svm_without_poly.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "df1 = pd.read_csv('PCA/PCA_without_poly_Co_test.csv', index_col = 0)\n",
    "df2 = pd.read_csv('PCA/PCA_without_poly_Pt_test.csv' , index_col = 0)"
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   "source": [
    "df1_len = df1.shape[0]\n",
    "df2_len = df2.shape[0]\n",
    "df2.shape + df1.shape"
   ]
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   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "X = pd.concat([df1, df2])"
   ]
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   "source": [
    "y1 = pd.Series([0]*df1_len)\n",
    "y1.shape"
   ]
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   "source": [
    "y2 = pd.Series([1]*df2_len, index = range(df1_len-1,(df1_len + df2_len)-1))\n",
    "y2.shape"
   ]
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   "source": [
    "y = pd.concat([y1,y2]) \n",
    "y.shape"
   ]
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    "y.tail()"
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   "source": [
    "X_train, X_test, y_train1, y_test1 = train_test_split(X, y)\n",
    "y_train = pd.DataFrame(y_train1)\n",
    "y_test = pd.DataFrame(y_test1)\n",
    "\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# Creating the classifier object\n",
    "this_C = 7.0\n",
    "clf = SVC(kernel = 'rbf', C=this_C).fit(X_train, np.ravel(y_train))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
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     "execution_count": 14,
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   "source": [
    "# Testing with a case\n",
    "\n",
    "clf.score(X_test,y_test)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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