From f05aff7a987ba243bbf4774bf430c0f48f26bbc9 Mon Sep 17 00:00:00 2001
From: Shalaka Saraogi <62024191+shalakasaraogi@users.noreply.github.com>
Date: Sun, 6 Dec 2020 15:32:09 +0530
Subject: [PATCH 1/4] Create README.md

---
 intern-basics/Stock Market Analysis and Prediction/README.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 intern-basics/Stock Market Analysis and Prediction/README.md

diff --git a/intern-basics/Stock Market Analysis and Prediction/README.md b/intern-basics/Stock Market Analysis and Prediction/README.md
new file mode 100644
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+

From ead4d49426adec998fd32fb35c9eb55376e87fd0 Mon Sep 17 00:00:00 2001
From: Shalaka Saraogi <62024191+shalakasaraogi@users.noreply.github.com>
Date: Sun, 6 Dec 2020 16:55:25 +0530
Subject: [PATCH 2/4] files added

---
 .../Stock_Market_Prediction- (ARIMA).ipynb    | 654 +++++++++++++
 ... (Random Forest & Linear Regression).ipynb | 869 ++++++++++++++++++
 2 files changed, 1523 insertions(+)
 create mode 100644 intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (ARIMA).ipynb
 create mode 100644 intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (Random Forest & Linear Regression).ipynb

diff --git a/intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (ARIMA).ipynb b/intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (ARIMA).ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stock Market Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Autoregressive Integrated Moving Average (ARIMA) algorithm for Time Series Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  import sys\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import math\n",
+    "\n",
+    "from pandas import datetime\n",
+    "from statsmodels.tsa.arima_model import ARIMA\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from pandas.plotting import autocorrelation_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "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>Code</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-18</td>\n",
+       "      <td>7.437910</td>\n",
+       "      <td>7.446311</td>\n",
+       "      <td>7.427869</td>\n",
+       "      <td>7.435041</td>\n",
+       "      <td>2538.135246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-19</td>\n",
+       "      <td>7.582241</td>\n",
+       "      <td>7.597414</td>\n",
+       "      <td>7.571207</td>\n",
+       "      <td>7.583621</td>\n",
+       "      <td>2778.203448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-22</td>\n",
+       "      <td>7.782296</td>\n",
+       "      <td>7.793385</td>\n",
+       "      <td>7.769650</td>\n",
+       "      <td>7.781907</td>\n",
+       "      <td>6414.482490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-23</td>\n",
+       "      <td>7.771465</td>\n",
+       "      <td>7.778030</td>\n",
+       "      <td>7.762879</td>\n",
+       "      <td>7.769444</td>\n",
+       "      <td>1944.929293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-24</td>\n",
+       "      <td>8.321127</td>\n",
+       "      <td>8.347465</td>\n",
+       "      <td>8.294648</td>\n",
+       "      <td>8.321972</td>\n",
+       "      <td>10216.726761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-26</td>\n",
+       "      <td>8.499172</td>\n",
+       "      <td>8.517881</td>\n",
+       "      <td>8.485762</td>\n",
+       "      <td>8.504636</td>\n",
+       "      <td>52045.635762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-29</td>\n",
+       "      <td>9.390217</td>\n",
+       "      <td>9.418071</td>\n",
+       "      <td>9.358560</td>\n",
+       "      <td>9.386957</td>\n",
+       "      <td>21635.451087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-30</td>\n",
+       "      <td>9.121958</td>\n",
+       "      <td>9.140356</td>\n",
+       "      <td>9.106677</td>\n",
+       "      <td>9.124332</td>\n",
+       "      <td>6166.682493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-31</td>\n",
+       "      <td>9.409392</td>\n",
+       "      <td>9.434392</td>\n",
+       "      <td>9.388122</td>\n",
+       "      <td>9.410359</td>\n",
+       "      <td>12830.422652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2015-01-01</td>\n",
+       "      <td>9.611657</td>\n",
+       "      <td>9.631461</td>\n",
+       "      <td>9.592275</td>\n",
+       "      <td>9.612921</td>\n",
+       "      <td>14989.721910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Code        Date      Open      High       Low     Close        Volume\n",
+       "0  3IINFOTECH  2014-12-18  7.437910  7.446311  7.427869  7.435041   2538.135246\n",
+       "1  3IINFOTECH  2014-12-19  7.582241  7.597414  7.571207  7.583621   2778.203448\n",
+       "2  3IINFOTECH  2014-12-22  7.782296  7.793385  7.769650  7.781907   6414.482490\n",
+       "3  3IINFOTECH  2014-12-23  7.771465  7.778030  7.762879  7.769444   1944.929293\n",
+       "4  3IINFOTECH  2014-12-24  8.321127  8.347465  8.294648  8.321972  10216.726761\n",
+       "5  3IINFOTECH  2014-12-26  8.499172  8.517881  8.485762  8.504636  52045.635762\n",
+       "6  3IINFOTECH  2014-12-29  9.390217  9.418071  9.358560  9.386957  21635.451087\n",
+       "7  3IINFOTECH  2014-12-30  9.121958  9.140356  9.106677  9.124332   6166.682493\n",
+       "8  3IINFOTECH  2014-12-31  9.409392  9.434392  9.388122  9.410359  12830.422652\n",
+       "9  3IINFOTECH  2015-01-01  9.611657  9.631461  9.592275  9.612921  14989.721910"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"C:/Users/DELL/Downloads/groupeddf.csv\")\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(79322, 7)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 79322 entries, 3IINFOTECH to TWL\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   Date    79322 non-null  object \n",
+      " 1   Open    79322 non-null  float64\n",
+      " 2   High    79322 non-null  float64\n",
+      " 3   Low     79322 non-null  float64\n",
+      " 4   Close   79322 non-null  float64\n",
+      " 5   Volume  79322 non-null  float64\n",
+      "dtypes: float64(5), object(1)\n",
+      "memory usage: 6.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_values = df[\"Code\"].unique()  \n",
+    "df=df.set_index(\"Code\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def arima_model(train, test):\n",
+    "    past = [x for x in train]\n",
+    "    predictions = list()\n",
+    "    for m in range(len(test)):\n",
+    "        model = ARIMA(past, order=(1,1 ,0))\n",
+    "        model_fit = model.fit(disp=0)\n",
+    "        output = model_fit.forecast()\n",
+    "        store = output[0]\n",
+    "        predictions.append(store[0])\n",
+    "        exp = test[m]\n",
+    "        past.append(exp)\n",
+    "        print('predicted value = %f, expected value = %f' % (store, exp))\n",
+    "    return predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parser(x):\n",
+    "    return datetime.strptime(x, '%Y-%m')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Company name : 3IINFOTECH\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Du9+fz/b/AHa6+xPh9RNAKnACeMrd385nvxHACIBatWpdNmHChLMJ+6wdPHiQihWLdw70/av3k/a7NE4cOkHLR1uS1DmpWI8XqZJoi3OF2iKT2iJTtLRF9+7dl7l7cs7ywjwHstTMRgOvhNeHUjSd6JZHWZ7ZzMxuA5KBblmKG7j7djNrDHxgZp+7+8ZcH+g+EhgJkJyc7CkpKWcd+NmYP38+xR5DCnQf0J2JgyaS9rs0uv93d7o82gWzvJo8OCXSFucItUUmtUWmaG+LwlzCuhdIA34K/AxYBfy4CI69DaifZb0esD3nRmbWC3gU6J/1YUZ33x5+3wTMB9oVQUznjUoXVOKOBXdwye2XMO9385h882SOHToWdFgich457RlI+I/2M+FXUVoCNDWzRsBXwBDg1qwbmFk74AVCl7q+yVJeFTjs7kfNLAm4iuwd7ALElYtj4LiB1LqkFrN/PZs96/cw5J0hVGlQJejQROQ8kO8ZiJlNCr9/Hr6NNtvrbA/s7ieA+4FZwGpgkrunmdnjZnbqrqr/BSoCb+S4XbcloUtrnwLzCPWBaITBPJgZnX7ZiVun3creTXsZmTySLxd9GXRYInIeKOgM5Gfh9+uL6+DuPgOYkaPs91mWe+Wz30fAxcUV1/mo6bVNGb54OK/3f51xPcZx7XPXctk9lwUdloicw/I9A3H3HeHF+9z9i6wv4L6SCU+KUlKLJIYvHk6jHo2YNmIa7/7oXU4cPRF0WCJyjipMJ/rVeZRdU9SBSMkoX7U8t06/lc4Pd2b5yOWMSxnHge0Hgg5LRM5BBfWB3GtmnwPNc/R/bAbOug9EghMTG0PP/+nJTW/cxNeff83Iy0by5YfqFxGRM1PQGchrQD9Cgyn2y/K6zN1vK4HYpJi1urEVwxcPp0zFMoxLGceS55dwugdLRUROKagPZJ+7b3H3W8L9HkcIPehX0cwalFiEUqxqtq7JPUvuoUnvJsy4bwZTh0/lxHfqFxGR0yvMYIr9zGw9sBlYAGwB3ivmuKQElUssxy3v3kLX33UldUwqL3V9if3b9gcdlohEucJ0oj8BXAmsc/dGQE9Cc6TLecRijO6Pd+fmKTeza80uRl42ki8WfhF0WCISxQqTQI67+24gxsxi3H0e0LaY45KAtBjYguGLh1Ouajle7vkyi/++WP0iIpKnwiSQdDOrCCwExpvZXwmNgCvnqRotazB88XCaXtuUmT+dyTt3vMPxI8eDDktEokxhEsgAQh3oDwIzgY2E7saS81i5KuW4ecrNpPwxhU9f/pSXOr9E+hfpQYclIlGkMFPaHnL3DHc/4e7j3P1v4Utacp6zGKPb77sxZOoQ9mzYw6jkUWyetznosEQkShT0IOEBM9uf5XUg63tJBinBat6vOcP/M5yEGgm8cvUrfPzsx+oXEZECnwOp5O6Vs7wqZX0vySAleEnNQ+NoNe/fnPd/8T5TbpvC8cPqFxEpzQrTB4KZdTazO8PLSeE5PKSUKVupLIMnD6bHkz34/PXPGXPVGPZu3ht0WCISkMI8SPgY8Bvg4XBRGeDV4gxKopfFGF0e6RKaX2TzXkYlj2Lj7FwzCYtIKVCYM5BBQH/gEHw/lWyl4gxKol/Ta5syYukIKl1QifF9x/Ph/36ofhGRUqYwCeSYh/4yOICZVSjekORcUe2iatz98d20/EFL5vx6Dm8OeVPzrouUIoVJIJPM7AUg0czuAeYAo4o3LDlXlKlYhhsn3kivp3uxavIqRncczZ6Ne4IOS0RKQGGeA/kLMBl4E2gO/N7d/14UBzezvma21sw2mNlDedSXNbOJ4frFZtYwS93D4fK1ZtanKOKRyJgZV/36Koa+N5T92/YzKnkUG2ZuCDosESlmBSYQM4s1sznuPtvdf+Xuv3T32UVxYDOLBZ4jNLthK+AWM2uVY7O7gb3ufhHwLPB0eN9WwBCgNdAX+Gf48yRATXo3YcTSEVRpUIXx147n33/6t/pFRM5jcQVVunuGmR02syruvq+Ij30FsMHdNwGY2QRCw6asyrLNAOAP4eXJwD/MzMLlE9z9KLDZzDaEP+/jgg64du1aUlJSivJnOGPp6ekkJiYGGkNxi6kcQ4saLfjgkQ+Y8MwE1jRfQ0ZcRq7tSkNbFJbaIpPaIlOgbeFQbU81Lth5Qb6bFJhAwr4DPjez2YTvxAJw95+eZXh1ga1Z1rcBHfLbxt1PmNk+oHq4/JMc+9bN6yBmNgIYARAfH096erDjOWVkZAQeQ0n4qPZHXBR3EW22t6HcgXJ80ugTDpU9lG2b0tIWhaG2yKS2yBREWyQcTeDCvRfSYE8DEo4ncDTuaL7bFiaBTA+/iprlUZbzekd+2xRm31Ch+0hgJEBycrIvXbr0TGIscvPnzw/8LKgkbZq7ick3T6bfjn7cMP4Gml3X7Pu60tYWBVFbZFJbZCqptjhx9ARr3l7DihdXsGnOJjC4qO9FtB/enmbXN+NPZf+U534FJpBwv8LVxTQH+jagfpb1esD2fLbZZmZxQBVgTyH3lSjQuGdjRiwdwcQbJvJ6v9dJ+UMKXX/bFYvJ638AESlJ36z8huWjl/PZK59xZPcRqlxYhZTHU2