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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Model Development \n",
+ "\n",
+ "This file is composed of components of the module development process for preliminary exploration of your data. This code is not a complete module development, but rather a preliminary examination of data. Please see the Table of Contents to explore different areas of MD.\n",
+ "***\n",
+ "\n",
+ "##### **Input:** .csv file with entire dataset. Will need to interpolate prior to using unsupervised learning if NaN exist in your dataset\n",
+ "##### **Output:** Figures for EDA\n",
+ "##### **Dependencies:** \n",
+ "***\n",
+ "\n",
+ "##### Format of input: \n",
+ ".csv file with entire dataset \n",
+ "***\n",
+ "\n",
+ "**Check:** \n",
+ "* Will need to interpolate data/remove NaN before doing any unsupervised learning for EDA\n",
+ "\n",
+ "**Sources:**\n",
+ "\n",
+ "***\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Table of Contents\n",
+ "\n",
+ "#### ML classification\n",
+ "* SVM\n",
+ "* Random Forest\n",
+ "* XGBoost\n",
+ "\n",
+ "#### Plot Visualization\n",
+ "* ROC-plot\n",
+ "* LOSO-CV\n",
+ "\n",
+ "#### Unsupervised Learning\n",
+ "* [Clustering](#cluster)\n",
+ " * [KNN Clustering](#knn)\n",
+ " * [Hierarchical Clustering](#hic)\n",
+ "* [Principal Component Analysis (PCA)](#pca)\n",
+ "\n",
+ "\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data:\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'filename' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#Change filename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'filename' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "data = pd.read_csv('/Users/christinayu/Desktop/AppleWatch.csv') "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Preliminary Exploratory Data Analysis:\n",
+ "\n",
+ "https://github.com/dformoso/sklearn-classification/blob/master/Data%20Science%20Workbook%20-%20Census%20Income%20Dataset.ipynb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "238595"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ECG | \n",
+ " Apple Watch | \n",
+ " Empatica | \n",
+ " Garmin | \n",
+ " Fitbit | \n",
+ " Miband | \n",
+ " Biovotion | \n",
+ " Skin Tone | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 221475.000000 | \n",
+ " 12791.000000 | \n",
+ " 138252.000000 | \n",
+ " 36625.000000 | \n",
+ " 10861.000000 | \n",
+ " 20632.000000 | \n",
+ " 160841.000000 | \n",
+ " 238595.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 90.549726 | \n",
+ " 89.579235 | \n",
+ " 88.962121 | \n",
+ " 84.418212 | \n",
+ " 84.253384 | \n",
+ " 86.713794 | \n",
+ " 86.938579 | \n",
+ " 3.638509 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 20.568018 | \n",
+ " 17.021518 | \n",
+ " 15.873166 | \n",
+ " 15.430226 | \n",
+ " 16.898867 | \n",
+ " 14.536440 | \n",
+ " 16.982374 | \n",
+ " 1.669205 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 24.174000 | \n",
+ " 36.000000 | \n",
+ " 48.200000 | \n",
+ " 39.000000 | \n",
+ " 46.000000 | \n",
+ " 45.000000 | \n",
+ " 45.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 76.355000 | \n",
+ " 78.000000 | \n",
+ " 77.630000 | \n",
+ " 73.000000 | \n",
+ " 70.000000 | \n",
+ " 74.000000 | \n",
+ " 75.000000 | \n",
+ " 2.000000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 89.291000 | \n",
+ " 89.000000 | \n",
+ " 87.430000 | \n",
+ " 84.000000 | \n",
+ " 81.000000 | \n",
+ " 87.000000 | \n",
+ " 86.000000 | \n",
+ " 4.000000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 101.030000 | \n",
+ " 100.000000 | \n",
+ " 99.120000 | \n",
+ " 96.000000 | \n",
+ " 95.000000 | \n",
+ " 98.000000 | \n",
+ " 98.000000 | \n",
+ " 5.000000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 240.000000 | \n",
+ " 155.000000 | \n",
+ " 178.740000 | \n",
+ " 134.000000 | \n",
+ " 180.000000 | \n",
+ " 128.000000 | \n",
+ " 168.000000 | \n",
+ " 6.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "