Logistic regression .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#from sklearn.decomposition import PCA\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "#from sklearn.model_selection import train_test_split\n",
    "#from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### todo es con data imputada, y con variables categoricas en funcion de la mas comun "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "archivo= pandas.read_csv(\"dataimputada2.csv\", decimal=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 200)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archivo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 119)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = archivo.isnull().sum()/91713\n",
    "r = result.sort_values(ascending=True) \n",
    "data2 = archivo[r[r<=0.00].reset_index()[\"index\"]].drop(columns=[\"encounter_id\", \"readmission_status\"]) \n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 118)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 =data2.drop(columns=[\"hospital_death\"])\n",
    "data3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NORMALIZAR LOS DATOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables_1=['gcs_motor_apache', 'gcs_eyes_apache', 'gcs_verbal_apache', 'weight', \"bmi\"\n",
    ", 'bun_apache', 'creatinine_apache', 'sodium_apache', 'temp_apache', 'wbc_apache', 'd1_diasbp_max', 'd1_diasbp_min', 'd1_diasbp_min', \"d1_diasbp_noninvasive_min\", \"d1_heartrate_max\", \"d1_heartrate_min\", \"d1_mbp_max\", \"d1_mbp_noninvasive_max\",  \"d1_mbp_noninvasive_min\", \"d1_resprate_max\", \"d1_resprate_min\", \"d1_spo2_max\", \"d1_spo2_min\", \"d1_sysbp_max\", \"d1_sysbp_min\", \"d1_sysbp_noninvasive_max\", \"d1_sysbp_noninvasive_min\", \"d1_temp_max\", \"d1_temp_min\", \"h1_diasbp_max\", \"h1_diasbp_min\", \"h1_diasbp_noninvasive_max\", \"h1_diasbp_noninvasive_min\", \"h1_heartrate_max\", \"h1_heartrate_min\", \"h1_mbp_max\", \"h1_mbp_min\", \"h1_mbp_noninvasive_max\", \"h1_mbp_noninvasive_min\", \"h1_resprate_max\", \"h1_spo2_max\", \"h1_spo2_min\", \"h1_sysbp_max\", \"h1_sysbp_min\", \"h1_sysbp_noninvasive_max\", \"h1_sysbp_noninvasive_min\", \"h1_temp_max\", \"h1_temp_min\", \"d1_bun_max\", \"d1_bun_min\", \"d1_calcium_max\", \"d1_calcium_min\", \"d1_creatinine_max\", \"d1_creatinine_min\", \"d1_glucose_max\", \"d1_glucose_min\", \"d1_hco3_max\", \"d1_hco3_min\", \"d1_hemaglobin_min\", \"d1_platelets_max\", \"d1_platelets_min\", \"d1_potassium_max\", \"d1_potassium_min\", \"d1_sodium_min\", \"d1_wbc_max\", \"d1_wbc_min\", \"apache_4a_hospital_death_prob\", \"apache_4a_icu_death_prob\"]\n",
    "variables_2=['age', 'height',  'hematocrit_apache','map_apache', \"d1_mbp_min\", \"d1_hemaglobin_max\", \"d1_hematocrit_max\", \"d1_hematocrit_min\", \"d1_sodium_max\"]\n",
    "\n",
    "variables_continuas= variables_1+variables_2\n",
    "\n",
    "# solo las columnas continuas\n",
    "continuas=[]\n",
    "for var in variables_continuas:\n",
    "    if var in list(data2.columns):\n",
    "        continuas.append(var)\n",
    "            \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()# Fit on training set only.\n",
    "dataNorm= scaler.fit_transform(data2[continuas]) \n",
    "norm= pd.DataFrame(dataNorm, columns= continuas)\n",
    "\n",
    "\n",
    "# sin continuas\n",
    "data_otros= data2.drop(columns= continuas)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.merge(data_otros.reset_index(),\n",
    "                  norm.reset_index(), \n",
    "                  left_index=True, \n",
    "                  right_index=True)\n",
    "\n",
    "data_norm=result.drop(columns=[\"index_x\",\"patient_id\" ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in list(result.columns):\n",
    "    if var not in list(data2.columns):\n",
    "        result.drop(columns= var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOGISTIC REGRESSION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 119)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 =data_norm.drop(columns=[\"hospital_death\"])\n",
    "data3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean recall: 0.732\n"
     ]
    }
   ],
   "source": [
    "from numpy import mean\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', class_weight='balanced')\n",
    "# define evaluation procedure\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "# evaluate model\n",
    "scores = cross_val_score(model, data3, data_norm[\"hospital_death\"], scoring='recall', cv=cv, n_jobs=-1)\n",
    "# summarize performance\n",
    "print('Mean recall: %.3f' % mean(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean recall: 0.749\n"
     ]
    }
   ],
   "source": [
    "# con data normalizda\n",
    "from numpy import mean\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', class_weight='balanced')\n",
    "# define evaluation procedure\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "# evaluate model\n",
    "scores = cross_val_score(model, data_norm, data2[\"hospital_death\"], scoring='recall', cv=cv, n_jobs=-1)\n",
    "# summarize performance\n",
    "print('Mean recall: %.3f' % mean(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularizacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[233., 233.,  15., ..., 119.,  37.,  73.],\n",
