Predictive Maintenance Analysis with Regression and Classification Methods.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1f99d817",
   "metadata": {},
   "source": [
    "# Predictive Maintenance Analysis with Regression and Classification Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "507e4ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.formula.api as smf\n",
    "from numpy import arange\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# !pip install tensorflow\n",
    "# !pip install keras\n",
    "#pip install scikeras\n",
    "# pip install graphviz\n",
    "from keras.models import Sequential\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.losses import MeanSquaredLogarithmicError\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import warnings\n",
    "\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "from numpy import arange\n",
    "import warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "# Ignore the specific warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b6b6b15b",
   "metadata": {},
   "source": [
    "## Data Import & EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffba54e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20631 entries, 0 to 20630\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   id         20631 non-null  int64  \n",
      " 1   cycle      20631 non-null  int64  \n",
      " 2   s1         20631 non-null  float64\n",
      " 3   s2         20631 non-null  float64\n",
      " 4   s3         20631 non-null  float64\n",
      " 5   s4         20631 non-null  float64\n",
      " 6   ttf        20631 non-null  int64  \n",
      " 7   label_bnc  20631 non-null  int64  \n",
      "dtypes: float64(4), int64(4)\n",
      "memory usage: 1.3 MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   id         100 non-null    int64  \n",
      " 1   cycle      100 non-null    int64  \n",
      " 2   s1         100 non-null    float64\n",
      " 3   s2         100 non-null    float64\n",
      " 4   s3         100 non-null    float64\n",
      " 5   s4         100 non-null    float64\n",
      " 6   ttf        100 non-null    int64  \n",
      " 7   label_bnc  100 non-null    int64  \n",
      "dtypes: float64(4), int64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "# Read the train and test dataset\n",
    "train = pd.read_csv(r'train_selected.csv')\n",
    "test = pd.read_csv(r'test_selected_ttf.csv')\n",
    "train.info() # Check the data types\n",
    "test.info();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "937a9239",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train.head() \n",
    "test.head();\n",
    "train.iloc[:,1:7].describe(); # General description table with summary statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a3cfc35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train[['ttf']].boxplot(); \n",
    "\n",
    "# How is the distribution of last cycle of each engine? Histogram\n",
    "\n",
    "train_g1 = train.groupby(['id'])[[\"id\" ,\"cycle\"]].max();\n",
    "\n",
    "plt.subplots(figsize=(12, 6));\n",
    "sns.histplot(train_g1[\"cycle\"], kde = True, color='blue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e442a995",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cycle', ylabel='ttf'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relationship between the independent variables (s1, s2 and s3) and dependent variable (ttf) \n",
    "\n",
    "fig, (ax1, ax2, ax3,ax4,ax5) = plt.subplots(nrows = 1, ncols = 5, figsize = (10, 5));\n",
    "sns.scatterplot(x = 's1', y = 'ttf', ax = ax1, data = train) # negative non-linear relationship\n",
    "sns.scatterplot(x = 's2', y ='ttf', ax = ax2, data = train) # positive non-linear relationship\n",
    "sns.scatterplot(x = 's3', y = 'ttf', ax = ax3, data = train) # negative non-linear relationship\n",
    "sns.scatterplot(x = 's4', y = 'ttf', ax = ax4, data = train) # positive non-linear relationship\n",
    "sns.scatterplot(x = 'cycle', y = 'ttf', ax = ax5, data = train) # negative linear relationship\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82cfb12d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train.iloc[:,1:7].corr()\n",
    "sns.heatmap(train.iloc[:,1:7].corr(), \n",
    "        xticklabels=train.iloc[:,1:7].corr().columns,\n",
    "        yticklabels=train.iloc[:,1:7].corr().columns)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "169aced2",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43aaf2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "independent = ['cycle','s1', 's2', 's3', 's4']\n",
    "X = train[independent]\n",
    "y = train.ttf\n",
    "\n",
    "X_test= test[independent]\n",
    "y_test=test.ttf"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6449a2f2",
   "metadata": {},
   "source": [
    "### Regression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1accf7e6",
   "metadata": {},
   "source": [
    "#### Elastic Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fdf02a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha: 0.000100\n",
      "l1_ratio_: 0.150000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "from numpy import arange\n",
    "\n",
    "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "\n",
    "# Define model\n",
    "ratios = arange(0, 1, 0.05)\n",
    "alphas = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0]\n",
    "elasticnet = ElasticNetCV(l1_ratio=ratios, alphas=alphas, cv=cv, n_jobs=-1)\n",
    "\n",
    "# Fit model\n",
    "elasticnet.fit(X, y)\n",
    "\n",
    "print('alpha: %f' % elasticnet.alpha_)\n",
    "print('l1_ratio_: %f' % elasticnet.l1_ratio_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2f18e981",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of test dataset: 1047.5214239156471\n"
     ]
    }
   ],
   "source": [
