cardano.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abedin Sherifi <br>\n",
    "## Cardano ADA Regression Analysis <br>\n",
    "## Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib.pyplot import figure, show\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from scipy.stats import zscore\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue)\n",
    "def encode_text_dummy(df, name):\n",
    "    dummies = pd.get_dummies(df[name])\n",
    "    for x in dummies.columns:\n",
    "        dummy_name = \"{}-{}\".format(name, x)\n",
    "        df[dummy_name] = dummies[x]\n",
    "    df.drop(name, axis=1, inplace=True)\n",
    "\n",
    "\n",
    "# Encode text values to a single dummy variable.  The new columns (which do not replace the old) will have a 1\n",
    "# at every location where the original column (name) matches each of the target_values.  One column is added for\n",
    "# each target value.\n",
    "def encode_text_single_dummy(df, name, target_values):\n",
    "    for tv in target_values:\n",
    "        l = list(df[name].astype(str))\n",
    "        l = [1 if str(x) == str(tv) else 0 for x in l]\n",
    "        name2 = \"{}-{}\".format(name, tv)\n",
    "        df[name2] = l\n",
    "\n",
    "\n",
    "# Encode text values to indexes(i.e. [1],[2],[3] for red,green,blue).\n",
    "def encode_text_index(df, name):\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    df[name] = le.fit_transform(df[name])\n",
    "    return le.classes_\n",
    "\n",
    "\n",
    "# Encode a numeric column as zscores\n",
    "def encode_numeric_zscore(df, name, mean=None, sd=None):\n",
    "    if mean is None:\n",
    "        mean = df[name].mean()\n",
    "\n",
    "    if sd is None:\n",
    "        sd = df[name].std()\n",
    "\n",
    "    df[name] = (df[name] - mean) / sd\n",
    "\n",
    "\n",
    "# Convert all missing values in the specified column to the median\n",
    "def missing_median(df, name):\n",
    "    med = df[name].median()\n",
    "    df[name] = df[name].fillna(med)\n",
    "\n",
    "\n",
    "# Convert all missing values in the specified column to the default\n",
    "def missing_default(df, name, default_value):\n",
    "    df[name] = df[name].fillna(default_value)\n",
    "\n",
    "\n",
    "# Convert a Pandas dataframe to the x,y inputs that TensorFlow needs\n",
    "def to_xy(df, target):\n",
    "    result = []\n",
    "    for x in df.columns:\n",
    "        if x != target:\n",
    "            result.append(x)\n",
    "    # find out the type of the target column.  Is it really this hard? :(\n",
    "    target_type = df[target].dtypes\n",
    "    target_type = target_type[0] if hasattr(target_type, '__iter__') else target_type\n",
    "    # Encode to int for classification, float otherwise. TensorFlow likes 32 bits.\n",
    "    if target_type in (np.int64, np.int32):\n",
    "        # Classification\n",
    "        dummies = pd.get_dummies(df[target])\n",
    "        return pd.DataFrame(df,columns=result).to_numpy(dtype=np.float32), dummies.to_numpy(dtype=np.float32)\n",
    "    else:\n",
    "        # Regression\n",
    "        return pd.DataFrame(df,columns=result).to_numpy(dtype=np.float32), pd.DataFrame(df,columns=[target]).to_numpy(dtype=np.float32)\n",
    "\n",
    "\n",
    "# Nicely formatted time string\n",
    "def hms_string(sec_elapsed):\n",
    "    h = int(sec_elapsed / (60 * 60))\n",
    "    m = int((sec_elapsed % (60 * 60)) / 60)\n",
    "    s = sec_elapsed % 60\n",
    "    return \"{}:{:>02}:{:>05.2f}\".format(h, m, s)\n",
    "\n",
    "\n",
    "# Regression chart.\n",
    "def chart_regression(pred,y,sort=True):\n",
    "    t = pd.DataFrame({'pred' : pred, 'y' : y.flatten()})\n",
    "    if sort:\n",
    "        t.sort_values(by=['y'],inplace=True)\n",
    "    a = plt.plot(t['y'].tolist(),label='expected')\n",
    "    b = plt.plot(t['pred'].tolist(),label='prediction')\n",
    "    plt.ylabel('output')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Remove all rows where the specified column is +/- sd standard deviations\n",
    "def remove_outliers(df, name, sd):\n",
    "    drop_rows = df.index[(np.abs(df[name] - df[name].mean()) >= (sd * df[name].std()))]\n",
    "    df.drop(drop_rows, axis=0, inplace=True)\n",
    "\n",
    "\n",
    "# Encode a column to a range between normalized_low and normalized_high.\n",
