finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Machine Learning with TensorFlow\n",
    "## Project: Finding Donors for *CharityML*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This is my first project of the Inro to Machine Learning with Tensorflow Nanodegree! \n",
    "\n",
    "In this project, I will employ several supervised algorithms of my choice to accurately model individuals' income using data collected from the 1994 U.S. Census. I will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. My goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. \n",
    "\n",
    "### Context\n",
    "This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Version\n",
    "Pyhon Version used in this notebook is: `Python 3.12.6`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset\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\"_. 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.\n",
    "\n",
    "**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": [
    "## Theory\n",
    "\n",
    "### One-hot encoding \n",
    "\n",
    "It creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`:\n",
    "\n",
    "|   | someFeature |                    \n",
    "| :-: | :-: |                            \n",
    "| 0 |  B  |  \n",
    "| 1 |  C  |\n",
    "| 2 |  A  |  \n",
    "\n",
    "We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`:\n",
    "\n",
    "|| someFeature_A | someFeature_B | someFeature_C |\n",
    "| :-: | :-: | :-: | :-: |\n",
    "|0| 0 | 1 | 0 |\n",
    "|1| 0 | 0 | 1 |\n",
    "|2| 1 | 0 | 0 |\n",
    "\n",
    "\n",
    "### Metrics and the Naive Predictor\n",
    "*CharityML*, equipped with their research, knows individuals that make more than $50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than $50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than $50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than $50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{\\text{precision} \\cdot \\text{recall}}{\\left( \\beta^2 \\cdot \\text{precision} \\right) + \\text{recall}} $$\n",
    "\n",
    "In particular, when $\\beta = 0.5$, more emphasis is placed on precision. This is called the $F_{0.5}$ score (or F-score for simplicity).\n",
    "\n",
    "Looking at the distribution of classes (those who make at most $50,000, and those who make more), it's clear most individuals do not make more than $50,000. This can greatly affect **accuracy**, since we could simply say *\"this person does not make more than $50,000\"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than $50,000, *CharityML* would identify no one as donors. \n",
    "\n",
    "\n",
    "#### Note: Recap of accuracy, precision, recall\n",
    "\n",
    "**Accuracy** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).\n",
    "\n",
    "**Precision** tells us what proportion of messages we classified as spam, actually were spam.\n",
    "It is a ratio of true positives (words classified as spam, and which are actually spam) to all positives (all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of\n",
    "\n",
    "$$\\text{Precision} = \\frac{\\text{True Positives}} {\\text{True Positives} + \\text{False Positives}}$$\n",
    "\n",
    "**Recall (sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam.\n",
    "It is a ratio of true positives (words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of\n",
    "\n",
    "$$\\text{Recall} = \\frac{\\text{True Positives}} {\\text{True Positives} + \\text{False Negatives}}$$\n",
    "\n",
    "For classification problems that are skewed in their classification distributions, like in our case, for example, if we had 100 text messages and only 2 were spam and the remaining 98 weren't, accuracy is not a very good metric. We could classify 90 messages as not spam (including the 2 that were spam, but we classify them as not spam, hence they would be false negatives) and 10 as spam (all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score and the weighted average (harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean when dealing with ratios)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  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 you may choose from:**\n",
    "- Gaussian Naive Bayes (GaussianNB)\n",
    "- Decision Trees\n",
    "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
    "- Stochastic Gradient Descent Classifier (SGDC)\n",
    "- K-Nearest Neighbors (KNeighbors)\n",
    "- Support Vector Machines (SVM)\n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Exploring the Data\n",
    "The following code cell below load necessary Python libraries and load the census data. \n",
    "\n",
    "Note the last column from this dataset, `income`, will be the 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": 1,
   "metadata": {},
   "outputs": [],
   "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",
    "import matplotlib.pyplot as plt\n",
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Import two metrics from sklearn - fbeta_score and accuracy_score, 'make_scorer'\n",
    "from sklearn.metrics import fbeta_score, accuracy_score, make_scorer\n",
    "\n",
    "# Import 'GridSearchCV'\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Import a supervised learning model that has 'feature_importances_'\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Import the three supervised learning models from sklearn\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Import supplementary visualization code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</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>HS-grad</td>\n",
       "      <td>9.0</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>0.0</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>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</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>0.0</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>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</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",
