AvijitChowdhury_CET182_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "**The aim of the project** is to apply data mining techniques to extract interesting patterns from the **Census income dataset** and evaluate the performance of the models used.\n",
    "\n",
    "The dataset was extracted from https://archive.ics.uci.edu/dataset/20/census+income, which contains 32561 rows and 15 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>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",
       "      <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>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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, I will load the needed Python libraries\n",
    "\n",
    "# import needed libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "#Now read the dataset and \n",
    "# show first few rows of data\n",
    "\n",
    "names_col = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', \n",
    "             'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', \n",
    "             'hours_per_week', 'native_country', 'income']\n",
    "df = pd.read_csv(\"adult.data\", names=names_col)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning\n",
    "\n",
    "We can first look at the data types for each column and check if there are missing or null values.\n",
    "Then, we can examine the summary statistics for each quantitative data column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amout of samples: 32561\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             32561 non-null  int64 \n",
      " 1   workclass       32561 non-null  object\n",
      " 2   fnlwgt          32561 non-null  int64 \n",
      " 3   education       32561 non-null  object\n",
      " 4   education_num   32561 non-null  int64 \n",
      " 5   marital_status  32561 non-null  object\n",
      " 6   occupation      32561 non-null  object\n",
      " 7   relationship    32561 non-null  object\n",
      " 8   race            32561 non-null  object\n",
      " 9   sex             32561 non-null  object\n",
      " 10  capital_gain    32561 non-null  int64 \n",
      " 11  capital_loss    32561 non-null  int64 \n",
      " 12  hours_per_week  32561 non-null  int64 \n",
      " 13  native_country  32561 non-null  object\n",
      " 14  income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n",
      "Unique values of feature workclass:\n",
      "[' State-gov' ' Self-emp-not-inc' ' Private' ' Federal-gov' ' Local-gov'\n",
      " ' ?' ' Self-emp-inc' ' Without-pay' ' Never-worked']\n",
      "\n",
      "Unique values of feature marital_status:\n",
      "[' Never-married' ' Married-civ-spouse' ' Divorced'\n",
      " ' Married-spouse-absent' ' Separated' ' Married-AF-spouse' ' Widowed']\n",
      "\n",
      "Unique values of feature occupation:\n",
      "[' Adm-clerical' ' Exec-managerial' ' Handlers-cleaners' ' Prof-specialty'\n",
      " ' Other-service' ' Sales' ' Craft-repair' ' Transport-moving'\n",
      " ' Farming-fishing' ' Machine-op-inspct' ' Tech-support' ' ?'\n",
      " ' Protective-serv' ' Armed-Forces' ' Priv-house-serv']\n",
      "\n",
      "Unique values of feature relationship:\n",
      "[' Not-in-family' ' Husband' ' Wife' ' Own-child' ' Unmarried'\n",
      " ' Other-relative']\n",
      "\n",
      "Unique values of feature race:\n",
      "[' White' ' Black' ' Asian-Pac-Islander' ' Amer-Indian-Eskimo' ' Other']\n",
      "\n",
      "Unique values of feature sex:\n",
      "[' Male' ' Female']\n",
      "\n",
      "Unique values of feature native_country:\n",
      "[' United-States' ' Cuba' ' Jamaica' ' India' ' ?' ' Mexico' ' South'\n",
      " ' Puerto-Rico' ' Honduras' ' England' ' Canada' ' Germany' ' Iran'\n",
      " ' Philippines' ' Italy' ' Poland' ' Columbia' ' Cambodia' ' Thailand'\n",
      " ' Ecuador' ' Laos' ' Taiwan' ' Haiti' ' Portugal' ' Dominican-Republic'\n",
      " ' El-Salvador' ' France' ' Guatemala' ' China' ' Japan' ' Yugoslavia'\n",
      " ' Peru' ' Outlying-US(Guam-USVI-etc)' ' Scotland' ' Trinadad&Tobago'\n",
      " ' Greece' ' Nicaragua' ' Vietnam' ' Hong' ' Ireland' ' Hungary'\n",
      " ' Holand-Netherlands']\n",
      "\n",
      "Unique values of feature income:\n",
      "[' <=50K' ' >50K']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Amout of samples: {len(df)}\")\n",
    "\n",
    "df.info()\n",
    "df.describe()\n",
    "\n",
    "def categorical_feature_analysis(df, var_name):\n",
    "  print('Unique values of feature %s:\\n%s\\n'  % (var_name, df[var_name].unique())) #feature's unique values\n",
    "\n",
    "# Let's look at the unique values for each categorical variable.\n",
    "categorical_feature_analysis(df,'workclass')\n",
    "categorical_feature_analysis(df,'marital_status')\n",
    "categorical_feature_analysis(df,'occupation')\n",
    "categorical_feature_analysis(df,'relationship')\n",
    "categorical_feature_analysis(df,'race')\n",
    "categorical_feature_analysis(df,'sex')\n",
    "categorical_feature_analysis(df,'native_country')\n",
    "categorical_feature_analysis(df,'income')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null data in any of the dataset columns. However, there are unknown data (information) in the 'workclass', 'occupation', 'native_country' and columns indicated by the symbol '?'. I will change these instances to of \"?\" symbol to \"Unknown\", for better interpretation and cleaner representation.\n",
    "\n",
    "Additionally, the education feature is a duplicate feature of education_num, but in a nonnumerical format. I will remove this column to clean the data.\n",
    "\n",
    "The matching education level of the education number:\n",
    "\n",
    "**1**: Preschool, **2**: 1st-4th, **3**: 5th-6th, **4**: 7th-8th, **5**: 9th, **6**: 10th, **7**: 11th, **8**: 12th, **9**: HS-grad,**10**: Some-college, **11**: Assoc-voc, **12**: Assoc-acdm, **13**: Bachelors, **14**: Masters, **15**: Prof-school, **16**: Doctorate ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop uneeded columns\n",
    "df.drop('education', inplace=True, axis=1)\n",
    "\n",
