AI_Investment_Model.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shivi13102/FinMitra/blob/main/AI_Investment_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from cryptography.fernet import Fernet\n",
        "import os\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "c_2PQl3nuP_g"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "btQff4WCuJCY"
      },
      "outputs": [],
      "source": [
        "rural_user_data = pd.read_csv('rural_indian_user_profiles.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rural_user_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJXgSWZxRknb",
        "outputId": "b97332cb-2677-49f3-a956-9cf5ffac613c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Full Name', 'Age', 'State', 'Village', 'Occupation', 'Annual Income',\n",
              "       'Monthly Expenses', 'Net Worth', 'Existing Investments',\n",
              "       'Risk Tolerance', 'Investment Time Horizon',\n",
              "       'Preferred Investment Types', 'Financial Goals', 'Goal Amount',\n",
              "       'Goal Timeline', 'Savings Behavior', 'Spending Habits',\n",
              "       'Emergency Fund Status', 'PAN Card Number', 'Bank Account Details',\n",
              "       'Aadhaar Number', 'Phone Number'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rural_user_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVGB7l0ISEyW",
        "outputId": "5fc6145a-fea0-4cc6-f635-9d1b8f4c0d0d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 300 entries, 0 to 299\n",
            "Data columns (total 22 columns):\n",
            " #   Column                      Non-Null Count  Dtype \n",
            "---  ------                      --------------  ----- \n",
            " 0   Full Name                   300 non-null    object\n",
            " 1   Age                         300 non-null    int64 \n",
            " 2   State                       300 non-null    object\n",
            " 3   Village                     300 non-null    object\n",
            " 4   Occupation                  300 non-null    object\n",
            " 5   Annual Income               300 non-null    int64 \n",
            " 6   Monthly Expenses            300 non-null    int64 \n",
            " 7   Net Worth                   300 non-null    int64 \n",
            " 8   Existing Investments        231 non-null    object\n",
            " 9   Risk Tolerance              300 non-null    object\n",
            " 10  Investment Time Horizon     300 non-null    object\n",
            " 11  Preferred Investment Types  300 non-null    object\n",
            " 12  Financial Goals             300 non-null    object\n",
            " 13  Goal Amount                 300 non-null    int64 \n",
            " 14  Goal Timeline               300 non-null    int64 \n",
            " 15  Savings Behavior            300 non-null    int64 \n",
            " 16  Spending Habits             300 non-null    object\n",
            " 17  Emergency Fund Status       300 non-null    object\n",
            " 18  PAN Card Number             300 non-null    int64 \n",
            " 19  Bank Account Details        300 non-null    object\n",
            " 20  Aadhaar Number              300 non-null    int64 \n",
            " 21  Phone Number                300 non-null    object\n",
            "dtypes: int64(9), object(13)\n",
            "memory usage: 51.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(rural_user_data)"
      ],
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        },
        "id": "GzO2rzhQTvQc",
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  Full Name  Age           State     Village  \\\n",
              "0                   उमा नाथ   46  Madhya Pradesh    Akbarpur   \n",
              "1               दीप्ति पटेल   39     West Bengal   Dungarpur   \n",
              "2                देवी मेहता   21           Bihar      Koriya   \n",
              "3          प्रतिमा द्विवेदी   49       Rajasthan    Chanchal   \n",
              "4                  लीना घोष   24     West Bengal    Chaibasa   \n",
              "..                      ...  ...             ...         ...   \n",
              "295    इन्द्रजित रोद्रिगुएस   44       Rajasthan     Sheopur   \n",
              "296  लक्ष्मी बलासुब्रमानियम   28          Odisha   Ratangarh   \n",
              "297              शीला वर्मा   33          Odisha  Kendrapara   \n",
              "298              माधवी गुहा   42       Jharkhand     Sheopur   \n",
              "299             दुर्गा सरीन   46  Madhya Pradesh      Sehore   \n",
              "\n",
              "               Occupation  Annual Income  Monthly Expenses  Net Worth  \\\n",
              "0                  Grocer         191780             17818     553797   \n",
              "1                   Other         377618             23446     976583   \n",
              "2                 Teacher         236866             14944     854051   \n",
