Copy_of_Tugas_statistika_UTS.ipynb

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      "source": [
        "# Tugas Naive Bayes Statistika\n",
        "\n",
        "## Oleh Marshall Al Karim\n",
        "## NIM: 2222038"
      ],
      "metadata": {
        "id": "ATkU1358wbaQ"
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      "cell_type": "markdown",
      "source": [
        "Naive Bayes adalah teknik klasifikasi statistik berdasarkan Teorema Bayes. Ini adalah salah satu algoritma pembelajaran terawasi yang paling sederhana. Klasifikasi Naive Bayes adalah algoritma yang cepat, akurat, dan dapat diandalkan. Klasifikasi Naive Bayes memiliki akurasi dan kecepatan tinggi pada kumpulan data besar.\n",
        "\n",
        "Klasifikasi Naive Bayes mengasumsikan bahwa efek fitur tertentu dalam sebuah kelas independen dari fitur lainnya. Misalnya, seorang calon peminjam dianggap layak atau tidak tergantung pada pendapatannya, riwayat pinjaman dan transaksi sebelumnya, usia, dan lokasi. Meskipun fitur-fitur ini saling terkait, fitur-fitur ini masih dianggap independen. Asumsi ini menyederhanakan perhitungan, dan itulah sebabnya dianggap sebagai naive. Asumsi ini disebut independensi kondisional kelas.\n",
        "\n",
        "$$ P(h | D) = \\frac{{P(D | h) \\times P(h)}}{{P(D)}} $$\n",
        "\n",
        "$ P(h) $ = Probabilitas hipotesis h menjadi benar (tanpa memperhitungkan data). Ini dikenal sebagai probabilitas sebelumnya (prior probability) dari $h$.\n",
        "\n",
        "$ P (D) $ = Probabilitas dari data (tanpa memperhitungkan hipotesis). Ini dikenal sebagai probabilitas sebelumnya.\n",
        "\n",
        "$ P(h | D) $ = Probabilitas hipotesis $h$ diberikan data $D$ Ini dikenal sebagai probabilitas posterior.\n",
        "\n",
        "$ P(D | h) $ = Probabilitas data $D$ diberikan bahwa hipotesis $h$ benar. Ini dikenal sebagai probabilitas posterior.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "Mengambil Library yang dibutuhkan"
      ],
      "metadata": {
        "id": "D_WEAD_xwu35"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Mengambil data dari google drive saya. Sumber data: https://www.kaggle.com/datasets/itssuru/loan-data"
      ],
      "metadata": {
        "id": "JIxEyrYCwQGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/dataset/loan_data.csv\" # Data yang saya taruh di Drive\n",
        "df = pd.read_csv(path)\n",
        "df.head() # 5 data teratas"
      ],
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          "height": 226
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        "outputId": "85d61b20-0c69-46ba-8f81-d9f5c5dbac68"
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              "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
              "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
              "1              1         credit_card    0.1071       228.22       11.082143   \n",
              "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
              "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
              "4              1         credit_card    0.1426       102.92       11.299732   \n",
              "\n",
              "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
              "0  19.48   737        5639.958333      28854        52.1               0   \n",
              "1  14.29   707        2760.000000      33623        76.7               0   \n",
              "2  11.63   682        4710.000000       3511        25.6               1   \n",
              "3   8.10   712        2699.958333      33667        73.2               1   \n",
              "4  14.97   667        4066.000000       4740        39.5               0   \n",
              "\n",
              "   delinq.2yrs  pub.rec  not.fully.paid  \n",
              "0            0        0               0  \n",
              "1            0        0               0  \n",
              "2            0        0               0  \n",
              "3            0        0               0  \n",