h7R1uq1K9y2v0L0wdSw8zKuHtR3+C/BGgaHhblK0Kd4rfm2GYqMIxQ38aNwAfu7mY2FXjNzJ4BLgCaAv8p4vikiCQ2TOSuD+9i2ohpzH9sPjuW72DQy4OCDkukVDp64ChpE9NY/uJyvlr8FTHxMbQc1JJ2w9vRuGfjM/rnrjCXsLYAH4b/aGftA3nmjCPPItyncT8wC4gFxrh7mpk9Dix196nAaOCVcCf5HkJJhvB2kwh1uJ8AfuLuuXtpJWrEl49n4MsDueDyC5j1i1mMumIUjR9pHHRYIqWCu/PV4q9Y/uJyVk5YyfFDx6nRqga9n+nNpbdfSkJSQkSfW5gEsj38iqGIhzBx9xnAjBxlv8+y/B1wUz77Pgk8WZTxSPEyMzr8tAO1LqnFG4PfYNmPllHjYA2Sf5ysS1oixeDwrsN8+sqnrBi9gm/TviW+QjxthrSh/fD21O1Ql9BNrZE7bQJx9z8CmFml0KofPKsjSqnXMKUhI5aNYNwN45jxkxmsemMV/V7sR7Um1YIOTeSc5yedTXM3seLFFax5ew0ZxzKo26Eu/Ub1o/XNrSlbqWyRHeu0CcTM2gCvANXC67uAH7p7WpFFIaVOlfpVuPjPF1NlYxXe/6/3+dcl/6LH//SgwwMddDYiEoF9W/eROjaVFaNXsO+LfZSvVp7k+5Jpf3d7arapWSzHLMwlrJHAL9x9HoCZpRAaC6tTsUQkpYaZ0X54ey7qexHTfjSNWT+fxao3VjFgzACqN6sedHgiUS/jWAbrpq1j+YvL2ThrI37SadyrMb2e7kWLgS2IK1uYP/GRK8ynVziVPADcfb5G5JWiVLleZW6ZdgufvfIZM382k39d+i9SHk+h4y86EhNbqDnPREqVXWt3sWL0Cj4d9ymHvjlEpbqV6PJoF9re2ZaqjaqWWByFSSCbzOx3hC5jAdwGbC6+kKQ0MjMu/eGlNL66MdPvnc6cX89h9eTVDHhpADVa1Qg6PJHAHTt0jFWTV7HixRV8uehLYuJiaNavGe2Ht6dJnyaB/LNVmARyF/BH4K3w+kLgzmKLSEq1SnUqcfOUm0mbmMaM+2fwQrsX6PZYNzr9qhOx8RovU0oXd2f7su2h229fW8nR/Uep3qw6vf7ci0t/eCkVa1UMNL7C3IW1Fzjbca9ECs3MaDOkDY16NGLG/TP44NEPWP1m6Gyk1iW1gg5PpNidzDjJZ69+xvInlrNww0LiysfR+qbWtBvejgadG5z17bdFpTB3Yc0GbnL39PB6VUIj4WoODilWFWpW4KZJN7HqzVXMuG8GIy8bSZdHu9DlkS7EltHZiJx/3J01b69h3m/n8e2qb6nQpALXPX8dbW5pQ7kq5YIOL5fCXMJKOpU8IHRGYmbFc0+YSB5a/aAVDbs1ZObPZ7LgjwtYM2UN/cf054LL8h9mWuRcs3neZuY+NJev/vMVSS2SGPzmYHZW3Uly9+SgQ8tXYXpdTppZg1MrZnYh+Yx8K1JcEpISuOHVGxjyzhAOfXuIFzu8yNxH5nLi6ImgQxM5K9uXbefVPq/yco+XObDjAP1H9+fez++l5Q0to+ZSVX4KcwbyKLDIzBaE17sSnl9DpKQ179+cBl0a8P4v3mfRnxax5u01DHhpAPU61As6NJEzsmvtLub9bh6r3lhF+erl6f1Mby6/93LiyhXvsxtFqTCd6DPNrD1wZbjoQXffVbxhieSvfNXyDHhpAK0Gt2LaiGmM6TSGK39xJd0f7058+figwxMp0P5t+1nw+AJWjFlBXLk4uv6+K53+qxNlKxfdECMlpbCprhOhM49TphVDLCJnpOk1Tbl35b3M/tVsPv7Lx6ybuo7+Y/rT4KoGp99ZpIQd3n2YRU8t4j9//w84XHH/FXR5pAsVap67z2UX5i6sp4DLgfHhop+Z2VXu/nCxRiZSCOWqlKPfyH60Htyad+95l5e6vESHn3agx5M9KFOhTNDhiXDs4DE++esnfPTnjzh28BiX3H4JKX9IIbHhuT/ve2HOQK4F2rr7SQAzGwesAJRAJGo07tWYez+/lzkPzWHxXxez7t119B/dn4YpDYMOTUqpjGMZLBu5jIVPLOTQ14doMbAF3Z/oTs3W589NrIV99j1rqjz9PIciAShTsQzX/uNahs0fBgbjuo9j+n3TOXrgaNChSSly6iHAf7T4B+898B5JLZK4++O7uXnKzedV8oDCnYH8CVhhZvMAI9QX8kixRiVyFhp2a8i9n93LB7/9gE/+7xPWz1hPv1H9aHJ1k6BDk/OYu7N++nrmPjKXbz7/htrtajN05lCa9G4S9bfjRqowd2G9bmbzCfWDGPAbd99Z3IGJnI34hHj6PNOHVje24p273uHV3q/Sbng7ev+ld1Q+0Svnti/+/QVzH5rL1o+2Uq1pNW6ceCOtbmx13s9tc9pLWGY21913uPtUd3/H3Xea2dyzOaiZVTOz2Wa2Pvyea/xhM2trZh+bWZqZfWZmN2epG2tmm80sNfxqezbxyPmrfqf6/GjFj+j0606kjknln63/yfoZ64MOS84TO1N38tp1rzG261jSt6Rz/QvXc1/afbQe3Pq8Tx5QQAIxs3JmVg1IMrOq4T/61cysIXC2Y0g8BMx196bA3PB6TocJzXzYGugL/J+ZZe2L+ZW7tw2/Us8yHjmPxZeP5+qnr+buT+6mXGI5XrvuNd4e9jZH9hwJOjQ5R+3ZsIc3b32TF9q9wNaPt9Lrz714YMMDXDbislI1anRBl7B+BPycULJYnqV8P/DcWR53AJASXh4HzAd+k3UDd1+XZXm7mX0D1ADSEYlA3cvrMmLZCBY+sZBFf1rExvc3ct2/rqPFgBZBhybniAM7DrDwvxeyfNRyYsvE0vmRzlz1q6sol1g6L4uae8HDWpnZA+7+9yI9qFm6uydmWd/r7vlOo2VmVxBKNK3d/aSZjQU6AkcJn8G4e5632pjZCMJDr9SqVeuyCRMmFN0PEoGDBw9SsWKwY/hHiyDb4sD6A6x9ei2HNh6iUotK1OhegxrdalCuVjB/CPS9yBSNbXH8wHG2TtjKV29+hZ9w6vSrw4W3X0iZasX7rFG0tEX37t2XuXuuUR0Lk0B+mFe5u798mv3mALXzqHoUGFfYBGJmdQidoQxz90+ylO0EyhCas32juz9e4A8CJCcn+9KlS0+3WbGaP38+KSkpgcYQLYJui4xjGSx5fgmfvfwZO5bvAKBex3q0HtyaVje1onLdyiUWS9BtEU2iqS2OHz7O4r8v5sOnPuS7fd9x8a0Xk/LHFKo1qVYix4+WtjCzPBNIYW7jvTzLcjmgJ6FLWgUmEHfvVUAwX5tZHXffEU4G3+SzXWVgOvDbU8kj/Nk7wotHzewl4JeF+DlEsoktE8uVP7uSK392JXs27CHtjTTSJqYx68FZzHpwFg06N6DV4Fa0urEVlepUCjpcKUEZxzNYMXoFCx5fwMEdB2l2fTN6PNlDE5rlUJjbeB/Ium5mVcicHz1SU4FhwFPh93dybmBmZYApwMvu/kaOulPJx4CBwMqzjP9q+EgAABFBSURBVEdKuWoXVaPLw13o8nAXdq3dxao3VpE2MY2ZP53JzJ/N5MKuF9J6cGta/qBl4NOISvE59M0hUsemsuyFZezdtJcGnRtw06SbaNBZ46vlJZJxgw8Dzc7yuE8Bk8zsbuBL4CYAM0sGfuzuw4HBhB5arG5md4T3uyN8x9V4M6tB6LmUVODHZxmPyPeSmifR9bdd6frbrny76lvSJoXOTGb8ZAbvPfAeDVMa0mpwK1re0JIKNc7dgfAkxE86m+ZuYvnI5ax5Zw0nj5+kQecG9P1bX5pe2/S8fQiwKBRmMMV3yZxAKhZoCUw6m4O6+25Cl8Jyli8FhoeXXwVezWf/HmdzfJHCqtGqBil/SKHbY934Nu1bVk5cSdrENKb/eDozfjKDRj0a0frm1rQY2IKE6glBhytn4MCOA6S+lMryF5eTvjmd8tXLc8UDV9B+eHtqtKwRdHjnhMKcgfwly/IJQv/131I84YhEJzOjZpua9GjTg+6Pd+frz74mbWIaaZPSeHf4u0z/8XQa92pM65tb03xAc8pXLR90yJKHkxkn2ThrI8tGLmPdtHV4htOwe0N6/k9PWgxqQVzZc2cyp2hQmD6QBeEnvW8ldFlpM/BmcQcmEq3MjNqX1qb2pbXp8WQPdq7YycqJK1k1aRXv3PkOMfExNOndJJRM+jfX0ClRYN/WfawYs4IVo1ewf+t+KtSsQKdfdqLd3e2o3rR60OGds/JNIGbWDBhC6GxjNzCR0G2/3UsoNpGoZ2bUaV+HOu3r0OupXmxfsj3UZzIpjfXT1xNbJpaL+l5E65tb06xfM8pWOvdmnTtXnTxxknXT17F81HI2vLcBd6fJ1U3o82wfmvdrTmyZ0vPEeHEp6AxkDfBvoJ+7bwAwswdLJCqRc5CZUfeKutS9oi5X//lqti3eRtqkNFa9sYq1U9cSVy6Optc2pdXgVjS7vpkmvComezfvZcXoFaS+lMqB7QeoWKcinR/uTLu721G1Ub7PK0sECkogPyB0BjLPzGYCEwj1f4jIaViMUb9jfep3rE+f/9eHrR9t/T6ZrH5rNXHl42h2fTNaD25N02ubBh3uOS/jWAZrp65l+ajlbJy9ETPjomsu4tp/Xkuz65oRE1fYqY/kTOSbQNx9CjDFzCoQetbiQaCWmT0PTHH390soRpFzmsUYDTo3oEHnBvR5tg9fLvqStElprJ68mlVvrCK+QjyVL63M0Q5HSWyY+P2ryoVV1H9yGrvX72b5i8v5dOynHPrmEJXrVabbY91od1c7qtTX3HfFrTCd6IcIzYc+Pjw6702ERs9VAhE5QzGxMTTs1pCG3Rpyzd+u4YsFX7By4krWzFzD0hVLOXHkRLbtyyWW+z6Z5EwuiQ0TKZdYrtQ9p3Di6AnWTFnDspHL2DJvCxZrNLu+GZeNuIwmfZoQE6uzjZJyRvesufse4IXwS0TOQkxsDI16NKJRj0ZUml+Jbt26cfjbw6RvSSf9i/TQ+5Z09n2xjz0b9rBpziaOHzqe7TPKVi6bb3JJbJhI+Wrlz5sEs2vNLpaNWsan4z7lyO4jJDZMpPsT3Wl3ZzsqXaChZoKgm55FooSZUaFmBSrUrEDdK+rmqnd3juw+kiu5nFreMn8Lxw4cy7ZPfIX4PBNLYsNEEi9MJKFGQlQnmONHjrNq8iqWj1zOl4u+JCYuhhYDW9D+nvY07tW4VEzaFM2UQETOEWZGQlICCUkJXHBZ7jnd3J3v0r/7PqHkTDBbP9zKd+nfZdsnrnzc98mkyoVVKFulLHFl44gtG0tcubgCl+PKhdfzWY6Ji4k4OX39+dcsH7Wcz175jO/Sv6PaRdXo9XQvLh12qcYiiyJKICLnCTOjfNXylK9anjrt6uS5zXfp331/BpM1uez7Yh9fLfmKYwePkXE0o4gCotDJJuvyxk82smD1AmLLxNLyBy1pf097GqY0jOozpdJKCUSkFCmXWI7aiaGn6PPj7mQcyyDjaAYnjp7gxHcnimQ547vcZccOHSNjT/byk2VO0vuZ3lx6+6UkJGl8sWimBCIi2ZhZ6IygbBxlKfkn5+fPn0/HlI4lflw5c7rfTUREIqIEIiIiEVECERGRiCiBiIhIRJRAREQkIoEkEDOrZmazzWx9+D3PMZbNLMPMUsOvqVnKG5nZ4vD+E81M42KLiJSwoM5AHgLmuntTYG54PS9H3L1t+NU/S/nTwLPh/fcCdxdvuCIiklNQCWQAMC68PI7QcPGFYqHHUXsAkyPZX0REioa5e8kf1Czd3ROzrO9191yXsczsBJAKnACecve3zSwJ+MTdLwpvUx94z93b5HOsEcAIgFq1al02YcKEov+BzsDBgwepWFFj+YDaIiu1RSa1RaZoaYvu3bsvc/fknOXF9iS6mc0B8hov4dEz+JgG7r7dzBoDH5jZ58D+PLbLNwu6+0hgJEBycrKnpKScweGL3vz58wk6hmihtsiktsiktsgU7W1RbAnE3XvlV2dmX5tZHXffYWZ1gG/y+Yzt4fdNZjYfaAe8CSSaWZy7nwDqAduL/AcQEZECBdUHMhUYFl4eBryTcwMzq2pmZcPLScBVwCoPXXObB9xY0P4iIlK8gkogTwFXm9l64OrwOmaWbGYvhrdpCSw1s08JJYyn3H1VuO43wC/MbANQHRhdotGLiEgwo/G6+26gZx7lS4Hh4eWPgIvz2X8TcEVxxigiIgXTk+giIhIRJRAREYmIEoiIiESkVM1IuHv3bsaOHRtoDOnp6WzZsiXQGKKF2iKT2iKT2iJTtLeFzkBERCQigQxlEpTk5GRfunRpoDFE+5OlJUltkUltkUltkSla2sLM8hzKRGcgIiISESUQERGJiBKIiIhERAlEREQiogQiIiIRUQIREZGIKIGIiEhElEBERCQiSiAiIhIRJRAREYmIEoiIiERECURERCISSAIxs2pmNtvM1offq+axTXczS83y+s7MBobrxprZ5ix1bUv+pxARKd2COgN5CJjr7k2BueH1bNx9nru3dfe2QA/gMPB+lk1+dare3VNLJGoREfleUAlkADAuvDwOGHia7W8E3nP3w8UalYiIFFpQCaSWu+8ACL/XPM32Q4DXc5Q9aWafmdmzZla2OIIUEZH8FduEUmY2B6idR9WjwDh3T8yy7V53z9UPEq6rA3wGXODux7OU7QTKACOBje7+eD77jwBGANSqVeuyCRMmRP5DFYGDBw9SsWLFQGOIFmqLTGqLTGqLTNHSFt27d89zQqlimxPd3XvlV2dmX5tZHXffEU4G3xTwUYOBKaeSR/izd4QXj5rZS8AvC4hjJKEkQ3Jysgc9u1e0zDAWDdQWmdQWmdQWmaK9LYotgZzGVGAY8FT4/Z0Ctr0FeDhrQZbkY4T6T1YW5qC7d+9m7Nix2cpat27N5ZdfzvHjxxk/fnyufdq2bUvbtm05fPgwkyZNylWfnJxMmzZt2LdvH1OmTMlV37FjR5o3b86uXbuYNm0a6enpbNmy5fv6rl270rhxY3bu3MnMmTNz7d+zZ0/q16/P1q1bmTt3bq76vn37Urt2bTZt2sTChQtz1V9//fUkJSWxdu1aPv7441z1gwYNokqVKqxcuZK8pvsdPHgwCQkJpKamkpqa+16FoUOHEh8fz5IlS0hLS8tVf8cddwDw0UcfsW7dumx1+/fv//6XY8GCBWzevDlbfUJCAoMHDwZgzpw5bNu2LVt95cqVueGGGwCYOXMmO3fuzFZfvXp1+vXrB8C7777L7t27s9XXrl2bvn37AvDWW2+xf//+bPX16tWjV6/Q/0GTJk3i8OHsXXCNGjWiW7duAIwfP57jx49nq2/WrBmdOnUCyPW9g+zfvdTU1GzfCyj6715O0frdO/U7Upzfvfj4eIYOHQrouxfJ371TguoDeQq42szWA1eH1zGzZDN78dRGZtYQqA8syLH/eDP7HPgcSAKeKIGYRUQki2LrA4lGycnJntd/2SUp2k9JS5LaIpPaIpPaIlO0tIWZ5dkHoifRRUQkIkogIiISESUQERGJiBKIiIhERAlEREQiogQiIiIRUQIREZGIKIGIiEhElEBERCQiSiAiIhIRJRAREYmIEoiIiERECURERCKiBCIiIhFRAhERkYgogYiISESUQEREJCJKICIiEhElEBERiUggCcTMbjKzNDM7aWa55tnNsl1fM1trZhvM7KEs5Y3MbLGZrTeziWZWpmQiFxGRU4I6A1kJ3AAszG8DM4sFngOuAVoBt5hZq3D108Cz7t4U2AvcXbzhiohIToEkEHdf7e5rT7PZFcAGd9/k7seACcAAMzOgBzA5vN04YGDxRSsiInmJCzqAAtQFtmZZ3wZ0AKoD6e5+Ikt53fw+xMxGACPCqwfN7HSJq7glAbsCjiFaqC0yqS0yqS0yRUtbXJhXYbElEDObA9TOo+pRd3+nMB+RR5kXUJ4ndx8JjCzE8UqEmS1193z7fUoTtUUmtUUmtUWmaG+LYksg7t7rLD9iG1A/y3o9YDuhbJxoZnHhs5BT5SIiUoKi+TbeJUDT8B1XZYAhwFR3d2AecGN4u2FAYc5oRESkCAV1G+8gM9sGdASmm9mscPkFZjYDIHx2cT8wC1gNTHL3tPBH/Ab4hZltINQnMrqkf4azEDWX06KA2iKT2iKT2iJTVLeFhf6hFxEROTPRfAlLRESimBKIiIhERAmkhJhZfTObZ2arw8O4/CzomIJmZrFmtsLMpgUdS5DMLNHMJpvZmvD3o2PQMQXFzB4M/36sNLPXzaxc0DGVFDMbY2bfmNnKLGXVzGx2eNim2WZWNcgYc1ICKTkngP9y95bAlcBPsgzNUlr9jNANEqXdX4GZ7t4CuJRS2iZmVhf4KZDs7m2AWEJ3X5YWY4G+OcoeAuaGh22aG16PGkogJcTdd7j78vDyAUJ/JPJ9gv58Z2b1gOuAF4OOJUhmVhnoSvhOQnc/5u7pwUYVqDigvJnFAQmUome83H0hsCdH8QBCwzVBFA7bpAQSADNrCLQDFgcbSaD+D/g1cDLoQALWGPgWeCl8Oe9FM6sQdFBBcPevgL8AXwI7gH3u/n6wUQWulrvvgNA/oUDNgOPJRgmkhJlZReBN4Ofuvj/oeIJgZtcD37j7sqBjiQJxQHvgeXdvBxwiyi5TlJTw9f0BQCPgAqCCmd0WbFRSECWQEmRm8YSSx3h3fyvoeAJ0FdDfzLYQGmW5h5m9GmxIgdkGbHP3U2ejkwkllNKoF7DZ3b919+PAW0CngGMK2tdmVgcg/P5NwPFkowRSQsLD0I8GVrv7M0HHEyR3f9jd67l7Q0KdpB+4e6n8T9PddwJbzax5uKgnsCrAkIL0JXClmSWEf196UkpvKMhiKqHhmiAKh22K5uHczzdXAbcDn5tZarjsEXefEWBMEh0eAMaHx3zbBNwZcDyBcPfFZjYZWE7orsUVRPlQHkXJzF4HUoCk8FBPjwFPAZPM7G5CCfam4CLMTUOZiIhIRHQJS0REIqIEIiIiEVECERGRiCiBiIhIRJRAREQkIkogIiXAzA4GHYNIUVMCERGRiCiBiATEzPqZ2eLwIIpzzKxWuLxGeO6H5Wb2gpl9YWZJQccrkpMSiEhwFgFXhgdRnEBodGIIPYH8gbu3B6YADQKKT6RAGspEJDj1gInhQfLKAJvD5Z2BQQDuPtPM9gYUn0iBdAYiEpy/A/9w94uBHwGnpm+14EISKTwlEJHgVAG+Ci8Py1K+CBgMYGa9gaiaB1vkFA2mKFICzOwk2adnfQbYCDxLKIl8Alzu7ilmVhN4nVDiWADcDDRy96MlG7VIwZRARKKMmZUFMtz9hJl1JDRbYdug4xLJSZ3oItGnAaE5IGKAY8A9AccjkiedgYiISETUiS4iIhFRAhERkYgogYiISESUQEREJCJKICIiEpH/D//giZbjv6OKAAAAAElFTkSuQmCC\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted value = 2.521238, expected value = 4.663048\n",
+      "predicted value = 3.813087, expected value = 3.206145\n",
+      "predicted value = 2.901418, expected value = 3.048606\n",
+      "predicted value = 2.162019, expected value = 3.348171\n",
