AI Ethics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YQv7xLkloyVa",
    "outputId": "aff29bee-d33e-419e-81e9-09018e4a857a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis) (1.3.1)\n",
      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis) (1.2.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow tensorflow-model-analysis fairness-indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "461vf-F0o4fC"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_model_analysis as tfma\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WE0Ay7vHo70G",
    "outputId": "47c2b2d1-5f20-4a2e-ced9-ddd685a914e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup successful!\n"
     ]
    }
   ],
   "source": [
    "eval_config = tfma.EvalConfig()\n",
    "print(\"Setup successful!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "ivq6hGrhr8bY",
    "outputId": "9b79396e-d2ad-4d0e-c2f6-42a02ca14668"
   },
   "outputs": [
    {
     "data": {
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       "     <output id=\"result-194d366a-b695-4ac5-b753-f016bb6c37f0\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving adult.data to adult.data\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aKaIMj1esUrr",
    "outputId": "f0159f86-f787-457e-de5f-75119fa753ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
      "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
      "1  38            Private  215646     HS-grad    9             Divorced   \n",
      "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
      "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
      "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
      "\n",
      "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
      "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
      "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
      "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
      "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
      "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
      "\n",
      "    United-States   <=50K  \n",
      "0   United-States   <=50K  \n",
      "1   United-States   <=50K  \n",
      "2   United-States   <=50K  \n",
      "3            Cuba   <=50K  \n",
      "4   United-States   <=50K  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('adult.data')\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 554
    },
    "id": "mVppIR6MsoX8",
    "outputId": "49988553-3a07-43f1-8947-4643afb84a9c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State-gov</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77516</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelors</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never-married</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not-in-family</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United-States</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "39                0\n",
       " State-gov        0\n",
       " 77516            0\n",
       " Bachelors        0\n",
       " 13               0\n",
       " Never-married    0\n",
       " Adm-clerical     0\n",
       " Not-in-family    0\n",
       " White            0\n",
       " Male             0\n",
       " 2174             0\n",
       " 0                0\n",
       " 40               0\n",
       " United-States    0\n",
       " <=50K            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  # This shows the number of missing values in each column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V4Z-4m9bsxFM"
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jTDmI0yntWOy"
   },
   "outputs": [],
   "source": [
    "columns = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\",\n",
    "           \"occupation\", \"relationship\", \"race\", \"sex\", \"capital-gain\", \"capital-loss\",\n",
