dv-coffee-consumption.ipynb

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     "text": [
      "/kaggle/input/coffee-consumption-in-india/coffee_consumption_india.csv\n"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "df = pd.read_csv(\"/kaggle/input/coffee-consumption-in-india/coffee_consumption_india.csv\")"
   ]
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    {
     "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>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Daily Cups Consumed</th>\n",
       "      <th>Preferred Coffee Brand</th>\n",
       "      <th>Consumption Setting</th>\n",
       "      <th>Monthly Coffee Expense (INR)</th>\n",
       "      <th>Reason for Consumption</th>\n",
       "      <th>Milk Preference</th>\n",
       "      <th>Sugar Preference</th>\n",
       "      <th>Awareness of Specialty Coffee</th>\n",
       "      <th>Frequency of Café Visits (Per Week)</th>\n",
       "      <th>Preferred Time of Consumption</th>\n",
       "      <th>Influence of Social Media on Choice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Other</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>4</td>\n",
       "      <td>Bru</td>\n",
       "      <td>Home</td>\n",
       "      <td>1060</td>\n",
       "      <td>Taste</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Sugar</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>26-35</td>\n",
       "      <td>Male</td>\n",
       "      <td>Cold Coffee</td>\n",
       "      <td>3</td>\n",
       "      <td>Others</td>\n",
       "      <td>Home</td>\n",
       "      <td>3972</td>\n",
       "      <td>Taste</td>\n",
       "      <td>No</td>\n",
       "      <td>Less Sugar</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>50+</td>\n",
       "      <td>Female</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>2</td>\n",
       "      <td>Bru</td>\n",
       "      <td>Home</td>\n",
       "      <td>666</td>\n",
       "      <td>Habit</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Sugar</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>Morning</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>36-50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Filter Coffee</td>\n",
       "      <td>3</td>\n",
       "      <td>Others</td>\n",
       "      <td>Home</td>\n",
       "      <td>3644</td>\n",
       "      <td>Socializing</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium Sugar</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>26-35</td>\n",
       "      <td>Female</td>\n",
       "      <td>Instant</td>\n",
       "      <td>3</td>\n",
       "      <td>Bru</td>\n",
       "      <td>Home</td>\n",
       "      <td>1885</td>\n",
       "      <td>Socializing</td>\n",
       "      <td>No</td>\n",
       "      <td>High Sugar</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City        State Age Group  Gender    Coffee Type  \\\n",
       "0      Delhi        Delhi     18-25   Other       Espresso   \n",
       "1      Delhi        Delhi     26-35    Male    Cold Coffee   \n",
       "2  Bangalore    Karnataka       50+  Female       Espresso   \n",
       "3    Kolkata  West Bengal     36-50    Male  Filter Coffee   \n",
       "4     Mumbai  Maharashtra     26-35  Female        Instant   \n",
       "\n",
       "   Daily Cups Consumed Preferred Coffee Brand Consumption Setting  \\\n",
       "0                    4                    Bru                Home   \n",
       "1                    3                 Others                Home   \n",
       "2                    2                    Bru                Home   \n",
       "3                    3                 Others                Home   \n",
       "4                    3                    Bru                Home   \n",
       "\n",
       "   Monthly Coffee Expense (INR) Reason for Consumption Milk Preference  \\\n",
       "0                          1060                  Taste             Yes   \n",
       "1                          3972                  Taste              No   \n",
       "2                           666                  Habit             Yes   \n",
       "3                          3644            Socializing             Yes   \n",
       "4                          1885            Socializing              No   \n",
       "\n",
       "  Sugar Preference Awareness of Specialty Coffee  \\\n",
       "0         No Sugar                            No   \n",
       "1       Less Sugar                            No   \n",
       "2         No Sugar                            No   \n",
       "3     Medium Sugar                            No   \n",
       "4       High Sugar                            No   \n",
       "\n",
       "   Frequency of Café Visits (Per Week) Preferred Time of Consumption  \\\n",
       "0                                    6                       Morning   \n",
       "1                                    4                       Evening   \n",
       "2                                    6                       Morning   \n",
       "3                                    3                     Afternoon   \n",
       "4                                    4                     Afternoon   \n",
       "\n",
       "  Influence of Social Media on Choice  \n",
       "0                                 Yes  \n",
       "1                                 Yes  \n",
       "2                                  No  \n",
       "3                                 Yes  \n",
       "4                                  No  "
      ]
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   ],
   "source": [
    "df.head()"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 16 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   City                                 10000 non-null  object\n",
      " 1   State                                10000 non-null  object\n",
      " 2   Age Group                            10000 non-null  object\n",
      " 3   Gender                               10000 non-null  object\n",
      " 4   Coffee Type                          10000 non-null  object\n",
      " 5   Daily Cups Consumed                  10000 non-null  int64 \n",
      " 6   Preferred Coffee Brand               10000 non-null  object\n",
      " 7   Consumption Setting                  10000 non-null  object\n",
      " 8   Monthly Coffee Expense (INR)         10000 non-null  int64 \n",
      " 9   Reason for Consumption               10000 non-null  object\n",
      " 10  Milk Preference                      10000 non-null  object\n",
      " 11  Sugar Preference                     10000 non-null  object\n",
      " 12  Awareness of Specialty Coffee        10000 non-null  object\n",
      " 13  Frequency of Café Visits (Per Week)  10000 non-null  int64 \n",
      " 14  Preferred Time of Consumption        10000 non-null  object\n",
      " 15  Influence of Social Media on Choice  10000 non-null  object\n",
      "dtypes: int64(3), object(13)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
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   "id": "821201aa",
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     "text": [
      "\n",
      "Estatísticas:\n",
      "       Daily Cups Consumed  Monthly Coffee Expense (INR)  \\\n",
      "count         10000.000000                  10000.000000   \n",
      "mean              3.006200                   2575.385300   \n",
      "std               1.421254                   1387.152155   \n",
      "min               1.000000                    200.000000   \n",
      "25%               2.000000                   1360.000000   \n",