+      "RMSE: 1.263\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RkXZhsEGD4OhRpytTyutooPuxp1s+zaDYQbwa/yrvrXzv7Dt//hluucVO41+82IalN6tf336aeOIJ+PBDOxnpl1+crkopr6KB7sdEhHfi3qHT5Z0YPH8wMzbPsHckJMDNN9shgosX2yGKvqBkSfjPf+wSvocPQ7NmdvkAvWCqFKCB7veCg4KZ1H0SsTViuX3a7ayb++nfYb5kiZ085GvatrUzTDt2tAt8tW0LyclOV6WU4woMdBEpJSIrRWSNiGwQkRF5HDNARFJEZHXW10DPlKsuRJnQMsy6fRY3p1Yiqsc9nCp/kW2Z+2KYZ6tUCaZOhbFj7bWAhg3hyy+drkopR7nSQj8BtDHGNAIaA7eJSLM8jvvCGNM46+tjt1apiqzKb7v46uPDpJcS2g4IIqVyGadLKjoRuOceu3rjZZdBr172+8OHna5MKUcUGOjGyt5FITTrS5fF8yWrV0PbtgSXD+Pg7C9ZWWI/HSZ14OgpPxkpUru2neX67LN2m7smTewFVKUCjEt96CISLCKrgf3AAmPMijwO6y4ia0VkqojkeZVNRO4XkQQRSUjRVfWKx5o1djOJsmVh8WKatOjGpO6T+HnXz9w+7XYyT2c6XaF7hIbCiy/a8eqnTtkVHP/1L8jw8OYfSnkRlwLdGJNpjGkMRALXiEiDXIfMAmoaYxoCC4E813E1xow2xsQaY2LDi7pRgirY2rU2zMuUsRdAa9UCoEvdLrwT9w4zt8xk8LzBntscwwnXXWd/ifXqBS+8ADfcAH/84XRVShWLQo1yMcakAkuA23L9/UFjTPYc8zHAVW6pTl24detsmJcqdVaYZ3v4mod5ssWTvJ/wPv/58T/O1OgpFSrAxInw+ef236FRI3tbKT/nyiiXcBGpkHW7NNAW2JzrmJwrOXUCNrmzSFVI69dDmzZ23PaSJXDppXke9krbV+jToA9PL3yaiesmFm+NxaFfP3v9oGFD6N8f+vaF1EIsWKaUj3GlhV4dWCwia4GfsX3os0VkpIh0yjpmcNaQxjXAYGCAZ8pVBcoO8xIl7NDEyy7L99AgCeLTzp9yfcz1DJg+gMV/LC7GQovJJZfYX2ojR8KUKba1/sMPTlellEeIU/2nsbGxJiEhwZHX9lsbNsCNN0JIiA2xOnVceljq8VRaftKSXem7WHbPMhpUyX2JxE+sWGFb7X/8AcOGwfDh9mKqUj5ERFYZY2Lzuk9nivqLjRttyzwkxLbMXQxzgAqlKjCv3zzKhJYhbkIcu9J3ebBQB117rR2zftdd8NJL0LIlbN3qdFVKuY0Guj/YtMmGeVCQ3Wno8ssL/RTRYdHM7TeX1OOptJvYjvQT6R4o1AuUKweffGJnlW7bZsesjx0L/jTSRwUsDXRft3mz7WYB2zKvW/eCn6pxtcZM6zWNjSkb6T6lOyczT7qpSC/Uo4cd1nnNNTBwoP3+4EGnq1KqSDTQfdmWLTbMjSlymGe75dJbGNNxDAt/X8h9s+7zrzHquUVGwsKFdsXGWbPsaJjvvnO6KqUumAa6r/rtNxvmmZk2zOvVc9tTD2g8gJE3jGT8mvG8sPgFtz2vVwoKgieftEsFlCtnV2584gk44cLWfUp5GQ10X7R1qw3zjAwb5vXru/0lnmv9HAObDOTFH15kzKoxbn9+r5O9YcaDD8Ibb9gLqBs3Ol2V8iMHjh5gxuYZPLXgKWZtmeWR1wjxyLMqz9m2zYb5yZM2zK+4wiMvIyK83/59dh3exaA5g4goH0G72u088lpeo0wZ+OADuyH1PffAVVfZcB80yK7sqJSLjDFs/3M7y5KWsSxpGfE749l8wM7HLBFcgnIlytHx8o5uf10dh+5Ltm+3a5McO2bD/MorPf6SR04e4fpPr2fzgc18P+B7YmvkOfzV/+zdCwMGwDffQPv2dmRMlSpOV6W81KnMU6zeu9oG+M5lxCfFs++vfQBULFWRFlEtaBXdilbRrYitEUupkFIX/FrnG4euge4rcob5okX2Al4x2XtkL83HNufoqaMsv3c5l1S8pNhe21GnT8O778JTT0FYmF2aNy7O6aqUF0g/kc7y5OVnWuArdq04sxz1JRUuoWV0S1pF2QCvF16PIHFf77YGuq/7/Xcb5n/9ZcO8UaNiL2Hzgc20GNuCKmWrEH9PPJXKVCr2Ghyzbp2dYbpuHTzyiB0VU7q001WpYpScnkx8UvyZFvjafWs5bU4TJEE0rtaYVlGtaBndkpZRLYkoH+HRWjTQfdkff9gwP3LEDqlr3NixUpYlLaPt+LZcVeMqFvZfSOnQAAq148ftcgFvvWUvQk+c6MgvVuV5p81pNuzfQPzO+DMt8MS0RMBu59g8sjmtolvRMqolzSKbUa5kuWKtTwPdV+3YYcM8Pd2GeZMmTlfE1I1T6fVlL7rV68YXPb4gOCjY6ZKK1zff2L71Q4fglVdgyBA79FH5rGOnjvHz7p9tC3znMn7c+SOpx+2qnNUuqmb7vrNa4I2qNiI02Nn1fzTQfVFiog3z1FQb5k2bOl3RGaN+GsVj3z7Go9c+yqjbRjldTvFLSbGzS2fOhJtvtn3rNWo4XZVy0YGjB4hPij/TAk/YncCp06cAqFe53pmLly2jWlKrYi3Ey0Y4nS/QddiiN0pK+jvMFy70qjAHGNp8KElpSby14i2iw6IZ2nyo0yUVr/BwmD4dRo+GoUPtBeoxY6BrV6crU7nkHD6Y3QLPOXwwtkYsQ5sNpVV0K1pEtfD5a0Ma6N5m504b5n/+acP8Ku/c/OmNW99gZ/pOHv/2cSLLR9Lzip5Ol1S8ROCBB+D66+0F027d4L77YNQou3+rckTO4YPZLfDs4YMVSlWgZVRL7mp0l1uGD3ojDXRvkh3mBw/aMI/13jHfQRLEZ10/Y++RvfT/uj/Vy1WnVXQrp8sqfnXrwk8/2f1LX3vNblI9YYJXv3f+JOfwwfid8SxPXn7W8MGbL73ZY8MHvZH2oXuL5GQb5ikpsGCBXQXQBxw8epCWn7Rk/1/7+bjTx9StXJeYsBjKlgjAVurixXDnnXZS0siRdvx6cIBdNPawXem7zpp9uWbfmrOGD7aManmm/9vTwwedohdFvd2uXTbM9+2zYX7ttU5XVCh//PkHLT5pwd4je8/8XaXSlahZoSYxFWKoGZb1Z4WaxITFEFMhhgqlKjhYsQf9+aftivnyS2jdGj77DKKjna7KJ502p9mYsvGsAN+RugP4e/hgdoA7MXzQKRro3mzXLrs2y9698O230KyZ0xVdkMMnDrN+/3p2pO4gMS3x7D9TEzmWceys48NKhp0d8mExf/8CqFCTSqUred3oApcZA+PH20lIwcHw0UfQu7fTVXm94xnH+XnXz2cm7+Q1fDA7wL1h+KBTNNC91e7dNsx377Zh3ry50xV5hDGGA0cPnBPy2bd3pO7g8MnDZz2mTGiZfMM+JiyGqhdV9f7+0O3b7QXTFStsV8w770D58k5X5TUOHD3Ajzt/PNMCz2v4YHaAe+PwQadooHujPXtsN8vu3TB/vt3fMkAZY0g9nppn2Gf/eejYobMeUzK4JNFh0Wd16eQM/ohyEd4x6enUKXjxRfsVEwOffw4tWjhdVbHLHj6Yc/p89vDB0KBQro64+szknRZRLahcprLDFXsvDXRvs3evDfPkZBvmrQJwdEghHT5xmMS0xDzDPjE18czQtGwhQSFElo/8O+RztfIjy0dSIrhE8Z1AfDzccYedY/D88/Dcc3ZDbz+VPXww5/T53MMHs1vgsTViA2sZiSLSQPcm+/bZMN+5E+bNg+uuc7oiv3Ds1DGS0pLOCvkdaVl/pu5g9+HdGP7+WReEGuVq5HvhNjos2v0hk5YG//d/9kJp8+a2tV6rlntfwyHZwwezJ+/kHD5Ys0LNs6bP1w+v7/3dZV5MA91b7Ntn+8wTE22Yt27tdEUB42TmSZLTk/8O+6wWfvYvgJ1pO8k0mWc9pmrZqvleuI0Ji7nwURWTJtlNMzIz4b33oH9/n9tAI3v4YHYLPOfwwUZVG501fd5fhw86RQPdG+zfb8N8xw6YO9fOMFReI+N0BrsP7z477HO08hPTEjmZefKsx1xc+uLzXritUKpC/hfyEhNtkP/wA/TqBR9+CBUrFsOZFl5BwwebRTY7M3knkIYPOkUD3Wn790ObNnZd87lzbZeL8imnzWn2Hdl3dtjnGqKZ3cWQrVyJcud06eQM/vCSFyOvvQbDh0O1arYrxgt+Ns43fLBq2apnWt+BPnzQKRroTkpJsWG+fTvMmWNb6crvZA/NzC/sd6TuIP1E+lmPKR1SmpgKMdx8sALPfrSJKnvS2XRPR9KGPUZMldpUu6hasfQ15x4+uGrPqjOfRupVrnfmAqYOH/QOGuhOOXDAhvnWrTbM27RxuiLloNTjqXmGfWJqIin7/uC56Ye47xdYVR36docdVUsQVT7qnFE62bcjykcQElS4kTLGGH7/8/cz4Z3X8MHsANfhg95JA90JBw7ATTfBb7/BrFnQtq3TFSkvd+TkEQ5N+Jhqj72AHD/BzAdv5MsWYezIunibc2kFgGAJtkMz87lwG1U+iiAJYs2+NX8HeB7DB7MDXIcP+gYN9OJ28KAN8y1b/t4EQSlX7dpld0VauBA6d4aPP4bKlTmecZyktKR8J1/tSt91ztDMEsElOJF5AtDhg/5CA704HTpkw3zTJhvmt9zidEXKF50+bfcvHTYMLr4Yxo0r8Gcpe2hmzrA/cvIIV9e4mpbRLYksH1lMxStPKlKgi0gpYClQErt++lRjzPBcx5QExgNXAQeB3saYHed7Xr8M9EOHbNfKxo0wYwbceqvTFSlft3o19O1rGwiPPgovvwyl/GtTBlU45wt0Vz5vnQDaGGMaAY2B20Qk95KA9wJ/GmMuA0YBrxalYJ/055+2a2XDBrs9mYa5cofGjWHVKrty41tv2XXy1693uirlpQoMdGMdyfo2NOsrd7O+MzAu6/ZU4CYJpLFN2WG+fj18/TXcdpvTFSl/Urq0Xalx9mw72zg21n7vUHep8l4uXRERkWARWQ3sBxYYY1bkOiQC2AlgjMkA0oBzdlsVkftFJEFEElJSUopWubdITbV9m+vWwVdfQbt2Tlek/FX79rB2rb1GM3iw/Vnbu7fgx6mA4VKgG2MyjTGNgUjgGhFpkOuQvFrj5zQfjDGjjTGxxpjY8PDwwlfrbdLSbJivWQPTptn/cEp5UtWqtqX+7ruwZAk0bGi/VwoXAz2bMSYVWALk7lNIBqIARCQECAMO4c+yw3z1ahvmHTo4XZEKFCLw8MOQkADVq0PHjvDQQ3D0aMGPVX6twEAXkXARqZB1uzTQFtic67CZwF1Zt3sAi4xT4yGLQ3q6vej5669278iOHZ2uSAWiK66AlSvh8cfhgw/gqqvsz6QKWK600KsDi0VkLfAztg99toiMFJFOWceMBSqJyDbgMeAZz5TrBdLT7UXPVatgyhQ78UMpp5QsCa+/brcwTEuzG4y//rodx64Cjk4sKozDh22Yr1xpw7xrV6crUupvBw/CfffZkVZt2tiNqiN0LXJ/U9Rx6ApsmMfF2Q1/J0/WMFfep1Ilez1nzBhYvhyuvNJ2Cfpx76c6mwa6Kw4ftkPEli+3Yd69u9MVKZU3ERg40PalX3qp3TyjXTs701T5PQ30ghw5Yocj/vST3TqsRw+nK1KqYHXqwI8/wqhR9mf3yithyBA7CU75LQ308/nrLxvmP/4IEydCz55OV6SU60JD7fovW7faVvu770Lt2vD++5CR4XR1ygM00POTHebLltnd2Xv1croipS5MeLjds/SXX2xL/eGHoUkT+O47pytTbqaBnpejR+1EoR9+sGHep4/TFSlVdI0awaJF9sLpX3/ZlUG7drXbIyq/oIGe29GjdqLQ0qV2097bb3e6IqXcRwS6dbNLPP/737BgAdSvD888Yy/+K5+mgZ7T0aPQqRMsXmw3FOjb1+mKlPKMUqXs5hm//WYbLa++avvXP/lEJyX5MA30bMeO2VmfixbZML/jDqcrUsrzatSATz+18ytq1YJ777VrrsfHO12ZugAa6PB3mH/3Hfzvf9C/v9MVKVW8skN8wgS7JG+rVrblnpTkdGWqEDTQjx+HLl3shryffAJ33cBdAKcAAAvxSURBVFXwY5TyRyK2m3HLFnjhBbvzVt268M9/6kqOPiKwA/34cXuVf8ECGDvW7rSuVKArWxZGjIDNm+01pREj4PLL7cQ6XUbAqwVuoB8/bq/2z59v1764+26nK1LKu8TE2KUuli6FKlVs671VK7sOu/JKgRnoJ07Y9VjmzbNhfu+9TleklPe67jq7wujYsbBtG1x9tW0A7dnjdGUql8AL9OwwnzsXPvrITolWSp1fcDDcc49dRuDJJ+3F0zp14JVX7Kdd5RUCK9BPnLDrscyZY6dC33+/0xUp5VvKl4fXXrMTk266yY5lv+IKuwa79q87LnAC/eRJux7LrFl2caIHHnC6IqV812WX2VEwCxZA6dL2etRNN8HatU5XFtACI9Czw3zmTHjvPRg0yOmKlPIPbdvajdLffRfWrLGLfg0aBAcOOF1ZQPL/QD91Cnr3hhkz7A/dQw85XZFS/iUkxK7guHWr/XPMGLuMwH//a///qWLj34F+6pRdKXH6dHj7bfvDppTyjIsvtv/P1q61M08ffRQaNrSjyVSx8N9AP3XKTl3+6it46y34v/9zuiKlAkP9+nZ+x6xZkJlpt8Br397OQFUe5Z+BfuqUnQQxbZrdgmvIEKcrUiqwiNg9Bdavh9dftxvFNGgAjz0GqalOV+e3/C/QMzLsSolTp8Kbb9qPfUopZ5QoAY8/bvvX777bflquXdvOAcnMdLo6v+NfgZ4d5lOm2FbB0KFOV6SUArt0wOjRsGqV7ZJ58EFo2tTuPaDcxn8CPSPDLnv7xRd24sPjjztdkVIqtyZNYMkS+PJLSEuDNm3szO3ff3e6Mr/gH4GemWmXvZ082e688uSTTleklMqPCPToAZs2wYsv2guo9evDP/6h2+AVke8HenaYT5wIL78MTz3ldEVKKVeULg3PPmu3wevVy/7/vfxyu2OYboN3QXw70DMz7YWWCRPshrfPPON0RUqpwoqIgPHj4aefIDra7kvQrJn9XhWK7wZ6ZqZd/e2zz+zHtmHDnK5IKVUUzZrBjz/acN+1C1q0sIMckpOdrsxn+GagZ2baNczHj4eRI+3HNqWU7wsKsoMbtmyx/6+nTrXdMCNH6jZ4Ligw0EUkSkQWi8gmEdkgIufM0hGRG0QkTURWZ3294JlysX1r991n+9lGjIDnn/fYSymlHHLRRfaT9+bNdpbp8OFQr54dxabL9ObLlRZ6BvC4MaYe0Ax4WETq53HcD8aYxllfI91aZU5jx8L//mff4Bc893tDKeUFata080qWLIGKFe3aTK1bwy+/OF2ZVyow0I0xe4wxv2TdPgxsAiI8XVi+7r7bvsHDhztWglKqmF1/vZ2UNHq07Y6JjbW7je3b53RlXqVQfegiUhNoAqzI4+7mIrJGROaJyBX5PP5+EUkQkYSUlJRCFwvYpTp79rRjWZVSgSM42Ha3bt1q14QZP94uI/Daa3Y3MuV6oIvIRcA04FFjTHquu38BYowxjYB3gOl5PYcxZrQxJtYYExseHn6hNSulAllYmF3aY/1623J/+mm7Dd6MGQHfv+5SoItIKDbMJxhjvsp9vzEm3RhzJOv2XCBURCq7tVKllMqpTh27RO/8+XYRsC5d4JZbbNAHKFdGuQgwFthkjHkzn2OqZR2HiFyT9bwH3VmoUkrl6dZb7fZ3b79t+9kbN4ZHHoGDgRdBrrTQWwL9gTY5hiW2E5EHReTBrGN6AOtFZA3wNtDHmAD/7KOUKj6hoXYTm61b7UqOH35o+9ffeSegtsETp3I3NjbWJCQkOPLaSik/t3693Qvhu+/s+PVRo2xL3g+IyCpjTGxe9/nmTFGllDqfBg1gwQK7n/DJk3DbbdCxo10IzI9poCul/JMIdO4MGzbYoY3ff2+D/okn7FrsfkgDXSnl30qWtHsk/PYb3Hmn3Zqydm0YM8bvtsHTQFdKBYZq1eDjjyEhwS74df/9dsbp0qVOV+Y2GuhKqcDStKkN8cmT7dDG66+3s8937HC6siLTQFdKBR4R6N3bruY4YgTMmQN168Jzz8GRI05Xd8E00JVSgatMGbtq65YtdrPql16y3TGff+6T2+BpoCulVFSU3coyPh5q1LCbbLRoASvyWofQe2mgK6VUtuwQ//RTSEy02+LdeafdEs8HaKArpVROQUFw1112mOOwYXb/hTp1bHfMsWNOV3deGuhKKZWXcuXg3/+GjRvtTNPnnrPLCEyd6rXL9GqgK6XU+dSqBdOmwaJFdi32nj3hhhtg9WqnKzuHBrpSSrnixhvtXqYffmhb7U2b2slJ+/c7XdkZGuhKKeWq4GB44AG7TO+jj9oN62vXhjfesIuAOUwDXSmlCqtCBbsmzLp10KqVXfCrQQOYPdvR/nUNdKWUulB169pZpnPn2tExHTvaC6gbNzpSjga6UkoVVVycba2PGmXHsTdsCIMHw6FDxVqGBrpSSrlDaKjtV9+6Fe67D957z/avv/ceZGQUSwka6Eop5U7h4fDBB/Drr9Cokd2wunFjWLjQ4y+tga6UUp7QsKHd0/Srr+wM05tvhi5dYNs2j72kBrpSSnmKCHTtarfBe/llG/D169u+dg/QQFdKKU8rVQqeecauD9Ovn5196gEhHnlWpZRS56pe3U5G8hBtoSullJ/QQFdKKT+hga6UUn5CA10ppfyEBrpSSvkJDXSllPITGuhKKeUnNNCVUspPiHFoMXYRSQESL/DhlYEDbizHSXou3slfzsVfzgP0XLLFGGPC87rDsUAvChFJMMbEOl2HO+i5eCd/ORd/OQ/Qc3GFdrkopZSf0EBXSik/4auBPtrpAtxIz8U7+cu5+Mt5gJ5LgXyyD10ppdS5fLWFrpRSKhcNdKWU8hNeHegicpuIbBGRbSLyTB73lxSRL7LuXyEiNYu/Ste4cC4DRCRFRFZnfQ10os6CiMgnIrJfRNbnc7+IyNtZ57lWRJoWd42ucuFcbhCRtBzvyQvFXaMrRCRKRBaLyCYR2SAiQ/I4xifeFxfPxVfel1IislJE1mSdy4g8jnFvhhljvPILCAa2A7WAEsAaoH6uYx4CPsy63Qf4wum6i3AuA4B3na7VhXNpDTQF1udzfztgHiBAM2CF0zUX4VxuAGY7XacL51EdaJp1uxzwWx4/Xz7xvrh4Lr7yvghwUdbtUGAF0CzXMW7NMG9uoV8DbDPG/G6MOQlMBjrnOqYzMC7r9lTgJhGRYqzRVa6ci08wxiwFDp3nkM7AeGMtByqISPXiqa5wXDgXn2CM2WOM+SXr9mFgExCR6zCfeF9cPBefkPVvfSTr29Csr9yjUNyaYd4c6BHAzhzfJ3PuG3vmGGNMBpAGVCqW6grHlXMB6J71cXiqiEQVT2lu5+q5+ormWR+Z54nIFU4XU5Csj+xNsK3BnHzufTnPuYCPvC8iEiwiq4H9wAJjTL7vizsyzJsDPa/fUrl/u7lyjDdwpc5ZQE1jTENgIX//1vY1vvKeuOIX7LoZjYB3gOkO13NeInIRMA141BiTnvvuPB7ite9LAefiM++LMSbTGNMYiASuEZEGuQ5x6/vizYGeDORspUYCu/M7RkRCgDC88yN0gedijDlojDmR9e0Y4Kpiqs3dXHnffIIxJj37I7MxZi4QKiKVHS4rTyISig3ACcaYr/I4xGfel4LOxZfel2zGmFRgCXBbrrvcmmHeHOg/A7VF5BIRKYG9YDAz1zEzgbuybvcAFpmsqwtepsBzydWf2Qnbd+iLZgJ3Zo2qaAakGWP2OF3UhRCRatn9mSJyDfb/y0FnqzpXVo1jgU3GmDfzOcwn3hdXzsWH3pdwEamQdbs00BbYnOswt2ZYyIU+0NOMMRki8gjwDXaUyCfGmA0iMhJIMMbMxL7xn4nINuxvtT7OVZw/F89lsIh0AjKw5zLAsYLPQ0QmYUcZVBaRZGA49mIPxpgPgbnYERXbgKPA3c5UWjAXzqUHMEhEMoBjQB8vbTC0BPoD67L6awH+AUSDz70vrpyLr7wv1YFxIhKM/aUzxRgz25MZplP/lVLKT3hzl4tSSqlC0EBXSik/oYGulFJ+QgNdKaX8hAa6Ukr5CQ10pZTyExroSinlJ/4fiuVU92Z55nIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Company name : 8KMILES\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted value = 721.484519, expected value = 879.681825\n",
