Grand File.ipynb

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   "id": "e085e5ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in c:\\users\\rahul\\anaconda3\\lib\\site-packages (1.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3549c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74355245",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ca4fb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('engine_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "355b0447",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0         700          2.493592      11.790927          3.178981   \n",
       "1         876          2.941606      16.193866          2.464504   \n",
       "2         520          2.961746       6.553147          1.064347   \n",
       "3         473          3.707835      19.510172          3.727455   \n",
       "4         619          5.672919      15.738871          2.052251   \n",
       "\n",
       "   lub oil temp  Coolant temp  Engine Condition  \n",
       "0     84.144163     81.632187                 1  \n",
       "1     77.640934     82.445724                 0  \n",
       "2     77.752266     79.645777                 1  \n",
       "3     74.129907     71.774629                 1  \n",
       "4     78.396989     87.000225                 0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2733986d",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Engine rpm</th>\n",
       "      <th>Lub oil pressure</th>\n",
       "      <th>Fuel pressure</th>\n",
       "      <th>Coolant pressure</th>\n",
       "      <th>lub oil temp</th>\n",
       "      <th>Coolant temp</th>\n",
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      ],
      "text/plain": [
       "   Engine rpm  Lub oil pressure  Fuel pressure  Coolant pressure  \\\n",
       "0         700          2.493592      11.790927          3.178981   \n",
       "1         876          2.941606      16.193866          2.464504   \n",
       "2         520          2.961746       6.553147          1.064347   \n",
       "3         473          3.707835      19.510172          3.727455   \n",
       "4         619          5.672919      15.738871          2.052251   \n",
       "\n",
       "   lub oil temp  Coolant temp  \n",
       "0     84.144163     81.632187  \n",
       "1     77.640934     82.445724  \n",
       "2     77.752266     79.645777  \n",
       "3     74.129907     71.774629  \n",
       "4     78.396989     87.000225  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:,:-1]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b43a69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Engine Condition, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = data.iloc[:,-1] \n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0290ee3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8124cbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_logistic = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ebc417e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rahul\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec26fb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model_logistic.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "88aeeb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Trained Model/LR_model.sav']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name='Trained Model/LR_model.sav'\n",
    "joblib.dump(model_logistic,file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1728c3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "54b9e5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6529602456918615\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e8e0f94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.28      0.38      2214\n",
      "           1       0.67      0.88      0.76      3647\n",
      "\n",
      "    accuracy                           0.65      5861\n",
      "   macro avg       0.63      0.58      0.57      5861\n",
      "weighted avg       0.64      0.65      0.62      5861\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f6c7abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix:\n",
      "[[ 619 1595]\n",
      " [ 439 3208]]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "850d3fbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Obtaining dependency information for xgboost from https://files.pythonhosted.org/packages/24/ec/ad387100fa3cc2b9b81af0829b5ecfe75ec5bb19dd7c19d4fea06fb81802/xgboost-2.0.3-py3-none-win_amd64.whl.metadata\n",
      "  Downloading xgboost-2.0.3-py3-none-win_amd64.whl.metadata (2.0 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\rahul\\anaconda3\\lib\\site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\rahul\\anaconda3\\lib\\site-packages (from xgboost) (1.11.1)\n",
      "Downloading xgboost-2.0.3-py3-none-win_amd64.whl (99.8 MB)\n",
      "   ---------------------------------------- 0.0/99.8 MB ? eta -:--:--\n",
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      "   ---------------------------------------- 99.8/99.8 MB 3.3 MB/s eta 0:00:00\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbbc37fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f8ad3d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgboost = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27274acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgboost.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d35fa85",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model_xgboost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6b87e9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Trained Model/XGB_model.sav']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name='Trained Model/XGB_model.sav'\n",
    "joblib.dump(model_xgboost,file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d473b80b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6432349428425184\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "55eb81db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.38      0.44      2214\n",
      "           1       0.68      0.81      0.74      3647\n",
      "\n",
      "    accuracy                           0.64      5861\n",
      "   macro avg       0.61      0.59      0.59      5861\n",
      "weighted avg       0.63      0.64      0.63      5861\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e58fd25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix:\n",
      "[[ 831 1383]\n",
