AI project- Saada Duaa.ipynb

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       "   Age  Gender        BMI  AlcoholConsumption  Smoking  GeneticRisk  \\\n",
       "0   58       0  35.857584           17.272828        0            1   \n",
       "1   71       1  30.732470            2.201266        0            1   \n",
       "2   48       0  19.971407           18.500944        0            0   \n",
       "3   34       1  16.615417           12.632870        0            0   \n",
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       "7   40       0  31.152253            1.396013        0            0   \n",
       "8   58       0  27.483346           11.093754        1            0   \n",
       "9   77       1  21.650482           14.955887        1            1   \n",
       "\n",
       "   PhysicalActivity  Diabetes  Hypertension  LiverFunctionTest  Diagnosis  \n",
       "0          0.658940         0             0          42.734240          1  \n",
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       "5          2.882027         0             0          50.535064          1  \n",
       "6          2.569917         0             0          45.527997          0  \n",
       "7          6.641528         0             0          88.226766          0  \n",
       "8          4.193878         0             0          37.607603          1  \n",
       "9          9.654107         0             0          34.419683          1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "df=pd.read_csv('Liver_disease1.csv')\n",
    "df.head(10)\n",
    "\n",
    "\n",
    "#data = pd.read_csv('Liver_disease1.csv')\n",
    "\n",
    "#missing_values = data.isnull().sum()\n",
    "#print(missing_values)\n",
    "\n",
    "#duplicates = data.duplicated().sum()\n",
    "#print(f'Duplicates: {duplicates}')\n",
    "\n"
   ]
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       "      <th>AlcoholConsumption</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>GeneticRisk</th>\n",
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      "text/plain": [
       "   Age  Gender        BMI  AlcoholConsumption  Smoking  GeneticRisk  \\\n",
       "0   58       0  35.857584           17.272828        0            1   \n",
       "1   71       1  30.732470            2.201266        0            1   \n",
       "2   48       0  19.971407           18.500944        0            0   \n",
       "3   34       1  16.615417           12.632870        0            0   \n",
       "4   62       1  16.065830            1.087815        0            1   \n",
       "\n",
       "   PhysicalActivity  Diabetes  Hypertension  LiverFunctionTest  \n",
       "0          0.658940         0             0          42.734240  \n",
       "1          1.670557         1             0          67.309822  \n",
       "2          9.928308         0             0          63.738956  \n",
       "3          5.630129         0             0          64.555873  \n",
       "4          3.566218         1             0          77.868689  "
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     "metadata": {},
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   ],
   "source": [
    "\n",
    "#if gender was in strings\n",
    "#le_Gender = LabelEncoder()\n",
    "#df['Gender'] = le_Gender.fit_transform(df['Gender'])\n",
    "\n",
    "\n",
    "x=df.drop(['Diagnosis'],axis='columns')\n",
    "y=df['Diagnosis']\n",
    "x.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "a9638696-aa70-4d57-bdc5-6076764d4f50",
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    {
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     "output_type": "stream",
     "text": [
      "The length for training is: 1190\n",
