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     "text": [
      "0.8705440900562852\n",
      "[[47  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1 48  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 27  1  6  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 37  4  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 33  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1 34  0  0  3  0  0  0]\n",
      " [ 0  0  0  0  0  2  1 26  0  3  0  2  0]\n",
      " [ 0  0  0  0  0  8  0  0 43  1  0  0  0]\n",
      " [ 0  0  0  0  1  2  9  0  0 31  0  1  0]\n",
      " [ 0  0  0  0  1  9  0  0  1  9 12  6  0]\n",
      " [ 0  0  0  0  0  3  0  0  0  0  0 39  0]\n",
      " [ 0  0  0  0  0  0  0  1  0 16  0  3 23]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        47\n",
      "           1       0.97      1.00      0.99        39\n",
      "           2       1.00      0.98      0.99        49\n",
      "           3       1.00      0.79      0.89        34\n",
      "           4       0.93      0.90      0.91        41\n",
      "           5       0.49      1.00      0.65        33\n",
      "           6       0.77      0.89      0.83        38\n",
      "           7       0.96      0.76      0.85        34\n",
      "           8       0.98      0.83      0.90        52\n",
      "           9       0.49      0.70      0.58        44\n",
      "          10       1.00      0.32      0.48        38\n",
      "          11       0.76      0.93      0.84        42\n",
      "          12       1.00      0.53      0.70        43\n",
      "\n",
      "    accuracy                           0.82       534\n",
      "   macro avg       0.87      0.82      0.82       534\n",
      "weighted avg       0.88      0.82      0.82       534\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "import time\n",
    "from sklearn import metrics\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.utils import shuffle\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\\IGfeatures\\full\\ig13.xlsx')\n",
    "#dfn=shuffle(df)#shuffle\n",
    "#dfn.to_excel(r'C:\\Users\\pc\\Documents\\IGfeatures\\full\\shuffle13.xlsx')\n",
    "dfs=pd.read_excel(r'C:\\Users\\pc\\Documents\\IGfeatures\\full\\shuffle13.xlsx')\n",
    "X=dfs.iloc[:,:-1]\n",
    "Y=dfs.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=(373), max_iter=10000) \n",
    "mlp.fit(X_train,Y_train)  \n",
    "result=mlp.score(X_train,Y_train)\n",
    "print(result)\n",
    "predictions = mlp.predict(X_test) \n",
    "print(confusion_matrix(Y_test,predictions))  \n",
    "print(classification_report(Y_test,predictions)) "
   ]
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