      " [ 708 2939]]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "62cd28ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "86148245",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_svm = SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bcc7cb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "76c11ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model_svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6bf05659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Trained Model/SVM_model.sav']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name='Trained Model/SVM_model.sav'\n",
    "joblib.dump(model_svm,file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6449de0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6543252004777341\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c6b19c52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.33      0.42      2214\n",
      "           1       0.68      0.85      0.75      3647\n",
      "\n",
      "    accuracy                           0.65      5861\n",
      "   macro avg       0.63      0.59      0.59      5861\n",
      "weighted avg       0.64      0.65      0.63      5861\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "568295e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix:\n",
      "[[ 725 1489]\n",
      " [ 537 3110]]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c176d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score\n",
    "\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 8, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28a2f338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5], &#x27;n_estimators&#x27;: [10, 20]},\n",
       "             scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5], &#x27;n_estimators&#x27;: [10, 20]},\n",
       "             scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [3, 5], 'n_estimators': [10, 20]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier1 = RandomForestClassifier() \n",
    "grid_values = {'n_estimators':[10, 20], 'max_depth':[3, 5]}\n",
    "classifier = GridSearchCV(classifier1, param_grid = grid_values, scoring = 'roc_auc', cv=5)\n",
    "\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4fafdc7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_preds =  classifier.predict(X_train)\n",
    "test_preds  = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6f886f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Trained Model/RF_model.sav']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name='Trained Model/RF_model.sav'\n",
    "joblib.dump(classifier,file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b402a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6839256984057335"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_train,train_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "24b8051f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6570551100494796"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "556dc561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.33      0.42      2214\n",
      "           1       0.68      0.85      0.75      3647\n",
      "\n",
      "    accuracy                           0.65      5861\n",
      "   macro avg       0.63      0.59      0.59      5861\n",
      "weighted avg       0.64      0.65      0.63      5861\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b13f524d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix:\n",
      "[[ 725 1489]\n",
      " [ 537 3110]]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a82cd6d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Obtaining dependency information for absl-py>=1.0.0 from https://files.pythonhosted.org/packages/a2/ad/e0d3c824784ff121c03cc031f944bc7e139a8f1870ffd2845cc2dd76f6c4/absl_py-2.1.0-py3-none-any.whl.metadata\n",
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      "Collecting astunparse>=1.6.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for astunparse>=1.6.0 from https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl.metadata\n",
      "  Downloading astunparse-1.6.3-py2.py3-none-any.whl.metadata (4.4 kB)\n",
      "Collecting flatbuffers>=23.5.26 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for flatbuffers>=23.5.26 from https://files.pythonhosted.org/packages/41/f0/7e988a019bc54b2dbd0ad4182ef2d53488bb02e58694cd79d61369e85900/flatbuffers-24.3.25-py2.py3-none-any.whl.metadata\n",
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      "Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 from https://files.pythonhosted.org/packages/fa/39/5aae571e5a5f4de9c3445dae08a530498e5c53b0e74410eeeb0991c79047/gast-0.5.4-py3-none-any.whl.metadata\n",
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      "  Obtaining dependency information for google-pasta>=0.1.1 from https://files.pythonhosted.org/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl.metadata\n",
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      "  Obtaining dependency information for h5py>=3.10.0 from https://files.pythonhosted.org/packages/d8/5e/b7b83cfe60504cc4d24746aed04353af7ea8ec104e597e5ae71b8d0390cb/h5py-3.11.0-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading h5py-3.11.0-cp311-cp311-win_amd64.whl.metadata (2.5 kB)\n",
      "Collecting libclang>=13.0.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for libclang>=13.0.0 from https://files.pythonhosted.org/packages/0b/2d/3f480b1e1d31eb3d6de5e3ef641954e5c67430d5ac93b7fa7e07589576c7/libclang-18.1.1-py2.py3-none-win_amd64.whl.metadata\n",
      "  Downloading libclang-18.1.1-py2.py3-none-win_amd64.whl.metadata (5.3 kB)\n",
      "Collecting ml-dtypes~=0.3.1 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for ml-dtypes~=0.3.1 from https://files.pythonhosted.org/packages/a4/db/1784b87285588788170f87e987bfb4bda218d62a70a81ebb66c94e7f9b95/ml_dtypes-0.3.2-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading ml_dtypes-0.3.2-cp311-cp311-win_amd64.whl.metadata (20 kB)\n",
      "Collecting opt-einsum>=2.3.2 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for opt-einsum>=2.3.2 from https://files.pythonhosted.org/packages/bc/19/404708a7e54ad2798907210462fd950c3442ea51acc8790f3da48d2bee8b/opt_einsum-3.3.0-py3-none-any.whl.metadata\n",
      "  Downloading opt_einsum-3.3.0-py3-none-any.whl.metadata (6.5 kB)\n",
      "Requirement already satisfied: packaging in c:\\users\\rahul\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (23.1)\n",