    "           \"hours-per-week\", \"native-country\", \"income\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QYX7cdQ6ta15",
    "outputId": "e6691ce0-5dd1-4404-8a83-a42bbea83fd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age         workclass  fnlwgt  education  education-num  \\\n",
      "0   39         State-gov   77516  Bachelors             13   \n",
      "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
      "2   38           Private  215646    HS-grad              9   \n",
      "3   53           Private  234721       11th              7   \n",
      "4   28           Private  338409  Bachelors             13   \n",
      "\n",
      "       marital-status         occupation   relationship   race     sex  \\\n",
      "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
      "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
      "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
      "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
      "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
      "\n",
      "   capital-gain  capital-loss  hours-per-week native-country income  \n",
      "0          2174             0              40  United-States  <=50K  \n",
      "1             0             0              13  United-States  <=50K  \n",
      "2             0             0              40  United-States  <=50K  \n",
      "3             0             0              40  United-States  <=50K  \n",
      "4             0             0              40           Cuba  <=50K  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"adult.data\", names=columns, sep=\",\", skipinitialspace=True)\n",
    "\n",
    "# Display the first few rows\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o_VQK6Y8tlRA"
   },
   "outputs": [],
   "source": [
    "# Features (all columns except 'income')\n",
    "X = df.drop(columns=\"income\")\n",
    "\n",
    "# Labels (target column 'income')\n",
    "y = df[\"income\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tdEo2WUCuU7P",
    "outputId": "f1c3daff-b24c-4dcd-af3b-564d2fd32a62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex Distribution:\n",
      "Male      21790\n",
      "Female    10771\n",
      "Name: sex, dtype: int64\n",
      "\n",
      "Race Distribution:\n",
      "White                 27816\n",
      "Black                  3124\n",
      "Asian-Pac-Islander     1039\n",
      "Amer-Indian-Eskimo      311\n",
      "Other                   271\n",
      "Name: race, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check distribution of 'sex' attribute\n",
    "print(\"Sex Distribution:\")\n",
    "print(df[\"sex\"].value_counts())\n",
    "\n",
    "# Check distribution of 'race' attribute\n",
    "print(\"\\nRace Distribution:\")\n",
    "print(df[\"race\"].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4vtD1U52uoBa",
    "outputId": "9e4071bd-afa8-43d0-ca5b-dd22a3bf165b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing Values per Column:\n",
      "age               0\n",
      "workclass         0\n",
      "fnlwgt            0\n",
      "education         0\n",
      "education-num     0\n",
      "marital-status    0\n",
      "occupation        0\n",
      "relationship      0\n",
      "race              0\n",
      "sex               0\n",
      "capital-gain      0\n",
      "capital-loss      0\n",
      "hours-per-week    0\n",
      "native-country    0\n",
      "income            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMissing Values per Column:\")\n",
    "print(df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hfHEi5ElvzGT",
