main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intoduction To Problem\n",
    "\n",
    "\n",
    "### Aim : To predict the failure of temperature sensors based on historical data and operational metrics, to inform preventive maintenance strategies and reduce downtime.\n",
    "\n",
    "### Tools:\n",
    "- Python (with libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn for data manipulation and analysis).\n",
    "- Time series analysis techniques (e.g., ARIMA, SARIMA) for failure forecasting.\n",
    "- Machine learning models (e.g., Random Forest, XGBoost) for predictive maintenance.\n",
    "- Evaluation metrics (e.g., accuracy, precision, recall, F1 score) to assess model performance.\n",
    "- Data visualization techniques (e.g., line plots, scatter plots, heatmaps) to explore patterns and trends in the data.\n",
    "- anomoaly detection techniques (e.g., Isolation Forest, One-Class SVM) to identify outliers and anomalies in the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"sensor-fault-detection.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methodology\n",
    "\n",
    "1. Data Collection\n",
    "2. Data Preprocessing\n",
    "3. Exploratory Data Analysis\n",
    "4. Model Development\n",
    "5. Anomaly Detection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to detect anomalies in the data?\n",
    "- Anomaly detection is the process of identifying outliers or anomalies in a dataset that deviate significantly from the norm or expected behavior.\n",
    "- Anomalies can be detected using statistical methods, machine learning models, or domain-specific knowledge.\n",
    "- Common techniques for anomaly detection include Isolation Forest, One-Class SVM, and Autoencoders.\n",
    "- Anomalies can be visualized using scatter plots, heatmaps, or other data visualization techniques.\n",
    "- Anomaly detection can help identify potential failures or issues in the data that may require further investigation or preventive maintenance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"sensor-fault-detection.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62629 entries, 0 to 62628\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Timestamp  62629 non-null  object \n",
      " 1   SensorId   62629 non-null  int64  \n",
      " 2   Value      62629 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"sensor-fault-detection.csv\",sep=\";\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"sensor-fault-detection.csv\", sep=\";\", parse_dates=[\"Timestamp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>SensorId</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-01 23:20:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.479807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-02 04:00:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.539112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-23 06:25:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.250198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-23 19:35:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.961285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-04 15:10:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>25.321623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62624</th>\n",
       "      <td>2017-05-05 17:00:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>24.743029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62625</th>\n",
       "      <td>2017-07-07 16:40:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>31.787771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62626</th>\n",
       "      <td>2017-05-07 09:20:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>26.382582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62627</th>\n",
       "      <td>2017-05-07 14:55:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>26.286133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62628</th>\n",
       "      <td>2017-05-07 20:40:00+03:00</td>\n",
       "      <td>1</td>\n",
       "      <td>26.671986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62629 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Timestamp  SensorId      Value\n",
       "0     2017-03-01 23:20:00+03:00         1  18.479807\n",
       "1     2017-03-02 04:00:00+03:00         1  19.539112\n",
       "2     2017-03-23 06:25:00+03:00         1  19.250198\n",
       "3     2017-03-23 19:35:00+03:00         1  18.961285\n",
       "4     2017-04-04 15:10:00+03:00         1  25.321623\n",
       "...                         ...       ...        ...\n",
       "62624 2017-05-05 17:00:00+03:00         1  24.743029\n",
       "62625 2017-07-07 16:40:00+03:00         1  31.787771\n",
       "62626 2017-05-07 09:20:00+03:00         1  26.382582\n",
       "62627 2017-05-07 14:55:00+03:00         1  26.286133\n",
       "62628 2017-05-07 20:40:00+03:00         1  26.671986\n",
       "\n",
       "[62629 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62629 entries, 0 to 62628\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype                    \n",
      "---  ------     --------------  -----                    \n",
      " 0   Timestamp  62629 non-null  datetime64[ns, UTC+03:00]\n",
      " 1   SensorId   62629 non-null  int64                    \n",
      " 2   Value      62629 non-null  float64                  \n",
      "dtypes: datetime64[ns, UTC+03:00](1), float64(1), int64(1)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp    0\n",
       "SensorId     0\n",
       "Value        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Timestamp'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the data to see the trend of the data over time\n",
    "import matplotlib.pyplot as plt\n",
    "df.plot(x=\"Timestamp\", y=\"Value\", figsize=(12,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ']' does not match opening parenthesis '(' (3492849991.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[25], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    plt.bar(x=\"df[\"Value]\")\u001b[0m\n\u001b[1;37m                        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m closing parenthesis ']' does not match opening parenthesis '('\n"
     ]
    }
   ],
   "source": [
    "plt.bar(x=\"df[\"Value]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}