+      "predicted value = 863.211695, expected value = 947.393459\n",
+      "predicted value = 990.871242, expected value = 1354.689960\n",
+      "predicted value = 1500.079643, expected value = 1423.700000\n",
+      "RMSE: 206.344\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Company name : ABAN\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YCbwE/AXYUR3DeEUk1HnNIUA34EER6VZmtc+AFFXtAcwF/sur7pSqJjuPu680nrro2uHXMn75eE4dOcWrfV7lwIYDbodkjKlDfOlE/x/gdlXtr6r9gDuAP1bDvm8EdqnqblU9C8wBhnuvoKorVfX8zcE/BtpUw37rlbY3t2XSh5MIaxBGRv8Mdi3Z5XZIxpg6QlSrnhpcRDY5RwBVll32jkVGAmmq+oiz/BBwk6o+Vsn6fwYOquqzznIxkAMUA1NU9Z1KtpsMTAZITEzsNWfOnCsJ+4oVFhbSuHHjGt/vmcNnyH0il5N7T9L5Z51pcUeLGo+hLLfaIhhZW5SytigVLG0xYMCADaqaUrbcl0709SLyKvCaszwWT1/IlaroIoUKs5mIjANSgP5exVep6gER6QisEJFcVf2i3AuqTgWmAqSkpGhqauoVB34lsrOzcSuG/rf1J2tEFtunbKdt07bc8rNbXInjPDfbIthYW5SytigV7G3hyyms7wNbgB8CPwK2Ao9Ww773A229ltsA5U7Si8hg4BfA3d4XM6rqAefvbiAbuL4aYqrTomKiGPveWK574DqW/XwZK361gksdgRpjTGUueQTifGm/4Dyq0zqgk4h0AL4CRgNjvFcQkeuBl/Gc6vrWqzwOOKmqZ0QkHriFizvYTSVCI0IZkTmCiMYRfPDsB5wpOEPaH9OQELtq3RhzeSpNICKSpaqjRCSXCk4tXWkfiKoWi8hjwBIgFJimqltE5BlgvarOB/4baAy86UzL8aUz4qor8LKIlOA5ipqiqluvJJ76JCQ0hGF/H0ZEkwg++d9POFt4lmFThxESapMzG2N8V9URyI+cv0MDtXNVXQQsKlP2tNfzwZVstxZIClRc9YGIcMcLdxAZHcnqZ1Zz9vhZRswaQWhEqNuhGWNqiUoTiKp+7Tz9N1V93LvOmaH38fJbmdrk/FXrkU0iWfqzpRSdKOL+ufcT3sCmPjPGXJov5yxuq6BsSHUHYtxz809vZujLQ9n53k4yh2Ry5rhNvGyMubRKE4iIfN/p/+jiTCVy/rEH2FRzIZqa0GtyL0bMGsGXa77ktcGvcerIKbdDMsYEuar6QF4H3gOeB7znqTquqkcCGpVxRdKYJMIbhTN31FwyUjN46P2HaNzC/YuYjDHBqdIjEFU9pqp7VfVBVf0XcArPaKzGInJVjUVoatS1w69lzMIxHP3iKNP7TefYl8fcDskYE6R8mUxxmIjsBPYAq4C9eI5MTB3VcXBHHlr6ECe+PcG0vtPI25nndkjGmCDkSyf6s8B3gB2q2gEYhOce6aYOa3tzWyasnEDxqWKm3zqdbzZ943ZIxpgg40sCKVLVPCBEREJUdSWQHOC4TBBoeX1LJn4wkZCwEDJSM/jqn1+5HZIxJoj4kkDyRaQxsBrIFJE/4ZkB19QD8dfGM/GDiTSIa8DMQTPZm73X7ZCMMUHClwQyHE8H+o+BxcAXwLBABmWCS1yHOCZ+MJGYq2LIHJLJzkU73Q7JGBMEfLml7QlVPaeqxao6Q1X/zzmlZeqRJq2akL4qnYTrEpgzfA5b3tzidkjGGJdVdSHhcREp8Hoc9/5bk0Ga4NAwviHjl4+nzXfa8Nbot/hs2mduh2SMcVFV14E0UdVor0cT7781GaQJHlExUYxbMo6Ogzsy/+H5fPynj90OyRjjEp/m7xaRviIy0Xke79zDw9RT4Q3DGT1/NF1HdGXJ/1vC6mdX242pjKmHfLmQ8Nd4Zt590imKAGYFMigT/MIiwxj5xkh6PNSDlb9aybLHl1kSMaae8eWe6PfiuV3sp+C5layINAloVKZWCAkL4Z6Me4hoEsHa/17LmeNnuOulu+zuhsbUE74kkLOqqiKiACLSKMAxmVpEQoQ7/3wnkdGRfDjlQ4oKixg+fTghYXZ3Q2PqOl8SSJaIvAzEish3gUnA3wMblqlNRITBzw8mMjqSFU+t4GzhWe6bcx9hkb58vIwxtZUv14H8AZgLvAV0AZ5W1RerY+cikiYi20Vkl4g8UUF9pIi84dR/IiLtveqedMq3i8gd1RGPuTK3PnkrQ14cwufvfM7sYbM5e+Ks2yEZYwKoyp+IIhIKLHHuTb60OnfsvPZLeO54uB9YJyLzVXWr12oPA0dV9RoRGQ38HnhARLoBo4HrgFbAMhHprKrnqjNGc/lufOxGIhpHMP/h+cy6YxZj3h1DVGyU22EZYwKgygSiqudE5KSIxKhqdd8Y4kZgl6ruBhCROXimTfFOIMOB3zjP5wJ/FhFxyueo6hlgj4jscl7vo6p2uH37dlJTU6vzPVy2/Px8YmNjXY2hJiRcm8C5tef4ZdtfsjFpI0URReXWqS9t4Qtri1LWFqXcbIuQkhCa5jUl8dvEStfx5ST1aSBXRJYCJ84XquoPrzC+1sA+r+X9wE2VraOqxSJyDGjmlH9cZtvWFe1ERCYDkwHCw8PJz8+/wrCvzLlz51yPoSbkh+dT0L6Am/beRI9Pe/Dh1R9yOvz0RevUl7bwhbVFKWuLUjXeFgrxJ+Jpe7QtrfJbEVESwemw05Wu7ksCWeg8qltFYz3LXkhQ2Tq+bOspVJ0KTAVISUnR9evXX06M1S47O9v1o6Ca9K/V/+L1oa/z4KkHGb9gPHEd4y7U1be2qIq1RSlri1I10RaqysGcg+Rm5rJ5zmaOf3WciMYRdB3XlaSxSXQY2IEp4VMq3NaXPpDbVHVcAOLeD7T1Wm4DHKhknf0iEgbEAEd83NYEgXb92jF++Xgy0zKZfut0Hlr2EAldE9wOy5h67+ieo+S+nktuZi6Htx0mJDyETkM6kfRCEp2Hdia8YfglX8OXPpAEEYlQ1eoeUrMO6ORMi/IVnk7xMWXWmQ9MwNO3MRJY4VyTMh94XURewNOJ3gn4ZzXHZ6pJ696tSV+Vzmu3vUZGvwzGLRlHyxtauh2WMfXOiUMn2JK1hc2vb2bfWk8PwlW3XsVdf7uLbiO70bBZw8t6PV9OYe0FPnS+tL37QF64rD2V4fRpPAYsAUKBaaq6RUSeAdar6nzgVeA1p5P8CJ4kg7NeFp4O92Lg320EVnBr3r05Ez+YyMxBM5kxYAZjFpX9rWCMCYSzJ86y/R/byc3M5Yv3v6CkuITmSc0ZNGUQ3Ud3J7ad/530viSQA84jBKjWKUxUdRGwqEzZ017PTwP3V7Ltc8Bz1RmPCaym1zRl4pqJvDb4NWbdPotrf3stpLodlTF1z7mic+xeupvc13P5/J3PKTpRRHTbaPr8Rx+SxiaRmFT5yKrLcckEoqq/BXDmv1JVLayWPZt6KaZtDOmr05l1xyw2P7mZTq06kTQmye2wjKn1VJX9H+8nNzOXLVlbOHnoJFFxUfQY14OkMUlc1feqap+n7pIJRES6A68BTZ3lw8B4VbVb0hm/NE5szISVE3h54Mu8PfZt8v+VT98n+uK5xMcYczkObTtE7uu5bH59M0d3HyUsKowud3chaWwS16RdQ2hEaMD27csprKnAT1R1JYCIpOKZC+vmgEVl6rwGcQ3o8fse5M/IZ8VTK8jfk89df7nLJmE0xgcFXxWwec5mcjNzOfjZQSRE6Di4I/2e7kfXe7sSGR1ZI3H4kkAanU8eAKqabTPymuoQEhHCvbPuJbZDLB889wEF+woYmTWSyCY18+E3pjY5nX+abW9vIzczlz0r94BCq96tuON/76D7A91p3KJxjcfkSwLZLSK/wnMaC2AcsCdwIZn6REQY+OxAYtrFsPD7C8nol8GYhWNo0spuOWNMydmSC0ljx8IdnDtzjqbXNKX/0/1JGpNEs87NXI3PlwQyCfgt8LazvBqYGLCITL3U67u9iGkbw5v3v8kr33mFsYvG0rx7c7fDMsYVp46c4oPnP+Cff/0n506co1FiI1IeTSFpbBKtUloFTX+hL6OwjgJXOu+VMZd0Tdo1TPxgIpl3ZjLtlmk8MO8BOgzs4HZYxtSY4tPFfPLiJ6z5zzWcPnaa5gOac/vjt9NhYIeg7B/05Z7oS0Uk1ms5TkSWBDYsU1+1SG7BIx8/QnTbaGalzWLjzI1uh2RMwJWcK2HjzI282PlFlv18GW1vbsujGx+l66+6cvXtVwdl8gAfEggQr6oXpoN0jkjs3IIJmJirYpi0ZhLtbm3HOxPeYdUzq1CtcK5MY2o1VWXX4l1MvWEq70x458IQ9zELx1TbxX6B5EsfSImIXKWqXwKISDsqmfnWmOoSFRvF2PfGsuC7C8j+dTb5e/MZ+vJQQsMDN6bdmJp0YMMBlj2+jD3L9xDXMY775tzHdfdfV+0X+wWSLwnkF8AaEVnlLPfDub+GMYEUGhHK8IzhxLSPYfUzqynYX8CouaNqbIy7MYFwdM9RVvxiBZtnb6ZhfEPS/pRGyqMpAb3gL1B86URfLCI3AN9xin6sqocDG5YxHiLCgN8OILZ9LO9Ofpfpt05nzMIxRLeJdjs0Yy7LybyTrH52NeteWkdIWAh9n+rLLT+/haiY2nvLZ1+OQMBz1Xk/r+V3AxCLMZW6fuL1RLeJJuu+rAvDfBN7BP85YmOKThXxyZ8+Yc3zazhbeJbkicmk/jaV6Na1/0eQL6OwpgA/wjN1+lbgRyLyfKADM6asq2+7mklrJgEwre80vnj/C5cjMqZyJedK+Gz6Z7zY6UWWP7mcdv3b8eimR7n7lbvrRPIA30Zh3YnnroTTVHUakAbcFdiwjKlYYo9EHvn4EeI6xPH6Xa/z2fTP3A7JmIuoKjsX7eTl5JeZP2k+0a2jSV+VzoPzH6T5dXVrAKuvp7Bi8dzQCTy3lTXGNdFtopn4wUSyRmYxf9J88vfmk/qb1KC5OtfUX1+t+4plP1/G3uy9NL2mKSOzRtJtZLc6+9n0JYE8D3wmIisBwdMX8lRAozLmEiKjIxmzcAzvfu9dVj+zmmN7jzHs78Nq5UgWU/sd+eIIK36xgi1vbKFhQkOGvDiEXpN71fnPoy+jsGaLSDbQG08CeVxVDwY6MGMuJTQ8lLtfvZvYDrFkP53tGeb71iiiYmvvqBZTu5w4dILVz65m/V/XExoeyq2/vJVbfnZLvRlq7ssNpZar6iBgfgVlfhGRpsAbQHs891wf5Vzh7r1OMvBXIBo4Bzynqm84dRlAf+CYs3q6qub4G4+pvUSE/r/qT2y7WOY/PJ9pfacxdtFYYq6yM60mcIpOFvHx/37MmilrKDpRxPWPXE/qb1Jp0rJ+zSJdaQIRkSigIRAvInF4jj7A84Xe6gr3+wSwXFWniMgTzvLjZdY5iefOhztFpBWwQUSWeE2r8jNVnXuFcZg6ouf4njRp3YSsEZ5hvmMWjqHl9S3dDsvUMSXFJeTMyCH76WyOHzhOl+FdGPT8IBK6JrgdmiuqGoX1PWADcC3wqfN8A/AP4KUr3O9wYIbzfAZwT9kVVHWHqu50nh8AvgXq57+S8UnHQR2Z9OEkQsJCyOiXwc73drodkqkjVJUd7+7gbz3/xoJHFhBzVQwTP5jI6HdG19vkASCXmqRORH6gqi9W605F8lXVe4bfo6oaV8X6N+JJNNepaolzCqsPcAZYDjyhqmcq2XYyztQriYmJvebMmVN9b8QPhYWFNG5c83cOC0aBaoszh8+w+cnNFO4upPOPO9NyaPAfidjnolSwtUXB1gJ2v7ybY5uO0aBNAzo80oH4fvE1MrIqWNpiwIABG1Q1pWy5LwlkfEXlqjrzEtstA1pUUPULYIavCUREWgLZwARV/dir7CAQgeee7V+o6jNVvhEgJSVF169ff6nVAio7O5vU1FRXYwgWgWyLM8fPMHfUXHYt3kXfp/oy8NmBQT2U0j4XpYKlLfJ25rHiqRVsnbuVRs0b0f83/bnhkRtqdELPYGkLEakwgfgyjLe31/MoYBCeU1pVJhBVHVxFMN+ISEtV/dpJBt9Wsl40sBD45fnk4bz2187TMyIyHfipD+/D1CORTSJ5cMGDLPy3haz5zzUc23uMu6fdTVikr5c+mfpIVTmYc5ANUzfw2SufERoZSv9f96fPf/Qhskn9GFl1OXwZxvsD72URiaH0/uj+mg9MAKY4f/9RdgURiQDmATNV9c0ydeeTj+DpP9l8hfGYOigkLIShLw8ltkMsK55aQcFXBTww7wEaxDVwOzQTZGFpmX0AABGSSURBVI7uOUru67nkZuZyeNthQsJDuP5hz8iqxi3cP4UUrPz5OXYS6HyF+50CZInIw8CXwP0AIpICPKqqjwCj8Fy02ExE0p3tzg/XzRSRBDwjw3KAR68wHlNHiQi3Pnkrse1ieSf9Habd4hnmG9s+9tIbmzrtZN5Jtr65lU2zNrHvw30AXNX3Ku762110G9mNhs0auhxh8PPlOpAFlN5AKhToCmRdyU5VNQ/PqbCy5euBR5zns4BZlWw/8Er2b+qfpDFJNGnVhDfufcMzzPfdMbRKudLR6Ka2KTpVxI4FO8jNzGXnezspKSohoVsCA58bSNKYJPthcZl8OQL5g9fzYjy/+h8MTDjGBE771PZM+nASmXdmktE/g5FvjKTz0Cs9mDbBruRcCXtX7iU3M5etb23l7PGzNGnVhJt+eBM9xvUgsWdiUA+wCGa+9IGscq4KH4PntNIe4K1AB2ZMICR0S+CRjx/h9aGvM2f4HIb8eQi9v9/70huaWuV8Z/imWZvYPHszhV8XEhkdSbeR3Ugam0T71PaEhPoyGbmpSlVXoncGRuM52sjDM/WIqOqAGorNmIBo3KIx6dnpvPXgWyz6t0Vsf2c7yZOSuXb4tYRF2Sit2ix/bz65r+eyadamC53hne7sRNLYJDoP7Ux4g3C3Q6xTqvrf8jnwATBMVXcBiMiPayQqYwIsonEED8x7gA//60M2vLyBt0a/RVRsFNeNvo7k9GRa39jaTmvUEuc7w3Mzc/lyzZeAdYbXlKoSyH14jkBWishiYA6l82EZU+uFhIVw61O30veJvuxZuYeNGRvZOGMjG/62gfhr4+mZ3pOeD/WkSav6NUFebVB0qogd7+4gd5Z1hrup0gSiqvOAeSLSCM+1Fj8GEkXkr8A8VX2/hmI0JqAkROg4qCMdB3XkzpfuZMubW9iYsZHlTyxnxVMruPqOq0lOT6bL3V3sFJeLSs6VsDd7L7mzrDM8WPjSiX4CyMRz7UVTPNdsPAFYAjF1TmR0JDc8fAM3PHwDeTvz2DjDc1Qy94G5RMVG0f3B7iSnJ9Oqdyv7sqoB5zvDczNz2Tx7M8cPHLfO8CByWT+nVPUI8LLzMKZOa9apGQOfHUjqb1PZu3IvORk55EzPYf1f1xPfNZ7k9GR6jOthp7gC4HxneG5mLoe2HrLO8CBlx+PGXEJIaAgdB3ek4+COnH7pNFuyPKe4lj2+jOVPLueatGvomd6TLsPsFJc/ik8Xk7cjj0PbDnF422E+e/szVuWuAqwzPNjZp92YyxAVE0Wv7/ai13d7kbcjj5wZOWyauYm5o+YSFed1iivFTnGVdeb4GQ5vO8yhbYc4tNWTLA5vO8zR3UfREs9kFxIiNGzf0DrDawlLIMb4qVnnZgx6bhADnhnAnhWeUVw503JY/5f1JHRLoGd6T88prnp2m9MTh05UmCgK9hdcWCckPIT4LvG0uL4F3cd0J6FrAgndEmjWuRlrPl7Dram3uvgOjK8sgRhzhUJCQ7j6tqu5+rarOZ3vOcWVk5HDsp+XnuJKTk+m87DOdWY6eVWlYH+BJ1FsPXTh9NOhrYc4lXfqwnrhjcJJ6JpA+wHtie8aT0K3BBK6JhDXMY6QMOv8ru3qxqfZmCARFRtFr8m96DW5F4e3H/aM4pq5kTfvf5OouCiSxiSRnJ5My14ta8UprpLiEo7uOXohOZw/sji87TBnC89eWK9B0wYkdEug64iuFyWK6DbRSEjwv0/jH0sgxgRIfJd4Bv3nIAb8bgB7lu8hZ3oOn77yKeteWkfz7s09p7jG9qix+02oKiVFJZw7e45zZ89RfKb4wvNzZ85RfLqYI18cuXDK6dDWQ+TtyOPc2XMXXqNJ6yYkdE0geWIyCd0SPMmiawINExrWioRoqpclEGMCLCQ0hKtvv5qrb/ec4tr8xmY2Zmxk6U+XsuzxZXQa0gm9Rlm3bd1FX+gVfdGXnC25+Iu/inXL1nkngioJxHWMI6FrAtcMueZCooi/Np6omKjANpapVSyBGFODomKjSPleCinfS+Hw54cvjOI6/u5xdrKz3PqhkaGERpQ+wiLDSpe96iKaRFxcV6a+ytfxqo9tH0uzzs3sOgvjE0sgxrgk/tp4Bj8/mIHPDuT9t96nb7++F33Rh4SF2GkhE9QsgRjjspDQEKKaR9m9t02t48o4OhFpKiJLRWSn8zeukvXOiUiO85jvVd5BRD5xtn9DRCJqLnpjjDHgUgLBMxnjclXtBCx3litySlWTncfdXuW/B/7obH8UeDiw4RpjjCnLrQQyHJjhPJ+BZ7p4n4jnpPBAYK4/2xtjjKkeoqo1v1ORfFWN9Vo+qqrlTmOJSDGQAxQDU1T1HRGJBz5W1WucddoC76lq90r2NRmYDJCYmNhrzpw51f+GLkNhYSGNG9u5brC28GZtUcraolSwtMWAAQM2qGpK2fKAdaKLyDKgRQVVv7iMl7lKVQ+ISEdghYjkAgUVrFdpFlTVqcBUgJSUFE1NTb2M3Ve/7Oxs3I4hWFhblLK2KGVtUSrY2yJgCURVB1dWJyLfiEhLVf1aRFoC31byGgecv7tFJBu4HngLiBWRMFUtBtoAB6r9DRhjjKmSW30g84EJzvMJwD/KriAicSIS6TyPB24BtqrnnNtKYGRV2xtjjAkstxLIFOA2EdkJ3OYsIyIpIvKKs05XYL2IbMSTMKao6lan7nHgJyKyC2gGvFqj0RtjjHHnQkJVzQMGVVC+HnjEeb4WSKpk+93AjYGM0RhjTNVsQn5jjDF+sQRijDHGL5ZAjDHG+KVeTaaYl5dHRkaGqzHk5+ezd+9eV2MIFtYWpawtSllblAr2trAjEGOMMX5xZSoTt6SkpOj69etdjSHYryytSdYWpawtSllblAqWthCRCqcysSMQY4wxfrEEYowxxi+WQIwxxvjFEogxxhi/WAIxxhjjF0sgxhhj/GIJxBhjjF8sgRhjjPGLJRBjjDF+sQRijDHGL5ZAjDHG+MUSiDHGGL+4kkBEpKmILBWRnc7fuArWGSAiOV6P0yJyj1OXISJ7vOqSa/5dGGNM/ebWEcgTwHJV7QQsd5YvoqorVTVZVZOBgcBJ4H2vVX52vl5Vc2okamOMMRe4lUCGAzOc5zOAey6x/kjgPVU9GdCojDHG+MytBJKoql8DOH+bX2L90cDsMmXPicgmEfmjiEQGIkhjjDGVC9gNpURkGdCigqpfADNUNdZr3aOqWq4fxKlrCWwCWqlqkVfZQSACmAp8oarPVLL9ZGAyQGJiYq85c+b4/6aqQWFhIY0bN3Y1hmBhbVHK2qKUtUWpYGmLAQMGVHhDqYDdE11VB1dWJyLfiEhLVf3aSQbfVvFSo4B555OH89pfO0/PiMh04KdVxDEVT5IhJSVF3b67V7DcYSwYWFuUsrYoZW1RKtjbImAJ5BLmAxOAKc7ff1Sx7oPAk94FXslH8PSfbPZlp3l5eWRkZFxUdt1119G7d2+KiorIzMwst01ycjLJycmcPHmSrKyscvUpKSl0796dY8eOMW/evHL1ffr0oUuXLhw+fJh3332X/Px89u7de6G+X79+dOzYkYMHD7J48eJy2w8aNIi2bduyb98+li9fXq4+LS2NFi1asHv3blavXl2ufujQocTHx7N9+3Y++uijcvX33nsvMTExbN68mYpu9ztq1CgaNmxITk4OOTnlxyqMHTuW8PBw1q1bx5YtW8rVp6enA7B27Vp27NhxUV1BQcGF/xyrVq1iz549F9U3bNiQUaNGAbBs2TL2799/UX10dDQjRowAYPHixRw8ePCi+mbNmjFs2DAAFixYQF5e3kX1LVq0IC0tDYC3336bgoKCi+rbtGnD4MGe30FZWVmcPHlxF1yHDh3o378/AJmZmRQVFV1U37lzZ26++WaAcp87uPizl5OTc9HnAqr/s1dWsH72zv8fCeRnLzw8nLFjxwL22fPne+88t/pApgC3ichO4DZnGRFJEZFXzq8kIu2BtsCqMttnikgukAvEA8/WQMzGGGO8BKwPJBilpKRoRb+ya1KwH5LWJGuLUtYWpawtSgVLW4hIhX0gdiW6McYYv1gCMcYY4xdLIMYYY/xiCcQYY4xfLIEYY4zxiyUQY4wxfrEEYowxxi+WQIwxxvjFEogxxhi/WAIxxhjjF0sgxhhj/GIJxBhjjF8sgRhjjPGLJRBjjDF+sQRijDHGL5ZAjDHG+MUSiDHGGL9YAjHGGOMXSyDGGGP84koCEZH7RWSLiJSISLn77HqtlyYi20Vkl4g84VXeQUQ+EZGdIvKGiETUTOTGGGPOc+sIZDMwAlhd2QoiEgq8BAwBugEPikg3p/r3wB9VtRNwFHg4sOEaY4wpy5UEoqrbVHX7JVa7EdilqrtV9SwwBxguIgIMBOY6680A7glctMYYYyoS5nYAVWgN7PNa3g/cBDQD8lW12Ku8dWUvIiKTgcnOYqGIXCpxBVo8cNjlGIKFtUUpa4tS1halgqUt2lVUGLAEIiLLgBYVVP1CVf/hy0tUUKZVlFdIVacCU33YX40QkfWqWmm/T31ibVHK2qKUtUWpYG+LgCUQVR18hS+xH2jrtdwGOIAnG8eKSJhzFHK+3BhjTA0K5mG864BOzoirCGA0MF9VFVgJjHTWmwD4ckRjjDGmGrk1jPdeEdkP9AEWisgSp7yViCwCcI4uHgOWANuALFXd4rzE48BPRGQXnj6RV2v6PVyBoDmdFgSsLUpZW5SytigV1G0hnh/0xhhjzOUJ5lNYxhhjgpglEGOMMX6xBFJDRKStiKwUkW3ONC4/cjsmt4lIqIh8JiLvuh2Lm0QkVkTmisjnzuejj9sxuUVEfuz8/9gsIrNFJMrtmGqKiEwTkW9FZLNXWVMRWepM27RUROLcjLEsSyA1pxj4D1XtCnwH+HevqVnqqx/hGSBR3/0JWKyq1wI9qadtIiKtgR8CKaraHQjFM/qyvsgA0sqUPQEsd6ZtWu4sBw1LIDVEVb9W1U+d58fxfElUegV9XScibYC7gFfcjsVNIhIN9MMZSaiqZ1U1392oXBUGNBCRMKAh9egaL1VdDRwpUzwcz3RNEITTNlkCcYGItAeuBz5xNxJX/S/wc6DE7UBc1hE4BEx3Tue9IiKN3A7KDar6FfAH4Evga+CYqr7vblSuS1TVr8HzIxRo7nI8F7EEUsNEpDHwFvD/VLXA7XjcICJDgW9VdYPbsQSBMOAG4K+qej1wgiA7TVFTnPP7w4EOQCugkYiMczcqUxVLIDVIRMLxJI9MVX3b7XhcdAtwt4jsxTPL8kARmeVuSK7ZD+xX1fNHo3PxJJT6aDCwR1UPqWoR8DZws8sxue0bEWkJ4Pz91uV4LmIJpIY409C/CmxT1RfcjsdNqvqkqrZR1fZ4OklXqGq9/KWpqgeBfSLSxSkaBGx1MSQ3fQl8R0QaOv9fBlFPBxR4mY9nuiYIwmmbgnk697rmFuAhIFdEcpyyp1R1kYsxmeDwAyDTmfNtNzDR5XhcoaqfiMhc4FM8oxY/I8in8qhOIjIbSAXinamefg1MAbJE5GE8CfZ+9yIsz6YyMcYY4xc7hWWMMcYvlkCMMcb4xRKIMcYYv1gCMcYY4xdLIMYYY/xiCcSYGiAihW7HYEx1swRijDHGL5ZAjHGJiAwTkU+cSRSXiUiiU57g3PvhUxF5WUT+JSLxbsdrTFmWQIxxzxrgO84kinPwzE4MniuQV6jqDcA84CqX4jOmSjaViTHuaQO84UySFwHsccr7AvcCqOpiETnqUnzGVMmOQIxxz4vAn1U1CfgecP72reJeSMb4zhKIMe6JAb5ynk/wKl8DjAIQkduBoLoPtjHn2WSKxtQAESnh4tuzvgB8AfwRTxL5GOitqqki0hyYjSdxrAIeADqo6pmajdqYqlkCMSbIiEgkcE5Vi0WkD567FSa7HZcxZVknujHB5yo894AIAc4C33U5HmMqZEcgxhhj/GKd6MYYY/xiCcQYY4xfLIEYY4zxiyUQY4wxfrEEYowxxi//H5vnRrW++x8tAAAAAElFTkSuQmCC\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted value = 270.351902, expected value = 301.090843\n",
+      "predicted value = 282.759995, expected value = 280.225539\n",
+      "predicted value = 262.294793, expected value = 236.175711\n",
+      "predicted value = 205.946926, expected value = 230.738636\n",
+      "RMSE: 23.707\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Company name : ABB\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted value = 1340.508085, expected value = 1385.768171\n",
+      "predicted value = 1381.581589, expected value = 1298.446334\n",
+      "predicted value = 1363.649699, expected value = 1134.712187\n",
+      "predicted value = 1139.773055, expected value = 1060.549670\n",
+      "RMSE: 130.047\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Company name : ABBOTINDIA\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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted value = 3939.385900, expected value = 4168.029799\n",
+      "predicted value = 4103.432054, expected value = 4706.805640\n",
+      "predicted value = 4982.864299, expected value = 5432.858477\n",
+      "predicted value = 6008.213684, expected value = 5750.471627\n",
+      "RMSE: 413.903\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"
+    }
+   ],
+   "source": [
+    "for company in unique_values[:5]:\n",
+    "    data=(df.loc[company,:]).reset_index()\n",
+    "    print (\"Company name : %s\" %company)\n",
+    "    data['Price'] = data['Close']\n",
+    "    data['Date'] = data['Date'].map(lambda x: str(x)[:7])\n",
+    "    Quantity_date = data[['Price','Date']]\n",
+    "    QDcount = Quantity_date.groupby(['Date'])['Price'].aggregate('mean').reset_index().sort_values(by='Date', ascending=0)\n",
+    "    date = list(QDcount['Date'])\n",
+    "    quantity=list(QDcount['Price'])\n",
+    "    date_quantity = pd.DataFrame({'dates': date, 'quantity':quantity})\n",
+    "    date_quantity.index = date_quantity['dates'].map(lambda x: parser(x))\n",
+    "    date_quantity['quantity'] = date_quantity['quantity'].map(lambda x: float(x))\n",
+    "    date_quantity = date_quantity.fillna(date_quantity.bfill())\n",
+    "    date_quantity = date_quantity['quantity'].resample('MS').mean()\n",
+    "    \n",
+    "    #autosorelation plot\n",
+    "    autocorrelation_plot(date_quantity,color='purple')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    #date and prices plot\n",
+    "    date_quantity.plot(color='orange')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    #train and test data\n",
+    "    quantity = date_quantity.values\n",
+    "    size = int(len(quantity) * 0.66)\n",
+    "    train, test = quantity[0:size], quantity[size:len(quantity)]\n",
+    "    \n",
+    "    #fit in model\n",
+    "    predictions = arima_model(train, test)\n",
+    "    \n",
+    "    #rmse \n",
+    "    error = math.sqrt(mean_squared_error(test, predictions))\n",
+    "    print('RMSE: %.3f' % error)\n",
+    "    \n",
+    "    #plot graph\n",
+    "    plt.plot(test,color='green' )\n",
+    "    plt.plot(predictions, color='red')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (Random Forest & Linear Regression).ipynb b/intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (Random Forest & Linear Regression).ipynb
new file mode 100644
index 0000000..d0f2f16
--- /dev/null
+++ b/intern-basics/Stock Market Analysis and Prediction/Stock_Market_Prediction- (Random Forest & Linear Regression).ipynb	
@@ -0,0 +1,869 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stock Market Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Forest and Linear regression algorithm for Time Series Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import math\n",
+    "import sklearn.preprocessing as prep\n",
+    "\n",
+    "from matplotlib.pyplot import rcParams\n",
+    "from datetime import date\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "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>Code</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-18</td>\n",
+       "      <td>7.437910</td>\n",
+       "      <td>7.446311</td>\n",
+       "      <td>7.427869</td>\n",
+       "      <td>7.435041</td>\n",
+       "      <td>2538.135246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-19</td>\n",
+       "      <td>7.582241</td>\n",
+       "      <td>7.597414</td>\n",
+       "      <td>7.571207</td>\n",
+       "      <td>7.583621</td>\n",
+       "      <td>2778.203448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-22</td>\n",
+       "      <td>7.782296</td>\n",
+       "      <td>7.793385</td>\n",
+       "      <td>7.769650</td>\n",
+       "      <td>7.781907</td>\n",
+       "      <td>6414.482490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-23</td>\n",
+       "      <td>7.771465</td>\n",
+       "      <td>7.778030</td>\n",
+       "      <td>7.762879</td>\n",
+       "      <td>7.769444</td>\n",
+       "      <td>1944.929293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-24</td>\n",
+       "      <td>8.321127</td>\n",
+       "      <td>8.347465</td>\n",
+       "      <td>8.294648</td>\n",
+       "      <td>8.321972</td>\n",
+       "      <td>10216.726761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-26</td>\n",
+       "      <td>8.499172</td>\n",
+       "      <td>8.517881</td>\n",
+       "      <td>8.485762</td>\n",
+       "      <td>8.504636</td>\n",
+       "      <td>52045.635762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-29</td>\n",
+       "      <td>9.390217</td>\n",
+       "      <td>9.418071</td>\n",
+       "      <td>9.358560</td>\n",
+       "      <td>9.386957</td>\n",
+       "      <td>21635.451087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-30</td>\n",
+       "      <td>9.121958</td>\n",
+       "      <td>9.140356</td>\n",
+       "      <td>9.106677</td>\n",
+       "      <td>9.124332</td>\n",
+       "      <td>6166.682493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2014-12-31</td>\n",
+       "      <td>9.409392</td>\n",
+       "      <td>9.434392</td>\n",
+       "      <td>9.388122</td>\n",