    "outputId": "c1ae57b0-65aa-4153-8699-83989874d156"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.2)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.8.0)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (11.0.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sHbZw9w5v1si"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "KlffcikVv3z8",
    "outputId": "278fb06d-c83a-4a18-cf1f-09e156bf4f0e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(df[\"age\"], bins=30, kde=True)\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "wfCGkR6hv8DT",
    "outputId": "ab52efca-33f8-4302-8a75-f60d74f86fb5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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17dq1U5MmTZScnCxJSk5OVufOne3QIkkRERFyuVzauXOnXXP6OkprStdRVFSk1NRUtxoPDw8NGjTIrjnTjBkzFBAQYD/CwsIudfMBAMB51JjgUlJSoqeeekr/9V//pU6dOkmSsrKy5O3trcDAQLfa4OBgZWVl2TWnh5bS+dK5c9W4XC4dP35cP/30k4qLi8utKV3HmSZNmqT8/Hz7ceDAgYptOAAAuGBe1d1AqZiYGKWnp2vjxo3V3coF8fHxkY+PT3W3AQDAVaVG7HGJjY3V8uXLtXbtWl1zzTX2eEhIiIqKipSXl+dWn52drZCQELvmzKuMSpfPV+N0OlW7dm01bNhQnp6e5daUrgMAAFS/ag0ulmUpNjZWn376qdasWaPmzZu7zffo0UO1atVSUlKSPbZnzx7t379f4eHhkqTw8HDt2LHD7eqfxMREOZ1OdejQwa45fR2lNaXr8Pb2Vo8ePdxqSkpKlJSUZNcAAIDqV62HimJiYrR48WJ99tlnqlu3rn0+SUBAgGrXrq2AgACNHj1acXFxql+/vpxOp8aPH6/w8HD16dNHkjRkyBB16NBBDz30kGbOnKmsrCxNnjxZMTEx9qGcxx9/XHPnztUzzzyjRx55RGvWrNGHH36oFStW2L3ExcUpOjpaPXv21HXXXafZs2fr2LFjGjVq1OV/YwAAQLmqNbi89dZbkqQbb7zRbXzhwoUaOXKkJGnWrFny8PDQiBEjVFhYqIiICL355pt2raenp5YvX65x48YpPDxcfn5+io6O1vTp0+2a5s2ba8WKFZowYYJef/11XXPNNXr33XcVERFh19x7773Kzc3VlClTlJWVpWuvvVYJCQllTtgFAADVp0bdx8VkF3MNekVwHxdcDbiPC3B1MvY+LgAAAOdCcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGKNag8uGDRs0bNgwhYaGyuFwaNmyZW7zI0eOlMPhcHsMHTrUrebIkSN64IEH5HQ6FRgYqNGjR6ugoMCt5rvvvlO/fv3k6+ursLAwzZw5s0wvH330kdq1aydfX1917txZX3zxRaVvLwAAuDTVGlyOHTumrl27at68eWetGTp0qA4dOmQ//vGPf7jNP/DAA9q5c6cSExO1fPlybdiwQWPHjrXnXS6XhgwZoqZNmyo1NVWvvPKK4uPj9c4779g1mzdv1n333afRo0fr22+/1fDhwzV8+HClp6dX/kYDAIAKc1iWZVV3E5LkcDj06aefavjw4fbYyJEjlZeXV2ZPTKldu3apQ4cO2rZtm3r27ClJSkhI0C233KJ///vfCg0N1VtvvaVnn31WWVlZ8vb2liT9/ve/17Jly7R7925J0r333qtjx45p+fLl9rr79Omja6+9VvPnz7+g/l0ulwICApSfny+n01mBd+Dcekx8r9LXCdQ0qa88XN0tAKgGF/M7tMaf47Ju3To1atRIbdu21bhx43T48GF7Ljk5WYGBgXZokaRBgwbJw8NDX3/9tV3Tv39/O7RIUkREhPbs2aOff/7Zrhk0aJDb60ZERCg5OfmsfRUWFsrlcrk9AABA1arRwWXo0KF67733lJSUpJdfflnr16/XzTffrOLiYklSVlaWGjVq5PYcLy8v1a9fX1lZWXZNcHCwW03p8vlqSufLM2PGDAUEBNiPsLCwS9tYAABwXl7V3cC5REVF2T937txZXbp0UcuWLbVu3ToNHDiwGjuTJk2apLi4OHvZ5XIRXgAAqGI1eo/LmVq0aKGGDRtq7969kqSQkBDl5OS41Zw6dUpHjhxRSEiIXZOdne1WU7p8vprS+fL4+PjI6XS6PQAAQNUyKrj8+9//1uHDh9W4cWNJUnh4uPLy8pSammrXrFmzRiUlJerdu7dds2HDBp08edKuSUxMVNu2bVWvXj27Jikpye21EhMTFR4eXtWbBAAALkK1BpeCggKlpaUpLS1NkrRv3z6lpaVp//79Kigo0MSJE7VlyxZlZmYqKSlJt99+u1q1aqWIiAhJUvv27TV06FA9+uij2rp1qzZt2qTY2FhFRUUpNDRUknT//ffL29tbo0eP1s6dO7VkyRK9/vrrbod5nnzySSUkJOjVV1/V7t27FR8fr5SUFMXGxl729wQAAJxdtQaXlJQUdevWTd26dZMkxcXFqVu3bpoyZYo8PT313Xff6bbbblObNm00evRo9ejRQ1999ZV8fHzsdbz//vtq166dBg4cqFtuuUV9+/Z1u0dLQECAVq1apX379qlHjx767//+b02ZMsXtXi/XX3+9Fi9erHfeeUddu3bVxx9/rGXLlqlTp06X780AAADnVWPu42I67uMCXDru4wJcna6o+7gAAACUIrgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYo0LBpUWLFjp8+HCZ8by8PLVo0eKSmwIAAChPhYJLZmamiouLy4wXFhbqP//5zyU3BQAAUB6viyn+/PPP7Z+//PJLBQQE2MvFxcVKSkpSs2bNKq05AACA011UcBk+fLgkyeFwKDo62m2uVq1aatasmV599dVKaw4AAOB0FxVcSkpKJEnNmzfXtm3b1LBhwyppCgAAoDwXFVxK7du3r7L7AAAAOK8KBRdJSkpKUlJSknJycuw9MaUWLFhwyY0BAACcqULBZdq0aZo+fbp69uypxo0by+FwVHZfAAAAZVQouMyfP1+LFi3SQw89VNn9AAAAnFWF7uNSVFSk66+/vrJ7AQAAOKcKBZcxY8Zo8eLFld0LAADAOVXoUNGJEyf0zjvvaPXq1erSpYtq1arlNv/aa69VSnMAAACnq1Bw+e6773TttddKktLT093mOFEXAABUlQoFl7Vr11Z2HwAAAOdVoXNcAAAAqkOF9rgMGDDgnIeE1qxZU+GGAAAAzqZCwaX0/JZSJ0+eVFpamtLT08t8+SIAAEBlqVBwmTVrVrnj8fHxKigouKSGAAAAzqZSz3F58MEH+Z4iAABQZSo1uCQnJ8vX17cyVwkAAGCr0KGiO++8023ZsiwdOnRIKSkpeu655yqlMQAAgDNVKLgEBAS4LXt4eKht27aaPn26hgwZUimNAQAAnKlCwWXhwoWV3QcAAMB5VSi4lEpNTdWuXbskSR07dlS3bt0qpSkAAIDyVCi45OTkKCoqSuvWrVNgYKAkKS8vTwMGDNAHH3ygoKCgyuwRAABAUgWvKho/fryOHj2qnTt36siRIzpy5IjS09Plcrn0xBNPVHaPAAAAkiq4xyUhIUGrV69W+/bt7bEOHTpo3rx5nJwLAACqTIWCS0lJiWrVqlVmvFatWiopKbnkpgDAND0mvlfdLQBVLvWVh6u7hYodKrrpppv05JNP6uDBg/bYf/7zH02YMEEDBw6stOYAAABOV6HgMnfuXLlcLjVr1kwtW7ZUy5Yt1bx5c7lcLs2ZM6eyewQAAJBUwUNFYWFh+uabb7R69Wrt3r1bktS+fXsNGjSoUpsDAAA43UXtcVmzZo06dOggl8slh8OhwYMHa/z48Ro/frx69eqljh076quvvqqqXgEAwFXuooLL7Nmz9eijj8rpdJaZCwgI0GOPPabXXnut0poDAAA43UUFl+3bt2vo0KFnnR8yZIhSU1MveH0bNmzQsGHDFBoaKofDoWXLlrnNW5alKVOmqHHjxqpdu7YGDRqkH374wa3myJEjeuCBB+R0OhUYGKjRo0eroKDArea7775Tv3795Ovrq7CwMM2cObNMLx999JHatWsnX19fde7cWV988cUFbwcAALg8Liq4ZGdnl3sZdCkvLy/l5uZe8PqOHTumrl27at68eeXOz5w5U2+88Ybmz5+vr7/+Wn5+foqIiNCJEyfsmgceeEA7d+5UYmKili9frg0bNmjs2LH2vMvl0pAhQ9S0aVOlpqbqlVdeUXx8vN555x27ZvPmzbrvvvs0evRoffvttxo+fLiGDx+u9PT0C94WAABQ9S7q5Nzf/OY3Sk9PV6tWrcqd/+6779S4ceMLXt/NN9+sm2++udw5y7I0e/ZsTZ48Wbfffrsk6b333lNwcLCWLVumqKgo7dq1SwkJCdq2bZt69uwpSZozZ45uueUW/elPf1JoaKjef/99FRUVacGCBfL29lbHjh2Vlpam1157zQ44r7/+uoYOHaqJEydKkp5//nklJiZq7ty5mj9//gVvDwAAqFoXtcfllltu0XPPPee2x6PU8ePHNXXqVN16662V0ti+ffuUlZXldqVSQECAevfureTkZElScnKyAgMD7dAiSYMGDZKHh4e+/vpru6Z///7y9va2ayIiIrRnzx79/PPPds2ZV0RFRETYr1OewsJCuVwutwcAAKhaF7XHZfLkyVq6dKnatGmj2NhYtW3bVpK0e/duzZs3T8XFxXr22WcrpbGsrCxJUnBwsNt4cHCwPZeVlaVGjRq5zXt5eal+/fpuNc2bNy+zjtK5evXqKSsr65yvU54ZM2Zo2rRpFdgyAABQURcVXIKDg7V582aNGzdOkyZNkmVZkiSHw6GIiAjNmzevTAC4Uk2aNElxcXH2ssvlUlhYWDV2BADAle+ib0DXtGlTffHFF/r555+1d+9eWZal1q1bq169epXaWEhIiKRfTwg+/byZ7OxsXXvttXZNTk6O2/NOnTqlI0eO2M8PCQlRdna2W03p8vlqSufL4+PjIx8fnwpsGQAAqKgK3fJfkurVq6devXrpuuuuq/TQIknNmzdXSEiIkpKS7DGXy6Wvv/5a4eHhkqTw8HDl5eW5XYK9Zs0alZSUqHfv3nbNhg0bdPLkSbsmMTFRbdu2tfsODw93e53SmtLXAQAANUOFg0tlKCgoUFpamtLS0iT9ekJuWlqa9u/fL4fDoaeeekovvPCCPv/8c+3YsUMPP/ywQkNDNXz4cEm/fs3A0KFD9eijj2rr1q3atGmTYmNjFRUVpdDQUEnS/fffL29vb40ePVo7d+7UkiVL9Prrr7sd5nnyySeVkJCgV199Vbt371Z8fLxSUlIUGxt7ud8SAABwDhX6rqLKkpKSogEDBtjLpWEiOjpaixYt0jPPPKNjx45p7NixysvLU9++fZWQkCBfX1/7Oe+//75iY2M1cOBAeXh4aMSIEXrjjTfs+YCAAK1atUoxMTHq0aOHGjZsqClTprjd6+X666/X4sWLNXnyZP3hD39Q69attWzZMnXq1OkyvAsAAOBCOazSM2xxSVwulwICApSfn1/uVyJcqh4T36v0dQI1TeorD1d3CxXGZxRXg6r6jF7M79BqPVQEAABwMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxanRwiY+Pl8PhcHu0a9fOnj9x4oRiYmLUoEED+fv7a8SIEcrOznZbx/79+xUZGak6deqoUaNGmjhxok6dOuVWs27dOnXv3l0+Pj5q1aqVFi1adDk2DwAAXKQaHVwkqWPHjjp06JD92Lhxoz03YcIE/fOf/9RHH32k9evX6+DBg7rzzjvt+eLiYkVGRqqoqEibN2/WX//6Vy1atEhTpkyxa/bt26fIyEgNGDBAaWlpeuqppzRmzBh9+eWXl3U7AQDA+XlVdwPn4+XlpZCQkDLj+fn5+stf/qLFixfrpptukiQtXLhQ7du315YtW9SnTx+tWrVK33//vVavXq3g4GBde+21ev755/W73/1O8fHx8vb21vz589W8eXO9+uqrkqT27dtr48aNmjVrliIiIs7aV2FhoQoLC+1ll8tVyVsOAADOVOP3uPzwww8KDQ1VixYt9MADD2j//v2SpNTUVJ08eVKDBg2ya9u1a6cmTZooOTlZkpScnKzOnTsrODjYromIiJDL5dLOnTvtmtPXUVpTuo6zmTFjhgICAuxHWFhYpWwvAAA4uxodXHr37q1FixYpISFBb731lvbt26d+/frp6NGjysrKkre3twIDA92eExwcrKysLElSVlaWW2gpnS+dO1eNy+XS8ePHz9rbpEmTlJ+fbz8OHDhwqZsLAADOo0YfKrr55pvtn7t06aLevXuradOm+vDDD1W7du1q7Ezy8fGRj49PtfYAAMDVpkbvcTlTYGCg2rRpo7179yokJERFRUXKy8tzq8nOzrbPiQkJCSlzlVHp8vlqnE5ntYcjAADgzqjgUlBQoIyMDDVu3Fg9evRQrVq1lJSUZM/v2bNH+/fvV3h4uCQpPDxcO3bsUE5Ojl2TmJgop9OpDh062DWnr6O0pnQdAACg5qjRweXpp5/W+vXrlZmZqc2bN+uOO+6Qp6en7rvvPgUEBGj06NGKi4vT2rVrlZqaqlGjRik8PFx9+vSRJA0ZMkQdOnTQQw89pO3bt+vLL7/U5MmTFRMTYx/mefzxx/Xjjz/qmWee0e7du/Xmm2/qww8/1IQJE6pz0wEAQDlq9Dku//73v3Xffffp8OHDCgoKUt++fbVlyxYFBQVJkmbNmiUPDw+NGDFChYWFioiI0Jtvvmk/39PTU8uXL9e4ceMUHh4uPz8/RUdHa/r06XZN8+bNtWLFCk2YMEGvv/66rrnmGr377rvnvBQaAABUD4dlWVZ1N3ElcLlcCggIUH5+vpxOZ6Wvv8fE9yp9nUBNk/rKw9XdQoXxGcXVoKo+oxfzO7RGHyoCAAA4HcEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMgssZ5s2bp2bNmsnX11e9e/fW1q1bq7slAADwfwgup1myZIni4uI0depUffPNN+ratasiIiKUk5NT3a0BAAARXNy89tprevTRRzVq1Ch16NBB8+fPV506dbRgwYLqbg0AAEjyqu4GaoqioiKlpqZq0qRJ9piHh4cGDRqk5OTkMvWFhYUqLCy0l/Pz8yVJLperSvorLjxeJesFapKq+vxcDnxGcTWoqs9o6XotyzpvLcHl//z0008qLi5WcHCw23hwcLB2795dpn7GjBmaNm1amfGwsLAq6xG40gXMeby6WwBwDlX9GT169KgCAgLOWUNwqaBJkyYpLi7OXi4pKdGRI0fUoEEDORyOauwMlcHlciksLEwHDhyQ0+ms7nYAnIHP6JXFsiwdPXpUoaGh560luPyfhg0bytPTU9nZ2W7j2dnZCgkJKVPv4+MjHx8ft7HAwMCqbBHVwOl08p8iUIPxGb1ynG9PSylOzv0/3t7e6tGjh5KSkuyxkpISJSUlKTw8vBo7AwAApdjjcpq4uDhFR0erZ8+euu666zR79mwdO3ZMo0aNqu7WAACACC5u7r33XuXm5mrKlCnKysrStddeq4SEhDIn7OLK5+Pjo6lTp5Y5HAigZuAzevVyWBdy7REAAEANwDkuAADAGAQXAABgDIILAAAwBsEFuECZmZlyOBxKS0ur7lYAVFCzZs00e/bs6m4Dl4DggivayJEj5XA49PjjZW9THRMTI4fDoZEjR17+xoCrQOnn78zH3r17q7s1GIzggiteWFiYPvjgAx0//v9fgnfixAktXrxYTZo0qcbOgCvf0KFDdejQIbdH8+bNq7stGIzggite9+7dFRYWpqVLl9pjS5cuVZMmTdStWzd7LCEhQX379lVgYKAaNGigW2+9VRkZGedcd3p6um6++Wb5+/srODhYDz30kH766acq2xbAND4+PgoJCXF7eHp66rPPPlP37t3l6+urFi1aaNq0aTp16pT9PIfDobffflu33nqr6tSpo/bt2ys5OVl79+7VjTfeKD8/P11//fVun9GMjAzdfvvtCg4Olr+/v3r16qXVq1efs7+8vDyNGTNGQUFBcjqduummm7R9+/Yqez9w6QguuCo88sgjWrhwob28YMGCMndEPnbsmOLi4pSSkqKkpCR5eHjojjvuUElJSbnrzMvL00033aRu3bopJSVFCQkJys7O1j333FOl2wKY7quvvtLDDz+sJ598Ut9//73efvttLVq0SC+++KJb3fPPP6+HH35YaWlpateune6//3499thjmjRpklJSUmRZlmJjY+36goIC3XLLLUpKStK3336roUOHatiwYdq/f/9Ze7n77ruVk5OjlStXKjU1Vd27d9fAgQN15MiRKtt+XCILuIJFR0dbt99+u5WTk2P5+PhYmZmZVmZmpuXr62vl5uZat99+uxUdHV3uc3Nzcy1J1o4dOyzLsqx9+/ZZkqxvv/3WsizLev75560hQ4a4PefAgQOWJGvPnj1VuVmAEaKjoy1PT0/Lz8/Pftx1113WwIEDrZdeesmt9m9/+5vVuHFje1mSNXnyZHs5OTnZkmT95S9/scf+8Y9/WL6+vufsoWPHjtacOXPs5aZNm1qzZs2yLMuyvvrqK8vpdFonTpxwe07Lli2tt99++6K3F5cHt/zHVSEoKEiRkZFatGiRLMtSZGSkGjZs6Fbzww8/aMqUKfr666/1008/2Xta9u/fr06dOpVZ5/bt27V27Vr5+/uXmcvIyFCbNm2qZmMAgwwYMEBvvfWWvezn56cuXbpo06ZNbntYiouLdeLECf3yyy+qU6eOJKlLly72fOlXr3Tu3Nlt7MSJE3K5XHI6nSooKFB8fLxWrFihQ4cO6dSpUzp+/PhZ97hs375dBQUFatCggdv48ePHz3uYGNWH4IKrxiOPPGLvVp43b16Z+WHDhqlp06b685//rNDQUJWUlKhTp04qKioqd30FBQUaNmyYXn755TJzjRs3rtzmAUP5+fmpVatWbmMFBQWaNm2a7rzzzjL1vr6+9s+1atWyf3Y4HGcdK/0j4+mnn1ZiYqL+9Kc/qVWrVqpdu7buuuuuc36GGzdurHXr1pWZCwwMvLANxGVHcMFVY+jQoSoqKpLD4VBERITb3OHDh7Vnzx79+c9/Vr9+/SRJGzduPOf6unfvrk8++UTNmjWTlxcfJeBCde/eXXv27CkTaC7Vpk2bNHLkSN1xxx2Sfg0mmZmZ5+wjKytLXl5eatasWaX2gqrDybm4anh6emrXrl36/vvv5enp6TZXr149NWjQQO+884727t2rNWvWKC4u7pzri4mJ0ZEjR3Tfffdp27ZtysjI0JdffqlRo0apuLi4KjcFMNqUKVP03nvvadq0adq5c6d27dqlDz74QJMnT76k9bZu3VpLly5VWlqatm/frvvvv/+sJ9dL0qBBgxQeHq7hw4dr1apVyszM1ObNm/Xss88qJSXlknpB1SG44KridDrldDrLjHt4eOiDDz5QamqqOnXqpAkTJuiVV14557pCQ0O1adMmFRcXa8iQIercubOeeuopBQYGysODjxZwNhEREVq+fLlWrVqlXr16qU+fPpo1a5aaNm16Set97bXXVK9ePV1//fUaNmyYIiIi1L1797PWOxwOffHFF+rfv79GjRqlNm3aKCoqSv/617/sc2pQ8zgsy7KquwkAAIALwZ+FAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcARsvNzdW4cePUpEkT+fj4KCQkRBEREdq0aVN1twagCvCVtgCMNmLECBUVFemvf/2rWrRooezsbCUlJenw4cPV3RqAKsAeFwDGysvL01dffaWXX35ZAwYMUNOmTXXddddp0qRJuu222+yaMWPGKCgoSE6nUzfddJO2b98u6de9NSEhIXrppZfsdW7evFne3t5KSkqqlm0CcG4EFwDG8vf3l7+/v5YtW6bCwsJya+6++27l5ORo5cqVSk1NVffu3TVw4EAdOXJEQUFBWrBggeLj45WSkqKjR4/qoYceUmxsrAYOHHiZtwbAheDboQEY7ZNPPtGjjz6q48ePq3v37rrhhhsUFRWlLl26aOPGjYqMjFROTo58fHzs57Rq1UrPPPOMxo4dK0mKiYnR6tWr1bNnT+3YsUPbtm1zqwdQcxBcABjvxIkT+uqrr7RlyxatXLlSW7du1bvvvqtjx47piSeeUO3atd3qjx8/rqefflovv/yyvdypUycdOHBAqamp6ty5c3VsBoALQHABcMUZM2aMEhMT9dvf/lZz5szRunXrytQEBgaqYcOGkqT09HT16tVLJ0+e1Keffqphw4Zd5o4BXCiuKgJwxenQoYOWLVum7t27KysrS15eXmrWrFm5tUVFRXrwwQd17733qm3bthozZox27NihRo0aXd6mAVwQ9rgAMNbhw4d1991365FHHlGXLl1Ut25dpaSkaPz48YqMjNS7776r/v376+jRo5o5c6batGmjgwcPasWKFbrjjjvUs2dPTZw4UR9//LG2b98uf39/3XDDDQoICNDy5cure/MAlIPgAsBYhYWFio+P16pVq5SRkaGTJ08qLCxMd999t/7whz+odu3aOnr0qJ599ll98skn9uXP/fv314wZM5SRkaHBgwdr7dq16tu3ryQpMzNTXbt21R//+EeNGzeumrcQwJkILgAAwBjcxwUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxvhfC2Nuq7mdGc8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(data=df, x=\"sex\")\n",
    "plt.title(\"Sex Distribution\")\n",
    "plt.xlabel(\"Sex\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 658
    },
    "id": "z3z-bo3lv_eL",
    "outputId": "a138a7d0-a3bf-4bf9-ec7b-b602938b29e6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=df, x=\"race\")\n",
    "plt.title(\"Race Distribution\")\n",
    "plt.xlabel(\"Race\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f7jLW8XaweL-"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Splitting data into training (80%) and testing (20%) sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "t3WBorEuyh2V"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qs2Djbw_yks2"
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "# Encode categorical columns (e.g., 'sex', 'race', and 'income')\n",
    "df[\"sex\"] = le.fit_transform(df[\"sex\"])\n",
    "df[\"race\"] = le.fit_transform(df[\"race\"])\n",
    "df[\"income\"] = le.fit_transform(df[\"income\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u6mYf5bGzDtB"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Create LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Apply label encoding to categorical columns\n",
    "df[\"workclass\"] = le.fit_transform(df[\"workclass\"])\n",
    "df[\"education\"] = le.fit_transform(df[\"education\"])\n",
    "df[\"marital-status\"] = le.fit_transform(df[\"marital-status\"])\n",
    "df[\"occupation\"] = le.fit_transform(df[\"occupation\"])\n",
    "df[\"relationship\"] = le.fit_transform(df[\"relationship\"])\n",
    "df[\"race\"] = le.fit_transform(df[\"race\"])\n",
    "df[\"sex\"] = le.fit_transform(df[\"sex\"])\n",
    "df[\"native-country\"] = le.fit_transform(df[\"native-country\"])\n",
    "df[\"income\"] = le.fit_transform(df[\"income\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ywkpXJpgzYU6"
   },
   "outputs": [],
   "source": [
    "# Separate features and target variable\n",
    "X = df.drop(\"income\", axis=1)\n",
    "y = df[\"income\"]  # Target variable is 'income'\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HaGztsbwzb7L",
    "outputId": "5faa941a-9772-4c84-d48a-d9652ba26c99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age               int64\n",
      "workclass         int64\n",
      "fnlwgt            int64\n",
      "education         int64\n",
      "education-num     int64\n",
      "marital-status    int64\n",
      "occupation        int64\n",
      "relationship      int64\n",
      "race              int64\n",
      "sex               int64\n",
      "capital-gain      int64\n",
      "capital-loss      int64\n",
      "hours-per-week    int64\n",
      "native-country    int64\n",
      "dtype: object\n",
      "age               0\n",
      "workclass         0\n",
      "fnlwgt            0\n",
      "education         0\n",
      "education-num     0\n",
      "marital-status    0\n",
      "occupation        0\n",
      "relationship      0\n",
      "race              0\n",
      "sex               0\n",
      "capital-gain      0\n",
      "capital-loss      0\n",
      "hours-per-week    0\n",
      "native-country    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train.dtypes)\n",
    "\n",
    "# Check for missing values in the training data\n",
    "print(X_train.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VkAFZHJczgBU"
   },
   "outputs": [],
   "source": [
    "# Handle missing values (fill with the median or mode)\n",
    "X_train = X_train.fillna(X_train.median())  # Example: filling missing values with the median\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 196
    },
    "id": "Z4_ZOF6Szk1c",
    "outputId": "f44caf4f-1770-40f1-f6bf-8642312b0e7c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model\n",
    "model = LogisticRegression(max_iter=1000)  # Increase max_iter to ensure convergence\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DGHt9cbjzoxO",
    "outputId": "1fbd535f-7764-42cf-ed89-83d645199424"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26048, 14)\n",
      "(26048,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 219
    },
    "id": "GKT803Vl0VQp",
    "outputId": "a6d98d13-2d5b-488a-a5d6-4a293816b481"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(max_iter=1000)  # Increase max_iter to ensure convergence\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4LQFYTxS0j_R"
   },
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nb_IaUQ60m1y",
    "outputId": "aa4641c9-7703-4ca5-9a84-5f6e207e9477"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8082\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.94      0.88      4942\n",
      "           1       0.68      0.38      0.49      1571\n",
      "\n",
      "    accuracy                           0.81      6513\n",
      "   macro avg       0.76      0.66      0.69      6513\n",
      "weighted avg       0.79      0.81      0.79      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "\n",
    "# Detailed classification report (includes precision, recall, F1-score)\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "NhLI5pGu3_Sq",
    "outputId": "464a7717-853e-44e6-8019-a4ac17b3762f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['<=50K', '>50K'], yticklabels=['<=50K', '>50K'])\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HEPh6Rey4VY0",
    "outputId": "8708b913-dab6-4fa9-ca75-5deec6b09dde"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', 'version']\n"
     ]
    }
   ],
   "source": [
    "import fairness_indicators\n",
    "print(dir(fairness_indicators))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G9kU5Nu85Mw6",
    "outputId": "060c38cb-1ddd-431c-fc12-fb4c28939ec4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting aif360\n",
      "  Downloading aif360-0.6.1-py3-none-any.whl.metadata (5.0 kB)\n",
      "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.13.1)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.5.3)\n",
      "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.5.2)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360) (3.8.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2024.2)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (3.5.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (24.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (11.0.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (3.2.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360) (1.16.0)\n",
      "Downloading aif360-0.6.1-py3-none-any.whl (259 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m259.7/259.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: aif360\n",
      "Successfully installed aif360-0.6.1\n"
     ]
    }
   ],
   "source": [
    "!pip install aif360\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lSXpj4qO5024",
    "outputId": "297d537f-3f74-45d2-906c-89a2b2700c10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disparate Impact: 0.35933885680457067\n",
      "Mean Difference: -0.1953965374049429\n"
     ]
    }
   ],
   "source": [
    "from aif360.datasets import BinaryLabelDataset\n",
    "from aif360.metrics import BinaryLabelDatasetMetric\n",
    "\n",
    "# Prepare your data (make sure the features and labels are correctly formatted)\n",
    "dataset = BinaryLabelDataset(df=pd.concat([X_test, y_test], axis=1), label_names=['income'], protected_attribute_names=['sex'])\n",
    "\n",
    "# Define the privileged and unprivileged groups (male as privileged, female as unprivileged)\n",
    "privileged_groups = [{'sex': 1}]  # Male\n",
    "unprivileged_groups = [{'sex': 0}]  # Female\n",
    "\n",
    "# Compute fairness metrics\n",
    "metric = BinaryLabelDatasetMetric(dataset, privileged_groups=privileged_groups, unprivileged_groups=unprivileged_groups)\n",
    "\n",
    "# Print the fairness metrics\n",
    "print(f\"Disparate Impact: {metric.disparate_impact()}\")\n",
    "print(f\"Mean Difference: {metric.mean_difference()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KQwOjJEo6pee",
    "outputId": "a43f99b6-175a-4b81-c793-1a60e8cc93e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: aif360[Reweighing] in /usr/local/lib/python3.10/dist-packages (0.6.1)\n",
      "\u001b[33mWARNING: aif360 0.6.1 does not provide the extra 'reweighing'\u001b[0m\u001b[33m\n",
      "\u001b[0mRequirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360[Reweighing]) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360[Reweighing]) (1.13.1)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360[Reweighing]) (1.5.3)\n",
      "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360[Reweighing]) (1.5.2)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360[Reweighing]) (3.8.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360[Reweighing]) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360[Reweighing]) (2024.2)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360[Reweighing]) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360[Reweighing]) (3.5.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (24.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (11.0.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360[Reweighing]) (3.2.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360[Reweighing]) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install 'aif360[Reweighing]'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KmX57O1P6t1G",
    "outputId": "a4e92245-7531-426d-b4e9-312414dddc45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disparate Impact after Reweighing: 0.9999999999999998\n",
      "Mean Difference after Reweighing: -5.551115123125783e-17\n"
     ]
    }
   ],
   "source": [
    "from aif360.algorithms.preprocessing import Reweighing\n",
    "\n",
    "# Apply Reweighing method to the dataset\n",
    "RW = Reweighing(privileged_groups=privileged_groups, unprivileged_groups=unprivileged_groups)\n",
    "dataset_transf = RW.fit_transform(dataset)\n",
    "\n",
    "# Check the fairness metrics after reweighing\n",
    "metric_transf = BinaryLabelDatasetMetric(dataset_transf, privileged_groups=privileged_groups, unprivileged_groups=unprivileged_groups)\n",
    "print(f\"Disparate Impact after Reweighing: {metric_transf.disparate_impact()}\")\n",
    "print(f\"Mean Difference after Reweighing: {metric_transf.mean_difference()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ryXK5TNL7xEG",
    "outputId": "bb8224bd-07a4-4429-a399-2f25e57a3662"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy before mitigation: 0.8082296944572394\n",
      "Accuracy after mitigation: 0.8082296944572394\n",
      "Disparate Impact before mitigation: 0.35933885680457067\n",
      "Disparate Impact after mitigation: 0.9999999999999998\n",
      "Mean Difference before mitigation: -0.1953965374049429\n",
      "Mean Difference after mitigation: -5.551115123125783e-17\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Make predictions on the test set before mitigation\n",
    "y_pred_before = model.predict(X_test)\n",
    "accuracy_before = accuracy_score(y_test, y_pred_before)\n",
    "\n",
    "# Make predictions on the transformed dataset after mitigation\n",
    "y_pred_after = model.predict(X_test)\n",
    "accuracy_after = accuracy_score(y_test, y_pred_after)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Accuracy before mitigation: {accuracy_before}\")\n",
    "print(f\"Accuracy after mitigation: {accuracy_after}\")\n",
    "\n",
    "# Compare fairness metrics before and after mitigation\n",
    "print(f\"Disparate Impact before mitigation: {metric.disparate_impact()}\")\n",
    "print(f\"Disparate Impact after mitigation: {metric_transf.disparate_impact()}\")\n",
    "print(f\"Mean Difference before mitigation: {metric.mean_difference()}\")\n",
    "print(f\"Mean Difference after mitigation: {metric_transf.mean_difference()}\")\n"
   ]
  }
 ],
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  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
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}