From cd1247979aec68cb103f4f5ed49ccd0f9e848864 Mon Sep 17 00:00:00 2001
From: Patrick Ayertey <patrick.ayertey@mckesson.com>
Date: Wed, 18 Jul 2018 13:19:52 -0700
Subject: [PATCH 1/4] Patrick- first commit

---
 .gitignore                                  |  0
 .ipynb_checkpoints/Balance-checkpoint.ipynb | 48 +++++++++++++++++++++
 Balance.ipynb                               | 48 +++++++++++++++++++++
 3 files changed, 96 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 .ipynb_checkpoints/Balance-checkpoint.ipynb
 create mode 100644 Balance.ipynb

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e69de29
diff --git a/.ipynb_checkpoints/Balance-checkpoint.ipynb b/.ipynb_checkpoints/Balance-checkpoint.ipynb
new file mode 100644
index 0000000..5c6fa24
--- /dev/null
+++ b/.ipynb_checkpoints/Balance-checkpoint.ipynb
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Balance.ipynb b/Balance.ipynb
new file mode 100644
index 0000000..5c6fa24
--- /dev/null
+++ b/Balance.ipynb
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 783243de4cadc96794e3fc6ae354c22c6de399c4 Mon Sep 17 00:00:00 2001
From: Patrick Ayertey <patrick.ayertey@mckesson.com>
Date: Wed, 18 Jul 2018 13:35:12 -0700
Subject: [PATCH 2/4] Patrick- second commit

---
 .gitignore    |   1 +
 Balance.ipynb | 552 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 553 insertions(+)

diff --git a/.gitignore b/.gitignore
index e69de29..4950807 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+ipynb.checkpoints
\ No newline at end of file
diff --git a/Balance.ipynb b/Balance.ipynb
index 5c6fa24..2c263ae 100644
--- a/Balance.ipynb
+++ b/Balance.ipynb
@@ -16,6 +16,558 @@
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Unnamed: 0</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Limit</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Cards</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Student</th>\n",
+       "      <th>Married</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Balance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.891</td>\n",
+       "      <td>3606</td>\n",
+       "      <td>283</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>106.025</td>\n",
+       "      <td>6645</td>\n",
+       "      <td>483</td>\n",
+       "      <td>3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>104.593</td>\n",
+       "      <td>7075</td>\n",
+       "      <td>514</td>\n",
+       "      <td>4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>148.924</td>\n",
+       "      <td>9504</td>\n",
+       "      <td>681</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>55.882</td>\n",
+       "      <td>4897</td>\n",
+       "      <td>357</td>\n",
+       "      <td>2</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>331</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0   Income  Limit  Rating  Cards  Age  Education  Gender Student  \\\n",
+       "0           1   14.891   3606     283      2   34         11    Male      No   \n",
+       "1           2  106.025   6645     483      3   82         15  Female     Yes   \n",
+       "2           3  104.593   7075     514      4   71         11    Male      No   \n",
+       "3           4  148.924   9504     681      3   36         11  Female      No   \n",
+       "4           5   55.882   4897     357      2   68         16    Male      No   \n",
+       "\n",
+       "  Married  Ethnicity  Balance  \n",
+       "0     Yes  Caucasian      333  \n",
+       "1     Yes      Asian      903  \n",
+       "2      No      Asian      580  \n",
+       "3      No      Asian      964  \n",
+       "4     Yes  Caucasian      331  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('balance.txt')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'Income', 'Limit', 'Rating', 'Cards', 'Age', 'Education',\n",
+       "       'Gender', 'Student', 'Married', 'Ethnicity', 'Balance'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(400, 12)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 400 entries, 0 to 399\n",
+      "Data columns (total 12 columns):\n",
+      "Unnamed: 0    400 non-null int64\n",
+      "Income        400 non-null float64\n",
+      "Limit         400 non-null int64\n",
+      "Rating        400 non-null int64\n",
+      "Cards         400 non-null int64\n",
+      "Age           400 non-null int64\n",
+      "Education     400 non-null int64\n",
+      "Gender        400 non-null object\n",
+      "Student       400 non-null object\n",
+      "Married       400 non-null object\n",
+      "Ethnicity     400 non-null object\n",
+      "Balance       400 non-null int64\n",
+      "dtypes: float64(1), int64(7), object(4)\n",
+      "memory usage: 31.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>200.500000</td>\n",
+       "      <td>115.614301</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>100.75000</td>\n",
+       "      <td>200.5000</td>\n",
+       "      <td>300.25000</td>\n",
+       "      <td>400.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Income</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>45.218885</td>\n",
+       "      <td>35.244273</td>\n",
+       "      <td>10.354</td>\n",
+       "      <td>21.00725</td>\n",
+       "      <td>33.1155</td>\n",
+       "      <td>57.47075</td>\n",
+       "      <td>186.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Limit</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>4735.600000</td>\n",
+       "      <td>2308.198848</td>\n",
+       "      <td>855.000</td>\n",
+       "      <td>3088.00000</td>\n",
+       "      <td>4622.5000</td>\n",
+       "      <td>5872.75000</td>\n",
+       "      <td>13913.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rating</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>354.940000</td>\n",
+       "      <td>154.724143</td>\n",
+       "      <td>93.000</td>\n",
+       "      <td>247.25000</td>\n",
+       "      <td>344.0000</td>\n",
+       "      <td>437.25000</td>\n",
+       "      <td>982.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cards</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>2.957500</td>\n",
+       "      <td>1.371275</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>4.00000</td>\n",
+       "      <td>9.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>55.667500</td>\n",
+       "      <td>17.249807</td>\n",
+       "      <td>23.000</td>\n",
+       "      <td>41.75000</td>\n",
+       "      <td>56.0000</td>\n",
+       "      <td>70.00000</td>\n",
+       "      <td>98.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>13.450000</td>\n",
+       "      <td>3.125207</td>\n",
+       "      <td>5.000</td>\n",
+       "      <td>11.00000</td>\n",
+       "      <td>14.0000</td>\n",
+       "      <td>16.00000</td>\n",
+       "      <td>20.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Balance</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>520.015000</td>\n",
+       "      <td>459.758877</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>68.75000</td>\n",
+       "      <td>459.5000</td>\n",
+       "      <td>863.00000</td>\n",
+       "      <td>1999.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            count         mean          std      min         25%        50%  \\\n",
+       "Unnamed: 0  400.0   200.500000   115.614301    1.000   100.75000   200.5000   \n",