       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "2   38            Private         HS-grad            9.0             Divorced   \n",
       "3   53            Private            11th            7.0   Married-civ-spouse   \n",
       "4   28            Private       Bachelors           13.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male        2174.0   \n",
       "1     Exec-managerial         Husband   White     Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black     Male           0.0   \n",
       "4      Prof-specialty            Wife   Black   Female           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country income  \n",
       "0           0.0            40.0   United-States  <=50K  \n",
       "1           0.0            13.0   United-States  <=50K  \n",
       "2           0.0            40.0   United-States  <=50K  \n",
       "3           0.0            40.0   United-States  <=50K  \n",
       "4           0.0            40.0            Cuba  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the Census dataset\n",
    "df = pd.read_csv(\"data/census.csv\")\n",
    "\n",
    "# Success - Display the first record\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45222 entries, 0 to 45221\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   age              45222 non-null  int64  \n",
      " 1   workclass        45222 non-null  object \n",
      " 2   education_level  45222 non-null  object \n",
      " 3   education-num    45222 non-null  float64\n",
      " 4   marital-status   45222 non-null  object \n",
      " 5   occupation       45222 non-null  object \n",
      " 6   relationship     45222 non-null  object \n",
      " 7   race             45222 non-null  object \n",
      " 8   sex              45222 non-null  object \n",
      " 9   capital-gain     45222 non-null  float64\n",
      " 10  capital-loss     45222 non-null  float64\n",
      " 11  hours-per-week   45222 non-null  float64\n",
      " 12  native-country   45222 non-null  object \n",
      " 13  income           45222 non-null  object \n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In this dataset we have 14 features with diffrent datatypes. They are all complete regarding the non-null counts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Exploration\n",
    "A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than $50,000. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The total number of records, `n_records`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n"
     ]
    }
   ],
   "source": [
    "# Total number of records\n",
    "n_records = df.shape[0]\n",
    "print(\"Total number of records: {}\".format(n_records))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The number of individuals making more than $50,000 annually, `n_greater_50k`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<=50K', '>50K'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].unique() ## looking into the values on income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Individuals making more than $50,000: 11208\n"
     ]
    }
   ],
   "source": [
    "# Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = df[df['income'] == \">50K\"]['age'].count() ## the count of any column in this filtering is the same so I chose the age to count\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The number of individuals making at most $50,000 annually, `n_at_most_50k`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Individuals making at most $50,000: 34014\n"
     ]
    }
   ],
   "source": [
    "# Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = df[df['income'] == \"<=50K\"]['age'].count() ## the count of any column in this filtering is the same so I chose the age to count\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The percentage of individuals making more than $50,000 annually, `greater_percent`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of individuals making more than $50,000: 24.78439697492371%\n"
     ]
    }
   ],
   "source": [
    "# Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = (n_greater_50k / n_records) * 100\n",
    "print(\"Percentage of individuals making more than $50,000: {}%\".format(greater_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Preparing the Data\n",
    "Before data can be used as input for machine learning algorithms, it must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Skewed Continuous Features\n",
    "A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: `capital-gain` and `capital-loss`. \n",
    "\n",
    "Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aqlamaai/Documents/Finding-Charity-Donors/visuals.py:50: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "Y = df['income'] ## I renamed the target feature as convention that is Y\n",
    "X = df.drop('income', axis = 1)\n",
    "\n",
    "# Visualize skewed continuous features of original data\n",
    "vs.distribution(df)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For highly-skewed feature distributions such as `capital-gain` and `capital-loss`, it is common practice to apply a [logarithmic transformation](https://en.wikipedia.org/wiki/Data_transformation_(statistics)) on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of 0 is undefined, so we must translate the values by a small amount above 0 to apply the the logarithm successfully.\n",
    "\n",
    "Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aqlamaai/Documents/Finding-Charity-Donors/visuals.py:50: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_log_transformed = pd.DataFrame(data = X)\n",
    "features_log_transformed[skewed] = X[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",
