Natarajan_Dwarkesh_Assignment1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project 1\n",
    "\n",
    "Project description: \n",
    "- Read data into Jupyter notebook, use pandas to import data into a data frame\n",
    "- preprocess data: explore data, address missing data, categorical data, if there is any, and data scaling. Justify the type of scaling used in this project. \n",
    "- train your dataset using all the linear regression models you've learned so far. If your model has a scaling parameter(s) use Grid Search to find the best scaling parameter. Use plots and graphs to help you get a better glimpse of the results. \n",
    "- Then use cross validation to find average training and testing score. \n",
    "- Your submission should have at least the following regression models: KNN repressor, linear regression, Ridge, Lasso, polynomial regression, SVM both simple and with kernels. \n",
    "- Finally find the best repressor for this dataset and train your model on the entire dataset using the best parameters and predict the market price for the test_set.\n",
    "- submit IPython notebook. Use markdown to provide an inline report for this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <font color = 'red'> Important note: All the group members should participate in completing this project.  This includes coding, preparing report and testing the models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#importing data\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.read_csv('bitcoin_dataset.csv')\n",
    "test = pd.read_csv('test_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>btc_market_price</th>\n",
       "      <th>btc_total_bitcoins</th>\n",
       "      <th>btc_market_cap</th>\n",
       "      <th>btc_trade_volume</th>\n",
       "      <th>btc_blocks_size</th>\n",
       "      <th>btc_avg_block_size</th>\n",
       "      <th>btc_n_orphaned_blocks</th>\n",
       "      <th>btc_n_transactions_per_block</th>\n",
       "      <th>btc_median_confirmation_time</th>\n",
       "      <th>...</th>\n",
       "      <th>btc_cost_per_transaction_percent</th>\n",
       "      <th>btc_cost_per_transaction</th>\n",
       "      <th>btc_n_unique_addresses</th>\n",
       "      <th>btc_n_transactions</th>\n",
       "      <th>btc_n_transactions_total</th>\n",
       "      <th>btc_n_transactions_excluding_popular</th>\n",
       "      <th>btc_n_transactions_excluding_chains_longer_than_100</th>\n",
       "      <th>btc_output_volume</th>\n",
       "      <th>btc_estimated_transaction_volume</th>\n",
       "      <th>btc_estimated_transaction_volume_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/17/2010 0:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2043200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.781022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241</td>\n",
       "      <td>244</td>\n",
       "      <td>41240</td>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>65173.13</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/18/2010 0:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2054650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>154.463801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>234</td>\n",
       "      <td>235</td>\n",
       "      <td>41475</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>18911.74</td>\n",
       "      <td>7413.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/19/2010 0:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2063600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1278.516635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185</td>\n",
       "      <td>183</td>\n",
       "      <td>41658</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>9749.98</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/20/2010 0:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2074700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22186.687990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>41882</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>11150.03</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/21/2010 0:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2085400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>689.179876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218</td>\n",
       "      <td>218</td>\n",
       "      <td>42100</td>\n",
       "      <td>218</td>\n",
       "      <td>218</td>\n",
       "      <td>12266.83</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  btc_market_price  btc_total_bitcoins  btc_market_cap  \\\n",
       "0  2/17/2010 0:00               0.0           2043200.0             0.0   \n",
       "1  2/18/2010 0:00               0.0           2054650.0             0.0   \n",
       "2  2/19/2010 0:00               0.0           2063600.0             0.0   \n",
       "3  2/20/2010 0:00               0.0           2074700.0             0.0   \n",
       "4  2/21/2010 0:00               0.0           2085400.0             0.0   \n",
       "\n",
       "   btc_trade_volume  btc_blocks_size  btc_avg_block_size  \\\n",
       "0               0.0              0.0            0.000235   \n",
       "1               0.0              0.0            0.000241   \n",
       "2               0.0              0.0            0.000228   \n",
       "3               0.0              0.0            0.000218   \n",
       "4               0.0              0.0            0.000234   \n",
       "\n",
       "   btc_n_orphaned_blocks  btc_n_transactions_per_block  \\\n",
       "0                      0                           1.0   \n",
       "1                      0                           1.0   \n",
       "2                      0                           1.0   \n",
       "3                      0                           1.0   \n",
       "4                      0                           1.0   \n",
       "\n",
       "   btc_median_confirmation_time                  ...                   \\\n",
       "0                           0.0                  ...                    \n",
       "1                           0.0                  ...                    \n",
       "2                           0.0                  ...                    \n",
       "3                           0.0                  ...                    \n",
       "4                           0.0                  ...                    \n",
       "\n",
       "   btc_cost_per_transaction_percent  btc_cost_per_transaction  \\\n",
       "0                         31.781022                       0.0   \n",
       "1                        154.463801                       0.0   \n",
       "2                       1278.516635                       0.0   \n",
       "3                      22186.687990                       0.0   \n",
       "4                        689.179876                       0.0   \n",
       "\n",
       "   btc_n_unique_addresses  btc_n_transactions  btc_n_transactions_total  \\\n",
       "0                     241                 244                     41240   \n",
       "1                     234                 235                     41475   \n",
       "2                     185                 183                     41658   \n",
       "3                     224                 224                     41882   \n",
       "4                     218                 218                     42100   \n",
       "\n",
       "   btc_n_transactions_excluding_popular  \\\n",
       "0                                   244   \n",
       "1                                   235   \n",
       "2                                   183   \n",
       "3                                   224   \n",
       "4                                   218   \n",
       "\n",
       "   btc_n_transactions_excluding_chains_longer_than_100  btc_output_volume  \\\n",
       "0                                                244             65173.13   \n",
       "1                                                235             18911.74   \n",
       "2                                                183              9749.98   \n",
       "3                                                224             11150.03   \n",
       "4                                                218             12266.83   \n",
       "\n",
       "   btc_estimated_transaction_volume  btc_estimated_transaction_volume_usd  \n",
       "0                           36500.0                                   0.0  \n",
       "1                            7413.0                                   0.0  \n",
       "2                             700.0                                   0.0  \n",
       "3                              50.0                                   0.0  \n",
       "4                            1553.0                                   0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2906, 24)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2906 entries, 0 to 2905\n",
      "Data columns (total 24 columns):\n",
      "Date                                                   2906 non-null object\n",
      "btc_market_price                                       2906 non-null float64\n",
      "btc_total_bitcoins                                     2879 non-null float64\n",
      "btc_market_cap                                         2906 non-null float64\n",
      "btc_trade_volume                                       2885 non-null float64\n",
      "btc_blocks_size                                        2877 non-null float64\n",
      "btc_avg_block_size                                     2906 non-null float64\n",
      "btc_n_orphaned_blocks                                  2906 non-null int64\n",
      "btc_n_transactions_per_block                           2906 non-null float64\n",
      "btc_median_confirmation_time                           2894 non-null float64\n",