      "The length for Testing is: 510\n"
     ]
    },
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      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3, random_state=120)\n",
    "print('The length for training is:',len(x_train))\n",
    "print('The length for Testing is:',len(x_test))\n",
    "\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "new_xtrain=scaler.fit_transform(x_train)\n",
    "new_xtest=scaler.fit_transform(x_test)\n",
    "\n",
    "KNN=KNeighborsClassifier(n_neighbors=5)\n",
    "KNN.fit(new_xtrain,y_train)\n"
   ]
  },
  {
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   "execution_count": 5,
   "id": "663e8e2d-047f-4bae-875e-2ec20b0b28e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for KNN is 0.8215686274509804\n",
      "The Predicited Values are [0 0 0 0 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 1\n",
      " 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1\n",
      " 1 0 1 1 1 1 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0\n",
      " 1 0 1 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 1 0 1\n",
      " 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 0\n",
      " 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 0 0 1\n",
      " 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1\n",
      " 1 1 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 0 1 0 1 1 1\n",
      " 1 1 1 0 0 1 0 1 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0\n",
      " 1 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 1 0 1 1 0 0 0 0\n",
      " 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 0 0\n",
      " 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 1 1 1 1 1 1\n",
      " 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 1 1 1 0 0 1 0\n",
      " 0 1 1 1 1 1 1 1 0 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1]\n",
      "The Mean Absolute Error for KNN is 0.1784313725490196\n"
     ]
    }
   ],
   "source": [
    "knn_score=KNN.score(new_xtest,y_test)\n",
    "print(\"The Accuracy for KNN is\",knn_score)\n",
    "\n",
    "ypredictKNN=KNN.predict(new_xtest)\n",
    "print(\"The Predicited Values are\",ypredictKNN)\n",
    "\n",
    "mae_knn = mean_absolute_error(y_test, ypredictKNN)\n",
    "print(\"The Mean Absolute Error for KNN is\", mae_knn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a79287fb-70b2-43d0-bbae-0a916f19c5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn \n",
    "from sklearn.metrics import confusion_matrix,precision_score,accuracy_score\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "binarizer = LabelBinarizer()\n",
    "y_test_binary = binarizer.fit_transform(y_test)\n",
    "ypredictKNN_binary = binarizer.transform(ypredictKNN)\n",
    "\n",
    "conf_matrixKnn = confusion_matrix(y_test_binary, ypredictKNN_binary)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sn.heatmap(conf_matrixKnn,annot=True,cmap='Blues',fmt='d',xticklabels=[0,1,],yticklabels=[0,1])\n",
    "plt.title(\"KNN Confusion Matrix\")\n",
    "plt.xlabel = 'Predicted'\n",
    "plt.ylabel = 'Actual'\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16a147d9-8ea1-4cbf-bc0f-7c539d10b4e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Gradient Boosting 0.9058823529411765\n",
      "The Predicted Values are [0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 1\n",
      " 1 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1\n",
      " 1 0 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0\n",
      " 1 0 1 1 0 0 0 1 1 0 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 0 1 0 1 0 0\n",
      " 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1\n",
      " 1 0 1 0 1 0 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1 0 1 0 1\n",
