_linear_regs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3641ea11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import joblib\n",
    "%store -r X_features_full\n",
    "%store -r X_features_poly_full\n",
    "%store -r y_labels_full\n",
    "\n",
    "X_features = X_features_full\n",
    "X_features_poly = X_features_poly_full\n",
    "y_labels = y_labels_full\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76f4f5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05092641166558951"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_features[0], y_labels[0])\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "visc_predictions = np.exp(lin_reg.predict(X_features[1]))\n",
    "lin_mse = mean_absolute_percentage_error(np.exp(y_labels[1]), visc_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "796d9c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['linear_regressor.pkl']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lin_reg, \"linear_regressor.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "360b1cee",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input y contains NaN.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_17500/2593821373.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msgd_reg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSGDRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpenalty\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meta0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.001\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msgd_reg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_features_poly\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_labels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mvisc_predictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msgd_reg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_features_poly\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m   1559\u001b[0m             \u001b[0mFitted\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mSGDRegressor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1560\u001b[0m         \"\"\"\n\u001b[1;32m-> 1561\u001b[1;33m         return self._fit(\n\u001b[0m\u001b[0;32m   1562\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1563\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m   1507\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1509\u001b[1;33m         self._partial_fit(\n\u001b[0m\u001b[0;32m   1510\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1511\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py\u001b[0m in \u001b[0;36m_partial_fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, max_iter, sample_weight, coef_init, intercept_init)\u001b[0m\n\u001b[0;32m   1414\u001b[0m     ):\n\u001b[0;32m   1415\u001b[0m         \u001b[0mfirst_call\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"coef_\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1416\u001b[1;33m         X, y = self._validate_data(\n\u001b[0m\u001b[0;32m   1417\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1418\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    594\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 596\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    597\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    598\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1088\u001b[0m     )\n\u001b[0;32m   1089\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m     \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmulti_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_numeric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1091\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_check_y\u001b[1;34m(y, multi_output, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1110\u001b[0m         \u001b[0mestimator_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_estimator_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1111\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1112\u001b[1;33m         \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimator_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mestimator_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1113\u001b[0m         \u001b[0m_ensure_no_complex_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1114\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_numeric\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"O\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m    144\u001b[0m                     \u001b[1;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    145\u001b[0m                 )\n\u001b[1;32m--> 146\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Input y contains NaN."
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "sgd_reg = SGDRegressor(loss='squared_error', penalty =None,max_iter=100000, tol=1e-3, eta0=0.001)\n",
    "sgd_reg.fit(X_features_poly[0], y_labels[0])\n",
    "\n",
    "visc_predictions = np.exp(sgd_reg.predict(X_features_poly[1]))\n",
    "sgd_mse = mean_absolute_percentage_error(np.exp(y_labels[1]), visc_predictions)\n",
    "sgd_rmse = np.sqrt(sgd_mse)\n",
    "sgd_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cde82b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sgd_reg.pkl']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(sgd_reg, \"sgd_reg.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a708801",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037772071901268085"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_features_poly[0], y_labels[0])\n",
    "\n",
    "visc_predictions = np.exp(lin_reg.predict(X_features_poly[1]))\n",
    "lin_mse = mean_absolute_percentage_error(np.exp(y_labels[1]), visc_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_mse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b863a65b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['poly_lin_reg.pkl']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lin_reg, \"poly_lin_reg.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "632f806a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03846159188580567"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_reg = Ridge(alpha=0.1, solver=\"cholesky\")\n",
    "ridge_reg.fit(X_features_poly[0], y_labels[0])\n",
    "visc_predictions = np.exp(ridge_reg.predict(X_features_poly[1]))\n",
    "ridge_mse = mean_absolute_percentage_error(np.exp(y_labels[1]), visc_predictions)\n",
    "ridge_rmse = np.sqrt(ridge_mse)\n",
    "ridge_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "581be919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['poly_ridge_reg.pkl']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(ridge_reg, \"poly_ridge_reg.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a25f202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0382302953851348"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso_reg = Lasso(alpha=0.001)\n",
    "lasso_reg.fit(X_features_poly[0], y_labels[0])\n",
    "visc_predictions = np.exp(lasso_reg.predict(X_features_poly[1]))\n",
    "lasso_mse = mean_absolute_percentage_error(np.exp(y_labels[1]), visc_predictions)\n",
    "lasso_rmse = np.sqrt(lasso_mse)\n",
    "lasso_mse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8cf3cf54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['poly_lasso_reg.pkl']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lasso_reg, \"poly_lasso_reg.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "549fb2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03875271324789725"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "elastic_net = ElasticNet(alpha=0.001, l1_ratio=0.7)\n",
    "elastic_net.fit(X_features_poly[0], y_labels[0])\n",
    "visc_predictions = np.exp(elastic_net.predict(X_features_poly[1]))\n",
    "elastic_mse = mean_absolute_percentage_error(np.exp(y_labels[1]), visc_predictions)\n",
    "elastic_rmse = np.sqrt(elastic_mse)\n",
    "elastic_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cc6f3660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['poly_elastic_reg.pkl']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(elastic_net, \"poly_elastic_reg.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b68c35e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}