       "       [487., 557.,  26., ..., 118.,  31.,  67.],\n",
       "       [187., 196.,  23., ...,  96.,  48., 105.],\n",
       "       ...,\n",
       "       [187., 196.,  23., ..., 103.,  89., 158.],\n",
       "       [116., 116.,  23., ...,  77.,  52.,  95.],\n",
       "       [182., 182.,  12., ..., 145.,  81., 125.]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "train, valid, _ = get_data_splits(data2)\n",
    "\n",
    "X, y = train[train.columns.drop(\"hospital_death\")], train['hospital_death']\n",
    "\n",
    "# Set the regularization parameter C=1\n",
    "alphas= [1e-10, 1e-9,1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1,2,3,4]\n",
    "for a in alphas: \n",
    "    logistic = LogisticRegression(C=1, penalty=\"l2\", random_state=7).fit(X, y)\n",
    "    model = SelectFromModel(logistic, prefit=True)\n",
    "    coefs.append(ridge.coef_)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "logistic = LogisticRegression(C=1, penalty=\"l2\", random_state=7).fit(X, y)\n",
    "model = SelectFromModel(logistic, prefit=True)\n",
    "\n",
    "X_new = model.transform(X)\n",
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = pd.DataFrame(model.inverse_transform(X_new), \n",
    "                                 index=X.index,\n",
    "                                 columns=X.columns)\n",
    "\n",
    "# Dropped columns have values of all 0s, keep other columns \n",
    "selected_columns = selected_features.columns[selected_features.var() != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = cp.Variable(n)\n",
    "lambd = cp.Parameter(nonneg=True)\n",
    "problem = cp.Problem(cp.Minimize(objective_fn(X_train, Y_train, beta, lambd)))\n",
    "\n",
    "lambd_values = np.logspace(-2, 3, 50)\n",
    "train_errors = []\n",
    "test_errors = []\n",
    "beta_values = []\n",
    "for v in lambd_values:\n",
    "    lambd.value = v\n",
    "    problem.solve()\n",
    "    train_errors.append(mse(X_train, Y_train, beta))\n",
    "    test_errors.append(mse(X_test, Y_test, beta))\n",
    "    beta_values.append(beta.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_regularization_path(lambd_values, beta_values):\n",
    "    num_coeffs = len(beta_values[0])\n",
    "    for i in range(num_coeffs):\n",
    "        plt.plot(lambd_values, [wi[i] for wi in beta_values])\n",
    "    plt.xlabel(r\"$\\lambda$\", fontsize=16)\n",
    "    plt.xscale(\"log\")\n",
    "    plt.title(\"Regularization Path\")\n",
    "    plt.show()\n",
    "\n",
    "plot_regularization_path(lambd_values, beta_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### recall con las variables seleccionadas   de regularizacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean recall: 0.695\n"
     ]
    }
   ],
   "source": [
    "variables_selecc=list(selected_columns)\n",
    "matriz= data_norm[variables_selecc]\n",
    "\n",
    "\n",
    "from numpy import mean\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', class_weight='balanced')\n",
    "# define evaluation procedure\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "# evaluate model\n",
    "scores = cross_val_score(model, matriz, data2[\"hospital_death\"], scoring='recall', cv=cv, n_jobs=-1)\n",
    "# summarize performance\n",
    "print('Mean recall: %.3f' % mean(scores))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['d1_platelets_min',\n",
       " 'd1_platelets_max',\n",
       " 'd1_hco3_min',\n",
       " 'd1_bun_min',\n",
       " 'd1_bun_max',\n",
       " 'h1_heartrate_max',\n",
       " 'h1_heartrate_min',\n",
       " 'h1_mbp_min',\n",
       " 'h1_mbp_noninvasive_min',\n",
       " 'h1_resprate_max',\n",
       " 'h1_spo2_min',\n",
       " 'h1_sysbp_min',\n",
       " 'h1_sysbp_noninvasive_min',\n",
       " 'h1_diasbp_noninvasive_min',\n",
       " 'd1_wbc_max',\n",
       " 'd1_wbc_min',\n",
       " 'wbc_apache',\n",
       " 'h1_diasbp_min',\n",
       " 'bun_apache',\n",
       " 'apache_2_diagnosis',\n",
       " 'weight',\n",
       " 'age',\n",
       " 'd1_diasbp_min',\n",
       " 'd1_mbp_noninvasive_min',\n",
       " 'd1_resprate_max',\n",
       " 'd1_spo2_min',\n",
       " 'd1_mbp_min',\n",
       " 'd1_sysbp_min',\n",
       " 'd1_heartrate_max',\n",
       " 'd1_diasbp_noninvasive_min',\n",
       " 'd1_sysbp_noninvasive_min']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables_selecc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "# X es la 10x10 matrix de Hilbert\n",
    "X = data3\n",
    "y = data2[\"hospital_death\"]\n",
    "n_alphas = 200\n",
    "alphas = np.logspace(-10, -2, n_alphas)\n",
    "coefs = []\n",
    "for a in alphas: \n",
    "    ridge = linear_model.Ridge(alpha=a, fit_intercept=False)\n",
    "    ridge.fit(X, y)\n",
    "    coefs.append(ridge.coef_)\n",
    "# mostramos resultados\n",
    "ax = plt.gca()\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(ax.get_xlim()[::-1]) # reverse axis\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('weights')\n",
    "plt.title('Coeficientes de cresta en función de la regularización')\n",
