From a555187e46a06c35b7e53ebc85a5218f192836ae Mon Sep 17 00:00:00 2001
From: nivekyoccm <34323611+nivekyoccm@users.noreply.github.com>
Date: Thu, 19 Jul 2018 01:49:37 -0500
Subject: [PATCH 1/3] Kevin M - first commit

---
 .gitignore    |  1 +
 balance.ipynb | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 33 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 balance.ipynb

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..58461f2
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints
\ No newline at end of file
diff --git a/balance.ipynb b/balance.ipynb
new file mode 100644
index 0000000..81f040b
--- /dev/null
+++ b/balance.ipynb
@@ -0,0 +1,32 @@
+{
+ "cells": [
+  {
+   "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 50e00f36bbdef0399b5b07df8f3b5c7ca5131b6c Mon Sep 17 00:00:00 2001
From: nivekyoccm <34323611+nivekyoccm@users.noreply.github.com>
Date: Thu, 19 Jul 2018 02:01:49 -0500
Subject: [PATCH 2/3] test update

---
 balance.ipynb | 45 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/balance.ipynb b/balance.ipynb
index 81f040b..b5b85a4 100644
--- a/balance.ipynb
+++ b/balance.ipynb
@@ -1,5 +1,50 @@
 {
  "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": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.eye(10)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 8373ea798e34d92db0aac3f663e824cc859a199b Mon Sep 17 00:00:00 2001
From: nivekyoccm <34323611+nivekyoccm@users.noreply.github.com>
Date: Thu, 19 Jul 2018 02:53:10 -0500
Subject: [PATCH 3/3] Kevin M - balance

---
 balance.ipynb | 1108 ++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 1105 insertions(+), 3 deletions(-)

