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  {
   "cell_type": "markdown",
   "id": "677c5e27",
   "metadata": {},
   "source": [
    "Starting with MLE and logistic regression model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2520b517",
   "metadata": {},
   "source": [
    "Prerequisites\n",
    "Make sure you have the necessary Python libraries installed:\n",
    "pip install numpy pandas scikit-learn scipy matplotlib statsmodels\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e1f9bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37c6709b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_path\n",
    "data_path = \"../data/\"\n",
    "suv_filename = data_path + \"suv_percentilesSLOthenUWM.mat\"\n",
    "flags_filename=  data_path + \"flags_combined.mat\"\n",
    "normal_range_filename =  data_path + \"normal_range.mat\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b103dce8",
   "metadata": {},
   "source": [
    "Step 1: Data Preprocessing\n",
    "Ensure that your data is in the correct format. \n",
    "\n",
    "We'll assume that:\n",
    "\n",
    "suv_percentiles is a matrix of SUV values.\n",
    "\n",
    "flags_combined contains the labels (1 for adverse effects and 0 for normal controls)."
   ]
  }
 ],
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