added a virtual environment to make running simpler; README contains …

…instructions on running

.gitignore

@@ -1 +1,3 @@
 archive
+.vscode
+venv

README.md

@@ -3,6 +3,20 @@
 ## Goal
 build a classification model to determine whether or not a loan applicant will receive a loan given some data about the loan and some objective data about an individual
 
+## Running This Notebook
+### Steps
+```
+python3 -m venv ./venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+# open this notebook in a Jupyter processor
+```
+### Links
+- download [python3.9.4](https://www.python.org/downloads/release/python-394/)
+- download [VSCode and its Jupyter processing extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
+
+
 ## Presenting
 - a 6 minute live presentation will be given to the class
 
@@ -22,7 +36,7 @@ build a classification model to determine whether or not a loan applicant will r
 ## Implementation
 - clean data
     - transform our target dimension into a binary number (simplifying from multiple values to a single value)
-    - for each dimension that has multiple possible string values, we will create a dimension; across each of these new dimensions, only one dimension will have a value of 1, and the rest will have 0's
+    - for each dimension that has multiple possible string values, we will create a dimension using one-hot encoding
 - create a class that represents a test
     - each test contains a training algorithm and arguments to run the training algorithm
     - results of run will be presented as a single-row numpy array (that can be hstacked onto the other results)

model-selection.ipynb

@@ -0,0 +1,51 @@
+{
+ "metadata": {
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python394jvsc74a57bd0daf19b0280700678d1b30283c052a0b4f397f6934ace6c653c7a54482fc82c11",
+   "display_name": "Python 3.9.4 64-bit ('venv')"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Imports"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sklearn as skl"
+   ]
+  },
+  {
+   "source": [
+    "# Data Cleaning"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Selecting Relevant Fields"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## One-Hot Encoding"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  }
+ ]
+}

requirements.txt

@@ -0,0 +1,29 @@
+appnope==0.1.2
+backcall==0.2.0
+decorator==5.0.7
+ipykernel==5.5.3
+ipython==7.22.0
+ipython-genutils==0.2.0
+jedi==0.18.0
+joblib==1.0.1
+jupyter-client==6.1.12
+jupyter-core==4.7.1
+numpy==1.20.2
+pandas==1.2.4
+parso==0.8.2
+pexpect==4.8.0
+pickleshare==0.7.5
+prompt-toolkit==3.0.18
+ptyprocess==0.7.0
+Pygments==2.8.1
+python-dateutil==2.8.1
+pytz==2021.1
+pyzmq==22.0.3
+scikit-learn==0.24.1
+scipy==1.6.2
+six==1.15.0
+sklearn==0.0
+threadpoolctl==2.1.0
+tornado==6.1
+traitlets==5.0.5
+wcwidth==0.2.5