Red Wine Quality Prediction (Practice Project - 3).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7fedd2d7",
   "metadata": {},
   "source": [
    "####  Practice Project - 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d1da48",
   "metadata": {},
   "source": [
    "# Red Wine Quality Prediction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7e60af",
   "metadata": {},
   "source": [
    "* Problem Statement:\n",
    "  The dataset is related to red and white variants of the Portuguese \"Vinho Verde\" wine. Due to privacy and logistic issues,       only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine   brand, wine selling price, etc.).\n",
    "\n",
    "  This dataset can be viewed as classification task. The classes are ordered and not balanced (e.g. there are many more normal     wines than excellent or poor ones). Also, we are not sure if all input variables are relevant. So it could be interesting to     test feature selection methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2e1a11d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import important libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f6c7f912",
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1345\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1346\u001b[1;33m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[0;32m   1347\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1278\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1279\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1324\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1273\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1274\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1033\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1034\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1035\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    973\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1441\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    944\u001b[0m         \u001b[1;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 945\u001b[1;33m         self.sock = self._create_connection(\n\u001b[0m\u001b[0;32m    946\u001b[0m             (self.host,self.port), self.timeout, self.source_address)\n",
      "\u001b[1;32m~\\anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    822\u001b[0m     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 823\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    824\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    953\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 954\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    955\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3792/1571372144.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# read file from github\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"https://raw.githubusercontent.com/dsrscientist/DSData/master/winequality-red.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    813\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1038\u001b[0m             )\n\u001b[0;32m   1039\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    607\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[1;31m# open URLs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 609\u001b[1;33m     ioargs = _get_filepath_or_buffer(\n\u001b[0m\u001b[0;32m    610\u001b[0m         \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    611\u001b[0m         \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    310\u001b[0m         \u001b[1;31m# assuming storage_options is to be interpreted as headers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m         \u001b[0mreq_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq_info\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m             \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Content-Encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"gzip\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m     \u001b[1;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    212\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    213\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 214\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    216\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m         \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'urllib.Request'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 534\u001b[1;33m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[0;32m    535\u001b[0m                                   '_open', req)\n\u001b[0;32m    536\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    492\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    493\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 494\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    495\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    496\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1387\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1388\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1389\u001b[1;33m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[0;32m   1390\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0;32m   1391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1347\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m   1348\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1349\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1350\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1351\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
