finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identifying Donors for Non-Profits (*CharityML*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "The objective of this project is to accurately model individuals' income using data collected from the 1994 U.S. Censu. This will help non-profits identify high-probability donors, as well as get more granular in their requests.\n",
    "\n",
    "Specifically, I will employ several supervised algorithms to determine the best candidate algorithm from preliminary results, and further optimize the selected model. Moreover, the individuals will be classified as whether they make more than $50,000/yr or not.\n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Step 1 - Exploration\n",
    "Load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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_level</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>13.0</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>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass education_level  education-num  marital-status  \\\n",
       "0   39   State-gov       Bachelors           13.0   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male        2174.0           0.0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "0            40.0   United-States  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualization code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the Census dataset\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "# Success - Display the first record\n",
    "display(data.head(n=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A cursory investigation of the dataset shows us how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \\$50,000..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78%\n"
     ]
    }
   ],
   "source": [
    "# Total # of records\n",
    "n_records = len(data)\n",
    "\n",
    "# Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = len(data[data['income']==\">50K\"])\n",
    "\n",
    "# Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = len(data[data['income']==\"<=50K\"])\n",
    "\n",
    "# Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = n_greater_50k/(n_records)*100\n",
    "\n",
    "# Print results\n",
    "print(\"Total number of records: {}\".format(n_records))\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
    "print(\"Percentage of individuals making more than $50,000: {}%\".format(round(greater_percent,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Featureset Exploration\n",
    "\n",
    "* **age**: continuous. \n",
    "* **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. \n",
    "* **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. \n",
    "* **education-num**: continuous. \n",
    "* **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. \n",
    "* **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. \n",
    "* **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. \n",
    "* **race**: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. \n",
    "* **sex**: Female, Male. \n",
    "* **capital-gain**: continuous. \n",
    "* **capital-loss**: continuous. \n",
    "* **hours-per-week**: continuous. \n",
    "* **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Step 2 - Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Skewed Continuous Features\n",
    "Algorithms can be sensitive to skewed distributions, potentially causing underperformance. With the census dataset, two features fit this description: '`capital-gain'` and `'capital-loss'`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop('income', axis = 1)\n",
    "\n",
    "# Visualize skewed continuous features of original data\n",
    "vs.distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To mitigate the large spread in values, I applied a logarithmic transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_log_transformed = pd.DataFrame(data = features_raw)\n",
    "features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "vs.distribution(features_log_transformed, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing Numerical Features\n",
    "Similar to treating skewed distributions, I also normalized several numerical features to ensure \"equal treatment\" of features when applying supervised learning.\n",
    "\n",
    "2 Key Points:\n",
    "1. Normalization doesn't change distribution, only the scales\n",
    "2. The description of the variable in its \"raw form\" no longer applies directly to this normalized value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
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       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
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       "      <td>Self-emp-not-inc</td>\n",
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       "      <td>0.800000</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",
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       "      <td>Divorced</td>\n",
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       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>0.150685</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
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       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153061</td>\n",
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       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.829751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.342466</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</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>0.742849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age          workclass education_level  education-num  \\\n",