    "def encode_numeric_range(df, name, normalized_low=-1, normalized_high=1,\n",
    "                         data_low=None, data_high=None):\n",
    "    if data_low is None:\n",
    "        data_low = min(df[name])\n",
    "        data_high = max(df[name])\n",
    "\n",
    "    df[name] = ((df[name] - data_low) / (data_high - data_low)) \\\n",
    "               * (normalized_high - normalized_low) + normalized_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('coin_Cardano.csv',na_values=['NA','?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-02 23:59:59</td>\n",
       "      <td>0.030088</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>0.024607</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>57641300.0</td>\n",
       "      <td>6.288991e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-03 23:59:59</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>16997800.0</td>\n",
       "      <td>5.396927e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-04 23:59:59</td>\n",
       "      <td>0.022806</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.021931</td>\n",
       "      <td>9000050.0</td>\n",
       "      <td>5.686195e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-05 23:59:59</td>\n",
       "      <td>0.022154</td>\n",
       "      <td>0.020859</td>\n",
       "      <td>0.021951</td>\n",
       "      <td>0.021489</td>\n",
       "      <td>5562510.0</td>\n",
       "      <td>5.571390e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-06 23:59:59</td>\n",
       "      <td>0.021542</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.021359</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>7780710.0</td>\n",
       "      <td>4.806646e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo     Name Symbol                 Date      High       Low      Open  \\\n",
       "0    1  Cardano    ADA  2017-10-02 23:59:59  0.030088  0.019969  0.024607   \n",
       "1    2  Cardano    ADA  2017-10-03 23:59:59  0.027425  0.020690  0.025757   \n",
       "2    3  Cardano    ADA  2017-10-04 23:59:59  0.022806  0.020864  0.020864   \n",
       "3    4  Cardano    ADA  2017-10-05 23:59:59  0.022154  0.020859  0.021951   \n",
       "4    5  Cardano    ADA  2017-10-06 23:59:59  0.021542  0.018360  0.021359   \n",
       "\n",
       "      Close      Volume     Marketcap  \n",
       "0  0.025932  57641300.0  6.288991e+08  \n",
       "1  0.020816  16997800.0  5.396927e+08  \n",
       "2  0.021931   9000050.0  5.686195e+08  \n",
       "3  0.021489   5562510.0  5.571390e+08  \n",
       "4  0.018539   7780710.0  4.806646e+08  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1245 entries, 0 to 1244\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   SNo        1245 non-null   int64  \n",
      " 1   Name       1245 non-null   object \n",
      " 2   Symbol     1245 non-null   object \n",
      " 3   Date       1245 non-null   object \n",
      " 4   High       1245 non-null   float64\n",
      " 5   Low        1245 non-null   float64\n",
      " 6   Open       1245 non-null   float64\n",
      " 7   Close      1245 non-null   float64\n",
      " 8   Volume     1245 non-null   float64\n",
      " 9   Marketcap  1245 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 97.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-02 23:59:59</td>\n",
       "      <td>0.030088</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>0.024607</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>57641300.0</td>\n",
       "      <td>6.288991e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-03 23:59:59</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>16997800.0</td>\n",
       "      <td>5.396927e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-04 23:59:59</td>\n",
       "      <td>0.022806</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.021931</td>\n",
       "      <td>9000050.0</td>\n",
       "      <td>5.686195e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-05 23:59:59</td>\n",
       "      <td>0.022154</td>\n",
       "      <td>0.020859</td>\n",
       "      <td>0.021951</td>\n",
       "      <td>0.021489</td>\n",
       "      <td>5562510.0</td>\n",
       "      <td>5.571390e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>2017-10-06 23:59:59</td>\n",
       "      <td>0.021542</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.021359</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>7780710.0</td>\n",
       "      <td>4.806646e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo     Name Symbol                 Date      High       Low      Open  \\\n",
       "0    1  Cardano    ADA  2017-10-02 23:59:59  0.030088  0.019969  0.024607   \n",
       "1    2  Cardano    ADA  2017-10-03 23:59:59  0.027425  0.020690  0.025757   \n",
       "2    3  Cardano    ADA  2017-10-04 23:59:59  0.022806  0.020864  0.020864   \n",
       "3    4  Cardano    ADA  2017-10-05 23:59:59  0.022154  0.020859  0.021951   \n",
       "4    5  Cardano    ADA  2017-10-06 23:59:59  0.021542  0.018360  0.021359   \n",
       "\n",
       "      Close      Volume     Marketcap  \n",
       "0  0.025932  57641300.0  6.288991e+08  \n",
       "1  0.020816  16997800.0  5.396927e+08  \n",
       "2  0.021931   9000050.0  5.686195e+08  \n",
       "3  0.021489   5562510.0  5.571390e+08  \n",
       "4  0.018539   7780710.0  4.806646e+08  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Date = df.Date.str.replace('-','')\n",
    "df['Date'] = df['Date'].str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['Date'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1245 entries, 0 to 1244\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   SNo        1245 non-null   int64  \n",
      " 1   Name       1245 non-null   object \n",
      " 2   Symbol     1245 non-null   object \n",
      " 3   Date       1245 non-null   int64  \n",
      " 4   High       1245 non-null   float64\n",
      " 5   Low        1245 non-null   float64\n",
      " 6   Open       1245 non-null   float64\n",
      " 7   Close      1245 non-null   float64\n",
      " 8   Volume     1245 non-null   float64\n",
      " 9   Marketcap  1245 non-null   float64\n",
      "dtypes: float64(6), int64(2), object(2)\n",
      "memory usage: 97.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1245, 10)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1245.000000</td>\n",
       "      <td>1.245000e+03</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>1.245000e+03</td>\n",
       "      <td>1.245000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>623.000000</td>\n",
       "      <td>2.019016e+07</td>\n",
       "      <td>0.146055</td>\n",
       "      <td>0.129342</td>\n",
       "      <td>0.137758</td>\n",
       "      <td>0.138757</td>\n",
       "      <td>4.729622e+08</td>\n",
       "      <td>3.788101e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>359.544851</td>\n",
       "      <td>1.021154e+04</td>\n",
       "      <td>0.190901</td>\n",
       "      <td>0.159500</td>\n",
       "      <td>0.174933</td>\n",
       "      <td>0.177812</td>\n",
       "      <td>1.427985e+09</td>\n",
       "      <td>5.099696e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.017100e+07</td>\n",
       "      <td>0.021050</td>\n",
       "      <td>0.017620</td>\n",
       "      <td>0.018414</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>1.739460e+06</td>\n",
       "      <td>4.806646e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>312.000000</td>\n",
       "      <td>2.018081e+07</td>\n",
       "      <td>0.045570</td>\n",
       "      <td>0.042981</td>\n",
       "      <td>0.044286</td>\n",
       "      <td>0.044328</td>\n",
       "      <td>4.642072e+07</td>\n",
       "      <td>1.149308e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>623.000000</td>\n",
       "      <td>2.019062e+07</td>\n",
       "      <td>0.083514</td>\n",
       "      <td>0.077631</td>\n",
       "      <td>0.080869</td>\n",
       "      <td>0.080952</td>\n",
       "      <td>1.011906e+08</td>\n",
       "      <td>2.102088e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>934.000000</td>\n",
       "      <td>2.020042e+07</td>\n",
       "      <td>0.149314</td>\n",
       "      <td>0.138102</td>\n",
       "      <td>0.143086</td>\n",
       "      <td>0.143573</td>\n",
       "      <td>2.576860e+08</td>\n",
       "      <td>3.779666e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1245.000000</td>\n",
       "      <td>2.021023e+07</td>\n",
       "      <td>1.476653</td>\n",
       "      <td>1.234138</td>\n",
       "      <td>1.242145</td>\n",
       "      <td>1.324860</td>\n",