     ]
    }
   ],
   "source": [
    "# read file from github\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/dsrscientist/DSData/master/winequality-red.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b21bf5",
   "metadata": {},
   "source": [
    "* as we can see it's a classification problem as label contains catagorical data and also mentioned in problem ststement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3014a82f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape of the df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738d404a",
   "metadata": {},
   "source": [
    "* here we have 1599 rows and 12 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "49207a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "# checking info of df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5b27af",
   "metadata": {},
   "source": [
    "* i can se that there is no null value in any column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e74e8f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking stats of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832ba24e",
   "metadata": {},
   "source": [
    "* i can see mean and std looks good of every column except 'free sulfurdioxide' and 'total sulfurdioxide' it means data is not     normally distributed in these columns, i will confirm it graphically\n",
    "* quantile range of some columns don't look good either the have outliers or data is not distributed normally , i will check it graphically  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d70d6e0",
   "metadata": {},
   "source": [
    "### EDA (Exploratory data analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7943ed2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity           0.982751\n",
       "volatile acidity        0.671593\n",
       "citric acid             0.318337\n",
       "residual sugar          4.540655\n",
       "chlorides               5.680347\n",
       "free sulfur dioxide     1.250567\n",
       "total sulfur dioxide    1.515531\n",
       "density                 0.071288\n",
       "pH                      0.193683\n",
       "sulphates               2.428672\n",
       "alcohol                 0.860829\n",
       "quality                 0.217802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking skewness of data\n",
    "df.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4fe3df",
   "metadata": {},
   "source": [
    "* If the skewness is between -0.5 and 0.5, the data are fairly symmetrical. and here in most of the column(residual sugar ,chlorides,free sulfur dioxide,total sulfur dioxide,sulphates,alcohol,volatile acidity,fixed acidity) data is highly skewed. i will check it graphically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d73897d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "volatile acidity       -0.390558\n",
       "total sulfur dioxide   -0.185100\n",
       "density                -0.174919\n",
       "chlorides              -0.128907\n",
       "pH                     -0.057731\n",
       "free sulfur dioxide    -0.050656\n",
       "residual sugar          0.013732\n",
       "fixed acidity           0.124052\n",
       "citric acid             0.226373\n",
       "sulphates               0.251397\n",
       "alcohol                 0.476166\n",
       "quality                 1.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking correlation\n",
    "df.corr()['quality'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c152f9f8",
   "metadata": {},
   "source": [
    "* here we can see half of the columns are negatively correlated with label."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417c0e42",
   "metadata": {},
   "source": [
    "### Checking Outliers, Correlation and Skewness Graphically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "147021c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see data distribution for every row\n",
    "plt.figure(figsize=(20,15))\n",
    "plotnumber = 1\n",
    "for column in df:\n",
    "    if plotnumber<=12:\n",
    "        plt.subplot(4,3,plotnumber)\n",
    "        sns.distplot(df[column])\n",
    "        plt.xlabel(column,fontsize = 15)\n",
    "    plotnumber+=1\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411a73a8",
   "metadata": {},
   "source": [
    "* here i can see residual sugar ,chlorides,free sulfur dioxide,total sulfur dioxide,sulphates,alcohol,volatile acidity,fixed acidity are right skewed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8e6a25f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking outliers\n",