+       "Income      400.0    45.218885    35.244273   10.354    21.00725    33.1155   \n",
+       "Limit       400.0  4735.600000  2308.198848  855.000  3088.00000  4622.5000   \n",
+       "Rating      400.0   354.940000   154.724143   93.000   247.25000   344.0000   \n",
+       "Cards       400.0     2.957500     1.371275    1.000     2.00000     3.0000   \n",
+       "Age         400.0    55.667500    17.249807   23.000    41.75000    56.0000   \n",
+       "Education   400.0    13.450000     3.125207    5.000    11.00000    14.0000   \n",
+       "Balance     400.0   520.015000   459.758877    0.000    68.75000   459.5000   \n",
+       "\n",
+       "                   75%        max  \n",
+       "Unnamed: 0   300.25000    400.000  \n",
+       "Income        57.47075    186.634  \n",
+       "Limit       5872.75000  13913.000  \n",
+       "Rating       437.25000    982.000  \n",
+       "Cards          4.00000      9.000  \n",
+       "Age           70.00000     98.000  \n",
+       "Education     16.00000     20.000  \n",
+       "Balance      863.00000   1999.000  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>Unnamed: 0</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Limit</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Cards</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Student</th>\n",
+       "      <th>Married</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Balance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.891</td>\n",
+       "      <td>3606</td>\n",
+       "      <td>283</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>106.025</td>\n",
+       "      <td>6645</td>\n",
+       "      <td>483</td>\n",
+       "      <td>3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>104.593</td>\n",
+       "      <td>7075</td>\n",
+       "      <td>514</td>\n",
+       "      <td>4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>148.924</td>\n",
+       "      <td>9504</td>\n",
+       "      <td>681</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>55.882</td>\n",
+       "      <td>4897</td>\n",
+       "      <td>357</td>\n",
+       "      <td>2</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>331</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0   Income  Limit  Rating  Cards  Age  Education  Gender Student  \\\n",
+       "0           1   14.891   3606     283      2   34         11    Male      No   \n",
+       "1           2  106.025   6645     483      3   82         15  Female     Yes   \n",
+       "2           3  104.593   7075     514      4   71         11    Male      No   \n",
+       "3           4  148.924   9504     681      3   36         11  Female      No   \n",
+       "4           5   55.882   4897     357      2   68         16    Male      No   \n",
+       "\n",
+       "  Married  Ethnicity  Balance  \n",
+       "0     Yes  Caucasian      333  \n",
+       "1     Yes      Asian      903  \n",
+       "2      No      Asian      580  \n",
+       "3      No      Asian      964  \n",
+       "4     Yes  Caucasian      331  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    333\n",
+       "1    903\n",
+       "2    580\n",
+       "3    964\n",
+       "4    331\n",
+       "Name: Balance, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df.Balance\n",
+    "y.head()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 0f1def1d3524e149555a206b13c2aa7272530132 Mon Sep 17 00:00:00 2001
From: Patrick Ayertey <patrick.ayertey@mckesson.com>
Date: Thu, 19 Jul 2018 10:22:28 -0700
Subject: [PATCH 3/4] Patrick- Credit Card Prediction

---
 .ipynb_checkpoints/Balance-checkpoint.ipynb | 939 +++++++++++++++++++-
 Balance.ipynb                               | 567 ++++++++++--
 2 files changed, 1412 insertions(+), 94 deletions(-)

diff --git a/.ipynb_checkpoints/Balance-checkpoint.ipynb b/.ipynb_checkpoints/Balance-checkpoint.ipynb
index 5c6fa24..625db16 100644
--- a/.ipynb_checkpoints/Balance-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Balance-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,942 @@
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.metrics import r2_score\n",
     "from pandas.plotting import scatter_matrix\n",
-    "%matplotlib inline"
+    "#matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>Unnamed: 0</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Limit</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Cards</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Student</th>\n",
+       "      <th>Married</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Balance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.891</td>\n",
+       "      <td>3606</td>\n",
+       "      <td>283</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>106.025</td>\n",
+       "      <td>6645</td>\n",
+       "      <td>483</td>\n",
+       "      <td>3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>104.593</td>\n",
+       "      <td>7075</td>\n",
+       "      <td>514</td>\n",
+       "      <td>4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>148.924</td>\n",
+       "      <td>9504</td>\n",
+       "      <td>681</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>55.882</td>\n",
+       "      <td>4897</td>\n",
+       "      <td>357</td>\n",
+       "      <td>2</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caucasian</td>\n",
+       "      <td>331</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0   Income  Limit  Rating  Cards  Age  Education  Gender Student  \\\n",
+       "0           1   14.891   3606     283      2   34         11    Male      No   \n",
+       "1           2  106.025   6645     483      3   82         15  Female     Yes   \n",
+       "2           3  104.593   7075     514      4   71         11    Male      No   \n",
+       "3           4  148.924   9504     681      3   36         11  Female      No   \n",
+       "4           5   55.882   4897     357      2   68         16    Male      No   \n",
+       "\n",
+       "  Married  Ethnicity  Balance  \n",
+       "0     Yes  Caucasian      333  \n",
+       "1     Yes      Asian      903  \n",
+       "2      No      Asian      580  \n",
+       "3      No      Asian      964  \n",
+       "4     Yes  Caucasian      331  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import the data\n",
+    "df = pd.read_csv('balance.txt', delimiter=',')\n",
+    "df.head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc85f748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show any relevant graphs\n",
+    "df.hist();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 400 entries, 0 to 399\n",
+      "Data columns (total 12 columns):\n",
+      "Unnamed: 0    400 non-null int64\n",
+      "Income        400 non-null float64\n",
+      "Limit         400 non-null int64\n",
+      "Rating        400 non-null int64\n",
+      "Cards         400 non-null int64\n",
+      "Age           400 non-null int64\n",
+      "Education     400 non-null int64\n",
+      "Gender        400 non-null object\n",
+      "Student       400 non-null object\n",
+      "Married       400 non-null object\n",
+      "Ethnicity     400 non-null object\n",
+      "Balance       400 non-null int64\n",
+      "dtypes: float64(1), int64(7), object(4)\n",
+      "memory usage: 37.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>200.500000</td>\n",
