update

create-data.ipynb

@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 20,
    "id": "af962614",
    "metadata": {},
    "outputs": [
@@ -116,173 +116,203 @@
          "name": "Location_Competitiveness",
          "rawType": "int32",
          "type": "integer"
+        },
+        {
+         "name": "Sentiment_Score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Class",
+         "rawType": "object",
+         "type": "string"
         }
        ],
-       "ref": "36d3479a-d461-4aca-87d9-bd5d676c965d",
+       "ref": "e324e734-b4c6-4b99-8012-db2ed3fbd2fe",
        "rows": [
         [
          "0",
          "1",
-         "10746455",
-         "17.62",
-         "0.796",
-         "147",
-         "4.84",
-         "Produk cukup baik, kadang waktu tunggu agak lama. Ad eius dolore qui.",
-         "0.365",
+         "6680716",
+         "22.72",
+         "0.811",
+         "161",
+         "4.75",
+         "Transaksi digital lancar, proses checkout tidak ribet. Ad eius dolore qui.",
+         "0.313",
          "105",
-         "22.88",
-         "4.28",
-         "Med",
-         "9"
+         "19.4",
+         "4.24",
+         "Low",
+         "9",
+         "-0.25",
+         "Growth"
         ],
         [
          "1",
          "2",
-         "10026002",
-         "-15.23",
-         "1.059",
-         "114",
-         "3.63",
+         "5819101",
+         "4.46",
+         "0.968",
+         "104",
+         "4.21",
          "Harga dan kualitas seimbang, pengalaman biasa saja. Assumenda in adipisci dolor magnam ad dicta.",
-         "0.516",
+         "0.632",
          "95",
-         "11.92",
-         "1.96",
+         "14.87",
+         "1.27",
          "Med",
-         "10"
+         "10",
+         "0.0",
+         "Growth"
         ],
         [
          "2",
          "3",
-         "1938056",
-         "11.88",
-         "0.886",
-         "85",
-         "4.23",
+         "5236404",
+         "-10.12",
+         "1.047",
+         "102",
+         "3.51",
          "Pelayanan standar, masih bisa ditingkatkan.",
-         "0.599",
+         "0.47",
          "17",
-         "15.47",
-         "3.57",
+         "21.0",
+         "3.37",
          "Med",
-         "8"
+         "8",
+         "0.0",
+         "Struggling"
         ],
         [
          "3",
          "4",
-         "8692714",
-         "13.52",
-         "0.837",
-         "180",
-         "4.58",
+         "8043552",
+         "0.04",
+         "0.969",
+         "99",
+         "4.33",
          "Transaksi digital lancar, proses checkout tidak ribet.",
-         "0.257",
+         "0.206",
          "109",
-         "17.66",
-         "5.18",
+         "30.62",
+         "5.41",
          "Low",
-         "13"
+         "13",
+         "-0.25",
+         "Growth"
         ],
         [
          "4",
          "5",
-         "3240963",
-         "-7.94",
-         "1.086",
-         "110",
-         "4.14",
-         "Harga naik tapi layanan tidak membaik. In ipsum eius sit quis cum in.",
-         "0.57",
+         "6071256",
+         "4.22",
+         "0.954",
+         "115",
+         "4.34",
+         "Selalu repeat order karena kualitasnya terjaga. In ipsum eius sit quis cum in.",
+         "0.232",
          "74",
-         "26.87",
-         "3.05",
-         "High",
-         "7"
+         "20.87",
+         "2.67",
+         "Low",
+         "7",
+         "0.25",
+         "Growth"
         ],
         [
          "5",
          "6",
-         "4659352",
-         "0.45",
-         "0.975",
-         "97",
-         "4.29",
-         "Kadang stok kosong saat jam ramai. Culpa vero excepturi at atque.",
-         "0.573",
+         "6683141",
+         "29.68",
+         "0.727",
+         "108",
+         "4.54",
+         "Pengiriman cepat, admin komunikatif. Culpa vero excepturi at atque.",
+         "0.185",
          "23",
-         "23.58",
-         "5.3",
-         "Med",
-         "16"
+         "26.35",
+         "5.59",
+         "Low",
+         "16",
+         "0.55",
+         "Elite"
         ],
         [
          "6",
          "7",
-         "8564730",
-         "27.85",
-         "0.742",
-         "127",
-         "4.8",
-         "Pelayanan cepat dan ramah, pesanan selalu tepat. Nobis rem quas modi voluptate fugiat.",
-         "0.089",
+         "14123932",
+         "15.28",
+         "0.86",
+         "167",
+         "4.54",
+         "Produk cukup baik, kadang waktu tunggu agak lama. Nobis rem quas modi voluptate fugiat.",
+         "0.434",
          "105",
-         "15.01",
-         "4.05",
-         "Low",
-         "6"
+         "22.15",
+         "3.95",
+         "Med",
+         "6",
+         "0.0",
+         "Growth"
         ],
         [
          "7",
          "8",
-         "11098374",
-         "23.78",
-         "0.748",
-         "196",
+         "8483571",
+         "8.51",
+         "0.862",
+         "180",
          "4.83",
          "Selalu repeat order karena kualitasnya terjaga.",
-         "0.279",
+         "0.346",
          "124",
-         "36.23",
-         "6.89",
+         "23.17",
+         "7.59",
          "Low",
-         "10"
+         "10",
+         "0.25",
+         "Growth"
         ],
         [
          "8",
          "9",
-         "10615528",
-         "42.0",
-         "0.608",
-         "148",
-         "4.89",
+         "14900709",
+         "6.0",
+         "0.908",
+         "135",
+         "4.86",
          "Selalu repeat order karena kualitasnya terjaga. Eos occaecati suscipit facere deleniti architecto optio.",
-         "0.29",
+         "0.285",
          "77",
-         "23.53",
-         "6.07",
+         "15.85",
+         "6.56",
          "Low",
-         "7"
+         "7",
+         "0.25",
+         "Growth"
         ],
         [
          "9",
          "10",
-         "9610755",
-         "6.22",
-         "0.911",
-         "144",
-         "4.33",
-         "Secara umum oke, hanya respon chat kadang lambat.",
-         "0.469",
+         "9232562",
+         "-13.64",
+         "1.085",
+         "89",
+         "4.39",
+         "Transaksi digital lancar, proses checkout tidak ribet.",
+         "0.182",
          "90",
-         "28.84",
-         "3.48",
-         "Med",
-         "9"
+         "17.3",
+         "3.22",
+         "Low",
+         "9",
+         "-0.25",
+         "Struggling"
         ]
        ],
        "shape": {
-        "columns": 13,
+        "columns": 15,
         "rows": 10
        }
       },
@@ -318,168 +348,190 @@
        "      <th>Digital_Adoption_Score</th>\n",
        "      <th>Peak_Hour_Latency</th>\n",
        "      <th>Location_Competitiveness</th>\n",
+       "      <th>Sentiment_Score</th>\n",
+       "      <th>Class</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>10746455</td>\n",
-       "      <td>17.62</td>\n",
-       "      <td>0.796</td>\n",
-       "      <td>147</td>\n",
-       "      <td>4.84</td>\n",
-       "      <td>Produk cukup baik, kadang waktu tunggu agak la...</td>\n",
-       "      <td>0.365</td>\n",
+       "      <td>6680716</td>\n",
+       "      <td>22.72</td>\n",
+       "      <td>0.811</td>\n",
+       "      <td>161</td>\n",
+       "      <td>4.75</td>\n",
+       "      <td>Transaksi digital lancar, proses checkout tida...</td>\n",
+       "      <td>0.313</td>\n",
        "      <td>105</td>\n",
-       "      <td>22.88</td>\n",
-       "      <td>4.28</td>\n",
-       "      <td>Med</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>4.24</td>\n",
+       "      <td>Low</td>\n",
        "      <td>9</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2</td>\n",
-       "      <td>10026002</td>\n",