    "plt.figure(figsize=(20,15))\n",
    "graph = 1\n",
    "for column in df:\n",
    "    if graph<=11:\n",
    "        plt.subplot(2,6,graph)\n",
    "        ax = sns.boxplot(data = df[column])\n",
    "        plt.xlabel(column,fontsize=15)\n",
    "    graph+=1\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0ad374",
   "metadata": {},
   "source": [
    "* here i can see evey column contains outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cf9712ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking Correlation\n",
    "plt.figure(figsize=(22,7))\n",
    "sns.heatmap(df.corr().abs(),annot=True,linewidths=0.1,linecolor=\"black\",fmt=\"0.2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7aa590",
   "metadata": {},
   "source": [
    "* here i see some features are correlated to each other . so i will remove some of them which are very less correlated with 'quality'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790d6cd9",
   "metadata": {},
   "source": [
    "* i will remove 'pH','residual sugar','fixed acidity','free sulfur dioxide','citric acid' as they are less correlated with label(quality) and highly correlated with each other"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c386a137",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c6be9187",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing unwanted columns\n",
    "new_df = df.drop(columns=[ 'pH','residual sugar','fixed acidity','free sulfur dioxide','citric acid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ad9f6658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 7)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new_df shape\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a66fc2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['volatile acidity', 'chlorides', 'total sulfur dioxide', 'density',\n",
       "       'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ebb84f7",
   "metadata": {},
   "source": [
    "* so after feature selection i will go with these features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0b6dc1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking outliers\n",
    "plt.figure(figsize=(20,15))\n",
    "graph = 1\n",
    "for column in new_df:\n",
    "    if graph<=7:\n",
    "        plt.subplot(2,4,graph)\n",
    "        ax = sns.boxplot(data = new_df[column])\n",
    "        plt.xlabel(column,fontsize=15)\n",
    "    graph+=1\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aef66494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's remove outliers from these "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e2e988f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.4, 12.9)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting quantile range for alcohol column\n",
    "min_a, max_a = new_df.alcohol.quantile([0.000, 0.98])\n",
    "min_a, max_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "010ac46a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing rows containing ouotliers\n",
    "new_df = new_df[(new_df.alcohol<max_a)&(new_df.alcohol>min_a)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cf5dc691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='alcohol'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# checking outliers\n",
    "sns.boxplot(x=new_df['alcohol'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96f973a6",
   "metadata": {},
   "source": [
    "* outliers are removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a63950d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.12, 0.9291199999999993)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting quantile range for volatile acidity column\n",
    "min_v, max_v = new_df['volatile acidity'].quantile([0.000, 0.976])\n",
    "min_v, max_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9b241918",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing rows containing ouotliers\n",
    "new_df = new_df[(new_df['volatile acidity']<max_v)&(new_df['volatile acidity']>min_v)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d40ba53b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='volatile acidity'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# checking outliers\n",
    "sns.boxplot(x=new_df['volatile acidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d4302d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.0, 104.0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting quantile range for total sulfur dioxide column\n",
    "min_t, max_t = new_df['total sulfur dioxide'].quantile([0.000, 0.93])\n",
    "min_t, max_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "51645dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total sulfur dioxide'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# removing rows containing ouotliers\n",
    "new_df = new_df[(new_df['total sulfur dioxide']<max_t)&(new_df['total sulfur dioxide']>min_t)]\n",
    "# checking outliers\n",