    "# changing \"?\" to Unknown\n",
    "change_columns = ['workclass', 'occupation', 'native_country']\n",
    "for column in change_columns:\n",
    "        df[column] = df[column].replace({'?': 'Unknown'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we check if there are duplicate rows in the dataset. If there are, we remove them to make the dataset more realistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before removing duplicates: 24\n",
      "After removing duplicates: 0\n"
     ]
    }
   ],
   "source": [
    "# check duplicates and remove it\n",
    "print(\"Before removing duplicates:\", df.duplicated().sum())\n",
    "#There are 24 duplicate rows in our dataset.\n",
    "df = df[~df.duplicated()]\n",
    "print(\"After removing duplicates:\", df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "# Classification method:\n",
    "\n",
    "## A interesting prediction that can be possibly made from this dataset is:\n",
    "\n",
    "**1. Does an individual make more than 50k income?**\n",
    "\n",
    "To answer this, first let us visually look at the distribution of the numerical variables as a function of income group, so that we may have an idea what are the most important features that help to define the income of an individual.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Features: ['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_num_feature():\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "\n",
    "    features = df.select_dtypes(include=numerics).columns.to_list()\n",
    "    return features\n",
    "num_features = get_num_feature()\n",
    "print(f\"Numerical Features: {num_features}\")\n",
    "\n",
    "def univariate_analysis(feature):\n",
    "    f, axes = plt.subplots(1, 2, figsize=(15,5))\n",
    "    sns.histplot(x=feature, data=df, ax=axes[0])\n",
    "    sns.boxplot(x=feature, data=df, ax=axes[1])\n",
    "    plt.show()\n",
    "\n",
    "#plot distribution of income classes\n",
    "var='income'\n",
    "ax=df[var].value_counts().plot.bar()\n",
    "ax.set_title('Data distribution of feature '+var)\n",
    "ax.set_ylabel('Number of people')\n",
    "ax.set_xlabel('Annual income')\n",
    "ax.set_xticklabels(labels=[' <=50K', ' >50K'])\n",
    "\n",
    "# For each quantitative variable, look at distribution\n",
    "\n",
    "for feature in num_features:\n",
    "    sns.boxplot(y=feature, x='income', data=df)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature income\n",
    "The dataset is somewhat unbalanced, in that there are more people with income <=50K than those with more >50k.\n",
    "\n",
    "# Feature *Age*\n",
    "The feature 'Age' seems to have unbalanced data, where most data points are within the range 28-50, and some outliers > 80. From visual inspection, older people tend to have slightly more income than younger individuals, but the boxplots are overlapping so other features might also be important.\n",
    "\n",
    "\n",
    "# Feature *fnlwgt*\n",
    "represents the estimated final weight of each person. The weight means that if two samples share similar fnlwgt, they also share similar characteristics. Regarding the relationship between features fnlwgt and income, the boxplots for both income classes does not show notable differences in *fnlwgt*. This may indicate that fnlwgt is not so useful for the model. There are also outliers in this feature for both income groups.\n",
    "\n",
    "# Feature *education_num*\n",
    "There is some indication that people with higher level education are more likely to make over 50k.\n",
    "\n",
    "# hours per week\n",
    "There is also some indication that people with higher working hours are more likely to make over 50k.\n",
    "\n",
    "\n",
    "Now, let us examine how some of the categorical variables might explain income differences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clustered Bar Chart \n",
    "plt.figure(figsize=[8,6])\n",
    "ax = sb.barplot(data = df, x = 'income', y = 'age', hue = 'sex')\n",
    "ax.legend(loc = 8, ncol = 3, framealpha = 1, title = 'Sex')\n",
    "plt.title('Average of Age for Sex relative to Income')\n",
    "plt.xlabel('Income ($)')\n",
    "plt.ylabel('Average of Age');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Males tend to make a little bit higher than females.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of People (normalized by class)')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot N by race\n",
    "ax=df[var].value_counts(normalize=True).plot.bar()\n",
    "ax.set_title('Data distribution of feature '+var)\n",
    "ax.set_ylabel('Percentage of people')\n",
    "ax.set_xlabel('Race')\n",
    "ax.set_xticklabels(labels=[' White', ' Black', ' Asian-Pac-Islander', ' Amer-Indian-Eskimo', ' Other'])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "##plot distribution of race by income\n",
    "var='race'\n",
    "\n",
    "pd.crosstab([df[var]], df['income'], normalize='index').plot.bar()\n",
    "plt.ylabel('Percentage of People (normalized by class)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Very unbalanced data values, where most people are white, followed by next highest datapoints in black people. Considering the large number of white people compared to other races, it's not possible to note a clear trend that these people are more likely to make more.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>?</th>\n",
       "      <td>0.749571</td>\n",
       "      <td>0.250429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.677686</td>\n",
       "      <td>0.322314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>0.966102</td>\n",
       "      <td>0.033898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuba</th>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominican-Republic</th>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>El-Salvador</th>\n",
       "      <td>0.915094</td>\n",
       "      <td>0.084906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.678832</td>\n",
       "      <td>0.321168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.275862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guatemala</th>\n",
