From 4c2f955d3025df4a11eac93e7c9df5163ceb3ee8 Mon Sep 17 00:00:00 2001
From: Simon <Rene.Simon@McKesson.com>
Date: Wed, 18 Jul 2018 16:19:53 -0400
Subject: [PATCH 1/3] initial ipynb file

---
 .gitignore    |  1 +
 balance.ipynb | 48 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 49 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 balance.ipynb

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..763513e
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints
diff --git a/balance.ipynb b/balance.ipynb
new file mode 100644
index 0000000..d11baa0
--- /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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 313af76701b6824fa4f900003ed863cf271a9162 Mon Sep 17 00:00:00 2001
From: Simon <Rene.Simon@McKesson.com>
Date: Wed, 18 Jul 2018 16:32:47 -0400
Subject: [PATCH 2/3] initial ipynb file - rene - dev

---
 balance2.ipynb | 48 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 balance2.ipynb

diff --git a/balance2.ipynb b/balance2.ipynb
new file mode 100644
index 0000000..8acbe5e
--- /dev/null
+++ b/balance2.ipynb
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 17ce39ffade1102722654aa5f7408555454d98f0 Mon Sep 17 00:00:00 2001
From: Simon <Rene.Simon@McKesson.com>
Date: Wed, 18 Jul 2018 17:42:49 -0400
Subject: [PATCH 3/3] final ipynb file - rene - dev

---
 balance2.ipynb | 689 ++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 688 insertions(+), 1 deletion(-)

diff --git a/balance2.ipynb b/balance2.ipynb
index 8acbe5e..fdeb522 100644
--- a/balance2.ipynb
+++ b/balance2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,6 +16,693 @@
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "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": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('balance.txt')\n",
+    "df = df.drop(columns=['Unnamed: 0'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "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": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Balance'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "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": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df.Balance\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "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>Rating</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Student</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>283</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>483</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>514</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>681</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>357</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rating  Age  Education Student\n",
+       "0     283   34         11      No\n",
+       "1     483   82         15     Yes\n",
+       "2     514   71         11      No\n",
+       "3     681   36         11      No\n",
+       "4     357   68         16      No"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=['Balance'])\n",
+    "X = X.drop(columns=['Limit'])\n",
+    "X = X.drop(columns=['Income'])\n",
+    "X = X.drop(columns=['Married'])\n",
+    "X = X.drop(columns=['Ethnicity'])\n",
+    "X = X.drop(columns=['Gender'])\n",
+    "X = X.drop(columns=['Cards'])\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "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>Rating</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Student_No</th>\n",
+       "      <th>Student_Yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>283</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>483</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>514</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>681</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>357</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rating  Age  Education  Student_No  Student_Yes\n",
+       "0     283   34         11           1            0\n",
+       "1     483   82         15           0            1\n",
+       "2     514   71         11           1            0\n",
+       "3     681   36         11           1            0\n",
+       "4     357   68         16           1            0"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(X)\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "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>Rating</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Student_Yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>283</td>\n",
+       "      <td>34</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>483</td>\n",
+       "      <td>82</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>514</td>\n",
+       "      <td>71</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>681</td>\n",
+       "      <td>36</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>357</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rating  Age  Education  Student_Yes\n",
+       "0     283   34         11            0\n",
+       "1     483   82         15            1\n",
+       "2     514   71         11            0\n",
+       "3     681   36         11            0\n",
+       "4     357   68         16            0"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = X.drop(columns=['Student_No'])\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "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": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((268, 4), (132, 4))"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "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>   0.924</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.923</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   807.5</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Wed, 18 Jul 2018</td> <th>  Prob (F-statistic):</th> <td>1.06e-146</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>17:40:01</td>     <th>  Log-Likelihood:    </th> <td> -1796.4</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   268</td>      <th>  AIC:               </th> <td>   3601.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   264</td>      <th>  BIC:               </th> <td>   3615.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     4</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>Rating</th>      <td>    2.5388</td> <td>    0.073</td> <td>   34.852</td> <td> 0.000</td> <td>    2.395</td> <td>    2.682</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Age</th>         <td>   -3.8174</td> <td>    0.606</td> <td>   -6.297</td> <td> 0.000</td> <td>   -5.011</td> <td>   -2.624</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Education</th>   <td>  -13.3397</td> <td>    2.693</td> <td>   -4.954</td> <td> 0.000</td> <td>  -18.642</td> <td>   -8.037</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Student_Yes</th> <td>  375.7067</td> <td>   41.147</td> <td>    9.131</td> <td> 0.000</td> <td>  294.688</td> <td>  456.726</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>10.420</td> <th>  Durbin-Watson:     </th> <td>   1.904</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  10.601</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.437</td> <th>  Prob(JB):          </th> <td> 0.00499</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.430</td> <th>  Cond. No.          </th> <td>1.34e+03</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, 1.34e+03. 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:                       0.924\n",
+       "Model:                            OLS   Adj. R-squared:                  0.923\n",
+       "Method:                 Least Squares   F-statistic:                     807.5\n",
+       "Date:                Wed, 18 Jul 2018   Prob (F-statistic):          1.06e-146\n",
+       "Time:                        17:40:01   Log-Likelihood:                -1796.4\n",
+       "No. Observations:                 268   AIC:                             3601.\n",
+       "Df Residuals:                     264   BIC:                             3615.\n",
+       "Df Model:                           4                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "===============================================================================\n",
+       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Rating          2.5388      0.073     34.852      0.000       2.395       2.682\n",
+       "Age            -3.8174      0.606     -6.297      0.000      -5.011      -2.624\n",
+       "Education     -13.3397      2.693     -4.954      0.000     -18.642      -8.037\n",
+       "Student_Yes   375.7067     41.147      9.131      0.000     294.688     456.726\n",
+       "==============================================================================\n",
+       "Omnibus:                       10.420   Durbin-Watson:                   1.904\n",
+       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               10.601\n",
+       "Skew:                          -0.437   Prob(JB):                      0.00499\n",
+       "Kurtosis:                       3.430   Cond. No.                     1.34e+03\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, 1.34e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 104,
+     "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": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x13add77a400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(X);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat = results.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7471322577153838"
+      ]
+     },
+     "execution_count": 107,
+     "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,