+       "      <td>115.614301</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>100.75000</td>\n",
+       "      <td>200.5000</td>\n",
+       "      <td>300.25000</td>\n",
+       "      <td>400.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Income</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>45.218885</td>\n",
+       "      <td>35.244273</td>\n",
+       "      <td>10.354</td>\n",
+       "      <td>21.00725</td>\n",
+       "      <td>33.1155</td>\n",
+       "      <td>57.47075</td>\n",
+       "      <td>186.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Limit</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>4735.600000</td>\n",
+       "      <td>2308.198848</td>\n",
+       "      <td>855.000</td>\n",
+       "      <td>3088.00000</td>\n",
+       "      <td>4622.5000</td>\n",
+       "      <td>5872.75000</td>\n",
+       "      <td>13913.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rating</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>354.940000</td>\n",
+       "      <td>154.724143</td>\n",
+       "      <td>93.000</td>\n",
+       "      <td>247.25000</td>\n",
+       "      <td>344.0000</td>\n",
+       "      <td>437.25000</td>\n",
+       "      <td>982.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cards</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>2.957500</td>\n",
+       "      <td>1.371275</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>4.00000</td>\n",
+       "      <td>9.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>55.667500</td>\n",
+       "      <td>17.249807</td>\n",
+       "      <td>23.000</td>\n",
+       "      <td>41.75000</td>\n",
+       "      <td>56.0000</td>\n",
+       "      <td>70.00000</td>\n",
+       "      <td>98.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>13.450000</td>\n",
+       "      <td>3.125207</td>\n",
+       "      <td>5.000</td>\n",
+       "      <td>11.00000</td>\n",
+       "      <td>14.0000</td>\n",
+       "      <td>16.00000</td>\n",
+       "      <td>20.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Balance</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>520.015000</td>\n",
+       "      <td>459.758877</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>68.75000</td>\n",
+       "      <td>459.5000</td>\n",
+       "      <td>863.00000</td>\n",
+       "      <td>1999.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            count         mean          std      min         25%        50%  \\\n",
+       "Unnamed: 0  400.0   200.500000   115.614301    1.000   100.75000   200.5000   \n",
+       "Income      400.0    45.218885    35.244273   10.354    21.00725    33.1155   \n",
+       "Limit       400.0  4735.600000  2308.198848  855.000  3088.00000  4622.5000   \n",
+       "Rating      400.0   354.940000   154.724143   93.000   247.25000   344.0000   \n",
+       "Cards       400.0     2.957500     1.371275    1.000     2.00000     3.0000   \n",
+       "Age         400.0    55.667500    17.249807   23.000    41.75000    56.0000   \n",
+       "Education   400.0    13.450000     3.125207    5.000    11.00000    14.0000   \n",
+       "Balance     400.0   520.015000   459.758877    0.000    68.75000   459.5000   \n",
+       "\n",
+       "                   75%        max  \n",
+       "Unnamed: 0   300.25000    400.000  \n",
+       "Income        57.47075    186.634  \n",
+       "Limit       5872.75000  13913.000  \n",
+       "Rating       437.25000    982.000  \n",
+       "Cards          4.00000      9.000  \n",
+       "Age           70.00000     98.000  \n",
+       "Education     16.00000     20.000  \n",
+       "Balance      863.00000   1999.000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Education    -0.008062\n",
+       "Age           0.001835\n",
+       "Unnamed: 0    0.006064\n",
+       "Cards         0.086456\n",
+       "Income        0.463656\n",
+       "Limit         0.861697\n",
+       "Rating        0.863625\n",
+       "Balance       1.000000\n",
+       "Name: Balance, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Balance'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xca02fd0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc81c978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show any relevant graphs\n",
+    "plt.scatter(df.Rating, df.Balance, marker='+')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    333\n",
+       "1    903\n",
+       "2    580\n",
+       "3    964\n",
+       "4    331\n",
+       "Name: Balance, dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get the \"y\" vector\n",
+    "y = df.Balance\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the \"X\" matrix\n",
+    "X = df.drop(columns=['Balance'])\n",
+    "X = pd.get_dummies(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>Unnamed: 0</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Limit</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Cards</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender_ Male</th>\n",
+       "      <th>Gender_Female</th>\n",
+       "      <th>Student_No</th>\n",
+       "      <th>Student_Yes</th>\n",
+       "      <th>Married_No</th>\n",
+       "      <th>Married_Yes</th>\n",
+       "      <th>Ethnicity_African American</th>\n",
+       "      <th>Ethnicity_Asian</th>\n",
+       "      <th>Ethnicity_Caucasian</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.891</td>\n",
+       "      <td>3606</td>\n",
+       "      <td>283</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>106.025</td>\n",
+       "      <td>6645</td>\n",
+       "      <td>483</td>\n",
+       "      <td>3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>104.593</td>\n",
+       "      <td>7075</td>\n",
+       "      <td>514</td>\n",
+       "      <td>4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>148.924</td>\n",
+       "      <td>9504</td>\n",
+       "      <td>681</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>55.882</td>\n",
+       "      <td>4897</td>\n",
+       "      <td>357</td>\n",
+       "      <td>2</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0   Income  Limit  Rating  Cards  Age  Education  Gender_ Male  \\\n",
+       "0           1   14.891   3606     283      2   34         11             1   \n",
+       "1           2  106.025   6645     483      3   82         15             0   \n",
+       "2           3  104.593   7075     514      4   71         11             1   \n",
+       "3           4  148.924   9504     681      3   36         11             0   \n",
+       "4           5   55.882   4897     357      2   68         16             1   \n",
+       "\n",
+       "   Gender_Female  Student_No  Student_Yes  Married_No  Married_Yes  \\\n",
+       "0              0           1            0           0            1   \n",
+       "1              1           0            1           0            1   \n",
+       "2              0           1            0           1            0   \n",
+       "3              1           1            0           1            0   \n",
+       "4              0           1            0           0            1   \n",
+       "\n",
+       "   Ethnicity_African American  Ethnicity_Asian  Ethnicity_Caucasian  \n",
+       "0                           0                0                    1  \n",
