2123 lines
521 KiB
Plaintext
2123 lines
521 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data Scientist Nanodegree\n",
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"## Supervised Learning\n",
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"## Project: Finding Donors for *CharityML*"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Welcome to the first project of the Data Scientist Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
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"\n",
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"In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. \n",
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"\n",
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">**Note:** Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Getting Started\n",
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"\n",
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"In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. 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. \n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"----\n",
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"## Exploring the Data\n",
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"Run the code cell below to 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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>age</th>\n",
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" <th>workclass</th>\n",
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" <th>education_level</th>\n",
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" <th>education-num</th>\n",
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" <th>marital-status</th>\n",
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" <th>occupation</th>\n",
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" <th>relationship</th>\n",
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" <th>race</th>\n",
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" <th>sex</th>\n",
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" <th>capital-gain</th>\n",
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" <th>capital-loss</th>\n",
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" <th>hours-per-week</th>\n",
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" <th>native-country</th>\n",
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" <th>income</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>39</td>\n",
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" <td>State-gov</td>\n",
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" <td>Bachelors</td>\n",
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" <td>13.0</td>\n",
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" <td>Never-married</td>\n",
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" <td>Adm-clerical</td>\n",
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" <td>Not-in-family</td>\n",
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" <td>White</td>\n",
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" <td>Male</td>\n",
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" <td>2174.0</td>\n",
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" <td>0.0</td>\n",
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" <td>40.0</td>\n",
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" <td>United-States</td>\n",
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" <td><=50K</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" age workclass education_level education-num marital-status \\\n",
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"0 39 State-gov Bachelors 13.0 Never-married \n",
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"\n",
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" occupation relationship race sex capital-gain capital-loss \\\n",
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"0 Adm-clerical Not-in-family White Male 2174.0 0.0 \n",
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"\n",
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" hours-per-week native-country income \n",
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"0 40.0 United-States <=50K "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Import libraries necessary for this project\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from time import time\n",
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"from IPython.display import display # Allows the use of display() for DataFrames\n",
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"import seaborn as sns\n",
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"import matplotlib as plt\n",
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"\n",
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"# Import supplementary visualization code visuals.py\n",
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"import visuals as vs\n",
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"\n",
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"# Pretty display for notebooks\n",
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"%matplotlib inline\n",
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"\n",
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"# Load the Census dataset\n",
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"data = pd.read_csv(\"census.csv\")\n",
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"\n",
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"# Success - Display the first record\n",
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"display(data.head(1))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Implementation: Data Exploration\n",
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"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. In the code cell below, you will need to compute the following:\n",
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"- The total number of records, `'n_records'`\n",
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"- The number of individuals making more than \\$50,000 annually, `'n_greater_50k'`.\n",
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"- The number of individuals making at most \\$50,000 annually, `'n_at_most_50k'`.\n",
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"- The percentage of individuals making more than \\$50,000 annually, `'greater_percent'`.\n",
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"\n",
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"** HINT: ** You may need to look at the table above to understand how the `'income'` entries are formatted. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Check for missing data\n",
