in-vehicle coupon recommendation Data Set_LR_06.05.2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8cfb12ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12529, 59) (12529,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import RandomState\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "seed = 42\n",
    "rng = RandomState(seed)\n",
    "\n",
    "df = pd.read_csv('Model.csv')\n",
    "\n",
    "X, y = df.iloc[:, 1:-1], df.iloc[:, -1]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8cea7447",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>destination_Home</th>\n",
       "      <th>destination_No Urgent Place</th>\n",
       "      <th>destination_Work</th>\n",
       "      <th>passanger_Alone</th>\n",
       "      <th>passanger_Friend(s)</th>\n",
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      "text/plain": [
       "   Unnamed: 0  destination_Home  destination_No Urgent Place  \\\n",
       "0           0                 0                            1   \n",
       "1           1                 0                            1   \n",
       "2           2                 0                            1   \n",
       "3           3                 0                            1   \n",
       "4           4                 0                            1   \n",
       "\n",
       "   destination_Work  passanger_Alone  passanger_Friend(s)  passanger_Kid(s)  \\\n",
       "0                 0                1                    0                 0   \n",
       "1                 0                0                    1                 0   \n",
       "2                 0                0                    1                 0   \n",
       "3                 0                0                    1                 0   \n",
       "4                 0                0                    1                 0   \n",
       "\n",
       "   passanger_Partner  weather_Rainy  weather_Snowy  ...  income  education  \\\n",
       "0                  0              0              0  ...       3          2   \n",
       "1                  0              0              0  ...       3          2   \n",
       "2                  0              0              0  ...       3          2   \n",
       "3                  0              0              0  ...       3          2   \n",
       "4                  0              0              0  ...       3          2   \n",
       "\n",
       "   age  time  expiration  driving_distance  gender  has_children  \\\n",
       "0    1     1           1                 0       0             1   \n",
       "1    1     0           0                 0       0             1   \n",
       "2    1     0           0                 1       0             1   \n",
       "3    1     1           0                 1       0             1   \n",
       "4    1     1           1                 1       0             1   \n",
       "\n",
       "   direction_opp  Y  \n",
       "0              1  1  \n",
       "1              1  0  \n",
       "2              1  1  \n",
       "3              1  0  \n",
       "4              1  0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aecfbeb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set:  (10023, 59) (10023,)\n",
      "Test set:  (2506, 59) (2506,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=seed)\n",
    "print('Train set: ', X_train.shape, y_train.shape)\n",
    "print('Test set: ', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "70cc399b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression(random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Base logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression(solver='liblinear',random_state=0)\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "412a6897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6859223785293824"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4c55d499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6743814844373504"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8b598f",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e82216c",
   "metadata": {},
   "source": [
    "#### Cfloat, default=1.0\n",
    "Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.\n",
    "\n",
    "#### penalty{‘l1’, ‘l2’, ‘elasticnet’, ‘none’}, default=’l2’\n",
    "Specify the norm of the penalty:\n",
    "\n",
    "'none': no penalty is added;\n",
    "\n",
    "'l2': add a L2 penalty term and it is the default choice;\n",
    "\n",
    "'l1': add a L1 penalty term;\n",
    "\n",
    "'elasticnet': both L1 and L2 penalty terms are added\n",
    "\n",
    "### solver{‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, default=’lbfgs’\n",
    "Algorithm to use in the optimization problem. Default is ‘lbfgs’. To choose a solver, you might want to consider the following aspects:\n",
    "\n",
    "For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones;\n",
    "\n",
    "For multiclass problems, only ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ handle multinomial loss;\n",
    "\n",
    "‘liblinear’ is limited to one-versus-rest schemes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c64aeb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_space = np.linspace(-0.05, 5, 20)\n",
    "param_grid = {'C': c_space, 'penalty': ['l1', 'l2'],'solver':[\"liblinear\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5b3fca2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 200 candidates, totalling 600 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(lr, param_grid = param_grid, cv = 3, verbose=True, n_jobs=-1)\n",
    "best_clf=clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4ad8901a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.030538555088334123, random_state=0, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.030538555088334123, random_state=0, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.030538555088334123, random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cb202bee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy - : 0.686\n",
      "Accuracy - : 0.673\n"
     ]
    }
   ],
   "source": [
    "print (f'Accuracy - : {best_clf.score(X_train,y_train):.3f}')\n",
    "print (f'Accuracy - : {best_clf.score(X_test,y_test):.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f7e3df13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(best_clf, X_test, y_test)\n",
    "plt.savefig('LogReg_confmat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba71bd8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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