    "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `capital-gain` or `capital-loss` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.\n",
    "\n",
    "Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.301370</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</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>0.667492</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.452055</td>\n",
       "      <td>Self-emp-not-inc</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493151</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <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",
       "    </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",
       "\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  \n",
       "0      0.667492           0.0        0.397959   United-States  \n",
       "1      0.000000           0.0        0.122449   United-States  \n",
       "2      0.000000           0.0        0.397959   United-States  \n",
       "3      0.000000           0.0        0.397959   United-States  \n",
       "4      0.000000           0.0        0.397959            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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 = 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Preprocessing\n",
    "\n",
    "From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. \n",
    "\n",
    "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `income` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as 0 and 1, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_income(value):\n",
    "    \"\"\"\n",
    "    Encoding the income feature to numerical, Set records with \"<=50K\" to 0 and records with \">50K\" to 1.\n",
    "    Args:\n",
    "        value (str) either \"<=50K\" or \"<=50K\"\n",
    "    Returns:\n",
    "        0 if value is \"<=50K\" or 1 if value is \">50K\"\n",
    "    \"\"\"\n",
    "    if value == \"<=50K\":\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13188    1\n",
       "37403    0\n",
       "21952    0\n",
       "4231     0\n",
       "33078    0\n",
       "31904    0\n",
       "9564     1\n",
       "10669    0\n",
       "9943     0\n",
       "12023    0\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode the 'income_raw' data to numerical values\n",
    "Y_encoded = Y.apply(encode_income)\n",
    "\n",
    "Y_encoded.sample(10) ## to ensure accurate encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n",
      "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n"
     ]
    }
   ],
   "source": [
    "# Performing One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()\n",
    "X_encoded = pd.get_dummies(features_log_minmax_transform) \n",
    "\n",
    "# Print the number of features after one-hot encoding\n",
    "encoded = list(X_encoded.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": "code",
   "execution_count": 15,
   "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>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass_ Federal-gov</th>\n",
       "      <th>workclass_ Local-gov</th>\n",
       "      <th>workclass_ Private</th>\n",
       "      <th>workclass_ Self-emp-inc</th>\n",
       "      <th>workclass_ Self-emp-not-inc</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_ Portugal</th>\n",
       "      <th>native-country_ Puerto-Rico</th>\n",
       "      <th>native-country_ Scotland</th>\n",
       "      <th>native-country_ South</th>\n",
       "      <th>native-country_ Taiwan</th>\n",
       "      <th>native-country_ Thailand</th>\n",
       "      <th>native-country_ Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_ United-States</th>\n",
       "      <th>native-country_ Vietnam</th>\n",
       "      <th>native-country_ Yugoslavia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22221</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12170</th>\n",
       "      <td>0.205479</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>0.342466</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17615</th>\n",
       "      <td>0.054795</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28535</th>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>0.136986</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.295918</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29007</th>\n",
       "      <td>0.342466</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.438776</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4612</th>\n",
       "      <td>0.342466</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.905759</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34694</th>\n",
       "      <td>0.136986</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39437</th>\n",
       "      <td>0.095890</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "22221  0.301370       0.533333           0.0      0.000000        0.397959   \n",
       "12170  0.205479       0.533333           0.0      0.000000        0.397959   \n",
       "2048   0.342466       0.733333           0.0      0.000000        0.397959   \n",
       "17615  0.054795       0.333333           0.0      0.000000        0.397959   \n",
       "28535  0.219178       0.600000           0.0      0.000000        0.244898   \n",
       "837    0.136986       0.533333           0.0      0.000000        0.295918   \n",
       "29007  0.342466       0.533333           0.0      0.000000        0.438776   \n",
       "4612   0.342466       0.933333           0.0      0.905759        0.500000   \n",
       "34694  0.136986       0.533333           0.0      0.000000        0.397959   \n",
       "39437  0.095890       0.533333           0.0      0.000000        0.448980   \n",
       "\n",
       "       workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
       "22221                   False                 False                True   \n",