              "3                   Other         267227             21228     750495   \n",
              "4                  Farmer         488581             23714     329614   \n",
              "..                    ...            ...               ...        ...   \n",
              "295                Farmer         362880             24692     836410   \n",
              "296   Agriculture Laborer         583421             18424     758251   \n",
              "297  Small Business Owner         253688             25982      67633   \n",
              "298            Shopkeeper         191426             29750     428037   \n",
              "299                Farmer         147891             10907     968800   \n",
              "\n",
              "    Existing Investments Risk Tolerance  ...          Financial Goals  \\\n",
              "0                  Bonds            Low  ...           Small Business   \n",
              "1            Real Estate       Moderate  ...           Emergency Fund   \n",
              "2                    NaN            Low  ...  Agriculture Improvement   \n",
              "3            Real Estate           High  ...                Education   \n",
              "4            Real Estate            Low  ...                Education   \n",
              "..                   ...            ...  ...                      ...   \n",
              "295         Mutual Funds       Moderate  ...               Retirement   \n",
              "296          Real Estate           High  ...                Education   \n",
              "297         Mutual Funds            Low  ...  Agriculture Improvement   \n",
              "298                  NaN            Low  ...                Education   \n",
              "299          Real Estate       Moderate  ...                Education   \n",
              "\n",
              "    Goal Amount Goal Timeline  Savings Behavior  Spending Habits  \\\n",
              "0        204220             5                11    Discretionary   \n",
              "1        396643            10                23        Essential   \n",
              "2        459938             1                21    Discretionary   \n",
              "3        250307             3                18        Essential   \n",
              "4        497144             3                23    Discretionary   \n",
              "..          ...           ...               ...              ...   \n",
              "295      328378             1                14        Essential   \n",
              "296      256463             3                19        Essential   \n",
              "297      471260            10                17    Discretionary   \n",
              "298      119921             5                15    Discretionary   \n",
              "299      418295            10                11    Discretionary   \n",
              "\n",
              "     Emergency Fund Status PAN Card Number    Bank Account Details  \\\n",
              "0                       No      3041200930  GB49ENME76647119379640   \n",
              "1                      Yes      2414164990  GB03KMDL75514382081345   \n",
              "2                      Yes      7299724579  GB84LVOB37910603127969   \n",
              "3                      Yes      2328120286  GB45STZE65543827651768   \n",
              "4                      Yes      7450467385  GB50DTPC50717634216074   \n",
              "..                     ...             ...                     ...   \n",
              "295                     No      4595592204  GB74EIDN11536674861023   \n",
              "296                    Yes      8988291769  GB43DEUL82892834807041   \n",
              "297                     No      4751366283  GB06LWPT10058872269178   \n",
              "298                    Yes      8414195092  GB39DCEH83542925508652   \n",
              "299                    Yes      8075913694  GB33RHZV10087356676211   \n",
              "\n",
              "     Aadhaar Number     Phone Number  \n",
              "0      755266556380     09537 672264  \n",
              "1      643625695673     004-23752614  \n",
              "2      794301329020     09751 028486  \n",
              "3      212142531566   +91 6007385673  \n",
              "4      861570408223      09650137495  \n",
              "..              ...              ...  \n",
              "295    983362094925  +91 452 8710589  \n",
              "296    301499796048  +91 461 7941466  \n",
              "297    782839032489  +91 883 1765233  \n",
              "298    816605597171   +91 1394222653  \n",
              "299    196549104926      07952666897  \n",
              "\n",
              "[300 rows x 22 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Full Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>State</th>\n",
              "      <th>Village</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Annual Income</th>\n",
