jet_engine_optimized.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0 ---> Early Stage of Deterioration\n",
    "#### 1 ---> Mid Stage of Deterioration\n",
    "#### 2 ---> End Stage of Deterioration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concatenate all the training csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Training_1_all_features.csv')\n",
    "d2 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Training_2_all_features.csv')\n",
    "d3 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Training_3_all_features.csv')\n",
    "d4 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Training_4_all_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.concat([d1,d2,d3,d4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160359, 26)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cycle              0\n",
       "OpSet1             0\n",
       "OpSet2             0\n",
       "OpSet3             0\n",
       "SensorMeasure1     0\n",
       "SensorMeasure2     0\n",
       "SensorMeasure3     0\n",
       "SensorMeasure4     0\n",
       "SensorMeasure5     0\n",
       "SensorMeasure6     0\n",
       "SensorMeasure7     0\n",
       "SensorMeasure8     0\n",
       "SensorMeasure9     0\n",
       "SensorMeasure10    0\n",
       "SensorMeasure11    0\n",
       "SensorMeasure12    0\n",
       "SensorMeasure13    0\n",
       "SensorMeasure14    0\n",
       "SensorMeasure15    0\n",
       "SensorMeasure16    0\n",
       "SensorMeasure17    0\n",
       "SensorMeasure18    0\n",
       "SensorMeasure19    0\n",
       "SensorMeasure20    0\n",
       "SensorMeasure21    0\n",
       "labels             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_frame.iloc[:,0:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cycle</th>\n",
       "      <th>OpSet1</th>\n",
       "      <th>OpSet2</th>\n",
       "      <th>OpSet3</th>\n",
       "      <th>SensorMeasure1</th>\n",
       "      <th>SensorMeasure2</th>\n",
       "      <th>SensorMeasure3</th>\n",
       "      <th>SensorMeasure4</th>\n",
       "      <th>SensorMeasure5</th>\n",
       "      <th>SensorMeasure6</th>\n",
       "      <th>...</th>\n",
       "      <th>SensorMeasure12</th>\n",
       "      <th>SensorMeasure13</th>\n",
       "      <th>SensorMeasure14</th>\n",
       "      <th>SensorMeasure15</th>\n",
       "      <th>SensorMeasure16</th>\n",
       "      <th>SensorMeasure17</th>\n",
       "      <th>SensorMeasure18</th>\n",
       "      <th>SensorMeasure19</th>\n",
       "      <th>SensorMeasure20</th>\n",
       "      <th>SensorMeasure21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.61</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.61</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.61</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.61</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.61</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cycle  OpSet1  OpSet2  OpSet3  SensorMeasure1  SensorMeasure2  \\\n",
       "0      1 -0.0007 -0.0004   100.0          518.67          641.82   \n",
       "1      2  0.0019 -0.0003   100.0          518.67          642.15   \n",
       "2      3 -0.0043  0.0003   100.0          518.67          642.35   \n",
       "3      4  0.0007  0.0000   100.0          518.67          642.35   \n",
       "4      5 -0.0019 -0.0002   100.0          518.67          642.37   \n",
       "\n",
       "   SensorMeasure3  SensorMeasure4  SensorMeasure5  SensorMeasure6  ...  \\\n",
       "0         1589.70         1400.60           14.62           21.61  ...   \n",
       "1         1591.82         1403.14           14.62           21.61  ...   \n",
       "2         1587.99         1404.20           14.62           21.61  ...   \n",
       "3         1582.79         1401.87           14.62           21.61  ...   \n",
       "4         1582.85         1406.22           14.62           21.61  ...   \n",
       "\n",
       "   SensorMeasure12  SensorMeasure13  SensorMeasure14  SensorMeasure15  \\\n",
       "0           521.66          2388.02          8138.62           8.4195   \n",
       "1           522.28          2388.07          8131.49           8.4318   \n",
       "2           522.42          2388.03          8133.23           8.4178   \n",
       "3           522.86          2388.08          8133.83           8.3682   \n",
       "4           522.19          2388.04          8133.80           8.4294   \n",
       "\n",
       "   SensorMeasure16  SensorMeasure17  SensorMeasure18  SensorMeasure19  \\\n",
       "0             0.03              392             2388            100.0   \n",
       "1             0.03              392             2388            100.0   \n",
       "2             0.03              390             2388            100.0   \n",
       "3             0.03              392             2388            100.0   \n",
       "4             0.03              393             2388            100.0   \n",
       "\n",
       "   SensorMeasure20  SensorMeasure21  \n",
       "0            39.06          23.4190  \n",
       "1            39.00          23.4236  \n",