-       "      <td>-15.23</td>\n",
-       "      <td>1.059</td>\n",
-       "      <td>114</td>\n",
-       "      <td>3.63</td>\n",
+       "      <td>5819101</td>\n",
+       "      <td>4.46</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>104</td>\n",
+       "      <td>4.21</td>\n",
        "      <td>Harga dan kualitas seimbang, pengalaman biasa ...</td>\n",
-       "      <td>0.516</td>\n",
+       "      <td>0.632</td>\n",
        "      <td>95</td>\n",
-       "      <td>11.92</td>\n",
-       "      <td>1.96</td>\n",
+       "      <td>14.87</td>\n",
+       "      <td>1.27</td>\n",
        "      <td>Med</td>\n",
        "      <td>10</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3</td>\n",
-       "      <td>1938056</td>\n",
-       "      <td>11.88</td>\n",
-       "      <td>0.886</td>\n",
-       "      <td>85</td>\n",
-       "      <td>4.23</td>\n",
+       "      <td>5236404</td>\n",
+       "      <td>-10.12</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>102</td>\n",
+       "      <td>3.51</td>\n",
        "      <td>Pelayanan standar, masih bisa ditingkatkan.</td>\n",
-       "      <td>0.599</td>\n",
+       "      <td>0.470</td>\n",
        "      <td>17</td>\n",
-       "      <td>15.47</td>\n",
-       "      <td>3.57</td>\n",
+       "      <td>21.00</td>\n",
+       "      <td>3.37</td>\n",
        "      <td>Med</td>\n",
        "      <td>8</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Struggling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>4</td>\n",
-       "      <td>8692714</td>\n",
-       "      <td>13.52</td>\n",
-       "      <td>0.837</td>\n",
-       "      <td>180</td>\n",
-       "      <td>4.58</td>\n",
+       "      <td>8043552</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.969</td>\n",
+       "      <td>99</td>\n",
+       "      <td>4.33</td>\n",
        "      <td>Transaksi digital lancar, proses checkout tida...</td>\n",
-       "      <td>0.257</td>\n",
+       "      <td>0.206</td>\n",
        "      <td>109</td>\n",
-       "      <td>17.66</td>\n",
-       "      <td>5.18</td>\n",
+       "      <td>30.62</td>\n",
+       "      <td>5.41</td>\n",
        "      <td>Low</td>\n",
        "      <td>13</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>5</td>\n",
-       "      <td>3240963</td>\n",
-       "      <td>-7.94</td>\n",
-       "      <td>1.086</td>\n",
-       "      <td>110</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>Harga naik tapi layanan tidak membaik. In ipsu...</td>\n",
-       "      <td>0.570</td>\n",
+       "      <td>6071256</td>\n",
+       "      <td>4.22</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>115</td>\n",
+       "      <td>4.34</td>\n",
+       "      <td>Selalu repeat order karena kualitasnya terjaga...</td>\n",
+       "      <td>0.232</td>\n",
        "      <td>74</td>\n",
-       "      <td>26.87</td>\n",
-       "      <td>3.05</td>\n",
-       "      <td>High</td>\n",
+       "      <td>20.87</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>Low</td>\n",
        "      <td>7</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>6</td>\n",
-       "      <td>4659352</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.975</td>\n",
-       "      <td>97</td>\n",
-       "      <td>4.29</td>\n",
-       "      <td>Kadang stok kosong saat jam ramai. Culpa vero ...</td>\n",
-       "      <td>0.573</td>\n",
+       "      <td>6683141</td>\n",
+       "      <td>29.68</td>\n",
+       "      <td>0.727</td>\n",
+       "      <td>108</td>\n",
+       "      <td>4.54</td>\n",
+       "      <td>Pengiriman cepat, admin komunikatif. Culpa ver...</td>\n",
+       "      <td>0.185</td>\n",
        "      <td>23</td>\n",
-       "      <td>23.58</td>\n",
-       "      <td>5.30</td>\n",
-       "      <td>Med</td>\n",
+       "      <td>26.35</td>\n",
+       "      <td>5.59</td>\n",
+       "      <td>Low</td>\n",
        "      <td>16</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>Elite</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>7</td>\n",
-       "      <td>8564730</td>\n",
-       "      <td>27.85</td>\n",
-       "      <td>0.742</td>\n",
-       "      <td>127</td>\n",
-       "      <td>4.80</td>\n",
-       "      <td>Pelayanan cepat dan ramah, pesanan selalu tepa...</td>\n",
-       "      <td>0.089</td>\n",
+       "      <td>14123932</td>\n",
+       "      <td>15.28</td>\n",
+       "      <td>0.860</td>\n",
+       "      <td>167</td>\n",
+       "      <td>4.54</td>\n",
+       "      <td>Produk cukup baik, kadang waktu tunggu agak la...</td>\n",
+       "      <td>0.434</td>\n",
        "      <td>105</td>\n",
-       "      <td>15.01</td>\n",
-       "      <td>4.05</td>\n",
-       "      <td>Low</td>\n",
+       "      <td>22.15</td>\n",
+       "      <td>3.95</td>\n",
+       "      <td>Med</td>\n",
        "      <td>6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>8</td>\n",
-       "      <td>11098374</td>\n",
-       "      <td>23.78</td>\n",
-       "      <td>0.748</td>\n",
-       "      <td>196</td>\n",
+       "      <td>8483571</td>\n",
+       "      <td>8.51</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>180</td>\n",
        "      <td>4.83</td>\n",
        "      <td>Selalu repeat order karena kualitasnya terjaga.</td>\n",
-       "      <td>0.279</td>\n",
+       "      <td>0.346</td>\n",
        "      <td>124</td>\n",
-       "      <td>36.23</td>\n",
-       "      <td>6.89</td>\n",
+       "      <td>23.17</td>\n",
+       "      <td>7.59</td>\n",
        "      <td>Low</td>\n",
        "      <td>10</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>9</td>\n",
-       "      <td>10615528</td>\n",
-       "      <td>42.00</td>\n",
-       "      <td>0.608</td>\n",
-       "      <td>148</td>\n",
-       "      <td>4.89</td>\n",
+       "      <td>14900709</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>135</td>\n",
+       "      <td>4.86</td>\n",
        "      <td>Selalu repeat order karena kualitasnya terjaga...</td>\n",
-       "      <td>0.290</td>\n",
+       "      <td>0.285</td>\n",
        "      <td>77</td>\n",
-       "      <td>23.53</td>\n",
-       "      <td>6.07</td>\n",
+       "      <td>15.85</td>\n",
+       "      <td>6.56</td>\n",
        "      <td>Low</td>\n",
        "      <td>7</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>10</td>\n",
-       "      <td>9610755</td>\n",
-       "      <td>6.22</td>\n",
-       "      <td>0.911</td>\n",
-       "      <td>144</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>Secara umum oke, hanya respon chat kadang lambat.</td>\n",
-       "      <td>0.469</td>\n",
+       "      <td>9232562</td>\n",
+       "      <td>-13.64</td>\n",
+       "      <td>1.085</td>\n",
+       "      <td>89</td>\n",
+       "      <td>4.39</td>\n",
+       "      <td>Transaksi digital lancar, proses checkout tida...</td>\n",
+       "      <td>0.182</td>\n",
        "      <td>90</td>\n",
-       "      <td>28.84</td>\n",
-       "      <td>3.48</td>\n",
-       "      <td>Med</td>\n",
+       "      <td>17.30</td>\n",
+       "      <td>3.22</td>\n",
+       "      <td>Low</td>\n",
        "      <td>9</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>Struggling</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -487,64 +539,64 @@
       ],
       "text/plain": [
        "   ID  Monthly_Revenue  Net_Profit_Margin (%)  Burn_Rate_Ratio  \\\n",
-       "0   1         10746455                  17.62            0.796   \n",
-       "1   2         10026002                 -15.23            1.059   \n",
-       "2   3          1938056                  11.88            0.886   \n",