    "plt.axis('tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.62843e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.78527e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.01652e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.17462e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.87979e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.60962e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.89595e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.14793e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.3706e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.77649e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.96385e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.46234e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.99913e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.56102e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.23937e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.60764e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.25613e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.99081e-25): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.04309e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.14201e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.25181e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.37673e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.51161e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.65715e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.81808e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.99585e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.83471e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.39853e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.63745e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.8855e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.16556e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.4755e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.81017e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.18459e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.59055e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.03482e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.52162e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.05885e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.64663e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.29299e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.99719e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.77391e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.62385e-24): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.05599e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.15822e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.27074e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.39353e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.52873e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.67744e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.83991e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.01789e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.21377e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.42903e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.66437e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.92286e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.20659e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.51738e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.85793e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.23267e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.64286e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.0937e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.5872e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.12911e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.7238e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.37643e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.09109e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.87602e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.73671e-23): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.06814e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.17169e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.28533e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.41e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.54677e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.69678e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.86135e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.04184e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.23991e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.45711e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.69547e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.95683e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.24366e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.55826e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.90331e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.28187e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.69719e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.15274e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.65247e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.20067e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.80205e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.46176e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.18549e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.97933e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.85018e-22): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.08055e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.18535e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.30031e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.42642e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.56477e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.71652e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.88301e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.06563e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.26597e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.48574e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.72682e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.99128e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.28139e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.59964e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.94875e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.33173e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.75184e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.2127e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.71826e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.27285e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.88123e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.54861e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.28071e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.08383e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.96483e-21): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.09313e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.19915e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.31545e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.44303e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.58298e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.7365e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.90492e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.08967e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.29234e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.51466e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.75855e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.02609e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.31958e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.64153e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.99471e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.38214e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.80714e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.27337e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.78481e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.34585e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.96131e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.63646e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.37709e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.18955e-20): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.00808e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.10585e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.2131e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.33075e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.45982e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.6014e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.75671e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.92709e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.11399e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.31902e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.54393e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.79065e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.06131e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.35821e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.68391e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.0412e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.43314e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.86309e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.33474e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.85213e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.4197e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.04232e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.72533e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.47457e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.29649e-19): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.01981e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.11872e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.22722e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.34624e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.47681e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.62004e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.77716e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.94951e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