              "      <th>Monthly Expenses</th>\n",
              "      <th>Net Worth</th>\n",
              "      <th>Existing Investments</th>\n",
              "      <th>Risk Tolerance</th>\n",
              "      <th>...</th>\n",
              "      <th>Financial Goals</th>\n",
              "      <th>Goal Amount</th>\n",
              "      <th>Goal Timeline</th>\n",
              "      <th>Savings Behavior</th>\n",
              "      <th>Spending Habits</th>\n",
              "      <th>Emergency Fund Status</th>\n",
              "      <th>PAN Card Number</th>\n",
              "      <th>Bank Account Details</th>\n",
              "      <th>Aadhaar Number</th>\n",
              "      <th>Phone Number</th>\n",
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              "      <th>0</th>\n",
              "      <td>उमा नाथ</td>\n",
              "      <td>46</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>Akbarpur</td>\n",
              "      <td>Grocer</td>\n",
              "      <td>191780</td>\n",
              "      <td>17818</td>\n",
              "      <td>553797</td>\n",
              "      <td>Bonds</td>\n",
              "      <td>Low</td>\n",
              "      <td>...</td>\n",
              "      <td>Small Business</td>\n",
              "      <td>204220</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>Discretionary</td>\n",
              "      <td>No</td>\n",
              "      <td>3041200930</td>\n",
              "      <td>GB49ENME76647119379640</td>\n",
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              "      <td>09537 672264</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>दीप्ति पटेल</td>\n",
              "      <td>39</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>Dungarpur</td>\n",
              "      <td>Other</td>\n",
              "      <td>377618</td>\n",
              "      <td>23446</td>\n",
              "      <td>976583</td>\n",
              "      <td>Real Estate</td>\n",
              "      <td>Moderate</td>\n",
              "      <td>...</td>\n",
              "      <td>Emergency Fund</td>\n",
              "      <td>396643</td>\n",
              "      <td>10</td>\n",
              "      <td>23</td>\n",
              "      <td>Essential</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2414164990</td>\n",
              "      <td>GB03KMDL75514382081345</td>\n",
              "      <td>643625695673</td>\n",
              "      <td>004-23752614</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>देवी मेहता</td>\n",
              "      <td>21</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>Koriya</td>\n",
              "      <td>Teacher</td>\n",
              "      <td>236866</td>\n",
              "      <td>14944</td>\n",
              "      <td>854051</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Low</td>\n",
              "      <td>...</td>\n",
              "      <td>Agriculture Improvement</td>\n",
              "      <td>459938</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>Discretionary</td>\n",
              "      <td>Yes</td>\n",
              "      <td>7299724579</td>\n",
              "      <td>GB84LVOB37910603127969</td>\n",
              "      <td>794301329020</td>\n",
              "      <td>09751 028486</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>प्रतिमा द्विवेदी</td>\n",
              "      <td>49</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>Chanchal</td>\n",
              "      <td>Other</td>\n",
              "      <td>267227</td>\n",
              "      <td>21228</td>\n",
              "      <td>750495</td>\n",
              "      <td>Real Estate</td>\n",
              "      <td>High</td>\n",
              "      <td>...</td>\n",
              "      <td>Education</td>\n",
              "      <td>250307</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>Essential</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2328120286</td>\n",
              "      <td>GB45STZE65543827651768</td>\n",
              "      <td>212142531566</td>\n",
              "      <td>+91 6007385673</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>लीना घोष</td>\n",
              "      <td>24</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>Chaibasa</td>\n",
              "      <td>Farmer</td>\n",
              "      <td>488581</td>\n",
              "      <td>23714</td>\n",
              "      <td>329614</td>\n",
              "      <td>Real Estate</td>\n",
              "      <td>Low</td>\n",
              "      <td>...</td>\n",
              "      <td>Education</td>\n",
              "      <td>497144</td>\n",
              "      <td>3</td>\n",
              "      <td>23</td>\n",
              "      <td>Discretionary</td>\n",
              "      <td>Yes</td>\n",
              "      <td>7450467385</td>\n",
              "      <td>GB50DTPC50717634216074</td>\n",
              "      <td>861570408223</td>\n",
