y=accident.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import lightgbm as lgb\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# import geopandas as gpd\n",
    "# from shapely.geometry import Polygon, LineString, Point\n",
    "# import fiona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager, rc\n",
    "font_path = \"data/THEdog.ttf\"   #폰트파일의 위치\n",
    "font_name = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font_name)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pydatavenv",
   "language": "python",
   "name": "pydatavenv"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
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