T4-4-Linear Regression- SciKit Learn.ipynb

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  {
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   "metadata": {},
   "source": [
    "## El paquete SciKit learn para regresión lineal y selección de rasgos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.svm import SVR\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('../GitHub/python-ml-course/datasets/ads/Advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=data.columns.values.tolist()[:-1]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[feature_cols]\n",
    "Y=data[\"Sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator=SVR(kernel='linear')\n",
    "selector=RFE(estimator,2,step=1)\n",
    "selector=selector.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "array([ True,  True, False])"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "selector.support_"
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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     "data": {
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   ]
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_pred=X[[\"TV\",\"Radio\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "lm=LinearRegression()\n",
    "lm.fit(X_pred,Y)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "data": {
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       "2.921099912405138"
      ]
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "lm.intercept_"
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04575482, 0.18799423])"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "lm.coef_"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8971942610828956"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "lm.score(X_pred,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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