       "      <td>0.953125</td>\n",
       "      <td>0.046875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holand-Netherlands</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honduras</th>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>0.581395</td>\n",
       "      <td>0.418605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0.657534</td>\n",
       "      <td>0.342466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>0.876543</td>\n",
       "      <td>0.123457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laos</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>0.948678</td>\n",
       "      <td>0.051322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicaragua</th>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlying-US(Guam-USVI-etc)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.064516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>0.691919</td>\n",
       "      <td>0.308081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto-Rico</th>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scotland</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>0.607843</td>\n",
       "      <td>0.392157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinadad&amp;Tobago</th>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United-States</th>\n",
       "      <td>0.754165</td>\n",
       "      <td>0.245835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.074627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yugoslavia</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income                          <=50K      >50K\n",
       "native_country                                 \n",
       " ?                           0.749571  0.250429\n",
       " Cambodia                    0.631579  0.368421\n",
       " Canada                      0.677686  0.322314\n",
       " China                       0.733333  0.266667\n",
       " Columbia                    0.966102  0.033898\n",
       " Cuba                        0.736842  0.263158\n",
       " Dominican-Republic          0.971429  0.028571\n",
       " Ecuador                     0.857143  0.142857\n",
       " El-Salvador                 0.915094  0.084906\n",
       " England                     0.666667  0.333333\n",
       " France                      0.586207  0.413793\n",
       " Germany                     0.678832  0.321168\n",
       " Greece                      0.724138  0.275862\n",
       " Guatemala                   0.953125  0.046875\n",
       " Haiti                       0.909091  0.090909\n",
       " Holand-Netherlands          1.000000  0.000000\n",
       " Honduras                    0.923077  0.076923\n",
       " Hong                        0.700000  0.300000\n",
       " Hungary                     0.769231  0.230769\n",
       " India                       0.600000  0.400000\n",
       " Iran                        0.581395  0.418605\n",
       " Ireland                     0.791667  0.208333\n",
       " Italy                       0.657534  0.342466\n",
       " Jamaica                     0.876543  0.123457\n",
       " Japan                       0.612903  0.387097\n",
       " Laos                        0.888889  0.111111\n",
       " Mexico                      0.948678  0.051322\n",
       " Nicaragua                   0.941176  0.058824\n",
       " Outlying-US(Guam-USVI-etc)  1.000000  0.000000\n",
       " Peru                        0.935484  0.064516\n",
       " Philippines                 0.691919  0.308081\n",
       " Poland                      0.800000  0.200000\n",
       " Portugal                    0.891892  0.108108\n",
       " Puerto-Rico                 0.894737  0.105263\n",
       " Scotland                    0.750000  0.250000\n",
       " South                       0.800000  0.200000\n",
       " Taiwan                      0.607843  0.392157\n",
       " Thailand                    0.833333  0.166667\n",
       " Trinadad&Tobago             0.894737  0.105263\n",
       " United-States               0.754165  0.245835\n",
       " Vietnam                     0.925373  0.074627\n",
       " Yugoslavia                  0.625000  0.375000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot N of country\n",
    "var='native_country'\n",
    "\n",
    "##plot distribution\n",
    "ax=df[var].value_counts(normalize=True).plot.bar()\n",
    "ax.set_title('Data distribution of feature '+var)\n",
    "ax.set_ylabel('Percentage of people')\n",
    "ax.set_xlabel('Native country')\n",
    "plt.show()\n",
    "\n",
    "pd.crosstab([df[var]], df['income'], normalize='index')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Very unbalanced data values, where most people were born in the United States. Considering the large number of people that were born in the United States compared to other countries, it's not possible to note a clear trend that these people are more likely to make more.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing\n",
    "\n",
    "## I will first split the target class (i.e., Income) from other attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the outcome variable as 1 and 0\n",
    "y = df.income.replace({\" <=50K\": 0, \" >50K\": 1}) # target\n",
    "X = df.drop([\"income\"], axis= 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split features into numerical and Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>feature_levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fnlwgt</th>\n",
       "      <td>21648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_num</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital_gain</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital_loss</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours_per_week</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                feature_levels\n",
       "age                         73\n",
       "fnlwgt                   21648\n",
       "education_num               16\n",
       "capital_gain               119\n",
       "capital_loss                92\n",