.13859e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.346e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.57353e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=2.82312e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.09693e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.39728e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.72677e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.08821e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.48471e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=4.91966e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.3968e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.9202e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.49438e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.12423e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=7.81518e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=8.57313e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.4046e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.03167e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.13173e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.24149e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.36189e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.49397e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.63886e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.79781e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.97217e-17): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.025118864315095794,\n",
       " 3.9810717055349695e-11,\n",
       " -0.2294903377930254,\n",
       " 0.13893041045974475)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kA1hcMLwI2DyGac8qmvaOUhXd/WrgaoD29vbqd4gvIoeMsC+HJQx3CPdlCbuz5Lujv7iDWXT1k0HvE7vofWB7yXaSc5uZ8vLFBI1J9t74DJYOmP3OY2lYNo0wk4/OZRwzE0slaD5u9jh/yvLVM4HcC6wws2XAc8DFwFvKnPYm4F/MbOAumVcCH65+iCIykbg7hA5hdGUS7ng+LnOPyyBoSmLJILrBruhBSmEmJL+rb7Ct3I5eACwZEPbnCXtzeF+OsDdH2JfHs3kSLSmyO3rJduwrP9iEMeUVi0nObCLf2U9ieiPJGQ0kZzWRaE0PVmtYMYMgHZCc2QRAkE7QfELtOkCsprolEHfPmdl7iJJBAvgfd3/UzD4OrHb3G8zsZOCnwAzgAjP7Z3c/xt13mdkniJIQwMcHTqjXwu6fraN/w96iD1CqZonCybjPMxljnihq8NS3ail3lX1hnaJKI03jBRX8hUXF4ygeNzA6jOfrUZmHA0mijHirwBoSBE1JgsYklgro395L0Jpi6rmHQdy3VKIlRdCaItGaJmhOgtnQJNacHJIohpOe31Lrj1MzVovHHE5U7e3tvnr16jFP13n7RrLPlfjlYWXeqFP7+3mqr9zPJi80gRddydDK+a7L+UyF7VjxKHthGzakwpDywfrx4SALDIKBw0MGCYvqBIYFcb2B8USHmjwbYqnEC2NJBdGd3HHd1KwmSBieCQkaE1hjclxuwptMzGyNu7cXl6srkzJMfblOvouIFFNXJiIiUhElEBERqYgSiIiIVEQJREREKqIEIiIiFVECERGRiiiBiIhIRZRARESkIrqRsBy/vhKef7jeUYiIVGbesfDqq6rerPZARESkItoDKUcNMreIyGSnPRAREamIEoiIiFRECURERCqiBCIiIhVRAhERkYoogYiISEWUQEREpCJKICIiUhElEBERqUhdE4iZvcrMnjSzdWZ2ZYnxDWb2g3j8/5rZ0rh8qZn1mtkD8eur4x27iMihrm5dmZhZAvhP4FygA7jXzG5w98cKql0O7Hb35WZ2MfAZ4E3xuKfd/YRxDVpEZBLpznbTkGggGdRmU1/PvrBOAda5+3oAM/s+cCFQmEAuBP4pfn898GUzs/EMEuD/ru3gkX294z1bEZGK9ef6eXzX46SCFOcvXMXnVx1Z9XnUM4EsBDYVDHcApw5Xx91zZrYXmBmPW2Zm9wOdwEfd/c5SMzGzK4ArAJYsWVK96EVE6iibz5ILc2TCDJ39nXRmOsmGWWY0zKAx2cjWnm0AZPIZfr3h1/zl/IDlM5ZXNYZ6JpBSexJeZp0twBJ332lmJwE/M7Nj3L3zBZXdrwauBmhvby9uvyyfWLGokslERMbM3dnRu4MdvTtYv3c9j+98HMeZ0TiDRVMW8eiOR7mz406e3fv04DTpIM2Zc09kRuMMbt90O135fuY3TOOr53yVxkQjX7zvi8xrmVf1WOuZQDqAxQXDi4DNw9TpMLMkMA3Y5e4O9AO4+xozexo4Elhd86hFRIB8mCewgIGj6qGH9GR76M52053tBmBO8xz29O9h877NdGY66cp00ZvrJRkkSQUp+vP9PLj9QToznSQtSWemk/V717Orb9fgfBoSDQQW0JuLDqMngyTtc9v5sxV/xtyWubQ1tHHc7ONoTDYC0JvrJZPP0JJqGTz38eWzv1yTZVDPBHIvsMLMlgHPARcDbymqcwNwKXA38AbgNnd3M5tNlEjyZnY4sAJYX6tAr330Wp7a/dSYpvEX7EzVRpRLxzhNBbFVNM04xTZuJnRoEzg4XhhfqXWj1Gdwdxx/4V+c0MNoGmfE4YF5FU/vHtUZfBEShiF5z+M4+TBP6PGwO3mPhvvz/fTn+8l7HoCkJUkECfrz/RUtm5mNM5nTPIec55iansrpC09n1cxVzG2ey6Ipi1g+fTnJIElXpotNXZtYOnUpzanmYdtrSjbRlGyqKJaxqlsCic9pvAe4CUgA/+Puj5rZx4HV7n4D8A3g22a2DthFlGQAzgQ+bmY5IA+8y913vXAu1fHjtT+mo6ujVs1PGnW4fkEOIlbyiHSZ0xase8XtGDbkYHfJ8fsHhpTZ/gIs/lc4v8JhMyNpSdKJNNNsGoYNJiM8qpOwBIkgQcISOE4mnyEZJAevhEpaksCCwQRnGOlEeki8m/dtZvO+4oMxB+aotqP40CkfqmqbUOcnErr7r4BfFZX9Y8H7PuCNJab7MfDjmgcYO2FLAy3ZxvGa3fC0AZdqmmzrU8lwR/sMPnTP0QziZDAoCIaMtyCIyszAHcIQD0MskcDSaQjDqF46jYdh1EQ6HY0LAjyTwTPZqLxpCgRB1KYZns9DLg+JBJZIgxm5XbuwICAxfXoUcT6PZ7NYKoUlEgUfxSfcd6ZH2pbhkm8/R35XzXZwRORQFQRRQgJIJKIEkcsNjrZUKkpWDQ0ETU0s/NKXaFx1NJkNG9hz/fVgAal585j2+j9j2+c+R/cf7uKw675L2NXF7u9ex8z3/jXpWbNqFr5Vcpx6smpvb/fVq8d+nj23ezdksxXPtyrL+ICbqEYMB9hGVZbDgbVRndW9zsthHJfj6NVGqTBSAwPjyoml1DmTcpeD2f69jGwOz2bw/v6oLJ8n39MTVzO8v598Tw/e04vnc1gySdDYiDU1Ee7tJLNlC0FzE57Jktu+naCpEdzJbd9BvqsLz+dITp1GMHUqnsuSeeZZCEMsnYr+plKQTBLu6ya3fTthby8tp50G+Tw9995LbudOLJUifcTh9D/2eEXfddDSgqXT5Hfv5n1nv4/g8CP44p+fzIq5U8bc1v5FaGvcvb24XHsgZUjOmFHvEETkEBBmMtH5llSKzMaNZDZsILVgAaRSdP3mZvb+9CfRXseCBbT+yZnktu9g329/S//atTQdfzyJthnsu/U26O6mM9XM41MWwPZezv/3O/nhu17K8YunVzVe7YGUYff3Xk5yxzPVD0hEpJrcCbu7IZmk26awsbONBcnnWZ9dwMlv+hotLzqmoma1ByIicrAzI2htBaC3O7q6qzXVy8rwGdLzD64bCSeNz7d/W31hicik8vTvniMTZll5+myW3PMHPrd7N8ycOfqEY6AEUoYnfvcF9uY21DsMEZGyuMO+rW+huXkTXY/dyfNty2hYXt1+sEAJpCzT+p+nG+2BiEx+0X0UbsU3HBbdXxHfb+H7p4jeWRD/LZw6uq9kf0l0kyAYQZBgdAPnoat3j0cm20oYNjG1aSctiQTzG1JVa7uQEkgZ/s+0TsLGnnqHISLlGnLz4NDy8bwVzw28aI4ev7AAAwLP40AYJPEgiXkeC/MQpEgk0uD5KHHZ6MnIgd5snnzYjc/+Mi0NSRJmtKRyo05bCSWQMjTPWkl3dmx9YYnIxDTY51ZBH1mFY/cPFmchBw/BB6bYPz66mtXj/ZSBO90dD/MlE1bgjsWJI28B5k4yn4N8tKF3A8v1Q25//1puhqeawYwwnyX0PGZGEKTozRqBhZjnyXmSZCIgMCNR4zvXlUDKsPKsTxLmw3qHITKpHVCHj9W63aBUn1pmgzcl2mBRQCKRIJfPk