      "btc_hash_rate                                          2906 non-null float64\n",
      "btc_difficulty                                         2890 non-null float64\n",
      "btc_miners_revenue                                     2906 non-null float64\n",
      "btc_transaction_fees                                   2896 non-null float64\n",
      "btc_cost_per_transaction_percent                       2906 non-null float64\n",
      "btc_cost_per_transaction                               2906 non-null float64\n",
      "btc_n_unique_addresses                                 2906 non-null int64\n",
      "btc_n_transactions                                     2906 non-null int64\n",
      "btc_n_transactions_total                               2906 non-null int64\n",
      "btc_n_transactions_excluding_popular                   2906 non-null int64\n",
      "btc_n_transactions_excluding_chains_longer_than_100    2906 non-null int64\n",
      "btc_output_volume                                      2906 non-null float64\n",
      "btc_estimated_transaction_volume                       2906 non-null float64\n",
      "btc_estimated_transaction_volume_usd                   2906 non-null float64\n",
      "dtypes: float64(17), int64(6), object(1)\n",
      "memory usage: 545.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                                                    0\n",
       "btc_market_price                                        0\n",
       "btc_total_bitcoins                                     27\n",
       "btc_market_cap                                          0\n",
       "btc_trade_volume                                       21\n",
       "btc_blocks_size                                        29\n",
       "btc_avg_block_size                                      0\n",
       "btc_n_orphaned_blocks                                   0\n",
       "btc_n_transactions_per_block                            0\n",
       "btc_median_confirmation_time                           12\n",
       "btc_hash_rate                                           0\n",
       "btc_difficulty                                         16\n",
       "btc_miners_revenue                                      0\n",
       "btc_transaction_fees                                   10\n",
       "btc_cost_per_transaction_percent                        0\n",
       "btc_cost_per_transaction                                0\n",
       "btc_n_unique_addresses                                  0\n",
       "btc_n_transactions                                      0\n",
       "btc_n_transactions_total                                0\n",
       "btc_n_transactions_excluding_popular                    0\n",
       "btc_n_transactions_excluding_chains_longer_than_100     0\n",
       "btc_output_volume                                       0\n",
       "btc_estimated_transaction_volume                        0\n",
       "btc_estimated_transaction_volume_usd                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>btc_market_price</th>\n",
       "      <th>btc_total_bitcoins</th>\n",
       "      <th>btc_market_cap</th>\n",
       "      <th>btc_trade_volume</th>\n",
       "      <th>btc_blocks_size</th>\n",
       "      <th>btc_avg_block_size</th>\n",
       "      <th>btc_n_orphaned_blocks</th>\n",
       "      <th>btc_n_transactions_per_block</th>\n",
       "      <th>btc_median_confirmation_time</th>\n",
       "      <th>btc_hash_rate</th>\n",
       "      <th>...</th>\n",
       "      <th>btc_cost_per_transaction_percent</th>\n",
       "      <th>btc_cost_per_transaction</th>\n",
       "      <th>btc_n_unique_addresses</th>\n",
       "      <th>btc_n_transactions</th>\n",
       "      <th>btc_n_transactions_total</th>\n",
       "      <th>btc_n_transactions_excluding_popular</th>\n",
       "      <th>btc_n_transactions_excluding_chains_longer_than_100</th>\n",
       "      <th>btc_output_volume</th>\n",
       "      <th>btc_estimated_transaction_volume</th>\n",
       "      <th>btc_estimated_transaction_volume_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2.879000e+03</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>2.885000e+03</td>\n",
       "      <td>2877.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2894.000000</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>2.906000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>839.104218</td>\n",
       "      <td>1.151138e+07</td>\n",
       "      <td>1.344255e+10</td>\n",
       "      <td>7.398381e+07</td>\n",
       "      <td>35505.502848</td>\n",
       "      <td>0.350366</td>\n",
       "      <td>0.364074</td>\n",
       "      <td>671.673651</td>\n",
       "      <td>7.501113</td>\n",
       "      <td>1.244070e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>66.747821</td>\n",
       "      <td>14.639125</td>\n",
       "      <td>1.937861e+05</td>\n",
       "      <td>102081.138334</td>\n",
       "      <td>6.844558e+07</td>\n",
       "      <td>94348.852374</td>\n",
       "      <td>63140.320028</td>\n",
       "      <td>1.566216e+06</td>\n",
       "      <td>2.036475e+05</td>\n",
       "      <td>2.024338e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2304.972497</td>\n",
       "      <td>4.200024e+06</td>\n",
       "      <td>3.866150e+10</td>\n",
       "      <td>2.924228e+08</td>\n",
       "      <td>43618.633821</td>\n",
       "      <td>0.353168</td>\n",
       "      <td>0.842259</td>\n",
       "      <td>689.561322</td>\n",
       "      <td>4.974549</td>\n",
       "      <td>2.924141e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1761.894646</td>\n",
       "      <td>20.536083</td>\n",
       "      <td>2.089146e+05</td>\n",
       "      <td>103896.929350</td>\n",
       "      <td>8.285341e+07</td>\n",
       "      <td>103966.111763</td>\n",
       "      <td>69687.052174</td>\n",
       "      <td>2.278910e+06</td>\n",
       "      <td>2.682781e+05</td>\n",
       "      <td>5.800513e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.043200e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.250000e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>4.124000e+04</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>6.150000e+03</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.653465</td>\n",
       "      <td>8.485300e+06</td>\n",
       "      <td>5.363081e+07</td>\n",
       "      <td>2.916456e+05</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>0.024177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>6.066667</td>\n",
       "      <td>1.160880e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.181945</td>\n",
       "      <td>4.156470</td>\n",
       "      <td>1.675475e+04</td>\n",
       "      <td>8025.250000</td>\n",
       "      <td>2.413376e+06</td>\n",
       "      <td>6813.500000</td>\n",
       "      <td>6765.500000</td>\n",
       "      <td>4.901712e+05</td>\n",
       "      <td>9.600325e+04</td>\n",
       "      <td>9.581680e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>235.130000</td>\n",
       "      <td>1.243115e+07</td>\n",
       "      <td>3.346869e+09</td>\n",
       "      <td>1.001414e+07</td>\n",
       "      <td>15183.000000</td>\n",
       "      <td>0.196022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>7.916667</td>\n",
       "      <td>2.176189e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.493564</td>\n",
       "      <td>7.822430</td>\n",
       "      <td>1.304450e+05</td>\n",
       "      <td>62337.000000</td>\n",
       "      <td>3.255271e+07</td>\n",
       "      <td>53483.000000</td>\n",
       "      <td>35283.500000</td>\n",
       "      <td>1.105205e+06</td>\n",
       "      <td>1.784685e+05</td>\n",
       "      <td>3.742576e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>594.191164</td>\n",
       "      <td>1.520051e+07</td>\n",
       "      <td>8.075525e+09</td>\n",
       "      <td>2.834038e+07</td>\n",
       "      <td>58293.000000</td>\n",
       "      <td>0.676065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1232.995223</td>\n",
       "      <td>10.208333</td>\n",
       "      <td>1.035363e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>5.915591</td>\n",
       "      <td>14.800589</td>\n",
       "      <td>3.603765e+05</td>\n",
       "      <td>190471.250000</td>\n",
       "      <td>1.080663e+08</td>\n",
       "      <td>185901.750000</td>\n",
       "      <td>113793.250000</td>\n",
       "      <td>2.031654e+06</td>\n",
       "      <td>2.588046e+05</td>\n",
       "      <td>1.312499e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19498.683330</td>\n",
       "      <td>1.683769e+07</td>\n",
       "      <td>3.265250e+11</td>\n",
       "      <td>5.352016e+09</td>\n",
       "      <td>154444.590300</td>\n",
       "      <td>1.110327</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2722.625000</td>\n",
       "      <td>47.733333</td>\n",
       "      <td>2.160975e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>88571.428570</td>\n",
       "      <td>161.686071</td>\n",
       "      <td>1.072861e+06</td>\n",
       "      <td>490644.000000</td>\n",
       "      <td>2.966888e+08</td>\n",
       "      <td>470650.000000</td>\n",
       "      <td>318896.000000</td>\n",
       "      <td>4.599222e+07</td>\n",
       "      <td>5.825066e+06</td>\n",
       "      <td>5.760245e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       btc_market_price  btc_total_bitcoins  btc_market_cap  btc_trade_volume  \\\n",
       "count       2906.000000        2.879000e+03    2.906000e+03      2.885000e+03   \n",
       "mean         839.104218        1.151138e+07    1.344255e+10      7.398381e+07   \n",
       "std         2304.972497        4.200024e+06    3.866150e+10      2.924228e+08   \n",
       "min            0.000000        2.043200e+06    0.000000e+00      0.000000e+00   \n",
