Practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36e59240-d12a-4f20-987c-38cffd3a6240",
   "metadata": {},
   "source": [
    "# --------------------------------------- Important Practice -------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f919478-4870-4c6a-9097-859bd24060af",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5692d34e-6233-4df3-b447-94459186d099",
   "metadata": {},
   "source": [
    "## Importing necessary Libraries  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "010a84b7-7b48-494f-853d-0cb5f003c2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49206aee",
   "metadata": {},
   "source": [
    "## Python Warmup exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4623e1e6",
   "metadata": {},
   "source": [
    "* Q1: Write a code that receives an optional text, print all letters in the text and store them in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad07b9b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w\n",
      "e\n",
      "l\n",
      "c\n",
      "o\n",
      "m\n",
      "e\n",
      " \n",
      "t\n",
      "o\n",
      " \n",
      "w\n",
      "o\n",
      "r\n",
      "k\n",
      "s\n",
      "h\n",
      "o\n",
      "p\n",
      " \n",
      "1\n",
      "['w', 'e', 'l', 'c', 'o', 'm', 'e', ' ', 't', 'o', ' ', 'w', 'o', 'r', 'k', 's', 'h', 'o', 'p', ' ', '1']\n"
     ]
    }
   ],
   "source": [
    "output=[]\n",
    "text1='''welcome to workshop 1'''\n",
    "for i in text1:\n",
    "    print(i)\n",
    "    output.append(i)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1ff49b",
   "metadata": {},
   "source": [
    "* Q2: Write a code that receives an optional text, prints all words in the text and store them in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9243aeee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "welcome\n",
      "to\n",
      "workshop\n",
      "1\n",
      "['welcome', 'to', 'workshop', '1']\n"
     ]
    }
   ],
   "source": [
    "output=[]\n",
    "text1='''welcome to workshop 1'''\n",
    "for word in text1.split():\n",
    "    print(word)\n",
    "    output.append(word)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43826077",
   "metadata": {},
   "source": [
    "* Q3: Write Q2 answer as a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f307f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "welcome\n",
      "to\n",
      "workshop\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "text1='''welcome to workshop 1'''\n",
    "def print_word(text):\n",
    "    for word in text.split():\n",
    "        print(word)\n",
    "print_word(text1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734f134a",
   "metadata": {},
   "source": [
    "* Q4: Write a program to find out the average of a set of integers. The program prompts the user to enter the count of numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "226d8035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average is:  5.0\n"
     ]
    }
   ],
   "source": [
    "count = int(input(\"Enter the count of numbers: \")) # This gets the count of numbers from the user and \n",
    "# puts it in an integer variable\n",
    "i = 0\n",
    "sum = 0\n",
    "for i in range(count):\n",
    "    x = int(input(\"Enter an integer: \"))\n",
    "    sum = sum + x\n",
    "avg = sum/count\n",
    "print(\"The average is: \", avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4223ccf8",
   "metadata": {},
   "source": [
    "* Q5: Write a program to check whether the given number is even or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f52d4f72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is Odd.\n"
     ]
    }
   ],
   "source": [
    "# If we divide an even number by 2, the remainder will be zero\n",
    "number = input(\"Enter a number: \")\n",
    "x = int(number)%2\n",
    "if x == 0:\n",
    "    print(\"The number is Even.\")\n",
    "else:\n",
    "    print(\"The number is Odd.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31395763",
   "metadata": {},
   "source": [
    "* Q6: Write a program to find the average of 4 numbers using while loop. The program prompts the user to enter the numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e7d532d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average is : 2.0\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "sum = 0.0\n",
    "while(count<4):\n",
    "    number = float(input(\"Enter a real number: \"))\n",
    "    count=count+1\n",
    "    sum = sum+number\n",
    "avg = sum/10\n",
    "print(\"Average is :\",avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b97feb",
   "metadata": {},
   "source": [
    "* Q7: Write a function to find the average of a list of integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ee34d09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average is : 2.5\n"
     ]
    }
   ],
   "source": [
    "def give_me_average(in_list):\n",
    "    count=len(in_list)\n",
    "    sum=0\n",
    "    for i in in_list:\n",
    "        sum=sum+i\n",
    "    avg = sum/count\n",
    "    return avg\n",
    "a=[1,2,3,4]\n",
    "print(\"Average is :\",give_me_average(a))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd64cf45",
   "metadata": {},
   "source": [
    "* Q8: Write a program to display all integers within the range 100-120 whose sum of digits is an even number and store them in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57752dd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n",
      "103\n",
      "105\n",
      "107\n",
      "109\n",
      "110\n",
      "112\n",
      "114\n",
      "116\n",
      "118\n",
      "[101, 103, 105, 107, 109, 110, 112, 114, 116, 118]\n"
     ]
    }
   ],
   "source": [
    "output=[]\n",
    "for i in range(100,120):\n",
    "    num = i\n",
    "    sum = 0\n",
    "    while(num!=0):\n",
    "        digit = num%10\n",
    "        sum = sum + digit\n",
    "        num = num//10\n",
    "    if(sum%2==0):\n",
    "        output.append(i)\n",
    "        print(i)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0b2c3f",
   "metadata": {},
   "source": [
    "* Q9: Write a program to check whether the given integer is a multiple of both 5 and 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a86b22e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 is not a multiple of both 5 and 7\n"
     ]
    }
   ],
   "source": [
    "number = int(input(\"Enter an integer: \"))\n",
    "if((number%5==0)and(number%7==0)):\n",
    "    print(number, \"is a multiple of both 5 and 7\")\n",
    "else:\n",
    "    print(number, \"is not a multiple of both 5 and 7\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6c224d",
   "metadata": {},
   "source": [
    "## Pandas warmup exercises"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1bea436c",
   "metadata": {},
   "source": [
    "• You can install Pandas on your system by using pip as follows:\n",
    "pip install pandas\n",
    "\n",
    "• You can import Pandas as follows:\n",
    "import pandas\n",
    "\n",
    "• Pandas is usually imported under the pd alias.\n",
    "import pandas as pd\n",
    "\n",
    "• You can check Pandas version as follows:\n",
    "import pandas as pd\n",
    "print(pd.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c00f0e3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   calories  duration\n",
      "0       420        50\n",
      "1       380        40\n",
      "2       390        45\n",
      "\n",
      "\n",
      "     0    1    2\n",
      "0  420  380  390\n",
      "1   50   40   45\n"
     ]
    }
   ],
   "source": [
    "# You can use the DataFrame() method to create a Pandas DataFrame from a dictionary, numpy array, or a list.\n",
    "data = {\"calories\": [420, 380, 390], \"duration\": [50, 40, 45]}\n",
    "#load data into a DataFrame object:\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n",
    "print('\\n') # Addine an empty line to the end of the data frame\n",
    "# ---\n",
    "data = [[420, 380, 390],[50, 40, 45]]\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "839eae7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   calories  duration\n",
      "0       420        50\n",
      "1       380        40\n",
      "2       390        45\n",
      "\n",
      "\n",
      "420\n",
      "\n",
      "\n",
      "calories    420\n",
      "duration     50\n",
      "Name: 0, dtype: int64\n",
      "\n",
      "\n",
      "0    420\n",
      "1    380\n",
      "2    390\n",
      "Name: calories, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# You can use loc() or iloc() to return values, rows, or columns inside a data frame. Using loc() you need to specify the\n",
    "# column name, but using iloc() you only need to specify the column index. \n",
    "data = {\"calories\": [420, 380, 390], \"duration\": [50, 40, 45]}\n",
    "#load data into a DataFrame object:\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n",
    "print(\"\\n\")\n",
    "print(df.loc[0,'calories'])\n",
    "print(\"\\n\")\n",
    "print(df.loc[0,:])\n",
    "print(\"\\n\")\n",
    "print(df.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d71360dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2]\n",
      "\n",
      "['calories', 'duration']\n"
     ]
    }
   ],
   "source": [
    "# You can get the indices and columns names using index and column attributes as follows\n",
    "\n",
    "data = {\"calories\": [420, 380, 390], \"duration\": [50, 40, 45]}\n",
    "#load data into a DataFrame object:\n",
    "df = pd.DataFrame(data)\n",
    "index_names=list(df.index)\n",
    "column_names=list(df.columns)\n",
    "print(index_names,end='\\n\\n')\n",
    "print(column_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c15413a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      calories  duration\n",
      "day1       420        50\n",
      "day2       380        40\n",
      "day3       390        45\n"
     ]
    }
   ],
   "source": [
