Returns_SLM (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source for code: https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Anaconda3-5.3.1-Linux-x86_64/envs/jupyter-atp/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (99,102) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline\n",
    "etfs_mfs=pd.read_csv('ETFs+MFs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</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>fund_name</th>\n",
       "      <th>fund_extended_name</th>\n",
       "      <th>category</th>\n",
       "      <th>fund_family</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>ytd_return</th>\n",
       "      <th>fund_yield</th>\n",
       "      <th>legal_type</th>\n",
       "      <th>investment</th>\n",
       "      <th>...</th>\n",
       "      <th>fund_sharpe_ratio_5years</th>\n",
       "      <th>category_sharpe_ratio_5years</th>\n",
       "      <th>fund_sharpe_ratio_10years</th>\n",
       "      <th>category_sharpe_ratio_10years</th>\n",
       "      <th>fund_treynor_ratio_3years</th>\n",
       "      <th>category_treynor_ratio_3years</th>\n",
       "      <th>fund_treynor_ratio_5years</th>\n",
       "      <th>category_treynor_ratio_5years</th>\n",
       "      <th>fund_treynor_ratio_10years</th>\n",
       "      <th>category_treynor_ratio_10years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>Daiwa ETF TOPIX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Daiwa Asset Management Co Ltd</td>\n",
       "      <td>4.010000e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.84</td>\n",
       "      <td>Exchange Traded Fund</td>\n",
       "      <td>Blend</td>\n",
       "      <td>...</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1306</td>\n",
       "      <td>Nomura TOPIX ETF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nomura Asset Management Co Ltd</td>\n",
       "      <td>8.430000e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.77</td>\n",
       "      <td>Exchange Traded Fund</td>\n",
       "      <td>Blend</td>\n",
       "      <td>...</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1308</td>\n",
       "      <td>Nikko Exchange Traded Index TOPIX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nikko Asset Management Co Ltd</td>\n",
       "      <td>3.730000e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.84</td>\n",
       "      <td>Exchange Traded Fund</td>\n",
       "      <td>Blend</td>\n",
       "      <td>...</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1309</td>\n",
       "      <td>Nomura SSE50 Index Link ETF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nomura Asset Management Co Ltd</td>\n",
       "      <td>4.450000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Exchange Traded Fund</td>\n",
       "      <td>Value</td>\n",
       "      <td>...</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1310</td>\n",
       "      <td>Daiwa ETF TOPIX Core 30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Daiwa Asset Management Co Ltd</td>\n",
       "      <td>1.920000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.57</td>\n",
       "      <td>Exchange Traded Fund</td>\n",
       "      <td>Blend</td>\n",
       "      <td>...</td>\n",
       "      <td>0.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 fund_name                 fund_extended_name category  \\\n",
       "0           0      1305                    Daiwa ETF TOPIX      NaN   \n",
       "1           1      1306                   Nomura TOPIX ETF      NaN   \n",
       "2           2      1308  Nikko Exchange Traded Index TOPIX      NaN   \n",
       "3           3      1309        Nomura SSE50 Index Link ETF      NaN   \n",
       "4           4      1310            Daiwa ETF TOPIX Core 30      NaN   \n",
       "\n",
       "                      fund_family    net_assets  ytd_return  fund_yield  \\\n",
       "0   Daiwa Asset Management Co Ltd  4.010000e+12         NaN        1.84   \n",
       "1  Nomura Asset Management Co Ltd  8.430000e+12         NaN        1.77   \n",
       "2   Nikko Asset Management Co Ltd  3.730000e+12         NaN        1.84   \n",
       "3  Nomura Asset Management Co Ltd  4.450000e+09         NaN        0.00   \n",
       "4   Daiwa Asset Management Co Ltd  1.920000e+09         NaN        2.57   \n",
       "\n",
       "             legal_type investment  ... fund_sharpe_ratio_5years  \\\n",
       "0  Exchange Traded Fund      Blend  ...                     0.59   \n",
       "1  Exchange Traded Fund      Blend  ...                     0.59   \n",
       "2  Exchange Traded Fund      Blend  ...                     0.59   \n",
       "3  Exchange Traded Fund      Value  ...                     0.36   \n",
       "4  Exchange Traded Fund      Blend  ...                     0.34   \n",
       "\n",
       "  category_sharpe_ratio_5years  fund_sharpe_ratio_10years  \\\n",
       "0                          NaN                       0.60   \n",
       "1                          NaN                       0.61   \n",
       "2                          NaN                       0.60   \n",
       "3                          NaN                       0.26   \n",
       "4                          NaN                       0.44   \n",
       "\n",
       "   category_sharpe_ratio_10years  fund_treynor_ratio_3years  \\\n",
       "0                            NaN                       3.92   \n",
       "1                            NaN                       3.94   \n",
       "2                            NaN                       3.93   \n",
       "3                            NaN                      -4.41   \n",
       "4                            NaN                          1   \n",
       "\n",
       "   category_treynor_ratio_3years  fund_treynor_ratio_5years  \\\n",
       "0                            NaN                       8.01   \n",
       "1                            NaN                       8.03   \n",
       "2                            NaN                       8.03   \n",
       "3                            NaN                       6.32   \n",
       "4                            NaN                       4.08   \n",
       "\n",
       "   category_treynor_ratio_5years  fund_treynor_ratio_10years  \\\n",
       "0                            NaN                        9.29   \n",
       "1                            NaN                        9.33   \n",
       "2                            NaN                        9.31   \n",
       "3                            NaN                        4.29   \n",
       "4                            NaN                        6.18   \n",
       "\n",
       "   category_treynor_ratio_10years  \n",
       "0                             NaN  \n",
       "1                             NaN  \n",
       "2                             NaN  \n",
       "3                             NaN  \n",
       "4                             NaN  \n",
       "\n",
       "[5 rows x 105 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etfs_mfs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop nas in order to predict\n",
    "etf_mfs_clean=etfs_mfs.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'fund_name', 'fund_extended_name', 'category',\n",
       "       'fund_family', 'net_assets', 'ytd_return', 'fund_yield', 'legal_type',\n",
       "       'investment',\n",
       "       ...\n",
       "       'fund_sharpe_ratio_5years', 'category_sharpe_ratio_5years',\n",
       "       'fund_sharpe_ratio_10years', 'category_sharpe_ratio_10years',\n",
       "       'fund_treynor_ratio_3years', 'category_treynor_ratio_3years',\n",
       "       'fund_treynor_ratio_5years', 'category_treynor_ratio_5years',\n",
       "       'fund_treynor_ratio_10years', 'category_treynor_ratio_10years'],\n",
       "      dtype='object', length=105)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns-possible predictors\n",
    "etf_mfs_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Simple Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.27</td>\n",
       "      <td>9.603509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.03</td>\n",
       "      <td>11.093574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.42</td>\n",
       "      <td>9.926356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.21</td>\n",
       "      <td>10.423045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.52</td>\n",
       "      <td>10.646554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.66</td>\n",
       "      <td>11.093574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.56</td>\n",
       "      <td>8.386623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.77</td>\n",
       "      <td>10.224369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.19</td>\n",
       "      <td>11.416421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.39</td>\n",
       "      <td>10.298873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.63</td>\n",
       "      <td>9.454503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.61</td>\n",
       "      <td>10.820395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.80</td>\n",
       "      <td>11.217746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.92</td>\n",
       "      <td>8.858477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11.63</td>\n",
       "      <td>9.553840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.81</td>\n",
       "      <td>9.901522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.26</td>\n",
       "      <td>9.827019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.15</td>\n",
       "      <td>10.373376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.84</td>\n",
       "      <td>10.845230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.94</td>\n",
       "      <td>7.989273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.84</td>\n",
       "      <td>10.323707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.80</td>\n",
       "      <td>10.348541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17.93</td>\n",
       "      <td>10.373376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.27</td>\n",
       "      <td>10.025694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.35</td>\n",
       "      <td>10.621720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.12</td>\n",
       "      <td>9.876688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19.16</td>\n",
       "      <td>11.043905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10.87</td>\n",
       "      <td>9.628344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21.17</td>\n",
       "      <td>10.398210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.72</td>\n",
       "      <td>9.777350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>7.65</td>\n",
       "      <td>10.472713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>6.08</td>\n",
       "      <td>10.621720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>12.99</td>\n",
       "      <td>10.721058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2408</th>\n",
       "      <td>2.14</td>\n",
       "      <td>10.547217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>12.06</td>\n",
       "      <td>9.206159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>9.31</td>\n",
       "      <td>9.678012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>18.88</td>\n",
       "      <td>11.490924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2.13</td>\n",
       "      <td>10.174701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>14.24</td>\n",
       "      <td>10.795561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>14.82</td>\n",
       "      <td>9.827019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>12.55</td>\n",
       "      <td>10.497548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>6.64</td>\n",
       "      <td>10.671389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>10.02</td>\n",
       "      <td>10.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418</th>\n",
       "      <td>13.56</td>\n",
       "      <td>11.143242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2419</th>\n",
       "      <td>12.70</td>\n",
       "      <td>8.535630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>7.97</td>\n",
       "      <td>10.398210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2421</th>\n",
       "      <td>15.81</td>\n",
       "      <td>10.323707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>20.96</td>\n",
       "      <td>11.689599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2423</th>\n",
       "      <td>10.51</td>\n",
       "      <td>10.323707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>2.44</td>\n",
       "      <td>10.770726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>6.42</td>\n",
       "      <td>10.373376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2426</th>\n",
       "      <td>9.21</td>\n",
       "      <td>10.149866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>9.51</td>\n",
       "      <td>10.398210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>2.15</td>\n",
       "      <td>10.323707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>9.16</td>\n",
       "      <td>10.547217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>15.41</td>\n",
       "      <td>10.894898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>13.16</td>\n",
       "      <td>10.671389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>12.28</td>\n",
       "      <td>9.404834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>22.16</td>\n",
       "      <td>11.490924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>10.53</td>\n",
       "      <td>9.653178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted\n",
       "0       9.27   9.603509\n",
       "1       5.03  11.093574\n",
       "2      13.42   9.926356\n",
       "3      12.21  10.423045\n",
       "4      20.52  10.646554\n",
       "5       2.66  11.093574\n",
       "6      12.56   8.386623\n",
       "7      11.77  10.224369\n",
       "8       2.19  11.416421\n",
       "9      14.39  10.298873\n",
       "10      5.63   9.454503\n",
       "11     11.61  10.820395\n",
       "12     16.80  11.217746\n",
       "13      1.92   8.858477\n",
       "14     11.63   9.553840\n",
       "15      0.81   9.901522\n",
       "16      3.26   9.827019\n",
       "17     16.15  10.373376\n",
       "18      6.84  10.845230\n",
       "19     10.94   7.989273\n",
       "20     12.84  10.323707\n",
       "21      1.80  10.348541\n",
       "22     17.93  10.373376\n",
       "23     16.27  10.025694\n",
       "24      2.35  10.621720\n",
       "25      2.12   9.876688\n",
       "26     19.16  11.043905\n",
       "27     10.87   9.628344\n",
       "28     21.17  10.398210\n",
       "29     10.72   9.777350\n",
       "...      ...        ...\n",
       "2405    7.65  10.472713\n",
       "2406    6.08  10.621720\n",
       "2407   12.99  10.721058\n",
       "2408    2.14  10.547217\n",
       "2409   12.06   9.206159\n",
       "2410    9.31   9.678012\n",
       "2411   18.88  11.490924\n",
       "2412    2.13  10.174701\n",
       "2413   14.24  10.795561\n",
       "2414   14.82   9.827019\n",
       "2415   12.55  10.497548\n",
       "2416    6.64  10.671389\n",
       "2417   10.02  10.000860\n",
       "2418   13.56  11.143242\n",
       "2419   12.70   8.535630\n",
       "2420    7.97  10.398210\n",
       "2421   15.81  10.323707\n",
       "2422   20.96  11.689599\n",
       "2423   10.51  10.323707\n",
       "2424    2.44  10.770726\n",
       "2425    6.42  10.373376\n",