+       "      <td>9.410359</td>\n",
+       "      <td>12830.422652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3IINFOTECH</td>\n",
+       "      <td>2015-01-01</td>\n",
+       "      <td>9.611657</td>\n",
+       "      <td>9.631461</td>\n",
+       "      <td>9.592275</td>\n",
+       "      <td>9.612921</td>\n",
+       "      <td>14989.721910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Code        Date      Open      High       Low     Close  \\\n",
+       "0  3IINFOTECH  2014-12-18  7.437910  7.446311  7.427869  7.435041   \n",
+       "1  3IINFOTECH  2014-12-19  7.582241  7.597414  7.571207  7.583621   \n",
+       "2  3IINFOTECH  2014-12-22  7.782296  7.793385  7.769650  7.781907   \n",
+       "3  3IINFOTECH  2014-12-23  7.771465  7.778030  7.762879  7.769444   \n",
+       "4  3IINFOTECH  2014-12-24  8.321127  8.347465  8.294648  8.321972   \n",
+       "5  3IINFOTECH  2014-12-26  8.499172  8.517881  8.485762  8.504636   \n",
+       "6  3IINFOTECH  2014-12-29  9.390217  9.418071  9.358560  9.386957   \n",
+       "7  3IINFOTECH  2014-12-30  9.121958  9.140356  9.106677  9.124332   \n",
+       "8  3IINFOTECH  2014-12-31  9.409392  9.434392  9.388122  9.410359   \n",
+       "9  3IINFOTECH  2015-01-01  9.611657  9.631461  9.592275  9.612921   \n",
+       "\n",
+       "         Volume  \n",
+       "0   2538.135246  \n",
+       "1   2778.203448  \n",
+       "2   6414.482490  \n",
+       "3   1944.929293  \n",
+       "4  10216.726761  \n",
+       "5  52045.635762  \n",
+       "6  21635.451087  \n",
+       "7   6166.682493  \n",
+       "8  12830.422652  \n",
+       "9  14989.721910  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.read_csv('C:/Users/DELL/Downloads/groupeddf.csv')\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 79322 entries, 0 to 79321\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   Code    79322 non-null  object \n",
+      " 1   Date    79322 non-null  object \n",
+      " 2   Open    79322 non-null  float64\n",
+      " 3   High    79322 non-null  float64\n",
+      " 4   Low     79322 non-null  float64\n",
+      " 5   Close   79322 non-null  float64\n",
+      " 6   Volume  79322 non-null  float64\n",
+      "dtypes: float64(5), object(2)\n",
+      "memory usage: 4.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "442"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_values = df[\"Code\"].unique()\n",
+    "len(unique_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=df.set_index(\"Code\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_df=pd.DataFrame()\n",
+    "\n",
+    "for i in unique_values:\n",
+    "    df2 = (df1.loc[i,:]).groupby(['Code','Date']).mean()\n",
+    "    grouped_df=grouped_df.append(df2)\n",
+    "grouped_df.reset_index()\n",
+    "del df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-18</td>\n",
+       "      <td>561.963918</td>\n",
+       "      <td>562.615636</td>\n",
+       "      <td>561.317698</td>\n",
+       "      <td>562.008419</td>\n",
+       "      <td>148.219931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-19</td>\n",
+       "      <td>588.985235</td>\n",
+       "      <td>589.995805</td>\n",
+       "      <td>588.078859</td>\n",
+       "      <td>589.031208</td>\n",
+       "      <td>335.969799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-22</td>\n",
+       "      <td>603.079123</td>\n",
+       "      <td>603.608772</td>\n",
+       "      <td>602.417544</td>\n",
+       "      <td>603.047193</td>\n",
+       "      <td>169.870175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-23</td>\n",
+       "      <td>600.358528</td>\n",
+       "      <td>600.876254</td>\n",
+       "      <td>599.931438</td>\n",
+       "      <td>600.357692</td>\n",
+       "      <td>97.444816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-24</td>\n",
+       "      <td>588.538106</td>\n",
+       "      <td>589.038987</td>\n",
+       "      <td>588.018722</td>\n",
+       "      <td>588.531498</td>\n",
+       "      <td>117.449339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>2015-09-24</td>\n",
+       "      <td>1425.896581</td>\n",
+       "      <td>1426.803205</td>\n",
+       "      <td>1425.048077</td>\n",
+       "      <td>1425.834615</td>\n",
+       "      <td>85.448718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>2015-09-28</td>\n",
+       "      <td>1413.101322</td>\n",
+       "      <td>1413.839868</td>\n",
+       "      <td>1412.314537</td>\n",
+       "      <td>1412.951322</td>\n",
+       "      <td>71.374449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>2015-09-29</td>\n",
+       "      <td>1383.679461</td>\n",
+       "      <td>1384.679253</td>\n",
+       "      <td>1382.544606</td>\n",
+       "      <td>1383.513900</td>\n",
+       "      <td>82.506224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>2015-09-30</td>\n",
+       "      <td>1400.488693</td>\n",
+       "      <td>1400.946985</td>\n",
+       "      <td>1399.891960</td>\n",
+       "      <td>1400.346734</td>\n",
+       "      <td>54.628141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>2015-10-01</td>\n",
+       "      <td>1423.937900</td>\n",
+       "      <td>1424.694521</td>\n",
+       "      <td>1422.964840</td>\n",
+       "      <td>1423.700000</td>\n",
+       "      <td>94.360731</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>183 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Date         Open         High          Low        Close  \\\n",
+       "0    2014-12-18   561.963918   562.615636   561.317698   562.008419   \n",
+       "1    2014-12-19   588.985235   589.995805   588.078859   589.031208   \n",
+       "2    2014-12-22   603.079123   603.608772   602.417544   603.047193   \n",
+       "3    2014-12-23   600.358528   600.876254   599.931438   600.357692   \n",
+       "4    2014-12-24   588.538106   589.038987   588.018722   588.531498   \n",
+       "..          ...          ...          ...          ...          ...   \n",
+       "178  2015-09-24  1425.896581  1426.803205  1425.048077  1425.834615   \n",
+       "179  2015-09-28  1413.101322  1413.839868  1412.314537  1412.951322   \n",
+       "180  2015-09-29  1383.679461  1384.679253  1382.544606  1383.513900   \n",
+       "181  2015-09-30  1400.488693  1400.946985  1399.891960  1400.346734   \n",
+       "182  2015-10-01  1423.937900  1424.694521  1422.964840  1423.700000   \n",
+       "\n",
+       "         Volume  \n",
+       "0    148.219931  \n",
+       "1    335.969799  \n",
+       "2    169.870175  \n",
+       "3     97.444816  \n",
+       "4    117.449339  \n",
+       "..          ...  \n",
+       "178   85.448718  \n",
+       "179   71.374449  \n",
+       "180   82.506224  \n",
+       "181   54.628141  \n",
+       "182   94.360731  \n",
+       "\n",
+       "[183 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3=grouped_df.loc[\"8KMILES\",:]\n",
+    "df4=df3.reset_index()\n",
+    "label=df4['Date'].values.tolist()\n",
+    "trainset=df4['Open'].values.tolist()\n",
+    "df4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ds(dataset,past=1):\n",
+    "    dataX, dataY = [], []\n",
+    "    for i in range(len(dataset)-past-1):\n",
+    "        j = dataset[i:(i+past), 0]\n",
+    "        dataX.append(j)\n",
+    "        dataY.append(dataset[i + past, 0])\n",
+    "    return np.array(dataX), np.array(dataY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_train_func(prices):\n",
+    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    prices = scaler.fit_transform(prices)\n",
+    "    trainsize = int(len(prices) * 0.80)\n",
+    "    testsize = len(prices) - trainsize\n",
+    "    train, test = prices[0:trainsize,:], prices[trainsize:len(prices),:]\n",
+    "    print(len(train), len(test))\n",
+    "    \n",
+    "    x_train,y_train = ds(train,1)\n",
+    "    x_test,y_test = ds(test,1)\n",
+    "    \n",
+    "    x_train = scaler.fit_transform(x_train)\n",
+    "    x_test = scaler.fit_transform(x_test)\n",
+    "    \n",
+    "    return x_train,y_train, x_test,y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(183, 1)"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prices = df4['Close'].values.astype('float32') \n",
+    "prices = prices.reshape(len(prices), 1)\n",
+    "prices.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146 37\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainX, trainY, testX, testY = test_train_func(prices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.\n",
+      "[Parallel(n_jobs=5)]: Done  31 tasks      | elapsed:    0.0s\n",
+      "[Parallel(n_jobs=5)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "building tree 1 of 100building tree 2 of 100\n",
+      "\n",
+      "building tree 3 of 100\n",
+      "building tree 4 of 100\n",
+      "building tree 5 of 100\n",
+      "building tree 6 of 100building tree 7 of 100\n",
+      "building tree 8 of 100\n",
+      "\n",
+      "building tree 9 of 100\n",
+      "building tree 10 of 100\n",
+      "building tree 11 of 100\n",
+      "building tree 12 of 100building tree 13 of 100\n",
+      "\n",
+      "building tree 14 of 100\n",
+      "building tree 15 of 100\n",
+      "building tree 16 of 100\n",
+      "building tree 17 of 100building tree 18 of 100\n",
+      "\n",
+      "building tree 19 of 100building tree 20 of 100\n",
+      "\n",
+      "building tree 21 of 100\n",
+      "building tree 22 of 100building tree 23 of 100\n",
+      "building tree 24 of 100building tree 25 of 100\n",
+      "\n",
+      "\n",
+      "building tree 26 of 100\n",
+      "building tree 27 of 100building tree 28 of 100\n",
+      "\n",
+      "building tree 29 of 100\n",
+      "building tree 30 of 100\n",
+      "building tree 31 of 100\n",
+      "building tree 32 of 100building tree 33 of 100building tree 34 of 100building tree 35 of 100\n",
+      "\n",
+      "\n",
+      "building tree 36 of 100\n",
+      "\n",
+      "building tree 37 of 100\n",
+      "building tree 38 of 100building tree 39 of 100\n",
+      "building tree 40 of 100\n",
+      "\n",
+      "building tree 41 of 100\n",
+      "building tree 42 of 100building tree 43 of 100\n",
+      "building tree 44 of 100\n",
+      "\n",
+      "building tree 45 of 100building tree 46 of 100\n",
+      "\n",
+      "building tree 47 of 100\n",
+      "building tree 48 of 100\n",
+      "building tree 49 of 100\n",
+      "building tree 50 of 100building tree 51 of 100\n",
+      "\n",
+      "building tree 52 of 100\n",
+      "building tree 53 of 100building tree 54 of 100\n",
+      "\n",
+      "building tree 55 of 100\n",
+      "building tree 56 of 100\n",
+      "building tree 57 of 100\n",
+      "building tree 58 of 100building tree 59 of 100building tree 60 of 100\n",
+      "building tree 61 of 100\n",
+      "\n",
+      "\n",
+      "building tree 62 of 100\n",
+      "building tree 63 of 100building tree 64 of 100\n",
+      "\n",
+      "building tree 65 of 100building tree 66 of 100\n",
+      "building tree 67 of 100\n",
+      "\n",
+      "building tree 68 of 100\n",
+      "building tree 69 of 100\n",
+      "building tree 70 of 100building tree 71 of 100\n",
+      "\n",
+      "building tree 72 of 100\n",
+      "building tree 73 of 100building tree 74 of 100building tree 75 of 100\n",
+      "building tree 76 of 100\n",
+      "\n",
+      "\n",
+      "building tree 77 of 100\n",
+      "building tree 78 of 100\n",
+      "building tree 79 of 100building tree 80 of 100building tree 81 of 100\n",
+      "building tree 82 of 100\n",
+      "\n",
+      "\n",
+      "building tree 83 of 100\n",
+      "building tree 84 of 100\n",
+      "building tree 85 of 100\n",
+      "building tree 86 of 100building tree 87 of 100\n",
+      "\n",
+      "building tree 88 of 100\n",
+      "building tree 89 of 100building tree 90 of 100building tree 91 of 100\n",
+      "\n",
+      "building tree 92 of 100\n",
+      "\n",
+      "building tree 93 of 100building tree 94 of 100\n",
+      "building tree 95 of 100\n",
+      "building tree 96 of 100\n",
+      "\n",
+      "building tree 97 of 100\n",
+      "building tree 98 of 100\n",
+      "building tree 99 of 100\n",
+      "building tree 100 of 100\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
+       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "                      max_samples=None, min_impurity_decrease=0.0,\n",
+       "                      min_impurity_split=None, min_samples_leaf=1,\n",
+       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "                      n_estimators=100, n_jobs=5, oob_score=False,\n",
+       "                      random_state=1, verbose=2, warm_start=False)"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "randomforest  = RandomForestRegressor(random_state=1,verbose=2,n_jobs=5)\n",
+    "randomforest.fit(trainX,trainY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=5)]: Using backend ThreadingBackend with 5 concurrent workers.\n",
+      "[Parallel(n_jobs=5)]: Done  31 tasks      | elapsed:    0.0s\n",
+      "[Parallel(n_jobs=5)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
+     ]
+    }
+   ],
+   "source": [
+    "test=[]\n",
+    "test= randomforest.predict(testX)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.3665992 , 0.34853208, 0.3367042 , 0.3455994 , 0.36045766,\n",
+       "       0.37352097, 0.3716843 , 0.46163356, 0.49432492, 0.4646306 ,\n",
+       "       0.42408788, 0.57748544, 0.7374294 , 0.7817353 , 0.75124586,\n",
+       "       0.7573086 , 0.76831853, 0.8345448 , 0.79339325, 0.8194858 ,\n",
+       "       0.82750404, 0.94312084, 0.9119475 , 0.9489646 , 0.9445609 ,\n",
+       "       0.95504785, 0.98091006, 0.97550523, 1.        , 0.99246335,\n",
+       "       0.9764079 , 0.9988419 , 0.9839449 , 0.94990647, 0.96937025],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5016376801744173"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For Random Forest\n",
+    "math.sqrt(mean_squared_error(test,testY))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# For Random Forest\n",
+    "plt.plot(test,color=\"green\")\n",
+    "plt.plot(testY,color='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regr = linear_model.LinearRegression()\n",
+    "regr.fit(trainX, trainY)\n",
+    "test= regr.predict(testX)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5048180544142529"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For Linear Regression\n",
+    "math.sqrt(mean_squared_error(test,testY))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/+mLmRTYmbWTNwTWsPrCajUkbSc9OB8wEodDAUHo16sXgmwdL/7dwWkqpGK11SIHvSaCLsrIjZQc95/bE082TyOGR3Fz95htue/DMQUYuGsmq/asYEDyAL+/6kjqV6vBX+l/sO72Pvaf2svf0XvP49F72ntrLobOHyNbZuCk32tRqQ2hgKKGBoXQN7Iq/j38Z/qRC2I4EurC7bce20WtuL7w9vIkcHklwteAiP5Ojc/hs82e8tPwlPNw8qOBRgRNpJ67ZpppPNYL8gwiqam6d6nWiS4Mu+Hn72epHEcKuCgt06UMXNrf12FZ6ze3FTZ43ETk8kib+TSz6nJtyY2yHsfQN6sub697E2937Sng3rtqYxlUbS3ALkYcEurCpLclb6D23NxW9KhI5PJIg/6Bi7yO4WjBz7pljg+qEcC0S6MJm0jLT6PtNXypXqEzk8EgaVW1k75KEcGkS6MJm1hxYw4m0E3w99GsJcyHKgCwSLWwmPDEcHw8fujfsbu9ShCgXJNCFzUTsjSC0YSjeHt72LkWIckECXdjE/tP7STiZQL8gWexBiLIigS5sImKvWapNVu8RouxIoAubiNgbQaBfoKyrKUQZkkAXVpeRncHKfSvp16SfXDNFiDIkgS6s7o/Df3Au45x0twhRxiTQhdVF7I3Aw82Dno162rsUIcoVCXRhdeGJ4XSu35nKFSrbuxQhyhUJdGFVKedTiD0WK8MVhbADCXRhVcv2LgNkuKIQ9iCBLqwqfG84NXxrcGutW+1dihDljgS6sJocncOyvcvoG9TX6gs8CyGKJr91wmq2JG/hRNoJ6W4Rwk4k0IXVhCeGo1D0adzH3qUIUS5JoAurCU8Mp32d9gT4Bti7FCHKJQl0YRVnLp1hY9JGGa4ohB1JoAurWLlvJdk6m75N+tq7FCHKLQl0YRXhieH4VfCjU71O9i5FiHJLAl2Umtaa8L3h9G7cGw83WaZWCHuRQBeltuvELpL+SqJvkHS3CGFPEuii1MITwwGk/1wIO5NAF6UWnhhO84DmNPBrYO9ShCjXLAp0pVQ/pVSCUipRKfXKDbZ5QCkVr5SKU0p9Z90yhaNKy0xj7cG10t0ihAMo8gyWUsod+BToAyQBm5VSi7TW8Xm2CQZeBe7QWp9WStWwVcHCsaw5sIb07HSZ7i+EA7Ckhd4BSNRa79NaZwDzgLvzbTMa+FRrfRpAa33cumUKRxWeGI6Phw/dArvZuxQhyj1LAr0ucDjP86Tc1/JqCjRVSm1QSm1UShXYXFNKjVFKRSulolNTU0tWsXAoEXsjCG0YireHt71LEaLcsyTQC1q2Xed77gEEA92Bh4CvlFJVrvuQ1l9orUO01iEBAXK9D2e3//R+Ek4myHR/IRyEJYGeBNTP87wecLSAbX7VWmdqrfcDCZiAFy4sYm8EIKsTCeEoLAn0zUCwUqqRUsoLeBBYlG+bX4AeAEqp6pgumH3WLFQ4noi9EQT6BdK0WlN7lyKEwIJA11pnAWOBCGAXMF9rHaeUmqSUGpy7WQRwUikVD0QCL2qtT9qqaGF/GdkZrNy3kr5BfVGqoF45IURZs+jCG1rrJcCSfK9NyPNYA8/n3kQ5sP7Qes5lnGNA8AB7lyKEyCUzRUWJhP0Zhpe7F70a97J3KUKIXBLookTC9oTRvWF3KnpVtHcpQohcEuii2Pae2kvCyQQGBg+0dylCiDwk0EWxLdljTqdI/7kQjkUCXRRb2J4wmlZrShP/JvYuRQiRhwS6KJYLGRdYfWC1dLcI4YAk0EWxrNy/kvTsdAl0IRyQBLooliV7llDRqyJdA7vauxQhRD4S6MJiWmvC9oTRp3EfvNy97F2OECIfCXRhsR3Hd5D0V5J0twjhoCTQhcXC/gwDoH9wfztXIoQoiAS6sNiSxCW0rdWWOpXq2LsUIUQBJNCFRU5dPMXvh3+X7hYhHJgEurBIRGIEOTqHgU0l0IVwVBLoLmbFvhVsSd5i9f2G7Qmj+k3Vua3ObVbftxDCOiTQXURaZhqPL36cPl/34bYvb+Ol5S9xMfOiVfadnZNNeGI4/Zr0w93N3Sr7FEJYn0ULXAjHtvP4Th5c+CBxqXG82PlFzl46y7u/v8uihEXMunsWt9e/vVT733RkEycvnpT+cyEcnLTQnZjWmmnR07jty9s4kXaCiGERvNPnHabfNZ1lw5ZxMesid8y8gxciXiAtM63ExwnbE4a7cqdvUF8rVi+EsDYJdCd16uIp7l9wP0+GPUloYCjbntjGnUF3Xnm/T1Afdj65k8fbP857G9+jzbQ2rD+0vkTHCtsTRuf6nanqU9Va5QshbEAC3QmtP7SeNtPasChhEe/2eZclQ5dQs2LN67arVKESnw/6nJWPriQzJ5Nus7rxz/B/Fqu1fuSvI2w9tlWufS6EE5BAdyLZOdm8seYNQmeH4unuye8jf2d85/G4qcL/M/Zs1JMdT+7gqdue4oOoD2j9eWuikqIsOubSxKUA0n8uhBOQQHciw34exoTVE3iw5YPEPh7LbXUtH0JY0asinwz4hMjhkeToHLrP6c7ihMVFfi5sTxj1K9enZY2WpSldCFEGJNCdRFRSFPN2zuO1Lq/xzb3fULlC5RLtp3vD7kT9I4pWNVpx7w/3MjN25g23Tc9KZ/ne5QwMHohSqqSlCyHKiAS6k3h9zetU86nGq11fLXW4BvgGsGr4Kno17sWoRaN4a91baK2v227twbVcyLwgs0OFcBIS6E5g05FNLE1cyvjO46noVdEq+6zoVZHFDy1maKuhvLbqNcaFjyNH51yzzZI9S6jgXoEeDXtY5ZhCCNuSiUVO4PU1r+Pv48/Ttz1t1f16uXsx99651PStyXsb3yPlQgpz7plDBY8KgOk/79GoB75evlY9rhDCNiTQHdzmI5tZsmcJU3pOoVKFSlbfv5tyY2rfqdSuVJsXl7/IibQT/PT3n0g5n8KeU3t4tuOzVj+mEMI2JNAd3OXW+dgOY216nPGdx1PDtwYjfx1J99nd6dekH4CMPxfCiUigO7Doo9GE7Qljcs/JNmmd5/forY8ScFMA9y+4n9hjsTSr3ozGVRvb/LhCCOuQk6IObNKaSVT1rmrz1nle/YP7s+rRVdT0rckjrR8ps+MKIUpPWugOKuZoDIv/XMybPd4s8ZjzkupYryNHnj9S5AxUIYRjseg3VinVTymVoJRKVEq9Ush29yultFIqxHollk+T1prW+TMdn7HL8d3d3GUykRBOpsgWulLKHfgU6AMkAZuVUou01vH5tqsEPAtYdpEQcUNbkrewKGERb/R4o8xb50I4vOxsWL8eFi6EX36B8+ehTh2oXdvc539cpw7Urw8eJeiQyMmB6Gj47TdYtgyqVIH27aFdO3MfGAgO1PCx5CfsACRqrfcBKKXmAXcD8fm2ewN4Bxhv1QrLoUlrJlHFuwrPdLBP61wIh5OZCatXw48/ws8/w/Hj4O0N/ftD3bpw9CgkJ8PateZxZua1n7/pJggJgY4doUMHc1+vXsFhfP48rFgBixdDWBikpICbG3TqBMeOwTvvQFaW2bZatavh3q6dOUbDhnYLeUsCvS5wOM/zJKBj3g2UUm2B+lrr35RSNwx0pdQYYAxAgwYNil9tORCbHMuvCb8yqfsk/Lz97F2OENaVkwMbNsAPP5gA9vc3t2rVrr2/fNu2zYT4L7/AqVPg6wsDB8L995swr1jAzGmt4eRJs/+jR+HIEbOfqCj48EPIyDDb1a5tgr1jRxPIf/5pWuKRkZCeDn5+0K8f3HWXOZa/v/ncpUuwfTvExFy9/d//XQ35Dh1g/Hi4996S/augFCw5WkF/aq5c+EMp5Qa8DzxW1I601l8AXwCEhIRcf/EQwaS1pnUuE3qEy9DadFvMm2eC/MgR8PGBRo1MSJ88eX2LOq/KlWHwYPjb36BvX/PZwigF1aubW6tW176Xnn413KOiYNMm88fisuBgePppGDQIunQBT8/r9+/tbUK7Q4err126BDt3wrp18Nln8MAD5uf75z9h5Ejzh6gMqIIuynTNBkrdDkzUWvfNff4qgNb6rdznfsBe4HzuR2oBp4DBWuvoG+03JCRER0ff8O1yaeuxrbSd3pbXu7/OhNAJ9i5HiNLZudOE+Lx5sHevCcf+/eHBB02r93LrWmtISzPBfjngL9/Xrw+9e0OFCrar89Qp2LLFHOvmm0u/v+xsWLQI3n0X/vgDqlaFp56CsWOhVq1S714pFaO1LnDgiSWB7gH8CfQCjgCbgYe11nE32H41ML6wMAcJ9ILc98N9rNq/igPjDlDFu4q9yxHCMllZcPAgJCZevS1fDnFxpu+5Vy8T4vfea8KtPPn9d5g61fT7e3rCI4/ACy/ALbeUeJeFBXqRXS5a6yyl1FggAnAHZmqt45RSk4BorfWiEldWDmitiUuNI+mvJG7yvAlfT19z7+V75bm3hzfbU7bz8+6fmRg6UcJcFE9mpjlJWLduyfexbRu8+SZs3my6OPz8bny7dMm0uC+H98GDV/uPwXSJhITAp5+abpKa1y+PWG507mxue/bA++/DrFkwYwZ88onp2rGyIlvotlLSFnpmdiZuyg13N3cbVGUdaZlprNq/irA/w1iSuIRDZw8Vur1C4e7mjq+nr7TORfE99hjMmQM9e5qQGDzY8pNxW7bApEnw668myAcOhIsX4ezZ6295+7n9/KBJE3MLCrr6uEkT063gQEP5HEpqKnz+OTz0kOmvL4FSdbnYSkkDfWbsTCaunsjodqMZ1W4UdSrVsUF1xbf/9H7C9oQRtieMyP2RpGen4+vpS