    "# With the index and columns argument, you can name your own indexes and columns.\n",
    "data = {\n",
    " \"calories\": [420, 380, 390],\n",
    " \"duration\": [50, 40, 45]\n",
    "}\n",
    "df = pd.DataFrame(data, index = [\"day1\", \"day2\", \"day3\"])\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b0af609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c1   c2   c3\n",
      "0  420  380  390\n",
      "1   50   40   45\n"
     ]
    }
   ],
   "source": [
    "# With the index argument, you can name your own indexes and columns\n",
    "data =[[420, 380, 390],[50, 40, 45]]\n",
    "df = pd.DataFrame(data, columns = [\"c1\", \"c2\", \"c3\"])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4665820d",
   "metadata": {},
   "source": [
    "## Importing a dataset for this workshop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b70001-2e41-4a2a-a468-19bb575b56b4",
   "metadata": {},
   "source": [
    "## Importing the dataset directly from the source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "25a2c3fb-2f10-4869-abeb-d62b40193f3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ucimlrepo in c:\\python312\\lib\\site-packages (0.0.6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "# Installing a package which is needed to download the dataset from its online source. This package is recommended by the online source at the following\n",
    "# URL : https://archive.ics.uci.edu/dataset/2/adult\n",
    "!pip3 install ucimlrepo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "39320f90-ed64-4d58-a594-f5a785795af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downloading the dataset from the online source. The first two lines are given by the online source mentioned above\n",
    "from ucimlrepo import fetch_ucirepo \n",
    "# fetch dataset \n",
    "adult = fetch_ucirepo(id=2) \n",
    "\n",
    "# Putting data in a pandas dataframe\n",
    "X = adult.data.features \n",
    "y = adult.data.targets \n",
    "data=pd.concat([X,y],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33b16900-3b27-4d10-b145-d102bbd7620f",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
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       "      <td>2174</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  education  education-num  \\\n",
       "0       39         State-gov   77516  Bachelors             13   \n",
       "1       50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2       38           Private  215646    HS-grad              9   \n",
       "3       53           Private  234721       11th              7   \n",
       "4       28           Private  338409  Bachelors             13   \n",
       "...    ...               ...     ...        ...            ...   \n",
       "48837   39           Private  215419  Bachelors             13   \n",
       "48838   64               NaN  321403    HS-grad              9   \n",
       "48839   38           Private  374983  Bachelors             13   \n",
       "48840   44           Private   83891  Bachelors             13   \n",
       "48841   35      Self-emp-inc  182148  Bachelors             13   \n",
       "\n",
       "           marital-status         occupation    relationship  \\\n",
       "0           Never-married       Adm-clerical   Not-in-family   \n",
       "1      Married-civ-spouse    Exec-managerial         Husband   \n",
       "2                Divorced  Handlers-cleaners   Not-in-family   \n",
       "3      Married-civ-spouse  Handlers-cleaners         Husband   \n",
       "4      Married-civ-spouse     Prof-specialty            Wife   \n",
       "...                   ...                ...             ...   \n",
       "48837            Divorced     Prof-specialty   Not-in-family   \n",
       "48838             Widowed                NaN  Other-relative   \n",
       "48839  Married-civ-spouse     Prof-specialty         Husband   \n",
       "48840            Divorced       Adm-clerical       Own-child   \n",
       "48841  Married-civ-spouse    Exec-managerial         Husband   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                   White    Male          2174             0              40   \n",
       "1                   White    Male             0             0              13   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male             0             0              40   \n",
       "4                   Black  Female             0             0              40   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "48837               White  Female             0             0              36   \n",
       "48838               Black    Male             0             0              40   \n",
       "48839               White    Male             0             0              50   \n",
       "48840  Asian-Pac-Islander    Male          5455             0              40   \n",
       "48841               White    Male             0             0              60   \n",
       "\n",
       "      native-country  income  \n",
       "0      United-States   <=50K  \n",
       "1      United-States   <=50K  \n",
       "2      United-States   <=50K  \n",
       "3      United-States   <=50K  \n",
       "4               Cuba   <=50K  \n",
       "...              ...     ...  \n",
       "48837  United-States  <=50K.  \n",
       "48838  United-States  <=50K.  \n",
       "48839  United-States  <=50K.  \n",
       "48840  United-States  <=50K.  \n",
       "48841  United-States   >50K.  \n",
       "\n",
       "[48842 rows x 15 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing data\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed2fb69-ae10-45eb-bf6c-57fd3c8bbbdc",
   "metadata": {},
   "source": [
    "## Importing the dataset from a folder on your local disk "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3fbd3442-c9bf-4ae9-8945-6edbd87e6c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the dataset as a Pandas DataFrame into Python if the dataset is stored on your local hard disk\n",
    "# You can download the dataset from the following URL: https://archive.ics.uci.edu/dataset/2/adult  \n",
    "data = pd.read_csv('C:/Users/Uche Buzz/Desktop/School stuff/Data Mining and Infomatics/Dataset/adult.csv') # Replace the current data path with the data path on your local disk "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a86e6b-7c5c-41d4-8d91-f2682bdb833d",
   "metadata": {},
   "source": [
    "## Exploring the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4192c739-f44e-498d-a12e-49e71b45a5e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
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       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing the first 5 rows of the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97640dbc-bfa5-4701-ab69-24aeab9253f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48842, 15)\n"
     ]
    }
   ],
   "source": [
    "# Finding the shape of the data\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "227906ee-a57a-441b-b921-d37d31b8c1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a dataset by randomly extracting 30000 rows (samples) \n",
    "data_new = data.sample(n=30000, random_state = 48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da55c2b6-765a-42cc-81a3-2b81821e7036",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>8029</th>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>27498</th>\n",
       "      <td>25</td>\n",
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       "      <td>100903</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
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       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
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       "      <th>48416</th>\n",
       "      <td>47</td>\n",
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       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
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       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43230</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>119422</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>37544</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>166371</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20482</th>\n",
       "      <td>80</td>\n",
       "      <td>Private</td>\n",
       "      <td>202483</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39667</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>175808</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40001</th>\n",
       "      <td>25</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>31350</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48523</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>239057</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt     education  education-num  \\\n",
       "8029    29    Private  216481       Masters             14   \n",
       "45203   36    Private  280570  Some-college             10   \n",
       "27498   25          ?  100903     Bachelors             13   \n",
       "48416   47    Private  145636     Assoc-voc             11   \n",
       "43230   33    Private  119422       HS-grad              9   \n",
       "...    ...        ...     ...           ...            ...   \n",
       "37544   20    Private  166371       HS-grad              9   \n",
       "20482   80    Private  202483       HS-grad              9   \n",
       "39667   20    Private  175808       HS-grad              9   \n",
       "40001   25  State-gov   31350  Some-college             10   \n",
       "48523   19    Private  239057       HS-grad              9   \n",
       "\n",
       "              marital-status         occupation    relationship   race  \\\n",
       "8029      Married-civ-spouse    Exec-managerial            Wife  White   \n",
       "45203     Married-civ-spouse       Craft-repair         Husband  White   \n",
       "27498     Married-civ-spouse                  ?            Wife  White   \n",
       "48416     Married-civ-spouse  Handlers-cleaners         Husband  White   \n",
       "43230     Married-civ-spouse    Exec-managerial         Husband  White   \n",
       "...                      ...                ...             ...    ...   \n",
       "37544          Never-married       Craft-repair  Other-relative  White   \n",
       "20482  Married-spouse-absent       Adm-clerical   Not-in-family  White   \n",
       "39667          Never-married       Craft-repair       Own-child  White   \n",
       "40001          Never-married      Other-service   Not-in-family  White   \n",
       "48523          Never-married       Craft-repair       Own-child  White   \n",
       "\n",
       "          sex  capital-gain  capital-loss  hours-per-week native-country  \\\n",
       "8029   Female             0             0              40  United-States   \n",
       "45203    Male             0             0              45  United-States   \n",
       "27498  Female             0             0              25  United-States   \n",
       "48416    Male             0             0              48  United-States   \n",
       "43230    Male             0             0              40  United-States   \n",
       "...       ...           ...           ...             ...            ...   \n",
       "37544    Male             0             0              40  United-States   \n",
       "20482  Female             0             0              16  United-States   \n",
       "39667    Male             0             0              40  United-States   \n",
       "40001    Male             0             0              40  United-States   \n",
       "48523    Male             0             0              40  United-States   \n",
       "\n",
       "       income  \n",
       "8029     >50K  \n",
       "45203  <=50K.  \n",
       "27498   <=50K  \n",
       "48416   >50K.  \n",
       "43230  <=50K.  \n",
       "...       ...  \n",
       "37544  <=50K.  \n",
       "20482   <=50K  \n",
       "39667  <=50K.  \n",
       "40001  <=50K.  \n",
       "48523  <=50K.  \n",
       "\n",
       "[30000 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the new dataset\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46e6defc-7747-4ce1-8447-40446682deb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The indices of different rows in the dataset are currently messy. This happens in many data science projects. Always reindex \n",
    "# the dataset if you are unsure the indices are correct.  \n",
    "data_new.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1935a258-c8f6-4fd3-b71f-bcc7b05e26e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>11</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
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       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>20</td>\n",
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       "      <td>166371</td>\n",
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       "      <td>9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>29996</th>\n",
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       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
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       "      <td>31350</td>\n",
       "      <td>Some-college</td>\n",
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       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
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       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>239057</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt     education  education-num  \\\n",
       "0       29    Private  216481       Masters             14   \n",
       "1       36    Private  280570  Some-college             10   \n",
       "2       25          ?  100903     Bachelors             13   \n",
       "3       47    Private  145636     Assoc-voc             11   \n",
       "4       33    Private  119422       HS-grad              9   \n",
       "...    ...        ...     ...           ...            ...   \n",
       "29995   20    Private  166371       HS-grad              9   \n",
       "29996   80    Private  202483       HS-grad              9   \n",
       "29997   20    Private  175808       HS-grad              9   \n",
       "29998   25  State-gov   31350  Some-college             10   \n",
       "29999   19    Private  239057       HS-grad              9   \n",
       "\n",
       "              marital-status         occupation    relationship   race  \\\n",
       "0         Married-civ-spouse    Exec-managerial            Wife  White   \n",
       "1         Married-civ-spouse       Craft-repair         Husband  White   \n",
       "2         Married-civ-spouse                  ?            Wife  White   \n",
       "3         Married-civ-spouse  Handlers-cleaners         Husband  White   \n",
       "4         Married-civ-spouse    Exec-managerial         Husband  White   \n",
       "...                      ...                ...             ...    ...   \n",
       "29995          Never-married       Craft-repair  Other-relative  White   \n",
       "29996  Married-spouse-absent       Adm-clerical   Not-in-family  White   \n",
       "29997          Never-married       Craft-repair       Own-child  White   \n",
       "29998          Never-married      Other-service   Not-in-family  White   \n",
       "29999          Never-married       Craft-repair       Own-child  White   \n",
       "\n",
       "          sex  capital-gain  capital-loss  hours-per-week native-country  \\\n",
       "0      Female             0             0              40  United-States   \n",
       "1        Male             0             0              45  United-States   \n",
       "2      Female             0             0              25  United-States   \n",
       "3        Male             0             0              48  United-States   \n",
       "4        Male             0             0              40  United-States   \n",
       "...       ...           ...           ...             ...            ...   \n",
       "29995    Male             0             0              40  United-States   \n",
       "29996  Female             0             0              16  United-States   \n",
       "29997    Male             0             0              40  United-States   \n",
       "29998    Male             0             0              40  United-States   \n",
       "29999    Male             0             0              40  United-States   \n",
       "\n",
       "       income  \n",
       "0        >50K  \n",
       "1      <=50K.  \n",
       "2       <=50K  \n",
       "3       >50K.  \n",
       "4      <=50K.  \n",
       "...       ...  \n",
       "29995  <=50K.  \n",
       "29996   <=50K  \n",
       "29997  <=50K.  \n",
       "29998  <=50K.  \n",
       "29999  <=50K.  \n",
       "\n",
       "[30000 rows x 15 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if the indices are correct\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "52d731d3-b660-4846-8611-240de23c2f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  education  education-num  \\\n",
       "0       39         State-gov   77516  Bachelors             13   \n",
       "1       50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2       38           Private  215646    HS-grad              9   \n",
       "3       53           Private  234721       11th              7   \n",
       "4       28           Private  338409  Bachelors             13   \n",
       "...    ...               ...     ...        ...            ...   \n",
       "48837   39           Private  215419  Bachelors             13   \n",
       "48838   64               NaN  321403    HS-grad              9   \n",
       "48839   38           Private  374983  Bachelors             13   \n",
       "48840   44           Private   83891  Bachelors             13   \n",
       "48841   35      Self-emp-inc  182148  Bachelors             13   \n",
       "\n",
       "           marital-status         occupation    relationship  \\\n",
       "0           Never-married       Adm-clerical   Not-in-family   \n",
       "1      Married-civ-spouse    Exec-managerial         Husband   \n",
       "2                Divorced  Handlers-cleaners   Not-in-family   \n",
       "3      Married-civ-spouse  Handlers-cleaners         Husband   \n",
       "4      Married-civ-spouse     Prof-specialty            Wife   \n",
       "...                   ...                ...             ...   \n",
       "48837            Divorced     Prof-specialty   Not-in-family   \n",
       "48838             Widowed                NaN  Other-relative   \n",
       "48839  Married-civ-spouse     Prof-specialty         Husband   \n",
       "48840            Divorced       Adm-clerical       Own-child   \n",
       "48841  Married-civ-spouse    Exec-managerial         Husband   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                   White    Male          2174             0              40   \n",
       "1                   White    Male             0             0              13   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male             0             0              40   \n",
       "4                   Black  Female             0             0              40   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "48837               White  Female             0             0              36   \n",
       "48838               Black    Male             0             0              40   \n",
       "48839               White    Male             0             0              50   \n",
       "48840  Asian-Pac-Islander    Male          5455             0              40   \n",
       "48841               White    Male             0             0              60   \n",
       "\n",
       "      native-country  income  \n",
       "0      United-States   <=50K  \n",
       "1      United-States   <=50K  \n",
       "2      United-States   <=50K  \n",
       "3      United-States   <=50K  \n",
       "4               Cuba   <=50K  \n",
       "...              ...     ...  \n",
       "48837  United-States  <=50K.  \n",
       "48838  United-States  <=50K.  \n",
       "48839  United-States  <=50K.  \n",
       "48840  United-States  <=50K.  \n",
       "48841  United-States   >50K.  \n",
       "\n",
       "[48842 rows x 15 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df69e811-5ed5-4976-aa38-8ca2dfd9362e",
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.000000</td>\n",
       "      <td>47879</td>\n",
       "      <td>4.884200e+04</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842</td>\n",
       "      <td>47876</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48568</td>\n",
       "      <td>48842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22379</td>\n",
       "      <td>6172</td>\n",
       "      <td>19716</td>\n",
       "      <td>41762</td>\n",
       "      <td>32650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43832</td>\n",
       "      <td>24720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.896641e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.078089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1079.067626</td>\n",
       "      <td>87.502314</td>\n",
       "      <td>40.422382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056040e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.570973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7452.019058</td>\n",
       "      <td>403.004552</td>\n",
       "      <td>12.391444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.175505e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.781445e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.376420e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age workclass        fnlwgt education  education-num  \\\n",
       "count   48842.000000     47879  4.884200e+04     48842   48842.000000   \n",
       "unique           NaN         9           NaN        16            NaN   \n",
       "top              NaN   Private           NaN   HS-grad            NaN   \n",
       "freq             NaN     33906           NaN     15784            NaN   \n",
       "mean       38.643585       NaN  1.896641e+05       NaN      10.078089   \n",
       "std        13.710510       NaN  1.056040e+05       NaN       2.570973   \n",
       "min        17.000000       NaN  1.228500e+04       NaN       1.000000   \n",
       "25%        28.000000       NaN  1.175505e+05       NaN       9.000000   \n",
       "50%        37.000000       NaN  1.781445e+05       NaN      10.000000   \n",
       "75%        48.000000       NaN  2.376420e+05       NaN      12.000000   \n",
       "max        90.000000       NaN  1.490400e+06       NaN      16.000000   \n",
       "\n",
       "            marital-status      occupation relationship   race    sex  \\\n",
       "count                48842           47876        48842  48842  48842   \n",
       "unique                   7              15            6      5      2   \n",
       "top     Married-civ-spouse  Prof-specialty      Husband  White   Male   \n",
       "freq                 22379            6172        19716  41762  32650   \n",
       "mean                   NaN             NaN          NaN    NaN    NaN   \n",
       "std                    NaN             NaN          NaN    NaN    NaN   \n",
       "min                    NaN             NaN          NaN    NaN    NaN   \n",
       "25%                    NaN             NaN          NaN    NaN    NaN   \n",
       "50%                    NaN             NaN          NaN    NaN    NaN   \n",
       "75%                    NaN             NaN          NaN    NaN    NaN   \n",
       "max                    NaN             NaN          NaN    NaN    NaN   \n",
       "\n",
       "        capital-gain  capital-loss  hours-per-week native-country income  \n",
       "count   48842.000000  48842.000000    48842.000000          48568  48842  \n",
       "unique           NaN           NaN             NaN             42      4  \n",
       "top              NaN           NaN             NaN  United-States  <=50K  \n",
       "freq             NaN           NaN             NaN          43832  24720  \n",
       "mean     1079.067626     87.502314       40.422382            NaN    NaN  \n",
       "std      7452.019058    403.004552       12.391444            NaN    NaN  \n",
       "min         0.000000      0.000000        1.000000            NaN    NaN  \n",
       "25%         0.000000      0.000000       40.000000            NaN    NaN  \n",
       "50%         0.000000      0.000000       40.000000            NaN    NaN  \n",
       "75%         0.000000      0.000000       45.000000            NaN    NaN  \n",
       "max     99999.000000   4356.000000       99.000000            NaN    NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting statistical information of the dataset for different columns (features) \n",
    "data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4130f80b-be56-4c06-96ae-d3d6908eb407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping row with Null values (This topic will be discussed in detail in the next lecture)\n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d00963d5-9d65-48b4-8c9b-fee890ce9d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
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       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48836</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>245211</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47621 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  education  education-num  \\\n",
       "0       39         State-gov   77516  Bachelors             13   \n",
       "1       50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2       38           Private  215646    HS-grad              9   \n",
       "3       53           Private  234721       11th              7   \n",
       "4       28           Private  338409  Bachelors             13   \n",
       "...    ...               ...     ...        ...            ...   \n",
       "48836   33           Private  245211  Bachelors             13   \n",
       "48837   39           Private  215419  Bachelors             13   \n",
       "48839   38           Private  374983  Bachelors             13   \n",
       "48840   44           Private   83891  Bachelors             13   \n",
       "48841   35      Self-emp-inc  182148  Bachelors             13   \n",
       "\n",
       "           marital-status         occupation   relationship  \\\n",
       "0           Never-married       Adm-clerical  Not-in-family   \n",
       "1      Married-civ-spouse    Exec-managerial        Husband   \n",
       "2                Divorced  Handlers-cleaners  Not-in-family   \n",
       "3      Married-civ-spouse  Handlers-cleaners        Husband   \n",
       "4      Married-civ-spouse     Prof-specialty           Wife   \n",
       "...                   ...                ...            ...   \n",
       "48836       Never-married     Prof-specialty      Own-child   \n",
       "48837            Divorced     Prof-specialty  Not-in-family   \n",
       "48839  Married-civ-spouse     Prof-specialty        Husband   \n",
       "48840            Divorced       Adm-clerical      Own-child   \n",
       "48841  Married-civ-spouse    Exec-managerial        Husband   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                   White    Male          2174             0              40   \n",
       "1                   White    Male             0             0              13   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male             0             0              40   \n",
       "4                   Black  Female             0             0              40   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "48836               White    Male             0             0              40   \n",
       "48837               White  Female             0             0              36   \n",
       "48839               White    Male             0             0              50   \n",
       "48840  Asian-Pac-Islander    Male          5455             0              40   \n",
       "48841               White    Male             0             0              60   \n",
       "\n",
       "      native-country  income  \n",
       "0      United-States   <=50K  \n",
       "1      United-States   <=50K  \n",
       "2      United-States   <=50K  \n",
       "3      United-States   <=50K  \n",
       "4               Cuba   <=50K  \n",
       "...              ...     ...  \n",
       "48836  United-States  <=50K.  \n",
       "48837  United-States  <=50K.  \n",
       "48839  United-States  <=50K.  \n",
       "48840  United-States  <=50K.  \n",
       "48841  United-States   >50K.  \n",
       "\n",
       "[47621 rows x 15 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4d6d5476-7e05-4bc1-b740-0ff11600fed4",
   "metadata": {},
   "source": [
    "NOTE: As you can see, because a few rows have been dropped, the index numbers don't correspond to the actual row number (the number of rows is 47623 but the last index is 48841). We need to reindex the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c1345c17-6c41-497c-8713-c1c3d8240061",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reindexing the original data\n",
    "data.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "70ab02a0-256a-4f04-8404-18dbfd08b787",
   "metadata": {},
   "outputs": [
    {
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       "      <th>workclass</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
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       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
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       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47616</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>245211</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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       "    <tr>\n",
       "      <th>47617</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47618</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>47619</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47620</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47621 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  education  education-num  \\\n",
       "0       39         State-gov   77516  Bachelors             13   \n",
       "1       50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2       38           Private  215646    HS-grad              9   \n",
       "3       53           Private  234721       11th              7   \n",
       "4       28           Private  338409  Bachelors             13   \n",
       "...    ...               ...     ...        ...            ...   \n",
       "47616   33           Private  245211  Bachelors             13   \n",
       "47617   39           Private  215419  Bachelors             13   \n",
       "47618   38           Private  374983  Bachelors             13   \n",
       "47619   44           Private   83891  Bachelors             13   \n",
       "47620   35      Self-emp-inc  182148  Bachelors             13   \n",
       "\n",
       "           marital-status         occupation   relationship  \\\n",
       "0           Never-married       Adm-clerical  Not-in-family   \n",
       "1      Married-civ-spouse    Exec-managerial        Husband   \n",
       "2                Divorced  Handlers-cleaners  Not-in-family   \n",
       "3      Married-civ-spouse  Handlers-cleaners        Husband   \n",
       "4      Married-civ-spouse     Prof-specialty           Wife   \n",
       "...                   ...                ...            ...   \n",
       "47616       Never-married     Prof-specialty      Own-child   \n",
       "47617            Divorced     Prof-specialty  Not-in-family   \n",
       "47618  Married-civ-spouse     Prof-specialty        Husband   \n",
       "47619            Divorced       Adm-clerical      Own-child   \n",
       "47620  Married-civ-spouse    Exec-managerial        Husband   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                   White    Male          2174             0              40   \n",
       "1                   White    Male             0             0              13   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male             0             0              40   \n",
       "4                   Black  Female             0             0              40   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "47616               White    Male             0             0              40   \n",
       "47617               White  Female             0             0              36   \n",
       "47618               White    Male             0             0              50   \n",
       "47619  Asian-Pac-Islander    Male          5455             0              40   \n",
       "47620               White    Male             0             0              60   \n",
       "\n",
       "      native-country  income  \n",
       "0      United-States   <=50K  \n",
       "1      United-States   <=50K  \n",
       "2      United-States   <=50K  \n",
       "3      United-States   <=50K  \n",
       "4               Cuba   <=50K  \n",
       "...              ...     ...  \n",
       "47616  United-States  <=50K.  \n",
       "47617  United-States  <=50K.  \n",
       "47618  United-States  <=50K.  \n",
       "47619  United-States  <=50K.  \n",
       "47620  United-States   >50K.  \n",
       "\n",
       "[47621 rows x 15 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5cc538fd-c134-4794-95db-666e8995e04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education-num     0\n",
       "marital-status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital-gain      0\n",
       "capital-loss      0\n",
       "hours-per-week    0\n",
       "native-country    0\n",
       "income            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the number of Null values for different columns\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "90917d1a-9c44-4e26-8504-cead1a08b837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47621 entries, 0 to 47620\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             47621 non-null  int64 \n",
      " 1   workclass       47621 non-null  object\n",
      " 2   fnlwgt          47621 non-null  int64 \n",
      " 3   education       47621 non-null  object\n",
      " 4   education-num   47621 non-null  int64 \n",
      " 5   marital-status  47621 non-null  object\n",
      " 6   occupation      47621 non-null  object\n",
      " 7   relationship    47621 non-null  object\n",
      " 8   race            47621 non-null  object\n",
      " 9   sex             47621 non-null  object\n",
      " 10  capital-gain    47621 non-null  int64 \n",
      " 11  capital-loss    47621 non-null  int64 \n",
      " 12  hours-per-week  47621 non-null  int64 \n",
      " 13  native-country  47621 non-null  object\n",
      " 14  income          47621 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Showing the dataset information\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "58aa6cab-81d9-4355-b224-36ec3af04165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47621, 15)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc8a02fa-9a5e-4e1d-a8f1-b4f7c7d33660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education-num\n",
       "9     15444\n",
       "10    10512\n",
       "13     7881\n",
       "14     2610\n",
       "11     2034\n",
       "7      1746\n",
       "12     1566\n",
       "6      1336\n",
       "4       912\n",
       "15      819\n",
       "5       735\n",
       "8       633\n",
       "16      582\n",
       "3       494\n",
       "2       239\n",
       "1        78\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the count of different values in the column \"education-num\"\n",
    "data['education-num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f3f64e4-430f-49fa-bf15-63bf0b6e9614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "HS-grad         15444\n",
       "Some-college    10512\n",
       "Bachelors        7881\n",
       "Masters          2610\n",
       "Assoc-voc        2034\n",
       "11th             1746\n",
       "Assoc-acdm       1566\n",
       "10th             1336\n",
       "7th-8th           912\n",
       "Prof-school       819\n",
       "9th               735\n",
       "12th              633\n",
       "Doctorate         582\n",
       "5th-6th           494\n",
       "1st-4th           239\n",
       "Preschool          78\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the count of different values in the column \"education\"\n",
    "data['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8b78b631-5287-49d9-b495-d72e4f27c1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping a column\n",
    "data = data.drop(['fnlwgt'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d73888eb-eccd-4333-8b68-d025ad3a59c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47621, 14)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9061442f-1a55-49e0-9e79-ebd295b34436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the number of unique values of a column\n",
    "data['education'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c51e6dc9-bf67-4a05-abbe-118845d258f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Male      31937\n",
       "Female    15684\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding how many rows are related to either gender \n",
    "data['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "770c18ea-4c60-4e29-ad37-e8dfb7b429f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Female    36.961043\n",
       "Male      39.465542\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the average age of different genders in the dataset \n",
    "data['age'].groupby([data['sex']]).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "029da728-a889-457e-877e-110f7d6895c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     education   \n",
       "Female  10th            36.520642\n",
       "        11th            29.891410\n",
       "        12th            29.716418\n",
       "        1st-4th         46.508475\n",
       "        5th-6th         45.346774\n",
       "        7th-8th         51.088889\n",
       "        9th             41.566038\n",
       "        Assoc-acdm      36.402619\n",
       "        Assoc-voc       38.000000\n",
       "        Bachelors       35.747837\n",
       "        Doctorate       45.557522\n",
       "        HS-grad         38.862285\n",
       "        Masters         42.520482\n",
       "        Preschool       44.409091\n",
       "        Prof-school     40.131783\n",
       "        Some-college    33.778719\n",
       "Male    10th            38.406667\n",
       "        11th            33.479185\n",
       "        12th            33.842593\n",
       "        1st-4th         46.050000\n",
       "        5th-6th         42.524324\n",
       "        7th-8th         48.608443\n",
       "        9th             40.556405\n",
       "        Assoc-acdm      38.537173\n",
       "        Assoc-voc       38.853881\n",
       "        Bachelors       40.190319\n",
       "        Doctorate       47.936034\n",
       "        HS-grad         39.073626\n",
       "        Masters         44.584270\n",
       "        Preschool       41.160714\n",
       "        Prof-school     46.017391\n",
       "        Some-college    37.176643\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the average age of different genders in the dataset broken down based on their education\n",
    "data['age'].groupby([data['sex'],data['education']]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a86bfefd-1359-4589-aee6-3989cfacbaa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race\n",
       "Amer-Indian-Eskimo    82\n",
       "Asian-Pac-Islander    90\n",
       "Black                 90\n",
       "Other                 77\n",
       "White                 90\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the maximum age of different races in the dataset\n",
    "data['age'].groupby([data['race']]).max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a95ca1f7-9939-4611-81b5-e59cdbcbb515",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the age and education columns and creating a new DataFrame using these columns\n",
    "a=data['age']\n",
    "b=data['education']\n",
    "new_data=pd.concat([a,b],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5d76340e-49d3-4ae9-823f-cfc1ef5c605a",
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47616</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47617</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47618</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47619</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47620</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47621 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  education  education-num      marital-status  \\\n",
       "0       39         State-gov  Bachelors             13       Never-married   \n",
       "1       50  Self-emp-not-inc  Bachelors             13  Married-civ-spouse   \n",
       "2       38           Private    HS-grad              9            Divorced   \n",
       "3       53           Private       11th              7  Married-civ-spouse   \n",
       "4       28           Private  Bachelors             13  Married-civ-spouse   \n",
       "...    ...               ...        ...            ...                 ...   \n",
       "47616   33           Private  Bachelors             13       Never-married   \n",
       "47617   39           Private  Bachelors             13            Divorced   \n",
       "47618   38           Private  Bachelors             13  Married-civ-spouse   \n",
       "47619   44           Private  Bachelors             13            Divorced   \n",
       "47620   35      Self-emp-inc  Bachelors             13  Married-civ-spouse   \n",
       "\n",
       "              occupation   relationship                race     sex  \\\n",
       "0           Adm-clerical  Not-in-family               White    Male   \n",
       "1        Exec-managerial        Husband               White    Male   \n",
       "2      Handlers-cleaners  Not-in-family               White    Male   \n",
       "3      Handlers-cleaners        Husband               Black    Male   \n",
       "4         Prof-specialty           Wife               Black  Female   \n",
       "...                  ...            ...                 ...     ...   \n",
       "47616     Prof-specialty      Own-child               White    Male   \n",
       "47617     Prof-specialty  Not-in-family               White  Female   \n",
       "47618     Prof-specialty        Husband               White    Male   \n",
       "47619       Adm-clerical      Own-child  Asian-Pac-Islander    Male   \n",
       "47620    Exec-managerial        Husband               White    Male   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country  income  \n",
       "0              2174             0              40  United-States   <=50K  \n",
       "1                 0             0              13  United-States   <=50K  \n",
       "2                 0             0              40  United-States   <=50K  \n",
       "3                 0             0              40  United-States   <=50K  \n",
       "4                 0             0              40           Cuba   <=50K  \n",
       "...             ...           ...             ...            ...     ...  \n",
       "47616             0             0              40  United-States  <=50K.  \n",
       "47617             0             0              36  United-States  <=50K.  \n",
       "47618             0             0              50  United-States  <=50K.  \n",
       "47619          5455             0              40  United-States  <=50K.  \n",
       "47620             0             0              60  United-States   >50K.  \n",
       "\n",
       "[47621 rows x 14 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae58e93-3a07-4811-b144-5c25f1a20d39",
   "metadata": {},
   "source": [
    "* Q10: Write a function that receives the dataset and replace Famle with F and Male with M (please try to write it yourself before checking the answer in the next cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fb964e0a-6c7e-47ce-9fa0-0587f1982a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer\n",
    "def encode_sex(data):\n",
    "    rows=data.shape[0]\n",
    "    a=data.loc[:,'sex']\n",
    "    for i in range(rows): \n",
    "        if a[i]==\"Male\":\n",
    "            data.loc[i,\"sex\"]=\"M\"\n",
    "        elif a[i]==\"Female\":\n",
    "            data.loc[i,\"sex\"]=\"F\"\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3d096649-1d9f-4ed6-8928-a7c69329cbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copying the data\n",
    "data_copy=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f0d8a2e4-e07c-485e-8e0d-6b6304ef45d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
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       "      <td>M</td>\n",
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       "      <td>Self-emp-not-inc</td>\n",
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       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  education  education-num      marital-status  \\\n",
       "0   39         State-gov  Bachelors             13       Never-married   \n",
       "1   50  Self-emp-not-inc  Bachelors             13  Married-civ-spouse   \n",
       "2   38           Private    HS-grad              9            Divorced   \n",
       "3   53           Private       11th              7  Married-civ-spouse   \n",
       "4   28           Private  Bachelors             13  Married-civ-spouse   \n",
       "\n",
       "          occupation   relationship   race sex  capital-gain  capital-loss  \\\n",
       "0       Adm-clerical  Not-in-family  White   M          2174             0   \n",
       "1    Exec-managerial        Husband  White   M             0             0   \n",
       "2  Handlers-cleaners  Not-in-family  White   M             0             0   \n",
       "3  Handlers-cleaners        Husband  Black   M             0             0   \n",
       "4     Prof-specialty           Wife  Black   F             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              13  United-States  <=50K  \n",
       "2              40  United-States  <=50K  \n",
       "3              40  United-States  <=50K  \n",
       "4              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying the encode_sex function to the copied data\n",
    "data_encoded=encode_sex(data_copy)\n",
    "data_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cd3a4c58-e849-41b8-a1a0-9c2f066a4fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>47616</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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       "    <tr>\n",
       "      <th>47617</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47618</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47619</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47620</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47621 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  education  education-num      marital-status  \\\n",
       "0       39         State-gov  Bachelors             13       Never-married   \n",
       "1       50  Self-emp-not-inc  Bachelors             13  Married-civ-spouse   \n",
       "2       38           Private    HS-grad              9            Divorced   \n",
       "3       53           Private       11th              7  Married-civ-spouse   \n",
       "4       28           Private  Bachelors             13  Married-civ-spouse   \n",
       "...    ...               ...        ...            ...                 ...   \n",
       "47616   33           Private  Bachelors             13       Never-married   \n",
       "47617   39           Private  Bachelors             13            Divorced   \n",
       "47618   38           Private  Bachelors             13  Married-civ-spouse   \n",
       "47619   44           Private  Bachelors             13            Divorced   \n",
       "47620   35      Self-emp-inc  Bachelors             13  Married-civ-spouse   \n",
       "\n",
       "              occupation   relationship                race     sex  \\\n",
       "0           Adm-clerical  Not-in-family               White    Male   \n",
       "1        Exec-managerial        Husband               White    Male   \n",
       "2      Handlers-cleaners  Not-in-family               White    Male   \n",
       "3      Handlers-cleaners        Husband               Black    Male   \n",
       "4         Prof-specialty           Wife               Black  Female   \n",
       "...                  ...            ...                 ...     ...   \n",
       "47616     Prof-specialty      Own-child               White    Male   \n",
       "47617     Prof-specialty  Not-in-family               White  Female   \n",
       "47618     Prof-specialty        Husband               White    Male   \n",
       "47619       Adm-clerical      Own-child  Asian-Pac-Islander    Male   \n",
       "47620    Exec-managerial        Husband               White    Male   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country  income  \n",
       "0              2174             0              40  United-States   <=50K  \n",