    "sns.boxplot(x=new_df['total sulfur dioxide'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7c87c6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.33, 0.9)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting quantile range for sulphates column\n",
    "min_s, max_s = new_df['sulphates'].quantile([0.000, 0.94])\n",
    "min_s, max_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "96fb8bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sulphates'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# removing rows containing ouotliers\n",
    "new_df = new_df[(new_df['sulphates']<max_s)&(new_df['sulphates']>min_s)]\n",
    "# checking outliers\n",
    "sns.boxplot(x=new_df['sulphates'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "716a362f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.063, 0.103)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting quantile range for column chlorides\n",
    "min_ch, max_ch = new_df['chlorides'].quantile([0.12, 0.90])\n",
    "min_ch, max_ch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "850aa477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='chlorides'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMKUlEQVR4nO3dX4zld1nH8c/Tbgq7GCjtprWs4lKXUhEbbCopQpooxNReiBhjSjTQC2MwslkwJDaBC7wgEYMmZVUMgoi9KEpDALVaKololCIrlKW0RYaSQhcoWzZQ0i0FyteL86uO253u7Mw585ztvl7JZM78/sx55jsz7znn7M6ZGmMEgK13RvcAAKcrAQZoIsAATQQYoIkAAzTZdjIH79y5c+zevXtBowA88ezcuTM333zzzWOMK4/dd1IB3r17dw4cODC/yQBOA1W183jbPQQB0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAk5P6m3Asxv79+7OystI9xoYdOnQoSbJr167mSfrs2bMne/fu7R6DU4wAL4GVlZXcdvudeWTHOd2jbMiZR7+VJPnaw6fnl9OZR490j8Ap6vT8jllCj+w4Jw9dfFX3GBuy/a6bkuSUnX+zHv344WR5DBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMmWBHj//v3Zv3//VlwVwFwtsl/bFvJej7GysrIVVwMwd4vsl4cgAJoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGabNuKKzl06FAeeuih7Nu3byuu7pSzsrKSM747usdgg874zgNZWfm2r+8nqJWVlWzfvn0h7/uEt4Cr6req6kBVHTh8+PBChgA4HZ3wFvAY4x1J3pEkl1122YZupu3atStJct11123k9Ce8ffv25b/uvq97DDboB09+avZceL6v7yeoRd6z8RgwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZosm0rrmTPnj1bcTUAc7fIfm1JgPfu3bsVVwMwd4vsl4cgAJoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBkW/cAzJx59Ei233VT9xgbcubRbyTJKTv/Zp159EiS87vH4BQkwEtgz5493SNsyqFD30+S7Np1ukbo/FP+c0gPAV4Ce/fu7R4BaOAxYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMECTGmOs/+Cqw0nuWdAsO5Pcv6D3PQ/m2xzzbY75NqdzvvuTZIxx5bE7TirAi1RVB8YYl3XPsRbzbY75Nsd8m7Os83kIAqCJAAM0WaYAv6N7gBMw3+aYb3PMtzlLOd/SPAYMcLpZplvAAKcVAQZospAAV9WVVfW5qlqpqmuPs7+q6m3T/oNVdemqfWdX1Y1VdVdV3VlVL5y2v6mqDlXVbdPLVVs9X1U9Z9X131ZVD1TVa6d951TVLVX1+en105dsvvb1m/a9rqo+W1W3V9UNVfXkaXv7+p1gvmVZv33TbJ999HM7bV+W9Vtrvrmt3zpnvLiqPlZVD1fV69dz7jzXcN3GGHN9SXJmki8kuTDJWUk+neS5xxxzVZJ/TFJJLk/y8VX73pPkN6fLZyU5e7r8piSv757vmPfztSQ/Nr39h0munS5fm+QtSzZf+/ol2ZXki0m2T2//bZJrlmX9TjDfMqzf85LcnmRHkm1J/jnJs5do/R5vvrms30nMeF6Sn0ny5tXX+3jnzmsNT+ZlEbeAX5BkZYxx9xjju0nem+RlxxzzsiR/PWZuTXJ2VV1QVU9NckWSdyXJGOO7Y4xvLst8xxzzkiRfGGPcs+qc90yX35Pkl5dsvnnZ7Hzbkmyvqm2ZfaN+ZdU5y7B+a803L5uZ7yeS3DrGODrG+H6SjyZ5+apzutfv8eabpxPOOMb4+hjjE0m+dxLnzmsN120RAd6V5Mur3r532raeYy5McjjJu6vqU1X1zqp6yqrjXjPd5fnLTdw92Mx8q12d5IZVb58/xvhqkkyvz1uy+ZLm9RtjHEry1iRfSvLVJN8aY3x4OqZ9/U4wX9L/9Xd7kiuq6tyq2pHZLdEfnY5pX78TzJfMZ/3WO+NGzp3XGq7bIgJcx9l27P91W+uYbUkuTfL2McZPJ3kws7sCSfL2JD+e5PmZfXP8UcN8s51VZyX5pSTv2+AMj2dR87Wv3/RN97Ikz0ryjCRPqarf2OAcWz1f+/qNMe5M8pYktyT5p8zuPn9/g3OsZVHzzWv91jvjIs6du0UE+N78/596P5LH3o1b65h7k9w7xvj4tP3GzIKcMcZ9Y4xHxhg/SPIXmd2V2Or5HvWLST45xrhv1bb7Hr0bO73++jLNtyTr99IkXxxjHB5jfC/J+5P87HTMMqzfmvMtyfpljPGuMcalY4wrkhxJ8vnpmGVYvzXnm+P6rXfGjZw7rzVct0UE+BNJnl1Vz5puiV2d5EPHHPOhJK+c/jX18szu6n11jPG1JF+uqudMx70kyR3J/y7Io16e2d2dLZ1v1f5X5LF37z+U5FXT5Vcl+eAyzbck6/elJJdX1Y6qqsw+v3euOqd7/dacb0nWL1V13vT6mUl+Jf/3eV6G9Vtzvjmu33pn3Mi581rD9dvIv9yd6CWzx37+O7N/bXzDtO3VSV49Xa4kfzrt/0ySy1ad+/wkB5IcTPKBJE+ftl8/HXsws4W6oGm+HUm+keRpx7zPc5N8JLOf+B9Jcs6Szbcs6/f7Se7K7Bvw+iRPWrL1W2u+ZVm/f8vsRsmnk7xkCb/+1ppvbuu3zhl/OLNbuw8k+eZ0+alrnTvvNVzvi19FBmjiN+EAmggwQBMBBmgiwABNBBigiQDTrqr+qqp+9Tjbd1fVSf1/0ap6RlXduMa+f6mqpfvDjJy+tnUPAPNSVdvGGF9J8piYwzJyC5gtV1WvnJ6U5dNVdf20+Yqq+o+qunuNW8NPrqp3V9VnavZETT83bb+mqt5XVX+X5MOrbzVX1faqeu90XX+TZPuq9/cLNXu+2E9O5//QtP0PquqO6Zy3LnwxOK25BcyWqqqfTPKGJC8aY9xfVeck+eMkFyR5cZKLM/tNqWMfRvidJBlj/FRVXZxZbC+a9r0wySVjjCNVtXvVOb+d5OgY45KquiTJJ6cZdiZ5Y5KXjjEerKrfS/K7VfUnmf2a7MVjjFFVZ8/744fVBJit9vNJbhxj3J8kUzST5ANj9kQtd1TV+cc578VJ9k/n3FVV9yR5NMC3jDGOHOecK5K8bTrnYFUdnLZfnuS5Sf59uu6zknwss19b/U6Sd1bVPyT5+81+sPB4BJitVjn+0/89fMwxxztvLQ8+zr7jXVdlFu1XPGZH1QsyexKeq5O8JrMfGLAQHgNmq30kya9V1bnJ7O9wrfO8f03y69M5FyV5ZpLPncQ5z0tyybT91iQvqqo9074dVXXR9Djw08YYNyV5bWZPDAUL4xYwW2qM8dmqenOSj1bVI0k+tc5T/yzJn1fVZzJ7ku9rxhgPTw8hrOXtmf11lYNJbkvyn9MMh6vqmiQ3VNWTpmPfmOTbST5Ysz/EWUled1IfHJwkz4YG0MRDEABNBBigiQADNBFggCYCDNBEgAGaCDBAk/8BdZAZpuf2kv0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# removing rows containing ouotliers\n",
    "new_df = new_df[(new_df['chlorides']<max_ch)&(new_df['chlorides']>min_ch)]\n",
    "# checking outliers\n",
    "sns.boxplot(x=new_df['chlorides'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "76e79563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.993364, 1.0002488)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting quantile range for column\n",