       "25%            6.653465        8.485300e+06    5.363081e+07      2.916456e+05   \n",
       "50%          235.130000        1.243115e+07    3.346869e+09      1.001414e+07   \n",
       "75%          594.191164        1.520051e+07    8.075525e+09      2.834038e+07   \n",
       "max        19498.683330        1.683769e+07    3.265250e+11      5.352016e+09   \n",
       "\n",
       "       btc_blocks_size  btc_avg_block_size  btc_n_orphaned_blocks  \\\n",
       "count      2877.000000         2906.000000            2906.000000   \n",
       "mean      35505.502848            0.350366               0.364074   \n",
       "std       43618.633821            0.353168               0.842259   \n",
       "min           0.000000            0.000216               0.000000   \n",
       "25%         781.000000            0.024177               0.000000   \n",
       "50%       15183.000000            0.196022               0.000000   \n",
       "75%       58293.000000            0.676065               0.000000   \n",
       "max      154444.590300            1.110327               7.000000   \n",
       "\n",
       "       btc_n_transactions_per_block  btc_median_confirmation_time  \\\n",
       "count                   2906.000000                   2894.000000   \n",
       "mean                     671.673651                      7.501113   \n",
       "std                      689.561322                      4.974549   \n",
       "min                        1.000000                      0.000000   \n",
       "25%                       54.000000                      6.066667   \n",
       "50%                      375.000000                      7.916667   \n",
       "75%                     1232.995223                     10.208333   \n",
       "max                     2722.625000                     47.733333   \n",
       "\n",
       "       btc_hash_rate                  ...                   \\\n",
       "count   2.906000e+03                  ...                    \n",
       "mean    1.244070e+06                  ...                    \n",
       "std     2.924141e+06                  ...                    \n",
       "min     2.250000e-05                  ...                    \n",
       "25%     1.160880e+01                  ...                    \n",
       "50%     2.176189e+04                  ...                    \n",
       "75%     1.035363e+06                  ...                    \n",
       "max     2.160975e+07                  ...                    \n",
       "\n",
       "       btc_cost_per_transaction_percent  btc_cost_per_transaction  \\\n",
       "count                       2906.000000               2906.000000   \n",
       "mean                          66.747821                 14.639125   \n",
       "std                         1761.894646                 20.536083   \n",
       "min                            0.136531                  0.000000   \n",
       "25%                            1.181945                  4.156470   \n",
       "50%                            2.493564                  7.822430   \n",
       "75%                            5.915591                 14.800589   \n",
       "max                        88571.428570                161.686071   \n",
       "\n",
       "       btc_n_unique_addresses  btc_n_transactions  btc_n_transactions_total  \\\n",
       "count            2.906000e+03         2906.000000              2.906000e+03   \n",
       "mean             1.937861e+05       102081.138334              6.844558e+07   \n",
       "std              2.089146e+05       103896.929350              8.285341e+07   \n",
       "min              1.100000e+02          118.000000              4.124000e+04   \n",
       "25%              1.675475e+04         8025.250000              2.413376e+06   \n",
       "50%              1.304450e+05        62337.000000              3.255271e+07   \n",
       "75%              3.603765e+05       190471.250000              1.080663e+08   \n",
       "max              1.072861e+06       490644.000000              2.966888e+08   \n",
       "\n",
       "       btc_n_transactions_excluding_popular  \\\n",
       "count                           2906.000000   \n",
       "mean                           94348.852374   \n",
       "std                           103966.111763   \n",
       "min                              118.000000   \n",
       "25%                             6813.500000   \n",
       "50%                            53483.000000   \n",
       "75%                           185901.750000   \n",
       "max                           470650.000000   \n",
       "\n",
       "       btc_n_transactions_excluding_chains_longer_than_100  btc_output_volume  \\\n",
       "count                                        2906.000000         2.906000e+03   \n",
       "mean                                        63140.320028         1.566216e+06   \n",
       "std                                         69687.052174         2.278910e+06   \n",
       "min                                           118.000000         6.150000e+03   \n",
       "25%                                          6765.500000         4.901712e+05   \n",
       "50%                                         35283.500000         1.105205e+06   \n",
       "75%                                        113793.250000         2.031654e+06   \n",
       "max                                        318896.000000         4.599222e+07   \n",
       "\n",
       "       btc_estimated_transaction_volume  btc_estimated_transaction_volume_usd  \n",
       "count                      2.906000e+03                          2.906000e+03  \n",
       "mean                       2.036475e+05                          2.024338e+08  \n",
       "std                        2.682781e+05                          5.800513e+08  \n",
       "min                        7.000000e+00                          0.000000e+00  \n",
       "25%                        9.600325e+04                          9.581680e+05  \n",
       "50%                        1.784685e+05                          3.742576e+07  \n",
       "75%                        2.588046e+05                          1.312499e+08  \n",
       "max                        5.825066e+06                          5.760245e+09  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2299\n",
       "1     326\n",
       "2     167\n",
       "3      72\n",
       "4      30\n",
       "5      11\n",
       "7       1\n",
       "Name: btc_n_orphaned_blocks, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['btc_n_orphaned_blocks'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "Name: btc_n_orphaned_blocks, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['btc_n_orphaned_blocks'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2791 entries, 0 to 2905\n",
      "Data columns (total 24 columns):\n",
      "Date                                                   2791 non-null object\n",
      "btc_market_price                                       2791 non-null float64\n",
      "btc_total_bitcoins                                     2791 non-null float64\n",
      "btc_market_cap                                         2791 non-null float64\n",
      "btc_trade_volume                                       2791 non-null float64\n",
      "btc_blocks_size                                        2791 non-null float64\n",
      "btc_avg_block_size                                     2791 non-null float64\n",
      "btc_n_orphaned_blocks                                  2791 non-null int64\n",
      "btc_n_transactions_per_block                           2791 non-null float64\n",
      "btc_median_confirmation_time                           2791 non-null float64\n",
      "btc_hash_rate                                          2791 non-null float64\n",
      "btc_difficulty                                         2791 non-null float64\n",
      "btc_miners_revenue                                     2791 non-null float64\n",
      "btc_transaction_fees                                   2791 non-null float64\n",
      "btc_cost_per_transaction_percent                       2791 non-null float64\n",
      "btc_cost_per_transaction                               2791 non-null float64\n",
      "btc_n_unique_addresses                                 2791 non-null int64\n",
      "btc_n_transactions                                     2791 non-null int64\n",
      "btc_n_transactions_total                               2791 non-null int64\n",
      "btc_n_transactions_excluding_popular                   2791 non-null int64\n",
      "btc_n_transactions_excluding_chains_longer_than_100    2791 non-null int64\n",
      "btc_output_volume                                      2791 non-null float64\n",
      "btc_estimated_transaction_volume                       2791 non-null float64\n",
      "btc_estimated_transaction_volume_usd                   2791 non-null float64\n",
      "dtypes: float64(17), int64(6), object(1)\n",
      "memory usage: 545.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#dropping the null values\n",
    "data_without_na= data.dropna()\n",
    "data_without_na.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wI5JSo/FXRUVEhoLuuREREZFU0eBGREREUkWDGxEREUkVc/fyC5sdBn42TLE0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\n3IiIiEiq1NTgxvsy9HV009fRU+1QREREZJSqmR/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Dgbsq5i8ALsg7rmHYr1bgKOAJYKdU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OTEyfYUOqfZ5VamOwT8A/A98HbqvWT94WA7ItHgY6+5S15WdVK3Tm8cAdJcsXARflHVe7T0A3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\noii5ERERkURRciMiIiKJ8v8DZYydaIEZNykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e73aa51860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "fig,axes=plt.subplots(23,1,figsize=(8,30),sharex=False)\n",
