1b_regresion_en_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/alturas-pesos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7620</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>61.235</td>\n",
       "      <td>162.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1837</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>97.432</td>\n",
       "      <td>181.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3311</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>73.324</td>\n",
       "      <td>172.459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9478</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>55.193</td>\n",
       "      <td>157.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7805</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>56.886</td>\n",
       "      <td>151.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6737</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>59.834</td>\n",
       "      <td>158.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4413</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>77.618</td>\n",
       "      <td>172.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6018</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>68.889</td>\n",
       "      <td>169.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5165</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>53.874</td>\n",
       "      <td>155.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>600</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>83.248</td>\n",
       "      <td>177.908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Genero    Peso   Altura\n",
       "0        7620   Mujer  61.235  162.402\n",
       "1        1837  Hombre  97.432  181.908\n",
       "2        3311  Hombre  73.324  172.459\n",
       "3        9478   Mujer  55.193  157.748\n",
       "4        7805   Mujer  56.886  151.798\n",
       "5        6737   Mujer  59.834  158.623\n",
       "6        4413  Hombre  77.618  172.705\n",
       "7        6018   Mujer  68.889  169.225\n",
       "8        5165   Mujer  53.874  155.487\n",
       "9         600  Hombre  83.248  177.908"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[162.402,  61.235],\n",
       "       [181.908,  97.432],\n",
       "       [172.459,  73.324],\n",
       "       ...,\n",
       "       [170.91 ,  75.461],\n",
       "       [166.137,  65.461],\n",
       "       [171.575,  80.323]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Altura', 'Peso']].values #[0:10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([162.402, 181.908, 172.459, ..., 170.91 , 166.137, 171.575])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Altura'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Altura'].values, df['Peso'].values)\n",
    "plt.title('Pesos y Alturas')\n",
    "plt.xlabel('Alturas')\n",
    "plt.ylabel('Pesos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(df['Altura'].values.reshape(-1, 1), df['Peso'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3753917739550743"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-158.41934475794554"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = np.linspace(130, 200, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Altura'].values, df['Peso'].values)\n",
    "plt.title('Pesos y Alturas')\n",
    "plt.xlabel('Alturas')\n",
    "plt.ylabel('Pesos')\n",
    "x = np.linspace(130, 200, 100)\n",
    "# y = lr.coef_[0,0]*x + lr.intercept_[0]\n",
    "y = lr.predict(x.reshape(-1, 1))\n",
    "plt.plot(x, y, c='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}