       "12170                   False                 False                True   \n",
       "2048                    False                 False                True   \n",
       "17615                   False                 False                True   \n",
       "28535                   False                 False                True   \n",
       "837                     False                 False                True   \n",
       "29007                   False                 False                True   \n",
       "4612                    False                 False               False   \n",
       "34694                   False                 False                True   \n",
       "39437                   False                 False                True   \n",
       "\n",
       "       workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  ...  \\\n",
       "22221                    False                        False  ...   \n",
       "12170                    False                        False  ...   \n",
       "2048                     False                        False  ...   \n",
       "17615                    False                        False  ...   \n",
       "28535                    False                        False  ...   \n",
       "837                      False                        False  ...   \n",
       "29007                    False                        False  ...   \n",
       "4612                      True                        False  ...   \n",
       "34694                    False                        False  ...   \n",
       "39437                    False                        False  ...   \n",
       "\n",
       "       native-country_ Portugal  native-country_ Puerto-Rico  \\\n",
       "22221                     False                        False   \n",
       "12170                     False                        False   \n",
       "2048                      False                        False   \n",
       "17615                     False                        False   \n",
       "28535                     False                        False   \n",
       "837                       False                        False   \n",
       "29007                     False                        False   \n",
       "4612                      False                        False   \n",
       "34694                     False                        False   \n",
       "39437                     False                        False   \n",
       "\n",
       "       native-country_ Scotland  native-country_ South  \\\n",
       "22221                     False                  False   \n",
       "12170                     False                  False   \n",
       "2048                      False                  False   \n",
       "17615                     False                  False   \n",
       "28535                     False                  False   \n",
       "837                       False                  False   \n",
       "29007                     False                  False   \n",
       "4612                      False                  False   \n",
       "34694                     False                  False   \n",
       "39437                     False                  False   \n",
       "\n",
       "       native-country_ Taiwan  native-country_ Thailand  \\\n",
       "22221                   False                     False   \n",
       "12170                   False                     False   \n",
       "2048                    False                     False   \n",
       "17615                   False                     False   \n",
       "28535                   False                     False   \n",
       "837                     False                     False   \n",
       "29007                   False                     False   \n",
       "4612                    False                     False   \n",
       "34694                   False                     False   \n",
       "39437                   False                     False   \n",
       "\n",
       "       native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "22221                            False                           True   \n",
       "12170                            False                           True   \n",
       "2048                             False                           True   \n",
       "17615                            False                           True   \n",
       "28535                            False                           True   \n",
       "837                              False                           True   \n",
       "29007                            False                           True   \n",
       "4612                             False                           True   \n",
       "34694                            False                           True   \n",
       "39437                            False                           True   \n",
       "\n",
       "       native-country_ Vietnam  native-country_ Yugoslavia  \n",
       "22221                    False                       False  \n",
       "12170                    False                       False  \n",
       "2048                     False                       False  \n",
       "17615                    False                       False  \n",
       "28535                    False                       False  \n",
       "837                      False                       False  \n",
       "29007                    False                       False  \n",
       "4612                     False                       False  \n",
       "34694                    False                       False  \n",
       "39437                    False                       False  \n",
       "\n",
       "[10 rows x 103 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded.sample(10) ## to ensure accurate encoding let's take a look "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and Split Data\n",
    "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, 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.\n",
    "\n",
    "Run the code cell below to perform this split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, Y_encoded, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "## Evaluating Model Performance\n",
    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners:\n",
    "\n",
    "- `clf_A = KNeighborsClassifier()` K neighbors.\n",
    "- `clf_B = SVC()` Suppportive vector\n",
    "- `clf_C = LogisticRegression()` Logestic Regression\n",
    "\n",
    "and the fourth algorithm is known as a *naive predictor*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - Naive Predictor Performace\n",
    "* If we chose a model that always predicted an individual made more than $50,000, what would  that model's accuracy and F-score be on this dataset? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = np.sum(Y_encoded) # Counting the ones as this is the naive case. Note that 'Y_encoded' is the 'income_raw' data encoded to numerical values done in the data preprocessing step.\n",
    "FP = Y_encoded.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating accuracy, precision and recall\n",
    "accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
    "recall =  TP / (TP + FN)\n",
    "precision =  TP / (TP + FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "beta = 0.5 \n",
    "beta_sqr = np.square(beta)\n",
    "f_score = (1 + beta_sqr) * ((precision * recall) / ((beta_sqr * precision) + recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, f_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Answer:**\n",
    "\n",
    "The naive predictor is a base model without any intelligence. based on the calculations above the accuracy is 0.2478, that is the base model is only correct for these cases. If it always predicts an indiviual makes more than 50K$. As for the F-score or the harmonic mean of percision and recall when beta is 0.5 then it is equal to 0.2917.\n",
    "\n",
    "- When we have a model that always predicts 1 (i.e., the individual makes more than 50k) then our model will have no True Negatives (TN) or False Negatives (FN) as we are not making any negative (0 value) predictions. Therefore Accuracy in this case becomes the same as our Precision (True Positives / (True Positives + False Positives)) as every prediction that we have made with value 1 that should have 0 becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. \n",
    "* Our Recall score (True Positives / (True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n",
    "\n",
    "- Describe one real-world application in industry where the model can be applied. \n",
    "- What are the strengths of the model; when does it perform well?\n",
    "- What are the weaknesses of the model; when does it perform poorly?\n",
    "- What makes this model a good candidate for the problem, given what you know about the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Answer:**\n",
    "\n",
    "three of the supervised learning models used are:\n",
    "\n",
    "- **K-Nearest Neighbors**\n",
    "    - Real World Application\n",
    "        - It is used to Improved investment decisions in finance,\n",
    "        - It is used in Early disease detection in healthcare.\n",
    "        - It is used to Increased sales and user satisfaction in e-commerce.\n",
    "        - It is used to Enhanced security and automation in Computer vision.\n",
    "        - https://keylabs.ai/blog/k-nearest-neighbors-knn-real-world-applications/\n",
    "\n",
    "    - Strengths of the model\n",
    "        -  Is is easy to implement, simple and fast.\n",
    "        - It addapts easily, and genralize well on new data.\n",
    "        - It have a few hyperparameters compared to other ML algorithims.\n",
    "        - https://www.ibm.com/topics/knn#:~:text=The%20k%2Dnearest%20neighbors%20(KNN,used%20in%20machine%20learning%20today.\n",
    "\n",
    "    - weaknesses of the model\n",
    "        - it tends to fall victim to the dimensionality, as it doesn't perform well with high-dimensional data inputs.\n",
    "        - it is prone to overfitting, depends on the value of K.\n",
    "        - https://www.ibm.com/topics/knn#:~:text=The%20k%2Dnearest%20neighbors%20(KNN,used%20in%20machine%20learning%20today.\n",
    "\n",
    "    - Reason for chosing it\n",
    "        - The reason is because the problem involves classifying for potentioal donoers, so the KNN would leverage the similarities in the data to make the predictions. In addition to it's adaptibality to new data.\n",
    "\n",
    "- **Support vector Machine**\n",
    "    - Real World Application \n",
    "        - Used in Face detection as it clrifies the face and the non-face boundaries between them.\n",
    "        - Text and hypertext categorization as to classify douments into diffrent categories.\n",
    "        - It is also used in Bioinformatics to classify cancer and classify genes.\n",
    "        - It is also widley used in Handwritten recognition.\n",
    "        - https://data-flair.training/blogs/applications-of-svm/\n",
    "    - Strengths of the model\n",
    "        - It works well when there is a clear margin of sepration between classes.\n",
    "        - It is effective in high dimensional spaces.\n",
    "        - Also affective when the number of dimensions is greater than the number of samples.\n",
    "        - It is also memory efficeint (relaivly)\n",
    "        - https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107\n",
    "    - Weaknesses of the model\n",
    "        - It is not suitable for large datasets.\n",