       "      <td>1.914198e+10</td>\n",
       "      <td>4.121970e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SNo          Date         High          Low         Open  \\\n",
       "count  1245.000000  1.245000e+03  1245.000000  1245.000000  1245.000000   \n",
       "mean    623.000000  2.019016e+07     0.146055     0.129342     0.137758   \n",
       "std     359.544851  1.021154e+04     0.190901     0.159500     0.174933   \n",
       "min       1.000000  2.017100e+07     0.021050     0.017620     0.018414   \n",
       "25%     312.000000  2.018081e+07     0.045570     0.042981     0.044286   \n",
       "50%     623.000000  2.019062e+07     0.083514     0.077631     0.080869   \n",
       "75%     934.000000  2.020042e+07     0.149314     0.138102     0.143086   \n",
       "max    1245.000000  2.021023e+07     1.476653     1.234138     1.242145   \n",
       "\n",
       "             Close        Volume     Marketcap  \n",
       "count  1245.000000  1.245000e+03  1.245000e+03  \n",
       "mean      0.138757  4.729622e+08  3.788101e+09  \n",
       "std       0.177812  1.427985e+09  5.099696e+09  \n",
       "min       0.018539  1.739460e+06  4.806646e+08  \n",
       "25%       0.044328  4.642072e+07  1.149308e+09  \n",
       "50%       0.080952  1.011906e+08  2.102088e+09  \n",
       "75%       0.143573  2.576860e+08  3.779666e+09  \n",
       "max       1.324860  1.914198e+10  4.121970e+10  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171002</td>\n",
       "      <td>0.030088</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>0.024607</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>5.764130e+07</td>\n",
       "      <td>6.288991e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171003</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>1.699780e+07</td>\n",
       "      <td>5.396927e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171004</td>\n",
       "      <td>0.022806</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.021931</td>\n",
       "      <td>9.000050e+06</td>\n",
       "      <td>5.686195e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171005</td>\n",
       "      <td>0.022154</td>\n",
       "      <td>0.020859</td>\n",
       "      <td>0.021951</td>\n",
       "      <td>0.021489</td>\n",
       "      <td>5.562510e+06</td>\n",
       "      <td>5.571390e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171006</td>\n",
       "      <td>0.021542</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.021359</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>7.780710e+06</td>\n",
       "      <td>4.806646e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>1241</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210223</td>\n",
       "      <td>1.133705</td>\n",
       "      <td>0.824145</td>\n",
       "      <td>1.098769</td>\n",
       "      <td>0.954709</td>\n",
       "      <td>1.120190e+10</td>\n",
       "      <td>2.970337e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>1242</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210224</td>\n",
       "      <td>1.074832</td>\n",
       "      <td>0.902583</td>\n",
       "      <td>0.954958</td>\n",
       "      <td>1.051657</td>\n",
       "      <td>8.481335e+09</td>\n",
       "      <td>3.271965e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>1243</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210225</td>\n",
       "      <td>1.172732</td>\n",
       "      <td>1.017986</td>\n",
       "      <td>1.051349</td>\n",
       "      <td>1.079116</td>\n",
       "      <td>8.248286e+09</td>\n",
       "      <td>3.357399e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>1244</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210226</td>\n",
       "      <td>1.290047</td>\n",
       "      <td>0.995150</td>\n",
       "      <td>1.080828</td>\n",
       "      <td>1.241105</td>\n",
       "      <td>1.488414e+10</td>\n",
       "      <td>3.861387e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>1245</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210227</td>\n",
       "      <td>1.476653</td>\n",
       "      <td>1.234138</td>\n",
       "      <td>1.242145</td>\n",
       "      <td>1.324860</td>\n",
       "      <td>1.914198e+10</td>\n",