              "      <td>09650137495</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>295</th>\n",
              "      <td>इन्द्रजित रोद्रिगुएस</td>\n",
              "      <td>44</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>Sheopur</td>\n",
              "      <td>Farmer</td>\n",
              "      <td>362880</td>\n",
              "      <td>24692</td>\n",
              "      <td>836410</td>\n",
              "      <td>Mutual Funds</td>\n",
              "      <td>Moderate</td>\n",
              "      <td>...</td>\n",
              "      <td>Retirement</td>\n",
              "      <td>328378</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>Essential</td>\n",
              "      <td>No</td>\n",
              "      <td>4595592204</td>\n",
              "      <td>GB74EIDN11536674861023</td>\n",
              "      <td>983362094925</td>\n",
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              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>लक्ष्मी बलासुब्रमानियम</td>\n",
              "      <td>28</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>Ratangarh</td>\n",
              "      <td>Agriculture Laborer</td>\n",
              "      <td>583421</td>\n",
              "      <td>18424</td>\n",
              "      <td>758251</td>\n",
              "      <td>Real Estate</td>\n",
              "      <td>High</td>\n",
              "      <td>...</td>\n",
              "      <td>Education</td>\n",
              "      <td>256463</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "      <td>Essential</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8988291769</td>\n",
              "      <td>GB43DEUL82892834807041</td>\n",
              "      <td>301499796048</td>\n",
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              "      <th>297</th>\n",
              "      <td>शीला वर्मा</td>\n",
              "      <td>33</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>Kendrapara</td>\n",
              "      <td>Small Business Owner</td>\n",
              "      <td>253688</td>\n",
              "      <td>25982</td>\n",
              "      <td>67633</td>\n",
              "      <td>Mutual Funds</td>\n",
              "      <td>Low</td>\n",
              "      <td>...</td>\n",
              "      <td>Agriculture Improvement</td>\n",
              "      <td>471260</td>\n",
              "      <td>10</td>\n",
              "      <td>17</td>\n",
              "      <td>Discretionary</td>\n",
              "      <td>No</td>\n",
              "      <td>4751366283</td>\n",
              "      <td>GB06LWPT10058872269178</td>\n",
              "      <td>782839032489</td>\n",
              "      <td>+91 883 1765233</td>\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>माधवी गुहा</td>\n",
              "      <td>42</td>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>Sheopur</td>\n",
              "      <td>Shopkeeper</td>\n",
              "      <td>191426</td>\n",
              "      <td>29750</td>\n",
              "      <td>428037</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Low</td>\n",
              "      <td>...</td>\n",
              "      <td>Education</td>\n",
              "      <td>119921</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>Discretionary</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8414195092</td>\n",
              "      <td>GB39DCEH83542925508652</td>\n",
              "      <td>816605597171</td>\n",
              "      <td>+91 1394222653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>दुर्गा सरीन</td>\n",
              "      <td>46</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>Sehore</td>\n",
              "      <td>Farmer</td>\n",
              "      <td>147891</td>\n",
              "      <td>10907</td>\n",
              "      <td>968800</td>\n",
              "      <td>Real Estate</td>\n",
              "      <td>Moderate</td>\n",
              "      <td>...</td>\n",
              "      <td>Education</td>\n",
              "      <td>418295</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>Discretionary</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8075913694</td>\n",
              "      <td>GB33RHZV10087356676211</td>\n",
              "      <td>196549104926</td>\n",
              "      <td>07952666897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>300 rows × 22 columns</p>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    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",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('rural_user_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('rural_user_data');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rural_user_data"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Remove PII for Privacy Compliance:\n",
        "\n",
        "Mask or encrypt sensitive information like *PAN Card Number, Bank Account Details, Aadhaar Number, and Phone Number*."