81mCQIbzBcOhPHd44kgIBEYQRBEHTHm86SSqajzxXyeZCo52G1K1Hmjk8vloldvL3s6t5NqnI6Zke7cSO/Ox7HmOaRmHs3Tj32X3XvWsT41ndS+Hbxs08PMyvXSQj9diWk8EhzFTmbwvcxpPB4ujOIhZE5yO68+6h6WbN1J68LzaZtzGCuOP746y694ceo+kNH99oPfpDlZ3asXRORgsv9sydD38fBgUdEewRj7t8pjBIMHxZz+MMlua2aK95L1BH2WZmbQTYoowZmHuAXsDLpYevGxHHXscRV9Ot0HcgDua91Ij2+sdxgicogrPN0+3GmeUpqAly04r+rxKIGUIZXuxPob6h2GiBzSfJjrxgr3fCiqEZWlE120Tpta9YiUQMqwOUzwVK6t3mGITDI1OoFb88uoypxByUNPxfsGY2ivBHcIMTK5JA40JnPRifcwIBXkyzj6FV1SvDLoZt2TP2fFMS94OsYBUQIpw3N9L6UrV5vrqEVESgkJhqSiAKc7t7+kfwzX9XTYclYcc2H1gospgZThFbkEc0MlEBGZnLZkM2T7+0g1VPfBeEogZfhs+066Gqt//FBEDmEFF2cNdy2slRxT4rhVqQvACm5pae7awflbn2f2kqUVh1uKEkgZPpu7lYbs9nqHISJSkf5gNrOXXFH1dpVAyjB1Vyc2I1PvMEREKtKwu7Mm7dY1gZjZq4AvAQng6+5+VdH4BuBa4CRgJ/Amd38mHvdh4HIgD7zX3W+qVZybb1vH1F26E11EJqfOtq3w7uq3W7cEYmYJ4D+Bc4EO4F4zu8HdHyuodjmw292Xm9nFwGeAN5nZKuBi4BhgAXCLmR3p7vlaxPrQCVk2Th3PLthERKpnSWeeN2V7IdVU1XbruQdyCrDO3dcDmNn3gQuBwgRyIfBP8fvrgS9b1LHMhcD33b0f2GBm6+L27q5FoC9++C85OlndZwmLiIyXptwe2LEW5lfWlclw6plAFgKbCoY7gFOHq+PuOTPbC8yMy+8pmnZhrQLtn7mI0Ftr1byISE31W2vVkwfUN4GUOiZUzi2cxT2VDTdt1IDZFcAVAEuWLBlLfIN2zJnKsozuAxGRyWlDOqhJu/VMIB3A4oLhRcDmYep0mFkSmAbsKnNaANz9auBqiHrjrSTQRJAlmr2IyOSTCLJsvPP3LDnj9Kq2W8+t4r3ACjNbBjxHdFL8LUV1bgAuJTq38QbgNnd3M7sBuM7MPk90En0F8MdaBTrzsP8i27qnVs2LiNTUzH3TSbf+e9XbrVsCic9pvAe4iegy3v9x90fN7OPAane/AfgG8O34JPkuoiRDXO+HRCfcc8Bf1eoKLIDnZzYypaG6Vy+IiIyXroY08048ourt1vW4jLv/CvhVUdk/FrzvA0p2H+nunwI+VdMAY0uf2kzz9K7xmJWISNX17Oklf15IIlndcyE6sF+Gvt80sSvRUu8wREQq0pgzdv1/3cxeMqWq7SqBlOHBk0/Dd+2odxgiMi6Kr7Up7PLQh9by4aaJmQ1OGV06OnABqRW8H+jlIihox174HPiRLgEq7JGxcBYDAzNm8pYqJw9QAilLX0+StGtRicgYFfSIW9EloIVG6wyjeHzBDDO9KTx0LKhujxraKpYhsbufIJutdxgiIhVJ9PaT295Dam51D8WXlUDM7Aigw937zews4DjgWnc/JK5tbTjmMJ7au6zeYYjIcEb49T1cXSsutHF4Wu4I3PO4ZwDDLEU+30PhcalEomns96OFDu4c1ZQjMSVd5YjL3wP5MdBuZsuJLq29AbgOeE3VI5qAks+t5Lg+9cYrcqhzG/K8pqHj4r82pP4wKanwPMXgKRDDLG4ndAjAzDELCMM+IE8y0UroWcKwH3ACSxMkGgDDwxyhZ0gkmihOhQ3NjQTN1e9No9wEEsb3bfwZ8EV3/w8zu7/q0UxQYSognzngI5gih7Z6/rwveuzfcNv1QaU6TPIhf0oMRAVeMKEVnwgfKC/4PeoDe0Tx884Hpx68sy0kII0bZMMcOBiN4EbenHw+R5BIEOYc8zR58iQSSdwdG/WDHphyE0jWzN5MdFf4BXHZIdM51FlHzaHjqd31DkNk7Eb53VNy+1ZUWLKJUhcIFUznRWeOvfBs8pDxTsFbPPT9r3H6zZZIBgRJI8w7jS0pGltTNLakSKUDEqkEQcLo78nRMi3N0mNnYQmjoSnJnKVTBmNMpRNjnm9/Lk9fJs+05jRhGPL8zh527Ool3ZhkwewWHn1sGz27+kilAnq7s9gTz7FqR4qcwZaWgExTksbeDPP2RXdi5xtS9MRb9L5UQFtvyGMnz+SVrzuKRKK+fWG9HXgX8Cl33xB3P/KdmkQ0AX258VtsXvF0vcMQOTQNezWrD1un+DjTkEeFO4MZbPDt4JW6vn8Y8LxDHixp+D7H7yodohlYYFhgcfKLGkgkA4LAost5D3BnwNvy5Kb2EgQpEomGwfJ8ro98OHC4y/BccnDPI9jqfPRrMK95Ob98278dWAAllJtAznX39w4MxEmkt+rRTFCdPX1kcjXrKUVERtm4VudAjA15ayWKwQqJDjUAABi8SURBVBj6W92Gvi+4JcTdMWxw7yvMO2HoUTIxw93JZfYfqzIbmK9hAQSJADPIZvKYWbQXM8IHNUuQTDYRPYtvv0SyAfL9BEEKHPLJHoKgESMgmzc85+zZ2cM9D23hJcfNL3NZlafcBHIp0aNnC11Wouyg9MqGTlqnHjL5UkTKMPTePR+8NTAEBlJL0qIaYTxsQIBjvj9XZIhuNmw0p6EhSS7vZHJ5pjQkaG1KkQ0hnTBaG1M0NTYPzj86xzEQSSP5fJ7+/j4ymWeAkM7+Vrb0zMMdNnX1M3XW/mmrZcQEEp/3eAuwLO4Bd8AUomeUHxLOOHIefX176x1GdVXh+LIuK5DKxYeQSqxExedThk4xWGFo2Qsq7S8ovI+8sGnHC94PLR/STnw3eeiQD6PjW6F7dNgobj8MIR/HFZiRCAx3oy8X7YEE8cl1B0I3QodcvBfTmIQwNLpDY1+fE+AEGNv6YFvf/vvPjH4aE10kcTKhk/EEgUFDAC0NSTr7MuRDY17LYvblk+zpM1rTIcvntnHmlMM4at7UFy7sAzTaHshdwBZgFlB4AK0LeKjq0UxQ/3v3cfRvrf7TvEREBrhDPheCQWBGJpPHDQKLLgfO+v6EZ0AiiBJQlLj2d5jybEGbgYV0pJI0zw84dlX1r8gaMYG4+7PAs8BpVZ+ziMhBLox3fwp7uXKH0D26xw8nGXcvkgudkDg5GISJ6IQ8Drn4arVEYORDJwiivZl8QUIpvHw4MEgGeTL5BN2ZPPv29tGfy9OQHPvVYiMp90701wOfAeawvxcwd/fq7xNNQCeedw5b9vbVO4xJY7wuvzxY+AuPu8gICtcvLyosPkw1wApOmudDpyebJ5MLyeWdXOgkAkgGAbkwJJt3MrmQbD565UNoSgf0ZUO2dfVjQCphBGZ09eXozeYJ4r0GiJJD3mFbZ9/gdmNgw19SwT0hU5uT9GTy5EKnsTEgFQR09eeY05LmxTvgopMX8fjMgO/fuwlyIScvmsG7XruSFXOn8K3fbeCOR7by6UtOYP60RvbuXcMPbvsQ1z3ydp7tnsUrH9nAy09YXuliL6nck+ifBS5w98erOvdJ4pq7nuH2J7fXOwwRqbJEYCTMyLuTD6O9gVQiIJUw0smAVCIgMKM3myedCJg7Nbp8NpuPzoNMaUwypTHajIbxIaZEYJgZh7U1c/LSGWTzzvZ9/bQ2JGlJJ5jalGLB9CYWzWginQz4/dodJBMBLztiJjNbG+jqy/LE810cs2AqySDgyee7WDlvCn/82dM8eMsmAC4moHlqIz33dfFk33qW/9VxLFrfyykP99KSja4CmzbtRI6evZerXvkjHtn0ds540dKqL79yE8jWQzV5APzbRSeQyakrk7Go8Q2wBx0trjEackVu3FF6UR9XA/dCeNHNioFFJ53TifgejcFxtb9zu5TXv3jRkOEpjSlOXto2OHzsomkAnHL+Mvq6srQtaGHlS+bRPCXNg7dt4g/Xr+OunzzN2nu3AbDhwe2ccM4S8IAHO/+Coxs/y4mH/wizV1Ht/nNHuwrr9fHb1Wb2A+BnQP/AeHf/SVWjmaDaWqrfCZmITCz1SB5jkW5Mcs7bVw0pO/7sxWx8bBcP3rqJIGm0zmhkw4M7SDcl+cP16/iv9AJOWPAW/nzVj3l6y6McueikqsY0Wjq6oOB9D/DKgmEHDokEIiIyEZkZZ77pSL7/yT9y9Evn09Cc5L4bn2Xnc/vI9OZYZAEnr7qErz1xKl8/7UVVn/9oV2G9vepzFBGRqpk+t5m3fuI0mqam2b6xizW/fpZMXx4PYJknec/Ll/O3Z68YcriuWsq9CuvfSxTvBVa7+8+rG5KIiIxFy/To5P6cJVOYtbiVpcfO4le3P8MKS5FO1qYjRYByW24ETgDWxq/jgDbgcjP74lhnamZtZnazma2N/84Ypt6lcZ21ZnZpQfkdZvakmT0Qv+aMNQYRkYONBcZFHzmZRWfO5ynP0twT0tddu6eplntKfjnwCnfPAZjZV4DfAOcCD1cw3yuBW939KjO7Mh7+UGEFM2sDPga0E51vWWNmN7j7QL/ql7j76grmLSJyULnlsa0cNX8KjakEF199D+u27WNRvOexZd0elh0/uybzLTeBLARaiA5bEb9f4O55M+sffrJhXQicFb+/BriDogQCnAfc7O67AMzsZuBVwPcqmJ+IyEHph6s38ffXP8SM5hQLZzTRsbuH9569gnktafZet4GOJ3bXPYF8FnjAzO4gusz6TOBfzKwFuKWC+c519y0A7r5lmENQC4FNBcMdcdmAb5pZnuhxu590L33/s5ldAVwBsGTJkgpCFRGZmG5/Yhsf/ekjnLKsjR37+nnkuU7+480ncsHxCwC48ZF9PH7XFtpfs5Smej0T3d2/YWa/Ak4hSiAfcffN8egPlprGzG4B5pUY9Q9lxlbqkoGBJHGJuz9nZlOIEshbgWuHif1q4GqA9vZ29RkhIpPSmmd3897v3c+ZR87mnKPn8L0/buKWx7eyfE4r//3nJ5FIGOu3d3PC4umD05xy/jKevn8b9930LC97w4qqxzTajYRHufsTZvbiuGhgj2Cemc1z9/uGm9bdzxmh3a1mNj/e+5gPbCtRrYP9h7kAFhEd6sLdn4v/dpnZdUSJrWQCERGZzNydmx/byt/94AGaG5L8cPUmvvfHjUxrSvHB81Zy+enLaExFnSQWJg8gumv91Hk8/NvnOOGcJYNXa1XLaHsg7yM6/FPqWYgOvKLC+d5A9JCqq+K/pS4FvonoMNnAFVqvBD5sZklgurvvMLMUcD6VHUYTEZmwfrR6Ezc/tpVndnbz1NZ9rJjTynfeeSr7+nNs2N7N6StmDSaOkZz8p8sGn5ZYbTbMqYOaMrOZwA+BJcBG4I3uvsvM2oF3ufs743rvAD4ST/Ypd/9mfN7ld0CK6FnytwDvc/dRnznb3t7uq1frwi0RmXg6+7Lc9MjzbOvqZ8OObq5f08GStmYWzWjidScs5HUnLqzpPR0jMbM17t7+gvJyEoiZNRPtjSxx9yvMbAWw0t1/Uf1Qa0cJREQmgp37+lm7bR9b9vYyoznNbU9s4wf3bqK/oNPWv/iTw/n7844iUYM7yMdquARS7lVY3wTWAC+NhzuAHwGTKoGIiIwXd2dbVz9rt+5j7bYu1m7bx7r4tas7M6RuKmG8/sRFvPnUJRw1bwr92ZBpzak6RV6+chPIEe7+pvgZ6bh7r030ritFRKrE3Xm+s48nnu9ib0+Wrv4c+/py7OvPsq8vVzCco7s/Gt7e1U9XX26wjWlNKVbMaeW8Y+ayfM4UVsxpZcH0Rnbuy7BkZjPzpzUN1i3n3MZEUG4CyZhZE/FltGZ2BAXduh/stnX10Z/V80Bk4hjuyPNwTzccvv5I8ximrTHGNDBV6Psf5zrwF4YOe9zOwDM88mH04KYwHHjSn0fPAY+HwzBqNz/4PnowVD50sqGTy+9/6uBA/VzoZPMhmVxIdybHlr19uEM6GdDZm6WzL0dnb5auviz9uXCwzVLnoBOB0dqQpLUherBUa0OStpY0S2a20Nac4og5rSyf08qKOVOY1Zou2WX88hp2xNTT08ODDz7II488wlvf+lYaGxur2n65CeRjwI3AYjP7LvAy4LKqRjKBfej6h/REQpGDSDKInjjYnE4wd2ojicDI5EKmNqZYOL2Jo+dNYUpjksZ0gsCipxbOntLAqgVTmdmSprUxyZSGFI2pYMI+RySTyXD11VezZ88eFi5cSFdXV90SyNuAXwLXA+uBv3H3HVWNZAK7/PTD+dPjFtQ7jEmjHlf2TWbO8E8kHGnjNPw0Yywf4XmIY902jhTvwHPDLa5nQ4bjZ4oXlSUsejxsIjASQTRdwqLnkQdBVDcR2GDbwWBdI5kwUkFAMmEkg2B/fbOadG0+0dx1113s2bOHSy65hBUrqn8TIYztJPrpRJ0nHk7Urcnv3P1LNYlqgjl9xax6hyAiUpZ8Ps/GjRv5wx/+wKpVq2qWPKD8rkxuM7PfAicDLwfeBRwDHBIJRESk3tyd9evXs3jxYtLpof1a7dmzhylTppDP5/na177G9u3baWho4Jxzhu0QpCrKfaDUrUQ98N4N3Amc7O6luh8REZEauOeee7jppps48sgjufjiiwmCgO7ubm699Vbuu+8+li9fzty5c9m+fTsXXHABq1atoqmpafSGD0C5h7AeAk4CXkTUpfseM7vb3XtrFpmIyCHM3QnDkEQiwdq1a/nNb35DW1sbTz31FL/85S+ZNm0ad999N319faxcuZInn3ySdevWcdxxx3HSSSeNS4zlHsL6OwAzawXeTnROZB5Q3Z65REQOcu7O3r172bJlC93d3axatYpMJsP9999PR0cHTU1NLFu2jN///vd0d3ezcOFCNmzYwOzZs7n88su58cYbWbNmDQCHHXYYr3nNa5g7dy533HEHDzzwAOeee+64fZZyuzJ5D3AG0V7Is0R9Ud3p7rfVNrzqUlcmIjJW7o6Zkc1m2bVrF7lcjjAMB/cQCv8Wvt+2bRuPPvooyWSSqVOn0tvbS09PD52dnfT19Q22n0qlyOfzuDtz5sxh79699PX1MXPmTBYtWsT69es59thjOeusswbPfXR3d+PutLS0DLnybSDWajvQrkyagM8DawYea3soWb16NTt21O+q5XpeZz5Rr3GfjA62y5tH+zzlfN5SdQY2xAPjCodLvUrVCYLo/oyBvwPMjHw+T09Pz+B0A+8TiQTZbJZcLkc2mx18H4Yh6XSabDY75u9wyZIlJBIJtm/fTlNTEzNmzGDx4sXMmzePefPmkUwmuffee0mn05x22mlMmzaNTCbDli1bWLhwIclk6U10S0tLyfLx/v9a7iGsf611IBPZ+vXrefrppyuatt4bjXrOv96ffSwOtkRZr89TznxHq2PxvR/Fr5HGFdcZ2BMIw6gHicJ1MQgCmpubB4fb2towM3K5HKlUimQyOeRvEAT09/fT0NDAnDlzBssG5lmcrAb+trS0MG3atFGXx2tf+9ohw+l0msMOO2zU6SaCcvdADmkXXXRRvUMQEZlw6tO5vIiITHpKICIiUhElEBERqYgSiIiIVEQJREREKqIEIiIiFalLAjGzNjO72czWxn9nDFPvRjPbY2a/KCpfZmb/G0//AzNLl5peRERqp157IFcCt7r7CuDWeLiUfwXeWqL8M8AX4ul3A5fXJEoRERlWvRLIhcA18ftrgNeVquTutwJdhWUW3W76CqKnI444vYiI1E69Eshcd98CEP8dy2PlZwJ7Cvrk6gAWVjk+EREZRc26MjGzW4i6fC/2DwfadImyYTtdMrMrgCsg6thMRESqo2YJxN2HfZaimW01s/nuvsXM5gNjebrhDmC6mSXjvZBFwOYR4rgauBqi7tzHMB8RERlBvQ5h3QBcGr+/FPh5uRN61K3m7cAbKpleRESqo14J5CrgXDNbC5wbD2Nm7Wb29YFKZnYn8CPgbDPrMLPz4lEfAt5nZuuIzol8Y1yjFxGR+nTn7u47gbNLlK8G3lkwfMYw068HTqlZgCIiMirdiS4iIhVRAhERkYoogYiISEWUQEREpCJKICIiUhElEBERqYgSiIiIVEQJREREKqIEIiIiFVECERGRiiiBiIhIRZRARESkIkogIiJSESUQERGpiBKIiIhURAlEREQqogQiIiIVUQIREZGKKIGIiEhFlEBERKQiSiAiIlKRuiQQM2szs5vNbG38d8Yw9W40sz1m9oui8m+Z2QYzeyB+nTA+kYuIyIB67YFcCdzq7iuAW+PhUv4VeOsw4z7o7ifErwdqEaSIiAyvXgnkQuCa+P01wOtKVXL3W4Gu8QpKRETKV68EMtfdtwDEf+dU0ManzOwhM/uCmTUMV8nMrjCz1Wa2evv27ZXGKyIiRWqWQMzsFjN7pMTrwio0/2HgKOBkoA340HAV3f1qd2939/bZs2dXYdYiIgKQrFXD7n7OcOPMbKuZzXf3LWY2H9g2xra3xG/7zeybwAcOIFQREalAvQ5h3QBcGr+/FPj5WCaOkw5mZkTnTx6panQiIjKqeiWQq4BzzWwtcG48jJm1m9nXByqZ2Z3Aj4CzzazDzM6LR33XzB4GHgZmAZ8c1+hFRKR2h7BG4u47gbNLlK8G3lkwfMYw07+idtGJiEg5dCe6iIhURAlEREQqogQiIiIVUQIREZGKKIGIiEhFlEBERKQiSiAiIlIRJRAREamIEoiIiFRECURERCqiBCIiIhVRAhERkYoogYiISEWUQEREpCJKICIiUhElEBERqYgSiIiIVEQJREREKqIEIiIiFVECERGRitQlgZhZm5ndbGZr478zStQ5wczuNrNHzewhM3tTwbhlZva/8fQ/MLP0+H4CERGp1x7IlcCt7r4CuDUeLtYDvM3djwFeBXzRzKbH4z4DfCGefjdw+TjELCIiBeqVQC4EronfXwO8rriCuz/l7mvj95uBbcBsMzPgFcD1I00vIiK1Va8EMtfdtwDEf+eMVNnMTgHSwNPATGCPu+fi0R3AwhrGKiIiJSRr1bCZ3QLMKzHqH8bYznzg28Cl7h7GeyDFfITprwCuAFiyZMlYZi0iIiOoWQJx93OGG2dmW81svrtviRPEtmHqTQV+CXzU3e+Ji3cA080sGe+FLAI2jxDH1cDVAO3t7cMmGhERGZt6HcK6Abg0fn8p8PPiCvGVVT8FrnX3Hw2Uu7sDtwNvGGl6ERGprXolkKuAc81sLXBuPIyZtZvZ1+M6FwFnApeZ2QPx64R43IeA95nZOqJzIt8Y3/BFRMSiH/SHhvb2dl+9enW9wxARmVTMbI27txeX6050ERGpSM1Ooh9Mdn7jG/Q9/kS9w5BSSl6UJ3Wnr2XCmfP+D5CaO+IdE2OmBFKGzDPP0PvwQ/UOQ4odOkdfJ5dD6LD4ZOL9fVVvUwmkDPM/8Yl6hyAiMuHoHIiIiFRECURERCqiBCIiIhVRAhERkYoogYiISEWUQEREpCJKICIiUhElEBERqcgh1ZmimW0Hnq13HFU2i+gZKVIeLa+x0fIam4N1eR3m7rOLCw+pBHIwMrPVpXrJlNK0vMZGy2tsDrXlpUNYIiJSESUQERGpiBLI5Hd1vQOYZLS8xkbLa2wOqeWlcyAiIlIR7YGIiEhFlEBERKQiSiAiIlIRJZCDmJkdbWZfNbPrzezd9Y5nojOzs8zszniZnVXveCY6MzsjXlZfN7O76h3PRGdmq8zsh2b2FTN7Q73jqQYlkAnKzP7HzLaZ2SNF5a8ysyfNbJ2ZXTlSG+7+uLu/C7gIOKhvbqrG8iJ6yvo+oBHoqFWsE0GV1q874/XrF8A1tYy33qq0fr0a+A93fzfwtpoFO450FdYEZWZnEm3MrnX3F8VlCeAp4FyiDdy9wJuBBPDpoibe4e7bzOy1wJXAl939uvGKf7xVY3kBO9w9NLO5wOfd/ZLxin+8VWv9iqf7IfBOd+8cp/DHXZXWL4CPAT3AS939ZeMQek0l6x2AlObuvzOzpUXFpwDr3H09gJl9H7jQ3T8NnD9MOzcAN5jZL4GDNoFUa3nFdgMNtYhzoqjW8jKzJcDegzl5QFXXr7+KE89PahXreFICmVwWApsKhjuAU4erHB/Hfz3RxvBXNY1sYhrr8no9cB4wHfhybUObkMa0vGKXA9+sWUQT21jXr6XAR4AW4F9rGdh4UQKZXKxE2bDHIN39DuCOWgUzCYx1ef2Eg+SXYYXGtLwA3P1jNYplMhjr+vUMcEXNoqkDnUSfXDqAxQXDi4DNdYplMtDyGhstr7E55JeXEsjkci+wwsyWmVkauBi4oc4xTWRaXmOj5TU2h/zyUgKZoMzse8DdwEoz6zCzy909B7wHuAl4HPihuz9azzgnCi2vsdHyGhstr9J0Ga+IiFREeyAiIlIRJRAREamIEoiIiFRECURERCqiBCIiIhVRAhERkYoogYiMEzN7xsxmHWgdkYlCCURERCqiBCJSA2b2MzNbY2aPmtkVReOWmtkTZnaNmT0UPzGyuaDKX5vZfWb2sJkdFU9zipndZWb3x39XjusHEilBCUSkNt7h7icRPQnyvWY2s2j8SuBqdz8O6AT+smDcDnd/MfAV4ANx2RPAme5+IvCPwL/UNHqRMiiBiNTGe83sQeAeoh5bVxSN3+Tuf4jffwc4vWDcQJfya4Cl8ftpwI/iR6p+ATimFkGLjIUSiEiVxQ/yOgc4zd2PB+4nes56oeJO6AqH++O/efY/s+cTwO3x41QvKNGeyLhTAhGpvmnAbnfvic9hvKREnSVmdlr8/s3A78to87n4/WVViVLkACmBiFTfjUDSzB4i2nO4p0Sdx4FL4zptROc7RvJZ4NNm9gcgUc1gRSql7txFxln8bOxfxIejRCYt7YGIiEhFtAciIiIV0R6IiIhURAlEREQqogQiIiIVUQIREZGKKIGIiEhFlEBERKQi/w/mCdqOBpgkdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "# X es la 10x10 matrix de Hilbert\n",