       "      <td>4.121970e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1245 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SNo     Name Symbol      Date      High       Low      Open     Close  \\\n",
       "0        1  Cardano    ADA  20171002  0.030088  0.019969  0.024607  0.025932   \n",
       "1        2  Cardano    ADA  20171003  0.027425  0.020690  0.025757  0.020816   \n",
       "2        3  Cardano    ADA  20171004  0.022806  0.020864  0.020864  0.021931   \n",
       "3        4  Cardano    ADA  20171005  0.022154  0.020859  0.021951  0.021489   \n",
       "4        5  Cardano    ADA  20171006  0.021542  0.018360  0.021359  0.018539   \n",
       "...    ...      ...    ...       ...       ...       ...       ...       ...   \n",
       "1240  1241  Cardano    ADA  20210223  1.133705  0.824145  1.098769  0.954709   \n",
       "1241  1242  Cardano    ADA  20210224  1.074832  0.902583  0.954958  1.051657   \n",
       "1242  1243  Cardano    ADA  20210225  1.172732  1.017986  1.051349  1.079116   \n",
       "1243  1244  Cardano    ADA  20210226  1.290047  0.995150  1.080828  1.241105   \n",
       "1244  1245  Cardano    ADA  20210227  1.476653  1.234138  1.242145  1.324860   \n",
       "\n",
       "            Volume     Marketcap  \n",
       "0     5.764130e+07  6.288991e+08  \n",
       "1     1.699780e+07  5.396927e+08  \n",
       "2     9.000050e+06  5.686195e+08  \n",
       "3     5.562510e+06  5.571390e+08  \n",
       "4     7.780710e+06  4.806646e+08  \n",
       "...            ...           ...  \n",
       "1240  1.120190e+10  2.970337e+10  \n",
       "1241  8.481335e+09  3.271965e+10  \n",
       "1242  8.248286e+09  3.357399e+10  \n",
       "1243  1.488414e+10  3.861387e+10  \n",
       "1244  1.914198e+10  4.121970e+10  \n",
       "\n",
       "[1245 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(subset='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='Close'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Date', y='Close', kind='scatter', alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo          False\n",
       "Name         False\n",
       "Symbol       False\n",
       "Date         False\n",
       "High         False\n",
       "Low          False\n",
       "Open         False\n",
       "Close        False\n",
       "Volume       False\n",
       "Marketcap    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "closee = df['Close']\n",
    "sns.kdeplot(data=closee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Close'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171002</td>\n",
       "      <td>0.030088</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>0.024607</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>57641300.0</td>\n",
       "      <td>6.288991e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171003</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>16997800.0</td>\n",
       "      <td>5.396927e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171004</td>\n",
       "      <td>0.022806</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.021931</td>\n",
       "      <td>9000050.0</td>\n",
       "      <td>5.686195e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171005</td>\n",
       "      <td>0.022154</td>\n",
       "      <td>0.020859</td>\n",
       "      <td>0.021951</td>\n",
       "      <td>0.021489</td>\n",
       "      <td>5562510.0</td>\n",
       "      <td>5.571390e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171006</td>\n",
       "      <td>0.021542</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.021359</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>7780710.0</td>\n",
       "      <td>4.806646e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Symbol      Date      High       Low      Open     Close  \\\n",
       "0  Cardano    ADA  20171002  0.030088  0.019969  0.024607  0.025932   \n",
       "1  Cardano    ADA  20171003  0.027425  0.020690  0.025757  0.020816   \n",
       "2  Cardano    ADA  20171004  0.022806  0.020864  0.020864  0.021931   \n",
       "3  Cardano    ADA  20171005  0.022154  0.020859  0.021951  0.021489   \n",
       "4  Cardano    ADA  20171006  0.021542  0.018360  0.021359  0.018539   \n",
       "\n",
       "       Volume     Marketcap  \n",
       "0  57641300.0  6.288991e+08  \n",
       "1  16997800.0  5.396927e+08  \n",
       "2   9000050.0  5.686195e+08  \n",