      " 1 1 1 0 0 1 1 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 1 0 1\n",
      " 1 1 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0\n",
      " 1 0 1 0 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0\n",
      " 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 0 1 1 1 0 1 1 1 0 1 0 0\n",
      " 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 1 1 0\n",
      " 0 0 0 1 1 1 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 0\n",
      " 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 0 1]\n",
      "The Mean Absolute Error for Gradient Boosting is 0.09411764705882353\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#------------------------------Gradient Boosting-----------------------------------#\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gradientboosting=GradientBoostingClassifier(n_estimators=300,learning_rate=0.02,max_features=5, random_state=120)\n",
    "gradientboosting.fit(new_xtrain,y_train)\n",
    "\n",
    "gradientboostingscore=gradientboosting.score(new_xtest,y_test)\n",
    "print(\"The Accuracy for Gradient Boosting\",gradientboostingscore)\n",
    "\n",
    "ypredictGradientBoositng=gradientboosting.predict(new_xtest)\n",
    "print('The Predicted Values are',ypredictGradientBoositng)\n",
    "\n",
    "mae_gb = mean_absolute_error(y_test, ypredictGradientBoositng)\n",
    "print(\"The Mean Absolute Error for Gradient Boosting is\", mae_gb)\n",
    "\n",
    "\n",
    "import seaborn as sn \n",
    "from sklearn.metrics import confusion_matrix,precision_score,accuracy_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "binarizer = LabelBinarizer()\n",
    "y_test_binary = binarizer.fit_transform(y_test)\n",
    "ypredictGradboosting_binary = binarizer.transform(ypredictGradientBoositng)\n",
    "\n",
    "conf_matrixGradientBoosting = confusion_matrix(y_test_binary, ypredictGradboosting_binary)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sn.heatmap(conf_matrixGradientBoosting,annot=True,cmap='pink',fmt='d',xticklabels=[0,1],yticklabels=[0,1])\n",
    "plt.title(\"Gradient Boosting Confusion Matrix\")\n",
    "plt.xlabel=('Prediciton')\n",
    "plt.y_label=('Actual')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05306da9-df21-461f-b603-e17086a6f389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Decision Tree 0.8254901960784313\n",
      "The Predicted Values are [1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 1\n",
      " 1 1 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 1 1\n",
      " 1 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 0\n",
      " 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 0 0\n",
      " 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1\n",
      " 1 0 1 0 1 0 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1\n",
      " 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1\n",
      " 1 1 1 0 0 1 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 1 0 1 0 1\n",
      " 0 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0\n",
      " 1 0 1 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0\n",
      " 0 0 1 0 1 1 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 0\n",
      " 0 0 0 1 0 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 0 0 1 1 1 0 0\n",
      " 0 0 1 1 1 0 1 0 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 0\n",
      " 0 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1]\n",
      "The Mean Absolute Error for Decision Tree is 0.17450980392156862\n"
     ]
    },
    {
     "data": {