              "4            1        0               0  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9578,\n  \"fields\": [\n    {\n      \"column\": \"credit.policy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"debt_consolidation\",\n          \"credit_card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"int.rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026846987213382245,\n        \"min\": 0.06,\n        \"max\": 0.2164,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          0.1367,\n          0.1496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"installment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207.07130149985824,\n        \"min\": 15.67,\n        \"max\": 940.14,\n        \"num_unique_values\": 4788,\n        \"samples\": [\n          326.02,\n          182.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log.annual.inc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6148127513545925,\n        \"min\": 7.547501683,\n        \"max\": 14.52835448,\n        \"num_unique_values\": 1987,\n        \"samples\": [\n          10.22557105,\n          11.19908503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dti\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.883969540539616,\n        \"min\": 0.0,\n        \"max\": 29.96,\n        \"num_unique_values\": 2529,\n        \"samples\": [\n          5.86,\n          21.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 612,\n        \"max\": 827,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          647,\n          717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days.with.cr.line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2496.930376811348,\n        \"min\": 178.9583333,\n        \"max\": 17639.95833,\n        \"num_unique_values\": 2687,\n        \"samples\": [\n          2432.0,\n          8669.958333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revol.bal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33756,\n        \"min\": 0,\n        \"max\": 1207359,\n        \"num_unique_values\": 7869,\n        \"samples\": [\n          11196,\n          462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revol.util\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.014416965641065,\n        \"min\": 0.0,\n        \"max\": 119.0,\n        \"num_unique_values\": 1035,\n        \"samples\": [\n          28.6,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inq.last.6mths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 33,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          33,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delinq.2yrs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub.rec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"not.fully.paid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Eksplorasi Data.\n",
        "Untuk memahami lebih lanjut tentang dataset, kita akan menggunakan .info().\n",
        "\n",
        "Dataset ini terdiri dari 14 kolom dan 9578 baris.\n",
        "\n",
        "Selain dari kolom \"purpose\", kolom-kolom lainnya berupa float atau integer.\n",
        "\n",
        "Kolom target kita adalah \"not.fully.paid\" sebagai **kelas**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "0UVVfE0vBzJg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info() #Jumlah column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wZdjFT9uL9p",
        "outputId": "bdbb5998-e29a-4f4c-f1a3-8db2928e65c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9578 entries, 0 to 9577\n",
            "Data columns (total 14 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   credit.policy      9578 non-null   int64  \n",
            " 1   purpose            9578 non-null   object \n",
            " 2   int.rate           9578 non-null   float64\n",
            " 3   installment        9578 non-null   float64\n",
            " 4   log.annual.inc     9578 non-null   float64\n",
            " 5   dti                9578 non-null   float64\n",
            " 6   fico               9578 non-null   int64  \n",
            " 7   days.with.cr.line  9578 non-null   float64\n",
            " 8   revol.bal          9578 non-null   int64  \n",
            " 9   revol.util         9578 non-null   float64\n",
            " 10  inq.last.6mths     9578 non-null   int64  \n",
            " 11  delinq.2yrs        9578 non-null   int64  \n",
            " 12  pub.rec            9578 non-null   int64  \n",
            " 13  not.fully.paid     9578 non-null   int64  \n",
            "dtypes: float64(6), int64(7), object(1)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terdapat 14 Baris Data dari metode **info()**"
      ],
      "metadata": {
        "id": "X9XPz7vyxYig"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Di contoh ini, kita akan mengembangkan model untuk memprediksi pelanggan yang belum sepenuhnya melunasi pinjaman (dalam sintaks: *not*.fully.paid). Mari jelajahi tujuan dan kolom target dengan menggunakan countplot dari seaborn.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9SfIt5Klvslh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.countplot(data=df,x='purpose',hue='not.fully.paid')\n",