    "min_d, max_d = new_df['density'].quantile([0.01, 0.97])\n",
    "min_d, max_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7ded842f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='density'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# removing rows containing ouotliers\n",
    "new_df = new_df[(new_df['density']<max_d)&(new_df['density']>min_d)]\n",
    "# checking outliers\n",
    "sns.boxplot(x=new_df['density'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "636e8860",
   "metadata": {},
   "source": [
    "* all the outliers are removed from features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "37679cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's work on skewness \n",
    "#let's check the skewness first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eda70dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(963, 7)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking shape of new df\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "57f18266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking skewness\n",
    "plt.figure(figsize=(20,15))\n",
    "plotnumber = 1\n",
    "for column in new_df:\n",
    "    if plotnumber<=7:\n",
    "        plt.subplot(2,4,plotnumber)\n",
    "        sns.distplot(new_df[column])\n",
    "        plt.xlabel(column,fontsize = 15)\n",
    "    plotnumber+=1\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dafd841a",
   "metadata": {},
   "source": [
    "* i can see skewness is removed as i removed outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c166eed",
   "metadata": {},
   "source": [
    "####  Now i am ready for model building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03024da",
   "metadata": {},
   "source": [
    "sacling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5f605fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate features and label\n",
    "x = new_df.drop(columns = ['quality'])\n",
    "y = new_df['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4b5cba23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling data\n",
    "scaler = StandardScaler()\n",
    "x_scaled = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "820317cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(963, 6)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8a3534",
   "metadata": {},
   "source": [
    "* now we will go to model testing phase our data before that let's split our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e28c6db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting data into train and test data\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_scaled,y,test_size=0.25,random_state=355)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e070441",
   "metadata": {},
   "source": [
    "##### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8b8d8da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training data with logistic regression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "75e35a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting y_test\n",
    "y_pred = log_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2f769336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6556016597510373"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking model accuracy\n",
    "accuracy = accuracy_score(y_test,y_pred) \n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0884465",
   "metadata": {},
   "source": [
    "* according to me model accuracy is not up to the mark . i guess data has some variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fde59d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           4       0.00      0.00      0.00         4\n",
      "           5       0.66      0.75      0.70        99\n",
      "           6       0.66      0.66      0.66       117\n",
      "           7       0.58      0.39      0.47        18\n",
      "           8       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.66       241\n",
      "   macro avg       0.38      0.36      0.37       241\n",
      "weighted avg       0.63      0.66      0.64       241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# chcek classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e29f50",
   "metadata": {},
   "source": [
    "accuracy is 66% but is can be increased by hyper parameter tuning "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46c4d4b",
   "metadata": {},
   "source": [
    "##### knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "28b83de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e6231e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_scaled,y,test_size=0.25,random_state=355)\n",
    "\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3775b1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  2,  0,  0],\n",
       "       [ 0, 74, 25,  0,  0],\n",
       "       [ 0, 36, 77,  4,  0],\n",
       "       [ 0,  0, 11,  7,  0],\n",
