Regression Class ML Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data=pd.read_csv(\"C:/Users/LILENDAR/Desktop/PROJECT 1/data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>Y1</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.90</td>\n",
       "      <td>563.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>122.50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X1     X2     X3      X4   X5   X6   X7   X8     Y1  Unnamed: 9  \\\n",
       "0  0.98  514.5  294.0  110.25  7.0  2.0  0.0  0.0  15.55         NaN   \n",
       "1  0.98  514.5  294.0  110.25  7.0  3.0  0.0  0.0  15.55         NaN   \n",
       "2  0.98  514.5  294.0  110.25  7.0  4.0  0.0  0.0  15.55         NaN   \n",
       "3  0.98  514.5  294.0  110.25  7.0  5.0  0.0  0.0  15.55         NaN   \n",
       "4  0.90  563.5  318.5  122.50  7.0  2.0  0.0  0.0  20.84         NaN   \n",
       "\n",
       "   Unnamed: 10  Unnamed: 11  \n",
       "0          NaN          NaN  \n",
       "1          NaN          NaN  \n",
       "2          NaN          NaN  \n",
       "3          NaN          NaN  \n",
       "4          NaN          NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>Y1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.98</td>\n",
       "      <td>514.5</td>\n",
       "      <td>294.0</td>\n",
       "      <td>110.25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.90</td>\n",
       "      <td>563.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>122.50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X1     X2     X3      X4   X5   X6   X7   X8     Y1\n",
       "0  0.98  514.5  294.0  110.25  7.0  2.0  0.0  0.0  15.55\n",
       "1  0.98  514.5  294.0  110.25  7.0  3.0  0.0  0.0  15.55\n",
       "2  0.98  514.5  294.0  110.25  7.0  4.0  0.0  0.0  15.55\n",
       "3  0.98  514.5  294.0  110.25  7.0  5.0  0.0  0.0  15.55\n",
       "4  0.90  563.5  318.5  122.50  7.0  2.0  0.0  0.0  20.84"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2= data.loc[:, ~data.columns.str.contains('^Unnamed')]\n",
    "data2.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              X1            X2            X3   X4            X5        X6  \\\n",
      "X1  1.000000e+00 -9.919015e-01 -2.037817e-01 -0.0  8.277473e-01  0.000000   \n",
      "X2 -9.919015e-01  1.000000e+00  1.955016e-01  0.0 -8.581477e-01  0.000000   \n",
      "X3 -2.037817e-01  1.955016e-01  1.000000e+00 -0.0  2.809757e-01  0.000000   \n",
      "X4 -0.000000e+00  0.000000e+00 -0.000000e+00  NaN -0.000000e+00  0.000000   \n",
      "X5  8.277473e-01 -8.581477e-01  2.809757e-01 -0.0  1.000000e+00  0.000000   \n",
      "X6  0.000000e+00  0.000000e+00  0.000000e+00  0.0  0.000000e+00  1.000000   \n",
      "X7  1.283986e-17  1.318356e-16 -7.969726e-19 -0.0  1.861418e-18  0.000000   \n",
      "X8  1.764620e-17 -3.558613e-16  0.000000e+00 -0.0  0.000000e+00  0.000000   \n",
      "Y1  6.222722e-01 -6.581202e-01  4.556712e-01 -0.0  8.894307e-01 -0.002587   \n",
      "\n",
      "              X7            X8        Y1  \n",
      "X1  1.283986e-17  1.764620e-17  0.622272  \n",
      "X2  1.318356e-16 -3.558613e-16 -0.658120  \n",
      "X3 -7.969726e-19  0.000000e+00  0.455671  \n",
      "X4 -0.000000e+00 -0.000000e+00 -0.000000  \n",
      "X5  1.861418e-18  0.000000e+00  0.889431  \n",
      "X6  0.000000e+00  0.000000e+00 -0.002587  \n",
      "X7  1.000000e+00  2.129642e-01  0.269841  \n",
      "X8  2.129642e-01  1.000000e+00  0.087368  \n",
      "Y1  2.698410e-01  8.736759e-02  1.000000  \n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = data2.corr()\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAamElEQVR4nO3dfZRcd33f8fd3dyWZtUglJEFtPS0lImHFocbaOE6BYJAAQykmp3DKQ0FNfSJYGYpTIGA4B0J6lAMhPJRy7MYEY6iEHZdHlzg15iEhNBiODMZYNhRRZFvIRnIA8xiD7G//uHevRrMzszu7c3dmte/XOffszO937+9+585oPpr7m4fITCRJAhjqdwGSpMFhKEiSKoaCJKliKEiSKoaCJKliKEiSKoaCJKliKGjJiYh9EXFFU9uTI+IfI+KMiHhsRFwfEfdGxIJ8kCciPhoRlze1fSIi3tti3Q9EREbEry9EbVpaDAUtRf8JeFZEPA0gIk4D3ge8OjPvBn4FXANcuIA1XQT824h4SlnTvwMeD7y+caWIeCLwqAWsS0uMoaAlJzP/EXglcHlEnA68GfhOZl5Z9n8rM98PHJhprIj47xHx501tn4yI/1xefl1EfC8ifhIR34qI7W1qugd4NfC+iNgEvAd4WWb+tGHcEeC/Aa/o/lZLsxN+zYWWqoj4CLAceALw+My8s6n/14FvZ2Z0GON3gX3ApszMiFgNHKH43/xDgc8Av52ZRyJiDBjOzO90GO96YBvw15m5s6nvtcCGzHxVeVprS2Ye7PZ2S534SkFL2UXAU4E/aQ6ELvw9kMCTyuvPA76UmUeAB4AVwHhELMvMQ50CoWG8NcDexsaI2Ai8DHjTHOuUZsVQ0JKVmd8H7mUWp4k6jJHA1cALy6YXUbxyoPxf/MXAHwNHI+LqiDiz3VgRsQV4DXAp8I6IWNbQ/W6K8LpvrrVKs2EoSPN3FfC8iNgM/Dbw0amOzPxwZj4R2EzxiuJtrQaIiAD+kuLJ/5XAz4DXNayyHXh7RNwTEfeUbV+KiBf1+sZoaRvpdwHSoCmfoFdQzDdMvTspM/P+Vutn5tci4hjFk/r1mfmjcrvfANYD/wf4J+AXtP+P2CSwFvjTzHwwIi4EboyIj2TmN4FHN217N/BvgK/P68ZKTXylIE23meIJfOq00i+Ab82wzVXADuDDDW0rgLdSnKK6B3g48IbmDcv5gj8FLszMXwJk5m3AOyjejRSZeTQz75layk3vzcxfzOUGSu347iNJUsVXCpKkiqEgSaoYCpKkiqEgSaos6rekrl27NsfGxvpdhiQtKjfddNO9mbmuVd+iDoWxsTH279/f7zIkaVGJiDva9Xn6SJJUMRQkSRVDQZJUMRQkSRVDQZJUWXqhsG8fnHYaRMx+WbGi2K7deCtXnrz+ypXt15+ydWt3NfRiWb0ahoZ6M9aOHScfg5GR7seYby27d/fucSGpkJm1LMBG4PPA7RTfNvmqsv3twDeBW4CPA6satrkEOEjxjZTPmGkf27Zty67s3ZsZkQlzW/bunT7e8HDrdYeGpq8/ZXx87jUM0rJ9e3Eb+1nD5GR3jwFJCezPNs+rtX1LakScAZyRmV+NiIcCNwHPBTYAn8vM4xHxtjKYXhcR4xRfP3wOcCbFb9s+OjMfaLePiYmJ7OpzCmNjcEfbt+fObPNmOHRo9uM1rz8l2v7k7+KzefP8jul8DQ/D8eP927+0CEXETZk50aqvttNHmXl3Zn61vPwTilcM6zPz05k59a/4RoqQALgAuDoz78/M71K8Yjinp0XdOdef4W2z/UzjzXd/i0G/b+MDbf/PIGkOFmROISLGgMcDX27q+o/A35SX1wN3NfQdLtuax9oVEfsjYv+xY8e6K2TTpu7Wn2n7mcab7/4Wg37fxuHh/u5fOsXUHgoRsZLiN2svzswfN7S/EThO+SPnQKtzKtPObWXm5Zk5kZkT69a1/OqO9vbsmd+pmz17pl9v96Q0NDR9/Snj43OvYZBs397+Ni6UXbv6u3/pFFPrdx9FxDKKQNiXmR9raN8JPBvYnicmNQ5TTE5P2QAc6WlBL35x8ffCC+H+lj+329ry5XDFFSe2bx7vZS+Dn/3sRPvpp8Nf/MX09accOFC8++i222ZfQy+sWgX33VdM0c7X9u3wmc+cuL5zZ/enciLmV8vkJFx66dy3lzRNnRPNAXwQ+EFmXtzQfj7wTuDJmXmsoX0rxe/bTk00fxbY0tOJZklSx4nmOl8pPAF4CfCNiLi5bHsD8B6KHzS/ocgNbszMl2fmgYi4BriN4rTSRZ0CQZLUe7WFQmZ+kdbzBNd12GYP0OeT1JK0dC29TzRLktoyFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklSpLRQiYmNEfD4ibo+IAxHxqrL9YRFxQ0R8u/y7umyPiHhPRByMiFsi4uy6apMktVbnK4XjwKsz8zHAucBFETEOvB74bGZuAT5bXgd4JrClXHYBl9VYmySphdpCITPvzsyvlpd/AtwOrAcuAD5YrvZB4Lnl5QuAD2XhRmBVRJxRV32SpOkWZE4hIsaAxwNfBh6RmXdDERzAw8vV1gN3NWx2uGxrHmtXROyPiP3Hjh2rs2xJWnJqD4WIWAl8FLg4M3/cadUWbTmtIfPyzJzIzIl169b1qkxJEjWHQkQsowiEfZn5sbL5+1Onhcq/R8v2w8DGhs03AEfqrE+SdLI6330UwPuB2zPznQ1d1wI7y8s7gU82tL+0fBfSucB9U6eZJEkLY6TGsZ8AvAT4RkTcXLa9AXgrcE1EXAjcCTy/7LsOeBZwEPg58Ps11iZJaqG2UMjML9J6ngBge4v1E7iornokSTPzE82SpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpIqhIEmqGAqSpEptoRARV0TE0Yi4taHtrIi4MSJujoj9EXFO2R4R8Z6IOBgRt0TE2XXVJUlqr85XClcC5ze1/Rnwlsw8C3hTeR3gmcCWctkFXFZjXZKkNmoLhcz8AvCD5mbg18rL/ww4Ul6+APhQFm4EVkXEGXXVJklqbWSB93cxcH1E/DlFIP2rsn09cFfDeofLtrubB4iIXRSvJti0aVOtxUrSUrPQE82TwB9m5kbgD4H3l+3RYt1sNUBmXp6ZE5k5sW7duprKlKSlaaFDYSfwsfLy/wTOKS8fBjY2rLeBE6eWJEkLZKFD4Qjw5PLyU4Fvl5evBV5avgvpXOC+zJx26kiSVK/a5hQi4irgPGBtRBwG3gz8AfBfI2IE+CfKuQHgOuBZwEHg58Dv11WXJKm92kIhM1/Ypmtbi3UTuKiuWiRJs+MnmiVJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklSZUyhEhL+MJkmnoLm+UnhLT6uQJA2Etj/HGRG3tOsCHlFPOZKkfur0G82PAJ4B/LCpPYB/qK0iSVLfdAqFTwErM/Pm5o6I+NvaKpIk9U2nOYUzge+16sjMF9VTjiSpnzqFwgeA6yPijRGxbKEKkiT1T9vTR5l5TUT8NfAmYH9E/A/gwYb+dy5AfZKkBdRpTgHgV8DPgBXAQ2kIBUnSqafTW1LPB94JXAucnZk/X7CqJEl90emVwhuB52fmgYUqRpLUX20nmjPzSfMJhIi4IiKORsStTe2vjIhvRcSBiPizhvZLIuJg2feMue5XkjR3M80pzMeVwHuBD001RMRTgAuAx2Xm/RHx8LJ9HHgBsJXirbCfiYhHZ+YDNdYnSWpS27ekZuYXgB80NU8Cb83M+8t1jpbtFwBXZ+b9mfld4CBwTl21SZJaW+ivzn408KSI+HJE/F1E/FbZvh64q2G9w2XbNBGxKyL2R8T+Y8eO1VyuJC0tCx0KI8Bq4FzgtcA1EREU36fULFsNkJmXZ+ZEZk6sW7euvkolaQla6FA4DHwsC1+h+NzD2rJ9Y8N6G4AjC1ybJC15Cx0KnwCeChARjwaWA/dSfBbiBRGxIiIeCWwBvrLAtUnSklfbu48i4irgPGBtRBwG3gxcAVxRvk31l8DOzEzgQERcA9wGHAcu8p1HkrTwonhOXpwmJiZy//79/S5DkhaViLgpMyda9S306SNJ0gAzFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBklQxFCRJFUNBc7NvH4yNwdBQ8Xffvun9a9dCRLGsXFlcb7X+TGO1Gm/tWti9e+bt5nq7pvYztQwNFfvbvXt6Xz+Wxtu7ezeMjPRu7BUrWt+frY5LN8vISFFrL+zY0XlfK1cWNe/eXdx3M63XbDbHdOvWE+uvX995/Mbj18199ZCHdO5fu7Y3j/tGmblol23btqX6YO/ezNHRTDixjI4W7VP9y5ef3N+8TK0/01izHa/Vdr24XYO8jI5mbt9e3/iN92cvj8vk5Nzvo8ze3+bh4ZMfN5OTs992fDzzzDM7rxMxu8fvXJdly7p+3AP7M1s/r/ojO+re2Bjcccf09s2b4dCh9v2t1ofOY3XaX7sxp7brVjf7WQq6vT9na3gYjh+f+/YRvatlSuPjZmQEHlhkP/zY5eO+04/sGArq3tBQ8X+UZhHw4IPt+1utD53H6rS/dmNObdetbvazFHR7f3ZjPuPVEQqNj5s6xq9bl497f3lNvbVpU+f2dv2t1p9prG7G63bdXm57Kur2/pyt4eHejtcLjbdxEOubSQ/vI0NB3duzB0ZHT24bHS3ap/qXL+88xtT6M4012/FabdetVrUMstFR2L69vvEb789eHpddu+a3fa9v8/DwyY+bbuobH4czz+y8TsTsHr9ztWzZ/B73zdpNNiyGxYnmPtq7N3Pz5mISbfPm6RNde/dmrllzYjLs9NOL663Wn2msVuOtWVNMCM603VxvV6vJwsnJ7iYh61wab+/kZDFZ2quxly9vfX+2Oi7dTujOd5J5ykyTzaefXtQ8OVncdzOt12w2x3R8/MT67Sabp8ZvPH7d3Fennda5f82aOT3ucaJZkjTFOQVJ0qwYCpKkiqEgSaoYCpKkSm2hEBFXRMTRiLi1Rd9rIiIjYm15PSLiPRFxMCJuiYiz66pLktRena8UrgTOb26MiI3A04A7G5qfCWwpl13AZTXWJUlqo7ZQyMwvAD9o0fUu4I+AxvfCXgB8qHwL7Y3Aqog4o67aJEmtLeicQkQ8B/heZn69qWs9cFfD9cNlmyRpAY0s1I4iYhR4I/D0Vt0t2lp+qi4idlGcYmKT31UjST21kK8UHgU8Evh6RBwCNgBfjYh/TvHKYGPDuhuAI60GyczLM3MiMyfWrVtXc8mStLQsWChk5jcy8+GZOZaZYxRBcHZm3gNcC7y0fBfSucB9mXn3QtUmSSrU+ZbUq4AvAb8REYcj4sIOq18H/D/gIPA+oEe/2SdJ6kZtcwqZ+cIZ+scaLidwUV21SJJmx080S5IqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIWn337YGwMhoaKv/v2Te9fuxYiimXt2qJtpu26tXs3jIwU+xgZKa7Ppq+X9u2DFStO3NaI4vrUbdux4+S+qWXr1pOP0dSyY8fs9/uQh7Qeu9ul8b5oPm47dhT9rbYbHp77PkdGunscNNe1deuJ60ND3e+/rsdDL2Tmol22bduWWmL27s0cHc2EE8voaNE+1b98+cn9kDk0NL29cbtuTU5O3wcU7Z36emnv3tb7mVrGxzv3t1u2b595v0NDcxu73TI6Wuy3l2N2s+9Oj4N29+d8l14/HroA7M82z6tR9C9OExMTuX///n6XoYU0NgZ33DG9ffNmOHSofX87U9t1a2QEHnhgevvwcPG3Xd/x493vq51ub2s3Oj0v1Lnffun0OGh3X89Xrx8PXYiImzJzolXfyEIXI83LnXd2bm/X3+14M2n3JNHpyaPXTyxzrX2x7rdOnW5THYFQ57jzVNucQkRcERFHI+LWhra3R8Q3I+KWiPh4RKxq6LskIg5GxLci4hl11aVFbtOmzu3t+rsdbyZTrwhatXfq66W51r5Y91unTrep1/db3ePOU50TzVcC5ze13QA8NjMfB/xf4BKAiBgHXgBsLbe5NCIG84ipv/bsgdHRk9tGR4v2qf7ly6dvNzQ0vb1xu27t2tW+vVNfL81U+/j43Mbdvn3m/Q71+KljdHTm/dZlpsdBr++3usedr3aTDb1YgDHg1jZ9vwfsKy9fAlzS0Hc98Dszje9E8xK1d2/m5s2ZEcXf5knCvXsz16w5MaG3Zk3RNtN23ZqczBweLvYxPHzyxGGnvl5qNbG+fPmJ29Zu8nZ8/ORjNNtJ5sb9nnZabyZcG++L5uO2fXvR32q7+Ux2Dw939zhormt8/MT1iEU1yZzZx4nmiBgDPpWZj23R97+Av8rMvRHxXuDGzNxb9r0f+JvM/EiL7XYBuwA2bdq07Y5TbcJLkmrWaaK5L59TiIg3AseBqTcIR4vVWqZVZl6emROZObFu3bq6SpSkJWnB330UETuBZwPb88TLlMPAxobVNgBHFro2SVrqFvSVQkScD7wOeE5m/ryh61rgBRGxIiIeCWwBvrKQtUmSanylEBFXAecBayPiMPBmignlFcANEQHFPMLLM/NARFwD3EZxWumizBzMN/FK0inMTzRL0hIzcBPNkqTBZChIkiqGgiSpYihIkiqGgiSpYihIkiqGgiSpYihIkiqGgiSpYiho9rZuhYgTy9at/a6o/3bvLn5wpvG4NC8LcZxWr+5cQ0RRa5127565hoWoQ/NiKGh2tm6F2247ue2225Z2MOzeDZdd1vlH7qH+47R6NfzoRzOvd9ll9T0hTx2L2aizDs2b332k2YlWP3lRWsSPoXkZGenux9frOk6d7ptmw8Nw/Hjva+j2WNRVh2bF7z6S6tDNk+CgqKvmbsddjMduiTAUpLkaHu53Bd2rq+Zux12Mx26JMBQ0O+Pj3bUvBbt2zX7dOo/TqlWzX7ebmrvR7bh11aF5MxQ0OwcOTH9iGx8v2peqSy+FycmZz+nXfZx++MPZBcPkZFFzHaaOxWzUWYfmzYlmSVpinGiWJM2KoSBJqhgKkqSKoSBJqhgKkqTKon73UUQcA+5o070WuHcBy5kLa+wNa+wNa+yNxVDj5sxc16pjUYdCJxGxv91brgaFNfaGNfaGNfbGYqixE08fSZIqhoIkqXIqh8Ll/S5gFqyxN6yxN6yxNxZDjW2dsnMKkqTuncqvFCRJXTIUJEmVUzIUIuJQRHwjIm6OiIH4GtWIuCIijkbErQ1tD4uIGyLi2+Xf1QNY4x9HxPfKY3lzRDyrzzVujIjPR8TtEXEgIl5Vtg/MsexQ48Acy4g4LSK+EhFfL2t8S9n+yIj4cnkc/yoilg9gjVdGxHcbjuNZ/aqxodbhiPhaRHyqvD4wx7Fbp2QolJ6SmWcN0PuFrwTOb2p7PfDZzNwCfLa83k9XMr1GgHeVx/KszLxugWtqdhx4dWY+BjgXuCgixhmsY9muRhicY3k/8NTM/JfAWcD5EXEu8Layxi3AD4ELB7BGgNc2HMeb+1di5VXA7Q3XB+k4duVUDoWBkplfAH7Q1HwB8MHy8geB5y5oUU3a1DhQMvPuzPxqefknFP8Q1zNAx7JDjQMjCz8try4rlwSeCnykbO/3cWxX40CJiA3Avwb+srweDNBx7NapGgoJfDoiboqIQf7dv0dk5t1QPJEAD+9zPe28IiJuKU8v9fUUV6OIGAMeD3yZAT2WTTXCAB3L8pTHzcBR4AbgO8CPMvN4ucph+hxmzTVm5tRx3FMex3dFxIo+lgjwbuCPgAfL62sYsOPYjVM1FJ6QmWcDz6R46f67/S5oEbsMeBTFy/e7gXf0t5xCRKwEPgpcnJk/7nc9rbSocaCOZWY+kJlnARuAc4DHtFptYatq2nlTjRHxWOAS4DeB3wIeBryuX/VFxLOBo5l5U2Nzi1UH7hVOO6dkKGTmkfLvUeDjFA/4QfT9iDgDoPx7tM/1TJOZ3y//YT4IvI8BOJYRsYziyXZfZn6sbB6oY9mqxkE8lgCZ+SPgbynmP1ZFxEjZtQE40q+6GjXUeH55ei4z837gA/T3OD4BeE5EHAKupjht9G4G9DjOxikXChFxekQ8dOoy8HTg1s5b9c21wM7y8k7gk32spaWpJ9rS79HnY1mer30/cHtmvrOha2COZbsaB+lYRsS6iFhVXn4IsINi7uPzwPPK1fp9HFvV+M2G8A+Kc/V9O46ZeUlmbsjMMeAFwOcy88UM0HHs1in3ieaI+BcUrw4ARoAPZ+aePpYEQERcBZxH8bW63wfeDHwCuAbYBNwJPD8z+zbR26bG8yhOdyRwCHjZ1Ln7foiIJwJ/D3yDE+dw30Bxzn4gjmWHGl/IgBzLiHgcxQToMMV/Dq/JzD8p//1cTXFa5mvAvy//Rz5INX4OWEdxmuZm4OUNE9J9ExHnAa/JzGcP0nHs1ikXCpKkuTvlTh9JkubOUJAkVQwFSVLFUJAkVQwFSVLFUJDmofxG1O9GxMPK66vL65vL679WfjPqe/tbqTQ7hoI0D5l5F8XXV7y1bHorcHlm3lFe/y/A3/WjNmkuDAVp/t4FnBsRFwNPpPxOo4jYBjwC+HQfa5O6MjLzKpI6ycxfRcRrgf8NPD0zfxkRQxTh8BJge18LlLrgKwWpN55J8c2njy2v7wauK08vSYuGrxSkeSp/DvJpFN8y+sWIuBr4HeBJEbEbWAksj4ifZma/f11P6sjvPpLmofymzn8A3pSZN0TEK4Fzy2/KnFrnPwATmfmKPpUpzZqnj6T5+QPgzsy8obx+KfCbEfHkPtYkzZmvFCRJFV8pSJIqhoIkqWIoSJIqhoIkqWIoSJIqhoIkqWIoSJIq/x9wuUKTZmaDTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "labels = data2['Y1']\n",
    "features = data2.drop(['Y1'], axis=1)\n",
    "\n",
    "for x in features:\n",
    "    plt.plot(labels, features[x], 'ro')  # arguments are passed to np.histogram\n",
    "    plt.title(\"Y1 vs \" + x)\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(\"Y1\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1296,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:403: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (a >= first_edge)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:404: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (a <= last_edge)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAASsUlEQVR4nO3df7DldV3H8ecrQCpFwfZCO+zS9Qc5o44udAct0iEpQ3JATQ1KRKNWGynsp0iTVjM1Vv4os7AlCCwgyBVFQxN/RU5h3kVawcVcbNOVnd0ba6iD0Sy+++N8bx4vZ/eee8+5vz4+HzNnzvd8vp9zvu/Pfmdf93s/9/v9nlQVkqS2fMdKFyBJGj/DXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcNe3vSSPSLIryU/3tR2V5AtJXti9PjnJLUm+lmRvkotWrmJpfvEiJgmSPBu4GnhiVc0kuRQ4rqpekGQd8Bngl4F3Ag8DNlTVjpWrWDo0w13qJLkSOBL4C2Ar8OSq2pPk94GNVXXeStYnLYTTMtI3/TJwGr2j81+rqj1d+9OB/Un+Ocm+JO9NcsJKFSkNw3CXOlX1ZeBO4LuBd/Wt2gCcD1wEnAD8B3DtshcoLYDhLnWSvASYBD4E/EHfqq8DN1TVJ6vqf4DfAX4oyaOWv0ppOIevdAHSapDkWOAtwIuBu4A7k1xTVbcA24H+P07NLmd5q5SG55G71PM24N1V9dFurv03gMuSHAn8FfD8JJuSHAH8FvDxqvrvFaxXOiTDXd/2kjwP+GHg12fbquovgd3A66rqI8AlwN8D+4DHAz894KOkVcNTISWpQR65S1KDDHdJapDhLkkNmjfck2xM8tEkO5LcOXvDpCSPTnJzks91z8d07Uny1iQ7k2xPcvJSD0KS9K3m/YNqkvXA+qq6LclRwDbgecDLgP1V9YYkFwPHVNVrkpwJ/CJwJvA04E+q6mmH2sa6detqcnJy5MFI0reTbdu2/VdVTQxaN+9FTN05v3u65a8m2QEcD5xN7z4cAFcBHwNe07W/o3o/NW5NcnSS9X336XiIyclJpqenhx+RJIkk/3mwdQuac08yCZwEfILe7VBnQ38PcGzX7Xjgi31v2921zf2szUmmk0zPzMwspAxJ0jyGDvckj6B3G9RXV9VXDtV1QNtD5n6qaktVTVXV1MTEwN8qJEmLNFS4d5dcbwWurqrZu+Xt7ebjZ+fl93Xtu4GNfW/fANwznnIlScMY5myZAJcDO6rqzX2rbqR3G1S65/f0tb+0O2vm6cB9h5pvlySN3zB3hTwVOA/4dJLbu7ZLgDcA1ye5APgC8KJu3U30zpTZCdwPvHysFUuS5jXM2TIf5+C3Nj19QP8CXjViXZKkEXiFqiQ1yHCXpAYZ7pLUoCa+Zm/y4r9ftm3tesNPLNu2VoL/lmvTcu43aHvftfJv6ZG7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWrQMF+QfUWSfUnu6Gu7Lsnt3WPX7HerJplM8vW+dW9fyuIlSYMNcz/3K4G3Ae+Ybaiqn5pdTvIm4L6+/ndX1aZxFShJWrhhviD7liSTg9YlCfBi4FnjLUuSNIpR59yfAeytqs/1tT0myaeS/GOSZxzsjUk2J5lOMj0zMzNiGZKkfqOG+7nAtX2v9wAnVNVJwK8A1yR55KA3VtWWqpqqqqmJiYkRy5Ak9Vt0uCc5HHgBcN1sW1U9UFX3dsvbgLuB7x+1SEnSwoxy5P6jwF1VtXu2IclEksO65ccCJwKfH61ESdJCDXMq5LXAvwBPSLI7yQXdqnP41ikZgGcC25P8G/BO4JVVtX+cBUuS5jfM2TLnHqT9ZQPatgJbRy9LkjQKr1CVpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSgYb5D9Yok+5Lc0df220m+lOT27nFm37rXJtmZ5LNJfnypCpckHdwwR+5XAmcMaH9LVW3qHjcBJHkivS/OflL3nj9Pcti4ipUkDWfecK+qW4D9Q37e2cDfVtUDVfUfwE7glBHqkyQtwihz7hcm2d5N2xzTtR0PfLGvz+6u7SGSbE4ynWR6ZmZmhDIkSXMtNtwvBR4HbAL2AG/q2jOgbw36gKraUlVTVTU1MTGxyDIkSYMsKtyram9VPVhV3wAu45tTL7uBjX1dNwD3jFaiJGmhFhXuSdb3vXw+MHsmzY3AOUmOTPIY4ETgX0crUZK0UIfP1yHJtcBpwLoku4HXA6cl2URvymUX8AqAqrozyfXAZ4ADwKuq6sGlKV2SdDDzhntVnTug+fJD9P894PdGKUqSNBqvUJWkBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KB5wz3JFUn2Jbmjr+2PktyVZHuSG5Ic3bVPJvl6ktu7x9uXsnhJ0mDDHLlfCZwxp+1m4MlV9RTg34HX9q27u6o2dY9XjqdMSdJCzBvuVXULsH9O2wer6kD38lZgwxLUJklapHHMuf8s8P6+149J8qkk/5jkGQd7U5LNSaaTTM/MzIyhDEnSrJHCPclvAgeAq7umPcAJVXUS8CvANUkeOei9VbWlqqaqampiYmKUMiRJcyw63JOcDzwX+JmqKoCqeqCq7u2WtwF3A98/jkIlScNbVLgnOQN4DXBWVd3f1z6R5LBu+bHAicDnx1GoJGl4h8/XIcm1wGnAuiS7gdfTOzvmSODmJAC3dmfGPBP43SQHgAeBV1bV/oEfLElaMvOGe1WdO6D58oP03QpsHbUoSdJovEJVkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDhgr3JFck2Zfkjr62Rye5OcnnuudjuvYkeWuSnUm2Jzl5qYqXJA027JH7lcAZc9ouBj5cVScCH+5eAzwHOLF7bAYuHb1MSdJCDBXuVXULsH9O89nAVd3yVcDz+trfUT23AkcnWT+OYiVJwxllzv24qtoD0D0f27UfD3yxr9/uru1bJNmcZDrJ9MzMzAhlSJLmWoo/qGZAWz2koWpLVU1V1dTExMQSlCFJ375GCfe9s9Mt3fO+rn03sLGv3wbgnhG2I0laoFHC/Ubg/G75fOA9fe0v7c6aeTpw3+z0jSRpeRw+TKck1wKnAeuS7AZeD7wBuD7JBcAXgBd13W8CzgR2AvcDLx9zzZKkeQwV7lV17kFWnT6gbwGvGqUoSdJovEJVkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDhvqavUGSPAG4rq/pscDrgKOBnwdmuvZLquqmRVcoSVqwRYd7VX0W2ASQ5DDgS8AN9L4Q+y1V9caxVChJWrBxTcucDtxdVf85ps+TJI1gXOF+DnBt3+sLk2xPckWSYwa9IcnmJNNJpmdmZgZ1kSQt0sjhnuRhwFnA33VNlwKPozdlswd406D3VdWWqpqqqqmJiYlRy5Ak9RnHkftzgNuqai9AVe2tqger6hvAZcApY9iGJGkBxhHu59I3JZNkfd+65wN3jGEbkqQFWPTZMgBJvhv4MeAVfc1/mGQTUMCuOeskSctgpHCvqvuB75nTdt5IFUmSRuYVqpLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGjTS1+wBJNkFfBV4EDhQVVNJHg1cB0zS+x7VF1fVl0fdliRpOOM6cv+RqtpUVVPd64uBD1fVicCHu9eSpGWyVNMyZwNXdctXAc9bou1IkgYYR7gX8MEk25Js7tqOq6o9AN3zsXPflGRzkukk0zMzM2MoQ5I0a+Q5d+DUqronybHAzUnuGuZNVbUF2AIwNTVVY6hDktQZ+ci9qu7pnvcBNwCnAHuTrAfonveNuh1J0vBGCvckD09y1Owy8GzgDuBG4Pyu2/nAe0bZjiRpYUadljkOuCHJ7GddU1UfSPJJ4PokFwBfAF404nYkSQswUrhX1eeBpw5ovxc4fZTPliQtnleoSlKDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0KLDPcnGJB9NsiPJnUku6tp/O8mXktzePc4cX7mSpGGM8h2qB4BfrarbkhwFbEtyc7fuLVX1xtHLkyQtxqLDvar2AHu65a8m2QEcP67CJEmLN5Y59ySTwEnAJ7qmC5NsT3JFkmMO8p7NSaaTTM/MzIyjDElSZ+RwT/IIYCvw6qr6CnAp8DhgE70j+zcNel9VbamqqaqampiYGLUMSVKfkcI9yRH0gv3qqnoXQFXtraoHq+obwGXAKaOXKUlaiFHOlglwObCjqt7c176+r9vzgTsWX54kaTFGOVvmVOA84NNJbu/aLgHOTbIJKGAX8IqRKpQkLdgoZ8t8HMiAVTctvhxJ0jh4haokNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYtWbgnOSPJZ5PsTHLxUm1HkvRQSxLuSQ4D/gx4DvBEel+a/cSl2JYk6aGW6sj9FGBnVX2+qv4X+Fvg7CXaliRpjlTV+D80eSFwRlX9XPf6POBpVXVhX5/NwObu5ROAz46wyXXAf43w/tWilXGAY1mNWhkHOJZZ31dVE4NWHL74eg4pA9q+5adIVW0BtoxlY8l0VU2N47NWUivjAMeyGrUyDnAsw1iqaZndwMa+1xuAe5ZoW5KkOZYq3D8JnJjkMUkeBpwD3LhE25IkzbEk0zJVdSDJhcA/AIcBV1TVnUuxrc5YpndWgVbGAY5lNWplHOBY5rUkf1CVJK0sr1CVpAYZ7pLUoDUR7kk2Jvlokh1J7kxy0YA+SfLW7nYH25OcvBK1zmfIsZyW5L4kt3eP161ErfNJ8p1J/jXJv3Vj+Z0BfY5Mcl23Xz6RZHL5K53fkGN5WZKZvv3ycytR6zCSHJbkU0neN2Ddmtgns+YZy1raJ7uSfLqrc3rA+rFm2FKd5z5uB4BfrarbkhwFbEtyc1V9pq/Pc4ATu8fTgEu759VmmLEA/FNVPXcF6luIB4BnVdXXkhwBfDzJ+6vq1r4+FwBfrqrHJzkH+APgp1ai2HkMMxaA6/ovxlvFLgJ2AI8csG6t7JNZhxoLrJ19AvAjVXWwC5bGmmFr4si9qvZU1W3d8lfp7ejj53Q7G3hH9dwKHJ1k/TKXOq8hx7ImdP/WX+teHtE95v6F/mzgqm75ncDpSQZd5LaihhzLmpBkA/ATwF8epMua2Ccw1FhaMtYMWxPh3q/7FfIk4BNzVh0PfLHv9W5WeWgeYiwAP9hNEbw/yZOWtbAF6H5lvh3YB9xcVQfdL1V1ALgP+J7lrXI4Q4wF4Ce7X5nfmWTjgPWrwR8DvwF84yDr18w+Yf6xwNrYJ9A7WPhgkm3d7VfmGmuGralwT/IIYCvw6qr6ytzVA96yao+85hnLbfTuGfFU4E+Bdy93fcOqqgerahO9q5BPSfLkOV3WzH4ZYizvBSar6inAh/jm0e+qkeS5wL6q2naobgPaVt0+GXIsq36f9Dm1qk6mN/3yqiTPnLN+rPtlzYR7Nw+6Fbi6qt41oMuaueXBfGOpqq/MThFU1U3AEUnWLXOZC1JV/w18DDhjzqr/3y9JDgceBexf1uIW6GBjqap7q+qB7uVlwA8sc2nDOBU4K8kuendjfVaSv5nTZ63sk3nHskb2CQBVdU/3vA+4gd7dc/uNNcPWRLh384GXAzuq6s0H6XYj8NLuL85PB+6rqj3LVuSQhhlLku+dnQNNcgq9/XTv8lU5nCQTSY7ulr8L+FHgrjndbgTO75ZfCHykVuGVc8OMZc7851n0/l6yqlTVa6tqQ1VN0rvtx0eq6iVzuq2JfTLMWNbCPgFI8vDuBAqSPBx4NnDHnG5jzbC1crbMqcB5wKe7OVGAS4ATAKrq7cBNwJnATuB+4OUrUOcwhhnLC4FfSHIA+Dpwzmr8zwesB65K78tZvgO4vqrel+R3gemqupHeD7K/TrKT3tHhOStX7iENM5ZfSnIWvTOe9gMvW7FqF2iN7pOB1ug+OQ64oTtmOxy4pqo+kOSVsDQZ5u0HJKlBa2JaRpK0MIa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatD/AQ5z2jBa2El4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for x in features:\n",
    "    plt.hist(features[x], bins='auto')  # arguments are passed to np.histogram\n",
    "    plt.title(x)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\morestats.py:1044: RuntimeWarning: invalid value encountered in less_equal\n",
      "  if any(x <= 0):\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "x = data2['X4']\n",
    "prob = stats.probplot(x, dist=stats.norm, plot=ax1)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_title('Probplot against normal distribution')\n",
    "#We now use boxcox to transform the data so it’s closest to normal:\n",
    "ax2 = fig.add_subplot(212)\n",
    "data2['X4'], _ = stats.boxcox(x)\n",
    "prob = stats.probplot(data2['X4'], dist=stats.norm, plot=ax2)\n",
    "ax2.set_title('Probplot after Box-Cox transformation')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:403: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (a >= first_edge)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:404: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (a <= last_edge)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\LILENDAR\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEVCAYAAAAb/KWvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAa4klEQVR4nO3df5wddX3v8debJASUQIBsMCSRUAxV8FECrojSWgpWIFaCD6ENrRK4aLAXFR5FK3BvBa1YtUW8VgWDoQlFgQgKKTdaIz9KvVeCGwyBECgLRLImJMuPBHJBJOFz/5jvmsnJ2T2ze87usl/ez8fjPHbmO9+Z+X5nct5n8t05s4oIzMwsL7sMdwPMzKz1HO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuNsOJO0n6S5Jz0u6bLjbY32TdLSkRyRtkXTycLenlqSLJH1nuNvxWuRwf5WTtIekNZL+slQ2TtITkk6pqburpIckdTWxy7nAU8CeEXG+pEskXdvE9naStvlyCqQtklZL+mAr9zFUJB3T5PFu1ueBb0TEHhFx8zC2o+6xiIgvRsRHhqtNr2UO91e5iNhCEbj/S1JbKv4K0BERN9ZU/zSwscldHgA8GC36dpuk0b0suiEF0h7AecC1kvZrxT5fbfo4Bq1wALBqICsOcrtsuEWEXyPgBSwArgOOAZ4GJtUsPxBYDZwIdPWxnb2BW4Fu4Nk0PaW0j5eB3wJbgD9L0y+n+ftSvb2A+cB64NfAF4BRadkZwP8BLgeeAb5Qpw2XANfWlG0E3lWa/yjQmbaxGNg/lb+L4n8WU9P8YcAm4M299PdQYGnazgbgolQ+FvgasC69vgaMTcs+A9wNjE7zf00RoLvVbPv1wIvAK+n4bAH2T/27EbgWeA74CHAk8PPU1vXAN4BdS9sK4GPAI+m8fBNQWvYm4D+AzanvN6TyR9O+X0z7Hpv2vzj1txP4aM1xr23XJcD3U9nzwP3AwcCF6ZysBd5b2saZFP/OngceA86ucCyuLa1/UjqWm4A7gbeUlq0BPgWsTH29ofaY+9WPzBjuBvhV8UQVobw+vbnPrLP8VuADFOHfV7jvC3wQeB0wLr2xby4tX0ApkGvfnKnsZuDb6Q09Ebin9CY/A9gKfAIYDexepw2/2yYg4H3pzT4+lR2b+nlECqx/Bu4qrX8pcDuwewqCj/fS13HpmJ0P7Jbm35GWfZ4iwCcCbcD/Bf4+LdsFuCu1czpF2B7eyz52Ot5pvZeBk9O2dgfeBhyVjsk0ioA8r7ROpHM4HngjxYfvCWnZdcD/SNvaDfjD0nprgPeU5v8D+FaqNyNt57g+2nUJ8Bvg+NS2a4DH0/7GUHzIPl7a/vuAg9J5+2PgBeCIBsei51wfDPw/4E/Ttv+W4gNo11Jf7qH4UNgnHaOPDfd7b6S+hr0BfvXjZMFP05tpr5ryDwA/TtM7vcEabHMG8GxpfgF9hDuwH/ASpdAGTgPuSNNnAE802OclFP8j2JT6sw3429Ly+cBXSvN7pFCalubHAMsprjJ/TLrCrbOf04Bf9rLsUWBmaf54YE1pfhrF1e9q4MI++tJboN3V2zqpznnAD0vzwY6hvQi4IE1fA8wj/Q+rZjtrSOEOTE3Hclxp+T8AC3prVypbWpp/P8VVd8//xMalto3vpR83A+c2OBY94f53wKLSsl0o/ud3TKkvHyot/wpw5VC/z3J5ecx9hJD0IYrA+Snw5VL56yneBJ+ouJ3XSfq2pF9Jeo7iCnW8pFEVm3IARbiul7RJ0iaKq/iJpTprK2xnUUSMj4jXUVwJni7p7LRsf+BXPRWj+L3D08DkNP8yxYfQW4HLIiVBHVMpQryeHfaRpvcv7XMNcAfFMf9mhf7U2uEYSDpY0q2SnkzH/YvAhJp1nixNv0DxoQbFFa6AeyStkvTfetnn/sAzEfF8qexXpONWr13JhtL0i8BTEbGtNE9PWySdKOluSc+kcz+zTj96U3teX0ntKbevt2Ng/eRwHwEkTaQYw/4ocDbw55LenRZPpwig/5T0JPADYFIKkWl1Nnc+8PsUwxN7Aj3bUS+7rw3OtRRX7hNSOI+PiD0j4tA+1ulTCtIfUVw1QjEGfkDP8vQBti/FVR6SJgMXA/8CXCZpbC+bXkvxwVHPDvugGApZV9rnTOCdwG3AP/bV/IrlVwAPAdPTcb+I3o/5jhuKeDIiPhoR+1Oc/29JelOdquuAfSSNK5W9kXTcGrS3oXScbwL+CdgvIsYDS9jej0bbrj2vovgA/nWva9iAOdxHhm9QjIvfERHrKa7krkpvtgco3iAz0usjFFdiM6h/lTaO4mpsk6R9KEKyLxuAaZJ2AUj7/wlFqO4paRdJB0n644F2TtIU4AS23/XxPeBMSTNSH78ILIuINSkQFlAM3ZxFMab+971s+lbgDZLOkzQ23UL6jrTsOuB/SmqTNAH4LMUvFUnz8ymO5Rzg/Sns69kA7CtprwbdHEfxS8wtkt5M8UvaSiSdmo4RFOP/QTH8soOIWEvxu4N/kLSbpD+gOEbfrbqvBnal+B1IN7BV0onAe0vLGx2LRcD7JB0naQzFhcZLqc3WYg73V7n0xZQ/pLjNEYCI+A7QBXw2IramK7snI+JJinHiV9L8TgFAcVfI7hS/sLybYsy6L99PP5+WdG+aPp3ijf4gRdjcCEzqZ9f+ouc+d+AXFHfYfC717zaK8dmbKML7IGB2Wu+TFOP+f5eGY86k+CD4o9