    "X = data3\n",
    "y = data2[\"hospital_death\"]\n",
    "n_alphas = 200\n",
    "alphas = np.logspace(-10, -2, n_alphas)\n",
    "coefs = []\n",
    "for a in alphas: \n",
    "    ridge = linear_model.Ridge(alpha=a, fit_intercept=False)\n",
    "    ridge.fit(X, y)\n",
    "    coefs.append(ridge.coef_)\n",
    "# mostramos resultados\n",
    "ax = plt.gca()\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(ax.get_xlim()[::-1]) # reverse axis\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('weights')\n",
    "plt.title('Coeficientes de cresta en función de la regularización')\n",
    "plt.axis('tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### otro metodo de regularizacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "X = data3\n",
    "y = data2[\"hospital_death\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train)\n",
    "X_train_std = sc.transform(X_train)\n",
    "X_test_std = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "weights, params = [], []\n",
    "for c in np.arange(1e-15,5):\n",
    "    lr = LogisticRegression(C=c, random_state=0)\n",
    "    lr.fit(X_train_std, y_train)\n",
    "    weights.append((lr.coef_)[0])\n",
    "    params.append(10**c)\n",
    "\n",
    "weights = np.array(weights)\n",
    "\n",
    "# Decision region drawing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(params, weights[:, 0], color='blue', marker='x', label='vive')\n",
    "plt.plot(params, weights[:, 1], color='green',  marker='o', label='muere')\n",
    "plt.ylabel('weight coefficient')\n",
    "plt.xlabel('C')\n",
    "plt.legend(loc='right')\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REGULARIZACION 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Lasso, LogisticRegression\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4= data_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 120)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_norm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Selecting Numerical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 120)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91713, 122)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int16','int32','int64','float16','float32','float64']\n",
    "numerical_vars = list(data4.select_dtypes(include=numerics).columns)\n",
    "data5 = data4[numerical_vars]\n",
    "data5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((64199, 121), (27514, 121))"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data5.drop(labels=['hospital_death'], axis=1),\n",
    "    data5['hospital_death'],\n",
    "    test_size=0.3,\n",
    "    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train.fillna(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\inspiron\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SelectFromModel(estimator=LogisticRegression(C=1, class_weight='balanced',\n",
       "                                             dual=False, fit_intercept=True,\n",
       "                                             intercept_scaling=1, l1_ratio=None,\n",
       "                                             max_iter=100, multi_class='auto',\n",
       "                                             n_jobs=None, penalty='l2',\n",
       "                                             random_state=None, solver='lbfgs',\n",
       "                                             tol=0.0001, verbose=0,\n",
       "                                             warm_start=False),\n",
       "                max_features=None, norm_order=1, prefit=False, threshold=None)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_ = SelectFromModel(LogisticRegression(C=10*1, penalty='l2', class_weight=\"balanced\"))\n",
    "sel_.fit(scaler.transform(X_train.fillna(0)), y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecc the fratures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total features: 121\n",
      "selected features: 37\n",
      "features with coefficients shrank to zero: 0\n"
     ]
    }
   ],
   "source": [
    "selected_feat = X_train.columns[(sel_.get_support())]\n",
    "print('total features: {}'.format((X_train.shape[1])))\n",
    "print('selected features: {}'.format(len(selected_feat)))\n",
    "print('features with coefficients shrank to zero: {}'.format(\n",
    "      np.sum(sel_.estimator_.coef_ == 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(sel_.estimator_.coef_ == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(model, matriz, data2[\"hospital_death\"], scoring='recall', cv=cv, n_jobs=-1)"
   ]
  }
 ],
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