+       "1                           0                1                    0  \n",
+       "2                           0                1                    0  \n",
+       "3                           0                1                    0  \n",
+       "4                           0                0                    1  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(X)\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linear regression on the modeling dataset\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((268, 17), (132, 17))"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 64 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>         <td>Balance</td>     <th>  R-squared:         </th> <td>   0.955</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.953</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   450.6</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 19 Jul 2018</td> <th>  Prob (F-statistic):</th> <td>5.37e-164</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>10:19:04</td>     <th>  Log-Likelihood:    </th> <td> -1615.2</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   268</td>      <th>  AIC:               </th> <td>   3256.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   255</td>      <th>  BIC:               </th> <td>   3303.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>                      <td>  -93.7521</td> <td>   16.351</td> <td>   -5.734</td> <td> 0.000</td> <td> -125.952</td> <td>  -61.552</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Unnamed: 0</th>                 <td>    0.0195</td> <td>    0.058</td> <td>    0.338</td> <td> 0.736</td> <td>   -0.094</td> <td>    0.133</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Income</th>                     <td>   -7.5929</td> <td>    0.310</td> <td>  -24.465</td> <td> 0.000</td> <td>   -8.204</td> <td>   -6.982</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Limit</th>                      <td>    0.2216</td> <td>    0.041</td> <td>    5.454</td> <td> 0.000</td> <td>    0.142</td> <td>    0.302</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Rating</th>                     <td>    0.6277</td> <td>    0.610</td> <td>    1.029</td> <td> 0.304</td> <td>   -0.573</td> <td>    1.829</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Cards</th>                      <td>   21.3910</td> <td>    5.356</td> <td>    3.994</td> <td> 0.000</td> <td>   10.844</td> <td>   31.938</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Age</th>                        <td>   -0.5304</td> <td>    0.385</td> <td>   -1.378</td> <td> 0.169</td> <td>   -1.288</td> <td>    0.227</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Education</th>                  <td>   -0.5830</td> <td>    2.067</td> <td>   -0.282</td> <td> 0.778</td> <td>   -4.653</td> <td>    3.487</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender_ Male</th>               <td>  -35.4891</td> <td>   10.522</td> <td>   -3.373</td> <td> 0.001</td> <td>  -56.210</td> <td>  -14.769</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender_Female</th>              <td>  -58.2630</td> <td>   10.271</td> <td>   -5.673</td> <td> 0.000</td> <td>  -78.489</td> <td>  -38.037</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Student_No</th>                 <td> -255.1080</td> <td>   12.030</td> <td>  -21.205</td> <td> 0.000</td> <td> -278.799</td> <td> -231.417</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Student_Yes</th>                <td>  161.3559</td> <td>   14.984</td> <td>   10.768</td> <td> 0.000</td> <td>  131.847</td> <td>  190.864</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Married_No</th>                 <td>  -45.8819</td> <td>   10.324</td> <td>   -4.444</td> <td> 0.000</td> <td>  -66.213</td> <td>  -25.551</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Married_Yes</th>                <td>  -47.8702</td> <td>   10.778</td> <td>   -4.442</td> <td> 0.000</td> <td>  -69.095</td> <td>  -26.645</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_African American</th> <td>  -42.7295</td> <td>   11.726</td> <td>   -3.644</td> <td> 0.000</td> <td>  -65.823</td> <td>  -19.636</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_Asian</th>            <td>  -24.4368</td> <td>   11.232</td> <td>   -2.176</td> <td> 0.030</td> <td>  -46.556</td> <td>   -2.318</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_Caucasian</th>        <td>  -26.5858</td> <td>   10.150</td> <td>   -2.619</td> <td> 0.009</td> <td>  -46.575</td> <td>   -6.597</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>18.586</td> <th>  Durbin-Watson:     </th> <td>   2.018</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  21.032</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.686</td> <th>  Prob(JB):          </th> <td>2.71e-05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.006</td> <th>  Cond. No.          </th> <td>4.84e+19</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 3.28e-30. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                Balance   R-squared:                       0.955\n",
+       "Model:                            OLS   Adj. R-squared:                  0.953\n",
+       "Method:                 Least Squares   F-statistic:                     450.6\n",
+       "Date:                Thu, 19 Jul 2018   Prob (F-statistic):          5.37e-164\n",
+       "Time:                        10:19:04   Log-Likelihood:                -1615.2\n",
+       "No. Observations:                 268   AIC:                             3256.\n",
+       "Df Residuals:                     255   BIC:                             3303.\n",
+       "Df Model:                          12                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================================\n",
+       "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "----------------------------------------------------------------------------------------------\n",
+       "const                        -93.7521     16.351     -5.734      0.000    -125.952     -61.552\n",
+       "Unnamed: 0                     0.0195      0.058      0.338      0.736      -0.094       0.133\n",
+       "Income                        -7.5929      0.310    -24.465      0.000      -8.204      -6.982\n",
+       "Limit                          0.2216      0.041      5.454      0.000       0.142       0.302\n",
+       "Rating                         0.6277      0.610      1.029      0.304      -0.573       1.829\n",
+       "Cards                         21.3910      5.356      3.994      0.000      10.844      31.938\n",
+       "Age                           -0.5304      0.385     -1.378      0.169      -1.288       0.227\n",
+       "Education                     -0.5830      2.067     -0.282      0.778      -4.653       3.487\n",
+       "Gender_ Male                 -35.4891     10.522     -3.373      0.001     -56.210     -14.769\n",
+       "Gender_Female                -58.2630     10.271     -5.673      0.000     -78.489     -38.037\n",
+       "Student_No                  -255.1080     12.030    -21.205      0.000    -278.799    -231.417\n",
+       "Student_Yes                  161.3559     14.984     10.768      0.000     131.847     190.864\n",
+       "Married_No                   -45.8819     10.324     -4.444      0.000     -66.213     -25.551\n",