      "image/png": 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NMt99950xpubdUk6cOGEmT55srrzyShMREWGaNGliLrvsMjNz5kxTVlbmPu9P74pizI93NpkxY4ZJSEgwwcHBJjY21txzzz2muLjYY11CQoIZPHhwjffUr18/069fvzpfu7PdFedMdxlavny5SUlJMREREcZut5uEhAQzcuRIs3LlSo91db2+Z3LkyBHz+9//3nTt2tU0a9bMBAcHmzZt2pg77rjD/POf//RY+9FHH5nrr7/ehIWFmSZNmpiePXuat99+22PNmd7XqWuwevVq977a7opjjDElJSXm7rvvNjExMSYsLMwMHTrU7N271+OuOFb4+evfd8U5m9rubLNjxw4zcOBAEx4ebpo3b25uvfVWs3///hp3BTLGmKysLBMXF2caNWrkcX3PVPupY6fuilNaWmo6duxofvWrX3lcN2OMmTJligkODjaffPLJOd8rAPiazZjTbskBAAAAwJLI2AMAAAABgMYeAAAACAA09gAAAEAAoLEHAAAAAgCNPQAAABAAaOwBAACAAEBjDwAAAASABvPNs7a12f4uAQB8a/hof1cAAD5likP9XUKtbJrt0/MZzfTp+S4WJvYAAABAAGgwE3sAAACgXoyPz2fz8fkuEhp7AAAAWJvxcSdu0caeKA4AAAAQAJjYAwAAwNp8HcWxKBp7AAAAWJuvozgWRRQHAAAACABM7AEAAGBtRHEk0dgDAADA6ojiSCKKAwAAAAQEJvYAAACwNqI4kmjsAQAAYHVEcSQRxQEAAAACAhN7AAAAWBtRHElM7AEAAGB1xsdbHTmdTl199dUKDw9Xq1atNHz4cH355Zfu45WVlZoxY4Y6deqksLAwxcXFaezYsfr22289zpOcnCybzeaxjR492uvLQGMPAAAA1MPatWs1ZcoUffzxx8rJydHJkyeVmpqqsrIySdLx48eVm5urRx99VLm5uVq6dKm++uorDRs2rMa5Jk6cqIKCAve2YMECr+shigMAAABr89OHZ1esWOHxeOHChWrVqpU2b96s6667TpGRkcrJyfFY88wzz+iaa67R/v371aZNG/f+pk2byuFwnFc9TOwBAABgbcbm262eSkpKJElRUVFnXWOz2XTJJZd47F+0aJGio6N1xRVXKDMzU8eOHfP69ZnYAwAAAKdxuVxyuVwe++x2u+x2+xmfY4zRtGnTdO211yopKanWNSdOnNCDDz6oMWPGKCIiwr3/9ttvV2JiohwOh/Ly8pSVlaWtW7fWmPafC409AAAArM3Hd8VxOp2aPXu2x76ZM2dq1qxZZ3zOfffdpy+++ELr16+v9XhlZaVGjx6t6upqzZ8/3+PYxIkT3f+clJSk9u3bq3v37srNzVXXrl3rXDeNPQAAAKzNxxn7rKwsTZs2zWPf2ab1U6dO1VtvvaV169apdevWNY5XVlZq1KhRys/P16pVqzym9bXp2rWrgoODtWvXLhp7AAAAoL7OFbs5xRijqVOnatmyZVqzZo0SExNrrDnV1O/atUurV69WixYtznne7du3q7KyUrGxsV7VTWMPAAAAa/PTF1RNmTJFr7zyit58802Fh4ersLBQkhQZGakmTZro5MmTGjlypHJzc/WPf/xDVVVV7jVRUVEKCQnRnj17tGjRIt14442Kjo7Wjh07NH36dHXp0kV9+vTxqh6bMaZBfFeXbW22v0sAAN8a7v2XiwBAQ2aKQ/1dQq1sR/7k0/OZS2bU7XVttUeAFi5cqHHjxmnv3r21TvElafXq1UpOTtaBAwd0xx13KC8vT6WlpYqPj9fgwYM1c+bMs95dpzZM7AEAAIB6ONd8vG3btudcEx8fr7Vr1/qkHhp7AAAAWFuDyJ/4H409AAAArM1P3zzb0PDNswAAAEAAYGIPAAAAayOKI4nGHgAAAFZHFEcSURwAAAAgIDCxBwAAgLURxZFEYw8AAACrI4ojiSgOAAAAEBCY2AMAAMDaiOJIorEHAACA1RHFkUQUBwAAAAgITOwBAABgbURxJNHYAwAAwOqI4kgiigMAAAAEBCb2AAAAsDaiOJJo7AEAAGB1RHEkEcUBAAAAAgITewAAAFgbURxJNPYAAACwOqI4kojiAAAAAAGBiT0AAACsjSiOJBp7AAAAWB1RHElEcQAAAICAwMQeAAAA1kYURxKNPQAAAKyOKI4kojgAAABAQGBiDwAAAGsjiiOJxh4AAABWRxRHElEcAAAAICAwsQcAAIC1EcWRRGMPAAAAqyOKI4koDgAAABAQmNgDAADA2ojiSKKxBwAAgNXR2EsiigMAAAAEBCb2AAAAsDY+PCuJxh4AAABWRxRHElEcAAAAoF6cTqeuvvpqhYeHq1WrVho+fLi+/PJLjzXGGM2aNUtxcXFq0qSJkpOTtX37do81LpdLU6dOVXR0tMLCwjRs2DAdPHjQ63po7AEAAGBtxubbrY7Wrl2rKVOm6OOPP1ZOTo5Onjyp1NRUlZWVudc89thjevLJJzVv3jxt2rRJDodDAwcO1LFjx9xrMjIytGzZMi1evFjr169XaWmphgwZoqqqKq8ug80Y0yD+eGFbm+3vEgDAt4aP9ncFAOBTpjjU3yXUypb3Z5+ezyTdU6/nHTp0SK1atdLatWt13XXXyRijuLg4ZWRkaMaMGZJ+nM7HxMToT3/6kyZNmqSSkhK1bNlSL730km677TZJ0rfffqv4+Hi9++67GjRoUJ1fn4k9AAAA4AMlJSWSpKioKElSfn6+CgsLlZqa6l5jt9vVr18/bdiwQZK0efNmVVZWeqyJi4tTUlKSe01d8eFZAAAAWJuP8ycul0sul8tjn91ul91uP3MJxmjatGm69tprlZSUJEkqLCyUJMXExHisjYmJ0b59+9xrQkJC1Lx58xprTj2/rpjYAwAAwNp8nLF3Op2KjIz02JxO51lLuO+++/TFF1/o1VdfrXHMZvPM7Rtjauyr8ZbqsOanaOwBAACA02RlZamkpMRjy8rKOuP6qVOn6q233tLq1avVunVr936HwyFJNSbvRUVF7im+w+FQRUWFiouLz7imrmjsAQAAYG3Gt5vdbldERITHVlsMxxij++67T0uXLtWqVauUmJjocTwxMVEOh0M5OTnufRUVFVq7dq169+4tSerWrZuCg4M91hQUFCgvL8+9pq7I2AMAAMDa/PTNs1OmTNErr7yiN998U+Hh4e7JfGRkpJo0aSKbzaaMjAzNmTNH7du3V/v27TVnzhw1bdpUY8aMca+dMGGCpk+frhYtWigqKkqZmZnq1KmTBgwY4FU9NPYAAABAPfz5zz/eZjM5Odlj/8KFCzVu3DhJ0gMPPKDy8nLde++9Ki4uVo8ePfTBBx8oPDzcvX7u3Llq3LixRo0apfLycvXv31/Z2dkKCgryqh7uYw8AFwr3sQcQYBrsfew/X+DT85kuk3x6vouFiT0AAACszU9RnIaGD88CAAAAAYCJPQAAAKytQQTL/Y/GHgAAANZGFEcSURwAAAAgIDCxBwAAgLURxZFEYw8AAACrI4ojiSgOAAAAEBCY2AMAAMDaiOJIorEHAACA1RHFkUQUBwAAAAgITOwBAABgbURxJDGxBwAAAAICE3sAAABYGxl7STT2AAAAsDqiOJKI4gAAAAABgcYeltc3MkZvJfXXNz1HyfQbp5tatPE4HtaosZ65tIcO9LxVx6+9Qzu6D9fk2MtqnKdnREt9eOUglV57u4r7jNHqzjcotFHQxXobAHBGM2c0likO9dgK/mV3H795SCOteD1Yh3bbZYpD1TmJWAJ+ZozNt5tFEcWB5YUFNdbW0h+0sHCXll5xfY3jcy+9RimXOHTHzo+090SpUqPiNL99T31bcVxvHT4g6cemfkWngXLu36apuz9RhalS57AoVRv+tgegYcjbWa0Bwyvcj6uq/nMsLEz65yfVeu3Naj37v8F+qA7wM/7vWhKNPQLAih++0Yofvjnj8V4RLfVC4W6tLSmUJP2t4CtNiu