       "0  0.301370          State-gov       Bachelors       0.800000   \n",
       "1  0.452055   Self-emp-not-inc       Bachelors       0.800000   \n",
       "2  0.287671            Private         HS-grad       0.533333   \n",
       "3  0.493151            Private            11th       0.400000   \n",
       "4  0.150685            Private       Bachelors       0.800000   \n",
       "5  0.273973            Private         Masters       0.866667   \n",
       "6  0.438356            Private             9th       0.266667   \n",
       "7  0.479452   Self-emp-not-inc         HS-grad       0.533333   \n",
       "8  0.191781            Private         Masters       0.866667   \n",
       "9  0.342466            Private       Bachelors       0.800000   \n",
       "\n",
       "           marital-status          occupation    relationship    race  \\\n",
       "0           Never-married        Adm-clerical   Not-in-family   White   \n",
       "1      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3      Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4      Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "5      Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "6   Married-spouse-absent       Other-service   Not-in-family   Black   \n",
       "7      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "8           Never-married      Prof-specialty   Not-in-family   White   \n",
       "9      Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "\n",
       "       sex  capital-gain  capital-loss  hours-per-week  native-country  \n",
       "0     Male      0.667492           0.0        0.397959   United-States  \n",
       "1     Male      0.000000           0.0        0.122449   United-States  \n",
       "2     Male      0.000000           0.0        0.397959   United-States  \n",
       "3     Male      0.000000           0.0        0.397959   United-States  \n",
       "4   Female      0.000000           0.0        0.397959            Cuba  \n",
       "5   Female      0.000000           0.0        0.397959   United-States  \n",
       "6   Female      0.000000           0.0        0.153061         Jamaica  \n",
       "7     Male      0.000000           0.0        0.448980   United-States  \n",
       "8   Female      0.829751           0.0        0.500000   United-States  \n",
       "9     Male      0.742849           0.0        0.397959   United-States  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler() # default=(0, 1)\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "\n",
    "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n",
    "features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_log_minmax_transform.head(n = 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding Categorical Features\n",
    "\n",
    "- There still are several categorical features, which is best treated with a **one-hot encoding** scheme. \n",
    "- Similarly, I'll convert the target label (`'income'`) into a boolean value for modeling purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n"
     ]
    }
   ],
   "source": [
    "# One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()\n",
    "features_final = pd.get_dummies(features_log_minmax_transform)\n",
    "\n",
    "# Encode the 'income_raw' data to numerical values\n",
    "income = pd.DataFrame(data=income_raw)\n",
    "# income.loc[income_raw == \"<=50K\"] = 0\n",
    "# income.loc[income_raw == \">50K\"] = 1\n",
    "income['income'] = income.income.map({'<=50K':0, '>50K':1})\n",
    "\n",
    "# Print the number of features after one-hot encoding\n",
    "encoded = list(features_final.columns)\n",
    "print(\"{} total features after one-hot encoding.\".format(len(encoded)))\n",
    "\n",
    "# Uncomment the following line to see the encoded feature names\n",
    "# print(encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle & Split Data\n",
    "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. Now, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final, \n",
    "                                                    income, \n",
    "                                                    test_size = 0.2, \n",
    "                                                    random_state = 0)\n",
    "\n",
    "# Show the results of the split\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Step 3 - Evaluating Model Performance\n",
    "\n",
    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Specifically, these will be 3 candidate supervised learners, and a fourth *naive predictor* algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics\n",
    "Since **accuracy** of the biggest concern here, it will be the most important metric when assessing model performance. A secondary, but relevant metric is **recall**. Therefore, we can use **F-beta score** as a metric that considers both precision and recall:\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "In particular, to place more emphasis on precision, we'll set the $\\beta = 0.5$\n",
    "\n",
    "Especially since our classification distributions are notably skewed, the F1 score will be a great metric to go with.\n",
    "\n",
    "### Naive Predictor\n",
    "The **naive predictor** here will be used as a benchmark for our supervised models, where the prediction defaults to *\"this person does not make more than \\$50,000\"*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3A - Naive Predictor Performace\n",