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"To check for missing data we need to see if any rows are missing:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" age education-num capital-gain capital-loss hours-per-week\n",
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"count 45222.000000 45222.000000 45222.000000 45222.000000 45222.000000\n",
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"mean 38.547941 10.118460 1101.430344 88.595418 40.938017\n",
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"std 13.217870 2.552881 7506.430084 404.956092 12.007508\n",
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"min 17.000000 1.000000 0.000000 0.000000 1.000000\n",
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"25% 28.000000 9.000000 0.000000 0.000000 40.000000\n",
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"50% 37.000000 10.000000 0.000000 0.000000 40.000000\n",
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"75% 47.000000 13.000000 0.000000 0.000000 45.000000\n",
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"max 90.000000 16.000000 99999.000000 4356.000000 99.000000\n",
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"Total number of rows for income = 45222\n",
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"\n",
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"No missing data\n"
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]
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}
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],
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"source": [
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"print(data.describe())\n",
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"print('Total number of rows for income = {}\\n'.format(data['income'].shape[0]))\n",
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"if (data.isnull().values.any()) == False:\n",
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" print('No missing data')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<=50K 34014\n",
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">50K 11208\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"from collections import Counter\n",
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"income_values = pd.Series(data['income'].str.replace('[\\[\\]\\']','').str.split(',').map(Counter).sum())\n",
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"print(income_values)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total number of records: 45222\n",
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"Individuals making more than $50,000: 11208\n",
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"Individuals making at most $50,000: 34014\n",
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"Percentage of individuals making more than $50,000: 24.78%\n"
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]
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}
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],
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"source": [
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"# TODO: Total number of records\n",
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"n_records = income_values.sum()\n",
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"\n",
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"# TODO: Number of records where individual's income is more than $50,000\n",
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"n_greater_50k = income_values[1]\n",
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"\n",
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"# TODO: Number of records where individual's income is at most $50,000\n",
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"n_at_most_50k = income_values[0]\n",
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"\n",
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"# TODO: Percentage of individuals whose income is more than $50,000\n",
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"greater_percent = 100 * (n_greater_50k/n_records)\n",
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"\n",
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"# Print the results\n",
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"print(\"Total number of records: {}\".format(n_records))\n",
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"print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
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"print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
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"print(\"Percentage of individuals making more than $50,000: {:.2f}%\".format(greater_percent))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"** Featureset Exploration **\n",
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"\n",
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"* **age**: continuous. \n",
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"* **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. \n",
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"* **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",
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"* **education-num**: continuous. \n",
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"* **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. \n",
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"* **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",
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"* **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. \n",
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"* **race**: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. \n",
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"* **sex**: Female, Male. \n",
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"* **capital-gain**: continuous. \n",
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"* **capital-loss**: continuous. \n",
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"* **hours-per-week**: continuous. \n",
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"* **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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"----\n",
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"## Preparing the Data\n",
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"Before data can be used as input for machine learning algorithms, it often 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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Transforming Skewed Continuous Features\n",