       "1                 0             0              13  United-States   <=50K  \n",
       "2                 0             0              40  United-States   <=50K  \n",
       "3                 0             0              40  United-States   <=50K  \n",
       "4                 0             0              40           Cuba   <=50K  \n",
       "...             ...           ...             ...            ...     ...  \n",
       "47616             0             0              40  United-States  <=50K.  \n",
       "47617             0             0              36  United-States  <=50K.  \n",
       "47618             0             0              50  United-States  <=50K.  \n",
       "47619          5455             0              40  United-States  <=50K.  \n",
       "47620             0             0              60  United-States   >50K.  \n",
       "\n",
       "[47621 rows x 14 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57dda1cf-634c-4db2-ba37-44e8b2769e2b",
   "metadata": {},
   "source": [
    "# Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfcedab2-db23-4ab1-8e91-329f4388605e",
   "metadata": {},
   "source": [
    "## TASK 1.1: Import the dataset from the URL we used in this workshop. Then generate a new dataset by randomly extracting 10000 samples. Reindex the generated dataset and remove NULL values. Name the new dataset 'task_dataset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d5627b0a-da2e-4873-a6f8-06af96167057",
   "metadata": {},
   "outputs": [],
   "source": [
    "############# WRITE THE CODE IN THIS CELL ####################\n",
    "from ucimlrepo import fetch_ucirepo \n",
    "# fetch dataset \n",
    "adult = fetch_ucirepo(id=2) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f56d6e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting data in a pandas dataframe\n",
    "S = adult.data.features \n",
    "T = adult.data.targets\n",
    "\n",
    "mydata=pd.concat([S,T],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "509b906a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 9747 entries, 0 to 9999\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             9747 non-null   int64 \n",
      " 1   workclass       9747 non-null   object\n",
      " 2   fnlwgt          9747 non-null   int64 \n",
      " 3   education       9747 non-null   object\n",
      " 4   education-num   9747 non-null   int64 \n",
      " 5   marital-status  9747 non-null   object\n",
      " 6   occupation      9747 non-null   object\n",
      " 7   relationship    9747 non-null   object\n",
      " 8   race            9747 non-null   object\n",
      " 9   sex             9747 non-null   object\n",
      " 10  capital-gain    9747 non-null   int64 \n",
      " 11  capital-loss    9747 non-null   int64 \n",
      " 12  hours-per-week  9747 non-null   int64 \n",
      " 13  native-country  9747 non-null   object\n",
      " 14  income          9747 non-null   object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Generate a random sample of 10000 samples\n",
    "mydata = mydata.sample(n=10000, random_state=42)\n",
    "\n",
    "# Reindex the dataset\n",
    "mydata.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Remove NULL values\n",
    "mydata.dropna(inplace=True)\n",
    "\n",
    "#renaming data\n",
    "task_dataset = mydata\n",
    "\n",
    "# Display the first few rows of the new dataset\n",
    "task_dataset.info()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "41d8711b-390d-472d-91fa-1044a199f321",
   "metadata": {},
   "source": [
    "############ WRITE EXPLANATIONS HERE (IF APPLICABLE) #############\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c730224-dbd9-4fc3-b561-ffb6eb697437",
   "metadata": {},
   "source": [
    "## TASK 1.2: Write a code to find how much contribution each sex and occupation category made to the capital-gain on average. Apply the code to the task_dataset and report the result (Hint: you need to use the groupby method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2bd771c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex     occupation       \n",
      "Female  ?                     657.111111\n",
      "        Adm-clerical          584.281088\n",
      "        Craft-repair          204.825397\n",
      "        Exec-managerial      1130.724138\n",
      "        Farming-fishing       958.260870\n",
      "        Handlers-cleaners       0.000000\n",
      "        Machine-op-inspct     125.893750\n",
      "        Other-service         101.237410\n",
      "        Priv-house-serv       114.722222\n",
      "        Prof-specialty       1106.946988\n",
      "        Protective-serv       626.760000\n",
      "        Sales                 263.618582\n",
      "        Tech-support          166.681416\n",
      "        Transport-moving      312.419355\n",
      "Male    ?                     838.892704\n",
      "        Adm-clerical          472.453083\n",
      "        Armed-Forces            0.000000\n",
      "        Craft-repair          646.430868\n",
      "        Exec-managerial      2355.091324\n",
      "        Farming-fishing      1271.208054\n",
      "        Handlers-cleaners     151.812139\n",
      "        Machine-op-inspct     594.172867\n",
      "        Other-service         131.432836\n",
      "        Priv-house-serv         0.000000\n",
      "        Prof-specialty       3240.946429\n",
      "        Protective-serv       415.945652\n",
      "        Sales                1884.821277\n",
      "        Tech-support          704.227545\n",
      "        Transport-moving      341.561404\n",
      "Name: capital-gain, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "############# WRITE THE CODE IN THIS CELL ####################\n",
    "\n",
    "# Group by 'sex' and 'occupation', calculate average capital-gain for each group\n",
    "average_capital_gain = task_dataset.groupby(['sex', 'occupation'])['capital-gain'].mean()\n",
    "\n",
    "# Print the result\n",
    "print(average_capital_gain)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d3de738c-b803-414b-8616-8c9d48859c86",
   "metadata": {},
   "source": [
    "############ WRITE EXPLANATIONS HERE (IF APPLICABLE) #############\n",
    "\n",
    "First you have to group it \n",
    "'.mean()' This funtion finds the mean for the variable appended to it\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da3be0b-b6d8-4edf-b859-f53490342e18",
   "metadata": {},
   "source": [
    "## TASK 1.3: Write a code to find the country with the highest number of people with a Bachelors degree. Apply the code to the task_dataset and report the result (Hint: you need to use the groupby method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "be9003c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country with the highest number of people with a Bachelor's degree: United-States\n"
     ]
    }
   ],
   "source": [
    "############# WRITE THE CODE IN THIS CELL ####################\n",
    "\n",
    "\n",
    "# Group by 'native-country' and count the occurrences of people with a Bachelor's degree\n",
    "bachelors_counts = task_dataset[task_dataset['education'] == 'Bachelors'].groupby('native-country').size()\n",
    "\n",
    "# Find the country with the highest number of people with a Bachelor's degree\n",
    "country_highest_bachelors = bachelors_counts.idxmax()\n",
    "\n",
    "# Print the result\n",
    "print(\"Country with the highest number of people with a Bachelor's degree:\", country_highest_bachelors)\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "78513aa9-1b10-45ae-aa7b-ee0b90c0a5d5",
   "metadata": {},
   "source": [
    "############ WRITE EXPLANATIONS HERE (IF APPLICABLE) #############\n",
    "\n",
    "The country with the highest number of people with a Bachelo's degree is United-States.\n",
    "The function 'idxmax()' Finds the maximum value linked with the function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9366d57c-099b-466a-88e4-ff0be8b32d7d",
   "metadata": {},
   "source": [
    "## TASK 1.4: Write a code to receive two lists including five names and their respective ages and print 'Hello Name Age' \n",
    "## For example, if it received a list of two names ['Amin', 'Michael'] and respective ages [27,38], it would print 'Hello Amin 27', 'Hello Michael 38'. Each hello statement should be printed in a new line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "330f9940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Amin 27\n",
      "Hello Michael 38\n"
     ]
    }
   ],
   "source": [
    "############# WRITE THE CODE IN THIS CELL ####################\n",
    "#make a list\n",
    "names = ['Amin', 'Michael']\n",
    "ages = [27, 38]\n",
    "\n",
    "greet = []\n",
    "\n",
    "for i in range(len(names)):\n",
    "    greet.append(\"Hello \" + names[i] + \" \" + str(ages[i]))\n",
    "\n",
    "for greeting in greet:\n",
    "    print(greeting)\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ad2f14d7-31d5-4119-9959-364a36f6863c",
   "metadata": {},
   "source": [
    "############ WRITE EXPLANATIONS HERE (IF APPLICABLE) #############\n",
    "\n",
    "I made 'for' loop that loops through the names and the age and retuns the result of the greeting function,\n",
    "Through this, you can add any name or age and it will return the result.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187d4c11-e11d-4bd7-bb2b-2b299be682ed",
   "metadata": {},
   "source": [
    "## TASK 1.5: Write a code to receive an optional text, capitalise all words in the text and print them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a03c192c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GHFF\n"
     ]
    }
   ],
   "source": [
    "############# WRITE THE CODE IN THIS CELL ####################\n",
    "\n",
    "Text = input(\"Enter some text: \")\n",
    "\n",
    "New_text = Text.upper()\n",
    "\n",
