Sesion-04/Reto01/Reto01.ipynb

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   "source": [
    "# Reto 01\n",
    "**_Dr. Antonio Arista Jalife._**\n",
    "\n",
    "En este reto debes modificar los datos de entrada para mejorar el desempeño. Trata de reducir el MSE lo mas que puedas y mantén un reporte de tus resultados para saber que funcionó mejor."
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "boston_dataset = load_boston()\n",
    "bostonDataFrame = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
    "bostonDataFrame['MEDV'] = boston_dataset.target"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "#Elige los datos que irán de entrada:\n",
    "X = bostonDataFrame[['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT']]\n",
    "Y = bostonDataFrame['MEDV']"
   ]
  },
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   "source": [
    "#Edita la cantidad de datos de entrenamiento y prueba.\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.4, random_state=5)"
   ]
  },
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   "source": [
    "### Entrenamiento y predicciones:\n",
    "Trataremos de predecir que tal funciona el regresor lineal. "
   ]
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       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "lin_model = LinearRegression()\n",
    "lin_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entrenamiento: MSE =18.893207583229778\n",
      "Pruebas: MSE =29.031958421324983\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediccion</th>\n",
       "      <th>valor_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.780277</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.733483</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.375163</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.102632</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.221546</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.879990</td>\n",
       "      <td>10.4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26.778161</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.024677</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26.723752</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22.319473</td>\n",
       "      <td>23.2</td>\n",
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       "   prediccion  valor_real\n",
       "0   37.780277        37.6\n",
       "1   30.733483        27.9\n",
       "2   27.375163        22.6\n",
       "3    7.102632        13.8\n",
       "4   34.221546        35.2\n",
       "5    5.879990        10.4\n",
       "6   26.778161        23.9\n",
       "7   30.024677        29.0\n",
       "8   26.723752        22.8\n",
       "9   22.319473        23.2"
      ]
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   "source": [
    "y_train_predict = lin_model.predict(X_train)\n",
    "MSE = mean_squared_error(Y_train,y_train_predict)\n",
    "print(\"Entrenamiento: MSE =\"+str(MSE))\n",
    "\n",
    "y_test_predict = lin_model.predict(X_test)\n",
    "MSE = (mean_squared_error(Y_test, y_test_predict))\n",
    "print(\"Pruebas: MSE =\"+str(MSE))\n",
    "\n",
    "df_predicciones = pd.DataFrame({'valor_real':Y_test, 'prediccion':y_test_predict})\n",
    "df_predicciones = df_predicciones.reset_index(drop = True)\n",
    "df_predicciones.head(10)"
   ]
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