odpOGJP6X4H8GTFHei/Ela/AWgg+IXsvcD96YyKMa3b4mIJRHxNEVAfkfSvnX28RDFB8VjaZhq/9o6yaeAv6S4y+QqijtBqno7sCwdq8UUY9yP91L3NIr/ya0DfghcHBFL+7GvXqXj+UmKkH6Woj+LS8v7PBYR8TDwIYpfkD9FcV7eHxG/bUX7bEc9t1qZmVlGfOVuZpYhh7uZWYYc7mZmGaoc7pJGSfqlpFvT/IGSlqUn0t0gaddUPjbNd6bl0wan6WZm1pv+PDjoXIpv6+2Z5r8MXB4R10u6kuKOgivSz2cj4k2SZqd6f9HXhidMmBDTpk3rb9vNzF7Tli9f/lREtNVbVulumXSP7UKKZ3r8DcUtTN3AGyJiq6R3ApdExPGS/j1N/zw9de5JoK2PbxHS3t4eHR0d/e6YmdlrmaTlEdFeb1nVYZmvUXxx5pU0vy+wKSK2pvkutn+FeDLpyzNp+eZUv7ZRcyV1SOro7u6u2AwzM6uiYbhL+jNgY0QsLxfXqRoVlm0viJgXEe0R0d7WVvd/FWZmNkBVxtyPBk5KX7/ejWLM/WsUD5sana7Op7D9uRxdFF+H70rDMntRfGvSzMyGSMMr94i4MCKmRMQ0iq+A3x4Rf0XxxLyeP/M2B7glTS9O86Tlt/c13m5mZq3XzH3unwH+RlInxZj6/FQ+n+LhQZ0Uv3y9oLkmmplZf/XrbyhGxJ0UfxqLiHiM4k+H1db5DXBqC9pmZmYD5G+ompllyOFuZpYhh7uZWYb6Neb+ajTtgv89pPtb86X3Den+zGxo5ZIpvnI3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMNw13SbpLukXSfpFWSPpfKF0h6XNKK9JqRyiXp65I6Ja2UdMRgd8LMzHZU5XnuLwHHRsQWSWOAn0n6UVr26Yi4sab+icD09HoHcEX6aWZmQ6ThlXsUtqTZMekVfawyC7gmrXc3MF7SpOabamZmVVUac5c0StIKYCOwNCKWpUWXpqGXyyWNTWWTgbWl1btSWe0250rqkNTR3d3dRBfMzKxWpXCPiG0RMQOYAhwp6a3AhcCbgbcD+wCfSdVVbxN1tjkvItojor2trW1AjTczs/r6dbdMRGwC7gROiIj1aejlJeBfgCNTtS5gamm1KcC6FrTVzMwqqnK3TJuk8Wl6d+A9wEM94+iSBJwMPJBWWQycnu6aOQrYHBHrB6X1ZmZWV5W7ZSYBCyWNovgwWBQRt0q6XVIbxTDMCuBjqf4SYCbQCbwAnNn6ZpuZWV8ahntErAQOr1N+bC/1Azin+aaZmdlA+RuqZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWWoyh/I3k3SPZLuk7RK0udS+YGSlkl6RNINknZN5WPTfGdaPm1wu2BmZrWqXLm/BBwbEYcBM4ATJB0FfBm4PCKmA88CZ6X6ZwHPRsSbgMtTPTMzG0INwz0KW9LsmPQK4FjgxlS+EDg5Tc9K86Tlx0lSy1psZmYNVRpzlzRK0gpgI7AUeBTYFBFbU5UuYHKangysBUjLNwP71tnmXEkdkjq6u7ub64WZme2gUrhHxLaImAFMAY4E3lKvWvpZ7yo9diqImBcR7RHR3tbWVrW9ZmZWQb/ulomITcCdwFHAeEmj06IpwLo03QVMBUjL9wKeaUVjzcysmip3y7RJGp+mdwfeA6wG7gBOSdXmALek6cVpnrT89ojY6crdzMwGz+jGVZgELJQ0iuLDYFFE3CrpQeB6SV8AfgnMT/XnA/8qqZPiin32ILTbzMz60DDcI2IlcHid8scoxt9ry38DnNqS1pmZ2YD4G6pmZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZajKH8ieKukOSaslrZJ0biq/RNKvJa1Ir5mldS6U1CnpYUnHD2YHzMxsZ1X+QPZW4PyIuFfSOGC5pKVp2eUR8U/lypIOofij2IcC+wM/lXRwRGxrZcPNzKx3Da/cI2J9RNybpp8HVgOT+1hlFnB9RLwUEY8DndT5Q9pmZjZ4+jXmLmkacDiwLBV9XNJKSVdL2juVTQbWllbros6HgaS5kjokdXR3d/e74WZm1rvK4S5pD+Am4LyIeA64AjgImAGsBy7rqVpn9dipIGJeRLRHRHtbW1u/G25mZr2rFO6SxlAE+3cj4gcAEbEhIrZFxCvAVWwfeukCppZWnwKsa12TzcyskSp3ywiYD6yOiK+WyieVqn0AeCBNLwZmSxor6UBgOnBP65psZmaNVLlb5mjgw8D9klaksouA0yTNoBhyWQOcDRARqyQtAh6kuNPmHN8pY2Y2tBqGe0T8jPrj6Ev6WOdS4NIm2mVmZk3wN1TNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy1CVP5A9VdIdklZLWiXp3FS+j6Slkh5JP/dO5ZL0dUmdklZKOmKwO2FmZjuqcuW+FTg/It4CHAWcI+kQ4ALgtoiYDtyW5gFOBKan11zgipa32szM+tQw3CNifUTcm6afB1YDk4FZwMJUbSFwcpqeBVwThbuB8ZImtbzlZmbWq36NuUuaBhwOLAP2i4j1UHwAABNTtcnA2tJqXamsdltzJXVI6uju7u5/y83MrFeVw13SHsBNwHkR8VxfVeuUxU4FEfMioj0i2tva2qo2w8zMKqgU7pLGUAT7dyPiB6l4Q89wS/q5MZV3AVNLq08B1rWmuWZmVkWVu2UEzAdWR8RXS4sWA3PS9BzgllL56emumaOAzT3DN2ZmNjRGV6hzNPBh4H5JK1LZRcCXgEWSzgKeAE5Ny5YAM4FO4AXgzJa22MzMGmoY7hHxM+qPowMcV6d+AOc02S4zM2uCv6FqZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYaq/IHsqyVtlPRAqewSSb+WtCK9ZpaWXSipU9LDko4frIabmVnvqly5LwBOqFN+eUTMSK8lAJIOAWYDh6Z1viVpVKsaa2Zm1TQM94i4C3im4vZmAddHxEsR8TjQCRzZRPvMzGwAmhlz/7iklWnYZu9UNhlYW6rTlcp2ImmupA5JHd3d3U00w8zMag003K8ADgJmAOuBy1K56tSNehuIiHkR0R4R7W1tbQNshpmZ1TOgcI+IDRGxLSJeAa5i+9BLFzC1VHUKsK65JpqZWX8NKNwlTSrNfgDouZNmMTBb0lhJBwLTgXuaa6KZmfXX6EYVJF0HHANMkNQFXAwcI2kGxZDLGuBsgIhYJWkR8CCwFTgnIrYNTtPNzKw3DcM9Ik6rUzy/j/qXApc20ygzM2uOv6FqZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYahrukqyVtlPRAqWwfSUslPZJ+7p3KJenrkjolrZR0xGA23szM6qty5b4AOKGm7ALgtoiYDtyW5gFOBKan11zgitY008zM+qNhuEfEXcAzNcWzgIVpeiFwcqn8mijcDYyXNKlVjTUzs2oGOua+X0SsB0g/J6byycDaUr2uVLYTSXMldUjq6O7uHmAzzMysnlb/QlV1yqJexYiYFxHtEdHe1tbW4maYmb22DTTcN/QMt6SfG1N5FzC1VG8KsG7gzTMzs4EYaLgvBuak6TnALaXy09NdM0cBm3uGb8zMbOiMblRB0nXAMcAESV3AxcCXgEWSzgKeAE5N1ZcAM4FO4AXgzEFos5mZNdAw3CPitF4WHVenbgDnNNsoMzNrjr+hamaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGGv6Zvb5IWgM8D2wDtkZEu6R9gBuAacAa4M8j4tnmmmlmZv3Riiv3P4mIGRHRnuYvAG6LiOnAbWnezMyG0GAMy8wCFqbphcDJg7APMzPrQ7PhHsBPJC2XNDeV7RcR6wHSz4n1VpQ0V1KHpI7u7u4mm2FmZmVNjbkDR0fEOkkTgaWSHqq6YkTMA+YBtLe3R5PtMDOzkqau3CNiXfq5EfghcCSwQdIkgPRzY7ONNDOz/hlwuEt6vaRxPdPAe4EHgMXAnFRtDnBLs400M7P+aWZYZj/gh5J6tvO9iPixpF8AiySdBTwBnNp8M83MrD8GHO4R8RhwWJ3yp4HjmmmUmZk1x99QNTPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDgxbukk6Q9LCkTkkXDNZ+zMxsZ4MS7pJGAd8ETgQOAU6TdMhg7MvMzHY2WFfuRwKdEfFYRPwWuB6YNUj7MjOzGqMHabuTgbWl+S7gHeUKkuYCc9PsFkkPD3BfE4CnBrhuv+nLQ7Wnuoa0r8PMfc3Ta6WvlfvZZKYc0NuCwQp31SmLHWYi5gHzmt6R1BER7c1uZyRwX/Pkvubn1dDPwRqW6QKmluanAOsGaV9mZlZjsML9F8B0SQdK2hWYDSwepH2ZmVmNQRmWiYitkj4O/DswCrg6IlYNxr5owdDOCOK+5sl9zc+w91MR0biWmZmNKP6GqplZhhzuZmYZGjHh3uhxBpLGSrohLV8madrQt7I1KvT1DEndklak10eGo53NknS1pI2SHuhluSR9PR2HlZKOGOo2tkqFvh4jaXPpnH52qNvYCpKmSrpD0mpJqySdW6dOFue1Yl+H77xGxKv+RfFL2UeB3wN2Be4DDqmp89+BK9P0bOCG4W73IPb1DOAbw93WFvT13cARwAO9LJ8J/IjiexNHAcuGu82D2NdjgFuHu50t6Ock4Ig0PQ74rzr/frM4rxX7OmzndaRcuVd5nMEsYGGavhE4TlK9L1O92r1mHt0QEXcBz/RRZRZwTRTuBsZLmjQ0rWutCn3NQkSsj4h70/TzwGqKb6yXZXFeK/Z12IyUcK/3OIPag/i7OhGxFdgM7DskrWutKn0F+GD6L+2NkqbWWZ6DqsciF++UdJ+kH0k6dLgb06w0NHo4sKxmUXbntY++wjCd15ES7g0fZ1CxzkhQpR//BkyLiD8Afsr2/7HkJpdzWsW9wAERcRjwz8DNw9yepkjaA7gJOC8inqtdXGeVEXteG/R12M7rSAn3Ko8z+F0dSaOBvRiZ/w1u2NeIeDoiXkqzVwFvG6K2DbXXzGMsIuK5iNiSppcAYyRNGOZmDYikMRRh992I+EGdKtmc10Z9Hc7zOlLCvcrjDBYDc9L0KcDtkX6jMcI07GvN+ORJFGN9OVoMnJ7urjgK2BwR64e7UYNB0ht6fkck6UiK9+bTw9uq/kt9mA+sjoiv9lIti/Napa/DeV4H66mQLRW9PM5A0ueBjohYTHGQ/1VSJ8UV++zha/HAVezrJyWdBGyl6OsZw9bgJki6juJuggmSuoCLgTEAEXElsITizopO4AXgzOFpafMq9PUU4K8lbQVeBGaP0IuTo4EPA/dLWpHKLgLeCNmd1yp9Hbbz6scPmJllaKQMy5iZWT843M3MMuRwNzPLkMPdzCxDDnczsyY1ejBcTd13S7pX0lZJp9Qs21Z6yNjiUvmxaZ0HJC1M3+Xpk8PdzKx5C4ATKtZ9guL25e/VWfZiRMxIr5MAJO1C8S302RHxVuBXbP9OT68c7mZmTar3YDhJB0n6saTlkv5T0ptT3TURsRJ4peLm9wVeioj/SvNLgQ82WsnhbmY2OOYBn4iItwGfAr5VYZ3dJHVIulvSyansKYrHFrSn+VPY8fENdY2Ib6iamY0k6WFi7wK+X3ry+NgKq74xItZJ+j3gdkn3R8SjkmYDl0saC/yE4tvpfXK4m5m13i7ApoiY0Z+VImJd+vmYpDspHiP8aET8HPgjAEnvBQ6u0gAzM2uh9OjfxyWdCr/704KH9bWOpL3TlTnpyZFHAw+m+Ynp51jgM8CVjdrgcDcza1J6MNzPgd+X1CXpLOCvgLMk3QesIv1FNUlvTw+POxX4tqRVaTNvATpS/TuAL0XEg2nZpyWtBlYC/xYRtzdskx8cZmaWH1+5m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYb+P+dHryTEzisqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data2['X4'], bins='auto')  # arguments are passed to np.histogram\n",
    "plt.title(\"X4 after Box cox transformation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbrklEQVR4nO3de7hcdX3v8fcn2bmQBMslWwQSshVpNeFUhA2FgpYj1AL1iD4FD8r9aFMDqDwHrYItIjat9TkFrFRoEBogEaFIMUUqReRaBd3BcAnRFrlGAtmI3CkY+J4/1m+TyeyZyczes/bM3r/P63nWs9flN2t995rLZ9ZvrZlRRGBmZvma1OkCzMyssxwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxBYNiSFpLe2aV03SfpYO9Y13kjqS/uyp9O1WHs4CGwTkvaT9ENJz0h6StJ/SNpzlOs8TtJtVfOWSvqr0VVbjlr1jnA9H5H0zRHedkG6D367av4Nkv4mjX9J0j2SNkg6Y7T1toOkWZIekvSRinlbSnpE0mFVbadK+pmktWNfqVVyENjrJL0BuAb4GrANsCPwReDlTtZVyzh5N3oIcO1IbhgRq4H/B1woSQCSPsrG+wTgfuDPge+OvtT2iIjngYXAVyX1ptlfAQYi4sqq5p8B1o9lfVZHRHjwQEQA9ANPb6bNnwJrgOeA+4Dd0/zPAb+omP/BNP/twH8DrwLPA09TvFD8BnglzfvX1HYH4NvAIPAg8MmK7Z4BXAksA54FPlajtqXA+cD1qY6bgXkVywN4axr/LeCStK2Hgb+geGM0rN46++Em4G+AHwPPAN8BtqlYPgl4ApgNXAyckubvmOo4IU2/FXgKUI1t9AA/BU4EtgOeBH6/RrtlwBmbud/2An6U9v864FxgatW++TjwX8CvgX8YqgmYTBFKTwIPpHoC6GmwvaXAZcD+wK+A7auWvzk9jg4G1nb6sZ/70PECPHTPALwhPWkvTk/QrauWHw78EtgTUHoRm1exbIf0Avi/gReGnvzAccBtVetaCvxVxfQkYCVwOjAVeEt60fmjtPwMivD4QGq7RY36l1IEwLuBacBXK7fLpkFwSXrx3hLoA/4T+Gi9emts66a0L3YFZlIE2LKK5XsDP0rj/4eNYfcRisC8vGLZdxps550UQXE9cE6dNs0EwR6ppp70/64BTq7aN9cAWwE7UQTkQWnZx4GfAXMpjhRvZPNBsDVF4DwJHF9j+TXABymCwkHQ4WFcdg1JukjSekn3NtH23ZLuTP2o1X2Ur0palYYVFfPfk25zr6SLx0k3xKhFxLPAfhRP8guAQUkrJG2XmnwM+EpE/CQK90fEw+m2/xwRj0XEaxFxOcU7y71a2PyeQG9EnBkRr0TEA6mGIyra/Cgirk7beKnOer4bEbdExMvA54F9JM2tbCBpMkVYnRoRz0XEQ8DfAUe3UC/ApRFxb0S8APwl8KG0boA/ZmO30M3AuyRNogiprwD7pmV/kJbXFBE/BS6kOFI5rcX6KtezMiJuj4gN6f/9x7TtSl+OiKcj4hGKF/vd0vwPUYTQoxHxFMWR0Oa292tgNTADuKpymaQPUoTIv4z0/7H2GpdBQPHO76Am2z5C8Q6v1km7lyJitzS8HyA9WS8GjoiIXSm6DY4ddcXjRESsiYjjImIOxbvdHYBz0uK5FO9mh5F0TArUpyU9nW47u4VNzwN2GLp9WsdpFF0iQx5tYj2vt4miv/qp9D9Umk1x1PFwxbyHKbptWlFZz8PAFDb+z6+fH4iIX1B0M+0GvIvi3fBjkn6HzQRBshp4KCJebLG+10n6bUnXSHpc0rPAXzP8/nm8YvxFYFYa34Hh/+vmtncUxZHH94G/rZg/kyIIP9Hq/2DlGZdBEBG3UDzBXydpZ0nfk7RS0q2S3pbaPhQRdwOvNbn6bYGXI+I/0/T1wJ+0q/bxJCJ+RhG6u6ZZjwI7V7eTNI/i3ftJwLYRsRVwL0X3ERRHGMNWXzX9KPBgRGxVMWwZEYc0uE0tr7/7lzSLoivjsao2T1J0M82rmLcTRVdPs9vZZFvp9r8BnpT0JmB74M6K5TcDh1H0y/8yTR9D0YWyqsntjcZ5FN07u0TEGyhCVo1v8rp1DP9f65L0RuBsivNJf0ZxpPTutHgXioC4VdLjFEcL26eA6muyHmuzcRkEdSwBPhERewCfBr7exG2mSxqQdLukD6R5TwJTJPWn6cPY9EkwYUl6m6RTJM1J03OBDwO3pybfAD4taQ8V3ppCYCbFi+dgut3xbAwPKE6azpE0tWreWyqmfww8K+mzkraQNFnSriO4dPWQdAnsVOBLwB0RscmRRES8ClwBLE6XNs4D/i9FX3u9ems5StJ8STOAM4Er07oPAb4XEZWBcjNFUN6Spm+ieFd8W7pNyyRNkTSd4nncI2l6RddUtS0pTrI/n94kLWphU1cAn5Q0R9LWFBcGNHIucHVE3BgR6yiubLpA0jSKNwhzKY6OdqPobnwijTdzxGclmBBBkN75/T7wz5JWUfR/bt/ETXeKiH6KE3jnSNo5PXmPAM6W9GOKk48bSiq92zwH/B5wh6QXKALgXuAUKM4DAIsputmeA66muFLmPoo+9h9RPKn/B/AfFev9AUX3xuOSnkzzLgTmp26gq9OL4f+ieEF4kCKQv0FxdU8rvgl8geKIcQ/gyDrtPkFxQvsB4LZ0u4sa1FvLpRRHTI8D04FPpvm1Lhu9meLFeCgIbqPoP7+FkbsAeIkirD+fxuud5/g0xeP8uXS7y1vcznXAXRRHOVfVa5jeUO1HcWkoABHxDWAtcHo6R/H40EBxP72WpkcUiDZ62vRNy/iRDiOviYhd0/XvP4+Iui/+kpam9tXXMjdcLum9FJcqfqhNpVtJ0n24NiL+ooM19FAEw84R8Uyn6jBrxYQ4IkhXuzwo6XCA1G3xjka3kbR1OlRF0myKqzjuS9NvTH+nAZ+luDbdrBnbAH/pELDxZFwGgaTLKLohfkfS2vSJyyOBj0q6i+Kw/tDUdk8VH2E/HPhHSavTat4ODKT2N1JcOndfWvYZSWuAuymu//7BmP1zNq5FxPqIOK/TdZi1Ytx2DZmZWXuMyyMCMzNrn3H3idnZs2dHX19fp8swMxtXVq5c+WRE9NZaNu6CoK+vj4GBgU6XYWY2rkiq+4lwdw2ZmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWUujyBYvhymTwep+WHatOJ29dY3a9am7WfNqt9+yIIFrdXQjmHrrWHSpPas68ADN90HPT2tr2O0tZxwQvseF2YGjMPLR1u2fDkcfTS0+gnqV16Bo44qxo+s+ALL5cvh2GPh1aovSnzhBTjmmOHthyxYAPfdN3x+2Z5+un3ruuGGIgyOP37jvmnVaD/Jfl769oavN/Mt42bWjHH3FRP9/f3R0ucI+vrg4c3+oFJ98+bBQw81v77q9kPU7G+AjAPz5o1un47W5MmwIZdvBjdrD0kr09fuDzPxu4YeeaS9t9/c+ka7vfGg0/9j9dGYmY3KxA+CnRr+ql7rt9/c+ka7vfGg0//j5Ho/wmVmIzHxg2Dx4tF1yyxePHy63gvRpEnD2w+ZP3/kNXSTAw6o/z+OlYULO7t9swlm4gfBkUfCpZcWVwG1YupUWLZs+InfI4+Eiy+GmTM3nT9zJlxySe0TxQCrV3cmDLbaqn3nJw44AL7//eJ/XLZsZO/MR1vLokU+UWzWZqWdLE4/fH4J8CbgNWBJRHy1qs3+wHcofqMW4KqIOLPRels+WWxmZg1PFpd5+egG4JSIuFPSlsBKSddX/ArYkFsj4n0l1mFmZg2U1jUUEesi4s40/hywBtixrO2ZmdnIjMk5Akl9wDuBO2os3kfSXZL+TdKCOrdfKGlA0sDg4GCJlZqZ5af0IJA0C/g2cHJEPFu1+E5gXkS8A/gacHWtdUTEkojoj4j+3t6aP7BjZmYjVGoQSJpCEQLLI+Kq6uUR8WxEPJ/GrwWmSJpdZk1mZrap0oJAkoALgTURcVadNm9K7ZC0V6rnV2XVZGZmw5V51dC+wNHAPZJWpXmnATsBRMT5wGHAIkkbgJeAI2K8ffmRmdk4V1oQRMRtQMNPD0XEucC5ZdVgZmabN/E/WWxmZg05CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8yVFgSS5kq6UdIaSaslfapGG0n6e0n3S7pb0u5l1WNmZrX1lLjuDcApEXGnpC2BlZKuj4j7KtocDOySht8Dzkt/zcxsjJR2RBAR6yLizjT+HLAG2LGq2aHAJVG4HdhK0vZl1WRmZsONyTkCSX3AO4E7qhbtCDxaMb2W4WGBpIWSBiQNDA4OllWmmVmWSg8CSbOAbwMnR8Sz1Ytr3CSGzYhYEhH9EdHf29tbRplmZtkqNQgkTaEIgeURcVWNJmuBuRXTc4DHyqzJzMw2VeZVQwIuBNZExFl1mq0AjklXD+0NPBMR68qqyczMhivzqqF9gaOBeyStSvNOA3YCiIjzgWuBQ4D7gReB40usx8zMaigtCCLiNmqfA6hsE8CJZdVgZmab508Wm5llzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllrrQgkHSRpPWS7q2zfH9Jz0halYbTy6rFzMzq6ylx3UuBc4FLGrS5NSLeV2INZma2GaUdEUTELcBTZa3fzMzao9PnCPaRdJekf5O0oF4jSQslDUgaGBwcHMv6zMwmvE4GwZ3AvIh4B/A14Op6DSNiSUT0R0R/b2/vmBVoZpaDjgVBRDwbEc+n8WuBKZJmd6oeM7NcdSwIJL1JktL4XqmWX3WqHjOzXJV21ZCky4D9gdmS1gJfAKYARMT5wGHAIkkbgJeAIyIiyqrHzMxqKy0IIuLDm1l+LsXlpWZm1kGdvmrIzMw6zEFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpa5EQWBpOPbXYiZmXXGSI8IvtjWKszMrGPqfqBM0t31FgHblVOOmZmNtUafLN4O+CPg11XzBfywtIrMzGxMNQqCa4BZEbGqeoGkm0qryMzMxlSjcwQ7AL+stSAiPlJOOWZmNtYaBcE/AddJ+rykKWNVkJmZja26XUMRcYWk7wKnAwOSLgVeq1h+1hjUZ2ZmJdvc11D/BngBmAZsSUUQmJnZxNDo8tGDgLOAFcDuEfHimFVlZmZjptERweeBwyNi9VgVY2ZmY6/ROYJ3jWUhZmbWGf7SOTOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8tcaUEg6SJJ6yXdW2e5JP29pPsl3S1p97JqMTOz+so8IlgKHNRg+cHALmlYCJxXYi1mZlZHaUEQEbcATzVocihwSRRuB7aStH1Z9ZiZWW2dPEewI/BoxfTaNM/MzMZQJ4NANeZFzYbSQkkDkgYGBwdLLsvMLC+dDIK1wNyK6TnAY7UaRsSSiOiPiP7e3t4xKc7MLBedDIIVwDHp6qG9gWciYl0H6zEzy1Kj3yweFUmXAfsDsyWtBb4ATAGIiPOBa4FDgPuBF4Hjy6rFzMzqKy0IIuLDm1kewIllbd/MzJrjTxabmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmlrlSg0DSQZJ+Lul+SZ+rsfw4SYOSVqXhY2XWY2Zmw/WUtWJJk4F/AP4QWAv8RNKKiLivqunlEXFSWXWYmVljZR4R7AXcHxEPRMQrwLeAQ0vcnpmZjUCZQbAj8GjF9No0r9qfSLpb0pWS5tZakaSFkgYkDQwODpZRq5lZtsoMAtWYF1XT/wr0RcTvAt8HLq61oohYEhH9EdHf29vb5jLNzPJWZhCsBSrf4c8BHqtsEBG/ioiX0+QFwB4l1mNmZjWUGQQ/AXaR9GZJU4EjgBWVDSRtXzH5fmBNifWYmVkNpV01FBEbJJ0EXAdMBi6KiNWSzgQGImIF8ElJ7wc2AE8Bx5VVj5mZ1aaI6m777tbf3x8DAwOdLsPMbFyRtDIi+mst8yeLzcwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkIzMwyV2oQSDpI0s8l3S/pczWWT5N0eVp+h6S+MuuxEViwAKSNw5QpMGkS9PXB8uWbtl2+HGbP3th21qxiulb75cuLefXWVWt9kyYVf3t6ir+Vt6tuO3t2Ma/Z7fT1bfp/Dm3vhBOKoXpZJ4ah+pcv37gv2jVMm1b7/qy1X9r5v0Cxf4fu054eOPDA8rZbeb9Wq6yjmXWUVd8WWzRePvTYbqeIKGUAJgO/AN4CTAXuAuZXtTkBOD+NHwFcvrn17rHHHmFjZP78CKg/zJgRsWxZ0XbZsoipU5trv2xZMV5vXc2ub+h2ixbVbjtp0vD5tbZTXUs3D83sk9EMlfdn2ftlxoyIAw7o3L5ctGjj42DRos7ft60MU6Zs+jhuAjAQUef1ut6C0Q7APsB1FdOnAqdWtbkO2CeN9wBPAmq0XgfBGGrmATlvXtF23rzm29drO7SuVtYHEZMnt/YkGul2chhavT/H8zB58sbHQauPoW4YKh/HTT2d6wdBmV1DOwKPVkyvTfNqtomIDcAzwLbVK5K0UNKApIHBwcGSyrUReeSRTf82075e28r5za4P4NVXm287mu3koNX7czyrfNy0+hjqBm28j8oMAtWYFyNoQ0QsiYj+iOjv7e1tS3HWJjvttOnfZtrXa1s5v9n1AUye3Hzb0WwnB63en+NZ5eOm1cdQN2jjfVRmEKwF5lZMzwEeq9dGUg/wW8BTJdZkrZg/v/HyGTNg8eJifPFimDq1ufaLFxfj9dbV7PqGbrdwYe22kyYNn19rO9W1dLNm9sloVN6fZe+XGTPggAPK3UYjCxfWHh8PpkzZ9HE8WvX6jEY7UPT5PwC8mY0nixdUtTmRTU8WX7G59focwRirPmHc0xMhFf2T1Serli2L2HbbjW1nziyma7VftqyYV29dtdYnbezbHeojrTy5Wdl22203nphuZju1+sSl4iRit5xIHKp/2bKN+6Jdw9Spte/Pss4VVN4XixZtvE8nTy5OIJd5jmLofq1WWUcz6yirvunTGy8femy3iAbnCFQsL4ekQ4BzKK4guigiFks6MxW0QtJ04FLgnRRHAkdExAON1tnf3x8DAwOl1WxmNhFJWhkR/bWW9ZS54Yi4Fri2at7pFeP/DRxeZg1mZtaYP1lsZpY5B4GZWeYcBGZmmXMQmJllrtSrhsogaRB4uM7i2RRfU9HNXGN7uMb2cI3tMR5qnBcRNT+RO+6CoBFJA/Uuj+oWrrE9XGN7uMb2GA81NuKuITOzzDkIzMwyN9GCYEmnC2iCa2wP19gerrE9xkONdU2ocwRmZta6iXZEYGZmLXIQmJllbsIEgaSHJN0jaZWkrvh6UkkXSVov6d6KedtIul7Sf6W/W3dhjWdI+mXal6vSt8h2ssa5km6UtEbSakmfSvO7Zl82qLFr9qWk6ZJ+LOmuVOMX0/w3S7oj7cfLJZX8owcjqnGppAcr9uNunaqxotbJkn4q6Zo03TX7sVUTJgiS/xkRu3XR9bxLgYOq5n0OuCEidgFuSNOdtJThNQKcnfblbulbZDtpA3BKRLwd2Bs4UdJ8umtf1qsRumdfvgy8JyLeAewGHCRpb+BvU427AL8GPtqFNQJ8pmI/rupcia/7FLCmYrqb9mNLJloQdJWIuIXhv7h2KHBxGr8Y+MCYFlWlTo1dJSLWRcSdafw5iiffjnTRvmxQY9dIv0/yfJqckoYA3gNcmeZ3ej/Wq7GrSJoD/DHwjTQtumg/tmoiBUEA/y5ppaRu/t257SJiHRQvHsAbO1xPPSdJujt1HXW0+6qSpD6KHzK6gy7dl1U1Qhfty9SdsQpYD1wP/AJ4OiI2pCZr6XCAVdcYEUP7cXHaj2dLmtbBEqH4wa0/B15L09vSZfuxFRMpCPaNiN2BgykOy9/d6YLGsfOAnSkOzdcBf9fZcgqSZgHfBk6OiGc7XU8tNWrsqn0ZEa9GxG4UvyG+F/D2Ws3GtqqqjVfVKGlX4FTgbcCewDbAZztVn6T3AesjYmXl7BpNu+5Ipp4JEwQR8Vj6ux74F4oHeTd6QtL2AOnv+g7XM0xEPJGejK8BF9AF+1LSFIoX2OURcVWa3VX7slaN3bgvASLiaeAmivMZW0ka+rXCOcBjnaqrUkWNB6Wut4iIl4F/orP7cV/g/ZIeAr5F0SV0Dl26H5sxIYJA0kxJWw6NA+8F7m18q45ZARybxo8FvtPBWmoaenFNPkiH92Xqf70QWBMRZ1Us6pp9Wa/GbtqXknolbZXGtwAOpDiXcSNwWGrW6f1Yq8afVQS+KPreO7YfI+LUiJgTEX3AEcAPIuJIumg/tmpCfLJY0lsojgKg+B3mb0bE4g6WBICky4D9Kb6i9gngC8DVwBXATsAjwOER0bGTtXVq3J+iKyOAh4A/G+qL7wRJ+wG3AvewsU/2NIo++K7Ylw1q/DBdsi8l/S7FSczJFG8Cr4iIM9Pz51sUXS4/BY5K77y7qcYfAL0UXTCrgI9XnFTuGEn7A5+OiPd1035s1YQIAjMzG7kJ0TVkZmYj5yAwM8ucg8DMLHMOAjOzzDkIzMwy5yAwa1H6ptEHJW2TprdO0/PS9BvSN46e29lKzZrjIDBrUUQ8SvHVEV9Os74MLImIh9P0l4CbO1Gb2Ug4CMxG5mxgb0knA/uRvkNI0h7AdsC/d7A2s5b0bL6JmVWLiN9I+gzwPeC9EfGKpEkUgXA0cEBHCzRrgY8IzEbuYIpvFN01TZ8AXJu6jszGDR8RmI1A+qnEP6T49s7bJH0L2Ad4l6QTgFnAVEnPR0Snf4XOrCF/15BZi9I3YP4QOD0irpf0CWDv9A2UQ22OA/oj4qQOlWnWNHcNmbXuT4FHIuL6NP114G2S/qCDNZmNmI8IzMwy5yMCM7PMOQjMzDLnIDAzy5yDwMwscw4CM7PMOQjMzDLnIDAzy9z/B/bS85XgKjkVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data2['Y1'], data2['X4'], 'ro')  # arguments are passed to np.histogram\n",