      "50%               3.000000                   2578.000000   \n",
      "75%               4.000000                   3775.250000   \n",
      "max               5.000000                   4999.000000   \n",
      "\n",
      "       Frequency of Café Visits (Per Week)  \n",
      "count                         10000.000000  \n",
      "mean                              3.021100  \n",
      "std                               2.002912  \n",
      "min                               0.000000  \n",
      "25%                               1.000000  \n",
      "50%                               3.000000  \n",
      "75%                               5.000000  \n",
      "max                               6.000000  \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nEstatísticas:\")\n",
    "print(df.describe())"
   ]
  },
  {
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   "id": "2c8b3fcd",
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   },
   "source": [
    "# Analises das frequências no dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58af997a",
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   },
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")"
   ]
  },
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   },
   "source": [
    "# City"
   ]
  },
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   "outputs": [
    {
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PR03asGEDgoKCsHXrVowaNQoAcOjQIXz88ceYOXMm4uPj4eTkhBUrVuDo0aMt5vfnn39i4cKFyM7ORmRkJPr06YP6+nr07t0bmzZtQkJCAubNmwd/f38kJiYCAFxcXDBmzBjZ21/m5uYA1Kvf9GA4ODjI/n+jo6Nhb29/T3kFBQVJHcT6+vpYunQp3nvvPbi5uSE6OhpCCHzwwQeyNIWFhUhKSkJwcDC2b9+Os2fPYvXq1VJ8ZWUl7OzsEBkZiS+//BJ6enpwc3OTdTrTw+XGjRuYOXMmoqOjsXPnTigUCsybN6/FaVbXr18PHx8fREVFQUdHB3PmzLnrDtrU1FTk5+cjNDQUW7duvZ+PQe0gOTkZ8+fPxyuvvILY2Fh8/vnnMDU1bXLfkJAQfPLJJ9ixYweGDRsmhe/btw+dO3fGl19+iXfffReBgYH44YcfANzqWPHw8EBZWRnCw8MRGhqKCxcuYPHixQBuvcFgbGyM48ePA7j1gFahUODnn3+WHrCmpaVh6NChbflneCRdu3YNKSkpmDJlCjp37qwS3/DNks2bN2PMmDGIi4vDyy+/DC8vL+k+qLy8HNOnT8fzzz+PqKgobN++HZcvX8aiRYtk+TVXT9Q5TkttIGpbTzzxBN58802V+4/o6GiMHj0aXbt2RWVlJdzd3WFgYICYmBh4enrCz89Ptv/d1BdNTU3s2rVLejtLoVBg5cqVOHDgANatW4ejR49iw4YNsnQ3b97Eli1b4Ofnh127dqG8vFz6vgGAqqoqvPLKK9i5cyf27dsHa2trzJkzBxcvXpT28fHxQXFxMcLCwhAQEIDIyMgWB809KpKSkqCvrw99fX2MHz9eams25O/vDx8fH0RHR0NDQwMrVqyQxRcWFuL777/H9u3b8emnnyIqKgpubm64dOkSwsPD4eXlhY0bNyIzM1NK09LzmMTERHz22WdYvHgxoqOjoauri8jISNlx1W3fbtq0CaNHj8b+/fsxbtw4LFmyBLm5uVJ8S/dU6pSF2tbjjz9+V+3Z0NBQDBw4ELGxsZg8eTJWr14tdSDW1NRg1qxZ6NKlCyIiIrBr1y507twZs2fP5tt6dGeCiJrk7e0t5s6d22TcokWLxJgxY0RaWpoYPHiwqK6ulsWPHDlS7N69WwghREBAgBg/frwsXxsbG1FbWyuFLViwQCxatEjatrGxEaGhoa34aag53t7ewtjYWJiZmQkzMzOhVCrFiBEjRFZW1h3TbN++XUyYMEHaDggIEC+88IKoqKiQwvz8/ISjo6MQQoiKigphYmIikpKSpPjy8nLxwgsviDVr1tzxOKdOnRJKpVJcv35dCCHE0aNHhVKpFGVlZUIIIZYsWSJmzpwpS+Pn5yfGjh0rbdvY2AgPDw/ZPitWrBD/+te/ZGFpaWnCyMhI3Lx5847lobvj7e0tTExMhFKpFD/++KMsbtKkSWLVqlWysAULFghXV1dpW6lUikOHDgkhhLhw4YJQKpUiLS1NTJ8+XTg7O4vy8vJmj//+++8LT09PWXnu9L3WUOP6TW3v9rm5fPmyGDhwoCgqKhJFRUVi0KBB4vLly2Lu3LnC29tbCNH0NWL8+PEiICBA2lYqlcLf31/azsjIEEqlUuzdu1cKO3DggBg0aJC0HRAQIIyNjcXvv/8uhR0+fFgYGRmJP/74o8ly19XVCXNzc/Htt9/Kjn273lL7aO5//fLly0KpVIqcnBwhxP+/W37++WchxP+vMwkJCVKaq1evClNTUymscdtGCCFCQ0OFjY2NrAzDhw9XaSPRg3WnuhAdHS1efPFFWdihQ4eEUqmUtidNmiSWLl16x7xvfxetX79ejBgxQvzyyy+y+KlTpwpnZ2dZmIODg9iwYYMQQogjR44IY2NjcfHiRSn+3LlzQqlUiszMTCGEEGvXrhVubm5CCCF27twpFi1aJMaPHy8OHz4shBBi1KhRYs+ePS3+HejuZGZmCqVSKf773/82u1/ja01lZaVQKpXS+QkMDBQuLi6yNMXFxUKpVIq8vDwhRMv1RJ3jNOVe20CkqvG90u2fQYMGSfclmZmZwtjYWFy6dEkIIURpaal4/vnnxbFjx4QQQuzevVtYWFjI7jMiIyNl1x9164udnV2LZU5KShIWFhbSdnR0tFAqleLkyZNS2Pnz52XfN0158803RXh4uBBCiLy8PJX9b+fBe/db14ydO3cKIYSoqakRlpaW4ujRo0KI/7ctGt4PJScnC6VSKdWJpu6nXVxchI2Njairq5PCRo8eLbZu3SqEEGo9j5k0aZJYvXq1LN7R0VGlHdPQndq37733nko+vr6+d8yn8T3VvZSF7l3D7/36+nrxww8/iIEDB4p169aJqVOnqjyHaXi/JcStdo6Xl5e0XV9fL4YNGyYiIyOFEELExsaK0aNHi/r6emmf6upqYWpqKlJSUtryo9FfWNtOBk30NyWEgEKhQE5ODqqqqmBpaSmLv3nzZpPTvdw2YMAAaGhoSNu6urqyKT/owbO0tJRGfZeVlWHXrl1wdXXF3r170bdvXyQmJiIsLAwXLlxAVVUVamtr0bVrV1keffv2lYX16tVLGiFVVFSEmpoa2UjSbt264bnnnpPlkZWVhc2bNyM7OxtlZWXSKKLi4mIMGDBApdx5eXl4/fXXZWGDBw9GWFgY6urqpHo2cOBA2T7Z2dnIyclBfHy8FCaEQH19PYqKimBgYKDW341aZmhoiKtXr+Kzzz6DqakpunTpAuDWuZs0aZJs39vnrjlLly5F79698fnnn6NTp06yuIiICERHR+PixYuorq5GTU0NjIyMWiyjOvWbHgwdHR1pOgchBF599VXo6OjcU14Npxjq2bMnAECpVMrCqqurcf36del8P/3003jqqaekfczNzVFfX4/8/Hzo6uqitLQUGzduxPHjx3H58mXU19fjxo0bstGi9HApKChAQEAAMjMzcfXqVdl1pWF9aMzMzEz6XVtbG88995xsChF1KJVKrhf0F3b27Fk4Ojo2u09oaChu3LiB6Oho9O/fXyW+8VRnurq6UtsoNzcXvXv3xtNPPy3FDxgwAFpaWsjLy4OpqSmGDh2KqKgo1NXVIS0tDSNGjMCTTz6J48ePw9DQEL/++issLCxa4dNSQ6LRKPbmNDzHnTt3RteuXaWppLKzs3Hs2DHpDdyGCgsLpXZwc/VEneMA994GIvU0vFe6LTMzE++++y4AwNTUFAMGDEBsbCzc3NwQFxeHPn36SG+s5ebmwtDQEI8//riUvnG9ULe+mJiYqMT/+OOP2Lp1K/Ly8nD9+nXU1dWhuroaN27ckNZe7dixIwYNGiSlMTAwgJaWFnJzc2FqaorKykps3rwZycnJKCkpQV1dHW7evCm1cXJzc9GxY0fZfdXtPB51eXl5OH36NAIDAwHc+luPHTsWUVFRsmclDf+PdXV1Adya7u32WoiN76effPJJaGhooEOHDrKw298P6jyPyc3NhZOTkyzezMxMmqIZgNrt28Z108zMTDbFXkv3VOqUhVpXcnIyzM3NUVNTAyEEbG1t4enpCXd3d7XSN6yzCoVCVv+ys7NRWFiIwYMHy9JUV1c3+zyQHm3siCG6B7m5uejXrx8qKyuhq6uL8PBwlX26det2x/SNF8RVKBR3dcNDre+JJ56Anp6etG1iYoIhQ4bgyy+/xKuvvgovLy94enrCysoK3bp1Q0JCAkJDQ2V5NLXQ8d2c16qqKsyaNQtWVlb45JNP0KNHDxQXF2PWrFn3vV7H7RuQhsdycnLCtGnTVPZt+ECE7t9TTz2FgIAAvPPOO5g9ezZCQkLuq5PjlVdeQVxcHDIyMmTTvyQkJMDPzw/e3t4wNzdHly5d8J///Ef26n5TMjIy1Krf9OA4ODhIU4b5+vqqxCsUCpWw2tpalTBNTU2VNE2FtTRFVUPe3t64du0aVq5ciT59+uCxxx7DpEmTuKbQQ2zOnDno27cv1qxZg169eqG+vh62trb3dc6aarc0VQcbX3vo4dGhQweVc9i4TjTu7G/KkCFDkJycjKSkJLi5uanE32+bd+jQoaisrMSZM2eQlpaGxYsXQ1dXF9u2bYORkRF69eqFZ599Vu38SD16enpQKBRqdb42vK4At87x7etKVVUVbGxs4OXlpZLu9kNYQL160txx7rUNROprfK8EAL///rts29HREREREXBzc0NMTAzs7e2bbLPcibr1pfG1paioCO7u7nB2dsbixYvRvXt3/PTTT1i5ciVqamrUvhb5+fnhxx9/hLe3N5555hl06tQJCxYsYBtHDVFRUaitrYW1tbUUJoTAY489Jq1PCsj/15tqhzb1XdBU2O009/o8prHWaN/ynurhdLsTWVNTE7169ZLqk7pt2eauT1VVVTAxMcEnn3yiku5eB9LR3x87YojuUmpqKn755RfMmDEDvXv3RmlpKTQ0NNCvX7/2Lhq1IoVCAYVCgerqamRkZKBPnz6YO3euFH+3o7/79esHTU1NnD59WhrxU1FRgYKCAmmx3Ly8PFy7dg1eXl5SZ0hWVlaz+err6yM9PV0Wlp6ejmeffVb21lVjzz//PM6fP69yQ0Vto2/fvvjiiy+kzpjt27dL527ChAnSfunp6U2++dSQs7Mz/vGPf8DDwwNbt26VRgKnp6fD3NwcU6ZMkfZtPBJHU1NT5aF7a9Rval3W1taoqamBQqGAlZWVSryOjg7++OMPafv69euttihkcXExLl26JL0Vc/LkSXTo0EEahZqeng5fX19pcezi4mJcvXq1VY5Nre/q1avIz8/HmjVrpGvNiRMn1Ep78uRJ6XpVVlaGgoIC6OvrA7hVB0tLS6U3hAE0uegyPbx69OiByspKVFVVSWuANF7cWKlUIjU1FQ4ODnfMZ9CgQZgyZQpmz54NDQ0NaeFsdRgYGOD3339HcXGx1O45f/48ysvLpTdztbS0YGhoiIiICGhqasLAwAA9e/bEokWL8N133/FtmDaira0NKysrREREYNq0aSrrxJSXl6v1FoCJiQm++uor9O3bt8kBS61FnTYQtb3x48djw4YNCAsLw/nz52VtXAMDA+zfvx/V1dXSWzEnT56Upb/X+nLmzBkIIeDj4yO9OZGUlKSyX21tLbKysqQZCvLy8mTfNxkZGZgwYYK0nmNlZSV+++03Kb2+vv4d83iU1dbWYv/+/fDx8cGIESNkcfPmzcOBAwek9kNrMzExafF5jIGBATIzM2FnZyeFNe6kVbd9e/LkSZV8jI2NAah3T6VOWah1NdWJDNxqy5aUlEjbdXV1OHfunMrbVc0xMTFBUlISevbsydkkSG0dWt6F6NH1559/oqSkBJcuXcKZM2cQHBwMDw8P2NjYwM7ODsOHD4eZmRnmzZuHI0eOoKioCOnp6fD398fp06fbu/h0F26f65KSEuTm5uLDDz+URmXp6emhuLgYCQkJKCwsRFhYGL7++uu7yr9r166ws7PD+vXrcfToUZw7dw4rV66UOnwAoE+fPtDU1ER4eDguXLiAb775BkFBQc3m6+LigtTUVAQGBiI/Px/79u1DREQEXFxcmk3n6uqKjIwMfPDBBzh79iwKCgrw9ddfqyzcTa3n6aefRnh4OK5cuYLZs2fDyckJ+/btQ2RkJAoKChAaGopDhw61eO4AYNq0aVi4cCHc3d2lh6p6enrIyspCSkoK8vPzsXHjRpXvob59+yInJwd5eXm4cuUKampqWqV+U+vS0NBAUlISEhMTm+xQfemllxAXF4cTJ04gJycH3t7esikb7sfjjz8OHx8fZGdn48SJE1izZg3GjBkjjUR99tlnERcXh9zcXGRmZsLLy0utUfPUPrp37w5tbW3s2bMHv/76K1JTU7Fu3Tq10gYFBUmDT3x8fNCjRw+MHDkSwK3RhVeuXEFISAgKCwsRERGBlJSUtvwodB8qKipw9uxZ2c+AAQPwxBNP4N///jcKCwsRHx+vstD2/PnzkZCQgICAAOTm5iInJwfbtm1TyX/w4MHYtm0bNm/eLFuUuCXDhw+HUqmEl5cXzpw5g1OnTmHZsmWwsLCQTR9kYWGB+Ph4aYojbW1tGBgYICkpSQqj1ufr64v6+no4Ojriq6++QkFBAXJzcxEWFqYyteqdTJ48GWVlZViyZAlOnTqFwsJCpKSkYPny5airq2u1sqrTBqK21717d4waNQrr16/HiBEj0Lt3bynO1tYWCoUCq1atwvnz53H48GHs2LFDlv5e64uenh5qamqke6jY2Fjs3r1bZT9NTU18+OGHyMzMRFZWFpYvXw4zMzOpU0VPTw+HDh3C2bNnkZ2djaVLl8oGMOnr68Pa2hq+vr5SHqtWrXrk20HJyckoKyvDxIkToVQqZT9vvPEGoqKi2uzY6jyPeeeddxAdHY3o6Gjk5+cjICAA586dk+Wjbvv24MGDiIqKkvI5deoUpk6dCgBq3VOpUxZ6MF566SUcPnwYycnJyM3NxerVq++6U3XcuHHo0aMH5s6dixMnTuDChQs4duwY1qxZo/LGINFt7IghakZKSgqsrKzw2muvYfbs2Th27BhWrVqFoKAgaGhoQKFQYNu2bRg6dCiWL1+Of/7zn1iyZAl+++03PPnkk+1dfLoLt8+1lZUVHB0dcfr0aWzatAmWlpZ4/fXXMX36dHzwwQd46623kJGRIRvpoi4fHx+YmZlhzpw5mDlzJgYPHgwDAwNpVJiOjg7WrVuHgwcPYuzYsQgJCYG3t3ezeZqYmGDjxo1ITEzEuHHjEBAQgAULFsDe3r7ZdEZGRggPD0dBQQEmT56MCRMmICAgAL169brrz0Xq6927N8LDw3H16lXs2rUL7777Lnbs2AFbW1vs3r0bH3/8sdqjcGbMmIEFCxbAzc0N6enpcHJywhtvvIHFixfj7bffxrVr1zB58mRZmrfffhvPPfccHBwcMGzYMKSnp7da/abW1bVr1zuOrHJ3d8fQoUPh7u4Od3d3jBw5Es8880yrHPeZZ57BqFGj4OrqChcXFxgaGsqmR/voo49QVlaGCRMmYNmyZZg2bZq0/gw9POrr69GxY0d06NAB/v7+OHPmDGxtbbF27VosW7ZMrTyWLl2Kjz76CPb29igtLcWWLVuk9V4MDAzg6+uLyMhIvPXWWzh16pRancjUPo4fPw47OzvZT1BQEDZs2IDvv/8e48aNQ0JCAjw9PWXpLC0tsWnTJnz77bd46623MH369Ds+3B4yZAi2bduGTZs2NTlFTFMUCgWCgoKgpaWFqVOnYsaMGejfvz/8/f1l+1lYWKCurk729ktTYdS6+vfvj5iYGFhaWsLPzw+2traYOXMmUlNTVdYKuZOnnnoKu3btQn19PWbNmoVx48bh448/Rrdu3VptAAEAtdpA9GBMnDgRNTU1Km/SdenSBcHBwfjll19gZ2cHf39/lSnI7rW+GBkZYfny5QgJCYGtrS3i4+OxZMkSlf06deoEV1dXLF26FM7OzujcubPs+8bHxwdaWlpwcnLCnDlzYG1trbIezdq1a9GrVy9MnToVnp6eePvttx/5dlBUVBSGDx/e5FRgo0ePRlZWFnJyctrk2Oo8jxk7diw8PDywYcMG2Nvb4+LFi3B2dpblo2771tPTE4mJiRg/fjxiY2Px6aefSrMZqHNPpU5Z6MFwcHCAnZ0dvL29MW3aNPTv3/+u3oYBbr1t88UXX6BPnz6YP38+xo4di5UrV6K6uppvyNAdKQQXpiAiahdVVVV4+eWX4e3t3eJiuEREROqaNWsW9PT0ZPOyq+vYsWN45513kJaWxgWIiYjorsTGxmLt2rVISUmROu+JiIjoFq4RQ0T0gPz888/Iy8uDqakpKioqEBgYCODW6BkiIqL7VVZWhvT0dBw/fpwjLImI6IG5ceMGSkpKEBISAicnJ3bCEBERNYEdMURED9COHTuQn58PTU1NmJiYICIiAjo6Ou1dLCIi+htYsWIFTp8+DRcXF3byExHRA7N9+3YEBwdjyJAhcHNza+/iEBERPZQ4NRkREREREREREREREVEbab0V8oiIiIiIiIiIiIiIiEiGHTFERERERERERERERERthB0xREREREREREREREREbYQdMURERERERERERERERG2EHTFERERERERERERERERthB0xRERERERELTA0NMTXX3/d3sUgIiIiIqK/IIUQQrR3IYiIiIiIiNpTSUkJgoODkZycjEuXLqFnz54wNjbG9OnTMWzYMJSUlKB79+547LHHUFRUhNdffx2xsbEwNjZu76ITEREREdFDrmN7F4CIiIiIiKg9FRUVwdnZGVpaWli2bBmUSiVqa2tx5MgRvP/++zh48CB0dXXbu5hERERERPQXxTdiiIiIiIjokebq6oqcnBwcPHgQnTt3lsWVl5dDS0sLhoaGCAwMxMiRI2FoaCjbx8LCAgsWLMCMGTOQnJws67T56KOPcObMGURGRj6Qz0JERERERA8frhFDRERERESPrGvXriElJQVTpkxR6YQBAC0tLZWwvXv3AgB27tyJI0eO4LPPPsPQoUPRr18/7N+/X9qvpqYG8fHxcHBwaLsPQEREREREDz12xBARERER0SOrsLAQQgjo6+urnUZHRwcAoK2tDV1dXWhrawMAJk6ciJiYGGm/7777DtXV1RgzZkyrlpmIiIiIiP5a2BFDRERERESPrNacqdne3h6FhYU4efIkACAmJgZjxoxp8k0bIiIiIiJ6dLAjhoiIiIiIHll6enpQKBTIy8u777x69uwJGxsbxMTEoLS0FCkpKZyWjIiIiIiI2BFDRERERESPLm1tbVhZWSEiIgJVVVUq8eXl5SphmpqaAIC6ujqVuIkTJyIxMRF79uxB//798eKLL7Z+oYmIiIiI6C+FHTFERERERPRI8/X1RX19PRwdHfHVV1+hoKAAubm5CAsLw6RJk1T279mzJzp16oSUlBSUlpaioqJCirO2tkbXrl2xZcsW2NvbP8iPQUREREREDyl2xBARERER0SOtf//+iImJgaWlJfz8/GBra4uZM2ciNTUVq1evVtm/Y8eOWLVqFfbs2QNra2t4eHhIcR06dMCECRNQX18POzu7B/chiIiIiIjooaUQrbk6JRERERER0SNuxYoVuHLlCoKDg9u7KERERERE9BDo2N4FICIiIiIi+juoqKhATk4ODhw4gC1btrR3cYiIiIiI6CHBjhgiIiIiIqJW4OHhgVOnTsHJyQkjRoxo7+IQEREREdFDglOTERERERERERERERERtZEO7V0AIiIiIiIiIiIiIiKivyt2xBAREREREREREREREbURdsQQERERERERERERERG1EXbEEBERERERERERERERtRF2xBAREREREREREREREbURdsQQERERERERERERERG1EXbEEBERERERERERERERtRF2xBAREREREREREREREbWR/wG5NiniqhbOwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['City'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição das Cidades')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d3df04d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:47.912146Z",
     "iopub.status.busy": "2025-02-08T19:14:47.911734Z",
     "iopub.status.idle": "2025-02-08T19:14:47.923228Z",
     "shell.execute_reply": "2025-02-08T19:14:47.922186Z"
    },
    "papermill": {
     "duration": 0.02712,
     "end_time": "2025-02-08T19:14:47.925041",
     "exception": false,
     "start_time": "2025-02-08T19:14:47.897921",
     "status": "completed"
    },
    "tags": []
   },
   "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>City</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>1037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pune</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lucknow</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jaipur</td>\n",
       "      <td>941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City  Frequencia\n",
       "0  Ahmedabad        1037\n",
       "1  Hyderabad        1028\n",
       "2      Delhi        1025\n",
       "3  Bangalore        1007\n",
       "4       Pune        1005\n",
       "5    Kolkata        1001\n",
       "6     Mumbai         999\n",
       "7    Lucknow         981\n",
       "8    Chennai         976\n",
       "9     Jaipur         941"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['City'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['City', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d293eaf7",
   "metadata": {
    "papermill": {
     "duration": 0.011486,
     "end_time": "2025-02-08T19:14:47.948591",
     "exception": false,
     "start_time": "2025-02-08T19:14:47.937105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "120fdd2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:47.973837Z",
     "iopub.status.busy": "2025-02-08T19:14:47.973470Z",
     "iopub.status.idle": "2025-02-08T19:14:48.348591Z",
     "shell.execute_reply": "2025-02-08T19:14:48.347368Z"
    },
    "papermill": {
     "duration": 0.389774,
     "end_time": "2025-02-08T19:14:48.350312",
     "exception": false,
     "start_time": "2025-02-08T19:14:47.960538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['State'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição dos Estados')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "21660efd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:48.381793Z",
     "iopub.status.busy": "2025-02-08T19:14:48.381411Z",
     "iopub.status.idle": "2025-02-08T19:14:48.393978Z",
     "shell.execute_reply": "2025-02-08T19:14:48.392841Z"
    },
    "papermill": {
     "duration": 0.028788,
     "end_time": "2025-02-08T19:14:48.395850",
     "exception": false,
     "start_time": "2025-02-08T19:14:48.367062",
     "status": "completed"
    },
    "tags": []
   },
   "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>State</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>1037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State  Frequencia\n",
       "0    Maharashtra        2004\n",
       "1        Gujarat        1037\n",
       "2      Telangana        1028\n",
       "3          Delhi        1025\n",
       "4      Karnataka        1007\n",
       "5    West Bengal        1001\n",
       "6  Uttar Pradesh         981\n",
       "7     Tamil Nadu         976\n",
       "8      Rajasthan         941"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['State'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['State', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cadd9b41",
   "metadata": {
    "papermill": {
     "duration": 0.011991,
     "end_time": "2025-02-08T19:14:48.420284",
     "exception": false,
     "start_time": "2025-02-08T19:14:48.408293",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Age Group\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3e2175d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:48.446549Z",
     "iopub.status.busy": "2025-02-08T19:14:48.446112Z",
     "iopub.status.idle": "2025-02-08T19:14:48.769090Z",
     "shell.execute_reply": "2025-02-08T19:14:48.768036Z"
    },
    "papermill": {
     "duration": 0.338963,
     "end_time": "2025-02-08T19:14:48.771624",
     "exception": false,