diff --git a/balance.ipynb b/balance.ipynb
index b5b85a4..820b8b9 100644
--- a/balance.ipynb
+++ b/balance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -36,7 +36,7 @@
        "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -45,6 +45,1108 @@
     "np.eye(10)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predit \"Balance\" of a credit card\n",
+    "# use the following predictors or features to predict \"Balance\"\n",
+    "# \"Income\",\"Limit\",\"Rating\",\"Cards\",\"Age\",\"Education\",\"Gender\",\"Student\",\"Married\",\"Ethnicity\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('balance.txt')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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": 58,
+   "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>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>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>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>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>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>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": [
+       "    Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
+       "0   14.891   3606     283      2   34         11    Male      No     Yes   \n",
+       "1  106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
+       "2  104.593   7075     514      4   71         11    Male      No      No   \n",
+       "3  148.924   9504     681      3   36         11  Female      No      No   \n",
+       "4   55.882   4897     357      2   68         16    Male      No     Yes   \n",
+       "\n",
+       "   Ethnicity  Balance  \n",
+       "0  Caucasian      333  \n",
+       "1      Asian      903  \n",
+       "2      Asian      580  \n",
+       "3      Asian      964  \n",
+       "4  Caucasian      331  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop(columns=['Unnamed: 0'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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>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",
+       "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",
+       "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": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 49 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Education   -0.008062\n",
+       "Age          0.001835\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": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Balance'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df.Balance\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
+       "0   14.891   3606     283      2   34         11    Male      No     Yes   \n",
+       "1  106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
+       "2  104.593   7075     514      4   71         11    Male      No      No   \n",
+       "3  148.924   9504     681      3   36         11  Female      No      No   \n",
+       "4   55.882   4897     357      2   68         16    Male      No     Yes   \n",
+       "\n",
+       "   Ethnicity  \n",
+       "0  Caucasian  \n",
+       "1      Asian  \n",
+       "2      Asian  \n",
+       "3      Asian  \n",
+       "4  Caucasian  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=['Balance'])\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "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>const</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>Student_No</th>\n",
+       "      <th>Married_No</th>\n",
+       "      <th>Ethnicity_African American</th>\n",
+       "      <th>Ethnicity_Asian</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   const   Income  Limit  Rating  Cards  Age  Education  Gender_ Male  \\\n",
+       "0    1.0   14.891   3606     283      2   34         11             1   \n",
+       "1    1.0  106.025   6645     483      3   82         15             0   \n",
+       "2    1.0  104.593   7075     514      4   71         11             1   \n",
+       "3    1.0  148.924   9504     681      3   36         11             0   \n",
+       "4    1.0   55.882   4897     357      2   68         16             1   \n",
+       "\n",
+       "   Student_No  Married_No  Ethnicity_African American  Ethnicity_Asian  \n",
+       "0           1           0                           0                0  \n",
+       "1           0           0                           0                1  \n",
+       "2           1           1                           0                1  \n",
+       "3           1           1                           0                1  \n",
+       "4           1           0                           0                0  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(X)\n",
+    "X = X.drop(columns=['Ethnicity_Caucasian','Student_Yes','Married_Yes','Gender_Female'])\n",
+    "X = sm.add_constant(X)\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((268, 12), (132, 12))"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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>   493.2</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 19 Jul 2018</td> <th>  Prob (F-statistic):</th> <td>2.50e-165</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>02:50:23</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>   3254.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   256</td>      <th>  BIC:               </th> <td>   3298.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    11</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>  -62.2769</td> <td>   51.128</td> <td>   -1.218</td> <td> 0.224</td> <td> -162.962</td> <td>   38.409</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Income</th>                     <td>   -7.5939</td> <td>    0.310</td> <td>  -24.511</td> <td> 0.000</td> <td>   -8.204</td> <td>   -6.984</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Limit</th>                      <td>    0.2218</td> <td>    0.041</td> <td>    5.471</td> <td> 0.000</td> <td>    0.142</td> <td>    0.302</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Rating</th>                     <td>    0.6232</td> <td>    0.609</td> <td>    1.024</td> <td> 0.307</td> <td>   -0.576</td> <td>    1.822</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Cards</th>                      <td>   21.4617</td> <td>    5.342</td> <td>    4.017</td> <td> 0.000</td> <td>   10.941</td> <td>   31.982</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Age</th>                        <td>   -0.5233</td> <td>    0.384</td> <td>   -1.364</td> <td> 0.174</td> <td>   -1.279</td> <td>    0.232</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Education</th>                  <td>   -0.5403</td> <td>    2.059</td> <td>   -0.262</td> <td> 0.793</td> <td>   -4.596</td> <td>    3.515</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender_ Male</th>               <td>   23.4593</td> <td>   12.663</td> <td>    1.853</td> <td> 0.065</td> <td>   -1.479</td> <td>   48.397</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Student_No</th>                 <td> -416.1576</td> <td>   21.650</td> <td>  -19.222</td> <td> 0.000</td> <td> -458.792</td> <td> -373.523</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Married_No</th>                 <td>    1.7017</td> <td>   13.297</td> <td>    0.128</td> <td> 0.898</td> <td>  -24.484</td> <td>   27.887</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_African American</th> <td>  -16.6673</td> <td>   15.234</td> <td>   -1.094</td> <td> 0.275</td> <td>  -46.668</td> <td>   13.334</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Ethnicity_Asian</th>            <td>    2.0155</td> <td>   16.054</td> <td>    0.126</td> <td> 0.900</td> <td>  -29.599</td> <td>   33.630</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>18.394</td> <th>  Durbin-Watson:     </th> <td>   2.021</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  20.780</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.682</td> <th>  Prob(JB):          </th> <td>3.07e-05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.003</td> <th>  Cond. No.          </th> <td>4.47e+04</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 condition number is large, 4.47e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "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:                     493.2\n",
+       "Date:                Thu, 19 Jul 2018   Prob (F-statistic):          2.50e-165\n",
+       "Time:                        02:50:23   Log-Likelihood:                -1615.2\n",
+       "No. Observations:                 268   AIC:                             3254.\n",
+       "Df Residuals:                     256   BIC:                             3298.\n",
+       "Df Model:                          11                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================================\n",
+       "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "----------------------------------------------------------------------------------------------\n",
+       "const                        -62.2769     51.128     -1.218      0.224    -162.962      38.409\n",
+       "Income                        -7.5939      0.310    -24.511      0.000      -8.204      -6.984\n",
+       "Limit                          0.2218      0.041      5.471      0.000       0.142       0.302\n",
+       "Rating                         0.6232      0.609      1.024      0.307      -0.576       1.822\n",
+       "Cards                         21.4617      5.342      4.017      0.000      10.941      31.982\n",
+       "Age                           -0.5233      0.384     -1.364      0.174      -1.279       0.232\n",
+       "Education                     -0.5403      2.059     -0.262      0.793      -4.596       3.515\n",
+       "Gender_ Male                  23.4593     12.663      1.853      0.065      -1.479      48.397\n",
+       "Student_No                  -416.1576     21.650    -19.222      0.000    -458.792    -373.523\n",
+       "Married_No                     1.7017     13.297      0.128      0.898     -24.484      27.887\n",
+       "Ethnicity_African American   -16.6673     15.234     -1.094      0.275     -46.668      13.334\n",
+       "Ethnicity_Asian                2.0155     16.054      0.126      0.900     -29.599      33.630\n",
+       "==============================================================================\n",
+       "Omnibus:                       18.394   Durbin-Watson:                   2.021\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               20.780\n",
+       "Skew:                           0.682   Prob(JB):                     3.07e-05\n",
+       "Kurtosis:                       3.003   Cond. No.                     4.47e+04\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 4.47e+04. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 67,
+     "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": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-6.25406342e+01, -7.80310179e+00,  1.90906737e-01,  1.13652652e+00,\n",
+       "        1.77244836e+01, -6.13908824e-01, -1.09885532e+00,  1.06532477e+01,\n",
+       "       -4.25747360e+02,  8.53390061e+00, -1.01070252e+01,  6.69715400e+00])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.inv(X.T @ X) @ (X.T@y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat = results.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9519642107235258"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_test,y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,