    "i=0\n",
    "for col in data.columns :\n",
    "    if col!='Date' :\n",
    "        sns.boxplot(data=data,palette='rainbow',orient='h',y=col,ax=axes[i])\n",
    "        i=i+1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above graph we can see that the range is different for different features and that the data is skewed, Hence this data requires scaling\n",
    "### Which Scaler should be used?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e73a9fa208>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B5XB3rnkkVLqgz0N10MfN552UWhBZ4ffwk987o3TZckHc8dwWFjU2ZCy8vP2Cifi8Hq58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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e73a9fa4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=data_without_na[['btc_market_cap','btc_trade_volume','btc_cost_per_transaction']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e73e88f1d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xb5/HymuaR0TP26BJg4hclvgC/gx8ANgMmPh9SqkSSFzwEssZUyXKSt/69PZ+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3E5izftiEpCLLD17IKd6pwm8nRZBGxcWK0P0eql8x8bhsuxoWgKRc6TybdsL/AF4FiW1qLIEE2T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e73e88f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Minmax\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X1=data_without_na[X]\n",
    "X_scaled=scaler.fit_transform(X1)\n",
    "X_scaled\n",
    "data_scaled=pd.DataFrame(X_scaled,columns=X1.columns)\n",
    "sns.pairplot(data=data_scaled[['btc_market_cap','btc_trade_volume','btc_cost_per_transaction']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e73fd49668>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xb5/HymuaR0TP26BJg4hclvgC/gx8ANgMmPh9SqkSSFzwEssZUyXKSt/69PZ+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3E5izftiEpCLLD17IKd6pwm8nRZBGxcWK0P0eql8x8bhsuxoWgKRc6TybdsL/AF4FiW1qLIEE2T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e73fd49b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalizer\n",
    "from sklearn.preprocessing import Normalizer\n",
    "scaler=Normalizer()\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "X_scaled=scaler.fit_transform(X)\n",
    "data_scaled2=pd.DataFrame(X_scaled,columns=X.columns)\n",
    "sns.pairplot(data=data_scaled[['btc_market_cap','btc_trade_volume','btc_cost_per_transaction']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e7403490f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xb5/HymuaR0TP26BJg4hclvgC/gx8ANgMmPh9SqkSSFzwEssZUyXKSt/69PZ+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3E5izftiEpCLLD17IKd6pwm8nRZBGxcWK0P0eql8x8bhsuxoWgKRc6TybdsL/AF4FiW1qLIEE2T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e740349160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Standard Scaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "X_scaled=scaler.fit_transform(X)\n",
    "data_scaled3=pd.DataFrame(X_scaled,columns=X.columns)\n",
    "sns.pairplot(data=data_scaled[['btc_market_cap','btc_trade_volume','btc_cost_per_transaction']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different scalers have been used, However from the above graphs we can see that minmax scaler maintains the structure of the data and brings all the features between 0 and 1, Hence not giving any importance to features with higher values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s1BXAW166l4AbvfVp3jbe8SvFHjNljDFBE0ig6AHs9dnO8/b5Wgfc7K3fBMSJ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\n2lVN1fam2rMSaiohNBJ6jaq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7409e45c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.98615418868097804, 0.98755058819995045, 0.98684781303036317, 0.98661026353531733, 0.98266351504554872, 0.981855956961453]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=10)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler() #scaling\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "param=[1,3,4,5,15,20]\n",
    "scores_test=[]\n",
    "scores_train=[]\n",
    "#plotting accuracy and number of neighbors\n",
    "for k in param :\n",
    "    knnreg=KNeighborsRegressor(n_neighbors=k)\n",
    "    knnreg.fit(X_train,y_train)\n",
    "    scores_test.append(knnreg.score(X_test,y_test))\n",
    "    scores_train.append(knnreg.score(X_train,y_train))\n",
    "plt.plot(param,scores_train,label='train')\n",
    "plt.plot(param,scores_test,label='test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(scores_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN regression with grid search to find the optimum number of neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since grid search is supposed to be done on the whole of the training data, I imported the whole of the training data with out the nulls and created a pipeline to scale and fit the knn model within the grid search. The data can't be scaled before hand as the train and validation data must be separated before scaling to avoid data leakage. I used 10 fold cross validation, and the score was negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Alpha Value :  {'knnreg__n_neighbors': 1}\n",
      "Best Score :  -2.26226241295\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Accuracy Graph')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dpznt0Olce+YhvcsNF5LNO9pYHnZVXVa/nfVbg8QwvrKMhbNjnLswwaI5cQ6b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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7409b5b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Kfold Cross validation in grid search\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"knnreg\",KNeighborsRegressor())]) #defining pipeline\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50) #shuffle split cross validation\n",
    "param_grid={'knnreg__n_neighbors':[1,3,4,15,20,25]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=10,scoring='r2')\n",
    "grid_search.fit(X,y)\n",
    "print(\"Best Alpha Value : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "plt.plot(param_grid['knnreg__n_neighbors'],results.mean_test_score)\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### However when I used shuffle split cross validation the score was high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Alpha Value :  {'knnreg__n_neighbors': 4}\n",
      "Best Score :  0.994692650153\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e742ba4c88>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BYIMWTITz562tT4Hig2KA9sBEVb3zKOfFAquBgTjPMfkBGKWqP/kc8yHwmapO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\njDHGVIgFiTHGmAr5f8bcd9/19Jt2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e741f1bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Shuffle split cross validation in Grid Search for knn regression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"knnreg\",KNeighborsRegressor())]) #defining pipeline\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=30,random_state=10) #shuffle split cross validation\n",
    "param_grid={'knnreg__n_neighbors':[1,3,4,15,20,25]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y)\n",
    "print(\"Best Alpha Value : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "plt.plot(param_grid['knnreg__n_neighbors'],results.mean_test_score,label='test')\n",
    "plt.plot(param_grid['knnreg__n_neighbors'],results.mean_train_score,label='train')\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 0.999958625092\n",
      "Test Score : 0.999956624767\n",
      "The coefficients are :  [[ -6.30740019e+01   1.87794125e+04   4.30388845e+01  -3.21577020e+03\n",
      "    1.94600572e+01  -3.05808121e+00  -3.38911819e+01   2.73651425e+01\n",
      "   -4.80203722e+02   2.07260349e+02   3.31171524e+02  -1.95069369e+02\n",
      "    3.77856278e+00   4.31520847e+02   1.33657722e+02   3.81189702e+01\n",
      "    3.34690574e+03  -2.38257464e+01   2.15376795e+01   9.06383514e+00\n",
      "    2.80209879e+00   9.90895635e+01]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=10)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm=LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "print(\"Train Score :\",lm.score(X_train,y_train))\n",
    "print(\"Test Score :\",lm.score(X_test,y_test))\n",
    "print(\"The coefficients are : \", lm.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regression \n",
    "### Again I used pipeline to scale it with in the grid search and compared both kfold and shufflesplit cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Alpha Value :  {'ridge__alpha': 0}\n",
      "Best Score :  -314332.263972\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Ridge\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"ridge\",Ridge())]) #defining pipeline\n",
    "param_grid={'ridge__alpha' : [0,1,3,8,15,25]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=10,scoring='r2')\n",
    "grid_search.fit(X,y)\n",
    "print(\"Best Alpha Value : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Alpha Value :  {'ridge__alpha': 1}\n",
      "Best Score :  0.996303873551\n"
     ]
    }
   ],
   "source": [
    "#With shuffle split cross validation\n",
    "import numpy as np\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"ridge\",Ridge())]) #defining pipeline\n",
    "param_grid={'ridge__alpha' : [1,3,8,15,25]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y)\n",
    "print(\"Best Alpha Value : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Alpha Value :  {'ridge__alpha': 0.5}\n",
      "Best Score :  0.997952905605\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e7430527f0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gn94Mc16F5HQe2RhjAtKlWsEfO3aMokWLkj9/fjZt2sTixYuzuLq/siDxoq71yvL5gCYc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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e742b00668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"ridge\",Ridge())]) #defining pipeline\n",
    "param_grid={'ridge__alpha' : [0.5,1,1.5,2,3]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y)\n",
