in-vehicle coupon recommendation Data Set_SVM_06.05.2022.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c12b170",
   "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",
    "\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": 2,
   "id": "77f3ca34",
   "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": 3,
   "id": "cb4b5611",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validation_score(estimator):\n",
    "    scores = cross_val_score(\n",
    "        estimator, X, y, cv=10, n_jobs=-1,\n",
    "        scoring='roc_auc'\n",
    "    )\n",
    "    \n",
    "    return scores.mean(), scores.std()\n",
    "\n",
    "\n",
    "def plot_cross_validation(title, X, Y, error, xlabel, ylabel='ROC AUC'):\n",
    "    plt.title(title)\n",
    "    plt.grid()\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    plt.errorbar(X, Y, error, linestyle='None', marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed800ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                            | 0/4 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "title = 'Change kernel, C=1.0'\n",
    "xlabel = 'Kernel'\n",
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "means = []\n",
    "stddevs = []\n",
    "\n",
    "for kernel in tqdm(kernels):\n",
    "    clf = SVC(kernel=kernel, C=1.0)\n",
    "    mean, std = cross_validation_score(clf)\n",
    "    means.append(mean)\n",
    "    stddevs.append(std)\n",
    "\n",
    "plot_cross_validation(title, kernels, means, stddevs, xlabel)\n",
    "plt.savefig('SVM_change_kernel.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "143a13da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "clf = SVC(kernel='rbf', C=15.0)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print('Accuracy on test set: ', accuracy_score(y_test, y_pred))\n",
    "print('ROC AUC on test set: ', roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "123b24ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(clf, X_test, y_test)\n",
    "plt.savefig('SVM_confmat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42591e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(clf.n_support_)"
   ]
  }
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