+       "Married_Yes                  -47.8702     10.778     -4.442      0.000     -69.095     -26.645\n",
+       "Ethnicity_African American   -42.7295     11.726     -3.644      0.000     -65.823     -19.636\n",
+       "Ethnicity_Asian              -24.4368     11.232     -2.176      0.030     -46.556      -2.318\n",
+       "Ethnicity_Caucasian          -26.5858     10.150     -2.619      0.009     -46.575      -6.597\n",
+       "==============================================================================\n",
+       "Omnibus:                       18.586   Durbin-Watson:                   2.018\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.032\n",
+       "Skew:                           0.686   Prob(JB):                     2.71e-05\n",
+       "Kurtosis:                       3.006   Cond. No.                     4.84e+19\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The smallest eigenvalue is 3.28e-30. This might indicate that there are\n",
+       "strong multicollinearity problems or that the design matrix is singular.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = sm.OLS(y_train, X_train)\n",
+    "results = model.fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "209    772.457011\n",
+       "280    558.077166\n",
+       "33    -125.543453\n",
+       "210    201.978650\n",
+       "93     871.205893\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat=results.predict(X_test)\n",
+    "y_hat[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9522383003309711"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#get the r-squared value for the testing dataset\n",
+    "r2_score(y_test, y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Actual vs Forecasted')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#display the results\n",
+    "\n",
+    "plt.scatter(X_test.Rating, y_test, label='Actual')\n",
+    "plt.scatter(X_test.Rating, y_hat, label='Forecasted', marker = '+')\n",
+    "plt.legend()\n",
+    "plt.title('Actual vs Forecasted')"
    ]
   },
   {
diff --git a/Balance.ipynb b/Balance.ipynb
index 2c263ae..625db16 100644
--- a/Balance.ipynb
+++ b/Balance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,12 +13,12 @@
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.metrics import r2_score\n",
     "from pandas.plotting import scatter_matrix\n",
-    "%matplotlib inline"
+    "#matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -152,61 +152,41 @@
        "4     Yes  Caucasian      331  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = pd.read_csv('balance.txt')\n",
-    "df.head()"
+    "# import the data\n",
+    "df = pd.read_csv('balance.txt', delimiter=',')\n",
+    "df.head() "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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b2c/M7O1DWY6ZPWNmo83s9Tj/HkmfGWxc9SwrSzLrV+3WtiT1Ap8xs/+uMV0n8HtgZNyhs4gl82W4/urdB1wxZZ3+knqA75rZt7OYf1aGxZ2xkkZI+rqkFyU9DRxeNn6LUoCkcyR9N9F/kKRfSForabmk4+LwwyX9WtIrcfg5idneGz/XxjOLd0s6TtJ9ifm+R9KDktbFz/ckxvVI+qqkn0taL+kuSRNS3TAFVtrWMd3XSPq9pEMT48dL+k9JK+P4/0qMOzHeJPSypAWSdkmMM0mfl7Q0pstXJb1V0i/jfnBzrOooTX+EpEfivvMLSX/dvK3gACR1S1qR6O+V9E+SHpO0UdLVkjok/Tim6X9LGhen3XRmLulrwHuBy4ZYW5CbYZHRAycCRwD7AF3Ax+r9oaQ3Az8GLgXeBEwFHomjNwLHAmMJfx6fk3RkHHdw/BwbT/1+WTbf8cDtwDeAHYELgdsl7ZiY7JPA8cBOwDbAGfXG7YDQHPApQpO2fweullRqTfIdYHvgnYTtexGApPcD/wc4CpgI/AG4qWy+M4D9gAOALxJOx48hXLx8F3B0nNe+wDXAZwlpfCWwQNIb0l9VN0h/T7gZ7G3ARwjH+JcJ+8pWwKnlPzCzs4CfAafEY/qU5oXbIDMrTAf0AhuAtYnuROB/gJMS000nNBXrBRYDfwKeiuPGA78DXgEWAv8KzK9z+RcDF8XvnXEZWyfGHwfcF79/CvhV2e9/CRwXv/cA/5wY93ngJ3XEcA2wGng8MWx8XJel8XNcHC7CH80y4DFg3yal0wxCBrwMmJvBPvDBuK2XJYZvH9NjZ+DG+P03FbbRujiP0jYaDbwehz0Wf3dgHDcLeI3QLnpWHHYBcHH8fgXw1bL4ngLeV+e67AbcQ6gjfwI4bajpGWNdGrtZzUjnRtaxbJrumC6PxO4rtdK/wu9XsOXx/nRi/A9jWpW23wtxeftSdhwTjsvP1FintydifYSQl5w+1HVKoytiif5IMxub6L4F7MKWN3/8IfH9EEKzsZNj/1xC3foC4G7Cv/3vKi1I0v6S7pH0gqR1wEmEEkE9dimLoxTXpET/c4nvrxIynVrmETLSpLnA3WY2mbBOpeeWHApMjt1sws6eKW2+tf5QYC/gaEl7ZbS4TdvPzF6NX0cTMpa1wF8S084lbJtfEA680jY6GOgjlNhLNwY9H8/Izo7TfgU4O57u/5HN6bQ7MCdW26yVtJaQsW2qCqqhD5hjZu8gnD2cHLfVoNIzEev+hDteS7G2gmrrWO5nZjY1duc2sLzS8T47MeyPwPNs3n5nEv6Qh3Q8mNlTpVgJZ36vAvMrTJrWOtVUxIy+klVs2Sb4zWXjNxJKfBBu8y61F742/u6tVeZ7A+EPYTcz2wH4JptvNKl1lXsl/e9qezPwbI3fDcjM7gVeLhs8k7AuxM8jE8Ovs+B+YKykiY0svw6bbq03sz8RqkVmZrzMcj8CdmDL/b+0jVYS0qC0jf4eGAE8G7cRhCq8DxNK032EA3kh/f9glwNfKyt4bG9mN9YTpJmtMrOH4/f1hFLvJAafnh8GFprZy2a2pkqsuRhgHfMwE7gufn+FUCX7prJpBtt65QPA78ysvFDXVMMlo78ZOFXSrrEkUyoBGXAX4XT+LEkjCaWtwyDshMBI4IOSjooXZHaUNDX+fgzwspm9JmkaoU695AVCifEtVWK6A3ibpE/G+f4DoYR7W1orndAR16W0TjvF4ZVuc8/6IMtjmVuI26AH2EXSuFK6x+E3EOvnY136+wlVPL2JWXRQ33p8CzgpnvlJ0qh4AX/MYGNWaMW1D/AAg0/P3Ld5PcrWsdy7JT0aL5i+s8asRkrattSx+QkApeN9IvEYL1NpO+1cNs3zVD+mK/kEoaqwksGsU0OKmNH/SFu2o59POODuBB4FHgZuidMebGb7Eko37yLUmW1LONhLjLBTzCGUlB8B9o7jPg+cK2k94fT95k0/ClUFXwN+Hk/bD0gGaWYvES4QzwFeIlzUO8LMmvlclbpucy/AMiv5x7jcJwnXNN4AYGZ3A/8CjCKcCW5PqPZIMupYDzNbRLhGdBmwhlB3ftxgA5U0mlCPfLqZvTLQpFViapVtXlWNdXwY2N3M9iY0iviv8t+XuYNQHVPqzonDD4zH+/PATEkHl/2unu10CfCx2FLrGwMFEVtffRT4foXRg12nxmR5AaCdOsLOcAahbm5iHDaReJG2nTrCBaTkxdiK60RoBXJ0pekyjO3dwJ2J/jOBM9ttGxFa1lyZGL7FdCnGOZJQSPlCq8ea5jrWmL4XmNDgMs8BzigblurxQKgKuqtZ6zRQV8QSfV3iafSY0ndCS5zHCXXus+Jks4Bb84kwVdXWaQFwbKxWOABYZ7FKIEOtemv9YLfRncD0WPUzjrD/3JlmQLEp6NXAEjO7sJVjHaoB1jE5zc6lZrGxinQrwlnwYJZT7XhPSvt4OJoq1TZprNOg5P1vnldHqGd7NHZPAGfF4TsSWjIsjZ/j8451kOt1I6HK4c+EOsYTqq0T4VT1ckKrosVAV5NiPAz4bVzuWe26jYBPE6pjlgHHZxDnQYSqg8fY3AzvsFaMNYN1PInYJJrwKIYn4rF6P/CeISyn2vGeXE5qxwOhyu8lYIfEsFTXaTBdzUcgSLqGUJe82szeFYeNB75HOP3tBY4yszXxH+qSmFCvEtqEPzzgApxzzmWqnqqbebRwu2znnHMDq+uhZrHZ022JEv1TQLeZrYrtdHvM7O2SrozfbyyfbqD5T5gwwTo7OxtakUZs3LiRUaNSfQ1byylfx4ceeuhFMytvI5yZLNM46/Rr1/kXKY1ryfsYzmv59abxUJ/HvkU7Xkm12vH2y+iVeAVZR0cHX//614cYSuM2bNjA6NH13HTavsrX8ZBDDmnqDRydnZ0sWrQok3n39PTQ3d2dybzbef6SCpPGtWSdRq26/HrTOO0Xb9TdXtcSryDr6uqy4ZhIzTQc1tE5V9lQM/rnJU1MVN2sjsP99WNlOmu88X2ob3Vvd7W2CwzfbePqV9qP5kzp47gq+5TvR0O/M7aV2mU755wbQM0SvaQbCY/UnKDw8P6zgfOAmyWdADwDfDxOfgehaeUyQvPK4zOI2Tnn3CDUzOjN7Ogqoz5QYVpj8+N+nXPOtYBh+wgE55wbLtJudeOcc6mp56K9q81L9M45V3Ce0TvnXMF5Ru+ccwXndfTOudx4HXxzeIneOecKzjN655wrOM/onXOu4Dyjd865gvOM3jnnCs4zeuecKzhvXjkAf2a6c64IvETvnHMF5xm9c84VnGf0DknXSFot6fHEsPGSFkpaGj/HxeGS9A1JyyQ9Jmnf/CJ3ztXDM3oHMA+YUTZsLnC3mU0G7o79AIcCk2M3G7iiSTE654bIL8Y6zOxeSZ1lg2cSXiEJcC3QA3wpDr8uvk3sfkljSy+KTzsuf7G6c+nwjN5V01HKvM1slaSd4vBJwPLEdCvisH4ZvaTZhFI/HR0d9PT0bBo3Z0pfwwGW5rdhw4Yt5p22dp+/c57Ru8FShWFWaUIzuwq4CqCrq8u6u7s3jTsuhacW9h4T5tfT00Ny3mlr9/nXIqkXWA+8DvSZWZek8cD3gE6gFzjKzNbkFaNrTEMZve8gzZFTFcbzpSoZSROB1XH4CmC3xHS7AiuzCMA11SFm9mKiv3SN5jxJc2P/l/IJzTUqjYuxh5jZVDPriv3VLuK59rIAmBW/zwJuTQw/Nra+OQBYl0X9vMvdTMK1GeLnkTnG4hqURdVNtYt4rkVJupGQZhMkrQDOBs4DbpZ0AvAM8PE4+R3AYcAy4FXg+KYH7NJmwF2SDLgyVrlVu0azhYGuwyx+dl3NBc+Z0nDsAHRsV/26TzOuf7T6dZZGM/oh7yCudZjZ0VVGfaDCtAacnG1ErskONLOV8VhdKOnJen+Y9XWYes2Z0scFiytnZ6VrOVnK+zpLLY1m9EPeQQYqCTRbtX/jelqG1Iq71jwuvf7WAceHeTQWA7R+icPlx8xWxs/VkuYD06h+jca1oYYy+kZ2kIFKAs1W7d+4nhJJrdJCM0o19ZRYWr3E4fIhaRSwlZmtj9+nA+ey+RrNeWx5jca1oSFfjJU0StKY0nfCDvI41S/iOedaTwdwn6RHgV8Bt5vZTwgZ/IckLQU+FPtdm2qkRN8BzJdUms8NZvYTSQ9S+SKec67FmNnTwN4Vhr9EhWs07cjvsG4gox8OO4hzzhWBP9TMOecKrrCPQPC3QznnXFDYjL5Z6vlDcc65PHnVjXPOFZxn9M45V3Ce0TvnXMF5Hb1rW6XrI3Om9FW9A9kvuDvnGb1zbpgbDi30vOrGOecKzjN655wruGFddVNPHa9zzrU7L9E751zBeUbvnHMF5xm9c84V3LCuo3fF588id85L9M45V3ie0TvnXMF51U0B1HNn37wZo5oQiXPFVOsYa/Xjy0v0zjlXcF6id8NaPWdDtW6o8wu6bvGz62redJnnftK2Gb2/2ck55+qTSUYvaQZwCTAC+LaZnTeY33sm3voaTeMiKWoTTk/j4kg9o5c0Argc+BCwAnhQ0gIz+03ay3L58DQenFp/BHOm9NHdnFDq5mmcvjwLBFmU6KcBy8zsaQBJNwEzAd9BisPTOGVpnMWmnFF4GjdZls/FzyKjnwQsT/SvAPYvn0jSbGB27N0g6akMYqnLqTABeDGv5TfDIef3W8fdG5hdS6Vx1unXLvPX+f0GFSaNa8n7GG7W8oeaxllk9KowzPoNMLsKuCqD5Q+apEVm1pV3HFlKeR1bKo2zTr92n/8QtVQa15L3Nsx7+bVk0Y5+BbBbon9XYGUGy3H58TQuPk/jAskio38QmCxpD0nbAJ8AFmSwnJYg6RhJd+UdR5MNqzROi6ROSSapHZo1exoXSOoZvZn1AacAdwJLgJvN7Im0l9MISb2S/ihpg6TngD9JGl3H7/odqGZ2vZlNzzTgdKR2ep11GsdtvGfZsHMkfbfKT7KuOshl/pLGS5ovaaOkP0j6ZMZxbNIOx3GZvKuP8l7+gGTWr9qt8CT1Ap8xs/+WtDNhZ77NzM6q8btO4PfAyHgguAxIMmCymS1LDDsH2NPM/ldugTVosPuPpBsJhbETgKnA7cB7WjzDdS1o2D/rxsyeI2T0UwEkHS7p15JekbQ8ZjAl98bPtfFs4N2SjpN0X2mCWBo9SdJSSWskXS5JcdwISRdIelHS7yWd0kan8i1DUrekFZLmSFotaZWk4xPj58Xtfruk9ZIekPTWxPhLYtq+IukhSe9NjDtH0vclfTf+drGkt0k6My5ruaTpiel3kHR1jOFZSf8W26CX0vvrMb2fBupuGydpFPD3wL+Y2QYzu49QdfKphjaeG5aGfUYvaVfgUKBUetwIHAuMJRyYn5N0ZBx3cPwca2ajzeyXVWZ7BPA3wN7AUcCH4/AT47KmAvsCR1b8tavHzsAOhGaAJwCXSxqXGH808K/AOELafi0x7kFCGowHbgC+L2nbxPiPAN+Jv/01oSCwVVzWucCViWmvBfqAPYF9gOnAZ+K4Ewn7wj5AF/Cx5ApImivptirr9zbgdTP7bWLYo8A7q0zvXHVmNqw64Brg9ditJzQZ+ylwD7AUWAiMS0x/MXBR/N4Zp986Mf444L5EvwEHJfpvBubG7/8DfDYx7oPl80th/XaL67IEeAI4LQ4fH9et3zq2Whe3yZ5lw84Bvhu/Pwf8BXgEWBSHvQD8Kq7fSuC6xG8PA54cYHn/D3gZeDwuZ2Fie60kZOQ7xmnHxPieJtw89Cdgu8S8jgbuSaT3SXGfWw30ltI7LufZuA6PAIcl5nEmodVLH/DhxPATgZ6806eJ+0EvsLgsnSvux4TmoN8g/Kk/BuybmM+sOP1SYFaNZZbS6vHEsNSWCewX12lZ/K2asS2HY4l+HvA88IyZjQG6CaXrRWY2Gfgt8IikFyStIxyoEwa5jOcS318FShd6d2HLm1CS39PSB8wxs3cABwAnS9oLmAvcHdfx7tjfql4HRpYNGwn8OdH/nJlNtc1tl7cBHovrt4pQwi5JpgGxymeJpHWS1sZ5n5mY/nni9iKc3a0HvhjHvS9+Hgx8Pf52laS1cV5XAjvFaUrpPQ+YQfhTSLoorsNUM7vg579vAAANBElEQVQjxrYXoYXL38X1/Y9SVRDwxhjLcHJIWTpX248PBSbHbjZwBYQL2sDZhJu9pgFnl535lZtHSKukNJd5RZy29LvyZWVi2GX0ZnYvoTRY6v8p4QB8Vxx0OLAdsJuZ7QB8k803jzR65XoVoT1yyW7VJhwqM1tlZg/H7+sJJftJhNvXr42TXUtrVxs9Qzh7StoD+MMAv9keKDVzXQa8vdJEsT7+S4QqtXFmNhZYB2womzS5vTaweXt9NDHNjwn7xLvMbGzs3mhmpeqVVYT96F7CGUP5n1clM4GbCGdjIwhnFNPiuL3j8OGs2n48k3AWZ2Z2PzBW0kRCtelCM3vZzNYQSuRVM9dEWqW+zDjujWb2SwvF++to0nE47DL6KrYGDpY0lZBhjDKz1yRNA5JN2l4g/Em8ZYjLuRk4TdIkSWMJGU5mYiuPfYAHgA4zWwXhz4DNpc5W9D3gnyXtKmkrSR8k1Jv/II43YEK8kFq6/X4Emw/QPxLSsZIxhLOeF4CtJX2FUFIut2l7Ec4wSttrUmmCOP4l4P9KemOM9a2SSqX+m4FT43WgNwJvKlvGKZIek3RNosQ3CVhuZhuBWwhnBW+RdCAhY/lOlfUqIgPuKkvnavtxpUc2TBpg+GCktcxJ8XsjsQyJZ/RB6d/1X4DPA9tKWg98hXCwhonMXiVc1Pt5PFU/YJDL+Rah1PkY4SLfHYRM5/WG16CMwn0BPwRON7NX0p5/xs4FfgHcB6wB/h04xswej+NPIdSjHkqomjq44lwqu5NQEv8t4QzhNQZXhVb+aIAnCAWF38RYfwBMjOO+FZf3KKFpZDIdrojjnyWU/C+oMP/Px3lfDdwIfM6GV9PKA81sX+pL52qPbKjrUQ5DNNhlZhnLwJpxIaDVOkK1QPJiy1PAxPh9IvBUk+I4FPhDBvMdSchgvpD3OjZhG54DnNHo+tW7TxDq4I+uNN1g5l9tHOFawZmJcXcC7857O+fd1UrnaulCuDh+ZWL4FtNlsS9UW2Yc92Ri+BbTZdl5iT5YQLhKTvy8NYuFSNpO0mGStpY0iXDBZn7KyxChBLjEzC5MjGrKOmZN0ihJY0rfCc0ZHyf99as2vwXAsQoOANbZ5iqeusX62pK/JaxDaf6fkPQGSXsQLtj9aigr0M6GkM7V0uVOYLqkcbF6bHocNhipLDOOWy/pgHicHkuzjsO8/6lzKBncSDhV/jOhjuwEYEfC1fSl8XN8RsventCGez2h6uE/CRdn0lzGQYTTwcdINN1r1jo2If3eQqgKeZRQbXJWHD7k9RvMPkE4/b4c+B2hmVzXEOf/nfj7xwgZxsTE9GfF+T8FHJr3Nm+HdB4oXYBPEy7QLwOOb8a+UG2ZhPspHo+/uYwmNa8clo9AcM654cSrbpxzruBa4hkrEyZMsM7OzrzDqMvGjRsZNWpU3mEMWSn+hx566EUzK2/uV1G8YWcR8KyZHRHrjm8i3DH4MPApMyu/GWgL5WncLtuxXeKE/rEOJo1dsbVERt/Z2cmiRYvyDqMuPT09dHd35x3GkJXilzTQzUflTiPceFVqb34+4a7OmyR9k1CPecVAMyhP43bZju0SJ/SPdZBp7ArMq27cgOLNPocD3479At7P5puXWv0uW+eGvZYo0eel1lvXh/rG9YK5mPCclzGxf0dgrW1+nnrVu/uUeHF0R0cHPT09m8atfnkdl17f+i08O7ajLeIE2GOHEVtsY+dKhnVG7wYm6QhgtZk9JKm7NLjCpBWbblnixdFdXV2WrFa49PpbuWBx6+9+c6b0tUWcAPNmjGqbaibXXO2xB7u8HAh8VNJhwLaEOvqLCQ9v2jqW6v2l0c61uMJm9LWqZVxtZnYm8fG9sUR/hpkdI+n7hJdo3EQb32Xr3HDhF2PdUHwJ+IKkZYQ6+6tzjsc5N4DCluhdusysB+iJ359m8zPSnXMtzkv0zjlXcJ7RO+dcwbVt1Y1fbHXOufp4id455wrOM3rnnCs4z+idc67gPKN3zrmC84zeOecKzjN655wrOM/onXOu4Dyjd865gvOM3jnnCs4zeuecKzjP6J1zruA8o3fOuYLzjN455wrOM3rnnCu4hh5TLKkXWA+8DvSZWZek8cD3gE6gFzjKzNY0FqZzzrmhSqNEf4iZTTWzrtg/F7jbzCYDd8d+55xzOcmi6mYmcG38fi1wZAbLcM45V6dG3zBlwF2SDLjSzK4COsxsFYCZrZK0U6UfSpoNzAbo6Oigp6dn07jFz66rueA5UxqMvA7JmEo2bNhQcXi7GEz8knYDrgN2Bv4CXGVml3j1nHPtpdGM/kAzWxkz84WSnqz3h/FP4SqArq4u6+7u3jTuuBZ5TWDvMd39hvX09JCMtd0MMv4+YI6ZPSxpDPCQpIXAcYTqufMkzSVUz30pi3idc41rKKM3s5Xxc7Wk+cA04HlJE2NpfiKwOoU4c1HpvbRzpvRt8UfUe97hzQypqeKZWensbL2kJcAkQvVcd5zsWqAHz+ida1lDzugljQK2ihnAKGA6cC6wAJgFnBc/b00jUJcvSZ3APsADpFA917Fd+NNsde0SJ7R/taLLTiMl+g5gvqTSfG4ws59IehC4WdIJwDPAxxsP0+VJ0mjgh8DpZvZKTPOaBqqeu/T6W7lgcaM1h9mbM6WvLeIEmDdjVFtXK7rsDHkPNrOngb0rDH8J+EAjQbnWIWkkIZO/3sxuiYMLUz3n3HDgd8a6qhSK7lcDS8zswsSoUvUcePWccy2vPc5JXV4OBD4FLJb0SBz2ZcL1F6+ec65NeEbvqjKz+4BqFfJePedcm/CqG+ecKzjP6J1zruA8o3fOuYLzjN455wrOM3rnnCs4z+idc67gPKN3zrmC84zeOecKzm+YalClRxknFfkxxs659uAleuecKzjP6J1zruA8o3fOuYLzjN455wrOM3rnnCs4z+idc67gPKN3zrmC83b0GfN29s65vHmJ3jnnCs4zeuecKzivuslZraod8Ood51xjMsnoJc0ALgFGAN82s/OyWM5w0Yr1/J7GzrWP1KtuJI0ALgcOBfYCjpa0V9rLcfnxNHauvWRRop8GLDOzpwEk3QTMBH6TwbIcuVT/eBo710ayyOgnAcsT/SuA/csnkjQbmB17N0h6KoNYUncqTABezDuOwdL5m76W4t+9gdmlkcZtsR3bKb0POb9frI2ksSuQLDJ6VRhm/QaYXQVclcHyMyVpkZl15R3HUKUUf8Np3C7bsV3ihPaK1TVXFs0rVwC7Jfp3BVZmsByXH09j59pIFhn9g8BkSXtI2gb4BLAgg+W4/HgaO9dGUq+6MbM+SacAdxKa3l1jZk+kvZwctV11U5mG408pjdtlO7ZLnNBesbomklm/qlXnnHMF4o9AcM65gvOM3jnnCs4z+jKSeiUtlvSIpEVx2HhJCyUtjZ/j4nBJ+oakZZIek7RvDvFeI2m1pMcTwwYdr6RZcfqlkmZlGO8MSU/FGOZmtZxBxLObpHskLZH0hKTT4vCWTHNJIyT9WtJtsX8PSQ/EOL8XL44j6Q2xf1kc39nMOF1r8Yy+skPMbGqiTfJc4G4zmwzcHfshPAJgcuxmA1c0PVKYB8woGzaoeCWNB84m3PQ0DTi7lLGlqUUfndAHzDGzdwAHACfHmFo1zU8DliT6zwcuinGuAU6Iw08A1pjZnsBFcTo3THlGX5+ZwLXx+7XAkYnh11lwPzBW0sRmBmZm9wIvlw0ebLwfBhaa2ctmtgZYSP8/jzRsenSCmf0JKD06ITdmtsrMHo7f1xMy0Um0YJpL2hU4HPh27BfwfuAHVeIsxf8D4ANxejcMeUbfnwF3SXoo3sIP0GFmqyBkDMBOcXilRwFMalqk1Q023matR6tuLwBi9cY+wAO0ZppfDHwR+Evs3xFYa2Z9FWLZFGccvy5O74Yhfx59fwea2UpJOwELJT05wLR1PQqghVSLt1nr0bLbS9Jo4IfA6Wb2ygCF31zWQdIRwGoze0hSdx2xtOy2ds3nJfoyZrYyfq4G5hOqG54vnZ7Hz9Vx8lZ9FMBg423WerTk9pI0kpDJX29mt8TBrZbmBwIfldRLqPJ6P6GEP1ZSqcCWjGVTnHH8DvSv4nPDhGf0CZJGSRpT+g5MBx4n3N5faokyC7g1fl8AHBtbYhwArCud7udssPHeCUyXNC5ehJ0eh6Wt5R6dEOutrwaWmNmFiVEtleZmdqaZ7WpmnYTt9j9mdgxwD/CxKnGW4v9YnN5L9MOVmXkXO+AtwKOxewI4Kw7fkdDyYmn8HB+Hi9CK5HfAYqArh5hvBFYBfyaU4k4YSrzAp4FlsTs+w3gPA34bYzirBdL8IEKVxmPAI7E7rMXTvBu4LbHP/iqm2/eBN8Th28b+ZXH8W/Le1t7l1/kjEJxzruC86sY55wrOM3rnnCs4z+idc67gPKN3zrmC84zeOecKzjN655wrOM/onXOu4P4/zaipkbeuUugAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "Index(['Unnamed: 0', 'Income', 'Limit', 'Rating', 'Cards', 'Age', 'Education',\n",
-       "       'Gender', 'Student', 'Married', 'Ethnicity', 'Balance'],\n",
-       "      dtype='object')"
+       "<matplotlib.figure.Figure at 0xc85f748>"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "df.columns"
+    "# show any relevant graphs\n",
+    "df.hist();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(400, 12)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +209,7 @@
       "Ethnicity     400 non-null object\n",
       "Balance       400 non-null int64\n",
       "dtypes: float64(1), int64(7), object(4)\n",
-      "memory usage: 31.3+ KB\n"
+      "memory usage: 37.6+ KB\n"
      ]
     }
    ],
@@ -239,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +368,7 @@
        "Balance      863.00000   1999.000  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -399,7 +379,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Education    -0.008062\n",
+       "Age           0.001835\n",
+       "Unnamed: 0    0.006064\n",
+       "Cards         0.086456\n",
+       "Income        0.463656\n",
+       "Limit         0.861697\n",
+       "Rating        0.863625\n",
+       "Balance       1.000000\n",
+       "Name: Balance, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Balance'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xca02fd0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc81c978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show any relevant graphs\n",