5+gPgxoMoAWNVpwMfMiaZlpXMi8YO4zzP3l13o07CGLSYjiCQ83/dL9+0N8vGkt160LY8bA6NFmJEdBNm2CN94wozqqVIFx4+DZZ2/cJaK1aZmfPWv+WFSrJqFtJy4V6JH7I3lr/Vss37ccd+XO4JsH83j7x+kT1KdMp6prrdmSvIUF8Qv4NeFXdp/YDUCwfzADgwcyIHgA3QK7Xbm2uBBWd+4ctGxpRlDExpqgXbLEjLIIDzfP77vPnJDr1s0E8MaN8Prr5n1/f3j+eXOyzk+GyDoLlwr0yxJPJfJlzJfM2jqL1LRUGlVpxOh2oxnZdiQ1K9qmz05rTfTRaBbGL2ThroXsO70PDzcPujfszqDgQQwIHkBwtZL9M0qIYnvmGdNPvWED3H77te8lJsK0aTBzJpw+DS1amL7sVatM63r8eNM9U8n2V/EU1uWSgX5ZelY6v+z+hekx04k8EImHmwf3NruXUW1HEdowFG8P71LtX2vN5qObWRC3gIW7FnLgzAE83Dzo3bg3Q5oP4Z5m9+Dv41/qn0OIYlm/Hrp2heeegw8+uPF2aWlm7Pdnn5mJNs89B08+WfCEHOEUXDrQ80o4kcAXMV8we9tsTl08RQX3CnRp0IVejXrRu3Fv2tVuV+TJ1EtZl9iesp2YozHEJMewYt8KDp49iKebJ32C+jCk+RAG3zxYQlzYz6VLcOutZsbjjh0SzuVMuQn0yy5lXWLV/lWs3LeSFftXsD1lOwBVvKvQo2EPejXqRa/GvQj0C2RbyrYr4b0leQtxqXFk5ZghWP4+/nSu35n7b7mfwTcPpqpPORtDKxzTa6/BW2+Zi0X16WPvakQZK3eBnt/xC8dZtX8VK/atuNLizq/6TdVpX7s97Wq3o33t9rSv055Av0C5hKxwLLGxcNtt8Oijpn9clDvlPtDz0lqz7/Q+VuxbwdFzR2lTqw3t67SnfuX6Et7CsWVmmuuHHDtmhiiWt1mXAijlTFFXo5QiyD+IIP8ge5ciRPFMnQpbt8JPP0mYiwLJGmNCOIOEBJg40Vw29t577V2NcFAS6EI4upwc+Mc/zCINH39s72qEA5NAF6K4cnJMH3ZZnX+aNs2MO3//fatcflW4Lgl0IYrj4kWzeEGLFmY6/caN1j9GVhYcPmz2/cMP8PLLcOedZmSLEIUodydFhSixkyfh7rvNVPvRo80iBrffbvq133rLXGnQUpdX8Vm5EpKSzCo+l+9TUsy/Ai7z94fp0+ViWKJIEuhCWGL/fnNFwwMHYP58GDLEjDqZOtWsTPPrr2ZK/X/+Y5Y+u5H4ePj+e7OKT2Kiea1qVXOFxGZ9MAEAAA7USURBVLp1oXVrs3jx5ef16kHjxnLNFWGRcjcOXYhii4kx1wnPyDDB3bXrte8nJ5sRKF99Zabhv/qquWbK5bUvDx40Af7dd2ZxYTc36NHDXBP73nuvLj4shAVkYpEQJbV0qWmNV69uHhe2dNiuXfDKK6Yrpl49GDHCdKn8/rt5v1MnE+IPPCAnN0WJFRboclJUiBuZMcMsZty0qVnst6h1IG+5xbTgV682C0u88Ya5ZvmUKbBvn9nHs89KmAubkT504diys+HLL+HDD6FdO3jiCejSxbYnCLU2XSiTJkHfvrBgQfH6sENDISrKLDdWo4bNyhQiP2mhC8e1YYO5ENWTT5pJNWFhZqhgy5Zmgs2ZM9Y93pkzZmm2ESNMmI8YAYsXl+yEpFIS5qLMSaALx5OcbMZcd+liWrnz5pkhfkeOmG4QX1/TdVGnDowaZVaqt/Rc0IULZoX7hQtNV8iIEXDHHSZ8q1aFjh3NgssTJphjeXra9mcVworkpKhwHBkZ8NFHpnWcnm6WSXvtNRPg+cXEmLHZ335rVuVp1w5GjgRvbzhxwvwhSE299nFqqgn0vOrUMauvN2169b5Fi+KNKReiDMkoF+H4li0zre6EBDNE8IMPLAvVs2dNqE+bZlbvuczbGwICrt6qVzf3NWpAUJAJ7yZNZHy3cDpy+VzhuLKyYOhQM1knKMj0WQ8aZPnn/fzMqvZPPgl79oCXlwnuglr1Qrg4CXRhX/Pnm9trr5lZlt4lXNRbKdNdIkQ5JoEu7EdrePttaN7cjNl2k3P0QpSGBLqwn6VLTb/3nDkS5kJYgfwWCft5+22oX99MhxdClJq00IV9bNgA69aZGaAy1lsIq5AWurCPt9+GatXMxCAhhFVIoIuyt2MH/PabGXcuwwuFsBqLAl0p1U8plaCUSlRKvVLA+88rpeKVUtuVUiuVUoHWL1W4jHfeMUH+9NP2rkQIl1JkoCul3IFPgf5Ac+AhpVTzfJvFAiFa69bAQuAdaxcqXMSBA2bFnjFjTJeLEMJqLGmhdwAStdb7tNYZwDzg7rwbaK0jtdZpuU83AvWsW6ZwGVOnmiGKzz9v70qEcDmWBHpd4HCe50m5r93IKGBpQW8opcYopaKVUtGpqamWVylcw/HjZpm2Rx4xK/oIIazKkkAvaCWBAq/opZQaBoQA7xb0vtb6C611iNY6JCAgwPIqhWv4+GNzFcUXX7R3JUK4JEvGoScB9fM8rwcczb+RUqo38C8gVGudbp3yhMs4dw4++cQsitysmb2rEcIlWdJC3wwEK6UaKaW8gAeBRXk3UEq1BaYDg7XWx61fpnB6X3xhVgR6+WV7VyKEyyoy0LXWWcBYIALYBczXWscppSYppQbnbvYuUBFYoJTaqpRadIPdifIoPR3eew969oQOHexdjRAuy6Kp/1rrJcCSfK9NyPO4t5XrEq7km2/g6FGYPdvelQjh0mSmqLCt7GwzkahdO+gtf/eFsCW5OJewrV9+gT//NItYqIIGTAkhrEVa6MJ2Li9gERwM991n72qEcHnSQhe2s2wZREfDl1+Cu7u9qxHC5UkLXdiG1mZZufr14dFH7V2NEOWCtNCFbaxebRax+OQT8PKydzVClAvSQhe2MWkS1K4tC1gIUYakhS6sb/1600J//33w9rZ3NUKUG9JCF9b3xhtQo4a55rkQosxIoAvr2rTJjG554QW46SZ7VyNEuSKBLqzrjTfA3x+efNLelQhR7kigC+vZssUs/vz881Cpkr2rEaLckZOi5c2+fTBjhlnP89FHoXp16+37zTehShUYO9Z6+xRCWExa6OWB1mbUyT33QJMmZjr+Cy9AnTrw4IOwciXk5JTuGDt2wM8/w7PPgp+fVcoWQhSPBLoru3QJZs2CNm2gRw8z0ee11+DwYdi5E556ypzA7N0bmjY1QX/sWMmONXkyVKwIzz1n3Z9BCGExCXRXlJwMEyZAgwYwcqRpfX/1FRw6ZLpF6tSBFi3ggw/Mdcq/+cYs2vzqq2aq/n33wdKl5tK3lti921xNcexYc0JUCGEXEuiuRGvTlRIYaIK7UyfTnbJ9u5mx6eNz/We8vWHoUNMls3s3jBsH69bBgAHQti0sWWL2W5jJk82+n3/eJj+WEMIyEuiuZMoUs9Tb0KHmGuSLFpll3yy9DvnNN8O778KRI/Dtt3DxIgwcaLproqIK/kxiInz3nRmmGBBgvZ9FCFFsEuiu4scf4d//hmHDYOZMc/KzpLy84OGHIT4ePv0Udu0yrf0hQ8wfirzeestsP3586eoXQpSaBLor2LLFDEHs1Mlce9xaKwN5epoTp4mJMHGi6Vdv3ty0xpOT4cABmDsXRo+GWrWsc0whRIkpXVT/qI2EhITo6OhouxzbpSQnw223gZsbbN4MNWva7lgpKWYm6PTpplV+880QFwd795qTqkIIm1NKxWitQwp6T1rozuziRTO2/MwZWLzYtmEOZv+ffGK6YAYNgthY+Mc/JMyFcBAyU9RZaW2GJG7aZCb03Hpr2R27SRP44Qczbr1u3bI7rhCiUBLozmryZJg3z5yUvOce+9TQqJF9jiuEKJB0uTijH3+E//wHHnkEXn7Z3tUIIRyE8wV6bCx8/rmZLGPpTEZXsmWLCfLbb4cvvrDeiBYhhNNzvi6X334z09rBXKK1Uyfo3NncOnZ0zgtDZWWZkSKnT0OFCmYEyeVb3uenT8PgwWYCz88/y/JuQohrON+wRa1h/374/fertx07zPVKlIKWLU2433EHdOtmpsE7Cq3NLMwdO8zFsS7fx8dDerpl+/D1NRfZKsuToEIIh1HYsEXnC/SC/PWXGe1xOeD/+MO8BuZiU926mVvXrtCsWdl0U2RmmuF9MTHmtm2bCe8zZ65uU6cOtGplbi1bmnU4MzKu3tLTr3/cty+EFPjfUghRDrh+oOeXnW3Cc906c1u79uplYQMCTLB37Wr6oW+5BSpXLt3xMjLMBJuYGNPHHRNj+vgvXTLvV6xoWtSXg/vyvVyZUAhRTOUv0PPT2kxfX7v2asDv33/1/Tp1TLA3a3b1vlkz87pSpo/7yBFz+dlDh+DgwWvvExNNqIPp12/XDtq3v3oLDjYzOYUQopRKHehKqX7Ah4A78JXW+u1871cA5gLtgZPA37XWBwrbp92n/iclQXS0uWTs7t2me2T37qtdNWDC2c/PXDM8/4o+1aub640HBkJQ0NXwDgqS8BZC2ExhgV7kKBellDvwKdAHSAI2K6UWaa3j82w2CjittW6ilHoQ+B/w99KXbkP16l0/ZV1r0zVzOdwvB3yDBtffbrrJPnULIcQNWDJssQOQqLXeB6CUmgfcDeQN9LuBibmPFwKfKKWUtld/TkkpBbVrm1vPnvauRgghisWSvoG6wOE8z5NyXytwG611FnAWqJZ/R0qpMUqpaKVUdGpqaskqFkIIUSBLAr2gMX75W96WbIPW+gutdYjWOiRAVrcRQgirsiTQk4D6eZ7XA47eaBullAfgB5yyRoFCCCEsY0mgbwaClVKNlFJewIPAonzbLAKG5z6+H1jldP3nQgjh5Io8Kaq1zlJKjQUiMMMWZ2qt45RSk4BorfUiYAbwtVIqEdMyf9CWRQshhLieRRfn0lovAZbke21CnseXgCHWLU0IIURxyAwYIYRwERLoQgjhIux2LRelVCpwsIQfrw6csGI5ZUFqLhvOVrOz1QtSc1m5Uc2BWusCx33bLdBLQykVfaNrGTgqqblsOFvNzlYvSM1lpSQ1S5eLEEK4CAl0IYRwEc4a6F/Yu4ASkJrLhrPV7Gz1gtRcVopds1P2oQshhLies7bQhRBC5COBLoQQLsLpAl0p1U8plaCUSlRKvWLveiyhlDqglNqhlNqqlLLjuns3ppSaqZQ6rpTamec1f6XUcqXUntz7qvasMa8b1DtRKXUk93veqpQaYM8a81NK1VdKRSqldiml4pRSz+W+7pDfcyH1Ouz3rJTyVkptUkpty6359dzXGymlonK/4x9yLzToEAqpebZSan+e77lNkTvTWjvNDXNxsL1AY8AL2AY0t3ddFtR9AKhu7zqKqLEb0A7Ymee1d4BXch+/AvzP3nUWUe9EYLy9ayuk5tpAu9zHlYA/geaO+j0XUq/Dfs+YtRkq5j72BKKATsB84MHc16cBT9q7Vgtqng3cX5x9OVsL/cpyeFrrDODycniilLTWa7n+GvZ3A3NyH88B7inTogpxg3odmtY6WWu9JffxOWAXZrUvh/yeC6nXYWnjfO5Tz9ybBnpilscEB/qOodCai83ZAt2S5fAckQaWKaVilFJj7F1MMdTUWieD+eUGati5HkuMVUptz+2ScYiui4IopRoCbTGtMYf/nvPVCw78PSul3JVSW4HjwHLMv+rPaLM8JjhgbuSvWWt9+XuenPs9v6+UqlDUfpwt0C1a6s4B3aG1bgf0B55WSnWzd0Eu6nMgCGgDJANT7VtOwZRSFYEfgXFa67/sXU9RCqjXob9nrXW21roNZnW1DsAtBW1WtlUVLn/NSqmWwKtAM+A2wB94uaj9OFugW7IcnsPRWh/NvT8O/Iz5n8wZpCilagPk3h+3cz2F0lqn5P5i5ABf4oDfs1LKExOO32qtf8p92WG/54LqdYbvGUBrfQZYjemPrpK7PCY4cG7kqblfbpeX1lqnA7Ow4Ht2tkC3ZDk8h6KU8lVKVbr8GLgT2Fn4pxxG3qUFhwO/2rGWIl0OxVz34mDfs1JKYVb32qW1fi/PWw75Pd+oXkf+npVSAUqpKrmPfYDemL7/SMzymOBA3zHcsObdef7IK0yff5Hfs9PNFM0dIvUBV5fDm2znkgqllGqMaZWDWSHqO0esWSn1PdAdc8nOFOC/wC+Y0QENgEPAEK21Q5yIvEG93THdABozsujxy33TjkAp1QVYB+wAcnJffg3TL+1w33Mh9T6Eg37PSqnWmJOe7pgG63yt9aTc38N5mK6LWGBYbsvX7gqpeRUQgOlq3go8kefkacH7crZAF0IIUTBn63IRQghxAxLoQgjhIiTQhRDCRUigCyGEi5BAF0IIFyGBLoQQLkICXQghXMT/A0kYipfKMppnAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# For Linear Regression\n",
+    "plt.plot(test,color=\"red\")\n",
+    "plt.plot(testY,color='green')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From f3df5635b5e9326efab20a143b5d1c1916607795 Mon Sep 17 00:00:00 2001
From: Shalaka Saraogi <62024191+shalakasaraogi@users.noreply.github.com>
Date: Sun, 6 Dec 2020 17:15:44 +0530
Subject: [PATCH 3/4] Update README.md

---
 .../README.md                                 | 35 +++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/intern-basics/Stock Market Analysis and Prediction/README.md b/intern-basics/Stock Market Analysis and Prediction/README.md
index 8b13789..510e912 100644
--- a/intern-basics/Stock Market Analysis and Prediction/README.md	
+++ b/intern-basics/Stock Market Analysis and Prediction/README.md	
@@ -1 +1,36 @@
+# Stock Market Analysis & Prediction
+In this project, we used some algorithm to forecast stock market prices for NSE India stock market. Main goal is to compare various algorithms and evaluate models by comparing prediction accuracy. We examined different models - Random Forest, Linear Regression and ARIMA based on the accuracy (RMSE) and predicted the price of different industries.
 
+## Content
+A data frame with 8 variables: index, date, time, open, high, low, close and id. For each year from 2013 to 2016, the number of trading data of each minute of given each date. The currency of the price is Indian Rupee (INR).
+
+* Code : market id
+* Date : numerical value (Ex. 20151203- to be converted as 2015-12-03)
+* Time : factor (Ex. 09:16)
+* Open : numeric (opening price)
+* High : numeric (high price)
+* Low : numeric (low price)
+* Close : numeric (closing price)
+* Volume : numeric (total volume traded)
+
+## Implementation
+
+Dataset:   
+Dataset can be found [here](https://www.kaggle.com/ramamet4/nse-company-stocks).
+
+Code file:   
+[Code for Random Forest & Linear Regression](https://github.com/shalakasaraogi/Contribution-program/blob/master/intern-basics/Stock%20Market%20Analysis%20and%20Prediction/Stock_Market_Prediction-%20(Random%20Forest%20%26%20Linear%20Regression).ipynb)  
+[Code for ARIMA](https://github.com/shalakasaraogi/Contribution-program/blob/master/intern-basics/Stock%20Market%20Analysis%20and%20Prediction/Stock_Market_Prediction-%20(ARIMA).ipynb)
+
+
+## Prediction
+
+We have used following algorithm for time series analysis:
+
+* Random Forest
+* Linear Regression
+* Autoregressive Integrated Moving Average (ARIMA)
+
+## Conclusion
+
+We have compared algorithms by RMSE. We found that ARIMA model gave lowest RMSE and accurate prediction. 

From cb1cff10baada8308b320e31dc45096e044d2723 Mon Sep 17 00:00:00 2001
From: Shalaka Saraogi <62024191+shalakasaraogi@users.noreply.github.com>
Date: Sun, 6 Dec 2020 17:22:16 +0530
Subject: [PATCH 4/4] Update Readme.md

---
 intern-basics/Readme.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/intern-basics/Readme.md b/intern-basics/Readme.md
index 357d8ff..1c95f22 100644
--- a/intern-basics/Readme.md
+++ b/intern-basics/Readme.md
@@ -16,6 +16,8 @@ Part3: Any Project-Numpy&Pandas                - Julian
 
 Part4: Basic functions in OpenCV               - Nirmal
 
+Stock Market Analysis & Prediction             - Shalaka
+
 Grayscaling
 Image Translations
 Rotation