    "plt.title(\"Scatter plot b/w Y1 and X4\")\n",
    "plt.xlabel(\"X4\")\n",
    "plt.ylabel(\"Y1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     Y1   R-squared:                      -4.019\n",
      "Model:                            OLS   Adj. R-squared:                 -4.019\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Wed, 26 Feb 2020   Prob (F-statistic):                nan\n",
      "Time:                        20:29:46   Log-Likelihood:                -3484.0\n",
      "No. Observations:                 768   AIC:                             6970.\n",
      "Df Residuals:                     767   BIC:                             6975.\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept  3.973e-318          0        inf      0.000   3.97e-318   3.97e-318\n",
      "X1                  0          0        nan        nan           0           0\n",
      "X2         2.675e-315          0        inf      0.000   2.68e-315   2.68e-315\n",
      "X3         1.269e-315          0        inf      0.000   1.27e-315   1.27e-315\n",
      "X4         5.154e-159          0        inf      0.000   5.15e-159   5.15e-159\n",
      "X5          2.09e-317          0        inf      0.000   2.09e-317   2.09e-317\n",
      "X6         1.394e-317          0        inf      0.000   1.39e-317   1.39e-317\n",
      "X7         9.363e-319          0        inf      0.000   9.36e-319   9.36e-319\n",
      "X8         1.121e-317          0        inf      0.000   1.12e-317   1.12e-317\n",
      "==============================================================================\n",
      "Omnibus:                     3878.877   Durbin-Watson:                   0.096\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               94.839\n",
      "Skew:                           0.150   Prob(JB):                     2.55e-21\n",
      "Kurtosis:                       1.305   Cond. No.                          inf\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 6.82e-31. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     Y1   R-squared:                       0.916\n",
      "Model:                            OLS   Adj. R-squared:                  0.915\n",
      "Method:                 Least Squares   F-statistic:                     1187.\n",
      "Date:                Wed, 26 Feb 2020   Prob (F-statistic):               0.00\n",
      "Time:                        20:29:47   Log-Likelihood:                -1912.5\n",
      "No. Observations:                 768   AIC:                             3841.\n",
      "Df Residuals:                     760   BIC:                             3878.\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     84.0145     19.034      4.414      0.000      46.650     121.379\n",
      "X1           -64.7740     10.289     -6.295      0.000     -84.973     -44.575\n",
      "X2            -0.0873      0.017     -5.112      0.000      -0.121      -0.054\n",
      "X3             0.0608      0.007      9.148      0.000       0.048       0.074\n",
      "X5             4.1699      0.338     12.337      0.000       3.506       4.833\n",
      "X6            -0.0233      0.095     -0.246      0.805      -0.209       0.163\n",
      "X7            19.9327      0.814     24.488      0.000      18.335      21.531\n",
      "X8             0.2038      0.070      2.914      0.004       0.067       0.341\n",
      "==============================================================================\n",
      "Omnibus:                       18.648   Durbin-Watson:                   0.654\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               37.708\n",
      "Skew:                           0.044   Prob(JB):                     6.48e-09\n",
      "Kurtosis:                       4.082   Cond. No.                     1.53e+05\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, 1.53e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     Y1   R-squared:                       0.916\n",
      "Model:                            OLS   Adj. R-squared:                  0.916\n",
      "Method:                 Least Squares   F-statistic:                     1387.\n",
      "Date:                Wed, 26 Feb 2020   Prob (F-statistic):               0.00\n",
      "Time:                        20:29:47   Log-Likelihood:                -1912.5\n",
      "No. Observations:                 768   AIC:                             3839.\n",
      "Df Residuals:                     761   BIC:                             3871.\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     83.9329     19.019      4.413      0.000      46.597     121.269\n",
      "X1           -64.7740     10.283     -6.299      0.000     -84.961     -44.587\n",
      "X2            -0.0873      0.017     -5.115      0.000      -0.121      -0.054\n",
      "X3             0.0608      0.007      9.153      0.000       0.048       0.074\n",
      "X5             4.1699      0.338     12.345      0.000       3.507       4.833\n",
      "X7            19.9327      0.813     24.503      0.000      18.336      21.530\n",
      "X8             0.2038      0.070      2.916      0.004       0.067       0.341\n",
      "==============================================================================\n",
      "Omnibus:                       18.654   Durbin-Watson:                   0.654\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               37.740\n",
      "Skew:                           0.044   Prob(JB):                     6.38e-09\n",
      "Kurtosis:                       4.082   Cond. No.                     1.53e+05\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, 1.53e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     Y1   R-squared:                       0.915\n",
      "Model:                            OLS   Adj. R-squared:                  0.915\n",
      "Method:                 Least Squares   F-statistic:                     1646.\n",
      "Date:                Wed, 26 Feb 2020   Prob (F-statistic):               0.00\n",
      "Time:                        20:29:47   Log-Likelihood:                -1916.8\n",
      "No. Observations:                 768   AIC:                             3846.\n",
      "Df Residuals:                     762   BIC:                             3873.\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     84.3876     19.112      4.415      0.000      46.870     121.906\n",
      "X1           -64.7740     10.334     -6.268      0.000     -85.060     -44.488\n",
      "X2            -0.0873      0.017     -5.090      0.000      -0.121      -0.054\n",
      "X3             0.0608      0.007      9.109      0.000       0.048       0.074\n",
      "X5             4.1699      0.339     12.285      0.000       3.504       4.836\n",
      "X7            20.4379      0.799     25.588      0.000      18.870      22.006\n",
      "==============================================================================\n",
      "Omnibus:                       20.756   Durbin-Watson:                   0.646\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               44.999\n",
      "Skew:                          -0.002   Prob(JB):                     1.69e-10\n",
      "Kurtosis:                       4.186   Cond. No.                     1.52e+05\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, 1.52e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "model1 = sm.ols(formula=\"Y1~X1+X2+X3+X4+X5+X6+X7+X8\", data=data2).fit()\n",
    "model2 = sm.ols(formula=\"Y1~X1+X2+X3+X5+X6+X7+X8\", data=data2).fit()\n",
    "model3 = sm.ols(formula=\"Y1~X1+X2+X3+X5+X7+X8\", data=data2).fit()\n",