      ],
      "metadata": {
        "id": "wBDDV9nJW5i5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# File to store the encryption key\n",
        "key_file_path = \"encryption_key.key\"\n",
        "\n",
        "# Generate a new encryption key if it doesn't already exist\n",
        "if not os.path.exists(key_file_path):\n",
        "    key = Fernet.generate_key()\n",
        "    with open(key_file_path, \"wb\") as key_file:\n",
        "        key_file.write(key)\n",
        "    print(\"Encryption key generated and saved.\")\n",
        "else:\n",
        "    # Load the existing encryption key\n",
        "    with open(key_file_path, \"rb\") as key_file:\n",
        "        key = key_file.read()\n",
        "    print(\"Encryption key loaded.\")\n",
        "\n",
        "cipher = Fernet(key)\n",
        "\n",
        "def mask_phone(phone):\n",
        "    \"\"\"Mask phone numbers to keep only the last 4 digits.\"\"\"\n",
        "    phone_str = str(phone)\n",
        "    return \"XXXXXX\" + phone_str[-4:]\n",
        "\n",
        "def encrypt_data(data):\n",
        "    \"\"\"Encrypt sensitive data using Fernet encryption.\"\"\"\n",
        "    data_str = str(data)  # Ensure the data is a string\n",
        "    return cipher.encrypt(data_str.encode()).decode()\n",
        "\n",
        "# Load the dataset\n",
        "file_path = \"rural_indian_user_profiles.csv\"\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Mask or encrypt sensitive columns\n",
        "df['PAN Card Number'] = df['PAN Card Number'].apply(encrypt_data)\n",
        "df['Bank Account Details'] = df['Bank Account Details'].apply(encrypt_data)\n",
        "df['Aadhaar Number'] = df['Aadhaar Number'].apply(encrypt_data)\n",
        "df['Phone Number'] = df['Phone Number'].apply(mask_phone)\n",
        "\n",
        "# Save the processed dataset to a new file\n",
        "output_path = \"processed_user_profiles.csv\"\n",
        "df.to_csv(output_path, index=False)\n",
        "\n",
        "print(f\"Processed data saved to {output_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQCXKthSUnQK",
        "outputId": "4b915769-3239-42c3-ec77-faf7c4f6d087"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encryption key loaded.\n",
            "Processed data saved to processed_user_profiles.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('/content/processed_user_profiles.csv')\n",
        "display(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "rkjlRgRBUnMr",
        "outputId": "3b9640ad-9a7b-4498-94f6-f8e419f067f6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  Full Name  Age           State     Village  \\\n",
              "0                   उमा नाथ   46  Madhya Pradesh    Akbarpur   \n",
              "1               दीप्ति पटेल   39     West Bengal   Dungarpur   \n",
              "2                देवी मेहता   21           Bihar      Koriya   \n",
              "3          प्रतिमा द्विवेदी   49       Rajasthan    Chanchal   \n",
              "4                  लीना घोष   24     West Bengal    Chaibasa   \n",
              "..                      ...  ...             ...         ...   \n",
              "295    इन्द्रजित रोद्रिगुएस   44       Rajasthan     Sheopur   \n",
              "296  लक्ष्मी बलासुब्रमानियम   28          Odisha   Ratangarh   \n",
              "297              शीला वर्मा   33          Odisha  Kendrapara   \n",
              "298              माधवी गुहा   42       Jharkhand     Sheopur   \n",
              "299             दुर्गा सरीन   46  Madhya Pradesh      Sehore   \n",
              "\n",
              "               Occupation  Annual Income  Monthly Expenses  Net Worth  \\\n",
              "0                  Grocer         191780             17818     553797   \n",
              "1                   Other         377618             23446     976583   \n",
              "2                 Teacher         236866             14944     854051   \n",
              "3                   Other         267227             21228     750495   \n",
              "4                  Farmer         488581             23714     329614   \n",
              "..                    ...            ...               ...        ...   \n",
              "295                Farmer         362880             24692     836410   \n",
              "296   Agriculture Laborer         583421             18424     758251   \n",
              "297  Small Business Owner         253688             25982      67633   \n",
              "298            Shopkeeper         191426             29750     428037   \n",
              "299                Farmer         147891             10907     968800   \n",
              "\n",
              "    Existing Investments Risk Tolerance  ...          Financial Goals  \\\n",
              "0                  Bonds            Low  ...           Small Business   \n",
              "1            Real Estate       Moderate  ...           Emergency Fund   \n",
              "2                    NaN            Low  ...  Agriculture Improvement   \n",
              "3            Real Estate           High  ...                Education   \n",
              "4            Real Estate            Low  ...                Education   \n",
              "..                   ...            ...  ...                      ...   \n",
              "295         Mutual Funds       Moderate  ...               Retirement   \n",
              "296          Real Estate           High  ...                Education   \n",
              "297         Mutual Funds            Low  ...  Agriculture Improvement   \n",
              "298                  NaN            Low  ...                Education   \n",
              "299          Real Estate       Moderate  ...                Education   \n",
              "\n",
              "    Goal Amount Goal Timeline  Savings Behavior  Spending Habits  \\\n",
              "0        204220             5                11    Discretionary   \n",
              "1        396643            10                23        Essential   \n",
              "2        459938             1                21    Discretionary   \n",
              "3        250307             3                18        Essential   \n",
              "4        497144             3                23    Discretionary   \n",
              "..          ...           ...               ...              ...   \n",
              "295      328378             1                14        Essential   \n",
              "296      256463             3                19        Essential   \n",
              "297      471260            10                17    Discretionary   \n",
              "298      119921             5                15    Discretionary   \n",
              "299      418295            10                11    Discretionary   \n",
              "\n",
              "     Emergency Fund Status                                    PAN Card Number  \\\n",
              "0                       No  gAAAAABnf1FDxIQlk4ZCg-kYt5027CLqV_AjY7WElyO90T...   \n",
              "1                      Yes  gAAAAABnf1FDu55pK6ok8n7K_SzUfT_gIBflpRqXd5VtVq...   \n",
              "2                      Yes  gAAAAABnf1FDW1UF1i9MCt96Sqg01DW1JMHbpxukOY9V1x...   \n",
              "3                      Yes  gAAAAABnf1FDNHPetVHJhw50uga_rm_CCbNzElmZ9D-R7D...   \n",
              "4                      Yes  gAAAAABnf1FD04hthv9SLqgn1Rwre4sO4_Ro4MdTJDGzrJ...   \n",
              "..                     ...                                                ...   \n",
              "295                     No  gAAAAABnf1FDhKFX9nagxxFQpGTJED8hbSI1Kv_yVOpXa7...   \n",
              "296                    Yes  gAAAAABnf1FDGe08lHq5NC2W9moILFDeej0mxcSJUtntLQ...   \n",
              "297                     No  gAAAAABnf1FDau49qt63YyqRor58tunQPigqCcDtLrir6K...   \n",
              "298                    Yes  gAAAAABnf1FDalwKOCkQclBvPCm0Y80ZW-0lI_MujmxMWi...   \n",
              "299                    Yes  gAAAAABnf1FDU35q8CBkbkE2eF15ddm_aoZ-vWJa9ntByd...   \n",
              "\n",
              "                                  Bank Account Details  \\\n",
              "0    gAAAAABnf1FDCXVkwqPfH59VF8IRS-h_zg_zoAp9DekZHu...   \n",
              "1    gAAAAABnf1FDa122MRKchEYKUoc3Ld4m_Hwy4_P-MpoM-u...   \n",
              "2    gAAAAABnf1FDQi5nFBmxekGaqx6jChiIHImy3ys3D9bzAv...   \n",
              "3    gAAAAABnf1FDJJt7irV8r5kiy7GiLUdrlHYrt_3_wSIy8e...   \n",
              "4    gAAAAABnf1FDFGtrk6dgL7qyiAbhvd12BaFZs0LGCX3_eU...   \n",
              "..                                                 ...   \n",
              "295  gAAAAABnf1FDOnxUkrNJI1nGtb-oSSWu_KhUdTqotz4njG...   \n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    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",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-21b11183-590b-49aa-8d26-dbcb0fb4e826 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7369e8f7-9afd-4bfe-83fd-bceecd57ffad\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7369e8f7-9afd-4bfe-83fd-bceecd57ffad button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering"
      ],
      "metadata": {
        "id": "DFhHO_kdXIWr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate Disposable Income:\n",
        "Disposable Income = Annual Income - (Monthly Expenses * 12)"
      ],
      "metadata": {
        "id": "XnRg8swGXK6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Disposable Income'] = df['Annual Income'] - (df['Monthly Expenses'] * 12)"
      ],
      "metadata": {
        "id": "x1LGGub4XYgx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Investment Readiness Score:\n",
        "Combine factors such as Net Worth, Emergency Fund Status, Risk Tolerance, and Savings Behavior."