       "       [ 0,  0,  2,  1,  0]], dtype=int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating confusion matrix\n",
    "cfm = confusion_matrix(y_test,y_pred)\n",
    "cfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4572e5f1",
   "metadata": {},
   "source": [
    "we have very less type 1 and type 2 error columns , its good for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0f1d6ec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           4       0.00      0.00      0.00         4\n",
      "           5       0.66      0.75      0.70        99\n",
      "           6       0.66      0.66      0.66       117\n",
      "           7       0.58      0.39      0.47        18\n",
      "           8       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.66       241\n",
      "   macro avg       0.38      0.36      0.37       241\n",
      "weighted avg       0.63      0.66      0.64       241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce9d925a",
   "metadata": {},
   "source": [
    "cross validation of model(to remove over fittng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7737b147",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold,cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "389458a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.56923077, 0.43076923, 0.6       , 0.59375   , 0.546875  ,\n",
       "       0.5       , 0.625     , 0.5625    , 0.5       , 0.46875   ,\n",
       "       0.546875  , 0.4375    , 0.5625    , 0.46875   , 0.59375   ])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(knn,x_scaled,y,cv=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "adcb678d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5223121761658032"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(knn,x_scaled,y,cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2b8364",
   "metadata": {},
   "source": [
    "score is less not good for model lets do Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b79a867",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6f916e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0b2ba5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_drid = {'algorithm': ['kd_tree','brute'],\n",
    "             'leaf_size':[3,4,5,6,7,8],\n",
    "             'n_neighbors':[3,4,7,9,11,13]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fa4d8dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gridsearch = GridSearchCV(estimator=knn, param_grid=param_drid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8877f4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['kd_tree', 'brute'],\n",
       "                         'leaf_size': [3, 4, 5, 6, 7, 8],\n",
       "                         'n_neighbors': [3, 4, 7, 9, 11, 13]})"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "aa422960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'kd_tree', 'leaf_size': 3, 'n_neighbors': 13}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d4e8f0",
   "metadata": {},
   "source": [
    "lets use these parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d187e39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(algorithm = 'kd_tree', leaf_size = 3, n_neighbors=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ff6ec72e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='kd_tree', leaf_size=3, n_neighbors=13)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3f0b82b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ebaecc34",
   "metadata": {},
   "outputs": [],
   "source": [
    "cfm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "049d1037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  1,  0,  0],\n",
       "       [ 0, 75, 24,  0,  0],\n",
       "       [ 0, 39, 71,  7,  0],\n",
       "       [ 0,  1, 13,  4,  0],\n",
       "       [ 0,  0,  3,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb923b0",
   "metadata": {},
   "source": [
    "errors are reduced "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5f1aeb0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           4       0.00      0.00      0.00         4\n",
      "           5       0.64      0.76      0.69        99\n",
      "           6       0.63      0.61      0.62       117\n",
      "           7       0.36      0.22      0.28        18\n",
      "           8       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.62       241\n",
      "   macro avg       0.33      0.32      0.32       241\n",
      "weighted avg       0.60      0.62      0.61       241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "dff01207",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_accuracy reduced "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1344cd",
   "metadata": {},
   "source": [
    "##### decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "065f3ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25, random_state=44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "be2167db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d08c7750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training score\n",