      "Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 from https://files.pythonhosted.org/packages/ad/6e/1bed3b7c904cc178cb8ee8dbaf72934964452b3de95b7a63412591edb93c/protobuf-4.25.3-cp310-abi3-win_amd64.whl.metadata\n",
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      "Collecting termcolor>=1.1.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for termcolor>=1.1.0 from https://files.pythonhosted.org/packages/d9/5f/8c716e47b3a50cbd7c146f45881e11d9414def768b7cd9c5e6650ec2a80a/termcolor-2.4.0-py3-none-any.whl.metadata\n",
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      "  Obtaining dependency information for grpcio<2.0,>=1.24.3 from https://files.pythonhosted.org/packages/d4/f0/4a936fbe350bead6fa885c9dcb2c269a85603dc0cbeb35cbcf107c123131/grpcio-1.64.1-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading grpcio-1.64.1-cp311-cp311-win_amd64.whl.metadata (3.4 kB)\n",
      "Collecting tensorboard<2.17,>=2.16 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for tensorboard<2.17,>=2.16 from https://files.pythonhosted.org/packages/3a/d0/b97889ffa769e2d1fdebb632084d5e8b53fc299d43a537acee7ec0c021a3/tensorboard-2.16.2-py3-none-any.whl.metadata\n",
      "  Downloading tensorboard-2.16.2-py3-none-any.whl.metadata (1.6 kB)\n",
      "Collecting keras>=3.0.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
      "  Obtaining dependency information for keras>=3.0.0 from https://files.pythonhosted.org/packages/8d/44/c604ecc5c9993b6574a681f2f505e980725871a89cfd9e48597b12ccb506/keras-3.3.3-py3-none-any.whl.metadata\n",
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      "Note: you may need to restart the kernel to use updated packages.\n",
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     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0240c854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import loadtxt\n",
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bb2e90e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rahul\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model_nn = Sequential()\n",
    "model_nn.add(Dense(6, input_dim=6, activation='relu'))\n",
    "model_nn.add(Dense(12, activation='relu'))\n",
    "model_nn.add(Dense(6, activation='relu'))\n",
    "model_nn.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b8a3b669",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3a5372f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.5684 - loss: 2.3533\n",
      "Epoch 2/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6136 - loss: 0.7897\n",
      "Epoch 3/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6125 - loss: 0.7476\n",
      "Epoch 4/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6289 - loss: 0.6942\n",
      "Epoch 5/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6291 - loss: 0.6938\n",
      "Epoch 6/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6333 - loss: 0.6552\n",
      "Epoch 7/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6439 - loss: 0.6406\n",
      "Epoch 8/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6387 - loss: 0.6477\n",
      "Epoch 9/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6409 - loss: 0.6284\n",
      "Epoch 10/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6505 - loss: 0.6229\n",
      "Epoch 11/11\n",
      "\u001b[1m3907/3907\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.6389 - loss: 0.6255\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1bf13a40f90>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nn.fit(X, Y, epochs=11, batch_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "00ed1379",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model_nn.save('Trained Model/NN_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91b7ec81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step\n",
      "[[45.554825]]\n",
      "Not Needed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr=np.array([[1067,4.434499033,4.109890158,2.596118485,77.35028763,77.04324442]])\n",
    "pred=model_nn.predict(arr)\n",
    "pred = pred*100\n",
    "print(pred)\n",
    "if pred>50:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "782e64c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "[100]\n",
      "Needed\n",
      "[100]\n",
      "Needed\n",
      "[100]\n",
      "Needed\n",
      "[[83.37269]]\n",
      "Needed\n",
      "[100]\n",
      "Needed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rahul\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\rahul\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\rahul\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr=np.array([[382,2.162553685,7.77492339,2.727348376,77.00087426,71.62384988]])\n",
    "\n",
    "pred_logistic=model_logistic.predict(arr)\n",
    "pred_logistic = pred_logistic*100\n",
    "\n",
    "pred_xgboost=model_xgboost.predict(arr)\n",
    "pred_xgboost = pred_xgboost*100\n",
    "\n",
    "pred_svm=model_svm.predict(arr)\n",
    "pred_svm = pred_svm*100\n",
    "\n",
    "pred_nn=model_nn.predict(arr)\n",
    "pred_nn = pred_nn*100\n",
    "\n",
    "pred_rf= classifier.predict(arr)\n",
    "pred_rf= pred_rf*100\n",
    "\n",
    "print(pred_logistic)\n",
    "if pred_logistic>50:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")\n",
    "    \n",
    "print(pred_xgboost)\n",
    "if pred_xgboost>50:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")\n",
    "\n",
    "print(pred_svm)\n",
    "if pred_svm>50:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")\n",
    "    \n",
    "print(pred_nn)\n",
    "if pred_nn>50:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")\n",
    "    \n",
    "print(pred_rf)\n",
    "if pred_rf>50:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "00ee2175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "Needed\n"
     ]
    }
   ],
   "source": [
    "\n",
    "result=0\n",
    "\n",
    "if pred_logistic>50:\n",
    "    result=result+1\n",
    "    \n",
    "\n",
    "if pred_xgboost>50:\n",
    "    result=result+1\n",
    "\n",
    "\n",
    "\n",
    "if pred_svm>50:\n",
    "    result=result+1\n",
    "    \n",
    "\n",
    "if pred_nn>50:\n",
    "    result=result+1\n",
    "    \n",
    "\n",
    "if pred_rf>50:\n",
    "    result=result+1\n",
    "    \n",
    "print (result)\n",
    "if result>2:\n",
    "    print(\"Needed\")\n",
    "else:\n",
    "    print(\"Not Needed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe70b57f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "005d3ef4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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