+    "plt.scatter(df.Rating, df.Balance, marker='+')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    333\n",
+       "1    903\n",
+       "2    580\n",
+       "3    964\n",
+       "4    331\n",
+       "Name: Balance, dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get the \"y\" vector\n",
+    "y = df.Balance\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the \"X\" matrix\n",
+    "X = df.drop(columns=['Balance'])\n",
+    "X = pd.get_dummies(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -430,11 +507,15 @@
        "      <th>Cards</th>\n",
        "      <th>Age</th>\n",
        "      <th>Education</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Student</th>\n",
-       "      <th>Married</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>Balance</th>\n",
+       "      <th>Gender_ Male</th>\n",
+       "      <th>Gender_Female</th>\n",
+       "      <th>Student_No</th>\n",
+       "      <th>Student_Yes</th>\n",
+       "      <th>Married_No</th>\n",
+       "      <th>Married_Yes</th>\n",
+       "      <th>Ethnicity_African American</th>\n",
+       "      <th>Ethnicity_Asian</th>\n",
+       "      <th>Ethnicity_Caucasian</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -447,11 +528,15 @@
        "      <td>2</td>\n",
        "      <td>34</td>\n",
        "      <td>11</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Caucasian</td>\n",
-       "      <td>333</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -462,11 +547,15 @@
        "      <td>3</td>\n",
        "      <td>82</td>\n",
        "      <td>15</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Asian</td>\n",
-       "      <td>903</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -477,11 +566,15 @@
        "      <td>4</td>\n",
        "      <td>71</td>\n",
        "      <td>11</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Asian</td>\n",
-       "      <td>580</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -492,11 +585,15 @@
        "      <td>3</td>\n",
        "      <td>36</td>\n",
        "      <td>11</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Asian</td>\n",
-       "      <td>964</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -507,65 +604,351 @@
        "      <td>2</td>\n",
        "      <td>68</td>\n",
        "      <td>16</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Caucasian</td>\n",
-       "      <td>331</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Unnamed: 0   Income  Limit  Rating  Cards  Age  Education  Gender Student  \\\n",
-       "0           1   14.891   3606     283      2   34         11    Male      No   \n",
-       "1           2  106.025   6645     483      3   82         15  Female     Yes   \n",
-       "2           3  104.593   7075     514      4   71         11    Male      No   \n",
-       "3           4  148.924   9504     681      3   36         11  Female      No   \n",
-       "4           5   55.882   4897     357      2   68         16    Male      No   \n",
+       "   Unnamed: 0   Income  Limit  Rating  Cards  Age  Education  Gender_ Male  \\\n",
+       "0           1   14.891   3606     283      2   34         11             1   \n",
+       "1           2  106.025   6645     483      3   82         15             0   \n",
+       "2           3  104.593   7075     514      4   71         11             1   \n",
+       "3           4  148.924   9504     681      3   36         11             0   \n",
+       "4           5   55.882   4897     357      2   68         16             1   \n",
        "\n",
-       "  Married  Ethnicity  Balance  \n",
-       "0     Yes  Caucasian      333  \n",
-       "1     Yes      Asian      903  \n",
-       "2      No      Asian      580  \n",
-       "3      No      Asian      964  \n",
-       "4     Yes  Caucasian      331  "
+       "   Gender_Female  Student_No  Student_Yes  Married_No  Married_Yes  \\\n",
+       "0              0           1            0           0            1   \n",
+       "1              1           0            1           0            1   \n",
+       "2              0           1            0           1            0   \n",
+       "3              1           1            0           1            0   \n",
+       "4              0           1            0           0            1   \n",
+       "\n",
+       "   Ethnicity_African American  Ethnicity_Asian  Ethnicity_Caucasian  \n",
+       "0                           0                0                    1  \n",
+       "1                           0                1                    0  \n",
+       "2                           0                1                    0  \n",
+       "3                           0                1                    0  \n",
+       "4                           0                0                    1  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head()"
+    "x = sm.add_constant(X)\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linear regression on the modeling dataset\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    333\n",
-       "1    903\n",
-       "2    580\n",
-       "3    964\n",
-       "4    331\n",
-       "Name: Balance, dtype: int64"
+       "((268, 17), (132, 17))"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "y = df.Balance\n",
-    "y.head()"
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 64 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>         <td>Balance</td>     <th>  R-squared:         </th> <td>   0.955</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.953</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   450.6</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 19 Jul 2018</td> <th>  Prob (F-statistic):</th> <td>5.37e-164</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>10:19:04</td>     <th>  Log-Likelihood:    </th> <td> -1615.2</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   268</td>      <th>  AIC:               </th> <td>   3256.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   255</td>      <th>  BIC:               </th> <td>   3303.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>                      <td>  -93.7521</td> <td>   16.351</td> <td>   -5.734</td> <td> 0.000</td> <td> -125.952</td> <td>  -61.552</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Unnamed: 0</th>                 <td>    0.0195</td> <td>    0.058</td> <td>    0.338</td> <td> 0.736</td> <td>   -0.094</td> <td>    0.133</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Income</th>                     <td>   -7.5929</td> <td>    0.310</td> <td>  -24.465</td> <td> 0.000</td> <td>   -8.204</td> <td>   -6.982</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Limit</th>                      <td>    0.2216</td> <td>    0.041</td> <td>    5.454</td> <td> 0.000</td> <td>    0.142</td> <td>    0.302</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Rating</th>                     <td>    0.6277</td> <td>    0.610</td> <td>    1.029</td> <td> 0.304</td> <td>   -0.573</td> <td>    1.829</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Cards</th>                      <td>   21.3910</td> <td>    5.356</td> <td>    3.994</td> <td> 0.000</td> <td>   10.844</td> <td>   