    "print(New_text)\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ffcf6cb7-19bb-40b0-ac11-ba2a6de45142",
   "metadata": {},
   "source": [
    "############ WRITE EXPLANATIONS HERE (IF APPLICABLE) #############\n",
    "\n",
    "The function'.upper()' capitalize words or variables appended to it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7edaa81b-5ee3-4467-b463-41c5b33ad8b3",
   "metadata": {},
   "source": [
    "## TASK 1.6: Write a function to split the task_dataset in half column-wise and swap the first half and the second half"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "43e695b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        relationship                race     sex  capital-gain  capital-loss  \\\n",
      "0      Not-in-family               White    Male             0             0   \n",
      "1          Own-child               White  Female             0             0   \n",
      "2     Other-relative               Black    Male             0             0   \n",
      "3          Own-child               White  Female             0             0   \n",
      "4          Unmarried  Amer-Indian-Eskimo    Male             0             0   \n",
      "...              ...                 ...     ...           ...           ...   \n",
      "9995   Not-in-family               White  Female          2964             0   \n",
      "9996   Not-in-family               White    Male             0             0   \n",
      "9997       Unmarried               White  Female             0             0   \n",
      "9998         Husband               White    Male             0             0   \n",
      "9999  Other-relative               White    Male             0             0   \n",
      "\n",
      "      hours-per-week native-country  income         occupation  \n",
      "0                 20  United-States   <=50K      Other-service  \n",
      "1                 20  United-States   <=50K              Sales  \n",
      "2                 40  United-States   <=50K  Handlers-cleaners  \n",
      "3                 30  United-States   <=50K              Sales  \n",
      "4                 56    Puerto-Rico   <=50K  Machine-op-inspct  \n",
      "...              ...            ...     ...                ...  \n",
      "9995              30  United-States   <=50K    Priv-house-serv  \n",
      "9996              48  United-States   <=50K   Transport-moving  \n",
      "9997              50    Puerto-Rico  <=50K.    Priv-house-serv  \n",
      "9998              45  United-States  <=50K.       Tech-support  \n",
      "9999              50  United-States   <=50K    Farming-fishing  \n",
      "\n",
      "[9747 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "############# WRITE THE CODE IN THIS CELL ####################\n",
    "\n",
    "def My_function(dataset):\n",
    "    num_columns = len(dataset.columns)\n",
    "    half_num_columns = num_columns // 2 #splitting the columns in two\n",
    "\n",
    "#assigning the columns\n",
    "    first_half = dataset.iloc[:,6 :half_num_columns]\n",
    "    second_half = dataset.iloc[:, half_num_columns:]\n",
    "\n",
    "    swapped_dataset = pd.concat([second_half, first_half], axis=1)\n",
    "\n",
    "    return swapped_dataset\n",
    "\n",
    "\n",
    "#print results\n",
    "swapped_dataset = My_function(task_dataset)\n",
    "print(swapped_dataset)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c980e058-e5f8-4c83-9038-daec0eba06a9",
   "metadata": {},
   "source": [
    "############ WRITE EXPLANATIONS HERE (IF APPLICABLE) #############\n",
    "\n",
    "Defining a function to solve this problem:\n",
    "    - Split the column in 2\n",
    "    - Assign the column halves\n",
    "    - Concatonate using pandas\n",
    "    -Then print\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff493d6-db04-4ca7-9c19-b826871ac22e",
   "metadata": {},
   "source": [
    "## Task 1.7: Write a function that receives two numerical columns' names and compare their values for all rows. If the value of the first column is greater than the second column, the function should produce True, otherwise, it should produce False. The function should append an additional column to the dataset to store the results of the comparison for all rows. Apply the function to the \"age\" and \"hours-per-week\" columns in the task_dataset and print the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ea86494e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to compare two columns in a DataFrame\n",
    "def Comparison(task_dataset):\n",
    "    task_dataset['Comparison'] = task_dataset[\"age\"] > task_dataset[\"hours-per-week\"]\n",
    "    return task_dataset\n",
    "\n",
    "\n",
    "task_dataset = Comparison(task_dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9084c221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      age  workclass  fnlwgt     education  education-num      marital-status  \\\n",
      "0      18    Private  423024       HS-grad              9       Never-married   \n",
      "1      17    Private  178953          12th              8       Never-married   \n",
      "2      25  Local-gov  348986       HS-grad              9       Never-married   \n",
      "3      20    Private  218215  Some-college             10       Never-married   \n",
      "4      47    Private  244025       HS-grad              9       Never-married   \n",
      "...   ...        ...     ...           ...            ...                 ...   \n",
      "9995   79    Private  172220       7th-8th              4             Widowed   \n",
      "9996   45  State-gov  183710       HS-grad              9            Divorced   \n",
      "9997   44    Private  193537           9th              5       Never-married   \n",
      "9998   44    Private  166740     Assoc-voc             11  Married-civ-spouse   \n",
      "9999   45    Private   28035  Some-college             10       Never-married   \n",
      "\n",
      "             occupation    relationship                race     sex  \\\n",
      "0         Other-service   Not-in-family               White    Male   \n",
      "1                 Sales       Own-child               White  Female   \n",
      "2     Handlers-cleaners  Other-relative               Black    Male   \n",
      "3                 Sales       Own-child               White  Female   \n",
      "4     Machine-op-inspct       Unmarried  Amer-Indian-Eskimo    Male   \n",
      "...                 ...             ...                 ...     ...   \n",
      "9995    Priv-house-serv   Not-in-family               White  Female   \n",
      "9996   Transport-moving   Not-in-family               White    Male   \n",
      "9997    Priv-house-serv       Unmarried               White  Female   \n",
      "9998       Tech-support         Husband               White    Male   \n",
      "9999    Farming-fishing  Other-relative               White    Male   \n",
      "\n",
      "      capital-gain  capital-loss  hours-per-week native-country  income  \\\n",
      "0                0             0              20  United-States   <=50K   \n",
      "1                0             0              20  United-States   <=50K   \n",
      "2                0             0              40  United-States   <=50K   \n",
      "3                0             0              30  United-States   <=50K   \n",
      "4                0             0              56    Puerto-Rico   <=50K   \n",
      "...            ...           ...             ...            ...     ...   \n",
      "9995          2964             0              30  United-States   <=50K   \n",
      "9996             0             0              48  United-States   <=50K   \n",
      "9997             0             0              50    Puerto-Rico  <=50K.   \n",
      "9998             0             0              45  United-States  <=50K.   \n",
      "9999             0             0              50  United-States   <=50K   \n",
      "\n",
      "      Comparison  \n",
      "0          False  \n",
      "1          False  \n",
      "2          False  \n",
      "3          False  \n",
      "4          False  \n",
      "...          ...  \n",
      "9995        True  \n",
      "9996       False  \n",
      "9997       False  \n",
      "9998       False  \n",
      "9999       False  \n",
      "\n",
      "[9747 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "print(task_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b774c85-354d-4ed6-b6bd-f760a2e7e70a",
   "metadata": {},
   "source": [
    "## Task 1.8: Write a function that returns the names of countries with maximum and minimum average ages in the task_dataset (Hint: you can use the numpy module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d8000d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country with maximum average age: Yugoslavia\n",
      "Country with minimum average age: Ireland\n"
     ]
    }
   ],
   "source": [
    "############# WRITE THE CODE IN THIS CELL####################\n",
    "import numpy as np\n",
    "\n",
    "def max_min_avg_age_countries(task_dataset):\n",
    "    # Group by 'native-country' and calculate the average age for each country\n",
    "    avg_age_by_country = task_dataset.groupby('native-country')['age'].mean()\n",
    "\n",
    "    # Find the country with the maximum average age\n",
    "    max_avg_age_country = avg_age_by_country.idxmax()\n",
    "\n",
    "    # Find the country with the minimum average age\n",
    "    min_avg_age_country = avg_age_by_country.idxmin()\n",
    "\n",
    "    return max_avg_age_country, min_avg_age_country\n",
    "\n",
    "# Call the function with the task_dataset\n",
    "max_country, min_country = max_min_avg_age_countries(task_dataset)\n",
    "\n",
    "# Print the result\n",
    "print(\"Country with maximum average age:\", max_country)\n",
    "print(\"Country with minimum average age:\", min_country)\n"
   ]
  }
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