      ],
      "metadata": {
        "id": "DxOQJKiQXR0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define ranges for Savings Behavior categories\n",
        "def categorize_savings_behavior(value):\n",
        "    if 10 <= value <= 11:\n",
        "        return 'Very Low'\n",
        "    elif 12 <= value <= 13:\n",
        "        return 'Low'\n",
        "    elif 14 <= value <= 16:\n",
        "        return 'Moderate'\n",
        "    elif 17 <= value <= 19:\n",
        "        return 'Good'\n",
        "    elif 20 <= value <= 22:\n",
        "        return 'Very Good'\n",
        "    elif 23 <= value <= 25:\n",
        "        return 'Excellent'\n",
        "    elif 26 <= value <= 28:\n",
        "        return 'Exceptional'\n",
        "    elif 29 <= value <= 30:\n",
        "        return 'Outstanding'\n",
        "    else:\n",
        "        return 'Unknown'  # Handle unexpected values\n",
        "\n",
        "# Apply the categorization to the Savings Behavior column\n",
        "df['Savings Behavior Category'] = df['Savings Behavior'].apply(categorize_savings_behavior)"
      ],
      "metadata": {
        "id": "5U3ROFrRUnJ3"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_investment_readiness(row):\n",
        "    # Assign weights to factors (customizable)\n",
        "    net_worth_weight = 0.4\n",
        "    emergency_fund_weight = 0.2\n",
        "    risk_tolerance_weight = 0.2\n",
        "    savings_behavior_weight = 0.2\n",
        "\n",
        "    # Normalize numerical factors (e.g., Net Worth, Risk Tolerance)\n",
        "    normalized_net_worth = row['Net Worth'] / df['Net Worth'].max()\n",
        "    normalized_risk_tolerance = row['Risk Tolerance'] / df['Risk Tolerance'].max()\n",
        "\n",
        "    # Encode Emergency Fund Status (Yes = 1, No = 0)\n",
        "    emergency_fund_score = 1 if row['Emergency Fund Status'].lower() == 'yes' else 0\n",
        "\n",
        "    # Savings Behavior numeric value\n",
        "    savings_behavior_score = row['Savings Behavior'] / 30  # Normalize to [0, 1]\n",
        "\n",
        "    # Weighted sum to calculate the Investment Readiness Score\n",
        "    investment_score = (\n",
        "        (normalized_net_worth * net_worth_weight) +\n",
        "        (emergency_fund_score * emergency_fund_weight) +\n",
        "        (normalized_risk_tolerance * risk_tolerance_weight) +\n",
        "        (savings_behavior_score * savings_behavior_weight)\n",
        "    )\n",
        "\n",
        "    return round(investment_score * 100, 2)  # Scale score to [0, 100]\n",
        "\n",
        "df['Investment Readiness Score'] = df.apply(calculate_investment_readiness, axis=1)\n",
        "\n",
        "display(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "-rVvLvOeUnG9",
        "outputId": "21a80f28-5f5e-4b38-8574-af4707de5236"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "unsupported operand type(s) for /: 'str' and 'str'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-bc887718ca7d>\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minvestment_score\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Scale score to [0, 100]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Investment Readiness Score'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalculate_investment_readiness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m  10372\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10373\u001b[0m         )\n\u001b[0;32m> 10374\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10376\u001b[0m     def map(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    914\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1061\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m             \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_numba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1079\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1081\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1082\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-16-bc887718ca7d>\u001b[0m in \u001b[0;36mcalculate_investment_readiness\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# Normalize numerical factors (e.g., Net Worth, Risk Tolerance)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mnormalized_net_worth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Net Worth'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Net Worth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mnormalized_risk_tolerance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Risk Tolerance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Risk Tolerance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;31m# Encode Emergency Fund Status (Yes = 1, No = 0)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = df['Savings Behavior'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "id": "QDSEmc3sUnEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SttFJIUvUnBY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KfFefRzYUm-u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xW5jzN4GUm74"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}