       "2            38.95          23.3442  \n",
       "3            38.88          23.3739  \n",
       "4            38.90          23.4044  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data_frame['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95933\n",
       "2    32348\n",
       "1    32078\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.1,shuffle=True,stratify=Y,random_state=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At this point it seems like the Model is subject to overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [200, 400, 300], 'max_depth': [10, 20, 30], 'max_samples': [0.2, 0.3, 0.4]}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Number of trees in random forest\n",
    "n_estimators=[200, 400, 300]\n",
    "# Maximum number of levels in tree\n",
    "max_depth= [10, 20, 30]\n",
    "# Max number of samples given to each tree \n",
    "max_samples= [0.2,0.3,0.4 ]\n",
    "\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_depth': max_depth,\n",
    "               'max_samples': max_samples}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#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>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [10, 20, 30],\n",
       "                                        &#x27;max_samples&#x27;: [0.2, 0.3, 0.4],\n",
       "                                        &#x27;n_estimators&#x27;: [200, 400, 300]})</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [10, 20, 30],\n",
       "                                        &#x27;max_samples&#x27;: [0.2, 0.3, 0.4],\n",
       "                                        &#x27;n_estimators&#x27;: [200, 400, 300]})</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [10, 20, 30],\n",
       "                                        'max_samples': [0.2, 0.3, 0.4],\n",
       "                                        'n_estimators': [200, 400, 300]})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestClassifier()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, cv = 3,n_jobs=-1)\n",
    "# Fit the random search model\n",
    "rf_random.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 300, 'max_samples': 0.4, 'max_depth': 30}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8902323120724022"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rf_randSer = RandomForestClassifier(n_estimators=300,max_samples=0.3,max_depth=30)\n",
    "Rf_randSer.fit(X_train,Y_train)\n",
    "new_test_pred = Rf_randSer.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8899975056123721"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Accuracy_after_optimization = accuracy_score(Y_test,new_test_pred)\n",
    "train_Accuracy_after_optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9041  545    7]\n",
      " [ 601 2352  255]\n",
      " [   0  356 2879]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cnf_matrix = confusion_matrix(Y_test,new_test_pred)\n",
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 355.0, 'Predicted label')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_names=['0','1','2'] # name  of classes\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g',square=True)\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion Matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Predicted as 0: 9041\n",
      "0 Mis-Predicted as 1: 545\n",
      "0 Mis-Predicted as 2: 7\n",
      "--------------------------------\n",
      "1 Mis-Predicted as 0: 601\n",
      "1 Predicted as 1: 2352\n",
      "1 Mis-Predicted as 2: 255\n",
      "--------------------------------\n",
      "2 Mis-Predicted as 0: 0\n",
      "2 Mis-Predicted as 1: 356\n",
      "2 Predicted as 2: 2879\n",
      "--------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"0 Predicted as 0: \" + str(cnf_matrix[0][0]))\n",
    "print(\"0 Mis-Predicted as 1: \" + str(cnf_matrix[0][1]))\n",
    "print(\"0 Mis-Predicted as 2: \" + str(cnf_matrix[0][2]))\n",
    "print(\"--------------------------------\")\n",
    "print(\"1 Mis-Predicted as 0: \" + str(cnf_matrix[1][0]))\n",
    "print(\"1 Predicted as 1: \" + str(cnf_matrix[1][1]))\n",
    "print(\"1 Mis-Predicted as 2: \" + str(cnf_matrix[1][2]))\n",
    "print(\"--------------------------------\")\n",
    "print(\"2 Mis-Predicted as 0: \" + str(cnf_matrix[2][0]))\n",
    "print(\"2 Mis-Predicted as 1: \" + str(cnf_matrix[2][1]))\n",
    "print(\"2 Predicted as 2: \" + str(cnf_matrix[2][2]))\n",
    "print(\"--------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.89000\n",
      "Precision: 0.89048\n",
      "Recall: 0.89000\n",
      "F1 Score: 0.89019\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",'%.5f' % metrics.accuracy_score(Y_test, new_test_pred))\n",
    "print(\"Precision:\",'%.5f' %metrics.precision_score(Y_test, new_test_pred,average = 'weighted'))\n",
    "print(\"Recall:\",'%.5f' %metrics.recall_score(Y_test, new_test_pred,average = 'weighted'))\n",
    "print(\"F1 Score:\",'%.5f' %metrics.f1_score(Y_test, new_test_pred,average = 'weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_test_frame = pd.read_csv('random_test_set.csv',header=None)\n",
