NormalizeGSR.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
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   "source": [
    "log_data = pd.read_csv(\"heregoesthedata\")\n",
    "\n",
    "# Convert the log data into time series data\n",
    "log_data['timestamp'] = pd.to_datetime(log_data['timestamp'])\n",
    "log_data.set_index('timestamp', inplace=True)\n",
    "\n",
    "print(log_data)\n",
    "\n",
    "# Create StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit scaler\n",
    "scaler.fit(log_data)\n",
    "\n",
    "# Transform time series using the scaler\n",
    "normalized_data = scaler.transform(log_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b0bf31d",
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   "source": [
    "print(normalized_data)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "10b3575c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the normalized data back to a data frame\n",
    "normalized_data = pd.DataFrame(normalized_data, columns=log_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09f7ee4b",
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   "source": [
    "# Write the normalized data to a new CSV file\n",
    "normalized_data.to_csv(\"normalized_gsrdata.csv\", index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97a04d72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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