    "print(\"Best Alpha Value : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "plt.plot(param_grid['ridge__alpha'],results.mean_test_score,label='test')\n",
    "plt.plot(param_grid['ridge__alpha'],results.mean_train_score,label='train')\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As alpha becomes closer to 0 the model improves. Basically linear regression is the best model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R2 score for Best alpha :  0.999910404082\n",
      "The coefficients are  [  7.42149233e+00   1.86437432e+04   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   7.10675675e+00   2.49724619e+00\n",
      "   0.00000000e+00   0.00000000e+00   1.08491583e+02   0.00000000e+00\n",
      "   0.00000000e+00   5.50426808e+02   8.78486700e+01   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00   1.44630630e+02]\n"
     ]
    },
    {
     "data": {
      "image/png": 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oMygkRYHHgAFAFjBYUlaxbg8Az5hZZ+Au4J5g2XOB7kBXoBdwu6QGwTKPA5eb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\nOZdCbj6rE7HCQh57bxnRiPj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22ebd8629e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=10)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import r2_score\n",
    "alphas=[0.3,1,2,5,9,16]\n",
    "scores=[]\n",
    "for a in alphas :\n",
    "    linlasso=Lasso(alpha=a)\n",
    "    linlasso.fit(X_train,y_train)\n",
    "    y_pred=linlasso.predict(X_test)\n",
    "    scores.append(r2_score(y_test,y_pred))\n",
    "plt.plot(alphas,scores) \n",
    "plt.title(\"Accuracy vs Alpha\")\n",
    "linlasso=Lasso(alpha=0.3)\n",
    "linlasso.fit(X_train,y_train)\n",
    "y_pred=linlasso.predict(X_test)\n",
    "print(\" R2 score for Best alpha : \", r2_score(y_test,y_pred))\n",
    "print(\"The coefficients are \" , linlasso.coef_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso with grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Alpha Value :  {'lasso__alpha': 1}\n",
      "Best Score :  0.999885149752\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e74312c860>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mSD4B/ph/WkSKiEhLAFVdf5b9KgA70r2PdZelt5o/z2oGAkVEpKS7vKeIRIhI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7430eeef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"lasso\",Lasso())])\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50)\n",
    "param_grid={'lasso__alpha' : [1,3,8,15,25]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y)\n",
    "print(\"Best Alpha Value : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "plt.plot(param_grid['lasso__alpha'],results.mean_test_score,label='test')\n",
    "plt.plot(param_grid['lasso__alpha'],results.mean_train_score,label='train')\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_lasso__alpha</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split10_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004211</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.999885</td>\n",
       "      <td>0.999894</td>\n",
       "      <td>1</td>\n",
       "      <td>{'lasso__alpha': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999840</td>\n",
       "      <td>0.999900</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.999895</td>\n",
       "      <td>0.999863</td>\n",
       "      <td>0.999893</td>\n",
       "      <td>0.999908</td>\n",
       "      <td>0.999892</td>\n",
       "      <td>0.005564</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>0.999768</td>\n",
       "      <td>3</td>\n",
       "      <td>{'lasso__alpha': 3}</td>\n",
       "      <td>2</td>\n",
       "      <td>0.999734</td>\n",
       "      <td>0.999783</td>\n",
       "      <td>0.999755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999736</td>\n",
       "      <td>0.999770</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>0.999768</td>\n",
       "      <td>0.999795</td>\n",
       "      <td>0.999760</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.998951</td>\n",
       "      <td>0.998987</td>\n",
       "      <td>8</td>\n",
       "      <td>{'lasso__alpha': 8}</td>\n",
       "      <td>3</td>\n",
       "      <td>0.999080</td>\n",
       "      <td>0.999063</td>\n",
       "      <td>0.998826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998926</td>\n",
       "      <td>0.999010</td>\n",
       "      <td>0.998794</td>\n",
       "      <td>0.998981</td>\n",
       "      <td>0.999022</td>\n",
       "      <td>0.998952</td>\n",
       "      <td>0.005422</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003438</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>0.996611</td>\n",
       "      <td>0.996681</td>\n",
       "      <td>15</td>\n",
       "      <td>{'lasso__alpha': 15}</td>\n",
       "      <td>4</td>\n",
       "      <td>0.997151</td>\n",
       "      <td>0.996938</td>\n",
       "      <td>0.996161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996603</td>\n",
       "      <td>0.996758</td>\n",
       "      <td>0.996359</td>\n",
       "      <td>0.996662</td>\n",
       "      <td>0.996672</td>\n",
       "      <td>0.996571</td>\n",
       "      <td>0.006473</td>\n",
       "      <td>0.004688</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.000172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.990827</td>\n",
       "      <td>0.990960</td>\n",
       "      <td>25</td>\n",
       "      <td>{'lasso__alpha': 25}</td>\n",
       "      <td>5</td>\n",
       "      <td>0.992356</td>\n",
       "      <td>0.991677</td>\n",
       "      <td>0.989632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.990930</td>\n",
       "      <td>0.991186</td>\n",
       "      <td>0.990504</td>\n",
       "      <td>0.990898</td>\n",
       "      <td>0.990723</td>\n",
       "      <td>0.990628</td>\n",
       "      <td>0.007161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.000478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0       0.004211         0.000806         0.999885          0.999894   \n",
       "1       0.002813         0.000938         0.999752          0.999768   \n",
       "2       0.002188         0.000938         0.998951          0.998987   \n",
       "3       0.003438         0.001563         0.996611          0.996681   \n",
       "4       0.004688         0.000000         0.990827          0.990960   \n",
       "\n",
       "  param_lasso__alpha                params  rank_test_score  \\\n",
       "0                  1   {'lasso__alpha': 1}                1   \n",
       "1                  3   {'lasso__alpha': 3}                2   \n",
       "2                  8   {'lasso__alpha': 8}                3   \n",
       "3                 15  {'lasso__alpha': 15}                4   \n",
       "4                 25  {'lasso__alpha': 25}                5   \n",
       "\n",
       "   split0_test_score  split0_train_score  split10_test_score       ...         \\\n",
       "0           0.999840            0.999900            0.999911       ...          \n",
       "1           0.999734            0.999783            0.999755       ...          \n",
       "2           0.999080            0.999063            0.998826       ...          \n",
       "3           0.997151            0.996938            0.996161       ...          \n",
       "4           0.992356            0.991677            0.989632       ...          \n",
       "\n",
       "   split7_test_score  split7_train_score  split8_test_score  \\\n",
       "0           0.999881            0.999895           0.999863   \n",
       "1           0.999736            0.999770           0.999684   \n",
       "2           0.998926            0.999010           0.998794   \n",
       "3           0.996603            0.996758           0.996359   \n",
       "4           0.990930            0.991186           0.990504   \n",
       "\n",
       "   split8_train_score  split9_test_score  split9_train_score  std_fit_time  \\\n",
       "0            0.999893           0.999908            0.999892      0.005564   \n",
       "1            0.999768           0.999795            0.999760      0.006003   \n",
       "2            0.998981           0.999022            0.998952      0.005422   \n",
       "3            0.996662           0.996672            0.996571      0.006473   \n",
       "4            0.990898           0.990723            0.990628      0.007161   \n",
       "\n",
       "   std_score_time  std_test_score  std_train_score  \n",
       "0        0.003049        0.000025         0.000004  \n",
       "1        0.003712        0.000043         0.000009  \n",
       "2        0.003711        0.000132         0.000051  \n",
       "3        0.004688        0.000353         0.000172  \n",
       "4        0.000000        0.000878         0.000478  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score :  0.999997557534\n",
      "Test Score :  0.999882451113\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "poly=PolynomialFeatures(degree=2)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=10)\n",
    "X_train=poly.fit_transform(X_train)\n",
    "X_test=poly.transform(X_test)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n",
    "lm=LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "print(\"Training Score : \",lm.score(X_train,y_train))\n",
    "print(\"Test Score : \",lm.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Regression with Ridge\n",
    "### Again I use a pipeline to scale within the grid search with shuffle split cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best Parameter Value :  {'ridge__alpha': 1}\n",