    "random_test_frame = random_test_frame.iloc[:,0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = random_test_frame.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (Temp/ipykernel_9564/3459467281.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\souvi\\AppData\\Local\\Temp/ipykernel_9564/3459467281.py\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    from sklearn.exceptions\u001b[0m\n\u001b[1;37m                           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from sklearn.exceptions\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "MODERATE CONDITION\n",
      "[1]\n",
      "MODERATE CONDITION\n",
      "[1]\n",
      "MODERATE CONDITION\n",
      "[0]\n",
      "GOOD CONDITION\n",
      "[0]\n",
      "GOOD CONDITION\n",
      "[0]\n",
      "GOOD CONDITION\n",
      "[1]\n",
      "MODERATE CONDITION\n",
      "[2]\n",
      "VERY BAD CONDITION\n",
      "[1]\n",
      "MODERATE CONDITION\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(arr)):\n",
    "    test_input = arr[i]\n",
    "    test_input = np.asarray(test_input)\n",
    "    test_input = test_input.reshape(1,-1)\n",
    "    prediction = Rf_randSer.predict(test_input)\n",
    "    print(prediction)\n",
    "\n",
    "    if prediction[0] == 0:\n",
    "        print(\"GOOD CONDITION\")\n",
    "    elif prediction[0] == 1:\n",
    "        print(\"MODERATE CONDITION\")\n",
    "    elif prediction[0] == 2:\n",
    "        print(\"VERY BAD CONDITION\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing on combined Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Test_classification_1.csv')\n",
    "df2 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Test_classification_2.csv')\n",
    "# df3 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Test_classification_3.csv')\n",
    "# df4 = pd.read_csv('NASA-s-Turbo-Engine-Remaining-Useful-Life-prediction-using-machine-learning-main\\Test_classification_4.csv')\n",
    "\n",
    "test_df = df2\n",
    "# test_df = pd.concat([df1,df2,df3,df4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = test_df['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = test_df.drop(columns='labels',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_test_pred = Rf_randSer.predict(test_df)\n",
    "test_cnf_matrix = confusion_matrix(Y,random_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9101526874760966"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data_Acc_Score = accuracy_score(Y,random_test_pred)\n",
    "Test_data_Acc_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25973  1572     4]\n",
      " [  774  3444   427]\n",
      " [    2   275  1520]]\n"
     ]
    }
   ],
   "source": [
    "print(test_cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 355.0, 'Predicted label')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_names=['0','1','2'] # name  of classes\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(test_cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g',square=True)\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion Matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Predicted as 0: 25973\n",
      "0 Mis-Predicted as 1: 1572\n",
      "0 Mis-Predicted as 2: 4\n",
      "--------------------------------\n",
      "1 Mis-Predicted as 0: 774\n",
      "1 Predicted as 1: 3444\n",
      "1 Mis-Predicted as 2: 427\n",
      "--------------------------------\n",
      "2 Mis-Predicted as 0: 2\n",
      "2 Mis-Predicted as 1: 275\n",
      "2 Predicted as 2: 1520\n",
      "--------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"0 Predicted as 0: \" + str(test_cnf_matrix[0][0]))\n",
    "print(\"0 Mis-Predicted as 1: \" + str(test_cnf_matrix[0][1]))\n",
    "print(\"0 Mis-Predicted as 2: \" + str(test_cnf_matrix[0][2]))\n",
    "print(\"--------------------------------\")\n",
    "print(\"1 Mis-Predicted as 0: \" + str(test_cnf_matrix[1][0]))\n",
    "print(\"1 Predicted as 1: \" + str(test_cnf_matrix[1][1]))\n",
    "print(\"1 Mis-Predicted as 2: \" + str(test_cnf_matrix[1][2]))\n",
    "print(\"--------------------------------\")\n",
    "print(\"2 Mis-Predicted as 0: \" + str(test_cnf_matrix[2][0]))\n",
    "print(\"2 Mis-Predicted as 1: \" + str(test_cnf_matrix[2][1]))\n",
    "print(\"2 Predicted as 2: \" + str(test_cnf_matrix[2][2]))\n",
    "print(\"--------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.91015\n",
      "Precision: 0.91711\n",
      "Recall: 0.91015\n",
      "F1 Score: 0.91299\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",'%.5f' % metrics.accuracy_score(Y, random_test_pred))\n",
    "print(\"Precision:\",'%.5f' %metrics.precision_score(Y, random_test_pred,average = 'weighted'))\n",
    "print(\"Recall:\",'%.5f' %metrics.recall_score(Y, random_test_pred,average = 'weighted'))\n",
    "print(\"F1 Score:\",'%.5f' %metrics.f1_score(Y, random_test_pred,average = 'weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "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": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "903d28fc8095cdeaee5be616f3ebb92fafee475741240b3f05159e4f3ac8bf72"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}