    "clf.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "cabfe4fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5892116182572614"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# move to prediction part\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "# check the accuracy\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ea7795b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are tuning hyper parameter now by passing different values for both parameters\n",
    "drid_param = {\n",
    "    'criterion':['gini','entropy'],\n",
    "    'max_depth': range(10,20),\n",
    "    'min_samples_leaf': range(2,15),\n",
    "    'min_samples_split': range(3,15),\n",
    "    'max_leaf_nodes': range(5,15)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c9f57596",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=clf,param_grid=drid_param,cv=5,\n",
    "                           n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0c056b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=DecisionTreeClassifier(max_depth=10, max_leaf_nodes=9,\n",
       "                                              min_samples_leaf=2,\n",
       "                                              min_samples_split=3),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': range(10, 20),\n",
       "                         'max_leaf_nodes': range(5, 15),\n",
       "                         'min_samples_leaf': range(2, 15),\n",
       "                         'min_samples_split': range(3, 15)})"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d5744aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 10, 'max_leaf_nodes': 10, 'min_samples_leaf': 2, 'min_samples_split': 3}\n"
     ]
    }
   ],
   "source": [
    "best_parameters = grid_search.best_params_\n",
    "print(best_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fa4ada",
   "metadata": {},
   "source": [
    "* these are the best parameter for decision tree for this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "80ab5d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_score(clf, x_train,x_test,y_train,y_test, train=True):\n",
    "    if train:\n",
    "        y_pred = clf.predict(x_train)\n",
    "        \n",
    "        print(\"\\n==================== Train Result =======================\")\n",
    "        \n",
    "        print(f\"Accuracy score :{accuracy_score(y_train,y_pred)*100:.2f}%\")\n",
    "        \n",
    "    elif train==False:\n",
    "        pred = clf.predict(x_test)\n",
    "        \n",
    "        print(\"\\n===================== Test Result =======================\")\n",
    "        \n",
    "        print(f\"Accuracy score :{accuracy_score(y_test, pred)*100:.2f}%\")\n",
    "        \n",
    "        print('\\n \\n Test Classification Report \\n',classification_report(y_test, pred,digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "8a38365f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=10, max_leaf_nodes=10, min_samples_leaf=2,\n",
       "                       min_samples_split=3)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initaiting DecisionTreeClassifier with new parameters and train\n",
    "clf = DecisionTreeClassifier(criterion = 'gini',min_samples_split=3,max_depth=10,min_samples_leaf=2,max_leaf_nodes=10)\n",
    "\n",
    "# train the model\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "f3395db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================== Train Result =======================\n",
      "Accuracy score :64.82%\n",
      "\n",
      "===================== Test Result =======================\n",
      "Accuracy score :56.85%\n",
      "\n",
      " \n",
      " Test Classification Report \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00         2\n",
      "           5       0.57      0.72      0.64       100\n",
      "           6       0.56      0.55      0.56       114\n",
      "           7       1.00      0.10      0.17        21\n",
      "           8       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.57       241\n",
      "   macro avg       0.35      0.23      0.23       241\n",
      "weighted avg       0.59      0.57      0.54       241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# call the function and pass dataset to check the train and test score\n",
    "metric_score(clf,x_train,x_test,y_train,y_test, train=True) # for tarining score\n",
    "\n",
    "metric_score(clf,x_train,x_test,y_train,y_test, train=False) # for testing score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43907fab",
   "metadata": {},
   "source": [
    "we get msx accuracy by logistic regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c584ee5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saveing the model\n",
    "import pickle\n",
    "filename = 'redwinw.pkl'\n",
    "pickle.dump(log_reg, open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fda2558f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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