        "plt.xticks(rotation=45, ha='right');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "Vo7Cf90wvcxJ",
        "outputId": "8cbd8436-6d3e-4ead-b034-f095f8a47f4b"
      },
      "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": "code",
      "source": [
        "fully_paid = df[df['not.fully.paid'] == 0]\n",
        "not_fully_paid = df[df['not.fully.paid'] == 1]\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Visualisasi tujuan untuk data yang sepenuhnya dibayar\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data=fully_paid, x='purpose', palette='Set1')\n",
        "plt.title('Tujuan untuk Data yang Sepenuhnya Dibayar')\n",
        "plt.xlabel('Tujuan')\n",
        "plt.ylabel('Jumlah')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()\n",
        "\n",
        "# Visualisasi tujuan untuk data yang tidak sepenuhnya dibayar\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data=not_fully_paid, x='purpose', palette='Set2')\n",
        "plt.title('Tujuan untuk Data yang Tidak Sepenuhnya Dibayar')\n",
        "plt.xlabel('Tujuan')\n",
        "plt.ylabel('Jumlah')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PjNybw96Ec2W",
        "outputId": "59ceddf9-ee1e-40af-f92e-7922b062974e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-0fc550218ba4>:9: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=fully_paid, x='purpose', palette='Set1')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-0fc550218ba4>:18: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=not_fully_paid, x='purpose', palette='Set2')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Atas berupa grafik, bawah berupa angka, berdasarkan materi Slide statistika"
      ],
      "metadata": {
        "id": "pc-bD1kLGQyP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hitung jumlah data yang sepenuhnya dibayar dan tidak sepenuhnya dibayar untuk setiap tujuan\n",
        "fully_paid_counts = df[df['not.fully.paid'] == 0]['purpose'].value_counts()\n",
        "not_fully_paid_counts = df[df['not.fully.paid'] == 1]['purpose'].value_counts()\n",
        "\n",
        "# Gabungkan kedua data menjadi satu dataframe\n",
        "data_summary = pd.DataFrame({\n",
        "    'Tujuan': fully_paid_counts.index,\n",
        "    'Fully Paid': fully_paid_counts.values,\n",
        "    'Not Fully Paid': not_fully_paid_counts.values\n",
        "})\n",
        "\n",
        "print(data_summary)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZN3l3V6oFE34",
        "outputId": "dfbc6874-bb2d-4bfb-c090-ccf3ef389772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               Tujuan  Fully Paid  Not Fully Paid\n",
            "0  debt_consolidation        3354             603\n",
            "1           all_other        1944             387\n",
            "2         credit_card        1116             172\n",
            "3    home_improvement         522             146\n",
            "4      small_business         447             107\n",
            "5      major_purchase         388              69\n",
            "6         educational         274              49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Tambahkan total data untuk setiap tujuan\n",
        "data_summary['Total'] = data_summary['Fully Paid'] + data_summary['Not Fully Paid']\n",
        "\n",
        "# Total jumlah data yang sepenuhnya dibayar\n",
        "total_fully_paid = data_summary['Fully Paid'].sum()\n",
        "total_not_fully_paid = data_summary['Not Fully Paid'].sum()\n",
        "\n",
        "\n",
        "print(\"Total Fully Paid (Dibayar Penuh):\", total_fully_paid)\n",
        "print(\"\")\n",
        "print(\"Total Not Fully Paid (Tidak Dibayar Penuh):\", total_not_fully_paid)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiSuK_GkPLTo",
        "outputId": "b52cef3f-2849-4a0b-cb4b-aac8b89c1a53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Fully Paid (Dibayar Penuh): 8045\n",
            "\n",
            "Total Not Fully Paid (Tidak Dibayar Penuh): 1533\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MATERI PERTEMUAN 4 & 5\n",
        "\n",
        "Setelah mengetahui parameter dibawah ini, saya ingin mengaplikasikan rumus **Atribut tidak saling terkait**\n",
        "\n",
        "dengan notasi:\n",
        "\n",
        "$$ P(X|c_i) = \\prod_{k=1}^{n}P(x_k | C_i)  $$\n",
        "\n",
        "Bila $P(X|c_i)$ dapat diketahui melalui perhitungan di atas, maka klas\n",
        "(label) dari data sampel $X$ adalah klas (label) yang memiliki $P(X|c_i)*P(c_i)$ Maksimum."
      ],
      "metadata": {
        "id": "DUEuP5iCGg2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Hasil Perihtungan: \")\n",
        "print(\"\")\n",
        "print(\"\")\n",
        "#kelas dibayar penuh\n",
        "fully_paid = 8045\n",
        "#kelas tidak dibayar penuh\n",
        "not_fully_paid = 1533\n",
        "\n",
        "#kelas debt_consolidation\n",
        "debt_fully_paid = 3354/fully_paid\n",
        "debt_not_fully_paid = 603 / not_fully_paid\n",
        "print(\"(debt_consolidation | fully_paid):\", debt_fully_paid, \", (debt_consolidation | not_fully_paid):\", debt_not_fully_paid)\n",
        "print(\"\")\n",
        "#kelas all_other\n",
        "all_fully_paid =  1944/fully_paid\n",
        "all_not_fully_paid = 387/not_fully_paid\n",
        "print(\"(all_other | fully_paid):\", all_fully_paid, \", (all_other | not_fully_paid):\", all_not_fully_paid)\n",
        "print(\"\")\n",
        "#kelas credit_card\n",
        "credit_fully_paid = 1116/fully_paid\n",
        "credit_not_fully_paid = 172/not_fully_paid\n",
        "print(\"(credit_card | fully_paid):\", credit_fully_paid, \", (credit_card | not_fully_paid):\", credit_not_fully_paid)\n",
        "print(\"\")\n",
        "#Kelas home_improvement\n",
        "home_fully_paid = 522 /fully_paid\n",
        "home_not_fully_paid = 146/not_fully_paid\n",
        "print(\"(home_improvement | fully_paid):\", home_fully_paid, \", (home_improvement | not_fully_paid):\", home_not_fully_paid)\n",
        "print(\"\")\n",
        "#Kelas small_business\n",
        "sb_fully_paid = 477/fully_paid\n",
        "sb_not_fully_paid = 107/not_fully_paid\n",
        "print(\"(small_business | fully_paid):\", sb_fully_paid, \", (small_business | not_fully_paid):\", sb_not_fully_paid)\n",
        "print(\"\")\n",
        "#Kelas major_purchase\n",
        "mp_fully_paid = 388 / fully_paid\n",
        "mp_not_fully_paid = 69/ not_fully_paid\n",
        "print(\"(major_purchase | fully_paid):\", mp_fully_paid, \", (major_purchase | not_fully_paid):\", mp_not_fully_paid)\n",
        "print(\"\")\n",
        "#kelas educational\n",
        "edu_fully_paid = 274 / fully_paid\n",
        "edu_not_fully_paid = 49 / not_fully_paid\n",
        "print(\"(educational | fully_paid):\", edu_fully_paid, \", (educational | not_fully_paid):\", edu_not_fully_paid)\n",
        "print(\"\")\n",
        "#(X | fully_paid/not_fully_paid)\n",
        "prod_fully_paid = debt_fully_paid * all_fully_paid * credit_fully_paid * home_fully_paid * sb_fully_paid * mp_fully_paid * edu_fully_paid\n",
        "prod_not_fully_paid = debt_not_fully_paid * all_not_fully_paid * credit_not_fully_paid * home_not_fully_paid * sb_not_fully_paid * mp_not_fully_paid * edu_not_fully_paid\n",
        "print(\"\")\n",
        "print(\"Hasil (X | fully_paid): \", prod_fully_paid, \", Sedangkan (X | not_fully_paid):\", prod_not_fully_paid)\n",
        "print(\"\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwFjz2xHOLC3",
        "outputId": "49630ce0-197e-437e-c14a-26259a5dcdbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hasil Perihtungan: \n",
            "\n",
            "\n",
            "(debt_consolidation | fully_paid): 0.41690490988191425 , (debt_consolidation | not_fully_paid): 0.3933463796477495\n",
            "\n",
            "(all_other | fully_paid): 0.24164077066500933 , (all_other | not_fully_paid): 0.25244618395303325\n",
            "\n",
            "(credit_card | fully_paid): 0.1387197016780609 , (credit_card | not_fully_paid): 0.11219830397912589\n",
            "\n",
            "(home_improvement | fully_paid): 0.0648850217526414 , (home_improvement | not_fully_paid): 0.09523809523809523\n",
            "\n",
            "(small_business | fully_paid): 0.059291485394655064 , (small_business | not_fully_paid): 0.06979778212654925\n",
            "\n",
            "(major_purchase | fully_paid): 0.04822871348663766 , (major_purchase | not_fully_paid): 0.04500978473581213\n",
            "\n",
            "(educational | fully_paid): 0.03405842137973897 , (educational | not_fully_paid): 0.0319634703196347\n",
            "\n",
            "\n",
            "Hasil (X | fully_paid):  8.83105002428993e-08 , Sedangkan (X | not_fully_paid): 1.065475442370111e-07\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Total probabilitas X dengan Syarat**\n",
        "\n",
        "Hasil perhitungan"
      ],
      "metadata": {
        "id": "cTHlVeo2swwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#total beli komputer\n",
        "print(\"total • P(X| fully_paid) * P(fully_paid= 1): \", prod_fully_paid * (fully_paid / (fully_paid + not_fully_paid)))\n",
        "print(\"total • P(X| not_fully_paid) * P(fully_paid= 0): \", prod_not_fully_paid * (not_fully_paid / (fully_paid + not_fully_paid)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AXTt6_Frzwl",
        "outputId": "f716c97e-d66e-41ba-d294-8514576a1128"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total • P(X| fully_paid) * P(fully_paid= 1):  7.417602573127217e-08\n",
            "total • P(X| not_fully_paid) * P(fully_paid= 0):  1.7053391659567555e-08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Kembali Pelajaran Datacamp"
      ],
      "metadata": {
        "id": "k63lJrdJhglm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dalam tutorial yang saya dapatkan, saya masih belum mengerti tentang imbalance dataset dikarenakan di website: https://www.datacamp.com/tutorial/naive-bayes-scikit-learn tempat di mana saya mengambil contoh ini, data tsb *imbalance*"
      ],
      "metadata": {
        "id": "4t5cbX82wFVK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita akan mengonversi kolom 'purpose' dari kategori menjadi bilangan bulat menggunakan fungsi **get_dummies** dari pandas.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "GN0cjWyu9BCf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pre_df = pd.get_dummies(df,columns=['purpose'],drop_first=True)\n",
        "pre_df.head()"
      ],
      "metadata": {
        "id": "1ei0J5xDw2aI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "4a0fb72e-db26-4421-8ee4-b4fc750dd994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
              "0              1    0.1189       829.10       11.350407  19.48   737   \n",
