Pandas_exercise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53121eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "traits = ['workclass', 'fnlwgt', 'education', 'educational-num', 'marital-status','occupation', 'relationship','race','gender','capital-gain','capital-loss','hours-per-week', 'native-country','income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c4fe8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "\n",
    "# Plotting cell\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# font\n",
    "plt.rcParams.update({'font.size': 8})\n",
    "\n",
    "# reset the default figsize value\n",
    "plt.rcParams[\"figure.figsize\"] = plt.rcParamsDefault[\"figure.figsize\"]\n",
    "\n",
    "# 144 is good for a high-resolution display. Try 100 if it's too big\n",
    "plt.rcParams[\"figure.dpi\"] = (120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30514d4e",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'adult_cleaned.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/3178931511.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'adult_cleaned.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mac\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;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[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;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[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'adult_cleaned.csv'"
     ]
    }
   ],
   "source": [
    "ac=pd.read_csv('adult_cleaned.csv')\n",
    "ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b11110",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ac.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46414f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "ac.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c23722ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ac.dropna(how='any', inplace=True)\n",
    "ac.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a04a0c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"We have {ac.duplicated().sum()} duplicate values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac32098a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ac = ac.drop_duplicates()\n",
    "\n",
    "print(f\"After dropping duplicate values, now we have {ac.duplicated().sum()} duplicate values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1037d1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ac = ac.drop(['capital-gain', 'capital-loss'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cac82e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "myDict = {'Federal-gov':1, 'Local-gov':2, 'Private':3,'Self-emp-inc':4,'Self-emp-not-inc':5,'State-gov':6,'Without-pay':7,}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d2e59d",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_workclass = []\n",
    "for i, workclass in enumerate(ac['workclass']):\n",
    "    if workclass == '?':\n",
    "        categorical_workclass.append(8)\n",
    "    else:\n",
    "        categorical_workclass.append(myDict[workclass])\n",
    "ac[\"categorical_workclass\"] = categorical_workclass\n",
    "\n",
    "#drops\n",
    "ac = ac.drop(['workclass'], axis = 1)\n",
    "ac = ac.drop(['education'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53719c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ismarried = {'Married-civ-spouse':1, 'Never-married':2, 'Divorced':3, 'Widowed':4,\n",
    "       'Separated':5, 'Married-spouse-absent':6, 'Married-AF-spouse':7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cde925ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_marital_status = []\n",
    "for i, married in enumerate(ac['marital-status']):\n",
    "    if pd.isnull(married):\n",
    "        num_marital_status.append(8)\n",
    "    else:\n",
    "        num_marital_status.append(Ismarried[married])\n",
    "ac[\"num_maritial_status\"] = num_marital_status\n",
    "\n",
    "#drop\n",
    "ac = ac.drop(['marital-status'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbd1afa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "occup_dict = {'Craft-repair':1, 'Handlers-cleaners':2, '4-service':3, 'Adm-clerical':4,\n",
    "       'Farming-fishing':5, 'Sales':6, 'Prof-specialty':7, 'Priv-house-serv':8,\n",
    "       'Transport-moving':9, 'Exec-managerial':10, 'Machine-op-inspct':11,\n",
    "       'Protective-serv':12, 'Tech-support':13, 'Armed-Forces':14}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc66476f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/338457825.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moccup_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moccupation\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'occupation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moccupation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0moccup_num\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0moccupation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'?'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "occup_num = []\n",
    "for i, occupation in enumerate(ac['occupation']):\n",
    "    if pd.isnull(occupation):\n",
    "        occup_num.append(15)\n",
    "    elif occupation == '?':\n",
    "        occup_num.append(16)\n",
    "    else:\n",
    "        occup_num.append(occup_dict[occupation])\n",
    "ac[\"occupation-num\"] = occup_num\n",
    "\n",
    "#drop\n",
    "ac = ac.drop(['occupation'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3047a701",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "relations_dict = {'Husband':1, 'Own-child':2, 'Not-in-family':3, 'Unmarried':4, '4-relative':5,\n",
    "       'Wife':6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6073028",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/2806717804.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrel_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'relationship'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mrel_num\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0moccupation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'?'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "rel_num = []\n",
    "for i, rel in enumerate(ac['relationship']):\n",
    "    if pd.isnull(rel):\n",
    "        rel_num.append(7)\n",
    "    elif occupation == '?':\n",
    "        rel_num.append(8)\n",
    "    else:\n",
    "        rel_num.append(relations_dict[rel])\n",
    "ac[\"relationship-num\"] = rel_num\n",
    "\n",
    "#drop\n",
    "ac = ac.drop(['relationship'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef5e1ca3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/1291758067.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'native-country'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mnative_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcountry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'native-country'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "ac['native-country'].unique()\n",
    "\n",
    "native_dict = {}\n",
    "\n",
    "for i, country in enumerate(ac['native-country'].unique()):\n",