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<seaborn.axisgrid.PairGrid at 0x7f032ee776d8>"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 900x900 with 30 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.pairplot(data, diag_kind='hist')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Using seaborn distplot we can use rug=True to see where the values lie. We can then adjust the y axis and the number of bins to get a better view."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1500)"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"hist0 = sns.distplot(data['capital-loss'], kde=False, rug=True, bins=10)\n",
|
||
"axes0 = hist0.axes\n",
|
||
"axes0.set_ylim(0,1500)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1000)"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"hist1 = sns.distplot(data['capital-gain'], kde=False, rug=True, bins=10)\n",
|
||
"axes1 = hist1.axes\n",
|
||
"axes1.set_ylim(0,1000)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"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": [
|
||
"# 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": [
|
||
"For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">logarithmic transformation</a> 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": 11,
|
||
"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": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1500)"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"hist2 = sns.distplot(features_log_transformed['capital-gain'], kde=False, rug=True, bins=10)\n",
|
||
"axes2 = hist2.axes\n",
|
||
"axes2.set_ylim(0,1500)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1500)"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"hist3 = sns.distplot(features_log_transformed['capital-loss'], kde=False, rug=True, bins=10)\n",
|
||
"axes3 = hist3.axes\n",
|
||
"axes3.set_ylim(0,1500)"
|
||
]
|
||
},
|
||
{
|
||
"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": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
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|
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"<style scoped>\n",
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||
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|
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||
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||
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|
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|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>workclass</th>\n",
|
||
" <th>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": [
|
||
"# 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 = 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. One-hot encoding 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`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.\n",
|
||
"\n",
|
||
"| | someFeature | | someFeature_A | someFeature_B | someFeature_C |\n",
|
||
"| :-: | :-: | | :-: | :-: | :-: |\n",
|
||
"| 0 | B | | 0 | 1 | 0 |\n",
|
||
"| 1 | C | ----> one-hot encode ----> | 0 | 0 | 1 |\n",
|
||
"| 2 | A | | 1 | 0 | 0 |\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. In code cell below, you will need to implement the following:\n",
|
||
" - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_log_minmax_transform'` data.\n",
|
||
" - Convert the target label `'income_raw'` to numerical entries.\n",
|
||
" - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
|
||
]
|
||
},
|
||
{
|
||
"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>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>31082</th>\n",
|
||
" <td>0.260274</td>\n",
|
||
" <td>Local-gov</td>\n",
|
||
" <td>Some-college</td>\n",
|
||
" <td>0.600000</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>0.397959</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>43423</th>\n",
|
||
" <td>0.356164</td>\n",
|
||
" <td>Self-emp-not-inc</td>\n",
|
||
" <td>HS-grad</td>\n",
|
||
" <td>0.533333</td>\n",
|
||
" <td>Married-civ-spouse</td>\n",
|
||
" <td>Adm-clerical</td>\n",
|
||
" <td>Wife</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.142857</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29225</th>\n",
|
||
" <td>0.383562</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>Some-college</td>\n",
|
||
" <td>0.600000</td>\n",
|
||
" <td>Married-civ-spouse</td>\n",
|
||
" <td>Craft-repair</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>0.500000</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8419</th>\n",
|
||
" <td>0.410959</td>\n",
|
||
" <td>Federal-gov</td>\n",
|
||
" <td>Some-college</td>\n",
|
||
" <td>0.600000</td>\n",
|
||
" <td>Married-civ-spouse</td>\n",
|
||
" <td>Adm-clerical</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>0.397959</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35362</th>\n",
|
||
" <td>0.164384</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>11th</td>\n",
|
||
" <td>0.400000</td>\n",
|
||
" <td>Never-married</td>\n",
|
||
" <td>Machine-op-inspct</td>\n",
|
||
" <td>Own-child</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.418367</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age workclass education_level education-num \\\n",
|
||
"31082 0.260274 Local-gov Some-college 0.600000 \n",
|
||
"43423 0.356164 Self-emp-not-inc HS-grad 0.533333 \n",
|
||
"29225 0.383562 Private Some-college 0.600000 \n",
|
||
"8419 0.410959 Federal-gov Some-college 0.600000 \n",
|
||
"35362 0.164384 Private 11th 0.400000 \n",
|
||
"\n",
|
||
" marital-status occupation relationship race sex \\\n",
|
||
"31082 Married-civ-spouse Exec-managerial Husband White Male \n",
|
||
"43423 Married-civ-spouse Adm-clerical Wife White Female \n",
|
||
"29225 Married-civ-spouse Craft-repair Husband White Male \n",
|
||
"8419 Married-civ-spouse Adm-clerical Husband Black Male \n",
|
||
"35362 Never-married Machine-op-inspct Own-child White Male \n",
|
||
"\n",
|
||
" capital-gain capital-loss hours-per-week native-country \n",
|
||
"31082 0.0 0.0 0.397959 United-States \n",
|
||
"43423 0.0 0.0 0.142857 United-States \n",
|
||
"29225 0.0 0.0 0.500000 United-States \n",
|
||
"8419 0.0 0.0 0.397959 United-States \n",
|
||
"35362 0.0 0.0 0.418367 United-States "
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# non_numeric_features = features_log_minmax_transform.drop(numerical, axis=1)\n",
|
||
"features_log_minmax_transform.sample(frac=1).head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"103 total features after one-hot encoding.\n",
|
||
"\n",
|
||
"Encoded feature names are:\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",
|
||