    "elasticnet.score(X, y)  # Gives the R-square value\n",
    "\n",
    "y_prediction = elasticnet.predict(X_test)\n",
    "mse = np.mean((y_prediction - y_test)**2)\n",
    "print(\"MSE of test dataset:\", mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8fa0961e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cycle</td>\n",
       "      <td>-0.451713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s1</td>\n",
       "      <td>-0.919379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s2</td>\n",
       "      <td>6.591764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s3</td>\n",
       "      <td>-42.061798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s4</td>\n",
       "      <td>8.486542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variables  Coefficient Estimate\n",
       "0     cycle             -0.451713\n",
       "1        s1             -0.919379\n",
       "2        s2              6.591764\n",
       "3        s3            -42.061798\n",
       "4        s4              8.486542"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff = pd.DataFrame()\n",
    "coeff[\"Variables\"] = X.columns\n",
    "coeff['Coefficient Estimate'] = pd.Series(elasticnet.coef_)\n",
    "coeff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "93ab96ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis = range(len(X_test))\n",
    "plt.scatter(x_axis, y_test, s=5, color=\"black\", label=\"Original values\")\n",
    "plt.plot(x_axis, y_prediction, lw=0.8, color=\"red\", label=\"Predicted values\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "82f99930",
   "metadata": {},
   "source": [
    "#### Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "69c9063b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "\n",
    "X_scaled = pd.DataFrame(scaler.transform(X), columns=independent)\n",
    "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=independent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3ad98dd4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_model = RandomForestRegressor(n_estimators=300, max_features=\"sqrt\", n_jobs=-1,\n",
    "                                 random_state=38, criterion='squared_error')\n",
    "\n",
    "rf_model.fit(X_scaled, y)\n",
    "rul_pred = rf_model.predict(X_test_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07789e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of test dataset: 800.7153798888888\n"
     ]
    }
   ],
   "source": [
    "# Plot\n",
    "x_axis = range(len(X_test))\n",
    "plt.scatter(x_axis, y_test, s=8, color=\"black\", label=\"Original values\")\n",
    "plt.plot(x_axis, rul_pred, lw=0.8, color=\"red\", label=\"Predicted values\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"MSE of test dataset:\", np.mean((rul_pred - y_test)**2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5bf095e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance_array = rf_model.feature_importances_\n",
    "sorted_indices = np.argsort(importance_array)[::-1]\n",
    "columns = X_scaled.columns[1:]\n",
    "\n",
    "plt.title('Feature Importance of RF Model to predict TTF')\n",
    "plt.bar(range(X_scaled.shape[1]), importance_array[sorted_indices], align='center')\n",
    "plt.xticks(range(X_scaled.shape[1]), X_scaled.columns[sorted_indices], rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b3137568",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7b0ca7d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preprocessing\n",
    "independent = ['cycle', 's1', 's2', 's3', 's4']\n",
    "X = train[independent]\n",
    "y = train.label_bnc\n",
    "\n",
    "X_test = test[independent]\n",
    "y_test = test.label_bnc\n",
    "\n",
    "scaler.fit(X)\n",
    "\n",
    "X_scaled = pd.DataFrame(scaler.transform(X), columns=independent)\n",
    "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=independent)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c366719c",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bf223247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 29, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "param_grid = {\n",
    "        'n_neighbors': range(1, 30, 2),\n",
    "        'weights': ['uniform', 'distance']\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=knn,\n",
    "                           param_grid=param_grid,\n",
    "                           cv=5,\n",
    "                           n_jobs=-1,\n",
    "                           scoring='accuracy')\n",
    "\n",
    "grid_search.fit(X_scaled, y)\n",
    "\n",
    "grid_search.best_score_\n",
    "grid_search.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c7e9a3e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.96      0.92        75\n",
      "           1       0.84      0.64      0.73        25\n",
      "\n",
      "    accuracy                           0.88       100\n",
      "   macro avg       0.87      0.80      0.83       100\n",
      "weighted avg       0.88      0.88      0.87       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Final model\n",
    "knn_best = KNeighborsClassifier(n_neighbors=29, weights='uniform')\n",
    "knn_best.fit(X_scaled, y)\n",
    "\n",
    "# Accuracy score after hyperparameter tuning\n",
    "knn_best.score(X_test_scaled, y_test)\n",
    "\n",
    "# Classification report\n",
    "print(classification_report(y_test, knn_best.predict(X_test_scaled)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "98b7880a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "confusion_matrix = metrics.confusion_matrix(y_test, knn_best.predict(X_test_scaled))\n",
    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix=confusion_matrix, display_labels=['0: Not-Fail', '1: Fail'])\n",
    "cm_display.plot()\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}