2123 lines
522 KiB
Plaintext
2123 lines
522 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": 1,
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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": 2,
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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": 3,
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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": 4,
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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": 5,
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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": 6,
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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 0x1c2d686de48>"
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]
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},
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"execution_count": 6,
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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": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1500)"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"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": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1000)"
|
||
]
|
||
},
|
||
"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": [
|
||
"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": 9,
|
||
"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": 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": [
|
||
"# 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": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0, 1500)"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"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": 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": [
|
||
"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": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .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",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
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" }\n",
|
||
"\n",
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" .dataframe thead th {\n",
|
||
" text-align: right;\n",
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" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>workclass</th>\n",
|
||
" <th>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": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
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" .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>2817</th>\n",
|
||
" <td>0.328767</td>\n",
|
||
" <td>Local-gov</td>\n",
|
||
" <td>Bachelors</td>\n",
|
||
" <td>0.800000</td>\n",
|
||
" <td>Widowed</td>\n",
|
||
" <td>Adm-clerical</td>\n",
|
||
" <td>Not-in-family</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Female</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>30811</th>\n",
|
||
" <td>0.123288</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>HS-grad</td>\n",
|
||
" <td>0.533333</td>\n",
|
||
" <td>Divorced</td>\n",
|
||
" <td>Adm-clerical</td>\n",
|
||
" <td>Not-in-family</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.397959</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30395</th>\n",
|
||
" <td>0.150685</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>Bachelors</td>\n",
|
||
" <td>0.800000</td>\n",
|
||
" <td>Never-married</td>\n",
|
||
" <td>Tech-support</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>0.500000</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>42586</th>\n",
|
||
" <td>0.123288</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>Bachelors</td>\n",
|
||
" <td>0.800000</td>\n",
|
||
" <td>Never-married</td>\n",
|
||
" <td>Sales</td>\n",
|
||
" <td>Not-in-family</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.397959</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8534</th>\n",
|
||
" <td>0.123288</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>HS-grad</td>\n",
|
||
" <td>0.533333</td>\n",
|
||
" <td>Never-married</td>\n",
|
||
" <td>Adm-clerical</td>\n",
|
||
" <td>Own-child</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.397959</td>\n",
|
||
" <td>United-States</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age workclass education_level education-num marital-status \\\n",
|
||
"2817 0.328767 Local-gov Bachelors 0.800000 Widowed \n",
|
||
"30811 0.123288 Private HS-grad 0.533333 Divorced \n",
|
||
"30395 0.150685 Private Bachelors 0.800000 Never-married \n",
|
||
"42586 0.123288 Private Bachelors 0.800000 Never-married \n",
|
||
"8534 0.123288 Private HS-grad 0.533333 Never-married \n",
|
||
"\n",
|
||
" occupation relationship race sex capital-gain \\\n",
|
||
"2817 Adm-clerical Not-in-family White Female 0.0 \n",
|
||
"30811 Adm-clerical Not-in-family White Male 0.0 \n",
|
||
"30395 Tech-support Own-child White Male 0.0 \n",
|
||
"42586 Sales Not-in-family White Male 0.0 \n",
|
||
"8534 Adm-clerical Own-child White Female 0.0 \n",
|
||
"\n",
|
||
" capital-loss hours-per-week native-country \n",
|
||
"2817 0.0 0.397959 United-States \n",
|
||
"30811 0.0 0.397959 United-States \n",
|
||
"30395 0.0 0.500000 United-States \n",
|
||
"42586 0.0 0.397959 United-States \n",
|
||
"8534 0.0 0.397959 United-States "
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"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": 15,
|
||
"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",
|
||
"33781 0\n",
|
||
"9143 0\n",
|
||
"17867 0\n",
|
||
"11402 0\n",
|
||
"635 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": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 0\n",
|
||
"1 0\n",
|
||
"2 0\n",
|
||
"Name: income, dtype: int32"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"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": 17,
|
||
"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>8794</th>\n",
|
||
" <td>0.109589</td>\n",
|
||
" <td>0.866667</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</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>32608</th>\n",
|
||
" <td>0.835616</td>\n",
|
||
" <td>0.200000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.193878</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>42465</th>\n",
|
||
" <td>0.506849</td>\n",
|
||
" <td>0.533333</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.448980</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</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>43216</th>\n",
|
||
" <td>0.342466</td>\n",
|
||
" <td>0.666667</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.397959</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>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8185</th>\n",
|
||
" <td>0.397260</td>\n",
|
||
" <td>0.866667</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.551020</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",
|
||
"8794 0.109589 0.866667 0.0 0.0 0.397959 \n",
|
||
"32608 0.835616 0.200000 0.0 0.0 0.193878 \n",
|
||
"42465 0.506849 0.533333 0.0 0.0 0.448980 \n",
|
||
"43216 0.342466 0.666667 0.0 0.0 0.397959 \n",
|
||
"8185 0.397260 0.866667 0.0 0.0 0.551020 \n",
|
||
"\n",
|
||
" workclass_ Federal-gov workclass_ Local-gov workclass_ Private \\\n",
|
||
"8794 0 0 1 \n",
|
||
"32608 0 0 0 \n",
|
||
"42465 0 1 0 \n",
|
||
"43216 0 0 0 \n",
|
||
"8185 0 0 1 \n",
|
||
"\n",
|
||
" workclass_ Self-emp-inc workclass_ Self-emp-not-inc ... \\\n",
|
||
"8794 0 0 ... \n",
|
||
"32608 0 1 ... \n",
|
||
"42465 0 0 ... \n",
|
||
"43216 0 1 ... \n",
|
||
"8185 0 0 ... \n",
|
||
"\n",
|
||
" native-country_ Portugal native-country_ Puerto-Rico \\\n",
|
||
"8794 0 0 \n",
|
||
"32608 0 0 \n",
|
||
"42465 0 0 \n",
|
||
"43216 0 0 \n",
|
||
"8185 0 0 \n",
|
||
"\n",
|
||
" native-country_ Scotland native-country_ South \\\n",
|
||
"8794 0 0 \n",
|
||
"32608 0 0 \n",
|
||
"42465 0 0 \n",
|
||
"43216 0 0 \n",
|
||
"8185 0 0 \n",
|
||
"\n",
|
||
" native-country_ Taiwan native-country_ Thailand \\\n",
|
||
"8794 0 0 \n",
|
||
"32608 0 0 \n",
|
||
"42465 0 0 \n",
|
||
"43216 0 0 \n",
|
||
"8185 0 0 \n",
|
||
"\n",
|
||
" native-country_ Trinadad&Tobago native-country_ United-States \\\n",
|
||
"8794 0 1 \n",
|
||
"32608 0 1 \n",
|
||
"42465 0 1 \n",
|
||
"43216 0 0 \n",
|
||
"8185 0 1 \n",
|
||
"\n",
|
||
" native-country_ Vietnam native-country_ Yugoslavia \n",
|
||
"8794 0 0 \n",
|
||
"32608 0 0 \n",
|
||
"42465 0 0 \n",
|
||
"43216 0 0 \n",
|
||
"8185 0 0 \n",
|
||
"\n",
|
||
"[5 rows x 103 columns]"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"features_final.sample(frac=1).head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"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": 19,
|
||
"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": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"'''\n",
|
||
"TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data \n",
|
||
"encoded to numerical values done in the data preprocessing step.\n",
|
||
"FP = income.count() - TP # Specific to the naive case\n",
|
||
"\n",
|
||
"TN = 0 # No predicted negatives in the naive case\n",
|
||
"FN = 0 # No predicted negatives in the naive case\n",
|
||
"'''\n",
|
||
"# 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": 21,
|
||
"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": 22,
|
||
"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": 23,
|
||
"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": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_train.head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"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": 25,
|
||
"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": 25,
|
||
"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": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"predictions_test = clf.predict(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.8245439469320066"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"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_jobs=-1)\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
|
||
}
|