"\n",
|
||
"The income col now looks like:\n",
|
||
"43910 1\n",
|
||
"21041 1\n",
|
||
"44207 0\n",
|
||
"7311 0\n",
|
||
"26982 0\n",
|
||
"Name: income, dtype: object\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# TODO: 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",
|
||
"# TODO: Encode the 'income_raw' data to numerical values\n",
|
||
"income_raw.iloc[::-1][income_raw.iloc[::-1] == '<=50K'] = 0\n",
|
||
"income_raw.iloc[::-1][income_raw.iloc[::-1] == '>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.\\n\".format(len(encoded)))\n",
|
||
"\n",
|
||
"# Uncomment the following line to see the encoded feature names\n",
|
||
"print(f'Encoded feature names are:\\n{encoded}\\n')\n",
|
||
"print(f'The income col now looks like:\\n{income_raw.sample(frac=1).head(5)}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Convert income raw to dtype of int32"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 0\n",
|
||
"1 0\n",
|
||
"2 0\n",
|
||
"Name: income, dtype: int32"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"income_raw = pd.Series(income_raw, dtype='int32')\n",
|
||
"income_raw.head(3)"
|
||
]
|
||
},
|
||
{
|
||
"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": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Correcting a few things here:\n",
|
||
"sklearn v0.21.2 uses model_selection for train_test_split\n",
|
||
"\n",
|
||
"'income_raw' is our data - not 'income'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"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&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>24894</th>\n",
|
||
" <td>0.123288</td>\n",
|
||
" <td>0.533333</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.397959</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8437</th>\n",
|
||
" <td>0.465753</td>\n",
|
||
" <td>0.866667</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.602041</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24178</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.466667</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.142857</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25776</th>\n",
|
||
" <td>0.315068</td>\n",
|
||
" <td>0.600000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.900201</td>\n",
|
||
" <td>0.683673</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17204</th>\n",
|
||
" <td>0.630137</td>\n",
|
||
" <td>0.266667</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.397959</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 103 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age education-num capital-gain capital-loss hours-per-week \\\n",
|
||
"24894 0.123288 0.533333 0.0 0.000000 0.397959 \n",
|
||
"8437 0.465753 0.866667 0.0 0.000000 0.602041 \n",
|
||
"24178 0.000000 0.466667 0.0 0.000000 0.142857 \n",
|
||
"25776 0.315068 0.600000 0.0 0.900201 0.683673 \n",
|
||
"17204 0.630137 0.266667 0.0 0.000000 0.397959 \n",
|
||
"\n",
|
||
" workclass_ Federal-gov workclass_ Local-gov workclass_ Private \\\n",
|
||
"24894 0 0 1 \n",
|
||
"8437 0 0 0 \n",
|
||
"24178 0 0 0 \n",
|
||
"25776 0 0 1 \n",
|
||
"17204 0 0 1 \n",
|
||
"\n",
|
||
" workclass_ Self-emp-inc workclass_ Self-emp-not-inc ... \\\n",
|
||
"24894 0 0 ... \n",
|
||
"8437 0 1 ... \n",
|
||
"24178 0 1 ... \n",
|
||
"25776 0 0 ... \n",
|
||
"17204 0 0 ... \n",
|
||
"\n",
|
||
" native-country_ Portugal native-country_ Puerto-Rico \\\n",
|
||
"24894 0 0 \n",
|
||
"8437 0 0 \n",
|
||
"24178 0 0 \n",
|
||
"25776 0 0 \n",
|
||
"17204 0 0 \n",
|
||
"\n",
|
||
" native-country_ Scotland native-country_ South \\\n",
|
||
"24894 0 0 \n",
|
||
"8437 0 0 \n",
|
||
"24178 0 0 \n",
|
||
"25776 0 0 \n",
|
||
"17204 0 0 \n",
|
||
"\n",
|
||
" native-country_ Taiwan native-country_ Thailand \\\n",
|
||
"24894 0 0 \n",
|
||
"8437 0 0 \n",
|
||
"24178 0 0 \n",
|
||
"25776 0 0 \n",
|
||
"17204 0 0 \n",
|
||
"\n",
|
||
" native-country_ Trinadad&Tobago native-country_ United-States \\\n",
|
||
"24894 0 1 \n",
|
||
"8437 0 1 \n",
|
||
"24178 0 1 \n",
|
||
"25776 0 1 \n",
|
||
"17204 0 1 \n",
|
||
"\n",
|
||
" native-country_ Vietnam native-country_ Yugoslavia \n",
|
||
"24894 0 0 \n",
|
||
"8437 0 0 \n",
|
||
"24178 0 0 \n",
|
||
"25776 0 0 \n",
|
||
"17204 0 0 \n",
|
||
"\n",
|
||
"[5 rows x 103 columns]"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"features_final.sample(frac=1).head(5)"
|
||
]
|
||
},
|
||
{
|
||
"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_raw, \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",
|
||
"## 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 of your choice, and the fourth algorithm is known as a *naive predictor*."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 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",
|
||
"$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\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",
|
||
"`[True Positives/(True Positives + 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",
|
||
"`[True Positives/(True Positives + False Negatives)]`\n",
|
||
"\n",
|
||
"For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself 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, which is 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 as we are dealing with ratios)."
|
||
]
|
||
},
|
||
{
|
||
"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? You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later.\n",
|
||
"\n",
|
||
"** Please note ** that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from.\n",
|
||
"\n",
|
||
"** HINT: ** \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 our 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": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"TP = np.sum(income_raw)\n",
|
||
"FP = income_raw.count() - TP\n",
|
||
"TN = 0\n",
|
||
"FN = 0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"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",
|
||
"# TODO: Calculate accuracy, precision and recall\n",
|
||
"accuracy = (TP + TN) / (FP + FN + TP + TN)\n",
|
||
"recall = TP / (TP + FN)\n",
|
||
"precision = TP / (TP + FP)\n",
|
||
"\n",
|
||
"# TODO: 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": [
|
||
"### 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",
|
||
"- K-Nearest Neighbors (KNeighbors)\n",
|
||
"- Stochastic Gradient Descent Classifier (SGDC)\n",
|
||
"- Support Vector Machines (SVM)\n",
|
||
"- Logistic Regression"
|
||
]
|
||
},
|
||
{
|
||
"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?\n",
|
||
"\n",
|
||
"** HINT: **\n",
|
||
"\n",
|
||
"Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Naive Bayes (Gaussian)\n",
|
||
"\n",
|
||
"## Applications\n",
|
||
"Naive Bayes has many applications in the real-world. It is very popular in the Medical industry - which itself is a huge area and has many applications where it can be applied.\n",
|
||
"\n",
|
||
"From my own research in my MSc, I used the results of [1]. Here Naive Bayes was used to categorically identify phage virion proteins from features such as amino acid composition and nucleo-capsid thickness. My own research was motivated in trying to understand the numerical mathematical dynamics of these features. The classifier had excellent results in this case - and this raised further questions about the dynamics of the features of viral proteins over time which were (and still) largely unknown. **This was an excellent use case of machine learning** to identify viral proteins from seemingly healthy ones - but mathematically was interesting as current numerical methods were slow, and the dyanmics of the equations were unknown. The classifier can be used to save a lot of time practically but mathematically raised more questions about what was happening to the current numerical models used. \n",
|
||
"The model also has extensive usage in text classification (such as the Spam example used in this nano-degree), and can also be used for predicting and recommendations if someone or something will do something (assuming the features are largely independent).\n",
|
||
"\n",
|
||
"## Advantages\n",
|
||
"The model performs very well in problems where the features are largely conditionally independent from each other. This means that we are assuming that features do not depend on each other (E.g if we considered height and weight we would exepct them to correlate, taller people would weigh more than shorter people, but weight could still be conditionally independent as there are other factors to consider in how heavy someone is).\n",
|
||
"Because of this this means that the model will converge much quicker than other methods (such as linear regression) - in practice this means we can use less training data. We can use the model for both discrete and continuous data. [2]\n",
|
||
"\n",
|
||
"## Disadvatanges\n",
|
||
"The model also has disadvantages. When the featurees are dependent the conditional independence does not hold: in such a case computing\n",
|
||
"\n",
|
||
"$P(X|C_i)$\n",
|
||
"\n",
|
||
"can be computationally expensive if we cannot leverage the conditional probability condition\n",
|
||
"\n",
|
||
"$P(X|C_i) = P(x_1 | C_i) * P(x_2 | C_i) * ... * P(x_N | C_i)$\n",
|
||
"\n",
|
||
"Interestingly, it has been shown that Naive Bayes (Gaussian) can be used and will even perform well even when this independence assumption does not hold. [3] accredits this to the underlying zero-loss function used in the algorithm - which calculates the error as the number of incorrect predictions. The zero-loss function does not penalise inaccurate probability estimates as long as the highest probability is assigned to the correct class [4]. In addition - special care must be taken if using this model **for non indpendent features** in the continuous case - as the model minimises the expected loss by maximising the posterior probability $P(C_i|X)$. Care must be taken when considering the zero-loss function, as integrating a discrete indicator over a probability density function (for a continuous case) would always be zero. Functions such as the Dirac delta function can be applied in this case [5]. There is much more research into why this model performs well when this condition does not hold and can be seen in [6].\n",
|
||
"Although the model is a very good classifier - it does fall short on estimating. This means we can use the model to evaluate future people given their features - but the probability that someone belongs to either class is not a good indicator. This means that future modelling will require all our data to classify someone, using the probabilities that someone belongs in either class is not a practical use for this model.\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"## Reasoning\n",
|
||
"Based on the above I believe this model is a good candidate.\n",
|
||
"* We have features that appear at face value to not closely depend on each other - by this I mean no features stand out as largely dependending on another. Age is largeley independent to Ethnicity. Although there could be some dependence on the Capital Gains or Loss with respect to the other features - further investigation would only be warrented in my opinion if the output of the model warrants it.\n",
|
||
"* We want to know categorically if someone earns above or below \\$50k"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Decision Trees\n",
|
||
"\n",
|
||
"## Applications\n",
|
||
"\n",
|
||
"Decision trees have many applications in industry:\n",
|
||
"* One example is in customer relations and reccomendations. Decision trees can be used to analyse how customers interact online and then analyse their usage to provide recomendations based on this information. [10] applied decision trees to investigate customers shopping habits online - by classing people into two categories, 1) Those who rarely used it and 2) Those who shopped online frequently. The result of the decision tree showed that the time it takes customers to complete a transaction and how urgently the item needed were the most important factors in whether a customer shopped online or not. \n",
|
||
"\n",
|
||
"## Advantages\n",
|
||
"\n",
|
||
"Decision trees have many advantages when being considered as a classifier\n",
|
||
"\n",
|
||
"* They perform well with missing data points. Other methods require the removal of data if any features are missing - which is inefficient and if not careful can affect the validity of your data. Decision trees work around this by classifying missing values as a seperate category - where other categories can be used to analyise the missing categories. Or you can build a decision tree to categorically predict the missing values based on the data as a preliminary step before using the model to obtain your results. [7]\n",
|
||
"* They have excellent use in determining whether or not features are relatively important to one another. By using a decision tree you can find how important a feature is by removing it from the tree and validating the result against the feature included. This is a popular method employed to finding importance of features - I myself have used such methods when considering the importance of states in Markov Chains. By removing a state one by one - you can find the relative importance of a state relative to the other states. This can tell you lots of information about how important each state is. [8]\n",
|
||
"* Unlike a Naive Bayes classifier, Decision Trees can be used to predict values. The resulting probabilites from the model can be used to predict whether or not someone will belong to a class without the need to run the model with the new data included each time. [7]\n",
|
||
"* They are excellent when using categorical data. If you have a category that has many values under it a decision tree is a very good model in deciding how these categories can be split or grouped together. They can break the category down into a more manageable group. We can see in the cell below that the Occuption column has 14 values present. A decision tree can handle this (and much higher counts) with ease. [7]\n",
|
||
"\n",
|
||
"## Disadvantages\n",
|
||
"* Although decision trees are excellent predictors - if the data changes or evolves over time (say the number of people belonging to a specific job goes up) then the model needs to be redrawn to account for this. This can be accounted for by using ensemble methods in tandem with a decision tree [9]\n",
|
||
"* The hyperparamters for the model are very important - popular hyperparamters to consider are max_depth which can be used to control over-fitting, min_samples_split and min_samples_leaf which consider how many samples are needed to split or be defined as a leaf and min_weight_fraction_leaf when considering weighted samples. The overabundance and consideration of the hyperparameters means there is a need to understand your data before considering values for these. Methods we have seen such as grid search can aid in the process when we have many to consider.\n",
|
||
"* Decision trees are sensitive if a category is dominated by a particular value. Careful consideration should be taken if this is the case and balancing the data can help. Methods used to balance could include resampling (adding copies of under respresented values) or under sampling (removal of some of the dominant class values. We should also pay close attention to the Precision, Recall and F1 Score when evaluating the model - as this can aid in selection. \n",
|
||
"\n",
|
||
"## Reasoning\n",
|
||
"Based on the above I believe Decision Trees are a good candidate for our problem.\n",
|
||
"* We have a single classification problem which lends itself well to decision trees. \n",
|
||
"* We have categories which contain many classes (such as occupation) - we know decision trees can be used to great affect with this kind of data.\n",
|
||
"* Easy to visulise and explain - justifying the model can be easily done with scikitlearn. We can plot the tree which will show us the categories and their values used to split at each node.\n",
|
||
"* Categorical data - although we have accounted for categories with pd.get_dummies(), the model lends itself well to categorical data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Number of unique values in occupation column is 14\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import collections\n",
|
||
"occupation_cat_count = collections.Counter(data['occupation'].unique())\n",
|
||
"occupation_cat_count = sum(occupation_cat_count.values())\n",
|
||
"print(f'Number of unique values in occupation column is {occupation_cat_count}')\n",
|
||
"# sum(collections.Counter(encoded).values())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# SVMs\n",
|
||
"\n",
|
||
"## Applications\n",
|
||
"\n",
|
||
"An application of a support vector machine in industry is:\n",
|
||
"* SVMs have a wide application in image recognition and classification problems. When applied to image recognition each feature of the data set corresponds to a single image pixel. An SVM can be used to classify these pixels into which category they belong to based on their features (such as edge, colour or shape). In [11] SVMs were used on microscopic images of cells to detect the location of the cell nucleus. Manual location of the cell nuclei is a long and arduous process especially when considering there may be many of thousands of images to consider. A SVM can be used to learn what part of on image corresponds to a nucleus, and what part does not and was used to great effect in this case - managing to locate the neclei across multiple scales and stains successfully. Their usage is not limited to the medical sector and have many practical uses such as in geography in identifying areas of land suitable for cultivation and farming [12]\n",
|
||
"\n",
|
||
"## Advantages\n",
|
||
"\n",
|
||
"SVMs have many advantages and are a powerful classification method\n",
|
||
"* They integerate well with kernel methods - this means that SVMs are very versatile. You can employ different Kernels when considering how you will map your points to a higher dimensional space and even write your own kernels if your data requires it.\n",
|
||
"* When compared to linear regression methods SVMs are more robust due to the maximising of the margin. With the hyperparamter C you can control how much an incorrectly classified point is penalised.\n",
|
||
"* They are excellent at non linear boundaries due to the kernels they employ. Although it is possible to employ kernels in other methods such as regression SVMs in scikitlearn have the kernels already implemented and it is much easier to use the \"kernel trick\" (the kernel trick using the fact that you can generalise kernels in higher dimensions by using the dot product in the original space and using a generalisation of the corresponding Kernel. [14] has an excellent write up on how this used for the Linear kernel) \n",
|
||
"\n",
|
||
"## Disadvantages\n",
|
||
"Although SVMs are powerful in classification problems there are disadvantages we must consider when using them\n",
|
||
"* Choosing a kernel function is not an easy task and can often be the main barrier to the success of the model. Although scikitlearn offers several kernals such as the linear kernel and the popular Radial Basis Function kernel or RBF. The RBF is often used and uses the squared euclidean distance between two points. The feature space of the kernel has an infinite number of dimensions [13] and this means the kernel can be used to project points any higher dimension - although this comes at a huge computational cost. \n",
|
||
"* The model has a long training time on large data sets and this is due to several reasons\n",
|
||
" * The C parameter is a hyperparameter used to penalise misclassified points - the higher this is, the more accurate the results but the slower the training process is.\n",
|
||
" * The general method is of $O(n^3)$ (where $O$ is big Oh notation) - meaning it has to run a number of operations proportional to $n^3$. With 10,000 data points this means the number of operations is proportional to $10^{12}$ - a huge number which can affect our computational time greatly.\n",
|
||
" \n",
|
||
"## Reasoning\n",
|
||
"Based on the above I believe SVMs can be used for our model\n",
|
||
"* We have a classification problem which is well suited to SVMs.\n",
|
||
"* They are very good when we have a large number of features and after transforming our category values into feature values we know we have 98 features for our category data.\n",
|
||
"* With careful selection of our C hyperparamter and a suitable kernel we can obtain good boundaries for our dataset."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# References\n",
|
||
"[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3671239/\n",
|
||
"\n",
|
||
"[2] https://www.slideshare.net/ashrafmath/naive-bayes-15644818\n",
|
||
"\n",
|
||
"[3] https://www.cs.waikato.ac.nz/~eibe/pubs/nbr.pdf\n",
|
||
"\n",
|
||
"[4] https://link.springer.com/article/10.1023/A:1009778005914\n",
|
||
"\n",
|
||
"[5] https://en.wikipedia.org/wiki/Dirac_delta_function\n",
|
||
"\n",
|
||
"[6] https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf\n",
|
||
"\n",
|
||
"[7] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466856/\n",
|
||
"\n",
|
||
"[8] https://www.analyticsvidhya.com/blog/2018/01/channel-attribution-modeling-using-markov-chains-in-r/\n",
|
||
"\n",
|
||
"[9] https://scikit-learn.org/stable/modules/tree.html\n",
|
||
"\n",
|
||
"[10] https://www.sciencedirect.com/science/article/pii/S0957417406001825\n",
|
||
"\n",
|
||
"[11] https://link.springer.com/article/10.1007/s00138-010-0275-y\n",
|
||
"\n",
|
||
"[12] https://www.ncbi.nlm.nih.gov/pubmed/20052093\n",
|
||
"\n",
|
||
"[13] https://en.wikipedia.org/wiki/Radial_basis_function_kernel\n",
|
||
"\n",
|
||
"[14] https://www.quora.com/What-is-the-kernel-trick|"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Answer: **"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Implementation - Creating a Training and Predicting Pipeline\n",
|
||
"To properly evaluate the performance of each model you've chosen, it's important that you 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. Your implementation here will be used in the following section.\n",
|
||
"In the code block below, you will need to implement the following:\n",
|
||
" - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).\n",
|
||
" - Fit the learner to the sampled training data and record the training time.\n",
|
||
" - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.\n",
|
||
" - Record the total prediction time.\n",
|
||
" - Calculate the accuracy score for both the training subset and testing set.\n",
|
||
" - Calculate the F-score for both the training subset and testing set.\n",
|
||
" - Make sure that you set the `beta` parameter!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"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",
|
||
" beta = 0.5\n",
|
||
" results = {}\n",
|
||
" \n",
|
||
" # TODO: 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",
|
||
" # TODO: Calculate the training time\n",
|
||
" results['train_time'] = end - start\n",
|
||
" \n",
|
||
" # TODO: Get the predictions on the test set(X_test),\n",
|
||
" # 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",
|
||
" # TODO: Calculate the total prediction time\n",
|
||
" results['pred_time'] = end - start\n",
|
||
" \n",
|
||
" # TODO: 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",
|
||
" # TODO: Compute accuracy on test set using accuracy_score()\n",
|
||
" results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
|
||
" \n",
|
||
" # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n",
|
||
" results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta)\n",
|
||
" \n",
|
||
" # TODO: Compute F-score on the test set which is y_test\n",
|
||
" results['f_test'] = fbeta_score(y_test, predictions_test, beta)\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": [
|
||
"### Implementation: Initial Model Evaluation\n",
|
||
"In the code cell, you will need to implement the following:\n",
|
||
"- Import the three supervised learning models you've discussed in the previous section.\n",
|
||
"- Initialize the three models and store them in `'clf_A'`, `'clf_B'`, and `'clf_C'`.\n",
|
||
" - Use a `'random_state'` for each model you use, if provided.\n",
|
||
" - **Note:** Use the default settings for each model — you will tune one specific model in a later section.\n",
|
||
"- Calculate the number of records equal to 1%, 10%, and 100% of the training data.\n",
|
||
" - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.\n",
|
||
"\n",
|
||
"**Note:** Depending on which algorithms you chose, the following implementation may take some time to run!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"13181 0\n",
|
||
"10342 0\n",
|
||
"20881 0\n",
|
||
"24972 1\n",
|
||
"43867 0\n",
|
||
"Name: income, dtype: int32"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_train.head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"36177 3617 361\n",
|
||
"\n",
|
||
"0 361\n",
|
||
"GaussianNB trained on 361 samples.\n",
|
||
"1 3617\n",
|
||
"GaussianNB trained on 3617 samples.\n",
|
||
"2 36177\n",
|
||
"GaussianNB trained on 36177 samples.\n",
|
||
"0 361\n",
|
||
"DecisionTreeClassifier trained on 361 samples.\n",
|
||
"1 3617\n",
|
||
"DecisionTreeClassifier trained on 3617 samples.\n",
|
||
"2 36177\n",
|
||
"DecisionTreeClassifier trained on 36177 samples.\n",
|
||
"0 361\n",
|
||
"SVC trained on 361 samples.\n",
|
||
"1 3617\n",
|
||
"SVC trained on 3617 samples.\n",
|
||
"2 36177\n",
|
||
"SVC 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": [
|
||
"# TODO: Import the three supervised learning models from sklearn\n",
|
||
"from sklearn.naive_bayes import GaussianNB\n",
|
||
"from sklearn.tree import DecisionTreeClassifier\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"\n",
|
||
"# TODO: Initialize the three models\n",
|
||
"clf_A = GaussianNB()\n",
|
||
"clf_B = DecisionTreeClassifier()\n",
|
||
"clf_C = SVC()\n",
|
||
"\n",
|
||
"# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
|
||
"# HINT: samples_100 is the entire training set i.e. len(y_train)\n",
|
||
"# HINT: samples_10 is 10% of samples_100 (ensure to set the count of the values to be `int` and not `float`)\n",
|
||
"# HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)\n",
|
||
"samples_100 = int(X_train.shape[0])\n",
|
||
"samples_10 = int(samples_100 * 0.1)\n",
|
||
"samples_1 = int(samples_100 * 0.01)\n",
|
||
"print(samples_100, samples_10, samples_1)\n",
|
||
"print()\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",
|
||
" print(i, samples)\n",
|
||
" results[clf_name][i] = \\\n",
|
||
" train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
|
||
"# Run metrics visualization for the three supervised learning models chosen\n",
|
||
"vs.evaluate(results, accuracy, fscore)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"----\n",
|
||
"## Improving Results\n",
|
||
"In this final section, you will choose from the three supervised learning models the *best* model to use on the student data. You will then 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 you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \\$50,000. \n",
|
||
"\n",
|
||
"** HINT: ** \n",
|
||
"Look at the graph at the bottom left from the cell above(the visualization created by `vs.evaluate(results, accuracy, fscore)`) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:\n",
|
||
"* metrics - F score on the testing when 100% of the training data is used, \n",
|
||
"* prediction/training time\n",
|
||
"* the algorithm's suitability for the data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Answer: **"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Question 4 - Describing the Model in Layman's Terms\n",
|
||
"\n",
|
||
"* In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.\n",
|
||
"\n",
|
||
"** HINT: **\n",
|
||
"\n",
|
||
"When explaining your model, if using external resources please include all citations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Answer: ** "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Implementation: Model Tuning\n",
|
||
"Fine tune the chosen model. Use grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to 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 a `random_state` if one is available to the same state you set before.\n",
|
||
"- Create a dictionary of parameters you wish to tune for the chosen model.\n",
|
||
" - Example: `parameters = {'parameter' : [list of values]}`.\n",
|
||
" - **Note:** Avoid tuning the `max_features` parameter of your learner if that parameter is available!\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`.\n",
|
||
"\n",
|
||
"**Note:** Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
|
||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||
" tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"from sklearn.metrics import make_scorer\n",
|
||
"\n",
|
||
"clf = SVC(C=0.01, gamma=0.1, kernel='rbf')\n",
|
||
"\n",
|
||
"clf.fit(X_train, y_train)\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"predictions_test = clf.predict(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.8245439469320066"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"accuracy_score(y_test, predictions_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# TODO: 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",
|
||
"# TODO: Initialize the classifier\n",
|
||
"clf = SVC()\n",
|
||
"\n",
|
||
"# TODO: Create the parameters list you wish to tune, using a dictionary if needed.\n",
|
||
"# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}\n",
|
||
"parameters = {'C': np.logspace(-4, 10, 12, base=10),\n",
|
||
" 'gamma': np.logspace(-11, 3, 12, base=10),\n",
|
||
" 'kernel': ['linear', 'rbf']}\n",
|
||
"\n",
|
||
"# TODO: Make an fbeta_score scoring object using make_scorer()\n",
|
||
"scorer = make_scorer(fbeta_score, beta=0.5)\n",
|
||
"\n",
|
||
"# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
|
||
"grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer)\n",
|
||
"\n",
|
||
"# TODO: 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)\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": [
|
||
"### Question 5 - Final Model Evaluation\n",
|
||
"\n",
|
||
"* What is your optimized model's accuracy and F-score on the testing data? \n",
|
||
"* Are these scores better or worse than the unoptimized model? \n",
|
||
"* How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_ \n",
|
||
"\n",
|
||
"**Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Results:\n",
|
||
"\n",
|
||
"| Metric | Unoptimized Model | Optimized Model |\n",
|
||
"| :------------: | :---------------: | :-------------: | \n",
|
||
"| Accuracy Score | | |\n",
|
||
"| F-score | | EXAMPLE |\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Answer: **"
|
||
]
|
||
},
|
||
{
|
||
"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. 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",
|
||
"Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier. In the next python cell 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. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Answer:**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Implementation - Extracting Feature Importance\n",
|
||
"Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.\n",
|
||
"\n",
|
||
"In the code cell below, you will need to implement the following:\n",
|
||
" - Import a supervised learning model from sklearn if it is different from the three used earlier.\n",
|
||
" - Train the supervised model on the entire training set.\n",
|
||
" - Extract the feature importances using `'.feature_importances_'`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# TODO: Import a supervised learning model that has 'feature_importances_'\n",
|
||
"\n",
|
||
"\n",
|
||
"# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n",
|
||
"model = None\n",
|
||
"\n",
|
||
"# TODO: Extract the feature importances using .feature_importances_ \n",
|
||
"importances = None\n",
|
||
"\n",
|
||
"# Plot\n",
|
||
"vs.feature_plot(importances, X_train, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Question 7 - Extracting Feature Importance\n",
|
||
"\n",
|
||
"Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000. \n",
|
||
"* How do these five features compare to the five features you discussed in **Question 6**?\n",
|
||
"* If you were close to the same answer, how does this visualization confirm your thoughts? \n",
|
||
"* If you were not close, why do you think these features are more relevant?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Answer:**"
|
||
]
|
||
},
|
||
{
|
||
"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. 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. The code cell below will use the same optimized model you found earlier, and train it on the same training set *with only the top five important features*. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"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": [
|
||
"### 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:**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n",
|
||
"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|