    "model4 = sm.ols(formula=\"Y1~X1+X2+X3+X5+X7\", data=data2).fit()\n",
    "\n",
    "#sales~TV+radio+newspaper\n",
    "print(model1.summary())\n",
    "print(model2.summary())\n",
    "print(model3.summary())\n",
    "print(model4.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  Intercept    84.014521\n",
      "X1          -64.773991\n",
      "X2           -0.087290\n",
      "X3            0.060813\n",
      "X5            4.169939\n",
      "X6           -0.023328\n",
      "X7           19.932680\n",
      "X8            0.203772\n",
      "dtype: float64\n",
      "R2:  0.9162020821915835\n",
      "Standard errors:  Intercept    19.033607\n",
      "X1           10.289445\n",
      "X2            0.017075\n",
      "X3            0.006648\n",
      "X5            0.337990\n",
      "X6            0.094705\n",
      "X7            0.813986\n",
      "X8            0.069918\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Parameters: ', model2.params)\n",
    "print('R2: ', model2.rsquared)\n",
    "print('Standard errors: ', model2.bse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.06690598 23.06690598 23.06690598 23.06690598 25.46152869 25.46152869\n",
      " 25.46152869 25.46152869 24.42394979 24.42394979 24.42394979 24.42394979\n",
      " 26.36622457 26.36622457 26.36622457 26.36622457 27.66075942 27.66075942\n",
      " 27.66075942 27.66075942 31.93514795 31.93514795 31.93514795 31.93514795\n",
      "  6.06774237  6.06774237  6.06774237  6.06774237  7.36227722  7.36227722\n",
      "  7.36227722  7.36227722  8.00907217  8.00907217  8.00907217  8.00907217\n",
      "  9.30360702  9.30360702  9.30360702  9.30360702  9.95040196  9.95040196\n",
      "  9.95040196  9.95040196 10.59719691 10.59719691 10.59719691 10.59719691\n",
      " 25.11069593 25.11069593 25.11069593 25.11069593 27.50531864 27.50531864\n",
      " 27.50531864 27.50531864 26.46773974 26.46773974 26.46773974 26.46773974\n",
      " 28.41001451 28.41001451 28.41001451 28.41001451 29.70454937 29.70454937\n",
      " 29.70454937 29.70454937 33.97893789 33.97893789 33.97893789 33.97893789\n",
      "  8.11153231  8.11153231  8.11153231  8.11153231  9.40606717  9.40606717\n",
      "  9.40606717  9.40606717 10.05286211 10.05286211 10.05286211 10.05286211\n",
      " 11.34739697 11.34739697 11.34739697 11.34739697 11.99419191 11.99419191\n",
      " 11.99419191 11.99419191 12.64098685 12.64098685 12.64098685 12.64098685\n",
      " 25.11069593 25.11069593 25.11069593 25.11069593 27.50531864 27.50531864\n",
      " 27.50531864 27.50531864 26.46773974 26.46773974 26.46773974 26.46773974\n",
      " 28.41001451 28.41001451 28.41001451 28.41001451 29.70454937 29.70454937\n",
      " 29.70454937 29.70454937 33.97893789 33.97893789 33.97893789 33.97893789\n",
      "  8.11153231  8.11153231  8.11153231  8.11153231  9.40606717  9.40606717\n",
      "  9.40606717  9.40606717 10.05286211 10.05286211 10.05286211 10.05286211\n",
      " 11.34739697 11.34739697 11.34739697 11.34739697 11.99419191 11.99419191\n",
      " 11.99419191 11.99419191 12.64098685 12.64098685 12.64098685 12.64098685\n",
      " 25.11069593 25.11069593 25.11069593 25.11069593 27.50531864 27.50531864\n",
      " 27.50531864 27.50531864 26.46773974 26.46773974 26.46773974 26.46773974\n",
      " 28.41001451 28.41001451 28.41001451 28.41001451 29.70454937 29.70454937\n",
      " 29.70454937 29.70454937 33.97893789 33.97893789 33.97893789 33.97893789\n",
      "  8.11153231  8.11153231  8.11153231  8.11153231  9.40606717  9.40606717\n",
      "  9.40606717  9.40606717 10.05286211 10.05286211 10.05286211 10.05286211\n",
      " 11.34739697 11.34739697 11.34739697 11.34739697 11.99419191 11.99419191\n",
      " 11.99419191 11.99419191 12.64098685 12.64098685 12.64098685 12.64098685\n",
      " 25.11069593 25.11069593 25.11069593 25.11069593 27.50531864 27.50531864\n",
      " 27.50531864 27.50531864 26.46773974 26.46773974 26.46773974 26.46773974\n",
      " 28.41001451 28.41001451 28.41001451 28.41001451 29.70454937 29.70454937\n",
      " 29.70454937 29.70454937 33.97893789 33.97893789 33.97893789 33.97893789\n",
      "  8.11153231  8.11153231  8.11153231  8.11153231  9.40606717  9.40606717\n",
      "  9.40606717  9.40606717 10.05286211 10.05286211 10.05286211 10.05286211\n",
      " 11.34739697 11.34739697 11.34739697 11.34739697 11.99419191 11.99419191\n",
      " 11.99419191 11.99419191 12.64098685 12.64098685 12.64098685 12.64098685\n",
      " 25.11069593 25.11069593 25.11069593 25.11069593 27.50531864 27.50531864\n",
      " 27.50531864 27.50531864 26.46773974 26.46773974 26.46773974 26.46773974\n",
      " 28.41001451 28.41001451 28.41001451 28.41001451 29.70454937 29.70454937\n",
      " 29.70454937 29.70454937 33.97893789 33.97893789 33.97893789 33.97893789\n",
      "  8.11153231  8.11153231  8.11153231  8.11153231  9.40606717  9.40606717\n",
      "  9.40606717  9.40606717 10.05286211 10.05286211 10.05286211 10.05286211\n",
      " 11.34739697 11.34739697 11.34739697 11.34739697 11.99419191 11.99419191\n",
      " 11.99419191 11.99419191 12.64098685 12.64098685 12.64098685 12.64098685\n",
      " 28.17638084 28.17638084 28.17638084 28.17638084 30.57100355 30.57100355\n",
      " 30.57100355 30.57100355 29.53342466 29.53342466 29.53342466 29.53342466\n",
      " 31.47569943 31.47569943 31.47569943 31.47569943 32.77023428 32.77023428\n",
      " 32.77023428 32.77023428 37.04462281 37.04462281 37.04462281 37.04462281\n",
      " 11.17721723 11.17721723 11.17721723 11.17721723 12.47175209 12.47175209\n",
      " 12.47175209 12.47175209 13.11854703 13.11854703 13.11854703 13.11854703\n",
      " 14.41308188 14.41308188 14.41308188 14.41308188 15.05987683 15.05987683\n",
      " 15.05987683 15.05987683 15.70667177 15.70667177 15.70667177 15.70667177\n",
      " 28.17638084 28.17638084 28.17638084 28.17638084 30.57100355 30.57100355\n",
      " 30.57100355 30.57100355 29.53342466 29.53342466 29.53342466 29.53342466\n",
      " 31.47569943 31.47569943 31.47569943 31.47569943 32.77023428 32.77023428\n",
      " 32.77023428 32.77023428 37.04462281 37.04462281 37.04462281 37.04462281\n",
      " 11.17721723 11.17721723 11.17721723 11.17721723 12.47175209 12.47175209\n",
      " 12.47175209 12.47175209 13.11854703 13.11854703 13.11854703 13.11854703\n",
      " 14.41308188 14.41308188 14.41308188 14.41308188 15.05987683 15.05987683\n",
      " 15.05987683 15.05987683 15.70667177 15.70667177 15.70667177 15.70667177\n",
      " 28.17638084 28.17638084 28.17638084 28.17638084 30.57100355 30.57100355\n",
      " 30.57100355 30.57100355 29.53342466 29.53342466 29.53342466 29.53342466\n",
      " 31.47569943 31.47569943 31.47569943 31.47569943 32.77023428 32.77023428\n",
      " 32.77023428 32.77023428 37.04462281 37.04462281 37.04462281 37.04462281\n",
      " 11.17721723 11.17721723 11.17721723 11.17721723 12.47175209 12.47175209\n",
      " 12.47175209 12.47175209 13.11854703 13.11854703 13.11854703 13.11854703\n",
      " 14.41308188 14.41308188 14.41308188 14.41308188 15.05987683 15.05987683\n",
      " 15.05987683 15.05987683 15.70667177 15.70667177 15.70667177 15.70667177\n",
      " 28.17638084 28.17638084 28.17638084 28.17638084 30.57100355 30.57100355\n",
      " 30.57100355 30.57100355 29.53342466 29.53342466 29.53342466 29.53342466\n",
      " 31.47569943 31.47569943 31.47569943 31.47569943 32.77023428 32.77023428\n",
      " 32.77023428 32.77023428 37.04462281 37.04462281 37.04462281 37.04462281\n",
      " 11.17721723 11.17721723 11.17721723 11.17721723 12.47175209 12.47175209\n",
      " 12.47175209 12.47175209 13.11854703 13.11854703 13.11854703 13.11854703\n",
      " 14.41308188 14.41308188 14.41308188 14.41308188 15.05987683 15.05987683\n",
      " 15.05987683 15.05987683 15.70667177 15.70667177 15.70667177 15.70667177\n",
      " 28.17638084 28.17638084 28.17638084 28.17638084 30.57100355 30.57100355\n",
      " 30.57100355 30.57100355 29.53342466 29.53342466 29.53342466 29.53342466\n",
      " 31.47569943 31.47569943 31.47569943 31.47569943 32.77023428 32.77023428\n",
      " 32.77023428 32.77023428 37.04462281 37.04462281 37.04462281 37.04462281\n",
      " 11.17721723 11.17721723 11.17721723 11.17721723 12.47175209 12.47175209\n",
      " 12.47175209 12.47175209 13.11854703 13.11854703 13.11854703 13.11854703\n",
      " 14.41308188 14.41308188 14.41308188 14.41308188 15.05987683 15.05987683\n",
      " 15.05987683 15.05987683 15.70667177 15.70667177 15.70667177 15.70667177\n",
      " 31.24206576 31.24206576 31.24206576 31.24206576 33.63668847 33.63668847\n",
      " 33.63668847 33.63668847 32.59910957 32.59910957 32.59910957 32.59910957\n",
      " 34.54138435 34.54138435 34.54138435 34.54138435 35.8359192  35.8359192\n",
      " 35.8359192  35.8359192  40.11030773 40.11030773 40.11030773 40.11030773\n",
      " 14.24290215 14.24290215 14.24290215 14.24290215 15.537437   15.537437\n",
      " 15.537437   15.537437   16.18423195 16.18423195 16.18423195 16.18423195\n",
      " 17.4787668  17.4787668  17.4787668  17.4787668  18.12556174 18.12556174\n",
      " 18.12556174 18.12556174 18.77235669 18.77235669 18.77235669 18.77235669\n",
      " 31.24206576 31.24206576 31.24206576 31.24206576 33.63668847 33.63668847\n",
      " 33.63668847 33.63668847 32.59910957 32.59910957 32.59910957 32.59910957\n",
      " 34.54138435 34.54138435 34.54138435 34.54138435 35.8359192  35.8359192\n",
      " 35.8359192  35.8359192  40.11030773 40.11030773 40.11030773 40.11030773\n",
      " 14.24290215 14.24290215 14.24290215 14.24290215 15.537437   15.537437\n",
      " 15.537437   15.537437   16.18423195 16.18423195 16.18423195 16.18423195\n",
      " 17.4787668  17.4787668  17.4787668  17.4787668  18.12556174 18.12556174\n",
      " 18.12556174 18.12556174 18.77235669 18.77235669 18.77235669 18.77235669\n",
      " 31.24206576 31.24206576 31.24206576 31.24206576 33.63668847 33.63668847\n",
      " 33.63668847 33.63668847 32.59910957 32.59910957 32.59910957 32.59910957\n",
      " 34.54138435 34.54138435 34.54138435 34.54138435 35.8359192  35.8359192\n",
      " 35.8359192  35.8359192  40.11030773 40.11030773 40.11030773 40.11030773\n",
      " 14.24290215 14.24290215 14.24290215 14.24290215 15.537437   15.537437\n",
      " 15.537437   15.537437   16.18423195 16.18423195 16.18423195 16.18423195\n",
      " 17.4787668  17.4787668  17.4787668  17.4787668  18.12556174 18.12556174\n",
      " 18.12556174 18.12556174 18.77235669 18.77235669 18.77235669 18.77235669\n",
      " 31.24206576 31.24206576 31.24206576 31.24206576 33.63668847 33.63668847\n",
      " 33.63668847 33.63668847 32.59910957 32.59910957 32.59910957 32.59910957\n",
      " 34.54138435 34.54138435 34.54138435 34.54138435 35.8359192  35.8359192\n",
      " 35.8359192  35.8359192  40.11030773 40.11030773 40.11030773 40.11030773\n",
      " 14.24290215 14.24290215 14.24290215 14.24290215 15.537437   15.537437\n",
      " 15.537437   15.537437   16.18423195 16.18423195 16.18423195 16.18423195\n",
      " 17.4787668  17.4787668  17.4787668  17.4787668  18.12556174 18.12556174\n",
      " 18.12556174 18.12556174 18.77235669 18.77235669 18.77235669 18.77235669\n",
      " 31.24206576 31.24206576 31.24206576 31.24206576 33.63668847 33.63668847\n",
      " 33.63668847 33.63668847 32.59910957 32.59910957 32.59910957 32.59910957\n",
      " 34.54138435 34.54138435 34.54138435 34.54138435 35.8359192  35.8359192\n",
      " 35.8359192  35.8359192  40.11030773 40.11030773 40.11030773 40.11030773\n",
      " 14.24290215 14.24290215 14.24290215 14.24290215 15.537437   15.537437\n",
      " 15.537437   15.537437   16.18423195 16.18423195 16.18423195 16.18423195\n",
      " 17.4787668  17.4787668  17.4787668  17.4787668  18.12556174 18.12556174\n",
      " 18.12556174 18.12556174 18.77235669 18.77235669 18.77235669 18.77235669]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model4.predict()\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       15.55\n",
      "1       15.55\n",
      "2       15.55\n",
      "3       15.55\n",
      "4       20.84\n",
      "        ...  \n",
      "1291      NaN\n",
      "1292      NaN\n",
      "1293      NaN\n",
      "1294      NaN\n",
      "1295      NaN\n",
      "Name: Y1, Length: 1296, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data2[\"Y1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array length 768 does not match index length 1296",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-c55e68be7076>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Create DataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    433\u001b[0m             )\n\u001b[0;32m    434\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 435\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    436\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    252\u001b[0m             \u001b[0marr\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    253\u001b[0m         ]\n\u001b[1;32m--> 254\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    376\u001b[0m                         \u001b[1;34mf\"length {len(index)}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m                     )\n\u001b[1;32m--> 378\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    379\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m                 \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mibase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: array length 768 does not match index length 1296"
     ]
    }
   ],
   "source": [
    "data = {'Name':y_pred, 'Age':data2[\"Y1\"]} \n",
    "  \n",
    "# Create DataFrame \n",
    "df = pd.DataFrame(data) \n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array length 768 does not match index length 1296",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-b5551b97385f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel4\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Actual'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdata2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Y1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Predicted'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#print(y_pred)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    433\u001b[0m             )\n\u001b[0;32m    434\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 435\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    436\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    252\u001b[0m             \u001b[0marr\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    253\u001b[0m         ]\n\u001b[1;32m--> 254\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    376\u001b[0m                         \u001b[1;34mf\"length {len(index)}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m                     )\n\u001b[1;32m--> 378\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    379\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m                 \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mibase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: array length 768 does not match index length 1296"
     ]
    }
   ],
   "source": [
    "y_pred = model4.predict()\n",
    "pd.DataFrame({'Actual': data2[\"Y1\"], 'Predicted': y_pred})  \n",
    "#print(y_pred)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}