\"Jumlah baris setelah NONE dihapus :\", len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUiYRM8CbnVf",
        "outputId": "9a8e3948-2c37-49a2-ef46-4734bbdda5b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah baris setelah NONE dihapus : 20507\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jumlah baris setelah NONE dihapus : 20507"
      ],
      "metadata": {
        "id": "oWWKR9tncCxB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seperti yang bisa kita lihat di atas, fitur Tanggal dan Waktu bukanlah tipe numerik. Untuk visualisasi dan pemahaman data yang lebih baik, kita bisa menambahkan beberapa fitur lagi ke DataFrame ini berdasarkan informasi dari dua fitur ini."
      ],
      "metadata": {
        "id": "CiXA5NzKby-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Year (ingat kembali penulisan function lambda)\n",
        "df['Year'] = df['Date'].apply(lambda x: x.split(\"-\")[0])\n",
        "# Month\n",
        "df['Month'] = df['Date'].apply(lambda x: x.split(\"-\")[1])\n",
        "# Day\n",
        "df['Day'] = df['Date'].apply(lambda x: x.split(\"-\")[2])"
      ],
      "metadata": {
        "id": "gPWL1Sq-asbF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61aa0eef-3cd3-4e7e-f3f8-05be01eb5346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-3999072ffe9c>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Year'] = df['Date'].apply(lambda x: x.split(\"-\")[0])\n",
            "<ipython-input-12-3999072ffe9c>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Month'] = df['Date'].apply(lambda x: x.split(\"-\")[1])\n",
            "<ipython-input-12-3999072ffe9c>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Day'] = df['Date'].apply(lambda x: x.split(\"-\")[2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cek kembali perubahan yang terjadi setelah nilai tahun, bulan dan hari ditampung di fitur baru (year, month, day)\n",
        "print(df.info())\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmrAdKZMcRcK",
        "outputId": "a619f237-f3cd-449c-9be5-fef91d31c43d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 20507 entries, 0 to 21292\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Date         20507 non-null  object\n",
            " 1   Time         20507 non-null  object\n",
            " 2   Transaction  20507 non-null  int64 \n",
            " 3   Item         20507 non-null  object\n",
            " 4   Year         20507 non-null  object\n",
            " 5   Month        20507 non-null  object\n",
            " 6   Day          20507 non-null  object\n",
            "dtypes: int64(1), object(6)\n",
            "memory usage: 1.3+ MB\n",
            "None\n",
            "         Date      Time  Transaction           Item  Year Month Day\n",
            "0  2016-10-30  09:58:11            1          Bread  2016    10  30\n",
            "1  2016-10-30  10:05:34            2   Scandinavian  2016    10  30\n",
            "2  2016-10-30  10:05:34            2   Scandinavian  2016    10  30\n",
            "3  2016-10-30  10:07:57            3  Hot chocolate  2016    10  30\n",
            "4  2016-10-30  10:07:57            3            Jam  2016    10  30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Visualisasi dan Memahami Data**\n",
        "Kita tahu bahwa dataset ini direkam dari 30/10/2016 hingga 09/04/2017. Sebelum kita masuk dalam pemodelan, kita harus mengeksplorasi dan memvisualisasikan penjualan dalam periode waktu ini. Barang apa yang paling banyak dibeli pelanggan? Bulan mana yang lebih sukses? Mari kita jawab ini secara visual.\n"
      ],
      "metadata": {
        "id": "wyge9sJWtyZk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung 20 data penjualan item terbanyak\n",
        "top_items = df['Item'].value_counts().head(20)\n",
        "\n",
        "# Mencetak 20 data penjualan item terbanyak\n",
        "print(\"20 data penjualan item terbanyak:\")\n",
        "print(top_items)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uePwUu-asPm",
        "outputId": "9e62af97-96d7-4860-9079-5619b3cf7ca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20 data penjualan item terbanyak:\n",
            "Coffee           5471\n",
            "Bread            3325\n",
            "Tea              1435\n",
            "Cake             1025\n",
            "Pastry            856\n",
            "Sandwich          771\n",
            "Medialuna         616\n",
            "Hot chocolate     590\n",
            "Cookies           540\n",
            "Brownie           379\n",
            "Farm House        374\n",
            "Muffin            370\n",
            "Alfajores         369\n",
            "Juice             369\n",
            "Soup              342\n",
            "Scone             327\n",
            "Toast             318\n",
            "Scandinavian      277\n",
            "Truffles          193\n",
            "Coke              185\n",
            "Name: Item, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transaction_count = df.groupby(by='Item')[['Transaction']].count().sort_values(by='Transaction', ascending=False)\n",
        "def convert_to_percentage(x):\n",
        "    return 100 * x / float(x.sum())\n",
        "\n",
        "transaction_percentage = transaction_count.apply(convert_to_percentage)\n",
        "transaction_percentage.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "SM5G3gbbv2jC",
        "outputId": "df194132-73f9-446a-8681-8d38dc36442f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Transaction\n",
              "Item               \n",
              "Coffee    26.678695\n",
              "Bread     16.213976\n",
              "Tea        6.997611\n",
              "Cake       4.998293\n",
              "Pastry     4.174184"
            ],
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasikan data penjualan item terbanyak ke line chart dan bar chart\n",
        "# Menghitung 20 data penjualan item terbanyak\n",
        "top_items = df['Item'].value_counts().head(20)\n",
        "\n",
        "# Menampilkan visualisasi data dalam bentuk line chart\n",
        "plt.figure(figsize=(20,6))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(top_items)\n",
        "plt.title('Items Most Sold (Line Chart)')\n",
        "\n",
        "# Menampilkan visualisasi data dalam bentuk bar chart\n",
        "plt.subplot(1,2,2)\n",
        "plt.barh(top_items.index, top_items.values)\n",
        "plt.title('Items Most Sold (Bar Chart)')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "W_3hF2KaasMm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "a8fc613f-3c14-46fd-9b5d-eb3ce40e3e4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x600 with 2 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil visualisasi, sebutkan 3 barang yang paling banyak terjual !\n",
        "=>\n"
      ],
      "metadata": {
        "id": "Sa6gzzQJVjFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#buat visualisasi data diagram batang dengan judul 'Monthly Sales' dengan sumbu x bulan dan sumbu y jumlah transaksi\n",
        "monthly_sales = df['Month'].value_counts().sort_index()\n",
        "\n",
        "plt.bar(monthly_sales.index, monthly_sales.values)\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.title('Monthly Sales')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3vTQomLFasJP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "e53224cb-28fb-44b9-cc40-7b2b002a3c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil visualisasi data penjualan perbulan, terlihat terjadi perbedaan penjualan yang drastis pada bulan 4 dan 10 ini disebut dengan outlier.\n",
        "Untuk mengetahui lebih lanjut, kita bisa periksa untuk melihat apakah ada lebih sedikit transaksi harian yang dicatat untuk bulan-bulan ini dibandingkan dengan yang lain."
      ],
      "metadata": {
        "id": "KcOEQ6joWP8i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.groupby('Month')['Day'].nunique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KUVgjgDa96F",
        "outputId": "4cbf74c0-c526-463f-d2a4-18af1441b695"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Month\n",
            "01    30\n",
            "02    28\n",
            "03    31\n",
            "04     9\n",
            "10     2\n",
            "11    30\n",
            "12    29\n",
            "Name: Day, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada Bulan 4 hanya tercatat 9 hari dan pada bulan  10 hanya tercatat 2 hari"
      ],
      "metadata": {
        "id": "ud0D6bgklyqb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. Market Basket Analysis**"
      ],
      "metadata": {
        "id": "sAnhfA6tmFyD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pertama kita mengubah item list menjadi data transaksi dengan menggunakan TransactionEncoder.\n",
        "Kita akan menggunakan frequent item-sets untuk menghasilkan aturan asosiasi dengan menggunakan algoritma Apriori.\n"
      ],
      "metadata": {
        "id": "R4E90s6umYB1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import association_rules, apriori"
      ],
      "metadata": {
        "id": "L82xUMNIa93T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "kita membuat list transaction_list() yang unik sehingga kita dapat mengubah data kita ke dalam format yang benar"
      ],
      "metadata": {
        "id": "M_lj3kvwqLE0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transaction_list = []\n",
        "\n",
        "# For loop to create a list of the unique transactions throughout the dataset:\n",
        "for i in df['Transaction'].unique():\n",
        "    tlist = list(set(df[df['Transaction']==i]['Item']))\n",
        "    if len(tlist)>0:\n",
        "        transaction_list.append(tlist)\n",
        "print(len(transaction_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0v8X7l5Ma90i",
        "outputId": "2333251c-1113-488c-8846-f3c0f99c73f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9465\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "kita menggunakan TransactionEncoder untuk merubah bentuk transaction_list()"
      ],
      "metadata": {
        "id": "QmtvWrBTq2F-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "te = TransactionEncoder()\n",
        "te_ary = te.fit(transaction_list).transform(transaction_list)\n",
        "df2 = pd.DataFrame(te_ary, columns=te.columns_)"
      ],
      "metadata": {
        "id": "19tdfChHa9xm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0be694c-e06e-4bbc-fb36-b02ea9ce4968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sekarang kita terapkan Apriori. Kita akan menggunakan parameter min_threshold (nilai ambang batas yang ditentukan) dalam aturan asosiasi untuk metrik lift menjadi 1,0 karena jika kurang dari satu, maka kedua item tersebut kemungkinan tidak akan dibeli bersama. Kita akan mengurutkan nilai berdasarkan keyakinan untuk melihat kemungkinan suatu barang dibeli jika pendahulunya dibeli."
      ],
      "metadata": {
        "id": "kffaMg3SraLA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets = apriori(df2, min_support=0.01, use_colnames=True)\n",
        "rules = association_rules(frequent_itemsets, metric=\"lift\" , min_threshold=1.0)\n",
        "metrix = pd.DataFrame(rules)\n",
        "metrix[['antecedents','consequents', 'support', 'confidence', 'lift']].sort_values('lift', ascending=False)"
      ],
      "metadata": {
        "id": "Q8e9VpIIa9u6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ab8b4df9-2e31-46c8-eea9-6df4d007559d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         antecedents       consequents   support  confidence      lift\n",
              "41            (Cake)     (Coffee, Tea)  0.010037    0.096643  1.937977\n",
              "38     (Coffee, Tea)            (Cake)  0.010037    0.201271  1.937977\n",
              "9             (Cake)   (Hot chocolate)  0.011410    0.109868  1.883874\n",
              "8    (Hot chocolate)            (Cake)  0.011410    0.195652  1.883874\n",
              "10             (Tea)            (Cake)  0.023772    0.166667  1.604781\n",
              "11            (Cake)             (Tea)  0.023772    0.228891  1.604781\n",
              "31           (Toast)          (Coffee)  0.023666    0.704403  1.472431\n",
              "30          (Coffee)           (Toast)  0.023666    0.049470  1.472431\n",
              "37          (Pastry)   (Bread, Coffee)  0.011199    0.130061  1.444872\n",
              "36   (Bread, Coffee)          (Pastry)  0.011199    0.124413  1.444872\n",
              "33        (Sandwich)             (Tea)  0.014369    0.200000  1.402222\n",
              "32             (Tea)        (Sandwich)  0.014369    0.100741  1.402222\n",
              "40             (Tea)    (Coffee, Cake)  0.010037    0.070370  1.285822\n",
              "39    (Coffee, Cake)             (Tea)  0.010037    0.183398  1.285822\n",
              "29  (Spanish Brunch)          (Coffee)  0.010882    0.598837  1.251766\n",
              "28          (Coffee)  (Spanish Brunch)  0.010882    0.022747  1.251766\n",
              "18       (Medialuna)          (Coffee)  0.035182    0.569231  1.189878\n",
              "19          (Coffee)       (Medialuna)  0.035182    0.073542  1.189878\n",
              "23          (Pastry)          (Coffee)  0.047544    0.552147  1.154168\n",
              "22          (Coffee)          (Pastry)  0.047544    0.099382  1.154168\n",
              "1           (Coffee)       (Alfajores)  0.019651    0.041078  1.130235\n",
              "0        (Alfajores)          (Coffee)  0.019651    0.540698  1.130235\n",
              "17           (Juice)          (Coffee)  0.020602    0.534247  1.116750\n",
              "16          (Coffee)           (Juice)  0.020602    0.043065  1.116750\n",
              "24          (Coffee)        (Sandwich)  0.038246    0.079947  1.112792\n",
              "25        (Sandwich)          (Coffee)  0.038246    0.532353  1.112792\n",
              "7             (Cake)          (Coffee)  0.054728    0.526958  1.101515\n",
              "6           (Coffee)            (Cake)  0.054728    0.114399  1.101515\n",
              "26           (Scone)          (Coffee)  0.018067    0.522936  1.093107\n",
              "27          (Coffee)           (Scone)  0.018067    0.037765  1.093107\n",
              "12          (Coffee)         (Cookies)  0.028209    0.058966  1.083723\n",
              "13         (Cookies)          (Coffee)  0.028209    0.518447  1.083723\n",
              "34   (Bread, Coffee)            (Cake)  0.010037    0.111502  1.073621\n",
              "35            (Cake)   (Bread, Coffee)  0.010037    0.096643  1.073621\n",
              "14          (Coffee)   (Hot chocolate)  0.029583    0.061837  1.060311\n",
              "15   (Hot chocolate)          (Coffee)  0.029583    0.507246  1.060311\n",
              "2            (Bread)          (Pastry)  0.029160    0.089119  1.034977\n",
              "3           (Pastry)           (Bread)  0.029160    0.338650  1.034977\n",
              "5           (Coffee)         (Brownie)  0.019651    0.041078  1.025860\n",
              "4          (Brownie)          (Coffee)  0.019651    0.490765  1.025860\n",
              "20          (Coffee)          (Muffin)  0.018806    0.039311  1.022193\n",
              "21          (Muffin)          (Coffee)  0.018806    0.489011  1.022193"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-7d2a7bb4-7900-40e3-8303-ceb3821d0c46\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Coffee, Tea)</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.096643</td>\n",
              "      <td>1.937977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>(Coffee, Tea)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.201271</td>\n",
              "      <td>1.937977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>0.011410</td>\n",
              "      <td>0.109868</td>\n",
              "      <td>1.883874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.011410</td>\n",
              "      <td>0.195652</td>\n",
              "      <td>1.883874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(Tea)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.023772</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.604781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Tea)</td>\n",
              "      <td>0.023772</td>\n",
              "      <td>0.228891</td>\n",
              "      <td>1.604781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>(Toast)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.023666</td>\n",
              "      <td>0.704403</td>\n",
              "      <td>1.472431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Toast)</td>\n",
              "      <td>0.023666</td>\n",
              "      <td>0.049470</td>\n",
              "      <td>1.472431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>(Bread, Coffee)</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>0.130061</td>\n",
              "      <td>1.444872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>(Bread, Coffee)</td>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>0.124413</td>\n",
              "      <td>1.444872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>(Sandwich)</td>\n",
              "      <td>(Tea)</td>\n",
              "      <td>0.014369</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.402222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>(Tea)</td>\n",
              "      <td>(Sandwich)</td>\n",
              "      <td>0.014369</td>\n",
              "      <td>0.100741</td>\n",
              "      <td>1.402222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>(Tea)</td>\n",
              "      <td>(Coffee, Cake)</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.070370</td>\n",
              "      <td>1.285822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>(Coffee, Cake)</td>\n",
              "      <td>(Tea)</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.183398</td>\n",
              "      <td>1.285822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>(Spanish Brunch)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.010882</td>\n",
              "      <td>0.598837</td>\n",
              "      <td>1.251766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Spanish Brunch)</td>\n",
              "      <td>0.010882</td>\n",
              "      <td>0.022747</td>\n",
              "      <td>1.251766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(Medialuna)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.035182</td>\n",
              "      <td>0.569231</td>\n",
              "      <td>1.189878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Medialuna)</td>\n",
              "      <td>0.035182</td>\n",
              "      <td>0.073542</td>\n",
              "      <td>1.189878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.047544</td>\n",
              "      <td>0.552147</td>\n",
              "      <td>1.154168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>0.047544</td>\n",
              "      <td>0.099382</td>\n",
              "      <td>1.154168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Alfajores)</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.041078</td>\n",
              "      <td>1.130235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Alfajores)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.540698</td>\n",
              "      <td>1.130235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(Juice)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.020602</td>\n",
              "      <td>0.534247</td>\n",
              "      <td>1.116750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Juice)</td>\n",
              "      <td>0.020602</td>\n",
              "      <td>0.043065</td>\n",
              "      <td>1.116750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Sandwich)</td>\n",
              "      <td>0.038246</td>\n",
              "      <td>0.079947</td>\n",
              "      <td>1.112792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>(Sandwich)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.038246</td>\n",
              "      <td>0.532353</td>\n",
              "      <td>1.112792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.054728</td>\n",
              "      <td>0.526958</td>\n",
              "      <td>1.101515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.054728</td>\n",
              "      <td>0.114399</td>\n",
              "      <td>1.101515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>(Scone)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.018067</td>\n",
              "      <td>0.522936</td>\n",
              "      <td>1.093107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Scone)</td>\n",
              "      <td>0.018067</td>\n",
              "      <td>0.037765</td>\n",
              "      <td>1.093107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Cookies)</td>\n",
              "      <td>0.028209</td>\n",
              "      <td>0.058966</td>\n",
              "      <td>1.083723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(Cookies)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.028209</td>\n",
              "      <td>0.518447</td>\n",
              "      <td>1.083723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>(Bread, Coffee)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.111502</td>\n",
              "      <td>1.073621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Bread, Coffee)</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.096643</td>\n",
              "      <td>1.073621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>0.029583</td>\n",
              "      <td>0.061837</td>\n",
              "      <td>1.060311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.029583</td>\n",
              "      <td>0.507246</td>\n",
              "      <td>1.060311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Bread)</td>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>0.029160</td>\n",
              "      <td>0.089119</td>\n",
              "      <td>1.034977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>(Bread)</td>\n",
              "      <td>0.029160</td>\n",
              "      <td>0.338650</td>\n",
              "      <td>1.034977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Brownie)</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.041078</td>\n",
              "      <td>1.025860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Brownie)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.490765</td>\n",
              "      <td>1.025860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Muffin)</td>\n",
              "      <td>0.018806</td>\n",
              "      <td>0.039311</td>\n",
              "      <td>1.022193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>(Muffin)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.018806</td>\n",
              "      <td>0.489011</td>\n",
              "      <td>1.022193</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d2a7bb4-7900-40e3-8303-ceb3821d0c46')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-e3a22db3-ce08-4e0a-b3dd-0d0f27c8e89a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e3a22db3-ce08-4e0a-b3dd-0d0f27c8e89a')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e3a22db3-ce08-4e0a-b3dd-0d0f27c8e89a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7d2a7bb4-7900-40e3-8303-ceb3821d0c46 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7d2a7bb4-7900-40e3-8303-ceb3821d0c46');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Kesimpulan**\n",
        "\n",
        "Penjelasan nilai-nilai metrik (support, confidence, dan lift)!\n",
        "\n",
        "**Berdasarkan analisis dengan nilai min_support sebesar 0.01, adapun hasil beberapa aturan asosiasi yang menunjukkan item yang sering dibeli bersamaan antara lain:**\n",
        "1. Coffee dan Bread.\n",
        "2. Medialuna dan Coffee.\n",
        "3. Pastry dan Coffee.\n",
        "4. Tea dan Coffee.\n",
        "\n",
        "\n",
        "**Kesimpulannya secara garis besar:**\n",
        "\n",
        "Aturan-aturan ini menunjukkan bahwa kombinasi produk-produk tersebut memiliki tingkat kejadian yang tinggi, dan pelanggan cenderung membeli item tersebut bersamaan. Ini dapat memberikan wawasan berharga untuk strategi pemasaran, penempatan produk di toko, atau penawaran bundel yang dapat meningkatkan penjualan dan kepuasan pelanggan. Karena Algoritma Apriori membantu mengidentifikasi aturan asosiasi yang dapat memberikan wawasan tentang kecenderungan pelanggan membeli produk bersamaan."
      ],
      "metadata": {
        "id": "fbgS4hYzzLAA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Refrensi Serupa:\n",
        "\n",
        "https://www.kaggle.com/code/xvivancos/market-basket-analysis"
      ],
      "metadata": {
        "id": "tG8f8ak40rF-"
      }
    }
  ]
}