    "        - It doesn't perform well when the dataset has noise and the target classes are overlapping.\n",
    "        - It is prone to underfit in casses where the number of features for each data points exceeds the numbers of training data sample.\n",
    "        - https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107\n",
    "    - Reason for chosing it:\n",
    "       - since the dataset after preprocessng and one hot encoding included a high-dimentional space (relativly) and it's binary-classification, so SVM would help to make decsion boundaries to classify the potentioal donoers.\n",
    "\n",
    "- **Logistic Regression**\n",
    "    - Real World Application \n",
    "        - It is used in Credit Scoring and it shows remarkble results.\n",
    "        - It is used in medical feilds, for exanple to identfy the relationship between certain micrio-RNA and genese.\n",
    "        - In text editing LR is used to transform this data into usable text with grammatical and semantic correct formatting. \n",
    "        - In hotel booking, logistic regression is used trying to predict either user will change a journey date or not.\n",
    "        - https://activewizards.com/blog/5-real-world-examples-of-logistic-regression-application\n",
    "    - Strengths of the model\n",
    "        - Outputs have a nice probabilistic interpretation, and the algorithm can be regularized to avoid overfitting. Logistic models can be updated easily with new data using stochastic gradient descent.\n",
    "        - https://elitedatascience.com/machine-learning-algorithms#:~:text=Strengths%3A%20Outputs%20have%20a%20nice,or%20non%2Dlinear%20decision%20boundaries.\n",
    "    - weaknesses of the model\n",
    "        - Logistic regression tends to underperform when there are multiple or non-linear decision boundaries. They are not flexible enough to naturally capture more complex relationships.\n",
    "        - https://elitedatascience.com/machine-learning-algorithms#:~:text=Strengths%3A%20Outputs%20have%20a%20nice,or%20non%2Dlinear%20decision%20boundaries.\n",
    "    - Reason for chosing it\n",
    "        - The problem is binary classification so the LR works well to linearly separte the data and make the task of prediction. it's simple and less prone to overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Creating a Training and Predicting Pipeline\n",
    "To properly evaluate the performance of each model I've chosen, it's important that we create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "   \"\"\"\n",
    "   Trains a model and predicts to return the results of performance metrics.\n",
    "   Args:\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",
    "   Returns:\n",
    "      results (set)\n",
    "   \"\"\"\n",
    "   results = {}\n",
    "\n",
    "   # Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])\n",
    "   train_start = time() # Get start time\n",
    "   learner = learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "   train_end = time() # Get end time\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",
    "   pred_start = time() # Get start time\n",
    "   predictions_test = learner.predict(X_test)\n",
    "   predictions_train = learner.predict(X_train[:300])\n",
    "   pred_end = time() # Get end time\n",
    "\n",
    "   results['train_time'] = train_end -  train_start # Calculate the training time\n",
    "   results['pred_time'] = pred_end - pred_start   # Calculate the total prediction time\n",
    "   results['acc_train'] = accuracy_score(y_train[:300], predictions_train) # Compute accuracy on the first 300 training samples which is y_train[:300]\n",
    "   results['acc_test'] = accuracy_score(y_test, predictions_test) # Compute accuracy on test set using accuracy_score()\n",
    "   results['f_train'] = fbeta_score(y_train[:300], predictions_train,  beta = 0.5)    # Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "   results['f_test'] = fbeta_score(y_test, predictions_test, beta = 0.5) # Compute F-score on the test set which is y_test\n",
    "\n",
    "   # Success\n",
    "   print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "\n",
    "   return results  # Return the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Initial Model Evaluation\n",
    "In this section, the default settings for each model are used, then one specific model is tuned in a later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize the three models\n",
    "\n",
    "clf_A = KNeighborsClassifier()\n",
    "clf_B = SVC(random_state=42)\n",
    "clf_C = LogisticRegression(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the number of samples for 1%, 10%, and 100% of the training data, samples_1 is 1% of samples_100 ( the count of the values is set to be `int` and not `float`)\n",
    "samples_100 = len(y_train)\n",
    "samples_10 = int(0.1*samples_100)\n",
    "samples_1 = int(0.01*samples_100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier trained on 361 samples.\n",
      "KNeighborsClassifier trained on 3617 samples.\n",
      "KNeighborsClassifier trained on 36177 samples.\n",
      "SVC trained on 361 samples.\n",
      "SVC trained on 3617 samples.\n",
      "SVC trained on 36177 samples.\n",
      "LogisticRegression trained on 361 samples.\n",
      "LogisticRegression trained on 3617 samples.\n",
      "LogisticRegression trained on 36177 samples.\n"
     ]
    }
   ],
   "source": [
    "# Collect results on the learners\n",
    "results = {}\n",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aqlamaai/Documents/Finding-Charity-Donors/visuals.py:122: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
      "  pl.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, f_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**interpretations on performance:** \n",
    "\n",
    "> Model training time:\n",