       "3   5562510.0  5.571390e+08  \n",
       "4   7780710.0  4.806646e+08  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('SNo',1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dino/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:2079: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "cols = ['Date', 'Close', 'Open']\n",
    "sns.pairplot(df[cols], size = 2.5)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram and normal probability plot\n",
    "from scipy import stats\n",
    "sns.distplot(df['Close'], fit=stats.norm);\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(df['Close'], plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1245 entries, 0 to 1244\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Name       1245 non-null   object \n",
      " 1   Symbol     1245 non-null   object \n",
      " 2   Date       1245 non-null   int64  \n",
      " 3   High       1245 non-null   float64\n",
      " 4   Low        1245 non-null   float64\n",
      " 5   Open       1245 non-null   float64\n",
      " 6   Close      1245 non-null   float64\n",
      " 7   Volume     1245 non-null   float64\n",
      " 8   Marketcap  1245 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(2)\n",
      "memory usage: 87.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Name',1,inplace=True)\n",
    "df.drop('Symbol',1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171002</td>\n",
       "      <td>0.030088</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>0.024607</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>5.764130e+07</td>\n",
       "      <td>6.288991e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171003</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>1.699780e+07</td>\n",
       "      <td>5.396927e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171004</td>\n",
       "      <td>0.022806</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.021931</td>\n",
       "      <td>9.000050e+06</td>\n",
       "      <td>5.686195e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171005</td>\n",
       "      <td>0.022154</td>\n",
       "      <td>0.020859</td>\n",
       "      <td>0.021951</td>\n",
       "      <td>0.021489</td>\n",
       "      <td>5.562510e+06</td>\n",
       "      <td>5.571390e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20171006</td>\n",
       "      <td>0.021542</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.021359</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>7.780710e+06</td>\n",
       "      <td>4.806646e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210223</td>\n",
       "      <td>1.133705</td>\n",
       "      <td>0.824145</td>\n",
       "      <td>1.098769</td>\n",
       "      <td>0.954709</td>\n",
       "      <td>1.120190e+10</td>\n",
       "      <td>2.970337e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210224</td>\n",
       "      <td>1.074832</td>\n",
       "      <td>0.902583</td>\n",
       "      <td>0.954958</td>\n",
       "      <td>1.051657</td>\n",
       "      <td>8.481335e+09</td>\n",
       "      <td>3.271965e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210225</td>\n",
       "      <td>1.172732</td>\n",
       "      <td>1.017986</td>\n",
       "      <td>1.051349</td>\n",
       "      <td>1.079116</td>\n",
       "      <td>8.248286e+09</td>\n",
       "      <td>3.357399e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210226</td>\n",
       "      <td>1.290047</td>\n",
       "      <td>0.995150</td>\n",
       "      <td>1.080828</td>\n",
       "      <td>1.241105</td>\n",
       "      <td>1.488414e+10</td>\n",
       "      <td>3.861387e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>20210227</td>\n",
       "      <td>1.476653</td>\n",
       "      <td>1.234138</td>\n",
       "      <td>1.242145</td>\n",
       "      <td>1.324860</td>\n",
       "      <td>1.914198e+10</td>\n",
       "      <td>4.121970e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1245 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name Symbol      Date      High       Low      Open     Close  \\\n",
       "0     Cardano    ADA  20171002  0.030088  0.019969  0.024607  0.025932   \n",
       "1     Cardano    ADA  20171003  0.027425  0.020690  0.025757  0.020816   \n",
