Support Vector Machine on scaled data.ipynb

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   "source": [
    "import pickle\n",
    "import pandas as pd"
   ]
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   "cell_type": "code",
   "execution_count": 35,
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   "source": [
    "# get the pre-processed data.\n",
    "with open('data_processed_scaled.pickle', 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "# data processing    \n",
    "X = data\n",
    "Y = data['CATEGORY']\n",
    "\n",
    "# drop the AVG and CATEGORY from training model\n",
    "X.drop( ['AVG','CATEGORY'] , axis=1, inplace=True)\n",
    "    \n",
    "# divide data into testing and training models\n",
    "from sklearn import model_selection\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y)"
   ]
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   "cell_type": "code",
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     "execution_count": 36,
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   "source": [
    "# importing SVM and training the classifier\n",
    "from sklearn import svm\n",
    "clf = svm.SVC()\n",
    "\n",
    "# train our model\n",
    "score = 0\n",
    "\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test,Y_test)\n",
    "    \n",
    "( score )"
   ]
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