       "hours_per_week              94"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = X.select_dtypes(include=\"number\")\n",
    "categorical_features = X.select_dtypes(include=\"object\")\n",
    "\n",
    "# Detect if any numerical feature has low variation\n",
    "def unique_levels(x):\n",
    "    x = x.value_counts().count()\n",
    "    return x\n",
    "\n",
    "df_value_counts = pd.DataFrame(numerical_features.apply(lambda x: unique_levels(x)))\n",
    "df_value_counts.columns = [\"feature_levels\"]\n",
    "df_value_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Analysis of Numerical Features\n",
    "\n",
    "For numerical features, identify outliers and discard them using extreme percentile values (lower than 0.01, higher than 0.99) as threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_features.describe(percentiles=[0.01, 0.05, 0.10, 0.25, 0.50, 0.75, 0.85,0.90, 0.99])\n",
    "\n",
    "def outlier_cap(x):\n",
    "    x = x.clip(lower=x.quantile(0.01))\n",
    "    x = x.clip(upper=x.quantile(0.99))\n",
    "    return x\n",
    "\n",
    "numerical_features = numerical_features.apply(lambda x: outlier_cap(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical data scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove features that has 0 variance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  39.0\n",
       "fnlwgt            77516.0\n",
       "education_num        13.0\n",
       "capital_gain       2174.0\n",
       "capital_loss          0.0\n",
       "hours_per_week       40.0\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt = VarianceThreshold(threshold=0)\n",
    "vt.fit_transform(numerical_features)\n",
    "\n",
    "# get columns to keep and create new dataframe with those only\n",
    "cols = vt.get_support(indices=True)\n",
    "num_feat_none_var = numerical_features.iloc[:,cols]\n",
    "\n",
    "num_feat_none_var.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I will scale the data to MinMaxScalling for numerical features, which converts the data to have a range between 0 and 1. This will help to make the data well-prepared for the model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scalling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "numerical = ['age', 'capital_gain', 'capital_loss', 'hours_per_week', 'fnlwgt']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X[numerical] = scaler.fit_transform(X[numerical])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical feature encoding\n",
    "**I will encode and create dummy variables using the hot-encoding approach for the categorical features, to make it as numerical data. It helps for easier processing and more numerical representation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding\n",
    "X['sex'] = X.sex.replace({\"Female\": 0, \"Male\": 1})\n",
    "\n",
    "# Create dummy variables\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some classification models\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# import needed functions\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Splitting to training and testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conduct a RandomForestClassifier to predict income\n",
    "**A Random forest is a meta estimator that fits several decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest:\n",
      "Accuracy: train:  0.99993853157698 | test:  0.8567478274307023\n",
      "F1-score: train:  0.9998724184668186 | test:  0.6769203852215517\n",
      "---------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "models = {}\n",
    "\n",
    "# models with default parameter\n",
    "models['RandomForest'] = RandomForestClassifier()\n",
    "\n",
    "# Cross validation\n",
    "for model_name in models:\n",
    "    model = models[model_name]\n",
    "    results = cross_validate(model, X, y, cv=5, scoring=['accuracy', 'f1'], return_train_score=True)\n",
    "    \n",
    "    print(model_name + \":\")\n",
    "    print(\"Accuracy:\" , 'train: ', results['train_accuracy'].mean(), '| test: ', results['test_accuracy'].mean())\n",
    "    print(\"F1-score:\" , 'train: ', results['train_f1'].mean(), '| test: ', results['test_f1'].mean())\n",
    "    print(\"---------------------------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**During our exploration of the dataset it surfaced that there is an imbalance between the classes of classifications: individuals making less than 50k as income represent 75% of the data. One way to fight this issue is to generate new samples in the classes which are under-represented (>50K income). The most naive strategy is to generate new samples by randomly sampling with replacement of the currently available samples. This is called Oversampling, it is a technique used to modify unequal data classes to create balanced data sets. \n",
    "\n",
    "To artificailly overcome imbalance in income classes, I will rerun  the random forest classifier with oversampling:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: train:  0.9999746944353063 | test:  0.9362703141206985\n",
      "F1-score: train:  0.9999746949475907 | test:  0.9392486261169456\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_resampled, y_resampled = ros.fit_resample(X, y)\n",
    "Xr_train, Xr_test, yr_train, yr_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "results = cross_validate(clf, X_resampled, y_resampled, cv=5, scoring=['accuracy', 'f1'], return_train_score=True)\n",
    "print(\"Accuracy:\" , 'train: ', results['train_accuracy'].mean(), '| test: ', results['test_accuracy'].mean())\n",
    "print(\"F1-score:\" , 'train: ', results['train_f1'].mean(), '| test: ', results['test_f1'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics used for Evaluation:\n",
    "\n",
    "I have used the accuracy metric for the evaluation of the RandomForestModel. The accuracy metric is described as the ratio between the number of correct predictions and the total number of predictions:\n",
    "\n",
    "$$\\text{Accuracy} = \\frac{\\text{Number of correct predictions}}{\\text{Total number of predictions}}$$\n",
    "\n",