    "- SVC takes the longer time to train, specialy when the training set is all used 100%. that seems to be computationally expensive.\n",
    "- The KNN and the LR take much less time in training overall samples (1%, 10%, 100%)\n",
    "\n",
    "> Accuracy Score on training:\n",
    "- All the algorthims acheive a high training accuracy on all subsets. LR seems to have almost the same accuracies on all subestes. The highest accuracy scored when the KNN model is trained on 100% of the training set. \n",
    "- SVC performs the same on all training subsets.\n",
    "\n",
    "> F-score on training:\n",
    "- they seem to score similarly high across all training subsets. That means there is a good balance between percision and recall training.\n",
    "\n",
    "> Model Prediction Times:\n",
    "- SVM is the sloweset in making predictions, with the it's increasing hugely when the whole training set is used as it reaches almost 14 seconds.\n",
    "- KNN time to predict also slightly increases when the training subset increases.\n",
    "- As for the logestic regresion, is is the fastest in prediction time, alsmost no time in comparisio to the other two algorthims.\n",
    "\n",
    "> Accuracy score on testing:\n",
    "- Models have almost the same performance, within each subset of the training samples. and within the same range of training. \n",
    "- That is the models are genralizing well on the unseen data, non of the algorthims have the overfitting issue.\n",
    "- The highest score on testing accuracy is the SVC model.\n",
    "\n",
    "> F-score on testing:\n",
    "- This scores drops from 0.8 to 0.6 for all models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Improving Results\n",
    "In this final section, I will choose from the three supervised learning models the *best* model to use on the data. then I perform a grid search optimization for the model 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": [
    "### Question 3 - Choosing the Best Model\n",
    "\n",
    "* Based on the evaluation performed earlier,here is an explaination to *CharityML* which of the three models I believe to be most appropriate for the task of identifying individuals that make more than $50,000. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Answer:**\n",
    "\n",
    "Looking at the graph from the cell above and checking the F score for the testing set when 100% of the training set is used. The model that has the highest score is SVC model.\n",
    "\n",
    "\n",
    "* The F score on the testing when 100% of the training data is used is when the SVC model is used. It has the highest balance between percision and recall. \n",
    "\n",
    "* Even though the SVC is highest f-score and the highest accuracy on testing it's prediction/training time where the highest.\n",
    "\n",
    "* As for the algorithm's suitability for the data, since its relativly high computational expemse, that makes it less suitable. \n",
    "\n",
    "* We have a tradeoff between optimal performance and computational resources here. So the choice is to go for the seconf best regarding f-score and testing accuracy that is the LR model. That would be more practical for CharityML task..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Describing the Model in Layman's Terms\n",
    "\n",
    "* Here is a paragraph to explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "\n",
    "In simple terms the reason that the LR model is chosen to be used for this task is that it is simple and effective that helps us predict the task. The model will learn to recognize the patterns using the diffrent features that will be related to the outcome. By looking at a new person's data and predict if they are likely to donate. Weighing the importance of each feature and combining them into a single number. If this number is above a certain threshold the model predicts that the person will earn more than 50K$. If it's below the prediction will be that the person's income is less that 50K$. It is fast and effective for a suitable solution to this problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Model Tuning\n",
    "In this section we Fine tune the LR model. Using grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. Using the entire training set for this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the classifier\n",
    "clf = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the parameters list to tune, using a dictionary \n",
    "## C is the regularization strength\n",
    "## penalty is the regularization type\n",
    "## solver has to match the penalty\n",
    "## max_iter is set because I got a  ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
    "\n",
    "parameters = {\n",
    "    'C': [0.1, 1, 10],\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'solver': ['liblinear', 'saga'],\n",
    "    'max_iter': [1000]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Performing grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(estimator= clf, param_grid= parameters, scoring= scorer, cv = 5)\n",
    "\n",
    "## Fiting the grid search object to the training data to find the optimal parameters using fit()\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "## Geting the estimator\n",
    "best_clf = grid_fit.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8456\n",
      "F-score on testing data: 0.7103\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8449\n",
      "Final F-score on the testing data: 0.7092\n"
     ]
    }
   ],
   "source": [
    "# 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": [
    "### Question 5 - Final Model Evaluation\n",
    "\n",
    "The table below shows the results of the optimzation done on the LR model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |      0.8456       |       0.8449    |\n",