31.938</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Age</th>                        <td>   -0.5304</td> <td>    0.385</td> <td>   -1.378</td> <td> 0.169</td> <td>   -1.288</td> <td>    0.227</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Education</th>                  <td>   -0.5830</td> <td>    2.067</td> <td>   -0.282</td> <td> 0.778</td> <td>   -4.653</td> <td>    3.487</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender_ Male</th>               <td>  -35.4891</td> <td>   10.522</td> <td>   -3.373</td> <td> 0.001</td> <td>  -56.210</td> <td>  -14.769</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender_Female</th>              <td>  -58.2630</td> <td>   10.271</td> <td>   -5.673</td> <td> 0.000</td> <td>  -78.489</td> <td>  -38.037</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Student_No</th>                 <td> -255.1080</td> <td>   12.030</td> <td>  -21.205</td> <td> 0.000</td> <td> -278.799</td> <td> -231.417</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Student_Yes</th>                <td>  161.3559</td> <td>   14.984</td> <td>   10.768</td> <td> 0.000</td> <td>  131.847</td> <td>  190.864</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Married_No</th>                 <td>  -45.8819</td> <td>   10.324</td> <td>   -4.444</td> <td> 0.000</td> <td>  -66.213</td> <td>  -25.551</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Married_Yes</th>                <td>  -47.8702</td> <td>   10.778</td> <td>   -4.442</td> <td> 0.000</td> <td>  -69.095</td> <td>  -26.645</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_African American</th> <td>  -42.7295</td> <td>   11.726</td> <td>   -3.644</td> <td> 0.000</td> <td>  -65.823</td> <td>  -19.636</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_Asian</th>            <td>  -24.4368</td> <td>   11.232</td> <td>   -2.176</td> <td> 0.030</td> <td>  -46.556</td> <td>   -2.318</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_Caucasian</th>        <td>  -26.5858</td> <td>   10.150</td> <td>   -2.619</td> <td> 0.009</td> <td>  -46.575</td> <td>   -6.597</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>18.586</td> <th>  Durbin-Watson:     </th> <td>   2.018</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  21.032</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.686</td> <th>  Prob(JB):          </th> <td>2.71e-05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.006</td> <th>  Cond. No.          </th> <td>4.84e+19</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 3.28e-30. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                Balance   R-squared:                       0.955\n",
+       "Model:                            OLS   Adj. R-squared:                  0.953\n",
+       "Method:                 Least Squares   F-statistic:                     450.6\n",
+       "Date:                Thu, 19 Jul 2018   Prob (F-statistic):          5.37e-164\n",
+       "Time:                        10:19:04   Log-Likelihood:                -1615.2\n",
+       "No. Observations:                 268   AIC:                             3256.\n",
+       "Df Residuals:                     255   BIC:                             3303.\n",
+       "Df Model:                          12                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================================\n",
+       "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "----------------------------------------------------------------------------------------------\n",
+       "const                        -93.7521     16.351     -5.734      0.000    -125.952     -61.552\n",
+       "Unnamed: 0                     0.0195      0.058      0.338      0.736      -0.094       0.133\n",
+       "Income                        -7.5929      0.310    -24.465      0.000      -8.204      -6.982\n",
+       "Limit                          0.2216      0.041      5.454      0.000       0.142       0.302\n",
+       "Rating                         0.6277      0.610      1.029      0.304      -0.573       1.829\n",
+       "Cards                         21.3910      5.356      3.994      0.000      10.844      31.938\n",
+       "Age                           -0.5304      0.385     -1.378      0.169      -1.288       0.227\n",
+       "Education                     -0.5830      2.067     -0.282      0.778      -4.653       3.487\n",
+       "Gender_ Male                 -35.4891     10.522     -3.373      0.001     -56.210     -14.769\n",
+       "Gender_Female                -58.2630     10.271     -5.673      0.000     -78.489     -38.037\n",
+       "Student_No                  -255.1080     12.030    -21.205      0.000    -278.799    -231.417\n",
+       "Student_Yes                  161.3559     14.984     10.768      0.000     131.847     190.864\n",
+       "Married_No                   -45.8819     10.324     -4.444      0.000     -66.213     -25.551\n",
+       "Married_Yes                  -47.8702     10.778     -4.442      0.000     -69.095     -26.645\n",
+       "Ethnicity_African American   -42.7295     11.726     -3.644      0.000     -65.823     -19.636\n",
+       "Ethnicity_Asian              -24.4368     11.232     -2.176      0.030     -46.556      -2.318\n",
+       "Ethnicity_Caucasian          -26.5858     10.150     -2.619      0.009     -46.575      -6.597\n",
+       "==============================================================================\n",
+       "Omnibus:                       18.586   Durbin-Watson:                   2.018\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.032\n",
+       "Skew:                           0.686   Prob(JB):                     2.71e-05\n",
+       "Kurtosis:                       3.006   Cond. No.                     4.84e+19\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The smallest eigenvalue is 3.28e-30. This might indicate that there are\n",
+       "strong multicollinearity problems or that the design matrix is singular.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = sm.OLS(y_train, X_train)\n",
+    "results = model.fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "209    772.457011\n",
+       "280    558.077166\n",
+       "33    -125.543453\n",
+       "210    201.978650\n",
+       "93     871.205893\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat=results.predict(X_test)\n",
+    "y_hat[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9522383003309711"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#get the r-squared value for the testing dataset\n",
+    "r2_score(y_test, y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Actual vs Forecasted')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#display the results\n",
+    "\n",
+    "plt.scatter(X_test.Rating, y_test, label='Actual')\n",
+    "plt.scatter(X_test.Rating, y_hat, label='Forecasted', marker = '+')\n",
+    "plt.legend()\n",
+    "plt.title('Actual vs Forecasted')"
    ]
   },
   {

From 26ec86319e7aeee090539e39d3b7cad1f740f8b9 Mon Sep 17 00:00:00 2001
From: Patrick Ayertey <patrick.ayertey@mckesson.com>
Date: Thu, 19 Jul 2018 10:40:18 -0700
Subject: [PATCH 4/4] Patrick- Credit Card Prediction

---
 Balance.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Balance.ipynb b/Balance.ipynb
index 625db16..0c52e2f 100644
--- a/Balance.ipynb
+++ b/Balance.ipynb
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [