Laboratorio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ddf31f77-8805-4b6b-bae6-2bb420cd311b",
   "metadata": {},
   "source": [
    "## **Librerías**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1ded37e1-bef8-4575-accb-d6df1ec4ddff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a966a4b8-6e2c-4bf0-a660-b7cc9633a642",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6db6f601-a77e-4fd5-b433-9d2160c4fd27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import winsound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96565514-8e0e-4279-acc2-68f3cd7493f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965a7c2b-036a-4fb4-b876-681fed50055f",
   "metadata": {},
   "source": [
    "## **Datos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd81b8b7-d945-4a4a-8067-7e08edc8d144",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/penguins_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c268d67-1cc7-4605-8d01-c44ff1128d37",
   "metadata": {},
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>224</td>\n",
       "      <td>5350</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g   sex\n",
       "227  Gentoo  Biscoe            50.0           15.9                224         5350  male"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a138ba1-1739-44f0-9092-3465456abbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "encode = ['sex', 'island']\n",
    "\n",
    "for col in encode:\n",
    "    dummy = pd.get_dummies(df[col], prefix=col)\n",
    "    df = pd.concat([df, dummy], axis=1)\n",
    "    del df[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0bc74365-8636-488c-8e07-839ce1a39fb5",
   "metadata": {},
   "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>species</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>island_Biscoe</th>\n",
       "      <th>island_Dream</th>\n",
       "      <th>island_Torgersen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>187</td>\n",
       "      <td>3700</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    species  bill_length_mm  bill_depth_mm  flipper_length_mm  ...  sex_male  island_Biscoe  island_Dream  island_Torgersen\n",
       "144  Adelie            36.0           17.1                187  ...     False          False          True             False\n",
       "\n",
       "[1 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d10de5d6-d3df-4547-9df5-7f614b1368d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['species', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm',\n",
       "       'body_mass_g', 'sex_female', 'sex_male', 'island_Biscoe',\n",
       "       'island_Dream', 'island_Torgersen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e9af629-74dd-46e0-893d-c35d940a0d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    'bill_length_mm': 43.9, \n",
    "    'bill_depth_mm': 17.2, \n",
    "    'flipper_length_mm': 201, \n",
    "    'body_mass_g': 4207,\n",
    "    'sex_female': False,\n",
    "    'sex_male': True,\n",
    "    'island_Biscoe': False,\n",
    "    'island_Dream': True, \n",
    "    'island_Torgersen': False\n",
    "}\n",
    "\n",
    "features = pd.DataFrame(data, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "568a534a-c6db-427a-8867-270e308067bb",
   "metadata": {},
   "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>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>island_Biscoe</th>\n",
       "      <th>island_Dream</th>\n",
       "      <th>island_Torgersen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>201</td>\n",
       "      <td>4207</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bill_length_mm  bill_depth_mm  flipper_length_mm  ...  island_Biscoe  island_Dream  island_Torgersen\n",
       "0            43.9           17.2                201  ...          False          True             False\n",
       "\n",
       "[1 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65422388-ad00-4baa-8b23-5fd59dfd05f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pickle.load(open('./Models/penguin_class.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "547a4073-8fda-4b97-bc72-10ce3d820f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233cbcae-d37b-43f2-8eef-bbd0f8c3f429",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11cfd539-f34a-4cd7-9ba9-4d8166364611",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efbbe8ad-e9e9-45a3-b26b-ab9e5786ab9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}