Part 1.  The Proportion of Target Variable Values .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import os\n",
    "from zipfile import ZipFile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_json(\"./train.json\")\n",
    "# df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['interest_level'].value_counts().plot(kind=\"bar\")\n",
    "plt.xlabel('Interest Level')\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Interest Level Distribution')\n",
    "plt.show()"
   ]
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