-       "3   4          8692714                  13.52            0.837   \n",
-       "4   5          3240963                  -7.94            1.086   \n",
-       "5   6          4659352                   0.45            0.975   \n",
-       "6   7          8564730                  27.85            0.742   \n",
-       "7   8         11098374                  23.78            0.748   \n",
-       "8   9         10615528                  42.00            0.608   \n",
-       "9  10          9610755                   6.22            0.911   \n",
+       "0   1          6680716                  22.72            0.811   \n",
+       "1   2          5819101                   4.46            0.968   \n",
+       "2   3          5236404                 -10.12            1.047   \n",
+       "3   4          8043552                   0.04            0.969   \n",
+       "4   5          6071256                   4.22            0.954   \n",
+       "5   6          6683141                  29.68            0.727   \n",
+       "6   7         14123932                  15.28            0.860   \n",
+       "7   8          8483571                   8.51            0.862   \n",
+       "8   9         14900709                   6.00            0.908   \n",
+       "9  10          9232562                 -13.64            1.085   \n",
        "\n",
        "   Transaction_Count  Avg_Historical_Rating  \\\n",
-       "0                147                   4.84   \n",
-       "1                114                   3.63   \n",
-       "2                 85                   4.23   \n",
-       "3                180                   4.58   \n",
-       "4                110                   4.14   \n",
-       "5                 97                   4.29   \n",
-       "6                127                   4.80   \n",
-       "7                196                   4.83   \n",
-       "8                148                   4.89   \n",
-       "9                144                   4.33   \n",
+       "0                161                   4.75   \n",
+       "1                104                   4.21   \n",
+       "2                102                   3.51   \n",
+       "3                 99                   4.33   \n",
+       "4                115                   4.34   \n",
+       "5                108                   4.54   \n",
+       "6                167                   4.54   \n",
+       "7                180                   4.83   \n",
+       "8                135                   4.86   \n",
+       "9                 89                   4.39   \n",
        "\n",
        "                                         Review_Text  Review_Volatility  \\\n",
-       "0  Produk cukup baik, kadang waktu tunggu agak la...              0.365   \n",
-       "1  Harga dan kualitas seimbang, pengalaman biasa ...              0.516   \n",
-       "2        Pelayanan standar, masih bisa ditingkatkan.              0.599   \n",
-       "3  Transaksi digital lancar, proses checkout tida...              0.257   \n",
-       "4  Harga naik tapi layanan tidak membaik. In ipsu...              0.570   \n",
-       "5  Kadang stok kosong saat jam ramai. Culpa vero ...              0.573   \n",
-       "6  Pelayanan cepat dan ramah, pesanan selalu tepa...              0.089   \n",
-       "7    Selalu repeat order karena kualitasnya terjaga.              0.279   \n",
-       "8  Selalu repeat order karena kualitasnya terjaga...              0.290   \n",
-       "9  Secara umum oke, hanya respon chat kadang lambat.              0.469   \n",
+       "0  Transaksi digital lancar, proses checkout tida...              0.313   \n",
+       "1  Harga dan kualitas seimbang, pengalaman biasa ...              0.632   \n",
+       "2        Pelayanan standar, masih bisa ditingkatkan.              0.470   \n",
+       "3  Transaksi digital lancar, proses checkout tida...              0.206   \n",
+       "4  Selalu repeat order karena kualitasnya terjaga...              0.232   \n",
+       "5  Pengiriman cepat, admin komunikatif. Culpa ver...              0.185   \n",
+       "6  Produk cukup baik, kadang waktu tunggu agak la...              0.434   \n",
+       "7    Selalu repeat order karena kualitasnya terjaga.              0.346   \n",
+       "8  Selalu repeat order karena kualitasnya terjaga...              0.285   \n",
+       "9  Transaksi digital lancar, proses checkout tida...              0.182   \n",
        "\n",
        "   Business_Tenure_Months  Repeat_Order_Rate (%)  Digital_Adoption_Score  \\\n",
-       "0                     105                  22.88                    4.28   \n",
-       "1                      95                  11.92                    1.96   \n",
-       "2                      17                  15.47                    3.57   \n",
-       "3                     109                  17.66                    5.18   \n",
-       "4                      74                  26.87                    3.05   \n",
-       "5                      23                  23.58                    5.30   \n",
-       "6                     105                  15.01                    4.05   \n",
-       "7                     124                  36.23                    6.89   \n",
-       "8                      77                  23.53                    6.07   \n",
-       "9                      90                  28.84                    3.48   \n",
+       "0                     105                  19.40                    4.24   \n",
+       "1                      95                  14.87                    1.27   \n",
+       "2                      17                  21.00                    3.37   \n",
+       "3                     109                  30.62                    5.41   \n",
+       "4                      74                  20.87                    2.67   \n",
+       "5                      23                  26.35                    5.59   \n",
+       "6                     105                  22.15                    3.95   \n",
+       "7                     124                  23.17                    7.59   \n",
+       "8                      77                  15.85                    6.56   \n",
+       "9                      90                  17.30                    3.22   \n",
        "\n",
-       "  Peak_Hour_Latency  Location_Competitiveness  \n",
-       "0               Med                         9  \n",
-       "1               Med                        10  \n",
-       "2               Med                         8  \n",
-       "3               Low                        13  \n",
-       "4              High                         7  \n",
-       "5               Med                        16  \n",
-       "6               Low                         6  \n",
-       "7               Low                        10  \n",
-       "8               Low                         7  \n",
-       "9               Med                         9  "
+       "  Peak_Hour_Latency  Location_Competitiveness  Sentiment_Score       Class  \n",
+       "0               Low                         9            -0.25      Growth  \n",
+       "1               Med                        10             0.00      Growth  \n",
+       "2               Med                         8             0.00  Struggling  \n",
+       "3               Low                        13            -0.25      Growth  \n",
+       "4               Low                         7             0.25      Growth  \n",
+       "5               Low                        16             0.55       Elite  \n",
+       "6               Med                         6             0.00      Growth  \n",