    "Model that always predicts an individual makes more than $50,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data \n",
    "encoded to numerical values done in the data preprocessing step.\n",
    "FP = income.count() - TP # Specific to the naive case\n",
    "\n",
    "TN = 0 # No predicted negatives in the naive case\n",
    "FN = 0 # No predicted negatives in the naive case\n",
    "'''\n",
    "# Calculate accuracy, precision and recall\n",
    "accuracy = (np.sum(income)/n_records)[0]\n",
    "recall = (np.sum(income)/(np.sum(income) + 0))[0]\n",
    "precision = (np.sum(income)/(income.count()))[0]\n",
    "\n",
    "# Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "fscore = (1 + 0.5**2)*(precision*recall)/((0.5**2 * precision) + recall)\n",
    "\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3B - Supervised Learning Models\n",
    "**The following are some of the supervised learning models that are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that I considered:**\n",
    "- Gaussian Naive Bayes (GaussianNB)\n",
    "- Decision Trees\n",
    "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
    "- K-Nearest Neighbors (KNeighbors)\n",
    "- Stochastic Gradient Descent Classifier (SGDC)\n",
    "- Support Vector Machines (SVM)\n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option A: Logistic Regression \n",
    "**Strengths:**  \n",
    "- Provides probability scores associated with each observation (instead of only discrete classifications), allowing the application of custom performance metrics to derive the final predictions.  \n",
    "- Low variance, thus tends to not overfit to the training dataset.  \n",
    "- Can handle both categorical & continuous data.  \n",
    "- Pretty efficient in time & memory requirements.  \n",
    "\n",
    "**Weaknesses:**  \n",
    "- Falters when dealing with large feature spaces and datasets with many missing data.  \n",
    "- High bias, so isn't too good when the data is non-linear.  \n",
    "\n",
    "**Why this model**  \n",
    "- Our problem deals with a relatively small (< 100K) dataset\n",
    "- Handles larget # of features (103 in our case) very well\n",
    "- Binary target variable (income is higher or lower than 50K?)  \n",
    "- No missing data.  \n",
    "- Simple model to implement & interpret.\n",
    "\n",
    "#### Option B: K-Nearest Neighbors (KNeighbors)  \n",
    "**Strengths:**  \n",
    "- Simple, instance-based learning algorithm with fast training performance.  \n",
    "- Easy implementation, even for multi-class problems.  \n",
    "- Decision boundary can take any form.  \n",
    "\n",
    "**Weaknesses:**  \n",
    "- Testing performance is slow & memory-intensive, particularly as the dataset grows (given it's a memory-based algorithm).  \n",
    "- As the algorithm centers on the Euclidean distance between the data points, the dataset must be scaled and have very few outliers.  \n",
    "\n",
    "**Why this model**\n",
    "- Relatively small dataset.  \n",
    "- Normalized dataset - as the model is distance-based, scaling is critical.  \n",
    "- All features in dataset is in numerical format (via one-hot encoding).  \n",
    "- Simple & intuitive to implement & interpret.  \n",
    "\n",
    "#### Option C: AdaBoost Classifiers  \n",
    "**Strengths:**  \n",
    "- Powerful model that address a decision tree's tendency of overfitting by randomly iterating through different decision trees via boosting (iteratively correcting mistakes of a previous weak learner in crafting the next weak learner).  \n",
    "- Can be used with any type of data (e.g. textual and numeric), and is very simple to implement.  \n",
    "\n",
    "**Weakness:**  \n",
    "- Memory-intensive, constrained by memory & time.  \n",
    "- It's also fairly sensitive to outliers.  \n",
    "\n",
    "**Why this model**  \n",
    "- Moderate dataset size, so memory-intensiveness won't be as big of a challenge.  \n",
    "- Prevents overfitting by boosting weak learners (then combining them together), to generate a somewhat balanced result between bias & variance.  \n",
    "- Has the flexibility to handle non-linear decision boundaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3C - ML Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "#     training_labels = np.ravel(y_train[:sample_size])\n",
    "#     print(y_train['income'])\n",
    "    \n",
    "    # Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])\n",
    "    start = time() # Get start time\n",
    "    learner = learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the training time\n",
    "    results['train_time'] = end - start\n",
    "        \n",
    "    # Get the predictions on the test set(X_test), then get predictions on the first 300 training samples(X_train) using .predict()\n",
    "    start = time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the total prediction time\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # Compute accuracy on the first 300 training samples which is y_train[:300]\n",
    "    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)\n",
    "        \n",
    "    # Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    # Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    results['f_train'] = fbeta_score(y_train[:300], predictions_train, 0.5)\n",
    "        \n",
    "    # Compute F-score on the test set which is y_test\n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test, 0.5)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D - Preliminary Evaluation\n",
    "\n",
    "To evaluate the performance for the 3 candidate models, I did the following:\n",
    "- Imported supervised learning models.\n",
    "- Initialize the 3 models and store them in `'clf_A'`, `'clf_B'`, and `'clf_C'`.\n",
    "- Calculate the number of records equal to 1%, 10%, and 100% of the training data, storing the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression trained on 361 samples.\n",
      "LogisticRegression trained on 3617 samples.\n",
      "LogisticRegression trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier trained on 361 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier trained on 3617 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier trained on 361 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier trained on 3617 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the three supervised learning models from sklearn from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# Initialize the three models\n",
    "# clf_A = SVC()\n",
    "clf_A = LogisticRegression(random_state = 30)\n",
    "clf_B = KNeighborsClassifier()\n",
    "clf_C = AdaBoostClassifier(random_state = 30)\n",
    "\n",
    "# Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "samples_100 = len(y_train)\n",
    "samples_10 = int(samples_100 * 0.1)\n",
    "samples_1 = int(samples_100 * 0.01)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Step 4 - Refining Model\n",
    "Once I choose a candidate model based on the observed metrics, I will the perform grid search optimization over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F-score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Winner: AdaBoost\n",
    "\n",
    "The **AdaBoost Classifier** seems to be the most appropiate for the task of identifying individuals that make more than $50,000. Not only does it demonstrate the highest F-score during testing when 100% of training data is used, but also has a relatively low training time, and is much suitable for the data, as it integrates randomization to prevent overfitting the data.\n",
    "\n",
    "Although the training time is the longest (by a miniscule 0.5 seconds), its prediction time is significantly low even when using 100% of the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More about AdaBoost\n",
    "\n",
    "Before moving on, I wanted to get a deeper understanding of the chosen model by answering the question \"What exactly is AdaBoost\"?\n",
    "\n",
    "A \"weak learner\" is a machine learning algorithm that gives a better result than simple guessing (hence the \"weak\"). The most common form (and the one used in this project) of weak learners is a decision tree that performs very accurately on a subset of the problem. An example of this would be accurately classifying a particular type of dog, but randomly guessing the other types.  \n",
    "\n",
    "The **AdaBoost Ensemble method** is a technique that combines many \"weak learners\" to create a better performing model. Specifically, it:  \n",
    "1. Iteratively generates classifiers that learn from the mistakes from the previously generated classifier by assigning heavier weights to incorrectly categorized data points (to match the # of correct points).  \n",
    "2. It does the above for a specified # of iterations, ending up with several weak models, each with its own weight (determined by the # of data points that were classified correctly and incorrectly).  \n",
    "3. The final \"composite\" model is generated by simply adding the weights of all the models by classification regions where they overlap (the sign of the weight depending on which side of the decision boundary).  \n",
    "\n",
    "The result is a combined model that integrates the cumulative learning for several iterations of weak learners to generate a better performing model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Tuning\n",
    "Fine tune the chosen AdaBoost model using grid search (`GridSearchCV`).\n",
    "\n",
    "Specifically, I will implement the following:\n",
    "- Import [`sklearn.grid_search.GridSearchCV`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n",
    "- Initialize the classifier you've chosen and store it in `clf`.\n",
    " - Set the same `random_state` as before.\n",
    "- Create a dictionary of parameters you wish to tune for the chosen model.\n",
    "- Use `make_scorer` to create an `fbeta_score` scoring object (with $\\beta = 0.5$).\n",
    "- Perform grid search on the classifier `clf` using the `'scorer'`, and store it in `grid_obj`.\n",
    "- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_fit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8576\n",
      "F-score on testing data: 0.7246\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8609\n",
      "Final F-score on the testing data: 0.7316\n"
     ]
    }
   ],
   "source": [
    "# Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# Initialize the classifier\n",
    "clf = AdaBoostClassifier(random_state = 30)\n",
    "\n",
    "# Create the parameters list you wish to tune, using a dictionary if needed.\n",
    "parameters = {'n_estimators': [10, 30, 50, 70, 90], }\n",
    "\n",
    "# Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "# Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=scorer)\n",
    "\n",
    "# Fit the grid search object to the training data and find the optimal parameters using fit()\n",
    "grid_fit = grid_obj.fit(X_train, y_train['income'])\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print(\"Unoptimized model\\n------\")\n",
    "print(\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
    "print(\"\\nOptimized Model\\n------\")\n",
    "print(\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model Evaluation\n",
    "\n",
    "Let's peek into what the final, grid search-optimized model key metrics (accuracy, F-score) are..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |      0.8576       |      0.8609     |\n",
    "| F-score        |      0.7246       |      0.7316     |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Grid Search optimization technique improved both the accuracy & F-scores for the model, by around 0.003 and 0.007, respectively. Furthermore, the optimized model performs significantly better (more than 2x) than the naive predictor benchmarks (accuracy = 0.25, F-score = 0.3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Step 5 - Features & Conclusion\n",
    "\n",
    "Now that we're done with our first cut of the modelling, we want to better understand the actual relationship between the independent & dependent variabels by observing the features' predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon. In other words, which features most strongly predict whether an individual makes at most or more than \\$50,000?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5A - Hypothesis\n",
    "\n",
    "Based on the feature exploration & intuition, I believe these would be the 5 strongest predictors of income:\n",
    "\n",
    "1.  **Capital Gain** - The amount of capital they gain is probably the best indicator for individuals who gain more than $50k.\n",
    "2.  **Capital Loss** - The amount they spend is also a good indicator (though not absolute) of how much they make.\n",
    "3.  **Occupation** - Occupation heavily influences how much an individual makes.\n",
    "4.  **Education** - Education is a decent indicator of the occupation & income of an individual, as it generally increases the range & sophistication of jobs an individual is qualified for.\n",
    "5.  **Workclass** - Workclass (particularly unemployed status) would also be a decent indicator for income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5B - Actual\n",
    "Now, I will use the `feature_importances_` feature to reveal the variables' strengths..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import a supervised learning model that has 'feature_importances_'\n",
    "\n",
    "# Train the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = AdaBoostClassifier().fit(X_train, y_train)\n",
    "\n",
    "# Extract the feature importances using .feature_importances_ \n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5C - Extracting Feature Importance\n",
    "The results from the visualization above reveals that capital loss (not capital gain) is the most important factor in determining income.\n",
    "\n",
    "Only 3 of the factors I hypothesized were found in the top features, where age & hours-per-week topped many of my guesses. My thoughts for why these features would be most relevant are as follows:\n",
    "1.  **Capital Loss** = Indicates how much an individual is losing in capital value, where a greater value would lessen their chances of earning above 50K.\n",
    "2.  **Age** = Age seems to be another indicator for income, where it would correlate with years of experience, another big indicator for income.\n",
    "3.  **Capital gain** = Like capital loss, would be an indicator for how much a household earns, but probably provides less visibility to how much the individual is making (as their spending habits muddle the waters).\n",
    "4.  **Hours per week** = Although less of a factor, # of hours still tends to be a decent indicator for income.\n",
    "5.  **Education** = For the reasons stated above, education tends (although not always) to improve the range/sophistication of career opportunities available upon graduation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra - Exploring the impact of Feature Reduction\n",
    "\n",
    "From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data.\n",
    "\n",
    "This led me to wonder what the impact on performance would be if I reduced the number of features (trading-off performance metrics for lower training & prediction time). So I tried a similar model that uses only the top-5 features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nickb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8609\n",
      "F-score on testing data: 0.7316\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8321\n",
      "F-score on testing data: 0.6740\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print(\"Final Model trained on full data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print(\"\\nFinal Model trained on reduced data\\n------\")\n",
    "print(\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Veredict**\n",
    "\n",
    "The final model's F-score lowered slightly (around -3%), whereas the decrease in accuracy score was a bit more significant (around -6%).\n",
    "\n",
    "If the training time was a lot longer (though this would require a way larger dataset), it would definitely be worth considering going for the reduced dataset (with feature selection). The drop in accuracy & f-score can be minimized through further experimentation/adjusting of hyperparameters and classification models, so there's a good chance it may not be as big of a compromise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[NbConvertApp] Converting notebook finding_donors.ipynb to html',\n",
       " '[NbConvertApp] Writing 455695 bytes to finding_donors.html']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export to HTML\n",
    "!!jupyter nbconvert *.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}