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     "text": [
      "0.31647940074906367\n",
      "[[42  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 33  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  8 25  0  0  0  0  0  0 20  0  0  0]\n",
      " [ 0  0  0  1  0  0  0  0  0 36  0  0  0]\n",
      " [ 0  0  0  0  5  0  0  0  0 31  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1 37  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 51  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 28  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 11 27  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 46  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 38  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 42  0  0  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        42\n",
      "           1       0.80      1.00      0.89        33\n",
      "           2       1.00      0.47      0.64        53\n",
      "           3       1.00      0.03      0.05        37\n",
      "           4       1.00      0.14      0.24        36\n",
      "           5       0.00      0.00      0.00        38\n",
      "           6       0.00      0.00      0.00        51\n",
      "           7       0.00      0.00      0.00        28\n",
      "           8       0.92      0.29      0.44        38\n",
      "           9       0.13      1.00      0.23        52\n",
      "          10       0.00      0.00      0.00        46\n",
      "          11       0.00      0.00      0.00        38\n",
      "          12       0.00      0.00      0.00        42\n",
      "\n",
      "    accuracy                           0.32       534\n",
      "   macro avg       0.45      0.30      0.27       534\n",
      "weighted avg       0.44      0.32      0.27       534\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import sys, os\n",
    "import numpy as np \n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "#df = pd.read_excel (r'C:\\Users\\pc\\Documents\\chisquare\\feature\\full\\chi13.xlsx')\n",
    "#dfn=shuffle(df)#shuffle\n",
    "#dfn.to_excel(r'C:\\Users\\pc\\Documents\\chisquare\\feature\\full\\shuffle13.xlsx')\n",
    "sdf=pd.read_excel(r'C:\\Users\\pc\\Documents\\chisquare\\feature\\full\\shuffle13.xlsx')\n",
    "X=sdf.iloc[:,:-1]\n",
    "Y=sdf.iloc[:,-1]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,shuffle=False)\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(503), max_iter=1000) \n",
    "#mlp=KNeighborsClassifier(n_neighbors=1)\n",
    "mlp.fit(X_train,Y_train)\n",
    "#filename1='chi92.sav'\n",
    "#filename2='chi3.sav'\n",
    "#filename3='chi61.sav'\n",
    "filename4='ch13.sav'\n",
    "joblib.dump(mlp,filename4)\n",
    "load=joblib.load(filename4)\n",
    "result=load.score(X_test,Y_test)\n",
    "print(result)\n",
    "predictions = load.predict(X_test) \n",
    "print(confusion_matrix(Y_test,predictions))  \n",
    "print(classification_report(Y_test,predictions)) "
   ]
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