    "For binary classification such as this dataset, accuracy can also be calculated in terms of the confusion matrix terminology:\n",
    "\n",
    "$$\\text{Accuracy} = \\frac{TP+TN}{TP+TN+FP+FN}$$\n",
    "\n",
    "Where TP = True Positives, TN = True Negatives, FP = False Positives, and FN = False Negatives.\n",
    "\n",
    "F1-score has been used as one of the metrics in the experiment, it can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. \n",
    "\n",
    "The formula for the F1 score is:\n",
    "\n",
    "$$\\text{F1} = \\frac{2 * (precision * recall)}{(precision + recall)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of classification model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get importance of individual features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for col in df.columns:\n",
    "    if df[col].dtypes == 'object':\n",
    "        encoder = LabelEncoder()\n",
    "        df[col] = encoder.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Features Importance:\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>14.562505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workclass</td>\n",
       "      <td>4.020120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fnlwgt</td>\n",
       "      <td>16.933568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>education_num</td>\n",
       "      <td>12.232387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>marital_status</td>\n",
       "      <td>7.455686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>occupation</td>\n",
       "      <td>6.891667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>relationship</td>\n",
       "      <td>9.992091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>race</td>\n",
       "      <td>1.416027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sex</td>\n",
       "      <td>1.160018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>capital_gain</td>\n",
       "      <td>11.694496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>capital_loss</td>\n",
       "      <td>3.931690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hours_per_week</td>\n",
       "      <td>8.044919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>native_country</td>\n",
       "      <td>1.664826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature  importance\n",
       "0              age   14.562505\n",
       "1        workclass    4.020120\n",
       "2           fnlwgt   16.933568\n",
       "3    education_num   12.232387\n",
       "4   marital_status    7.455686\n",
       "5       occupation    6.891667\n",
       "6     relationship    9.992091\n",
       "7             race    1.416027\n",
       "8              sex    1.160018\n",
       "9     capital_gain   11.694496\n",
       "10    capital_loss    3.931690\n",
       "11  hours_per_week    8.044919\n",
       "12  native_country    1.664826"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partioning the data\n",
    "Xc = df.drop('income', axis=1)\n",
    "yc = df['income']\n",
    "\n",
    "# Splitting to training and testing\n",
    "Xc_train, Xc_test, yc_train, yc_test = train_test_split(Xc, yc, test_size=0.2, random_state=42)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(Xc_train, yc_train)\n",
    "\n",
    "# View a list of the features and their importance scores\n",
    "print('\\nFeatures Importance:')\n",
    "feat_imp = pd.DataFrame(zip(Xc.columns.tolist(), clf.feature_importances_ * 100), columns=['feature', 'importance'])\n",
    "feat_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot importance of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Features importance plot\n",
    "plt.figure(figsize=[20,6])\n",
    "sb.barplot(data=feat_imp, x='feature', y='importance')\n",
    "plt.title('Features Importance', weight='bold', fontsize=20)\n",
    "plt.xlabel('Feature', weight='bold', fontsize=13)\n",
    "plt.ylabel('Importance (%)', weight='bold', fontsize=13);\n",
    "\n",
    "# add annotations\n",
    "impo = feat_imp['importance']\n",
    "locs, labels = plt.xticks()\n",
    "\n",
    "for loc, label in zip(locs, labels):\n",
    "    count = impo[loc]\n",
    "    pct_string = '{:0.2f}%'.format(count)\n",
    "\n",
    "    plt.text(loc, count-0.8, pct_string, ha = 'center', color = 'w', weight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the relative importance of features, certain features that have little importance can be dropped from the model. I will drop features that have less than 4% impartance, to speed up the process of fitting the model as well as reduce potential overfitting.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: train:  0.9999746944353063 | test:  0.9332943474339098\n",
      "F1-score: train:  0.99997469520372 | test:  0.9366133832951622\n"
     ]
    }
   ],
   "source": [
    "df_final=df.copy()\n",
    "df_final.head(2)\n",
    "df_final.drop(['race', 'sex', 'capital_loss', 'native_country'], axis=1, inplace=True)\n",
    "\n",
    "# Scalling\n",
    "numerical = ['age', 'capital_gain', 'hours_per_week', 'fnlwgt']\n",
    "scaler = MinMaxScaler()\n",
    "df_final[numerical] = scaler.fit_transform(df_final[numerical])\n",
    "\n",
    "# Encoding\n",
    "df_final['income'] = df_final.income.replace({\"<=50K\": 0, \">50K\": 1})\n",
    "\n",
    "# Create dummy variables\n",
    "df_final = pd.get_dummies(df_final)\n",
    "\n",
    "# Partioning\n",
    "Xf = df_final.drop('income', axis=1)\n",
    "yf = df_final['income']\n",
    "\n",
    "# Oversampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_resampled, y_resampled = ros.fit_resample(Xf, yf)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "results = cross_validate(clf, X_resampled, y_resampled, cv=5, scoring=['accuracy', 'f1'], return_train_score=True)\n",
    "print(\"Accuracy:\" , 'train: ', results['train_accuracy'].mean(), '| test: ', results['test_accuracy'].mean())\n",