      "Best Score :  0.999511708704\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e7442277f0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2plY5uM77xRpj/CpHfSTq9Lwecn/SgTLAFBH5pxdrM37WtEpppj7SjsplinLv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\n80KQtAVeVNUu7ucRAKr6iscxm4Auqpo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e74317bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "poly=PolynomialFeatures(degree=2)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "X_poly=poly.fit_transform(X)\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"ridge\",Ridge())])\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50,random_state=10)\n",
    "param_grid= {'ridge__alpha' : [1,3,7,11,18,20,22,28,36,40,44,50,55,60,65]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv)\n",
    "grid_search.fit(X_poly,y)\n",
    "print(\" Best Parameter Value : \", grid_search.best_params_)\n",
    "print(\"Best Score : \",  grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "plt.plot(param_grid['ridge__alpha'],results.mean_test_score,label='test')\n",
    "plt.plot(param_grid['ridge__alpha'],results.mean_train_score,label='train')\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Regression with Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best Parameter Value :  {'lasso__alpha': 1}\n",
      "Best Score :  0.999902546858\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e744202668>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Os79TTMnwp7+2vxgEu34q2gNUaQTtnj3pZs8zi/nz59O+fXtWr15N7dq1Adi/fz9xcXEc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\npK2IrBORjSIyqIDttURkroi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e743101518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "poly=PolynomialFeatures(degree=2)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "X_poly=poly.fit_transform(X)\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"lasso\",Lasso())])\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=50,random_state=10)\n",
    "param_grid= {'lasso__alpha' : [1,3,7,11,18,20,22,28,36,40,44,50,55,60,65]}\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv)\n",
    "grid_search.fit(X_poly,y)\n",
    "print(\" Best Parameter Value : \", grid_search.best_params_)\n",
    "print(\"Best Score : \",  grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "plt.plot(param_grid['lasso__alpha'],results.mean_test_score,label='test')\n",
    "plt.plot(param_grid['lasso__alpha'],results.mean_train_score,label='train')\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM\n",
    "### Ran grid search on linear kernel with C and epsilon as parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameter :  {'SVR__C': 10, 'SVR__epsilon': 1}\n",
      "Best Score :  0.532516889733\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.]),\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BCxgyC+Z1Ze0TW3LlzQ80PQUAAGgx48aNy8MPPyxyjTC11jz88MMZN27cbr2PSxSBIfMb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\nQYNP8X03AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e740d45a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=20,random_state=10)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"SVR\",SVR(kernel='linear'))]) #pipeline\n",
    "param_grid = {'SVR__C':[0.01,0.1,1,10],'SVR__epsilon' : [0.01,0.1,1]} #parameter grid\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y.values.ravel())\n",
    "print(\"Best Parameter : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "results=results.sort_values(by='mean_test_score',ascending=False)\n",
    "fig,axes=plt.subplots(figsize=(20,8))\n",
    "results.plot(x='params',y='mean_test_score',ax=axes)\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.xlabel(\"Parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ran grid search on poly kernel with C and gamma as parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameter :  {'SVR__C': 10, 'SVR__gamma': 1}\n",
      "Best Score :  0.998175336714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.]),\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObttYgzOS9cTCDRo7oK3qxvv5f6EAAAAAEDl8O8ElaYukJc65hc65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f+NcD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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e740c1d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=20,random_state=10)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"SVR\",SVR(kernel='poly'))]) #pipeline\n",
    "param_grid = {'SVR__C':[0.01,0.1,1,10],'SVR__gamma' : [0.01,0.1,1]} #parameter grid\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y.values.ravel())\n",
    "print(\"Best Parameter : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "results=results.sort_values(by='mean_test_score',ascending=False)\n",
    "fig,axes=plt.subplots(figsize=(20,8))\n",
    "results.plot(x='params',y='mean_test_score',ax=axes)\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.xlabel(\"Parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search with rbf kernel, C, gamma parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameter :  {'SVR__C': 10, 'SVR__gamma': 1}\n",
      "Best Score :  0.220049034001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.]),\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+EgHha1DKOerRoq4e/2itrureVPGx0b4jAQAAAABw1rwXXJIml/pc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e742bb0978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import ShuffleSplit\n",
    "cv=ShuffleSplit(X.shape[0],n_iter=20,random_state=10)\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X=data_without_na[X]\n",
    "y=data_without_na[['btc_market_price']]\n",
    "pipe=Pipeline([(\"scaler\",MinMaxScaler()),(\"SVR\",SVR(kernel='rbf'))]) #pipeline\n",
    "param_grid = {'SVR__C':[0.01,0.1,1,10],'SVR__gamma' : [0.01,0.1,1]} #parameter grid\n",
    "grid_search=GridSearchCV(pipe,param_grid,cv=cv,scoring='r2')\n",
    "grid_search.fit(X,y.values.ravel())\n",
    "print(\"Best Parameter : \",grid_search.best_params_)\n",
    "print(\"Best Score : \",grid_search.best_score_)\n",
    "results=pd.DataFrame(grid_search.cv_results_)\n",
    "results=results.sort_values(by='mean_test_score',ascending=False)\n",
    "fig,axes=plt.subplots(figsize=(20,8))\n",
    "results.plot(x='params',y='mean_test_score',ax=axes)\n",
    "plt.title(\"Accuracy Graph\")\n",
    "plt.xlabel(\"Parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The best model is linear model as it has the highest test score with 0.9999 hence I will use the same for training and testtng the whole data. The mean score that has been obtained in these models are actually the cross validated score which is done by grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#training the linear regression model on the entire data set\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X=[col for col in data.columns.values if col!= 'Date' if col!='btc_market_price']\n",
    "X1=data_without_na[X]\n",
    "X_scaled=scaler.fit_transform(X1)\n",
    "y=data_without_na[['btc_market_price']]\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm=LinearRegression()\n",
    "lm.fit(X_scaled,y)\n",
    "X2=[col for col in test.columns.values if col!= 'Date']\n",
    "test_without_date=test[X2]\n",
    "test_scaled=scaler.transform(test_without_date)\n",
    "test['btc_market_price']=lm.predict(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>btc_total_bitcoins</th>\n",
       "      <th>btc_market_cap</th>\n",
       "      <th>btc_trade_volume</th>\n",
       "      <th>btc_blocks_size</th>\n",
       "      <th>btc_avg_block_size</th>\n",
       "      <th>btc_n_orphaned_blocks</th>\n",
       "      <th>btc_n_transactions_per_block</th>\n",
       "      <th>btc_median_confirmation_time</th>\n",
       "      <th>btc_hash_rate</th>\n",
       "      <th>...</th>\n",
       "      <th>btc_cost_per_transaction</th>\n",
       "      <th>btc_n_unique_addresses</th>\n",
       "      <th>btc_n_transactions</th>\n",
       "      <th>btc_n_transactions_total</th>\n",
       "      <th>btc_n_transactions_excluding_popular</th>\n",
       "      <th>btc_n_transactions_excluding_chains_longer_than_100</th>\n",
       "      <th>btc_output_volume</th>\n",
       "      <th>btc_estimated_transaction_volume</th>\n",
       "      <th>btc_estimated_transaction_volume_usd</th>\n",
       "      <th>btc_market_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/1/2018 0:00</td>\n",
       "      <td>16839687.5</td>\n",
       "      <td>1.529590e+11</td>\n",
       "      <td>1.509688e+09</td>\n",
       "      <td>154613.2244</td>\n",
       "      <td>1.053963</td>\n",
       "      <td>0</td>\n",
       "      <td>1610.400000</td>\n",
       "      <td>12.475000</td>\n",
       "      <td>20703947.91</td>\n",
       "      <td>...</td>\n",
       "      <td>78.049647</td>\n",
       "      <td>591550</td>\n",
       "      <td>257664</td>\n",
       "      <td>296946448</td>\n",
       "      <td>249466</td>\n",
       "      <td>179686</td>\n",
       "      <td>2.190613e+06</td>\n",
       "      <td>276923.32070</td>\n",
       "      <td>2.515366e+09</td>\n",
       "      <td>9055.259423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/2/2018 0:00</td>\n",
       "      <td>16841787.5</td>\n",
       "      <td>1.499240e+11</td>\n",
       "      <td>2.213437e+09</td>\n",
       "      <td>154785.0008</td>\n",
       "      <td>1.022479</td>\n",
       "      <td>0</td>\n",
       "      <td>1404.273810</td>\n",
       "      <td>11.225000</td>\n",
       "      <td>21739145.31</td>\n",
       "      <td>...</td>\n",
       "      <td>89.591902</td>\n",
       "      <td>551198</td>\n",
       "      <td>235918</td>\n",
       "      <td>297182366</td>\n",
       "      <td>229894</td>\n",
       "      <td>155128</td>\n",
       "      <td>1.460796e+06</td>\n",
       "      <td>330740.21920</td>\n",
       "      <td>2.944217e+09</td>\n",
       "      <td>8886.563817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/3/2018 0:00</td>\n",
       "      <td>16843762.5</td>\n",
       "      <td>1.528850e+11</td>\n",
