SLR.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='darkorange'>Prediction: Simple Linear Regression SLR\n",
    "---\n",
    "</font>\n",
    "\n",
    "*@j*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es un modelo de predicción de variables continuas que utiliza 1 variable independiente para predecir otra variable dependiente.\n",
    "\n",
    "Problema: Predecir el Co2 emitido por un automóvil tomando como referencia el tamaño del motor."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Librerías:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1067, 13)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODELYEAR</th>\n",
       "      <th>MAKE</th>\n",
       "      <th>MODEL</th>\n",
       "      <th>VEHICLECLASS</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>TRANSMISSION</th>\n",
       "      <th>FUELTYPE</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
       "\n",
       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0          AS5        Z                   9.9                  6.7   \n",
       "1           M6        Z                  11.2                  7.7   \n",
       "2          AV7        Z                   6.0                  5.8   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
       "0                   8.5                        33           196  \n",
       "1                   9.6                        29           221  \n",
       "2                   5.9                        48           136  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./FuelConsumptionCo2.csv')\n",
    "print(df.shape)\n",
    "df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleccionar datos útiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ENGINESIZE</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CO2EMISSIONS\n",
       "0         2.0           196\n",
       "1         2.4           221\n",
       "2         1.5           136"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['ENGINESIZE', 'CO2EMISSIONS']]\n",
    "df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['ENGINESIZE']].values # [[]]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['CO2EMISSIONS'].values # []"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizar los Splits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: (853, 1) (853,)\n",
      "Test set size:  (214, 1) (214,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=11)\n",
    "print ('Train set size:', X_train.shape,  y_train.shape)\n",
    "print ('Test set size: ', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "modelSLR = LinearRegression()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entrenar Modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelSLR.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hacer los test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = modelSLR.predict(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluar resultados de los test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_Score: 79.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(f'R2_Score: {round(r2_score(y_test, y_hat),2)*100} %')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hacer nuevas predicciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicción para tamaño de motor: 6.4\t Co2 = 378.66\n",
      "\n",
      "Predicción para tamaño de motor: 3.3\t Co2 = 255.38\n",
      "\n",
      "Predicción para tamaño de motor: 7.9\t Co2 = 438.31\n",
      "\n"
     ]
    }
   ],
   "source": [
    "p1 = 6.4\n",
    "p2 = 3.3\n",
    "p3 = 7.9\n",
    "\n",
    "pred1 = modelSLR.intercept_ + modelSLR.coef_ * p1\n",
    "pred2 = modelSLR.intercept_ + modelSLR.coef_ * p2\n",
    "pred3 = modelSLR.intercept_ + modelSLR.coef_ * p3\n",
    "\n",
    "print(f'Predicción para tamaño de motor: {p1}\\t Co2 = {round(pred1[0],2)}\\n')\n",
    "print(f'Predicción para tamaño de motor: {p2}\\t Co2 = {round(pred2[0],2)}\\n')\n",
    "print(f'Predicción para tamaño de motor: {p3}\\t Co2 = {round(pred3[0],2)}\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfica del modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "trainD = plt.scatter(X_train,y_train, color='#31f', alpha=0.5)\n",
    "regP = plt.plot(X_train, modelSLR.intercept_ + modelSLR.coef_[0]*X_train, color='#f22')\n",
    "plt.title('Regresión Lineal Simple', fontdict={'size': 18})\n",
    "plt.xlabel('Tamaño Motor', fontdict={'size': 15})\n",
    "plt.ylabel('Co2', fontdict={'size': 15})\n",
    "plt.legend(['Regresion', 'Train Data'])\n",
    "plt.show();"
   ]
  }
 ],
 "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": "3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b5f98589597bf928df7c3b4b7221661d9594090b96ffc8472b8fdbc1b88b3ff7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}