     "start_time": "2025-02-08T19:14:48.432661",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Age Group'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição das Idades dos Grupos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66134416",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:48.814463Z",
     "iopub.status.busy": "2025-02-08T19:14:48.813987Z",
     "iopub.status.idle": "2025-02-08T19:14:48.832512Z",
     "shell.execute_reply": "2025-02-08T19:14:48.830857Z"
    },
    "papermill": {
     "duration": 0.038884,
     "end_time": "2025-02-08T19:14:48.834850",
     "exception": false,
     "start_time": "2025-02-08T19:14:48.795966",
     "status": "completed"
    },
    "tags": []
   },
   "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>Age Group</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50+</td>\n",
       "      <td>2576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36-50</td>\n",
       "      <td>2488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18-25</td>\n",
       "      <td>2469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26-35</td>\n",
       "      <td>2467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group  Frequencia\n",
       "0       50+        2576\n",
       "1     36-50        2488\n",
       "2     18-25        2469\n",
       "3     26-35        2467"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Age Group'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Age Group', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44603361",
   "metadata": {
    "papermill": {
     "duration": 0.0157,
     "end_time": "2025-02-08T19:14:48.870738",
     "exception": false,
     "start_time": "2025-02-08T19:14:48.855038",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25c56449",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:48.899905Z",
     "iopub.status.busy": "2025-02-08T19:14:48.899475Z",
     "iopub.status.idle": "2025-02-08T19:14:49.220417Z",
     "shell.execute_reply": "2025-02-08T19:14:49.218975Z"
    },
    "papermill": {
     "duration": 0.338572,
     "end_time": "2025-02-08T19:14:49.222646",
     "exception": false,
     "start_time": "2025-02-08T19:14:48.884074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Gender'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição dos gêneros')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3790da59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:49.251833Z",
     "iopub.status.busy": "2025-02-08T19:14:49.251456Z",
     "iopub.status.idle": "2025-02-08T19:14:49.263497Z",
     "shell.execute_reply": "2025-02-08T19:14:49.262236Z"
    },
    "papermill": {
     "duration": 0.028659,
     "end_time": "2025-02-08T19:14:49.265445",
     "exception": false,
     "start_time": "2025-02-08T19:14:49.236786",
     "status": "completed"
    },
    "tags": []
   },
   "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>Gender</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other</td>\n",
       "      <td>3373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>3361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>3266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Frequencia\n",
       "0   Other        3373\n",
       "1    Male        3361\n",
       "2  Female        3266"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Gender'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Gender', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa6532c",
   "metadata": {
    "papermill": {
     "duration": 0.014018,
     "end_time": "2025-02-08T19:14:49.293889",
     "exception": false,
     "start_time": "2025-02-08T19:14:49.279871",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Coffee Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "673c1830",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:49.323864Z",
     "iopub.status.busy": "2025-02-08T19:14:49.323481Z",
     "iopub.status.idle": "2025-02-08T19:14:49.652191Z",
     "shell.execute_reply": "2025-02-08T19:14:49.650800Z"
    },
    "papermill": {
     "duration": 0.346135,
     "end_time": "2025-02-08T19:14:49.654214",
     "exception": false,
     "start_time": "2025-02-08T19:14:49.308079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Coffee Type'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição dos Preços')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da0eaf85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:49.684739Z",
     "iopub.status.busy": "2025-02-08T19:14:49.684410Z",
     "iopub.status.idle": "2025-02-08T19:14:49.695755Z",
     "shell.execute_reply": "2025-02-08T19:14:49.694558Z"
    },
    "papermill": {
     "duration": 0.028689,
     "end_time": "2025-02-08T19:14:49.697637",
     "exception": false,
     "start_time": "2025-02-08T19:14:49.668948",
     "status": "completed"
    },
    "tags": []
   },
   "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>Coffee Type</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Filter Coffee</td>\n",
       "      <td>2091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Espresso</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cold Coffee</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brewed</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Instant</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Coffee Type  Frequencia\n",
       "0  Filter Coffee        2091\n",
       "1       Espresso        1992\n",
       "2    Cold Coffee        1980\n",
       "3         Brewed        1977\n",
       "4        Instant        1960"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Coffee Type'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Coffee Type', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c44b9e69",
   "metadata": {
    "papermill": {
     "duration": 0.0153,
     "end_time": "2025-02-08T19:14:49.727727",
     "exception": false,
     "start_time": "2025-02-08T19:14:49.712427",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Daily Cups Consumed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc3d4f9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:49.759715Z",
     "iopub.status.busy": "2025-02-08T19:14:49.759290Z",
     "iopub.status.idle": "2025-02-08T19:14:50.185726Z",
     "shell.execute_reply": "2025-02-08T19:14:50.184275Z"
    },
    "papermill": {
     "duration": 0.444882,
     "end_time": "2025-02-08T19:14:50.187821",
     "exception": false,
     "start_time": "2025-02-08T19:14:49.742939",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Daily Cups Consumed'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição das doses diarias consumidas')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37a041b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:50.222294Z",
     "iopub.status.busy": "2025-02-08T19:14:50.221779Z",
     "iopub.status.idle": "2025-02-08T19:14:50.235400Z",
     "shell.execute_reply": "2025-02-08T19:14:50.234075Z"
    },
    "papermill": {
     "duration": 0.03263,
     "end_time": "2025-02-08T19:14:50.237259",
     "exception": false,
     "start_time": "2025-02-08T19:14:50.204629",
     "status": "completed"
    },
    "tags": []
   },
   "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>Daily Cups Consumed</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Cups Consumed  Frequencia\n",
       "0                    5        2044\n",
       "1                    1        2010\n",
       "2                    2        1994\n",
       "3                    4        1988\n",
       "4                    3        1964"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Daily Cups Consumed'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Daily Cups Consumed', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d2da993",
   "metadata": {