       "      <td>9.524038e+08</td>\n",
       "      <td>154942.4583</td>\n",
       "      <td>0.996567</td>\n",
       "      <td>0</td>\n",
       "      <td>1233.487342</td>\n",
       "      <td>10.475000</td>\n",
       "      <td>20445148.56</td>\n",
       "      <td>...</td>\n",
       "      <td>98.824757</td>\n",
       "      <td>436196</td>\n",
       "      <td>194891</td>\n",
       "      <td>297377257</td>\n",
       "      <td>184856</td>\n",
       "      <td>131568</td>\n",
       "      <td>9.100425e+05</td>\n",
       "      <td>164374.02440</td>\n",
       "      <td>1.491970e+09</td>\n",
       "      <td>9071.208047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/4/2018 0:00</td>\n",
       "      <td>16845987.5</td>\n",
       "      <td>1.415170e+11</td>\n",
       "      <td>1.080683e+09</td>\n",
       "      <td>155118.7652</td>\n",
       "      <td>0.990488</td>\n",
       "      <td>0</td>\n",
       "      <td>975.769663</td>\n",
       "      <td>9.275000</td>\n",
       "      <td>23033142.05</td>\n",
       "      <td>...</td>\n",
       "      <td>112.999677</td>\n",
       "      <td>396694</td>\n",
       "      <td>173687</td>\n",
       "      <td>297550944</td>\n",
       "      <td>165753</td>\n",
       "      <td>125143</td>\n",
       "      <td>9.722485e+05</td>\n",
       "      <td>193244.21400</td>\n",
       "      <td>1.623377e+09</td>\n",
       "      <td>8402.392216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/5/2018 0:00</td>\n",
       "      <td>16848300.0</td>\n",
       "      <td>1.152220e+11</td>\n",
       "      <td>1.793319e+09</td>\n",
       "      <td>155322.7709</td>\n",
       "      <td>1.102733</td>\n",
       "      <td>0</td>\n",
       "      <td>1169.529730</td>\n",
       "      <td>6.133333</td>\n",
       "      <td>23938939.78</td>\n",
       "      <td>...</td>\n",
       "      <td>78.586115</td>\n",
       "      <td>486553</td>\n",
       "      <td>216363</td>\n",
       "      <td>297767307</td>\n",
       "      <td>208757</td>\n",
       "      <td>144850</td>\n",
       "      <td>1.848913e+06</td>\n",
       "      <td>257109.29930</td>\n",
       "      <td>1.758323e+09</td>\n",
       "      <td>6765.556851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2/6/2018 0:00</td>\n",
       "      <td>16850425.0</td>\n",
       "      <td>1.295060e+11</td>\n",
       "      <td>3.094258e+09</td>\n",
       "      <td>155504.2500</td>\n",
       "      <td>1.067524</td>\n",
       "      <td>0</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>7.433333</td>\n",
       "      <td>24293141.32</td>\n",
       "      <td>...</td>\n",
       "      <td>72.073119</td>\n",
       "      <td>589896</td>\n",
       "      <td>244120</td>\n",
       "      <td>298011427</td>\n",
       "      <td>238351</td>\n",
       "      <td>164738</td>\n",
       "      <td>1.914333e+06</td>\n",
       "      <td>286153.34030</td>\n",
       "      <td>2.199270e+09</td>\n",
       "      <td>7593.445999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2/7/2018 0:00</td>\n",
       "      <td>16852187.5</td>\n",
       "      <td>1.365020e+11</td>\n",
       "      <td>1.670288e+09</td>\n",
       "      <td>155655.3629</td>\n",
       "      <td>1.071722</td>\n",
       "      <td>0</td>\n",
       "      <td>1515.737589</td>\n",
       "      <td>8.866667</td>\n",
       "      <td>20149017.21</td>\n",
       "      <td>...</td>\n",
       "      <td>71.480272</td>\n",
       "      <td>489402</td>\n",
       "      <td>213719</td>\n",
       "      <td>298225146</td>\n",
       "      <td>205988</td>\n",
       "      <td>144287</td>\n",
       "      <td>1.160112e+06</td>\n",
       "      <td>208207.33170</td>\n",
       "      <td>1.686471e+09</td>\n",
       "      <td>8069.377019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2/8/2018 0:00</td>\n",
       "      <td>16853825.0</td>\n",
       "      <td>1.388850e+11</td>\n",
       "      <td>1.336662e+09</td>\n",
       "      <td>155792.6909</td>\n",
       "      <td>1.048305</td>\n",
       "      <td>0</td>\n",
       "      <td>1322.816794</td>\n",
       "      <td>13.675000</td>\n",
       "      <td>18720008.90</td>\n",
       "      <td>...</td>\n",
       "      <td>82.053232</td>\n",
       "      <td>398607</td>\n",
       "      <td>173289</td>\n",
       "      <td>298398435</td>\n",
       "      <td>166517</td>\n",
       "      <td>126556</td>\n",
       "      <td>1.171510e+06</td>\n",
       "      <td>214089.82690</td>\n",
       "      <td>1.764215e+09</td>\n",
       "      <td>8256.778645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2/9/2018 0:00</td>\n",
       "      <td>16855512.5</td>\n",
       "      <td>1.438710e+11</td>\n",
       "      <td>1.093135e+09</td>\n",
       "      <td>155937.9769</td>\n",
       "      <td>1.076193</td>\n",
       "      <td>0</td>\n",
       "      <td>1317.481481</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>19291612.22</td>\n",
       "      <td>...</td>\n",
       "      <td>85.223878</td>\n",
       "      <td>462086</td>\n",
       "      <td>177860</td>\n",
       "      <td>298576295</td>\n",
       "      <td>173698</td>\n",
       "      <td>131499</td>\n",
       "      <td>1.144163e+06</td>\n",
       "      <td>199472.83090</td>\n",
       "      <td>1.702604e+09</td>\n",
       "      <td>8550.416315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2/10/2018 0:00</td>\n",
       "      <td>16857300.0</td>\n",
       "      <td>1.402510e+11</td>\n",
       "      <td>9.185402e+08</td>\n",
       "      <td>156085.9522</td>\n",
       "      <td>1.034792</td>\n",
       "      <td>0</td>\n",
       "      <td>1271.209790</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>20434818.87</td>\n",
       "      <td>...</td>\n",
       "      <td>85.874632</td>\n",
       "      <td>421547</td>\n",
       "      <td>181783</td>\n",
       "      <td>298758078</td>\n",
       "      <td>177603</td>\n",
       "      <td>133091</td>\n",
       "      <td>1.537986e+06</td>\n",
       "      <td>116211.04860</td>\n",
       "      <td>9.668616e+08</td>\n",
       "      <td>8297.176611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2/11/2018 0:00</td>\n",
       "      <td>16859187.5</td>\n",
       "      <td>1.406640e+11</td>\n",
       "      <td>7.963917e+08</td>\n",
       "      <td>156237.9970</td>\n",
       "      <td>1.006919</td>\n",
       "      <td>0</td>\n",
       "      <td>974.887417</td>\n",
       "      <td>7.533333</td>\n",
       "      <td>21578025.52</td>\n",
       "      <td>...</td>\n",
       "      <td>110.491753</td>\n",
       "      <td>368624</td>\n",
       "      <td>147208</td>\n",
       "      <td>298905286</td>\n",
       "      <td>144165</td>\n",
       "      <td>108927</td>\n",
       "      <td>1.398605e+06</td>\n",
       "      <td>85791.95132</td>\n",
       "      <td>7.158013e+08</td>\n",
       "      <td>8354.926122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2/12/2018 0:00</td>\n",
       "      <td>16861262.5</td>\n",
       "      <td>1.485700e+11</td>\n",
       "      <td>8.366237e+08</td>\n",
       "      <td>156410.8514</td>\n",
       "      <td>1.041292</td>\n",
       "      <td>0</td>\n",
       "      <td>1126.012048</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>23721537.99</td>\n",
       "      <td>...</td>\n",
       "      <td>101.114203</td>\n",
       "      <td>437380</td>\n",
       "      <td>186918</td>\n",
       "      <td>299092204</td>\n",
       "      <td>183345</td>\n",
       "      <td>133062</td>\n",
       "      <td>1.674742e+06</td>\n",
       "      <td>181029.65410</td>\n",
       "      <td>1.595114e+09</td>\n",
       "      <td>8780.020426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2/13/2018 0:00</td>\n",
       "      <td>16863312.5</td>\n",
       "      <td>1.449870e+11</td>\n",
       "      <td>1.884544e+08</td>\n",
       "      <td>156580.2562</td>\n",
       "      <td>1.032956</td>\n",
       "      <td>0</td>\n",
       "      <td>1115.756098</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>23435736.33</td>\n",
       "      <td>...</td>\n",
       "      <td>99.544224</td>\n",
       "      <td>414749</td>\n",
       "      <td>182984</td>\n",
       "      <td>299275188</td>\n",
       "      <td>179616</td>\n",
       "      <td>128687</td>\n",
       "      <td>1.315763e+06</td>\n",
       "      <td>146929.34580</td>\n",
       "      <td>1.263264e+09</td>\n",
       "      <td>8558.701296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2/14/2018 0:00</td>\n",
       "      <td>16865550.0</td>\n",
       "      <td>1.574340e+11</td>\n",
       "      <td>9.151655e+08</td>\n",
       "      <td>156762.0906</td>\n",
       "      <td>1.015835</td>\n",
       "      <td>0</td>\n",
       "      <td>1097.301676</td>\n",
       "      <td>10.225000</td>\n",
       "      <td>25579248.80</td>\n",
       "      <td>...</td>\n",
       "      <td>109.366318</td>\n",
       "      <td>450367</td>\n",
       "      <td>196417</td>\n",
       "      <td>299471605</td>\n",
       "      <td>192621</td>\n",
       "      <td>140594</td>\n",
       "      <td>1.557017e+06</td>\n",
       "      <td>148097.50310</td>\n",
       "      <td>1.382436e+09</td>\n",
       "      <td>9282.250869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date  btc_total_bitcoins  btc_market_cap  btc_trade_volume  \\\n",
       "0    2/1/2018 0:00          16839687.5    1.529590e+11      1.509688e+09   \n",
       "1    2/2/2018 0:00          16841787.5    1.499240e+11      2.213437e+09   \n",
       "2    2/3/2018 0:00          16843762.5    1.528850e+11      9.524038e+08   \n",
       "3    2/4/2018 0:00          16845987.5    1.415170e+11      1.080683e+09   \n",
       "4    2/5/2018 0:00          16848300.0    1.152220e+11      1.793319e+09   \n",
       "5    2/6/2018 0:00          16850425.0    1.295060e+11      3.094258e+09   \n",
       "6    2/7/2018 0:00          16852187.5    1.365020e+11      1.670288e+09   \n",
       "7    2/8/2018 0:00          16853825.0    1.388850e+11      1.336662e+09   \n",
       "8    2/9/2018 0:00          16855512.5    1.438710e+11      1.093135e+09   \n",
       "9   2/10/2018 0:00          16857300.0    1.402510e+11      9.185402e+08   \n",
       "10  2/11/2018 0:00          16859187.5    1.406640e+11      7.963917e+08   \n",
       "11  2/12/2018 0:00          16861262.5    1.485700e+11      8.366237e+08   \n",
       "12  2/13/2018 0:00          16863312.5    1.449870e+11      1.884544e+08   \n",
       "13  2/14/2018 0:00          16865550.0    1.574340e+11      9.151655e+08   \n",
       "\n",
       "    btc_blocks_size  btc_avg_block_size  btc_n_orphaned_blocks  \\\n",
       "0       154613.2244            1.053963                      0   \n",
       "1       154785.0008            1.022479                      0   \n",
       "2       154942.4583            0.996567                      0   \n",
       "3       155118.7652            0.990488                      0   \n",
       "4       155322.7709            1.102733                      0   \n",
       "5       155504.2500            1.067524                      0   \n",
       "6       155655.3629            1.071722                      0   \n",
       "7       155792.6909            1.048305                      0   \n",
       "8       155937.9769            1.076193                      0   \n",
       "9       156085.9522            1.034792                      0   \n",
       "10      156237.9970            1.006919                      0   \n",
       "11      156410.8514            1.041292                      0   \n",
       "12      156580.2562            1.032956                      0   \n",
       "13      156762.0906            1.015835                      0   \n",
       "\n",
       "    btc_n_transactions_per_block  btc_median_confirmation_time  btc_hash_rate  \\\n",
       "0                    1610.400000                     12.475000    20703947.91   \n",
       "1                    1404.273810                     11.225000    21739145.31   \n",
       "2                    1233.487342                     10.475000    20445148.56   \n",
       "3                     975.769663                      9.275000    23033142.05   \n",
       "4                    1169.529730                      6.133333    23938939.78   \n",
       "5                    1436.000000                      7.433333    24293141.32   \n",
       "6                    1515.737589                      8.866667    20149017.21   \n",
       "7                    1322.816794                     13.675000    18720008.90   \n",
       "8                    1317.481481                     12.200000    19291612.22   \n",
       "9                    1271.209790                      9.450000    20434818.87   \n",
       "10                    974.887417                      7.533333    21578025.52   \n",
       "11                   1126.012048                     10.200000    23721537.99   \n",
       "12                   1115.756098                     11.050000    23435736.33   \n",
       "13                   1097.301676                     10.225000    25579248.80   \n",
       "\n",
       "          ...         btc_cost_per_transaction  btc_n_unique_addresses  \\\n",
       "0         ...                        78.049647                  591550   \n",
       "1         ...                        89.591902                  551198   \n",
       "2         ...                        98.824757                  436196   \n",
       "3         ...                       112.999677                  396694   \n",
       "4         ...                        78.586115                  486553   \n",
       "5         ...                        72.073119                  589896   \n",
       "6         ...                        71.480272                  489402   \n",
       "7         ...                        82.053232                  398607   \n",
       "8         ...                        85.223878                  462086   \n",
       "9         ...                        85.874632                  421547   \n",
       "10        ...                       110.491753                  368624   \n",
       "11        ...                       101.114203                  437380   \n",
       "12        ...                        99.544224                  414749   \n",
       "13        ...                       109.366318                  450367   \n",
       "\n",
       "    btc_n_transactions  btc_n_transactions_total  \\\n",
       "0               257664                 296946448   \n",
       "1               235918                 297182366   \n",
       "2               194891                 297377257   \n",
       "3               173687                 297550944   \n",
       "4               216363                 297767307   \n",
       "5               244120                 298011427   \n",
       "6               213719                 298225146   \n",
       "7               173289                 298398435   \n",
       "8               177860                 298576295   \n",
       "9               181783                 298758078   \n",
       "10              147208                 298905286   \n",
       "11              186918                 299092204   \n",
       "12              182984                 299275188   \n",
       "13              196417                 299471605   \n",
       "\n",
       "    btc_n_transactions_excluding_popular  \\\n",
       "0                                 249466   \n",
       "1                                 229894   \n",
       "2                                 184856   \n",
       "3                                 165753   \n",
       "4                                 208757   \n",
       "5                                 238351   \n",
       "6                                 205988   \n",
       "7                                 166517   \n",
       "8                                 173698   \n",
       "9                                 177603   \n",
       "10                                144165   \n",
       "11                                183345   \n",
       "12                                179616   \n",
       "13                                192621   \n",
       "\n",
       "    btc_n_transactions_excluding_chains_longer_than_100  btc_output_volume  \\\n",
       "0                                              179686         2.190613e+06   \n",
       "1                                              155128         1.460796e+06   \n",
       "2                                              131568         9.100425e+05   \n",
       "3                                              125143         9.722485e+05   \n",
       "4                                              144850         1.848913e+06   \n",
       "5                                              164738         1.914333e+06   \n",
       "6                                              144287         1.160112e+06   \n",
       "7                                              126556         1.171510e+06   \n",
       "8                                              131499         1.144163e+06   \n",
       "9                                              133091         1.537986e+06   \n",
       "10                                             108927         1.398605e+06   \n",
       "11                                             133062         1.674742e+06   \n",
       "12                                             128687         1.315763e+06   \n",
       "13                                             140594         1.557017e+06   \n",
       "\n",
       "    btc_estimated_transaction_volume  btc_estimated_transaction_volume_usd  \\\n",
       "0                       276923.32070                          2.515366e+09   \n",
       "1                       330740.21920                          2.944217e+09   \n",
       "2                       164374.02440                          1.491970e+09   \n",
       "3                       193244.21400                          1.623377e+09   \n",
       "4                       257109.29930                          1.758323e+09   \n",
       "5                       286153.34030                          2.199270e+09   \n",
       "6                       208207.33170                          1.686471e+09   \n",
       "7                       214089.82690                          1.764215e+09   \n",
       "8                       199472.83090                          1.702604e+09   \n",
       "9                       116211.04860                          9.668616e+08   \n",
       "10                       85791.95132                          7.158013e+08   \n",
       "11                      181029.65410                          1.595114e+09   \n",
       "12                      146929.34580                          1.263264e+09   \n",
       "13                      148097.50310                          1.382436e+09   \n",
       "\n",
       "    btc_market_price  \n",
       "0        9055.259423  \n",
       "1        8886.563817  \n",
       "2        9071.208047  \n",
       "3        8402.392216  \n",
       "4        6765.556851  \n",
       "5        7593.445999  \n",
       "6        8069.377019  \n",
       "7        8256.778645  \n",
       "8        8550.416315  \n",
       "9        8297.176611  \n",
       "10       8354.926122  \n",
       "11       8780.020426  \n",
       "12       8558.701296  \n",
       "13       9282.250869  \n",
       "\n",
       "[14 rows x 24 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
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