2g7uHR7sZ+brtr9L/f7NSfDmxzP293+bELWzgAeOHkSem7otqPvbykWpKUEG/dSSOA8+d1FOfgwYN6+OGHlZKSossvv1y/+tWvlJKSoocfflgHDhy4EDUC52V9SZGGtWijuJCmkqTkSxzq0CRS7//7l4GWwaHqGdFSRRXl+udVN6qw121a0/kG9Ylo5c+yAcBD+1/a9M0Ou77eEqJXnwtWYgJNPOBGFEeSlxP79evXKy0tTfHx8UpNTVVqaqqMMSoqKtLy5cv1zDPP6L333lOfPn0uVL2A136z+xP9rUNvfdNrlCqrq1Uto7u//Kf+efTH0dcvQ8MlSbPaXqXMPZ9pS9kPGhvTTh92HqSkz5YzuQfgd59srtbYe6r11R6jmJY2PZLZWBveD9EVvVz6odjf1QENAFEcSV429r/97W919913a+7cuWc8npGRoU2bNp31PC6XSy6Xy3NnRaUUQi4QvvebX1yunhEtNTRvpfadKNN1kTGa376XCirK9eGRAjX69y/mCwq+UvZ3uyVJW0p/UP9LYjXe0V4P5ef6sXoAkFasrHb/c56MNm6q0J5cu9J/HaS586vO8kwAPydeRXHy8vI0efLkMx6fNGmS8vLyznkep9OpyMhIj02L3vGmFKBOQhsFaU5iV03bs0n/OHxQ28qK9X/f/ktLDuUrMz5JklRQUS5J2lF2xOO5O4+XqI097GKXDADndPy4tG1Htdq3s25kAPApojiSvGzsY2NjtWHDhjMe37hxo2JjY895nqysLJWUlHhsun2wN6UAdRJsa6SQRkGq/snf6KqMcf/Lv/dEqb5xlemyppEeazo0idA+V9lFqhQA6i4kRLq8QyMVFJI/ACT9GMXx5WZRXkVxMjMzNXnyZG3evFkDBw5UTEyMbDabCgsLlZOTo2effVZPPfXUOc9jt9tlt9s9dxLDQT2FNWqsS5tEuB8nhjZT57Ao/XDSpQOuMq05UqjHf9ld5VVV2ucqVb9Ih8bGtNO0Pf+JjD1+YLtmt71KW0t/0JbSH5TuuFQdm0Zq5I41fnhHAODp8d811tsrqrT/oNSqpfRIZmNFhEsvLP4xhtP8EqlNa5viYn+cNF7W/sf/LSwyZ7yTDoDA41Vjf++996pFixaaO3euFixYoKp/30Q3KChI3bp104svvqhRo0ZdkEKBM+keHq01V93gfjz30mskSdmFu3XXl+s1esdaOX/ZVYsu76uoxnbtc5Xp4b25+kvBl+7nPP3NDoU2CtLcdtcoKjhEW0uLNfCLD/T1CT44C8D/Wv/CplefDVF0C+nQ99LHn1WrZ2qF9v/7ZnTD0oKUPf8/A7Ilz4dIkmb990nN/tNJf5QMXFwWjs/4ks2Y+n0DT2Vlpb7//ntJUnR0tIKDz2/iblubfV7PB4AGZ/hof1cAAD5likP9XUKtbB++6NPzmf5jfXq+i6XeX1AVHBxcpzw9AAAAgAuPb54FAACAtRHFkURjDwAAAKuz8J1sfMmr210CAAAAaJiY2AMAAMDaiOJIorEHAACA1RHFkUQUBwAAAAgITOwBAABgbUzsJdHYAwAAwOrI2EsiigMAAAAEBCb2AAAAsDaiOJKY2AMAAMDqjM23mxfWrVunoUOHKi4uTjabTcuXL/c4brPZat0ef/xx95rk5OQax0ePHu31ZaCxBwAAAOqprKxMnTt31rx582o9XlBQ4LE9//zzstlsuuWWWzzWTZw40WPdggULvK6FKA4AAACszY8fnk1LS1NaWtoZjzscDo/Hb775plJSUvTLX/7SY3/Tpk1rrPUWE3sAAABYm/HxdoF89913eueddzRhwoQaxxYtWqTo6GhdccUVyszM1LFjx7w+PxN7AAAA4DQul0sul8tjn91ul91uP6/zvvDCCwoPD9eIESM89t9+++1KTEyUw+FQXl6esrKytHXrVuXk5Hh1fib2AAAAsDYff3jW6XQqMjLSY3M6nedd5vPPP6/bb79doaGhHvsnTpyoAQMGKCkpSaNHj9brr7+ulStXKjc316vzM7EHAACAtfk4PpOVlaVp06Z57Dvfaf1HH32kL7/8UkuWLDnn2q5duyo4OFi7du1S165d6/waNPYAAADAaXwRu/mp5557Tt26dVPnzp3PuXb79u2qrKxUbGysV69BYw8AAABr8+NdcUpLS7V792734/z8fG3ZskVRUVFq06aNJOno0aN67bXX9MQTT9R4/p49e7Ro0SLdeOONio6O1o4dOzR9+nR16dJFffr08aoWGnsAAABYmx+/efazzz5TSkqK+/GpCE96erqys7MlSYsXL5YxRr/+9a9rPD8kJEQffvihnn76aZWWlio+Pl6DBw/WzJkzFRQU5FUtNmNMg/gSXtvabH+XAAC+Ndz7bw0EgIbMFIeee5Ef2JadO7fuDXPzbT4938XCxB4AAADW5scoTkNCYw8AAABraxD5E//jPvYAAABAAGBiDwAAAGsjiiOJxh4AAABWRxRHElEcAAAAICAwsQcAAIC1EcWRRGMPAAAAqyOKI4koDgAAABAQmNgDAADA2ojiSKKxBwAAgNURxZFEFAcAAAAICEzsAQAAYG1EcSTR2AMAAMDqiOJIIooDAAAABAQm9gAAALA2ojiSaOwBAABgdURxJBHFAQAAAAICE3sAAABYG1EcSTT2AAAAsDqiOJKI4gAAAAABgYk9AAAArI0ojiQaewAAAFgdURxJRHEAAACAgMDEHgAAANZGFEcSjT0AAACsjiiOJKI4AAAAQEBgYg8AAABrI4ojicYeAAAAVkcURxJRHAAAACAgMLEHAACAtRHFkURjDwAAAKsjiiOJKA4AAAAQEJjYAwAAwNqY2EuisQcAAIDVkbGXRBQHAAAACAhM7AEAAGBtRHEk0dgDAADA6ojiSCKKAwAAANTbunXrNHToUMXFxclms2n58uUex8eNGyebzeax9ezZ02ONy+XS1KlTFR0drbCwMA0bNkwHDx70uhYaewAAAFibsfl280JZWZk6d+6sefPmnXHNDTfcoIKCAvf27rvvehzPyMjQsmXLtHjxYq1fv16lpaUaMmSIqqqqvKqFKA4AAACszY8Z+7S0NKWlpZ11jd1ul8PhqPVYSUmJnnvuOb300ksaMGCAJOnll19WfHy8Vq5cqUGDBtW5Fib2AAAAwGlcLpeOHj3qsblcrnqfb82aNWrVqpU6dOigiRMnqqioyH1s8+bNqqysVGpqqntfXFyckpKStGHDBq9eh8YeAAAA1ubjKI7T6VRkZKTH5nQ661VaWlqaFi1apFWrVumJJ57Qpk2bdP3117t/USgsLFRISIiaN2/u8byYmBgVFhZ69VpEcQAAAGBtPo7iZGVladq0aR777HZ7vc512223uf85KSlJ3bt3V0JCgt555x2NGDHijM8zxshm8y7vT2MPAAAAnMZut9e7kT+X2NhYJSQkaNeuXZIkh8OhiooKFRcXe0zti4qK1Lt3b6/OTRQHAAAA1ubHu+J46/Dhwzpw4IBiY2MlSd26dVNwcLBycnLcawoKCpSXl+d1Y8/EHgAAANbmx7vilJaWavfu3e7H+fn52rJli6KiohQVFaVZs2bplltuUWxsrPbu3auHHnpI0dHRuvnmmyVJkZGRmjBhgqZPn64WLVooKipKmZmZ6tSpk/suOXVFYw8AAADU02effaaUlBT341PZ/PT0dP35z3/Wtm3b9OKLL+rIkSOKjY1VSkqKlixZovDwcPdz5s6dq8aNG2vUqFEqLy9X//79lZ2draCgIK9qsRlj/Pg7zn/Y1mb7uwQA8K3ho/1dAQD4lCkO9XcJtbI9+YFPz2empZ57UQPExB4AAADW1iDG1P7Hh2cBAACAAMDEHgAAANZ2ge9kYxU09gAAALA2ojiSiOIAAAAAAYGJPQAAAKyNKI4kGnsAAABYHVEcSURxAAAAgIDAxB4AAADWRhRHEo09AAAArI4ojiSiOAAAAEBAYGIPAAAAayOKI4nGHgAAAFZHFEcSURwAAAAgIDCxBwAAgLURxZFEYw8AAACrI4ojiSgOAAAAEBCY2AMAAMDaiOJIorEHAACA1RHFkUQUBwAAAAgIDWdinzzO3xUAgG+Z2f6uAAB8bKa/C6gdURxJDamxBwAAAOqDKI4kojgAAABAQGBiDwAAAGsjiiOJxh4AAABWRxRHElEcAAAAICAwsQcAAIC1EcWRRGMPAAAAqyOKI4koDgAAABAQmNgDAADA2ojiSKKxBwAAgNURxZFEFAcAAAAICEzsAQAAYG1M7CXR2AMAAMDqyNhLIooDAAAABAQm9gAAALA2JvaSaOwBAABgdWTsJRHFAQAAAAICjT0AAACszdh8u3lh3bp1Gjp0qOLi4mSz2bR8+XL3scrKSs2YMUOdOnVSWFiY4uLiNHbsWH377bce50hOTpbNZvPYRo8e7fVloLEHAACAtRkfb14oKytT586dNW/evBrHjh8/rtzcXD366KPKzc3V0qVL9dVXX2nYsGE11k6cOFEFBQXubcGCBd4VIjL2AAAAQL2lpaUpLS2t1mORkZHKycnx2PfMM8/ommuu0f79+9WmTRv3/qZNm8rhcJxXLUzsAQAAYG0+juK4XC4dPXrUY3O5XD4ptaSkRDabTZdcconH/kWLFik6OlpXXHGFMjMzdezYMa/PTWMPAAAAa/NxFMfpdCoyMtJjczqd513miRMn9OCDD2rMmDGKiIhw77/99tv16quvas2aNXr00Uf1xhtvaMSIEV6fnygOAAAAcJqsrCxNmzbNY5/dbj+vc1ZWVmr06NGqrq7W/PnzPY5NnDjR/c9JSUlq3769unfvrtzcXHXt2rXOr0FjDwAAAGvz8RdU2e32827kT1dZWalRo0YpPz9fq1at8pjW16Zr164KDg7Wrl27aOwBAADwM9KAv6DqVFO/a9curV69Wi1atDjnc7Zv367KykrFxsZ69Vo09gAAAEA9lZaWavfu3e7H+fn52rJli6KiohQXF6eRI0cqNzdX//jHP1RVVaXCwkJJUlRUlEJCQrRnzx4tWrRIN954o6Kjo7Vjxw5Nnz5dXbp0UZ8+fbyqxWaMaRC/49h8+xcUAPA/M9vfFQCATxnN9HcJtbL95lOfns/87zV1XrtmzRqlpKTU2J+enq5Zs2YpMTGx1uetXr1aycnJOnDggO644w7l5eWptLRU8fHxGjx4sGbOnKmoqCiv6mZiDwAAAGvz45g6OTlZZ5uTn2uGHh8fr7Vr1/qkFm53CQAAAAQAJvYAAACwNh/fFceqaOwBAABgbQ3iE6P+RxQHAAAACABM7AEAAGBtRHEk0dgDAADA6ojiSCKKAwAAAAQEJvYAAACwNqI4kmjsAQAAYHVEcSQRxQEAAAACAhN7AAAAWBtRHEk09gAAALA6ojiSiOIAAAAAAYGJPQAAAKyNKI4kGnsAAABYHVEcSURxAAAAgIDAxB4AAADWRhRHEo09AAAArI4ojiSiOAAAAEBAYGIPAAAAayOKI4nGHgAAAFZHFEcSURwAAAAgIDCxBwAAgLURxZFEYw8AAACrI4ojiSgOAAAAEBCY2AMAAMDaiOJIorEHAACA1RHFkUQUBwAAAAgITOwBAABgbURxJNHYAwAAwOqI4kgiigMAAAAEBCb2AAAAsDYm9pJo7AEAAGB1ZOwlEcUBAAAAAgITewAAAFgbE3tJNPYAAACwOjL2kojiAAAAAAGBiT0AAACsjSiOJCb2AAAAsDrj480L69at09ChQxUXFyebzably5d7lmaMZs2apbi4ODVp0kTJycnavn27xxqXy6WpU6cqOjpaYWFhGjZsmA4ePOhdIaKxBwAAAOqtrKxMnTt31rx582o9/thjj+nJJ5/UvHnztGnTJjkcDg0cOFDHjh1zr8nIyNCyZcu0ePFirV+/XqWlpRoyZIiqqqq8qsVmjGkQHzew8RcUAIHGzPZ3BQDgU0Yz/V1CrWwj/uXT85mlHetXh82mZcuWafjw4T+exxjFxcUpIyNDM2bMkPTjdD4mJkZ/+tOfNGnSJJWUlKhly5Z66aWXdNttt0mSvv32W8XHx+vdd9/VoEGD6vz6TOwBAABgbT6O4rhcLh09etRjc7lcXpeVn5+vwsJCpaamuvfZ7Xb169dPGzZskCRt3rxZlZWVHmvi4uKUlJTkXlNXNPYAAADAaZxOpyIjIz02p9Pp9XkKCwslSTExMR77Y2Ji3McKCwsVEhKi5s2bn3FNXXFXHAAAAFibj++Kk5WVpWnTpnnss9vt9T6f7SeZc2NMjX0/VZc1P8XEHgAAANbm4yiO3W5XRESEx1afxt7hcEhSjcl7UVGRe4rvcDhUUVGh4uLiM66pKxp7AAAA4AJITEyUw+FQTk6Oe19FRYXWrl2r3r17S5K6deum4OBgjzUFBQXKy8tzr6krojgAAACwNj9+QVVpaal2797tfpyfn68tW7YoKipKbdq0UUZGhubMmaP27durffv2mjNnjpo2baoxY8ZIkiIjIzVhwgRNnz5dLVq0UFRUlDIzM9WpUycNGDDAq1po7AEAAGBtfrx5+2effaaUlBT341PZ/PT0dGVnZ+uBBx5QeXm57r33XhUXF6tHjx764IMPFB4e7n7O3Llz1bhxY40aNUrl5eXq37+/srOzFRQU5FUt3MceAC4U7mMPIMA02PvYD/3Kp+czb3fw6fkuFib2AAAAsDY/RnEaEhp7AAAAWFuDyJ/4H3fFAQAAAAIAE3sAAABYG1EcSTT2AAAAsDqiOJKI4gAAAAABgYk9AAAArI0ojiQm9gAAAEBAYGIPAAAAayNjL4nGHgAAAFZHFEcSURwAAAAgIDCxBwAAgLURxZFEYw8AAACrI4ojiSgOAAAAEBCY2AMAAMDaiOJIorEHAACA1RHFkUQUBwAAAAgIPm/sDxw4oPHjx591jcvl0tGjRz02yeXrUgAAAPBzYHy8WZTPG/sffvhBL7zwwlnXOJ1ORUZGemyS09elAAAA4OfA2Hy7WZTXGfu33nrrrMe//vrrc54jKytL06ZN89gXGWn3thQAAAAA/+Z1Yz98+HDZbDYZc+a/U9hsZ/9Nx263y26nkQcAAIAPWDg+40teR3FiY2P1xhtvqLq6utYtNzf3QtQJAAAA1I4ojqR6NPbdunU7a/N+rmk+AAAAAN/zOopz//33q6ys7IzHL730Uq1evfq8igIAAADqjJmypHo09n379j3r8bCwMPXr16/eBQEAAABesXB8xpf4gioAAAAgAHg9sQcAAAAaFKI4kmjsAQAAYHVEcSQRxQEAAAACAhN7AAAAWBtRHEk09gAAALA6GntJRHEAAACAgMDEHgAAANbGh2cl0dgDAADA6mjsJRHFAQAAAAICE3sAAABYGx+elURjDwAAAKsjiiOJKA4AAAAQEGjsAQAAYG3Gx5sX2rZtK5vNVmObMmWKJGncuHE1jvXs2fN833GtiOIAAADA2vwYxdm0aZOqqqrcj/Py8jRw4EDdeuut7n033HCDFi5c6H4cEhJyQWqhsQcAAADqqWXLlh6P//u//1vt2rVTv3793PvsdrscDscFr4UoDgAAAKzNx1Ecl8ulo0ePemwul+ucZVRUVOjll1/W+PHjZbP9568Ia9asUatWrdShQwdNnDhRRUVFPnvrp6OxBwAAgLUZm083p9OpyMhIj83pdJ6zjOXLl+vIkSMaN26ce19aWpoWLVqkVatW6YknntCmTZt0/fXX1+kXBW/ZjDEN4s6fNu5SBCDQmNn+rgAAfMpopr9LqJXtqu98er4Tn1xSo/G22+2y2+1nfd6gQYMUEhKit99++4xrCgoKlJCQoMWLF2vEiBE+qfcUMvYAAACwNh+PqevSxP/Uvn37tHLlSi1duvSs62JjY5WQkKBdu3adT4m1orEHAACAtTWAL6hauHChWrVqpcGDB5913eHDh3XgwAHFxsb6vAYy9gAAAMB5qK6u1sKFC5Wenq7Gjf8zNy8tLVVmZqY2btyovXv3as2aNRo6dKiio6N18803+7wOJvYAAACwNj9/YnTlypXav3+/xo8f77E/KChI27Zt04svvqgjR44oNjZWKSkpWrJkicLDw31eBx+eBYALhQ/PAggwDfbDs0mHfHo+k9fy3IsaIKI4AAAAQAAgigMAAABraxD5E/+jsQcAAIC1NYC74jQERHEAAACAAMDEHgAAANZGFEcSjT0AAACsjiiOJKI4AAAAQEBgYg8AAABrI4ojicYeAAAAVkcURxJRHAAAACAgMLEHAACAtRHFkURjDwAAAKsjiiOJKA4AAAAQEJjYAwAAwNqI4kiisQcAAIDVEcWRRBQHAAAACAhM7AEAAGBtRHEk0dgDAADA6ojiSCKKAwAAAAQEJvYAAACwNqI4kmjsAQAAYHVEcSQRxQEAAAACAhN7AAAAWBtRHEk09gAAALA6ojiSiOIAAAAAAYGJPQAAAKyNKI4kGnsAAABYHVEcSURxAAAAgIDAxB4AAADWRhRHEo09AAAArI4ojiSiOAAAAEBAYGIPAAAAayOKI4nGHgAAAFZHFEcSURwAAAAgIDCxBwAAgLURxZFEYw8AAACrI4ojiSgOAAAAEBBo7AEAAGBtxsebF2bNmiWbzeaxORyO/5RmjGbNmqW4uDg1adJEycnJ2r59+/m82zOisQcAAIC1GZtvNy9dccUVKigocG/btm1zH3vsscf05JNPat68edq0aZMcDocGDhyoY8eO+fIKSKKxBwAAAM5L48aN5XA43FvLli0l/Titf+qpp/Twww9rxIgRSkpK0gsvvKDjx4/rlVde8XkdNPYAAACwNj9GcSRp165diouLU2JiokaPHq2vv/5akpSfn6/CwkKlpqa619rtdvXr108bNmyo11s9G+6KAwAAAGvz8V1xXC6XXC6Xxz673S673V5jbY8ePfTiiy+qQ4cO+u677/SHP/xBvXv31vbt21VYWChJiomJ8XhOTEyM9u3b59OaJSb2AAAAgAen06nIyEiPzel01ro2LS1Nt9xyizp16qQBAwbonXfekSS98MIL7jU2m+cvHsaYGvt8gcYeAAAA1ubjKE5WVpZKSko8tqysrDqVEhYWpk6dOmnXrl3uu+OcmtyfUlRUVGOK7ws09gAAALA2H98Vx263KyIiwmOrLYZTG5fLpZ07dyo2NlaJiYlyOBzKyclxH6+oqNDatWvVu3dvn18GMvYAAABAPWVmZmro0KFq06aNioqK9Ic//EFHjx5Venq6bDabMjIyNGfOHLVv317t27fXnDlz1LRpU40ZM8bntdDYAwAAwNrqcScbXzl48KB+/etf6/vvv1fLli3Vs2dPffzxx0pISJAkPfDAAyovL9e9996r4uJi9ejRQx988IHCw8N9XovNGOPHS/EfF+DzAwDgX2a2vysAAJ8ymunvEmpla1bp0/OZ0mCfnu9iIWMPAAAABACiOAAAALC2BpE/8T8aewAAAFibj7+gyqqI4gAAAAABgIk9AAAArI0ojiQaewAAAFgdURxJRHEAAACAgMDEHgAAANZGFEcSjT0AAACsjiiOJKI4AAAAQEBgYg8AAABrI4ojicYeAAAAVkcURxJRHAAAACAgMLEHAACAtRHFkURjDwAAAKsjiiOJKA4AAAAQEJjYAwAAwNqI4kiSbMYYLgV+Nlwul5xOp7KysmS32/1dDgCcN/67BuAUGnv8rBw9elSRkZEqKSlRRESEv8sBgPPGf9cAnELGHgAAAAgANPYAAABAAKCxBwAAAAIAjT1+Vux2u2bOnMkHzAAEDP67BuAUPjwLAAAABAAm9gAAAEAAoLEHAAAAAgCNPQAAABAAaOwBAACAAEBjj5+N+fPnKzExUaGhoerWrZs++ugjf5cEAPW2bt06DR06VHFxcbLZbFq+fLm/SwLgZzT2+FlYsmSJMjIy9PDDD+vzzz9X3759lZaWpv379/u7NACol7KyMnXu3Fnz5s3zdykAGghud4mfhR49eqhr167685//7N53+eWXa/jw4XI6nX6sDADOn81m07JlyzR8+HB/lwLAj5jYI+BVVFRo8+bNSk1N9difmpqqDRs2+KkqAAAA36KxR8D7/vvvVVVVpZiYGI/9MTExKiws9FNVAAAAvkVjj58Nm83m8dgYU2MfAACAVdHYI+BFR0crKCioxnS+qKioxhQfAADAqmjsEfBCQkLUrVs35eTkeOzPyclR7969/VQVAACAbzX2dwHAxTBt2jTdeeed6t69u3r16qW//vWv2r9/vyZPnuzv0gCgXkpLS7V792734/z8fG3ZskVRUVFq06aNHysD4C/c7hI/G/Pnz9djjz2mgoICJSUlae7cubruuuv8XRYA1MuaNWuUkpJSY396erqys7MvfkEA/I7GHgAAAAgAZOwBAACAAEBjDwAAAAQAGnsAAAAgANDYAwAAAAGAxh4AAAAIADT2AAAAQACgsQcAAAACAI09AAAAEABo7AEAAIAAQGMPAAAABAAaewAAACAA0NgDAAAAAeD/ARSbrk3MlvSiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#----------------------------------------Decision Tree------------------------------------#\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "Dt=DecisionTreeClassifier()\n",
    "Dt.fit(new_xtrain,y_train)\n",
    "DecisionTreeScore=Dt.score(new_xtest,y_test)\n",
    "print(\"The Accuracy for Decision Tree\",DecisionTreeScore)\n",
    "\n",
    "ypredictDt=Dt.predict(new_xtest)\n",
    "print('The Predicted Values are',ypredictDt)\n",
    "mae_dt = mean_absolute_error(y_test, ypredictDt)\n",
    "print(\"The Mean Absolute Error for Decision Tree is\", mae_dt)\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "binarizer = LabelBinarizer()\n",
    "y_test_binary = binarizer.fit_transform(y_test)\n",
    "ypredictDecisionTree_binary = binarizer.transform(ypredictDt)\n",
    "\n",