              "1              1    0.1071       228.22       11.082143  14.29   707   \n",
              "2              1    0.1357       366.86       10.373491  11.63   682   \n",
              "3              1    0.1008       162.34       11.350407   8.10   712   \n",
              "4              1    0.1426       102.92       11.299732  14.97   667   \n",
              "\n",
              "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
              "0        5639.958333      28854        52.1               0            0   \n",
              "1        2760.000000      33623        76.7               0            0   \n",
              "2        4710.000000       3511        25.6               1            0   \n",
              "3        2699.958333      33667        73.2               1            0   \n",
              "4        4066.000000       4740        39.5               0            1   \n",
              "\n",
              "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
              "0        0               0                False                        True   \n",
              "1        0               0                 True                       False   \n",
              "2        0               0                False                        True   \n",
              "3        0               0                False                        True   \n",
              "4        0               0                 True                       False   \n",
              "\n",
              "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
              "0                False                     False                   False   \n",
              "1                False                     False                   False   \n",
              "2                False                     False                   False   \n",
              "3                False                     False                   False   \n",
              "4                False                     False                   False   \n",
              "\n",
              "   purpose_small_business  \n",
              "0                   False  \n",
              "1                   False  \n",
              "2                   False  \n",
              "3                   False  \n",
              "4                   False  "
            ],
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              "      <th>credit.policy</th>\n",
              "      <th>int.rate</th>\n",
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              "      <th>purpose_educational</th>\n",
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              "      <th>purpose_small_business</th>\n",
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              "      <td>162.34</td>\n",
              "      <td>11.350407</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e328e3ad-6a70-47d8-9d77-62b18aa04e9f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pre_df",
              "summary": "{\n  \"name\": \"pre_df\",\n  \"rows\": 9578,\n  \"fields\": [\n    {\n      \"column\": \"credit.policy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"int.rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026846987213382245,\n        \"min\": 0.06,\n        \"max\": 0.2164,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          0.1367,\n          0.1496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"installment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207.07130149985824,\n        \"min\": 15.67,\n        \"max\": 940.14,\n        \"num_unique_values\": 4788,\n        \"samples\": [\n          326.02,\n          182.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log.annual.inc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6148127513545925,\n        \"min\": 7.547501683,\n        \"max\": 14.52835448,\n        \"num_unique_values\": 1987,\n        \"samples\": [\n          10.22557105,\n          11.19908503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dti\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.883969540539616,\n        \"min\": 0.0,\n        \"max\": 29.96,\n        \"num_unique_values\": 2529,\n        \"samples\": [\n          5.86,\n          21.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 612,\n        \"max\": 827,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          647,\n          717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days.with.cr.line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2496.930376811348,\n        \"min\": 178.9583333,\n        \"max\": 17639.95833,\n        \"num_unique_values\": 2687,\n        \"samples\": [\n          2432.0,\n          8669.958333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revol.bal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33756,\n        \"min\": 0,\n        \"max\": 1207359,\n        \"num_unique_values\": 7869,\n        \"samples\": [\n          11196,\n          462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revol.util\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.014416965641065,\n        \"min\": 0.0,\n        \"max\": 119.0,\n        \"num_unique_values\": 1035,\n        \"samples\": [\n          28.6,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inq.last.6mths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 33,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          33,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delinq.2yrs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub.rec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"not.fully.paid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose_credit_card\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose_debt_consolidation\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose_educational\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose_home_improvement\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose_major_purchase\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purpose_small_business\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah itu, kita akan mendefinisikan variabel fitur (X) dan target (y), dan membagi dataset menjadi set pelatihan dan pengujian."
      ],
      "metadata": {
        "id": "wggi9fNl9jtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = pre_df.drop('not.fully.paid', axis=1)\n",
        "y = pre_df['not.fully.paid']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.33, random_state=125\n",
        ")"
      ],
      "metadata": {
        "id": "Co-_r1rn9gAE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Model building and Training***\n",
        "\n",
        "Pembangunan dan pelatihan model cukup sederhana. Kita akan melatih sebuah model pada dataset pelatihan menggunakan hiperparameter default."
      ],
      "metadata": {
        "id": "EL3tGKhm9yc4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "model = GaussianNB()\n",
        "\n",
        "model.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "wYDTrS2T-GeL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**\n",
        "\n",
        "Kita akan menggunakan akurasi dan skor f1 untuk menentukan kinerja model, dan sepertinya algoritma Gaussian Naive Bayes telah berperforma cukup baik."
      ],
      "metadata": {
        "id": "CPHjDhZ9-McR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    confusion_matrix,\n",
        "    ConfusionMatrixDisplay,\n",
        "    f1_score,\n",
        "    classification_report,\n",
        ")\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "accuray = accuracy_score(y_pred, y_test)\n",
        "f1 = f1_score(y_pred, y_test, average=\"weighted\")\n",
        "\n",
        "print(\"Akurasi:\", accuray)\n",
        "print(\"F1:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfqgG4Yb-Him",
        "outputId": "ade75466-0af5-446e-e85b-d9d151e24da3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi: 0.8206263840556786\n",
            "F1: 0.8686606980013266\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karena sifat data yang tidak seimbang, kita bisa melihat bahwa matriks kebingungan memberikan cerita yang berbeda. Pada target minoritas: *not fully paid*, kita memiliki lebih banyak pengklasifikasian yang salah.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "fqmYvujT_1Y-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [\"Dibayar Penuh\", \"Tidak Dibayar Penuh\"]\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "CYM1o8Dd_yUY",
        "outputId": "db799aa0-6289-4e5c-df90-c13355314cc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Kesimpulan\n",
        "\n",
        "Saya telah memahami Implementasi Naive Bayes Classifier di python, dan saya sudah juga sudah mengkalkulasi rumus **Atribut tidak saling terkait** yang dislide 17.\n",
        "\n",
        "Dari sini kita dapat menyimpulkan kelas **(X | fully_paid)** lebih menonjol dalam kalkulasi kelas atribut. Dari data yang di uji dapat disimpulkan bahwa akan masuk kelas **fully_paid**.\n",
        "\n",
        "\n",
        "**REFERENSI**\n",
        "\n",
        "[1] https://www.datacamp.com/tutorial/naive-bayes-scikit-learn\n",
        "\n",
        "[2] Slide pert 4 & 5 (Hal 17)\n",
        "\n",
        "[3]  ChatGPT\n",
        "\n",
        "[4] https://www.kaggle.com/datasets/itssuru/loan-data\n",
        "\n"
      ],
      "metadata": {
        "id": "L7y-OUNcTnw-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cwuRZBw_MHXO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}