       "2426    9.21  10.149866\n",
       "2427    9.51  10.398210\n",
       "2428    2.15  10.323707\n",
       "2429    9.16  10.547217\n",
       "2430   15.41  10.894898\n",
       "2431   13.16  10.671389\n",
       "2432   12.28   9.404834\n",
       "2433   22.16  11.490924\n",
       "2434   10.53   9.653178\n",
       "\n",
       "[2435 rows x 2 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#our two variables. y is what we want to predict\n",
    "X = etf_mfs_clean['fund_sharpe_ratio_5years'].values.reshape(-1,1)\n",
    "y = etf_mfs_clean['ytd_return'].values.reshape(-1,1)\n",
    "\n",
    "#creating the training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "#creating our model\n",
    "regressor = LinearRegression()\n",
    "#training the algorithm \n",
    "regressor.fit(X_train, y_train) \n",
    "#predicting the Y values\n",
    "y_pred = regressor.predict(X_test)\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bargraph comparing actual vs predicted\n",
    "df1 = df.head(25)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph showing actual (in grey) and predicted line (in red)\n",
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.502833579183225\n",
      "Mean Squared Error: 31.23610324514389\n",
      "Root Mean Squared Error: 5.588926841992467\n"
     ]
    }
   ],
   "source": [
    "#some statistics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Multiple Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#note- only numerical values can be put into the model\n",
    "X = etf_mfs_clean[['net_assets', 'fund_yield','fund_sharpe_ratio_5years', 'category_sharpe_ratio_5years',\n",
    "       'price_book']].values\n",
    "y = etf_mfs_clean['ytd_return'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.27</td>\n",
       "      <td>8.991151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.03</td>\n",
       "      <td>7.335367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.42</td>\n",
       "      <td>6.020583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.21</td>\n",
       "      <td>10.043689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.52</td>\n",
       "      <td>17.563991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.66</td>\n",
       "      <td>9.338088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.56</td>\n",
       "      <td>6.875648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.77</td>\n",
       "      <td>9.240662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.19</td>\n",
       "      <td>8.284834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.39</td>\n",
       "      <td>12.814139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.63</td>\n",
       "      <td>9.654860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.61</td>\n",
       "      <td>9.414896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.80</td>\n",
       "      <td>14.516305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.92</td>\n",
       "      <td>5.620373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11.63</td>\n",
       "      <td>12.147345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.81</td>\n",
       "      <td>9.217265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.26</td>\n",
       "      <td>8.850939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.15</td>\n",
       "      <td>13.065548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.84</td>\n",
       "      <td>3.701278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.94</td>\n",
       "      <td>10.282303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.84</td>\n",
       "      <td>12.049641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.80</td>\n",
       "      <td>7.170756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17.93</td>\n",
       "      <td>13.149309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.27</td>\n",
       "      <td>12.436534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.35</td>\n",
       "      <td>8.800468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0     9.27   8.991151\n",
       "1     5.03   7.335367\n",
       "2    13.42   6.020583\n",
       "3    12.21  10.043689\n",
       "4    20.52  17.563991\n",
       "5     2.66   9.338088\n",
       "6    12.56   6.875648\n",
       "7    11.77   9.240662\n",
       "8     2.19   8.284834\n",
       "9    14.39  12.814139\n",
       "10    5.63   9.654860\n",
       "11   11.61   9.414896\n",
       "12   16.80  14.516305\n",
       "13    1.92   5.620373\n",
       "14   11.63  12.147345\n",
       "15    0.81   9.217265\n",
       "16    3.26   8.850939\n",
       "17   16.15  13.065548\n",
       "18    6.84   3.701278\n",
       "19   10.94  10.282303\n",
       "20   12.84  12.049641\n",
       "21    1.80   7.170756\n",
       "22   17.93  13.149309\n",
       "23   16.27  12.436534\n",
       "24    2.35   8.800468"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df1 = df.head(25)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fZi5pdGdEbBARG624DhwA3NPG8iRJkrrWiKUrIuYAPwF2jIglEXFU/a7DgDnD5t0qIq6u39wcuCUi7gJuA67KzGs7N3RJkqTuMZp3Lx7eZPqMBtMGgQPr138F7Nbm+CRJkiYEz0gvSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVMCIZ6SXpG7TM+uqhtNnTC88EEkawi1dkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgGWLkmSpAIsXZIkSQVYuiRJkgrwY4AkSdK41+jjvbrto73c0iVJklSAW7okSePKRNiiITXili5JkqQCLF2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgGWLkmSpAIsXZIkSQVYuiRJkgoYsXRFxHkR8duIuGfItP6I+E1ELKhfDmzy2OkRcX9ELIqIWZ0cuCRJUjcZzZau84HpDaafkZnT6perh98ZEesAXwfeC7wRODwi3tjOYCVJkrrViKUrM28Gnmwhey9gUWb+KjP/CFwEHNxCjiRJUteLzBx5poge4MrM3KV+ux+YAfwBmAd8NjN/P+wxHwCmZ+bH6rc/ArwlM49psoyZwEyAKVtO2fOYCxvOtoqBxQP09fSNat6x6LbcTmSfecMDDadPm3QxfUxa9Y6+z7e8LBjf62Ki5FaZPZ5zmz6XXz84bsdcMrfK7Kp+ft32s6syezznVvm7N56fFye986T5mdk70nwN/pKOyjeBLwFZ//pV4K+HzRMNHte04WXmOcA5AL29vdnf1z+qgfQP9DPaecei23I7kX3+tVc1nN436VL6mdzgjtaXBeN7XUyU3Cqzx3Nu0+dyz+3jdswlc6vMrurn120/uyqzx3Nulb974/l5cRInjWq+lt69mJmPZ+aLmfkS8H+o7Uocbgmw7ZDb2wCDrSxPkiSp27VUuiJiyyE33w/c02C224HtI2JqRKwHHAZc3sryJEmSut2IuxcjYg7QB7w6IpYAs4G+iJhGbXfhYuAT9Xm3Av45Mw/MzOURcQxwHbAOcF5m/qKS70KSJGmcG7F0ZebhDSaf22TeQeDAIbevBlY5nYQkSdLaxjPSS5IkFWDpkiRJKsDSJUmSVIClS5IkqYBWT44qSd1n4BQYOGPlaf3L1sxYJK113NIlSZJUgKVLkiSpAHcvShNEz6zGn3k2Y3rhgUiSGnJLlyRJUgGWLkmSpAIsXZIkSQVYuiRJkgqwdEmSJBVg6ZIkSSrA0iVJklSApUuSJKkAS5ckSVIBli5JkqQCLF2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAiat6QFMZD2zrlpl2ozpa2AgkiRpjXNLlyRJUgGWLkmSpAIsXZIkSQVYuiRJkgqwdEmSJBXguxclSVK1Bk6BgTNWnta/bM2MZQ2ydEmSxr9Gf7RhrfzDre7l7kVJkqQCLF2SJEkFWLokSZIKsHRJkiQV4IH00kTnu4akruVn+E4sbumSJEkqwNIlSZJUwIilKyLOi4jfRsQ9Q6adFhH3RcTPI+L7EbFJk8cujoi7I2JBRMzr5MAlSZK6yWi2dJ0PDN+DfD2wS2a+CXgA+PxqHv/OzJyWmb2tDVGSJKn7jVi6MvNm4Mlh0+Zm5vL6zZ8C21QwNkmSpAmjE+9e/Gvg4ib3JTA3IhL4Vmae04HlSS9r9M4e8N09kqTxJzJz5JkieoArM3OXYdNPBHqBQ7JBUERslZmDEfEaarskP1PfctZoGTOBmQBTtpyy5zEXHjOqb2Bg8QB9PX2jmncsOpF75g0PrDJt2usHKxkvtD/mRuMFmDbpYvoa9fO+1e1VHllV6xiqW8/d9nyDJj+/Nn924LoYajyvi9LZlb12juPXoSqzS/4dGe+/e+N5XZz0zpPmj+YwqpZLV0QcCXwSeFdmPjuKjH7gmcw8faR5e3t7c9680R133z/QT39f/6jmHYtO5DY+v8rtlYwX2h9z061Gkw+hn8kNFtjeuZ6qWsdQ3XrutucbNPn5deA8Xa6LIRHjeF2Uzq7stXMcvw5VmV3y78h4/90bz8+LiBhV6WrplBERMR04ATioWeGKiA0iYqMV14EDgHsazStJkjTRjeaUEXOAnwA7RsSSiDgKOAvYCLi+fjqIs+vzbhURV9cfujlwS0TcBdwGXJWZ11byXUiSJI1zIx5In5mHN5h8bpN5B4ED69d/BezW1ugkSZImCM9IL0mSVIAfeN2FPE2CJEndx9IlSePVwCkwcMaq0zvwjktJ5bl7UZIkqQC3dEmS1E3cAtq13NIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAjyQXmrAc6FJkjrNLV2SJEkFWLokSZIKcPeiJI0DjXZpz5i8BgYiqTKWLklrTMOi4XFzkiYody9KkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQA/8FqSpDY0+uB28MPbtSq3dEmSJBVg6ZIkSSrA0iVJklSApUuSJKkAS5ckSVIBli5JkqQCLF2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZPW9AAkSZqQBk6BgTNWnta/bM2MReOCW7okSZIKGLF0RcR5EfHbiLhnyLTNIuL6iHiw/nXTJo+dHhH3R8SiiJjVyYFLkiR1k9Fs6TofmD5s2izgxszcHrixfnslEbEO8HXgvcAbgcMj4o1tjVaSJKlLjXhMV2beHBE9wyYfDPTVr38HGABOGDbPXsCizPwVQERcVH/cvS2PVqvn8QOSJI1brR5Iv3lmPgqQmY9GxGsazLM18MiQ20uAtzQLjIiZwEyAKVtOoX+gf1QDGVg8MOp5x6ITuU9NeqBB7mAluQADLKef54dNHP2yxpQ7xuyGuRWt41p2e+u5qtxmKl0XbT4vmlkbf0eaqWxdVPS7B1342tmNr0MdeL65LlafPZ7XRSNVvnsxGkzLZjNn5jnAOQC9vb3Z39c/qoX0D/Qz2nnHohO551971SrT+npuryQXoG/SpfQzedjE0S9rTLljzG6kqnUM7a/nqnKbqXRdtPm8aGZt/B1pprJ1UdHvHnTha2c3vg514Pnmulh99nhZFydx0qjma/Xdi49HxJYA9a+/bTDPEmDbIbe3AQZbXJ4kSVJXa7V0XQ4cWb9+JHBZg3luB7aPiKkRsR5wWP1xkiRJa53RnDJiDvATYMeIWBIRRwGnAvtHxIPA/vXbRMRWEXE1QGYuB44BrgMWAt/NzF9U821IkiSNb6N59+LhTe56V4N5B4EDh9y+Gri65dFJkiRNEJ6RXpIkqQA/e1FSaxqdFw48N5wkNeGWLkmSpAIsXZIkSQVYuiRJkgqwdEmSJBXggfSSxhcP0Jc0QbmlS5IkqQC3dGnNcYuGJGkt0jWlq2dW408unzG98EAkSZJa0DWlSxoX3DqniaLRc9nnsVQpj+mSJEkqwNIlSZJUgKVLkiSpAEuXJElSAd1/IL0Hg0qSpC7gli5JkqQCun9LlyRp/HDvg9SUpUuSJGmois7J6O5FSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgGWLkmSpAIsXZIkSQX4MUDSeFDRR05IksYPt3RJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVEDLpSsidoyIBUMuf4iI/z5snr6IWDZknv/V/pAlSZK6T8sfeJ2Z9wPTACJiHeA3wPcbzPrjzPzzVpcjSZI0EXRq9+K7gF9m5sMdypMkSZpQOlW6DgPmNLlvn4i4KyKuiYidO7Q8SZKkrtLy7sUVImI94CDg8w3uvgPYLjOfiYgDgR8A2zfJmQnMBJiy5RT6B/pXuv+pSQ80XP4Ay+nn+WET+xvOOxYDiwdWGcNYNRrzwOLBSnKh/XUxptwxZleV23TMba7nCbUuqvodqWjMXbkuqnq9qOj51jR7nOSWXBdV/ezAdbFSdreti4pyIzPbC4g4GPh0Zh4winkXA72Z+cTq5uvt7c158+atNK1n1lUN550x+RD6mbzyxP5lIw1lRP0D/fT39beV0WjMM6bfXkkutL8uxpQ7xuxG+vtf2XZu0zG3uZ4n1Lqo6nekojF3Yh1PmNeLip5v0OTnN05yS66Lqn520Jnnm+ti9dnj5TU5IuZnZu9IuZ3YvXg4TXYtRsQWERH163vVl7e0A8uUJEnqKm3tXoyI9YH9gU8MmfZJgMw8G/gA8KmIWA48BxyW7W5aK2XgFBg4Y9XpHfhPUJIkrX3aKl2Z+SwwZdi0s4dcPws4q51lSJIkTQSekV6SJKmAtt+9KEmSJgAPq6mcW7okSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAN+9qImp0btwfAeOJGkNsnRJktYKjT8Pdw0MRGstdy9KkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgGWLkmSpAIsXZIkSQVYuiRJkgqYtKYHsNYZOAUGzlh1ev+y8mORJEnFuKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFeApIyStVs+sqxpOnzG58EAkqctZuiSpXY3Ov+e59yQN4+5FSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgFtla6IWBwRd0fEgoiY1+D+iIh/jIhFEfHziNijneVJkiR1q06ckf6dmflEk/veC2xfv7wF+Gb9qyRJ0lql6t2LBwMXZM1PgU0iYsuKlylJkjTutLulK4G5EZHAtzLznGH3bw08MuT2kvq0R4cHRcRMYCbAlC2n0D/Qv9L9T016oOEABlhOP88Pm9jfcN5Gzryhce60SQ1yx5jdaMwNx9uB3KbZVeWOMbuqXNfFf+rGddFtvyPNdCK3qnXRzNq4LhrmLh5c5e/NWFX5fCv5vBjvv3vdti4aicxs/cERW2XmYES8Brge+Exm3jzk/quAUzLzlvrtG4HPZeb81eX29vbmvHkrHyLWM+uqhvPOmHwI/UxeeeIYPmh2TLkdyK4qt2n2OFkXjfT3v9J1seLha+m66LbfkWYa/vzGmFvVumimE2OuKrfk82LG9Nvp7+sfy/BGlQudeb6VfF6M59ehZtnjZV1ExPzM7B0pt63di5k5WP/6W+D7wF7DZlkCbDvk9jbAYDvLlCRJ6kYtl66I2CAiNlpxHTgAuGfYbJcDR9Tfxbg3sCwzV9m1KEmSNNG1c0zX5sD3I2JFzoWZeW1EfBIgM88GrgYOBBYBzwIfbW+4kiRJ3anl0pWZvwJ2azD97CHXE/h0q8uQJEmaKDwjvSRJUgGWLkmSpAIsXZIkSQVYuiRJkgqwdEmSJBVg6ZIkSSrA0iVJklSApUuSJKkAS5ckSVIBli5JkqQCLF2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCpi0pgcgSdIaM3AKDJyx6vT+ZeXHognPLV2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAjw5qiRpzHpmXdVw+ozJhQcidRG3dEmSJBVg6ZIkSSrA0iVJklSApUuSJKkAS5ckSVIBli5JkqQCLF2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUQMufvRgR2wIXAFsALwHnZOY/DJunD7gMeKg+6XuZ+b9bXaa6V6PPafMz2iRJa5N2PvB6OfDZzLwjIjYC5kfE9Zl577D5fpyZf97GciRJkrpey7sXM/PRzLyjfv1pYCFFPO9ZAAANBklEQVSwdacGJkmSNJF05JiuiOgBdgd+1uDufSLiroi4JiJ27sTyJEmSuk07uxcBiIgNgUuB/56Zfxh29x3Adpn5TEQcCPwA2L5JzkxgJsCULafQP9C/0v1PTXqg4fIHWE4/zw+b2N9w3kbGlNuB7Kpym2a7LqrPHWN2VbnduC667XnRTCdyq1oXzRR9XoyTddGNz7eSzwvXRfW5kZmtPzhiXeBK4LrM/Noo5l8M9GbmE6ubr7e3N+fNm7fStEYHYgPMmHwI/Qw7Irt/2UhDaS23A9lV5TbNdl1UnzvG7Eb6+1+5Vq6LbnteNNPw5zfG3KrWRTPtjrnKdVzyeTHen28lnxfj+XWoWfZ4WRcRMT8ze0fKbXn3YkQEcC6wsFnhiogt6vMREXvVl7e01WVKkiR1q3Z2L74N+Ahwd0QsqE/7AvBagMw8G/gA8KmIWA48BxyW7WxakyRJ6qCSpzRquXRl5i1AjDDPWcBZrS5DkiRpovCM9JIkSQW0/e5FSWPj2fklae3kli5JkqQCLF2SJEkFWLokSZIKsHRJkiQV4IH0kjRKzc+2XXggkrqSW7okSZIKsHRJkiQV4O5FSZLWMp4vcM1wS5ckSVIBli5JkqQCLF2SJEkFWLokSZIKsHRJkiQVYOmSJEkqwNIlSZJUgKVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgGWLkmSpAIsXZIkSQVMWtMDkCRVp2fWVQ2nz5hceCCS3NIlSZJUgqVLkiSpAEuXJElSAZYuSZKkAixdkiRJBVi6JEmSCrB0SZIkFWDpkiRJKsDSJUmSVIClS5IkqQBLlyRJUgGWLkmSpALaKl0RMT0i7o+IRRExq8H9ERH/WL//5xGxRzvLkyRJ6lYtl66IWAf4OvBe4I3A4RHxxmGzvRfYvn6ZCXyz1eVJkiR1s3a2dO0FLMrMX2XmH4GLgIOHzXMwcEHW/BTYJCK2bGOZkiRJXSkys7UHRnwAmJ6ZH6vf/gjwlsw8Zsg8VwKnZuYt9ds3Aidk5rwGeTOpbQ0D2BG4f5RDeTXwREvfxMTKrTK723KrzO623Cqzuy23yuxuy60y29zqs7stt8rs8ZK7XWb+6UgzTWp9PESDacMb3GjmqU3MPAc4Z8yDiJiXmb1jfdxEy60yu9tyq8zuttwqs7stt8rsbsutMtvc6rO7LbfK7G7LbWf34hJg2yG3twEGW5hHkiRpwmundN0ObB8RUyNiPeAw4PJh81wOHFF/F+PewLLMfLSNZUqSJHWllncvZubyiDgGuA5YBzgvM38REZ+s3382cDVwILAIeBb4aPtDXsWYd0lO0Nwqs7stt8rsbsutMrvbcqvM7rbcKrPNrT6723KrzO6q3JYPpJckSdLoeUZ6SZKkAixdkiRJBVi6JEmSCmjnPF3FRcRO1M5yvzW1830NApdn5sI1OrDVqI95a+BnmfnMkOnTM/PaNnL3AjIzb69//NJ04L7MvLrtQa+8nAsy84hOZtZz307tUw3uycy5beS8BViYmX+IiFcBs4A9gHuBkzNzWRvZxwLfz8xHWs1okrvi3b6DmXlDRPw34K3AQuCczHyhjezXAe+ndqqW5cCDwJx21oMkrW0i4jWZ+dtO53bNlq6IOIHaRw0FcBu1U1YEMKfRh213cLktv+Oy/kf7MuAzwD0RMfRjkk5uI3c28I/ANyPiFOAsYENgVkSc2Ebu5cMuVwCHrLjdam49+7Yh1z9eH/NGwOw2f37nUXtnLMA/ABsDf1ef9u02cgG+BPwsIn4cEUdHxIhnGx6lbwPvA46LiH8BPgj8DHgz8M+thtafb2cDk+tZr6JWvn4SEX1tjllrQES8Zk2PYawiYsqaHsNEEhEbR8SpEXFfRCytXxbWp21S0TKvafPxfxIRp0TEv9T/qRx63zfayN0iIr4ZEV+PiCkR0R8Rd0fEd9v5iMGI2GzYZQpwW0RsGhGbtZrbUGZ2xQV4AFi3wfT1gAcrXO6v23js3cCG9es9wDzguPrtO9vMXQdYH/gD8Cf16a8Cft5G7h3A/wX6gHfUvz5av/6ONtfjnUOu3w78af36BsDdbeQuHDr+YfctaHfM1P4xOQA4F/gdcC1wJLBRG7k/r3+dBDwOrFO/HW3+/O4ekrU+MFC//tp2nm/1jI2BU4H7gKX1y8L6tE3ayV7NMq9p8/F/ApwC/Avw34bd9402crcAvgl8HZgC9NfX/XeBLdvI3WzYZQqwGNgU2KzNdTF92M/yXODnwIXA5m3kngq8un69F/gVtVMEPdzOa0b9teh/Aq+r4HnVC/yw/lq3LXA9sKz+urR7G7kbAv8b+EU973fAT4EZbY73OuAEYIthz8ETgOvbyN2jyWVP4NE2x3xp/bnxF9TO13kp8MoVP9s2cq+lthFjVv35e0L99e0zwGVt5L4EPDTs8kL96686+fzrpt2LLwFbUftlHmrL+n0ti4ifN7sL2LyN6HWyvksxMxfXtzZcEhHb0fgjkkZreWa+CDwbEb/MzD/Ul/FcRLSzLnqB44ATgb/NzAUR8Vxm/qiNzBVeERGbUisxkZm/A8jM/4iI5W3k3hMRH83MbwN3RURvZs6LiB2o/dK0IzPzJWAuMDci1gXeCxwOnA60uuXrFfVdjBtQK0cbA08CrwTWbXPMk4AX61kbAWTmr+tjb8d3gZuAvsx8DGr/dVIroP8G7N9KaETs0ewuYFormUN8m9ru1UuBv46IQ6mVr/8H7N1G7vnAVdR+fj8E/pXalsuDqW1pPLjpI1fvCVZ9fduaWgFJ4L+0mAu1LesrDmf4KrV/pv4rcAjwLWp/HFvxvsxcsaX6NOBDWTvkYQdqha7Vj1HZFNgE+GFEPAbMAS7OzE58osk3gNn1/FuBv8nM/SPiXfX79mkx91+B7wPvAf6S2vPjIuB/RsQOmfmFFnN7MvPvhk6o/w7+XUT8dYuZUCuZP6Lx36J2t6C9LjMPrV//QX0PzE0RcVCbuZtn5j8BRMTRQ9bLP0XEUW3kfg54N7W/e3fX8x/KzKntDbeBTja4Ki/UjllaBFxD7aRl51B7EVnEkP/iWsx+nNoL/HbDLj3UjrtpNfcmYNqwaZOAC4AX28j9GbB+/forhkzfmDb+ixiSsw21P6Rn0caWvmGZi6n9F/xQ/esW9ekb0sYWqfr3fD7wy/p6eaGe/yNgtzbH3HTrEPCqNnL/pj7Gh4FjgRuB/0Nta8nsNnKPo/bf3znUtkh9tD79T4Gb21wX97dy3yhyX6z/nvywweW5Nse8YNjtE4F/p7YFqZ3/todutf316pY5xtzj669puw6Z9lA762BIzh1Drg9fL+2M+T5gUv36T4fd184W7KHj3ZdaGXqs/ryY2ea6WN3Pr509EHcNu317/esrqB1v22ruXGqlYPMh0zantpXnhjZy7wG2b3LfI22u44UM+dtUn3Ykta2AD3diHQNf7tTzrf74FX/3vkbtH9aObuF6eTlVhFZ1qT959wYOBT5Qv75OB3LPBd7e5L4L2/whbtHkvre1kfvKJtNfPfQFuwPr5X3UDkav8me6PjC1AzkbAbtR2zTe8u6SYZk7VPh9bwVsVb++Sf35vFcHcneuZ+3U4fH6wv+fGV33wk/tc3D/B/BZaoU/htzXzi7tz9SfG/tR2816JvBnwEnAv7SRu0oppnZIxXTg222ui59QO2Tgg9T+8fmL+vR3APPayL11xd8RalsRrxtyXzv/mGxK7TjV+4DfU9sqvrA+reXdzvXXiR2b3PcXba7jvwfe3WD6dNo4HIja7tsNG0x/PXBJO2MekvVfqe0WfqwTeavkVxHqxYuXiXUZ9sL/5LAX/k3byPWFv/EyOvrCT2132tDLimMqtwAuaDO7D7iY2jGQd1P7+LeZ1LeAtZh5USe+7ybZu1E7TuoaYCdqb8B5iloZf2sbuW+i9iavp4BbqP/TRm1L87Ftjnknaru/Nhw2vd29PDsB7+p07gjZ7x2PYx6aS+346F06tS5WWk4nw7x48bL2XajvxuyW3G4Z87AX/rV6XazNzwtqhyDcD/yA2mEaBw+5r53d5JXk1h//mYrGXFVuZeti+MXPXpTUloj4dWa+tltyq8zuttwqs7stt8rsdnIj4m5gn8x8JiJ6gEuo7br9h4i4MzN3H0+53TjmKtfFcN307kVJa0hV7/Ct8J3DXTdm10X1uVVmd+G74KvK7cYxV7kuVmLpkjQam1N7K/zvh00PagcQj7fcKrO7LbfK7G7LrTK7qtzHImJaZi4AqG+N+XNqJ4bedRzmduOYq1wXK7F0SRqNK6kduLpg+B0RMTAOc6vM7rbcKrO7LbfK7Kpyj6D2kV4vy8zlwBER8a1xmFtldrflrsJjuiRJkgroms9elCRJ6maWLkmSpAIsXZIkSQVYuiRJkgqwdEmSJBXw/wEJI9qnykM2KwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind='bar',figsize=(10,8))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.4349430947191273\n",
      "Mean Squared Error: 19.976077406480467\n",
      "Root Mean Squared Error: 4.469460527455239\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  }
 ],
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