    "print(\"F1-score:\" , 'train: ', results['train_f1'].mean(), '| test: ', results['test_f1'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Load test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</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>89814.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951.0</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age   workclass    fnlwgt      education  education_num  \\\n",
       "1  25     Private  226802.0           11th            7.0   \n",
       "2  38     Private   89814.0        HS-grad            9.0   \n",
       "3  28   Local-gov  336951.0     Assoc-acdm           12.0   \n",
       "4  44     Private  160323.0   Some-college           10.0   \n",
       "5  18           ?  103497.0   Some-college           10.0   \n",
       "\n",
       "        marital_status          occupation relationship    race      sex  \\\n",
       "1        Never-married   Machine-op-inspct    Own-child   Black     Male   \n",
       "2   Married-civ-spouse     Farming-fishing      Husband   White     Male   \n",
       "3   Married-civ-spouse     Protective-serv      Husband   White     Male   \n",
       "4   Married-civ-spouse   Machine-op-inspct      Husband   Black     Male   \n",
       "5        Never-married                   ?    Own-child   White   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country   income  \n",
       "1           0.0           0.0            40.0   United-States   <=50K.  \n",
       "2           0.0           0.0            50.0   United-States   <=50K.  \n",
       "3           0.0           0.0            40.0   United-States    >50K.  \n",
       "4        7688.0           0.0            40.0   United-States    >50K.  \n",
       "5           0.0           0.0            30.0   United-States   <=50K.  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_col = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', \n",
    "             'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', \n",
    "             'hours_per_week', 'native_country', 'income']\n",
    "df_test = pd.read_csv(\"adult.test\", names=names_col)\n",
    "df_test.drop(labels=0, axis=0, inplace=True)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income\n",
       "0    12435\n",
       "1     3846\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[\"age\"] = [int(age) for age in df_test['age'].to_list()]\n",
    "\n",
    "y_test = df_test.income.replace({\" <=50K.\": 0, \" >50K.\": 1}) # target\n",
    "X_test = df_test.drop([\"income\"], axis= 1)\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Cross-validation (k-fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML model prediction | Classification metric:\n",
      "Accuracy: 0.8557993730407524\n",
      "Recall: 0.6226510919248349\n",
      "Precision: 0.7403381642512077\n",
      "F1: 0.6764137931034482\n",
      "\n",
      "\n",
      "ML model prediction | Classification metric:\n",
      "Accuracy: 0.8565281534300467\n",
      "Recall: 0.6265060240963856\n",
      "Precision: 0.7359229147847034\n",
      "F1: 0.6768208252561618\n",
      "\n",
      "\n",
      "ML model prediction | Classification metric:\n",
      "Accuracy: 0.8567828385272604\n",
      "Recall: 0.631592356687898\n",
      "Precision: 0.7371394588165329\n",
      "F1: 0.6802963776070251\n",
      "\n",
      "\n",
      "ML model prediction | Classification metric:\n",
      "Accuracy: 0.8579419719695107\n",
      "Recall: 0.6205927439959121\n",
      "Precision: 0.7462365591397849\n",
      "F1: 0.6776398381922165\n",
      "\n",
      "\n",
      "Accuracy | mean: 0.856763 | min: 0.855799 | max: 0.857942\n",
      "Recall | mean: 0.625336 | min: 0.620593 | max: 0.631592\n",
      "Precision | mean: 0.739909 | min: 0.735923 | max: 0.746237\n",
      "F1 | mean: 0.677793 | min: 0.676414 | max: 0.680296\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold, RepeatedKFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "def calc_performance_metrics(model_name, real_y, prediction):\n",
    "  ##classification metrics\n",
    "  calc_accuracy=accuracy_score(real_y, prediction)\n",
    "  calc_recall=recall_score(real_y, prediction)\n",
    "  calc_precision=precision_score(real_y, prediction)\n",
    "  calc_f1=f1_score(real_y, prediction)\n",
    "\n",
    "  print(model_name+' prediction | Classification metric:')\n",
    "  print('Accuracy:', calc_accuracy)\n",
    "  print('Recall:', calc_recall)\n",
    "  print('Precision:', calc_precision)\n",
    "  print('F1:', calc_f1)\n",
    "  return calc_accuracy, calc_recall, calc_precision, calc_f1\n",
    "\n",
    "def cross_validation_prediction(ml_model, X, y):\n",
    "  list_metrics=[]\n",
    "\n",
    "  kf=RepeatedKFold(n_splits=2, n_repeats=2, random_state=376)\n",
    "\n",
    "  train_X, val_X, train_y, val_y=None, None, None, None\n",
    "  for l_train, l_val in kf.split(X):\n",
    "    #print(l_train.shape[0], l_val.shape[0])\n",
    "    train_X, val_X = X.iloc[l_train], X.iloc[l_val]\n",
    "    train_y, val_y = y.iloc[l_train], y.iloc[l_val]\n",
    "\n",
    "    ##optimized Random Forest\n",
    "    ml_model = RandomForestClassifier(n_estimators=500, random_state=376)\n",
    "    ml_model.fit(train_X, train_y)\n",
    "    ml_prediction=ml_model.predict(val_X)\n",
    "    acc, recall, prec, f1=calc_performance_metrics('ML model', val_y, ml_prediction)\n",
    "\n",
    "    list_metrics.append([acc, recall, prec, f1])\n",
    "\n",
    "    #plt.barh(features, ml_model.feature_importances_)\n",
    "    #plt.xlabel(\"Random Forest feature importance\")\n",
    "    #plt.show()\n",
    "\n",
    "    print('\\n')\n",
    "\n",
    "  print('Accuracy | mean: %f | min: %f | max: %f'  % (np.mean([list_metrics[i][0] for i in range(len(list_metrics))]), np.min([list_metrics[i][0] for i in range(len(list_metrics))]), np.max([list_metrics[i][0] for i in range(len(list_metrics))])))\n",
    "  print('Recall | mean: %f | min: %f | max: %f'    % (np.mean([list_metrics[i][1] for i in range(len(list_metrics))]), np.min([list_metrics[i][1] for i in range(len(list_metrics))]), np.max([list_metrics[i][1] for i in range(len(list_metrics))])))\n",
    "  print('Precision | mean: %f | min: %f | max: %f' % (np.mean([list_metrics[i][2] for i in range(len(list_metrics))]), np.min([list_metrics[i][2] for i in range(len(list_metrics))]), np.max([list_metrics[i][2] for i in range(len(list_metrics))])))\n",
    "  print('F1 | mean: %f | min: %f | max: %f'        % (np.mean([list_metrics[i][3] for i in range(len(list_metrics))]), np.min([list_metrics[i][3] for i in range(len(list_metrics))]), np.max([list_metrics[i][3] for i in range(len(list_metrics))])))\n",
    "\n",
    "  ##plotting accuracy values\n",
    "  plt.hist([list_metrics[i][0] for i in range(len(list_metrics))])\n",
    "  plt.show()\n",
    "\n",
    "  sns.boxplot(y=[list_metrics[i][0] for i in range(len(list_metrics))])\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "#cross_validation_prediction(rf_model, X, y)\n",
    "cross_validation_prediction(clf, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final remarks on classification\n",
    "**From the analysis, it can be seen that it is possible to determine whether a person makes over 50K a year based on census data, with a reasonable accuracy of around 86% in the testing step. However, there are features that are more relevant than others, such as age, working hours-per-week, education level, and these should be better considered in order to increase the accuracy of the models. Furthermore, future dataset collection should try to reduce the imbalance in the dataset by conducting random sampling.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering method\n",
    "**We can also use Clustering to reveal segments of the population with similar characteristics. One clustering methods is to Apply PCA to the data, which can be used on numerical variables.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MinMaxScaler to scale the numerical columns so that they fall between 0 and 1\n",
    "\n",
    "X_numerical=df_final[numerical]\n",
    "X_numerical.head()\n",
    "pca = PCA()\n",
    "model = pca.fit_transform(X_numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make function for plotting\n",
    "\n",
    "# Investigate the variance accounted for by each principal component.\n",
    "\n",
    "def plot_pc(pca):\n",
    "    '''\n",
    "    Creates a scree plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "            \n",
    "    OUTPUT: None\n",
    "    '''\n",
    "    num_components=len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(18, 8))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\n",
    "    for i in range(num_components):\n",
    "        ax.annotate(r\"%s%%\" % ((str(vals[i]*100)[:4])), (ind[i]+0.2, vals[i]), va=\"bottom\", ha=\"center\", fontsize=12)\n",
    " \n",
    "    ax.xaxis.set_tick_params(width=0)\n",
    "    ax.yaxis.set_tick_params(width=2, length=12)\n",
    " \n",
    "    ax.set_xlabel(\"Principal Component\")\n",
    "    ax.set_ylabel(\"Variance Explained (%)\")\n",
    "    plt.title('Explained Variance Per Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pc(pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the appropriarte number of components to retain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00011897087097167969\n"
     ]
    }
   ],
   "source": [
    "# Find appropriate number of components to retain\n",
    "from time import time\n",
    "\n",
    "start = time()\n",
    "for i in np.arange(20, len(pca.explained_variance_ratio_), 3):\n",
    "    print('For {} components, explained variance:'.format(i), \n",
    "          pca.explained_variance_ratio_[:i].sum())\n",
    "end = time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=4)\n",
    "pca_80 = pca.fit_transform(X_numerical)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpret Principal Components\n",
    "\n",
    "**Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion. To investigate the features, map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to show the weight of each features by dimension\n",
    "def show_weight(full_dataset, pca, comp_n, feat_n):\n",
    "    '''\n",
    "    Display the weight of each feature in dimension i\n",
    "    INPUT: \n",
    "        full_dataset: dataset\n",
    "        pca: PCA model fitted with data\n",
    "        comp_n: index of component\n",
    "        feat_n: feature number\n",
    "    OUTPUT: weight of each feature \n",
    "    '''\n",
    "    components = pd.DataFrame(np.round(pca.components_, 4), columns=full_dataset.keys()).iloc[comp_n - 1]\n",
    "    components.sort_values(ascending=False, inplace=True)\n",
    "    top2n_components = pd.concat([components.head(feat_n), components.tail(feat_n)])\n",
    "\n",
    "    # Plot the result\n",
    "    top2n_components.plot(kind='bar', \n",
    "                          title='Top {} weighted features for PCA component {}'.format(feat_n*2, comp_n),\n",
    "                          figsize=(12, 6))\n",
    "    plt.show()\n",
    "#     top_components = pd.concat([components.iloc[:5,:],components.iloc[-5:]]).reset_index()\n",
    "    return top2n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "age               0.9950\n",
       "hours_per_week    0.0851\n",
       "capital_gain      0.0381\n",
       "fnlwgt           -0.0345\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "components1 = show_weight(X_numerical, pca, 1, 2)\n",
    "components1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "def get_kmeans_score(data, center):\n",
    "    '''\n",
    "    return the kmeans score regarding SSE for points to centers\n",
    "    INPUT:\n",
    "        data - the dataset you want to fit kmeans to\n",
    "        center - the number of centers you want\n",
    "    OUTPUT:\n",
    "        score - the SSE score for the kmeans model fit to the data\n",
    "    '''\n",
    "    # instantiate kmeans\n",
    "    kmeans = KMeans(n_clusters=center)\n",
    "    \n",
    "    # Then fit the model to you data using the fit mothod\n",
    "    model = kmeans.fit(data)\n",
    "    \n",
    "    # Obtain a score related to the model fit\n",
    "    score = np.abs(model.score(data))\n",
    "    \n",
    "    return score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting k = 1 \n",
      "Fitting k = 2 \n",
      "Fitting k = 3 \n",
      "Fitting k = 4 \n",
      "Fitting k = 5 \n",
      "Fitting k = 6 \n",
      "Fitting k = 7 \n",
      "Fitting k = 8 \n",
      "Fitting k = 9 \n",
      "Fitting k = 10 \n",
      "Fitting k = 11 \n",
      "--- Run time: 0.11 mins ---\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "centers = list(range(1, 12))\n",
    "\n",
    "start = time()\n",
    "\n",
    "for center in centers:\n",
    "    print('Fitting k = {} '.format(center))\n",
    "    scores.append(get_kmeans_score(pca_80, center))\n",
    "end = time()\n",
    "\n",
    "print(\"--- Run time: %s mins ---\" % np.round(((end - start)/60),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SSE vs. K')"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the SSE value to decide the K value\n",
    "plt.plot(centers, scores, linestyle='--', marker='o', color='b')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('SSE vs. K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-fit the k-means model with the selected number of clusters and obtain\n",
    "# cluster predictions for the general population demographics data.\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "model = kmeans.fit(pca_80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction\n",
       "0    19003\n",
       "1    13534\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Prediction]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Store the prediction\n",
    "adult_predict = pd.DataFrame(np.array(model.predict(pca_80)), columns=['Prediction'])\n",
    "\n",
    "# Count the cluster number\n",
    "cluster_cnt = adult_predict['Prediction'].value_counts().sort_index()\n",
    "display(cluster_cnt)\n",
    "\n",
    "cluster_prop = pd.DataFrame((cluster_cnt/cluster_cnt.sum()), columns=['Prediction']).reset_index()\n",
    "cluster_prop.set_index('index', inplace=True)\n",
    "display(cluster_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/avijitchowdhury/Desktop/CET182/census+income/AvijitChowdhury_CET182_Assignment.ipynb Cell 73\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/avijitchowdhury/Desktop/CET182/census%2Bincome/AvijitChowdhury_CET182_Assignment.ipynb#Y164sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m cluster_prop \u001b[39m=\u001b[39m cluster_prop\u001b[39m.\u001b[39mreset_index()\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/avijitchowdhury/Desktop/CET182/census%2Bincome/AvijitChowdhury_CET182_Assignment.ipynb#Y164sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m cluster_prop\u001b[39m.\u001b[39;49mplot(x \u001b[39m=\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mindex\u001b[39;49m\u001b[39m'\u001b[39;49m, y \u001b[39m=\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mPrediction\u001b[39;49m\u001b[39m'\u001b[39;49m, kind \u001b[39m=\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mbar\u001b[39;49m\u001b[39m'\u001b[39;49m, figsize \u001b[39m=\u001b[39;49m (\u001b[39m18\u001b[39;49m,\u001b[39m8\u001b[39;49m ))\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/avijitchowdhury/Desktop/CET182/census%2Bincome/AvijitChowdhury_CET182_Assignment.ipynb#Y164sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39mCluster Distribution\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/avijitchowdhury/Desktop/CET182/census%2Bincome/AvijitChowdhury_CET182_Assignment.ipynb#Y164sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mCluster\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/pandas/plotting/_core.py:1031\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1028\u001b[0m             label_name \u001b[39m=\u001b[39m label_kw \u001b[39mor\u001b[39;00m data\u001b[39m.\u001b[39mcolumns\n\u001b[1;32m   1029\u001b[0m             data\u001b[39m.\u001b[39mcolumns \u001b[39m=\u001b[39m label_name\n\u001b[0;32m-> 1031\u001b[0m \u001b[39mreturn\u001b[39;00m plot_backend\u001b[39m.\u001b[39;49mplot(data, kind\u001b[39m=\u001b[39;49mkind, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m         kwargs[\u001b[39m\"\u001b[39m\u001b[39max\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(ax, \u001b[39m\"\u001b[39m\u001b[39mleft_ax\u001b[39m\u001b[39m\"\u001b[39m, ax)\n\u001b[1;32m     70\u001b[0m plot_obj \u001b[39m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m plot_obj\u001b[39m.\u001b[39;49mgenerate()\n\u001b[1;32m     72\u001b[0m plot_obj\u001b[39m.\u001b[39mdraw()\n\u001b[1;32m     73\u001b[0m \u001b[39mreturn\u001b[39;00m plot_obj\u001b[39m.\u001b[39mresult\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/pandas/plotting/_matplotlib/core.py:451\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    449\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgenerate\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    450\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_args_adjust()\n\u001b[0;32m--> 451\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_compute_plot_data()\n\u001b[1;32m    452\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setup_subplots()\n\u001b[1;32m    453\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_plot()\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/pandas/plotting/_matplotlib/core.py:636\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[39m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[1;32m    635\u001b[0m \u001b[39mif\u001b[39;00m is_empty:\n\u001b[0;32m--> 636\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mno numeric data to plot\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    638\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata \u001b[39m=\u001b[39m numeric_data\u001b[39m.\u001b[39mapply(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_convert_to_ndarray)\n",
      "\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "cluster_prop = cluster_prop.reset_index()\n",
    "cluster_prop.plot(x = 'index', y = 'Prediction', kind = 'bar', figsize = (18,8 ))\n",
    "plt.title('Cluster Distribution')\n",
    "plt.xlabel('Cluster')\n",
    "plt.ylabel('Proportion of persons in cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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