preprocessing-distance.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide the correct path to your CSV file\n",
    "file_path = r\"C:\\Users\\parul\\OneDrive\\Desktop\\Projects\\ShellAI\\Shell_AI_Hackathon\\dataset\\Distance_Matrix.csv\"\n",
    "\n",
    "# Read the CSV file into a DataFrame\n",
    "distance_matrix = pd.read_csv(file_path, index_col=\"Unnamed: 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "distance_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "distance_matrix.isna().sum()"
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   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_mms = MinMaxScaler()\n",
    "scaled_distances = pd.DataFrame(scaler_mms.fit_transform(distance_matrix),\n",
    "                                 columns = distance_matrix.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "scaled_distances.head()"
   ]
  }
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