    "| F-score        |      0.7103     |    0.7092      |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "When comparing the optimized and the unoptimized, there is a very small diffrences on performance. The optimized slightly improved with both metrics (the accyracy and the f-score.). Imporvements are minimal.\n",
    "\n",
    "When comaring the optimized model to the naive predictor, the optimzed is way better than the base naive model off course. As the accuracy for the naive is 0.2478 as for the optimzed model it's 0.8449. Similarly regarding the f-score metic, is much better for the optimzed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Feature Importance\n",
    "\n",
    "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. \n",
    "\n",
    "In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than $50,000.\n",
    "\n",
    "A scikit-learn classifier that is random forests has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  \n",
    "\n",
    "I fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6 - Feature Relevance Observation\n",
    "When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data.\n",
    "\n",
    "Of these thirteen records, let's determine which five features do you believe to be most important for prediction, and in what order would I rank them and why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'education_level', 'education-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "From the important features I would chose these 5 to be the most important for predicting in this order and for the following reasons:\n",
    "- `occupation`: higher hierarchy in a company would usually earn more income.\n",
    "- `workclass`: it defines how much a person could earn.\n",
    "- `marital-status`: maried people tend to consume more in a year, that might cause less income.\n",
    "- `hours-per-week`: didcated hours will could gain more per week to the total outcome in a year.\n",
    "- `education_level`: higher educational levels would get paid more usually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Extracting Feature Importance\n",
    "A `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it would be the RandomForest classifier. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Training the supervised model on the training set using .fit(X_train, y_train)\n",
    "model = RandomForestClassifier()\n",
    "model = model.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": [
    "> Interprestions:\n",
    "\n",
    "- Education_num feature: has the highest weight overall features. with 0.6 contribution.\n",
    "- Martial- status: comes a secnd weight with 0.6 weight.\n",
    "- Capital-gain: between 0.5 and 0.4.\n",
    "- Hours-per week: between 0.3 and 0.4\n",
    "- then finally comes the age where it has a contribution of wight between 0.2 and 0.3/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7 - Extracting Feature Importance\n",
    "\n",
    "Observing the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above $50,000.  Let's discuss how my answer compares to the actual important features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Answer:**\n",
    "\n",
    "- My selction was very close to the actual importance of the features. With the follwong diffrences: as the occuplation and the workclass didn't wight as I thought they would have.\n",
    "\n",
    "- The maretial status and the hours per week were spot on and their significance were confirmed too. As the visulaization shows how importnat they are. with 0.5 and higher for the martial status and 0.3 and higher for the hours per week wirghts.\n",
    "\n",
    "- As for the education level it didn't show in the visualozation, instead the education num has the highest contribution over all features. That is teh idea of importance of education regards income.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. \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. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. \n",
    "\n",
    "Let's use the same optimized model found earlier, and train it on the same training set *with only the top five important features*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8449\n",
      "F-score on testing data: 0.7092\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8284\n",
      "F-score on testing data: 0.6731\n"
     ]
    }
   ],
   "source": [
    "# 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": [
    "> Results:\n",
    "\n",
    "| Metric  | Final Model (Full Data) | Final Model (Reduced Data) |\n",
    "|------------|-----------------------|--------------------------|\n",
    "| **Accuracy**  =    | 0.8449         | 0.8284    |\n",
    "| **F-score**       | 0.7092        | 0.6731      |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8 - Effects of Feature Selection\n",
    "\n",
    "* How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?\n",
    "* If training time was a factor, would you consider using the reduced data as your training set?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Answer:**\n",
    "\n",
    "- The final model trained on the full dataset (all features) has a slightly higher accuracy (0.8449 vs. 0.8284) and F-score (0.7092 vs. 0.6731) than the model trained on the reduced dataset with only five features.\n",
    "\n",
    "- If the training time was a factor, I would consider using the redced data as the training set. Even though it slightly reduces the accuracy and f-score. Based on the computational resources, using fewer features would consume less resources."
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    "> I had dependecies problems in my Visual Code when trying to convert the notebook to html file, so I used an online conerter as I'm working to fix the problem on my visual code, hope that's not gonna cause failing the submission."
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