    "papermill": {
     "duration": 0.015365,
     "end_time": "2025-02-08T19:14:50.268669",
     "exception": false,
     "start_time": "2025-02-08T19:14:50.253304",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preferred Coffee Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47d2ed73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:50.301781Z",
     "iopub.status.busy": "2025-02-08T19:14:50.301386Z",
     "iopub.status.idle": "2025-02-08T19:14:50.775874Z",
     "shell.execute_reply": "2025-02-08T19:14:50.774531Z"
    },
    "papermill": {
     "duration": 0.493254,
     "end_time": "2025-02-08T19:14:50.777663",
     "exception": false,
     "start_time": "2025-02-08T19:14:50.284409",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Preferred Coffee Brand'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição das marcas de café preferidas')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "38cd617b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:50.813390Z",
     "iopub.status.busy": "2025-02-08T19:14:50.812926Z",
     "iopub.status.idle": "2025-02-08T19:14:50.825328Z",
     "shell.execute_reply": "2025-02-08T19:14:50.824151Z"
    },
    "papermill": {
     "duration": 0.032213,
     "end_time": "2025-02-08T19:14:50.827110",
     "exception": false,
     "start_time": "2025-02-08T19:14:50.794897",
     "status": "completed"
    },
    "tags": []
   },
   "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>Preferred Coffee Brand</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Others</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bru</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nestlé</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Local Brand</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Preferred Coffee Brand  Frequencia\n",
       "0                 Others        2079\n",
       "1                    Bru        2025\n",
       "2                 Nestlé        2011\n",
       "3              Starbucks        1946\n",
       "4            Local Brand        1939"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Preferred Coffee Brand'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Preferred Coffee Brand', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b306768",
   "metadata": {
    "papermill": {
     "duration": 0.016808,
     "end_time": "2025-02-08T19:14:50.861840",
     "exception": false,
     "start_time": "2025-02-08T19:14:50.845032",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Consumption Setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "64a2740a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:50.897320Z",
     "iopub.status.busy": "2025-02-08T19:14:50.896860Z",
     "iopub.status.idle": "2025-02-08T19:14:51.195163Z",
     "shell.execute_reply": "2025-02-08T19:14:51.194004Z"
    },
    "papermill": {
     "duration": 0.317936,
     "end_time": "2025-02-08T19:14:51.196800",
     "exception": false,
     "start_time": "2025-02-08T19:14:50.878864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Consumption Setting'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição das caracteristicas do consumo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "31443293",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:51.234632Z",
     "iopub.status.busy": "2025-02-08T19:14:51.234181Z",
     "iopub.status.idle": "2025-02-08T19:14:51.246986Z",
     "shell.execute_reply": "2025-02-08T19:14:51.245866Z"
    },
    "papermill": {
     "duration": 0.034488,
     "end_time": "2025-02-08T19:14:51.248914",
     "exception": false,
     "start_time": "2025-02-08T19:14:51.214426",
     "status": "completed"
    },
    "tags": []
   },
   "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>Consumption Setting</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>3344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Office</td>\n",
       "      <td>3329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Café</td>\n",
       "      <td>3327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Consumption Setting  Frequencia\n",
       "0                Home        3344\n",
       "1              Office        3329\n",
       "2                Café        3327"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Consumption Setting'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Consumption Setting', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8549ce5",
   "metadata": {
    "papermill": {
     "duration": 0.017054,
     "end_time": "2025-02-08T19:14:51.283464",
     "exception": false,
     "start_time": "2025-02-08T19:14:51.266410",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Monthly Coffee Expense (INR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a56ee4a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:51.320327Z",
     "iopub.status.busy": "2025-02-08T19:14:51.319787Z",
     "iopub.status.idle": "2025-02-08T19:14:51.710189Z",
     "shell.execute_reply": "2025-02-08T19:14:51.708962Z"
    },
    "papermill": {
     "duration": 0.411167,
     "end_time": "2025-02-08T19:14:51.712227",
     "exception": false,
     "start_time": "2025-02-08T19:14:51.301060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Monthly Coffee Expense (INR)'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição dos preços')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9416f3fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:51.751714Z",
     "iopub.status.busy": "2025-02-08T19:14:51.751279Z",
     "iopub.status.idle": "2025-02-08T19:14:51.765441Z",
     "shell.execute_reply": "2025-02-08T19:14:51.764235Z"
    },
    "papermill": {
     "duration": 0.035678,
     "end_time": "2025-02-08T19:14:51.767252",
     "exception": false,
     "start_time": "2025-02-08T19:14:51.731574",
     "status": "completed"
    },
    "tags": []
   },
   "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>Monthly Coffee Expense (INR)</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2968</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2628</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4428</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3281</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4464</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4171</th>\n",
       "      <td>4991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>4927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>737</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>4884</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>4285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4176 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Monthly Coffee Expense (INR)  Frequencia\n",
       "0                             2968           9\n",
       "1                             2628           9\n",
       "2                             4428           9\n",
       "3                             3281           9\n",
       "4                             4464           8\n",
       "...                            ...         ...\n",
       "4171                          4991           1\n",
       "4172                          4927           1\n",
       "4173                           737           1\n",
       "4174                          4884           1\n",
       "4175                          4285           1\n",
       "\n",
       "[4176 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Monthly Coffee Expense (INR)'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Monthly Coffee Expense (INR)', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecd54c5",
   "metadata": {
    "papermill": {
     "duration": 0.018143,
     "end_time": "2025-02-08T19:14:51.803705",
     "exception": false,
     "start_time": "2025-02-08T19:14:51.785562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reason for Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5464a658",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:51.907924Z",
     "iopub.status.busy": "2025-02-08T19:14:51.907558Z",
     "iopub.status.idle": "2025-02-08T19:14:52.204997Z",
     "shell.execute_reply": "2025-02-08T19:14:52.203830Z"
    },
    "papermill": {
     "duration": 0.319233,
     "end_time": "2025-02-08T19:14:52.206872",
     "exception": false,
     "start_time": "2025-02-08T19:14:51.887639",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Reason for Consumption'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição dos motivos pelo consumo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "85b2b29e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:52.249802Z",
     "iopub.status.busy": "2025-02-08T19:14:52.249412Z",
     "iopub.status.idle": "2025-02-08T19:14:52.260507Z",
     "shell.execute_reply": "2025-02-08T19:14:52.259472Z"
    },
    "papermill": {
     "duration": 0.035281,
     "end_time": "2025-02-08T19:14:52.262605",
     "exception": false,
     "start_time": "2025-02-08T19:14:52.227324",
     "status": "completed"
    },
    "tags": []
   },
   "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>Reason for Consumption</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Energy</td>\n",
       "      <td>2655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Socializing</td>\n",
       "      <td>2498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taste</td>\n",
       "      <td>2481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Habit</td>\n",
       "      <td>2366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Reason for Consumption  Frequencia\n",
       "0                 Energy        2655\n",
       "1            Socializing        2498\n",
       "2                  Taste        2481\n",
       "3                  Habit        2366"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Reason for Consumption'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Reason for Consumption', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182b14d5",
   "metadata": {
    "papermill": {
     "duration": 0.018667,
     "end_time": "2025-02-08T19:14:52.300968",
     "exception": false,
     "start_time": "2025-02-08T19:14:52.282301",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Milk Preference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff79ac7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:52.341446Z",
     "iopub.status.busy": "2025-02-08T19:14:52.341039Z",
     "iopub.status.idle": "2025-02-08T19:14:52.614326Z",
     "shell.execute_reply": "2025-02-08T19:14:52.612955Z"
    },
    "papermill": {
     "duration": 0.295134,
     "end_time": "2025-02-08T19:14:52.616090",
     "exception": false,
     "start_time": "2025-02-08T19:14:52.320956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Milk Preference'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição da Preferência do Tipo de Leite')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "882c1a28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:52.659002Z",
     "iopub.status.busy": "2025-02-08T19:14:52.658513Z",
     "iopub.status.idle": "2025-02-08T19:14:52.671426Z",
     "shell.execute_reply": "2025-02-08T19:14:52.670243Z"
    },
    "papermill": {
     "duration": 0.036416,
     "end_time": "2025-02-08T19:14:52.673157",
     "exception": false,
     "start_time": "2025-02-08T19:14:52.636741",
     "status": "completed"
    },
    "tags": []
   },
   "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>Milk Preference</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>5016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>4984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Milk Preference  Frequencia\n",
       "0             Yes        5016\n",
       "1              No        4984"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Milk Preference'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Milk Preference', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b66e6aa",
   "metadata": {
    "papermill": {
     "duration": 0.020487,
     "end_time": "2025-02-08T19:14:52.713713",
     "exception": false,
     "start_time": "2025-02-08T19:14:52.693226",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Sugar Preference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f75d3a95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:52.756160Z",
     "iopub.status.busy": "2025-02-08T19:14:52.755673Z",
     "iopub.status.idle": "2025-02-08T19:14:53.058951Z",
     "shell.execute_reply": "2025-02-08T19:14:53.057644Z"
    },
    "papermill": {
     "duration": 0.327114,
     "end_time": "2025-02-08T19:14:53.061069",
     "exception": false,
     "start_time": "2025-02-08T19:14:52.733955",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Sugar Preference'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição da Preferência por açúcar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "88323261",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:53.108067Z",
     "iopub.status.busy": "2025-02-08T19:14:53.107638Z",
     "iopub.status.idle": "2025-02-08T19:14:53.122913Z",
     "shell.execute_reply": "2025-02-08T19:14:53.121341Z"
    },
    "papermill": {
     "duration": 0.042188,
     "end_time": "2025-02-08T19:14:53.125030",
     "exception": false,
     "start_time": "2025-02-08T19:14:53.082842",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sugar Preference</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Medium Sugar</td>\n",
       "      <td>2507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High Sugar</td>\n",
       "      <td>2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Less Sugar</td>\n",
       "      <td>2498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Sugar</td>\n",
       "      <td>2491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sugar Preference  Frequencia\n",
       "0     Medium Sugar        2507\n",
       "1       High Sugar        2504\n",
       "2       Less Sugar        2498\n",
       "3         No Sugar        2491"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Sugar Preference'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Sugar Preference', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b13589",
   "metadata": {
    "papermill": {
     "duration": 0.021092,
     "end_time": "2025-02-08T19:14:53.169996",
     "exception": false,
     "start_time": "2025-02-08T19:14:53.148904",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Awareness of Specialty Coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e6a1fc8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:53.215416Z",
     "iopub.status.busy": "2025-02-08T19:14:53.214961Z",
     "iopub.status.idle": "2025-02-08T19:14:53.498623Z",
     "shell.execute_reply": "2025-02-08T19:14:53.497452Z"
    },
    "papermill": {
     "duration": 0.308868,
     "end_time": "2025-02-08T19:14:53.500510",
     "exception": false,
     "start_time": "2025-02-08T19:14:53.191642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Awareness of Specialty Coffee'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição do conhecimento sobre o café especial')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a2033337",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:53.548668Z",
     "iopub.status.busy": "2025-02-08T19:14:53.548211Z",
     "iopub.status.idle": "2025-02-08T19:14:53.560372Z",
     "shell.execute_reply": "2025-02-08T19:14:53.559168Z"
    },
    "papermill": {
     "duration": 0.037087,
     "end_time": "2025-02-08T19:14:53.562255",
     "exception": false,
     "start_time": "2025-02-08T19:14:53.525168",
     "status": "completed"
    },
    "tags": []
   },
   "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>Awareness of Specialty Coffee</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>5057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Awareness of Specialty Coffee  Frequencia\n",
       "0                            No        5057\n",
       "1                           Yes        4943"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Awareness of Specialty Coffee'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Awareness of Specialty Coffee', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c251a3",
   "metadata": {
    "papermill": {
     "duration": 0.021683,
     "end_time": "2025-02-08T19:14:53.605925",
     "exception": false,
     "start_time": "2025-02-08T19:14:53.584242",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Frequency of Café Visits (Per Week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4308143a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:53.653618Z",
     "iopub.status.busy": "2025-02-08T19:14:53.653099Z",
     "iopub.status.idle": "2025-02-08T19:14:54.047871Z",
     "shell.execute_reply": "2025-02-08T19:14:54.046550Z"
    },
    "papermill": {
     "duration": 0.419792,
     "end_time": "2025-02-08T19:14:54.049904",
     "exception": false,
     "start_time": "2025-02-08T19:14:53.630112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Frequency of Café Visits (Per Week)'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição da visita dos cafés (por semana)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a0259365",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:54.096020Z",
     "iopub.status.busy": "2025-02-08T19:14:54.095678Z",
     "iopub.status.idle": "2025-02-08T19:14:54.106524Z",
     "shell.execute_reply": "2025-02-08T19:14:54.105386Z"
    },
    "papermill": {
     "duration": 0.03606,
     "end_time": "2025-02-08T19:14:54.108526",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.072466",
     "status": "completed"
    },
    "tags": []
   },
   "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>Frequency of Café Visits (Per Week)</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequency of Café Visits (Per Week)  Frequencia\n",
       "0                                    3        1495\n",
       "1                                    6        1483\n",
       "2                                    1        1436\n",
       "3                                    4        1430\n",
       "4                                    0        1408\n",
       "5                                    5        1392\n",
       "6                                    2        1356"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Frequency of Café Visits (Per Week)'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Frequency of Café Visits (Per Week)', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea66606e",
   "metadata": {
    "papermill": {
     "duration": 0.022608,
     "end_time": "2025-02-08T19:14:54.154371",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.131763",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preferred Time of Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c822be12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:54.202719Z",
     "iopub.status.busy": "2025-02-08T19:14:54.202294Z",
     "iopub.status.idle": "2025-02-08T19:14:54.502971Z",
     "shell.execute_reply": "2025-02-08T19:14:54.501502Z"
    },
    "papermill": {
     "duration": 0.32673,
     "end_time": "2025-02-08T19:14:54.504941",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.178211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Preferred Time of Consumption'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição dos tempos preferiveis para consumo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b5e957be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:54.554341Z",
     "iopub.status.busy": "2025-02-08T19:14:54.553775Z",
     "iopub.status.idle": "2025-02-08T19:14:54.566831Z",
     "shell.execute_reply": "2025-02-08T19:14:54.565707Z"
    },
    "papermill": {
     "duration": 0.039701,
     "end_time": "2025-02-08T19:14:54.568414",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.528713",
     "status": "completed"
    },
    "tags": []
   },
   "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>Preferred Time of Consumption</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Morning</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Night</td>\n",
       "      <td>2531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afternoon</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Evening</td>\n",
       "      <td>2449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Preferred Time of Consumption  Frequencia\n",
       "0                       Morning        2535\n",
       "1                         Night        2531\n",
       "2                     Afternoon        2485\n",
       "3                       Evening        2449"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd = df['Preferred Time of Consumption'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Preferred Time of Consumption', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7983a1",
   "metadata": {
    "papermill": {
     "duration": 0.023942,
     "end_time": "2025-02-08T19:14:54.615617",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.591675",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Influence of Social Media on Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2605775",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:54.663646Z",
     "iopub.status.busy": "2025-02-08T19:14:54.663207Z",
     "iopub.status.idle": "2025-02-08T19:14:54.942515Z",
     "shell.execute_reply": "2025-02-08T19:14:54.941273Z"
    },
    "papermill": {
     "duration": 0.305516,
     "end_time": "2025-02-08T19:14:54.944352",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.638836",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.histplot(df['Influence of Social Media on Choice'], bins=30, kde=False, color='blue')\n",
    "plt.title('Distribuição do impacto das midias sociais na escolha')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "be0bdd2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:54.995194Z",
     "iopub.status.busy": "2025-02-08T19:14:54.994696Z",
     "iopub.status.idle": "2025-02-08T19:14:55.007478Z",
     "shell.execute_reply": "2025-02-08T19:14:55.006277Z"
    },
    "papermill": {
     "duration": 0.040632,
     "end_time": "2025-02-08T19:14:55.009550",
     "exception": false,
     "start_time": "2025-02-08T19:14:54.968918",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Influence of Social Media on Choice</th>\n",
       "      <th>Frequencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>5025</td>\n",
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       "  Influence of Social Media on Choice  Frequencia\n",
       "0                                 Yes        5025\n",
       "1                                  No        4975"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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   "source": [
    "qtd = df['Influence of Social Media on Choice'].value_counts().reset_index(name='Frequencia')\n",
    "qtd.columns = ['Influence of Social Media on Choice', 'Frequencia']\n",
    "qtd"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# ML"
   ]
  },
  {
   "cell_type": "code",
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   "id": "0766b1cd",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "86a014c0",
   "metadata": {
    "execution": {
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     "end_time": "2025-02-08T19:14:55.939601",
     "exception": false,
     "start_time": "2025-02-08T19:14:55.872490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 1. Pré-processamento de Dados\n",
    "\n",
    "# Convertendo variáveis categóricas em numéricas\n",
    "label_encoder = LabelEncoder()\n",
    "for coluna in df.select_dtypes(include=['object']).columns:\n",
    "    df[coluna] = label_encoder.fit_transform(df[coluna])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "54e06d01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:55.989892Z",
     "iopub.status.busy": "2025-02-08T19:14:55.989554Z",
     "iopub.status.idle": "2025-02-08T19:14:56.000976Z",
     "shell.execute_reply": "2025-02-08T19:14:55.999739Z"
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     "end_time": "2025-02-08T19:14:56.003440",
     "exception": false,
     "start_time": "2025-02-08T19:14:55.964105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Separando variáveis independentes (X) e dependentes (y)\n",
    "X = df.drop(columns=['Gender'])\n",
    "y = df['Gender'] \n",
    "\n",
    "# Dividindo os dados em treino e teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1ea34942",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-08T19:14:56.056765Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                Feature  Importance\n",
      "7          Monthly Coffee Expense (INR)    0.169207\n",
      "12  Frequency of Café Visits (Per Week)    0.088210\n",
      "3                           Coffee Type    0.073426\n",
      "5                Preferred Coffee Brand    0.072955\n",
      "4                   Daily Cups Consumed    0.070148\n",
      "0                                  City    0.068744\n",
      "2                             Age Group    0.066401\n",
      "8                Reason for Consumption    0.065048\n",
      "1                                 State    0.062292\n",
      "10                     Sugar Preference    0.061002\n"
     ]
    }
   ],
   "source": [
    "# Precisamos descobrir quais são as colunas que vamos analisar\n",
    "# Treinando o modelo Random Forest\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Obtendo a importância das características\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Criando um DataFrame para visualizar as importâncias\n",
    "features = X.columns\n",
    "importance_df = pd.DataFrame({'Feature': features, 'Importance': importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Exibindo as 10 variáveis mais importantes\n",
    "print(importance_df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c55898",
   "metadata": {
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     "start_time": "2025-02-08T19:14:57.316369",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## OBS: Como as variáveis quase não tem relação umas com as outras, nossos modelos não vão ser bons (ou eficientes)\n",
    "Basta convtermos a tabela de importance para porcentagem, o maior indice de ~17%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "69faaf02",
   "metadata": {
    "execution": {
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    },
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     "duration": 2.210689,
     "end_time": "2025-02-08T19:14:59.576531",
     "exception": false,
     "start_time": "2025-02-08T19:14:57.365842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Acurácia do modelo Árvore de Decisão: 0.3435\n",
      "Relatório de Classificação para Árvore de Decisão:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.31      0.31       672\n",
      "           1       0.34      0.37      0.35       649\n",
      "           2       0.37      0.36      0.36       679\n",
      "\n",
      "    accuracy                           0.34      2000\n",
      "   macro avg       0.34      0.34      0.34      2000\n",
      "weighted avg       0.34      0.34      0.34      2000\n",
      "\n",
      "\n",
      "Acurácia do modelo Random Forest: 0.3295\n",
      "Relatório de Classificação para Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.31      0.29      0.30       672\n",
      "           1       0.32      0.34      0.33       649\n",
      "           2       0.35      0.35      0.35       679\n",
      "\n",
      "    accuracy                           0.33      2000\n",
      "   macro avg       0.33      0.33      0.33      2000\n",
      "weighted avg       0.33      0.33      0.33      2000\n",
      "\n",
      "\n",
      "Acurácia do modelo KNN (K-Vizinhos): 0.3250\n",
      "Relatório de Classificação para KNN (K-Vizinhos):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.45      0.38       672\n",
      "           1       0.33      0.34      0.34       649\n",
      "           2       0.30      0.19      0.23       679\n",
      "\n",
      "    accuracy                           0.33      2000\n",
      "   macro avg       0.32      0.33      0.32      2000\n",
      "weighted avg       0.32      0.33      0.32      2000\n",
      "\n",
      "\n",
      "Acurácia do modelo Naive Bayes: 0.3245\n",
      "Relatório de Classificação para Naive Bayes:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.19      0.23       672\n",
      "           1       0.33      0.43      0.38       649\n",
      "           2       0.34      0.36      0.35       679\n",
      "\n",
      "    accuracy                           0.32      2000\n",
      "   macro avg       0.32      0.33      0.32      2000\n",
      "weighted avg       0.32      0.32      0.32      2000\n",
      "\n",
      "\n",
      "Acurácia do modelo Redes Neurais: 0.3405\n",
      "Relatório de Classificação para Redes Neurais:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.36      0.01      0.01       672\n",
      "           1       0.00      0.00      0.00       649\n",
      "           2       0.34      1.00      0.51       679\n",
      "\n",
      "    accuracy                           0.34      2000\n",
      "   macro avg       0.23      0.33      0.17      2000\n",
      "weighted avg       0.24      0.34      0.18      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Passo 2: Treinamento dos Modelos\n",
    "# Modelos a serem testados:\n",
    "models = {\n",
    "    \"Árvore de Decisão\": DecisionTreeClassifier(random_state=42),\n",
    "    \"Random Forest\": RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "    \"KNN (K-Vizinhos)\": KNeighborsClassifier(),\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"Redes Neurais\": MLPClassifier(max_iter=1000, random_state=42)  # Rede Neural MLP (Multilayer Perceptron)\n",
    "}\n",
    "\n",
    "\n",
    "# Passo 3: Testando todos os modelos\n",
    "for model_name, model in models.items():\n",
    "    # Treinando o modelo\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Fazendo previsões no conjunto de teste\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculando a acurácia\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(f\"\\nAcurácia do modelo {model_name}: {accuracy:.4f}\")\n",
    "    \n",
    "    # Exibindo o relatório de classificação\n",
    "    print(f\"Relatório de Classificação para {model_name}:\")\n",
    "    print(classification_report(y_test, y_pred))\n"
   ]
  }
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