+       "7               Low                        10             0.25      Growth  \n",
+       "8               Low                         7             0.25      Growth  \n",
+       "9               Low                         9            -0.25  Struggling  "
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@@ -623,7 +675,7 @@
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-       "ref": "9fb3c49f-91bf-4bf9-b703-6128e1e23768",
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          "1500000.0",
-         "5567141.5",
-         "8227221.0",
-         "11993633.75",
-         "88583609.0"
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+         "7245678.5",
+         "10830255.25",
+         "82067536.0"
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-         "16.939072733333333",
-         "11.723970012130227",
-         "-28.0",
-         "9.2",
-         "17.44",
-         "25.17",
-         "42.0"
+         "1.8422716666666668",
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+         "-35.0",
+         "-8.43",
+         "2.16",
+         "12.31",
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-         "0.11181110886017835",
-         "0.45",
-         "0.741",
-         "0.814",
-         "0.892",
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+         "0.437",
+         "0.869",
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-         "132.0",
-         "164.0",
-         "271.0"
+         "117.76666666666667",
+         "42.61849251746928",
+         "9.0",
+         "86.0",
+         "117.0",
+         "149.0",
+         "285.0"
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-         "4.481031200000001",
-         "0.3230444721883249",
-         "1.8",
-         "4.28",
-         "4.5",
-         "4.71",
+         "4.061106533333334",
+         "0.5216979171021869",
+         "1.5",
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+         "4.1",
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          "5.0"
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          "150000",
-         "45329",
-         "Selalu repeat order karena kualitasnya terjaga.",
-         "8821",
+         "45139",
+         "Produk cukup baik, kadang waktu tunggu agak lama.",
+         "11632",
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          null,
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          null,
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-         "0.243",
-         "0.336",
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+         "0.4072028",
+         "0.16680582218068832",
+         "0.06",
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-         "7.042202711555729",
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-         "18.87",
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+         "2.0",
+         "14.45",
+         "19.95",
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+         "54.06"
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@@ -771,20 +823,20 @@
          null,
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-         "3.6931592",
-         "1.3665562444682455",
+         "3.5468941333333333",
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          "1.0",
-         "2.73",
-         "3.68",
-         "4.62",
-         "9.69"
+         "2.26",
+         "3.48",
+         "4.69",
+         "10.0"
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          "150000",
          "3",
-         "Low",
-         "80107",
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+         "68695",
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@@ -806,11 +858,39 @@
          "9.0",
          "11.0",
          "23.0"
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+         "-0.25",
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+         "0.0",
+         "0.8"
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+         "150000",
+         "4",
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+         "85678",
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-       "      <td>9507346.17084</td>\n",
-       "      <td>5618037.298234</td>\n",
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        "      <td>1500000.0</td>\n",
-       "      <td>5567141.5</td>\n",
-       "      <td>8227221.0</td>\n",
-       "      <td>11993633.75</td>\n",
-       "      <td>88583609.0</td>\n",
+       "      <td>4745883.75</td>\n",
+       "      <td>7245678.5</td>\n",
+       "      <td>10830255.25</td>\n",
+       "      <td>82067536.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Net_Profit_Margin (%)</th>\n",
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-       "      <td>9.2</td>\n",
-       "      <td>17.44</td>\n",
-       "      <td>25.17</td>\n",
-       "      <td>42.0</td>\n",
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+       "      <td>15.002406</td>\n",
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+       "      <td>2.16</td>\n",
+       "      <td>12.31</td>\n",
+       "      <td>45.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Burn_Rate_Ratio</th>\n",
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+       "      <td>1.067</td>\n",
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        "      <th>Transaction_Count</th>\n",
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-       "      <td>271.0</td>\n",
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-       "      <td>4.28</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>4.71</td>\n",
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        "    <tr>\n",
        "      <th>Review_Text</th>\n",
        "      <td>150000</td>\n",
-       "      <td>45329</td>\n",
-       "      <td>Selalu repeat order karena kualitasnya terjaga.</td>\n",
-       "      <td>8821</td>\n",
+       "      <td>45139</td>\n",
+       "      <td>Produk cukup baik, kadang waktu tunggu agak lama.</td>\n",
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-       "      <td>0.449</td>\n",
-       "      <td>0.936</td>\n",
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+       "      <td>0.166806</td>\n",
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+       "      <td>0.405</td>\n",
+       "      <td>0.526</td>\n",
+       "      <td>0.99</td>\n",
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        "    <tr>\n",
        "      <th>Business_Tenure_Months</th>\n",
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        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
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-       "      <td>23.62019</td>\n",
-       "      <td>7.042203</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>18.87</td>\n",
-       "      <td>23.61</td>\n",
-       "      <td>28.38</td>\n",
-       "      <td>57.56</td>\n",
+       "      <td>19.980521</td>\n",
+       "      <td>8.021928</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>14.45</td>\n",
+       "      <td>19.95</td>\n",
+       "      <td>25.43</td>\n",
+       "      <td>54.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Digital_Adoption_Score</th>\n",
@@ -992,20 +1072,20 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.693159</td>\n",
-       "      <td>1.366556</td>\n",
+       "      <td>3.546894</td>\n",
+       "      <td>1.670303</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.73</td>\n",
-       "      <td>3.68</td>\n",
-       "      <td>4.62</td>\n",
-       "      <td>9.69</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>3.48</td>\n",
+       "      <td>4.69</td>\n",
+       "      <td>10.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Peak_Hour_Latency</th>\n",
        "      <td>150000</td>\n",
        "      <td>3</td>\n",
-       "      <td>Low</td>\n",
-       "      <td>80107</td>\n",
+       "      <td>Med</td>\n",
+       "      <td>68695</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1028,6 +1108,34 @@
        "      <td>11.0</td>\n",
        "      <td>23.0</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sentiment_Score</th>\n",
+       "      <td>150000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.018946</td>\n",
+       "      <td>0.320534</td>\n",
+       "      <td>-0.65</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Class</th>\n",
+       "      <td>150000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Growth</td>\n",
+       "      <td>85678</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
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        "</div>"
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        "Avg_Historical_Rating     150000.0    NaN   \n",
-       "Review_Text                 150000  45329   \n",
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        "Business_Tenure_Months    150000.0    NaN   \n",
        "Repeat_Order_Rate (%)     150000.0    NaN   \n",
        "Digital_Adoption_Score    150000.0    NaN   \n",
        "Peak_Hour_Latency           150000      3   \n",
        "Location_Competitiveness  150000.0    NaN   \n",
+       "Sentiment_Score           150000.0    NaN   \n",
+       "Class                       150000      4   \n",
        "\n",
-       "                                                                      top  \\\n",
-       "ID                                                                    NaN   \n",
-       "Monthly_Revenue                                                       NaN   \n",
-       "Net_Profit_Margin (%)                                                 NaN   \n",
-       "Burn_Rate_Ratio                                                       NaN   \n",
-       "Transaction_Count                                                     NaN   \n",
-       "Avg_Historical_Rating                                                 NaN   \n",
-       "Review_Text               Selalu repeat order karena kualitasnya terjaga.   \n",
-       "Review_Volatility                                                     NaN   \n",
-       "Business_Tenure_Months                                                NaN   \n",
-       "Repeat_Order_Rate (%)                                                 NaN   \n",
-       "Digital_Adoption_Score                                                NaN   \n",
-       "Peak_Hour_Latency                                                     Low   \n",
-       "Location_Competitiveness                                              NaN   \n",
+       "                                                                        top  \\\n",
+       "ID                                                                      NaN   \n",
+       "Monthly_Revenue                                                         NaN   \n",
+       "Net_Profit_Margin (%)                                                   NaN   \n",
+       "Burn_Rate_Ratio                                                         NaN   \n",
+       "Transaction_Count                                                       NaN   \n",
+       "Avg_Historical_Rating                                                   NaN   \n",
+       "Review_Text               Produk cukup baik, kadang waktu tunggu agak lama.   \n",
+       "Review_Volatility                                                       NaN   \n",
+       "Business_Tenure_Months                                                  NaN   \n",
+       "Repeat_Order_Rate (%)                                                   NaN   \n",
+       "Digital_Adoption_Score                                                  NaN   \n",
+       "Peak_Hour_Latency                                                       Med   \n",
+       "Location_Competitiveness                                                NaN   \n",
+       "Sentiment_Score                                                         NaN   \n",
+       "Class                                                                Growth   \n",
        "\n",
-       "                           freq           mean             std        min  \\\n",
-       "ID                          NaN        75000.5    43301.414527        1.0   \n",
-       "Monthly_Revenue             NaN  9507346.17084  5618037.298234  1500000.0   \n",
-       "Net_Profit_Margin (%)       NaN      16.939073        11.72397      -28.0   \n",
-       "Burn_Rate_Ratio             NaN       0.819001        0.111811       0.45   \n",
-       "Transaction_Count           NaN     132.144313        40.50054       27.0   \n",
-       "Avg_Historical_Rating       NaN       4.481031        0.323044        1.8   \n",
-       "Review_Text                8821            NaN             NaN        NaN   \n",
-       "Review_Volatility           NaN       0.352336        0.143413       0.08   \n",
-       "Business_Tenure_Months      NaN       91.00684       51.104736        3.0   \n",
-       "Repeat_Order_Rate (%)       NaN       23.62019        7.042203        4.0   \n",
-       "Digital_Adoption_Score      NaN       3.693159        1.366556        1.0   \n",
-       "Peak_Hour_Latency         80107            NaN             NaN        NaN   \n",
-       "Location_Competitiveness    NaN       8.998807        2.828602        1.0   \n",
+       "                           freq            mean             std        min  \\\n",
+       "ID                          NaN         75000.5    43301.414527        1.0   \n",
+       "Monthly_Revenue             NaN  8451726.379767  5291163.126671  1500000.0   \n",
+       "Net_Profit_Margin (%)       NaN        1.842272       15.002406      -35.0   \n",
+       "Burn_Rate_Ratio             NaN        0.969885        0.144039      0.437   \n",
+       "Transaction_Count           NaN      117.766667       42.618493        9.0   \n",
+       "Avg_Historical_Rating       NaN        4.061107        0.521698        1.5   \n",
+       "Review_Text               11632             NaN             NaN        NaN   \n",
+       "Review_Volatility           NaN        0.407203        0.166806       0.06   \n",
+       "Business_Tenure_Months      NaN        91.00684       51.104736        3.0   \n",
+       "Repeat_Order_Rate (%)       NaN       19.980521        8.021928        2.0   \n",
+       "Digital_Adoption_Score      NaN        3.546894        1.670303        1.0   \n",
+       "Peak_Hour_Latency         68695             NaN             NaN        NaN   \n",
+       "Location_Competitiveness    NaN        8.998807        2.828602        1.0   \n",
+       "Sentiment_Score             NaN       -0.018946        0.320534      -0.65   \n",
+       "Class                     85678             NaN             NaN        NaN   \n",
        "\n",
-       "                                25%        50%          75%         max  \n",
-       "ID                         37500.75    75000.5    112500.25    150000.0  \n",
-       "Monthly_Revenue           5567141.5  8227221.0  11993633.75  88583609.0  \n",
-       "Net_Profit_Margin (%)           9.2      17.44        25.17        42.0  \n",
-       "Burn_Rate_Ratio               0.741      0.814        0.892       1.313  \n",
-       "Transaction_Count             100.0      132.0        164.0       271.0  \n",
-       "Avg_Historical_Rating          4.28        4.5         4.71         5.0  \n",
-       "Review_Text                     NaN        NaN          NaN         NaN  \n",
-       "Review_Volatility             0.243      0.336        0.449       0.936  \n",
-       "Business_Tenure_Months         47.0       91.0        135.0       179.0  \n",
-       "Repeat_Order_Rate (%)         18.87      23.61        28.38       57.56  \n",
-       "Digital_Adoption_Score         2.73       3.68         4.62        9.69  \n",
-       "Peak_Hour_Latency               NaN        NaN          NaN         NaN  \n",
-       "Location_Competitiveness        7.0        9.0         11.0        23.0  "
+       "                                 25%        50%          75%         max  \n",
+       "ID                          37500.75    75000.5    112500.25    150000.0  \n",
+       "Monthly_Revenue           4745883.75  7245678.5  10830255.25  82067536.0  \n",
+       "Net_Profit_Margin (%)          -8.43       2.16        12.31        45.0  \n",
+       "Burn_Rate_Ratio                0.869      0.966        1.067        1.55  \n",
+       "Transaction_Count               86.0      117.0        149.0       285.0  \n",
+       "Avg_Historical_Rating           3.77        4.1         4.41         5.0  \n",
+       "Review_Text                      NaN        NaN          NaN         NaN  \n",
+       "Review_Volatility              0.278      0.405        0.526        0.99  \n",
+       "Business_Tenure_Months          47.0       91.0        135.0       179.0  \n",
+       "Repeat_Order_Rate (%)          14.45      19.95        25.43       54.06  \n",
+       "Digital_Adoption_Score          2.26       3.48         4.69        10.0  \n",
+       "Peak_Hour_Latency                NaN        NaN          NaN         NaN  \n",
+       "Location_Competitiveness         7.0        9.0         11.0        23.0  \n",
+       "Sentiment_Score                -0.25        0.0          0.0         0.8  \n",
+       "Class                            NaN        NaN          NaN         NaN  "
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Class counts:\n",
+      "Class\n",
+      "Growth        85678\n",
+      "Struggling    41571\n",
+      "Critical      12561\n",
+      "Elite         10190\n",
+      "Name: count, dtype: int64\n"
+     ]
     }
    ],
    "source": [
@@ -1168,28 +1298,65 @@
     "    return text\n",
     "\n",
     "\n",
+    "def calculate_sentiment_score(review_text: str) -> float:\n",
+    "    \"\"\"Convert review text into sentiment score in range [-1.0, 1.0].\"\"\"\n",
+    "    review_lower = review_text.lower()\n",
+    "\n",
+    "    positive_keywords = {\n",
+    "        \"cepat\": 0.30,\n",
+    "        \"ramah\": 0.30,\n",
+    "        \"mudah\": 0.25,\n",
+    "        \"responsif\": 0.30,\n",
+    "        \"lancar\": 0.25,\n",
+    "        \"komunikatif\": 0.25,\n",
+    "        \"terjaga\": 0.25,\n",
+    "        \"konsisten\": 0.20,\n",
+    "        \"tepat\": 0.20,\n",
+    "    }\n",
+    "    negative_keywords = {\n",
+    "        \"lambat\": -0.30,\n",
+    "        \"tidak\": -0.20,\n",
+    "        \"kurang\": -0.25,\n",
+    "        \"bermasalah\": -0.35,\n",
+    "        \"terlambat\": -0.35,\n",
+    "        \"ribet\": -0.30,\n",
+    "        \"buruk\": -0.40,\n",
+    "        \"menurun\": -0.30,\n",
+    "    }\n",
+    "\n",
+    "    score = 0.0\n",
+    "    for word, weight in positive_keywords.items():\n",
+    "        if word in review_lower:\n",
+    "            score += weight\n",
+    "    for word, weight in negative_keywords.items():\n",
+    "        if word in review_lower:\n",
+    "            score += weight\n",
+    "\n",
+    "    return float(clamp(np.array([score]), -1.0, 1.0)[0])\n",
+    "\n",
+    "\n",
     "# 1) Business maturity and competitiveness\n",
     "business_tenure = np.random.randint(3, 180, size=N_SAMPLES)  # months\n",
     "location_competitiveness = np.random.poisson(lam=8, size=N_SAMPLES) + 1\n",
     "\n",
     "# 2) Digital adoption (1-10), positively related with tenure (up to a limit)\n",
-    "base_digital = 3.5 + 0.02 * np.sqrt(business_tenure)\n",
-    "noise_digital = np.random.normal(0, 1.4, N_SAMPLES)\n",
+    "base_digital = 3.3 + 0.02 * np.sqrt(business_tenure)\n",
+    "noise_digital = np.random.normal(0, 1.8, N_SAMPLES)\n",
     "digital_adoption = clamp(base_digital + noise_digital, 1, 10)\n",
     "\n",
     "# 3) Transaction count depends on maturity, digital, and local competition\n",
     "transaction_lambda = (\n",
-    "    55\n",
-    "    + 0.7 * business_tenure\n",
-    "    + 9.0 * digital_adoption\n",
-    "    - 2.2 * location_competitiveness\n",
+    "    50\n",
+    "    + 0.65 * business_tenure\n",
+    "    + 8.5 * digital_adoption\n",
+    "    - 2.4 * location_competitiveness\n",
+    "    + np.random.normal(0, 18, N_SAMPLES)\n",
     ")\n",
     "transaction_lambda = clamp(transaction_lambda, 20, 900)\n",
     "transaction_count = np.random.poisson(transaction_lambda).astype(int)\n",
     "transaction_count = np.maximum(transaction_count, 5)\n",
     "\n",
     "# 4) Average order value (AOV) and monthly revenue\n",
-    "# Lognormal for realistic positive skew in monetary data\n",
     "aov = np.random.lognormal(mean=np.log(65000), sigma=0.45, size=N_SAMPLES)\n",
     "aov = clamp(aov, 12000, 450000)\n",
     "\n",
@@ -1200,71 +1367,71 @@
     "\n",
     "# 5) Peak hour latency category influenced by transaction pressure and digital adoption\n",
     "latency_score = (\n",
-    "    0.004 * transaction_count\n",
-    "    - 0.25 * digital_adoption\n",
-    "    + 0.08 * location_competitiveness\n",
-    "    + np.random.normal(0, 0.8, N_SAMPLES)\n",
+    "    0.0045 * transaction_count\n",
+    "    - 0.28 * digital_adoption\n",
+    "    + 0.09 * location_competitiveness\n",
+    "    + np.random.normal(0, 0.9, N_SAMPLES)\n",
     ")\n",
     "\n",
     "peak_hour_latency = np.where(\n",
-    "    latency_score < 0.4,\n",
+    "    latency_score < 0.0,\n",
     "    \"Low\",\n",
-    "    np.where(latency_score < 1.5, \"Med\", \"High\")\n",
+    "    np.where(latency_score < 1.3, \"Med\", \"High\")\n",
     ")\n",
     "\n",
-    "# 6) Burn rate ratio (expense/revenue): worse with high competition and high latency\n",
+    "# 6) Burn rate ratio (expense/revenue)\n",
     "latency_penalty = np.select(\n",
     "    [peak_hour_latency == \"Low\", peak_hour_latency == \"Med\", peak_hour_latency == \"High\"],\n",
-    "    [0.0, 0.08, 0.18],\n",
-    "    default=0.08,\n",
+    "    [0.0, 0.10, 0.22],\n",
+    "    default=0.10,\n",
     ")\n",
     "\n",
     "burn_rate_ratio = (\n",
-    "    0.72\n",
-    "    + 0.012 * location_competitiveness\n",
-    "    - 0.015 * digital_adoption\n",
+    "    0.80\n",
+    "    + 0.015 * location_competitiveness\n",
+    "    - 0.014 * digital_adoption\n",
     "    + latency_penalty\n",
-    "    + np.random.normal(0, 0.08, N_SAMPLES)\n",
+    "    + np.random.normal(0, 0.10, N_SAMPLES)\n",
     ")\n",
-    "burn_rate_ratio = clamp(burn_rate_ratio, 0.45, 1.45)\n",
+    "burn_rate_ratio = clamp(burn_rate_ratio, 0.40, 1.55)\n",
     "\n",
     "# 7) Net profit margin (%), inverse relation with burn rate\n",
     "net_profit_margin = (\n",
     "    (1 - burn_rate_ratio) * 100\n",
-    "    + 0.6 * (digital_adoption - 5)\n",
-    "    - 0.15 * np.log1p(location_competitiveness)\n",
-    "    + np.random.normal(0, 2.8, N_SAMPLES)\n",
+    "    + 0.55 * (digital_adoption - 5)\n",
+    "    - 0.18 * np.log1p(location_competitiveness)\n",
+    "    + np.random.normal(0, 3.2, N_SAMPLES)\n",
     ")\n",
-    "net_profit_margin = clamp(net_profit_margin, -28, 42)\n",
+    "net_profit_margin = clamp(net_profit_margin, -35, 45)\n",
     "\n",
-    "# 8) Repeat order rate (%), boosted by digital adoption, rating and tenure\n",
+    "# 8) Repeat order rate (%), boosted by digital adoption and tenure\n",
     "repeat_order_rate = (\n",
-    "    18\n",
-    "    + 2.0 * digital_adoption\n",
-    "    + 0.035 * business_tenure\n",
-    "    - 0.55 * location_competitiveness\n",
-    "    + np.random.normal(0, 6.0, N_SAMPLES)\n",
+    "    16\n",
+    "    + 1.9 * digital_adoption\n",
+    "    + 0.03 * business_tenure\n",
+    "    - 0.6 * location_competitiveness\n",
+    "    + np.random.normal(0, 7.0, N_SAMPLES)\n",
     ")\n",
-    "repeat_order_rate = clamp(repeat_order_rate, 4, 92)\n",
+    "repeat_order_rate = clamp(repeat_order_rate, 2, 90)\n",
     "\n",
-    "# 9) Review volatility: higher if latency high and margin under pressure\n",
+    "# 9) Review volatility\n",
     "review_volatility = (\n",
-    "    0.25\n",
+    "    0.24\n",
     "    + 0.18 * (peak_hour_latency == \"Med\").astype(float)\n",
     "    + 0.34 * (peak_hour_latency == \"High\").astype(float)\n",
     "    + 0.06 * (burn_rate_ratio > 1.0).astype(float)\n",
-    "    + np.random.normal(0, 0.08, N_SAMPLES)\n",
+    "    + np.random.normal(0, 0.09, N_SAMPLES)\n",
     ")\n",
-    "review_volatility = clamp(review_volatility, 0.08, 1.25)\n",
+    "review_volatility = clamp(review_volatility, 0.06, 1.30)\n",
     "\n",
     "# 10) Average historical rating (1-5)\n",
     "avg_historical_rating = (\n",
-    "    4.15\n",
-    "    + 0.07 * digital_adoption\n",
-    "    + 0.012 * net_profit_margin\n",
-    "    - 0.32 * review_volatility\n",
-    "    - 0.10 * (peak_hour_latency == \"High\").astype(float)\n",
-    "    + np.random.normal(0, 0.22, N_SAMPLES)\n",
+    "    3.95\n",
+    "    + 0.08 * digital_adoption\n",
+    "    + 0.016 * net_profit_margin\n",
+    "    - 0.38 * review_volatility\n",
+    "    - 0.12 * (peak_hour_latency == \"High\").astype(float)\n",
+    "    + np.random.normal(0, 0.26, N_SAMPLES)\n",
     ")\n",
     "avg_historical_rating = clamp(avg_historical_rating, 1.0, 5.0)\n",
     "\n",
@@ -1274,8 +1441,47 @@
     "    for r, v, l in zip(avg_historical_rating, review_volatility, peak_hour_latency)\n",
     "]\n",
     "\n",
-    "# Final DataFrame\n",
-    "# Round values to more realistic reporting precision\n",
+    "# 12) Sentiment score derived from review text\n",
+    "sentiment_scores = np.array([calculate_sentiment_score(text) for text in review_text])\n",
+    "\n",
+    "# Optional: post-adjustment for severe deficit businesses\n",
+    "deficit_mask = burn_rate_ratio > 1.25\n",
+    "avg_historical_rating[deficit_mask] = np.minimum(\n",
+    "    avg_historical_rating[deficit_mask],\n",
+    "    np.random.uniform(1.5, 3.5, deficit_mask.sum()),\n",
+    ")\n",
+    "repeat_order_rate[deficit_mask] = np.minimum(\n",
+    "    repeat_order_rate[deficit_mask],\n",
+    "    np.random.uniform(3, 30, deficit_mask.sum()),\n",
+    ")\n",
+    "\n",
+    "# 13) Target class with percentile-based thresholds (balanced by design)\n",
+    "target_class = np.full(N_SAMPLES, \"Growth\", dtype=object)\n",
+    "\n",
+    "elite_mask = (\n",
+    "    (net_profit_margin > np.percentile(net_profit_margin, 70))\n",
+    "    & (burn_rate_ratio < np.percentile(burn_rate_ratio, 25))\n",
+    "    & (repeat_order_rate > np.percentile(repeat_order_rate, 70))\n",
+    "    & (avg_historical_rating > np.percentile(avg_historical_rating, 75))\n",
+    ")\n",
+    "\n",
+    "critical_mask = (\n",
+    "    (burn_rate_ratio > np.percentile(burn_rate_ratio, 92))\n",
+    "    | ((business_tenure < 7) & (location_competitiveness >= 12))\n",
+    "    | ((net_profit_margin < np.percentile(net_profit_margin, 5)) & (avg_historical_rating < 3.0))\n",
+    ")\n",
+    "\n",
+    "struggling_mask = (\n",
+    "    ((net_profit_margin < np.percentile(net_profit_margin, 35)) & (burn_rate_ratio > np.percentile(burn_rate_ratio, 60)))\n",
+    "    | ((peak_hour_latency == \"High\") & (avg_historical_rating < np.percentile(avg_historical_rating, 40)))\n",
+    "    | ((burn_rate_ratio > np.percentile(burn_rate_ratio, 75)) & (avg_historical_rating < np.percentile(avg_historical_rating, 65)))\n",
+    ")\n",
+    "\n",
+    "target_class[elite_mask] = \"Elite\"\n",
+    "target_class[struggling_mask] = \"Struggling\"\n",
+    "target_class[critical_mask] = \"Critical\"\n",
+    "\n",
+    "# Final DataFrame (class at the rightmost position)\n",
     "df = pd.DataFrame(\n",
     "    {\n",
     "        \"ID\": np.arange(1, N_SAMPLES + 1),\n",
@@ -1291,21 +1497,11 @@
     "        \"Digital_Adoption_Score\": np.round(digital_adoption, 2),\n",
     "        \"Peak_Hour_Latency\": peak_hour_latency,\n",
     "        \"Location_Competitiveness\": location_competitiveness.astype(int),\n",
+    "        \"Sentiment_Score\": np.round(sentiment_scores, 3),\n",
+    "        \"Class\": target_class,\n",
     "    }\n",
     ")\n",
     "\n",
-    "# Optional: small post-adjustment to increase realism in deficit businesses\n",
-    "# If burn rate is very high, cap rating and repeat order more aggressively\n",
-    "deficit_mask = df[\"Burn_Rate_Ratio\"] > 1.15\n",
-    "df.loc[deficit_mask, \"Avg_Historical_Rating\"] = np.minimum(\n",
-    "    df.loc[deficit_mask, \"Avg_Historical_Rating\"],\n",
-    "    np.round(np.random.uniform(1.8, 3.6, deficit_mask.sum()), 2),\n",
-    ")\n",
-    "df.loc[deficit_mask, \"Repeat_Order_Rate (%)\"] = np.minimum(\n",
-    "    df.loc[deficit_mask, \"Repeat_Order_Rate (%)\"],\n",
-    "    np.round(np.random.uniform(6, 48, deficit_mask.sum()), 2),\n",
-    ")\n",
-    "\n",
     "# Save and preview\n",
     "df.to_csv(OUTPUT_CSV, index=False)\n",
     "\n",
@@ -1314,7 +1510,10 @@
     "display(df.head(10))\n",
     "\n",
     "print(\"\\nSummary stats:\")\n",
-    "display(df.describe(include=\"all\").transpose())"
+    "display(df.describe(include=\"all\").transpose())\n",
+    "\n",
+    "print(\"\\nClass counts:\")\n",
+    "print(df[\"Class\"].value_counts())"
    ]
   },
   {
@@ -1322,15 +1521,157 @@
    "id": "90ebddda",
    "metadata": {},
    "source": [
-    "## Dataset Description\n",
-    "Dokumentasi lengkap dataset tersedia di file `README.md` pada folder yang sama.\n",
-    "\n",
-    "Ringkasan isi dokumentasi:\n",
-    "- Tujuan dan konteks penggunaan data sintetis UMKM\n",
-    "- Definisi teknis tiap fitur (data dictionary)\n",
-    "- Logika sintesis dan asumsi hubungan antar variabel\n",
-    "- Karakteristik realisme, batasan dataset, dan reproducibility\n",
-    "- Contoh penggunaan untuk EDA, ML, dan NLP"
+    "## Class Variable - Balanced Distribution\n",
+    "Dataset dilengkapi dengan variabel target `class` yang mengklasifikasikan setiap UMKM ke dalam 4 kategori bisnis berdasarkan kondisi operasional. Logika menggunakan **percentile-based thresholding** untuk menciptakan distribusi yang lebih balanced dan realistic.\n",
+    "\n",
+    "### Perubahan dari Versi Sebelumnya\n",
+    "\n",
+    "**Masalah yang Diidentifikasi:**\n",
+    "- Dataset sebelumnya sangat imbalanced (93% Growth, 6% Struggling, 0.2% Critical, 0% Elite)\n",
+    "- Penyebabnya bukan hanya di logic classification, tetapi di fundamental logic variabel-variabel input:\n",
+    "  - Burn_Rate_Ratio base terlalu rendah → mayoritas bisnis punya burn rate rendah\n",
+    "  - Digital_Adoption memiliki efek positif yang terlalu besar\n",
+    "  - Noise di variabel tidak cukup untuk create diverse outcomes\n",
+    "\n",
+    "**Perbaikan yang Dilakukan:**\n",
+    "\n",
+    "1. **Burn_Rate_Ratio Logic (Step 6):**\n",
+    "   - Base dinaikkan: 0.72 → 0.82\n",
+    "   - Noise ditingkatkan: σ=0.08 → σ=0.11 (lebih banyak variance)\n",
+    "   - Latency penalty diperkuat: [0, 0.08, 0.18] → [0, 0.10, 0.22]\n",
+    "   - Location competitiveness effect: +0.018 (meningkat efeknya)\n",
+    "\n",
+    "2. **Net Profit Margin Logic (Step 7):**\n",
+    "   - Base rating lowered: 4.15 → 4.0 (lebih realistis marginal bisnis)\n",
+    "   - Noise ditingkatkan: σ=2.8 → σ=3.2\n",
+    "   - Ini menghasilkan distribusi margin yang lebih spread\n",
+    "\n",
+    "3. **Digital Adoption Logic (Step 2):**\n",
+    "   - Noise ditingkatkan: σ=1.4 → σ=2.2 (lebih independent dari tenure)\n",
+    "   - Ini mengurangi strong coupling dengan business maturity\n",
+    "\n",
+    "4. **Transaction Count & Repeat Order (Step 3 & 8):**\n",
+    "   - Noise ditambahkan untuk lebih realistic variability\n",
+    "   - Repeat Order ratios lebih conservative\n",
+    "\n",
+    "5. **Classification Logic (Step 14):**\n",
+    "   - Dari hard-coded thresholds → **Percentile-based thresholds**\n",
+    "   - Elite: top 10% dengan kombinasi high margin, low burn, strong rating\n",
+    "   - Struggling: bottom 45% dengan kombinasi low margin & high burn, atau high latency & low rating\n",
+    "   - Critical: bottom 8% dengan severe burn rate, new+competitive, atau deep losses\n",
+    "   - Growth: sisa bisnis (default)\n",
+    "\n",
+    "### Class Distribution Logic (Percentile-Based)\n",
+    "\n",
+    "| Class | % Target | Kriteria |\n",
+    "|-------|----------|----------|\n",
+    "| **Elite** | ~10% | Margin > P70 AND Burn < P25 AND Repeat > P70 AND Rating > P75 |\n",
+    "| **Growth** | ~37% | Default - bisnis dengan metrik moderate / recovering |\n",
+    "| **Struggling** | ~45% | (Margin < P35 AND Burn > P60) OR (High Latency AND Rating < P40) OR (Burn > P75 AND Rating < P65) |\n",
+    "| **Critical** | ~8% | Burn > P92 OR (Tenure < 7 AND Competition ≥ 12) OR (Margin < P5 AND Rating < 3.0) |\n",
+    "\n",
+    "### Kolom Baru\n",
+    "- `class`: Klasifikasi tier (Elite, Growth, Struggling, Critical)\n",
+    "- `Sentiment_Score`: Angka sentimen dari review text, skala -1.0 (sangat negatif) hingga 1.0 (sangat positif)\n",
+    "\n",
+    "### Penggunaan\n",
+    "Dataset dengan balanced class distribution cocok untuk:\n",
+    "- **Classification Model**: Training lebih stabil dengan distribusi lebih balanced\n",
+    "- **Risk Assessment**: Bisa identify minority classes (Elite dan Critical)\n",
+    "\n",
+    "Dokumentasi lengkap di [README.md](README.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b4494280",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "CLASS DISTRIBUTION - UPDATED\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'class'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3811\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3812\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3813\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:167\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:196\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7096\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[31mKeyError\u001b[39m: 'class'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mCLASS DISTRIBUTION - UPDATED\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m * \u001b[32m70\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m class_counts = \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mclass\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m.value_counts().sort_index()\n\u001b[32m      7\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mClass counts:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m      8\u001b[39m \u001b[38;5;28mprint\u001b[39m(class_counts)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\frame.py:4107\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m   4106\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4107\u001b[39m indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4108\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4109\u001b[39m     indexer = [indexer]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\indexes\\base.py:3819\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3814\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3815\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3816\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3817\u001b[39m     ):\n\u001b[32m   3818\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[32m-> \u001b[39m\u001b[32m3819\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m   3820\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3821\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3822\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3823\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3824\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
+      "\u001b[31mKeyError\u001b[39m: 'class'"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Verification: Class Distribution\n",
+    "print(\"=\" * 70)\n",
+    "print(\"CLASS DISTRIBUTION - UPDATED\")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "class_counts = df['class'].value_counts().sort_index()\n",
+    "print(\"\\nClass counts:\")\n",
+    "print(class_counts)\n",
+    "\n",
+    "print(\"\\n\\nClass percentages:\")\n",
+    "total = len(df)\n",
+    "for cls in sorted(df['class'].unique()):\n",
+    "    if cls in class_counts.index:\n",
+    "        count = class_counts[cls]\n",
+    "    else:\n",
+    "        count = 0\n",
+    "    pct = (count / total * 100)\n",
+    "    print(f\"  {cls:15s}: {count:7,d} ({pct:6.2f}%)\")\n",
+    "\n",
+    "print(\"\\n\\n\" + \"=\" * 70)\n",
+    "print(\"CLASS - METRICS CORRELATION\")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "class_metrics = df.groupby('class')[['Net_Profit_Margin (%)', 'Burn_Rate_Ratio', \n",
+    "                                      'Repeat_Order_Rate (%)', 'Avg_Historical_Rating', \n",
+    "                                      'Sentiment_Score']].agg(['mean', 'min', 'max'])\n",
+    "print(class_metrics.round(2))\n",
+    "\n",
+    "print(\"\\n\\n\" + \"=\" * 70)\n",
+    "print(\"BURN_RATE_RATIO DISTRIBUTION\")\n",
+    "print(\"=\" * 70)\n",
+    "print(f\"Mean: {df['Burn_Rate_Ratio'].mean():.3f}\")\n",
+    "print(f\"Std:  {df['Burn_Rate_Ratio'].std():.3f}\")\n",
+    "print(f\"Min:  {df['Burn_Rate_Ratio'].min():.3f}\")\n",
+    "print(f\"Max:  {df['Burn_Rate_Ratio'].max():.3f}\")\n",
+    "print(f\"\\n% with Burn_Rate < 0.75: {(df['Burn_Rate_Ratio'] < 0.75).sum() / len(df) * 100:.1f}%\")\n",
+    "print(f\"% with Burn_Rate < 1.00: {(df['Burn_Rate_Ratio'] < 1.00).sum() / len(df) * 100:.1f}%\")\n",
+    "print(f\"% with Burn_Rate >= 1.00: {(df['Burn_Rate_Ratio'] >= 1.00).sum() / len(df) * 100:.1f}%\")\n",
+    "print(f\"% with Burn_Rate >= 1.20: {(df['Burn_Rate_Ratio'] >= 1.20).sum() / len(df) * 100:.1f}%\")\n",
+    "\n",
+    "print(\"\\n\\n\" + \"=\" * 70)\n",
+    "print(\"NET_PROFIT_MARGIN DISTRIBUTION\")\n",
+    "print(\"=\" * 70)\n",
+    "print(f\"Mean: {df['Net_Profit_Margin (%)'].mean():.2f}%\")\n",
+    "print(f\"Std:  {df['Net_Profit_Margin (%)'].std():.2f}%\")\n",
+    "print(f\"Min:  {df['Net_Profit_Margin (%)'].min():.2f}%\")\n",
+    "print(f\"Max:  {df['Net_Profit_Margin (%)'].max():.2f}%\")\n",
+    "print(f\"\\n% with Profit < 0: {(df['Net_Profit_Margin (%)'] < 0).sum() / len(df) * 100:.1f}%\")\n",
+    "print(f\"% with Profit 0-10%: {((df['Net_Profit_Margin (%)'] >= 0) & (df['Net_Profit_Margin (%)'] < 10)).sum() / len(df) * 100:.1f}%\")\n",
+    "print(f\"% with Profit >= 10%: {(df['Net_Profit_Margin (%)'] >= 10).sum() / len(df) * 100:.1f}%\")\n",
+    "print(f\"% with Profit >= 20%: {(df['Net_Profit_Margin (%)'] >= 20).sum() / len(df) * 100:.1f}%\")\n"
    ]
   }
  ],

synthetic_umkm_data.csv

@@ -1,3 +1,3 @@
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+size 20345909