    "conf_matrixDecisionTree = confusion_matrix(y_test_binary, ypredictDecisionTree_binary)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sn.heatmap(conf_matrixDecisionTree,annot=True,cmap='winter',fmt='d',xticklabels=[0,1],yticklabels=[0,1])\n",
    "plt.title(\"Decision Tree Confusion Matrix\")\n",
    "plt.xlabel=('Prediciton')\n",
    "plt.ylabel=('Actual')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84fee8cc-44b2-4cff-8fb8-afb624b55e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Random Forest 0.8941176470588236\n",
      "The Predicted Values are [1 0 0 0 0 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 1\n",
      " 1 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1\n",
      " 1 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0\n",
      " 1 0 1 1 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0\n",
      " 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1\n",
      " 1 0 1 0 1 0 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 0 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1 1 1 0 1\n",
      " 1 1 1 0 0 1 1 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 1 0 1\n",
      " 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0\n",
      " 1 0 1 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 0 0 1 1 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0\n",
      " 0 0 1 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 0\n",
      " 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 1 1 0\n",
      " 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 1 1 0 0 0 0\n",
      " 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 0 1]\n",
      "The Mean Absolute Error for Random Forest is 0.10588235294117647\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#----------------------------------------Random Forest------------------------------------#\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "randomForest= RandomForestClassifier(n_estimators=100)\n",
    "randomForest.fit(new_xtrain,y_train)\n",
    "\n",
    "randomforestscore=randomForest.score(new_xtest,y_test)\n",
    "\n",
    "print(\"The Accuracy for Random Forest\",randomforestscore)\n",
    "\n",
    "ypredictrandomforest=randomForest.predict(new_xtest)\n",
    "print('The Predicted Values are',ypredictrandomforest)\n",
    "\n",
    "mae_rf = mean_absolute_error(y_test, ypredictrandomforest)\n",
    "print(\"The Mean Absolute Error for Random Forest is\", mae_rf)\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "binarizer = LabelBinarizer()\n",
    "y_test_binary = binarizer.fit_transform(y_test)\n",
    "ypredictRandomforest_binary = binarizer.transform(ypredictrandomforest)\n",
    "\n",
    "conf_matrixRandomForest = confusion_matrix(y_test_binary, ypredictRandomforest_binary)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sn.heatmap(conf_matrixRandomForest,annot=True,cmap='grey',fmt='d',xticklabels=[0,1],yticklabels=[0,1])\n",
    "plt.title(\"Random Forest Confusion Matrix\")\n",
    "plt.xlabel=('Prediction')\n",
    "plt.ylabel=('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3e69efe-008c-440d-8de9-880bd43e7759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming accuracy scores are defined\n",
    "accuracy = {\n",
    "     'KNN':knn_score,\n",
    "    'Gradient Boosting':gradientboostingscore,\n",
    "    'Decision Tree':DecisionTreeScore,\n",
    "    'Random Forest':randomforestscore\n",
    "}\n",
    "\n",
    "# Creating a horizontal bar chart\n",
    "bars = plt.barh(list(accuracy.keys()), list(accuracy.values()), color=['lightBlue', 'pink', 'grey', 'lightgreen'])\n",
    "plt.yticks(fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96291069-551e-4b6e-808c-08901aee168e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}