    "    native_dict[country] = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cf9a45a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/2572064317.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnative_country_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcountry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'native-country'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mnative_country_num\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnative_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcountry\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"native-country-num\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnative_country_num\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "native_country_num = []\n",
    "for i, country in enumerate(ac['native-country']):\n",
    "    native_country_num.append(native_dict[country])\n",
    "ac[\"native-country-num\"] = native_country_num\n",
    "\n",
    "#drop\n",
    "ac = ac.drop(['native-country'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef2350ab",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/3199980565.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Get a list of the animal classes to test the predictions (the TARGET)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'income'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Remove the `type` and `animal name` from the **training data** (this is the DATA)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "# Load it into a Pandas Dataframe\n",
    "# zoo=Zoo.iloc[:,1:]\n",
    "\n",
    "# Get a list of the animal classes to test the predictions (the TARGET)\n",
    "y=ac['income'].values\n",
    "\n",
    "# Remove the `type` and `animal name` from the **training data** (this is the DATA)\n",
    "X=ac.drop(['age','income'],axis=1).values\n",
    "\n",
    "# Haave a look. Do you need to normalize the data? Look at the numeric columns:\n",
    "ac.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f785247",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/1028594520.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9165a03",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/1869471164.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# See how `y=zoo['type'].values` encodes the TARGET types into integers:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined"
     ]
    }
   ],
   "source": [
    "# See how `y=zoo['type'].values` encodes the TARGET types into integers:\n",
    "\n",
    "y = y.reshape(-1,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eac25c0b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/3865899973.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mincome\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "# How many animals in each target type? \n",
    "type_names = [\"less than 50k/yr\", \"more than 50k/yr\"]\n",
    "\n",
    "from collections import Counter\n",
    "c = Counter(ac.income)\n",
    "print(c)\n",
    "\n",
    "ct = {}\n",
    "for i,t in enumerate(type_names):\n",
    "  print(type_names[i],c[i])\n",
    "  ct[type_names[i]] = c[i]\n",
    "    #CHECK THIS\n",
    "\n",
    "ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9973b64e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ac' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/1811405742.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneighbors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnumpts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mac\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# about 15% of the number of rows in the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0merror_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0merror_rate1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ac' is not defined"
     ]
    }
   ],
   "source": [
    "# What is the best value for k?'\n",
    "# Try the elbow method\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "numpts = int(len(ac)/6) # about 15% of the number of rows in the data\n",
    "error_rate = []\n",
    "error_rate1 = []\n",
    "\n",
    "# This might take some time!\n",
    "for i in range(1,20):\n",
    "  knn = KNeighborsClassifier(n_neighbors=i)\n",
    "  knn.fit(X_train,y_train)\n",
    "  pred_i = knn.predict(X_test)\n",
    "  error_rate.append(np.mean(pred_i != y_test))\n",
    "  pred_j = knn.predict(X_train)\n",
    "  error_rate1.append(np.mean(pred_j != y_train))\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,20),error_rate,color='blue', \n",
    "         linestyle='dashed', marker='o',markerfacecolor='red', markersize=7)\n",
    "plt.plot(range(1,20),error_rate1,color='gray', \n",
    "         linestyle='dashed', marker='^',markerfacecolor='green', markersize=7)\n",
    "\n",
    "plt.legend(['testing data','training data'])\n",
    "plt.title('Error Rate vs. value of k')\n",
    "plt.xlabel('k value')\n",
    "plt.ylabel('Error Rate')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10cbb67d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/691077663.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mknn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "k=11\n",
    "knn = KNeighborsClassifier(n_neighbors=11)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b782c9f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/1782882809.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "knn.score(X_train,y_train), knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0f240ad",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'ConfusionMatrixDisplay' has no attribute 'from_estimator'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/106202968.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m ]\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtitles_options\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     disp = ConfusionMatrixDisplay.from_estimator(\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mknn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: type object 'ConfusionMatrixDisplay' has no attribute 'from_estimator'"
     ]
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
    "\n",
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "for title, normalize in titles_options:\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(\n",
    "        knn,\n",
    "        X_test,\n",
    "        y_test,\n",
    "        display_labels=type_names,\n",
    "        cmap=plt.cm.Blues,\n",
    "        normalize=normalize,\n",
    "    )\n",
    "    disp.ax_.set_title(title)\n",
    "\n",
    "    print(title)\n",
    "    # print(disp.confusion_matrix)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9f0ffe4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/xf/8808rrps3wb3r170fzcq_6t00000gn/T/ipykernel_13896/3428559310.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzero_division\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_test' is not defined"
     ]
    }
   ],
   "source": [
    "y_pred = []\n",
    "for i in X_test:\n",
    "    y_pred.append(knn.predict(i.reshape(1,-1)))\n",
    "from sklearn.metrics import classification_report\n",
    "classification_report(y_test, y_pred, zero_division=0)\n",
    "\n",
    "# We set the type_names at the top\n",
    "print(classification_report(y_test, y_pred, target_names=type_names))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "345ccbbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8348a624",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fa097cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "909dc6fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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