       "2     Cardano    ADA  20171004  0.022806  0.020864  0.020864  0.021931   \n",
       "3     Cardano    ADA  20171005  0.022154  0.020859  0.021951  0.021489   \n",
       "4     Cardano    ADA  20171006  0.021542  0.018360  0.021359  0.018539   \n",
       "...       ...    ...       ...       ...       ...       ...       ...   \n",
       "1240  Cardano    ADA  20210223  1.133705  0.824145  1.098769  0.954709   \n",
       "1241  Cardano    ADA  20210224  1.074832  0.902583  0.954958  1.051657   \n",
       "1242  Cardano    ADA  20210225  1.172732  1.017986  1.051349  1.079116   \n",
       "1243  Cardano    ADA  20210226  1.290047  0.995150  1.080828  1.241105   \n",
       "1244  Cardano    ADA  20210227  1.476653  1.234138  1.242145  1.324860   \n",
       "\n",
       "            Volume     Marketcap  \n",
       "0     5.764130e+07  6.288991e+08  \n",
       "1     1.699780e+07  5.396927e+08  \n",
       "2     9.000050e+06  5.686195e+08  \n",
       "3     5.562510e+06  5.571390e+08  \n",
       "4     7.780710e+06  4.806646e+08  \n",
       "...            ...           ...  \n",
       "1240  1.120190e+10  2.970337e+10  \n",
       "1241  8.481335e+09  3.271965e+10  \n",
       "1242  8.248286e+09  3.357399e+10  \n",
       "1243  1.488414e+10  3.861387e+10  \n",
       "1244  1.914198e+10  4.121970e+10  \n",
       "\n",
       "[1245 rows x 9 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "encode_numeric_zscore(df, 'Volume')\n",
    "encode_numeric_zscore(df, 'Marketcap')\n",
    "encode_numeric_zscore(df, 'Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>-1.875632</td>\n",
       "      <td>0.030088</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>0.024607</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>-0.290844</td>\n",
       "      <td>-0.619488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>-1.875534</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>-0.319306</td>\n",
       "      <td>-0.636981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>-1.875436</td>\n",
       "      <td>0.022806</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.021931</td>\n",
       "      <td>-0.324907</td>\n",
       "      <td>-0.631309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>-1.875338</td>\n",
       "      <td>0.022154</td>\n",
       "      <td>0.020859</td>\n",
       "      <td>0.021951</td>\n",
       "      <td>0.021489</td>\n",
       "      <td>-0.327314</td>\n",
       "      <td>-0.633560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>-1.875240</td>\n",
       "      <td>0.021542</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.021359</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>-0.325761</td>\n",
       "      <td>-0.648556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>1.965221</td>\n",
       "      <td>1.133705</td>\n",
       "      <td>0.824145</td>\n",
       "      <td>1.098769</td>\n",
       "      <td>0.954709</td>\n",
       "      <td>7.513343</td>\n",
       "      <td>5.081729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>1.965319</td>\n",
       "      <td>1.074832</td>\n",
       "      <td>0.902583</td>\n",
       "      <td>0.954958</td>\n",
       "      <td>1.051657</td>\n",
       "      <td>5.608165</td>\n",
       "      <td>5.673191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>1.965417</td>\n",
       "      <td>1.172732</td>\n",
       "      <td>1.017986</td>\n",
       "      <td>1.051349</td>\n",
       "      <td>1.079116</td>\n",
       "      <td>5.444964</td>\n",
       "      <td>5.840718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>1.965515</td>\n",
       "      <td>1.290047</td>\n",
       "      <td>0.995150</td>\n",
       "      <td>1.080828</td>\n",
       "      <td>1.241105</td>\n",
       "      <td>10.091972</td>\n",
       "      <td>6.828989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>1.965612</td>\n",
       "      <td>1.476653</td>\n",
       "      <td>1.234138</td>\n",
       "      <td>1.242145</td>\n",
       "      <td>1.324860</td>\n",
       "      <td>13.073684</td>\n",
       "      <td>7.339966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1245 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name Symbol      Date      High       Low      Open     Close  \\\n",
       "0     Cardano    ADA -1.875632  0.030088  0.019969  0.024607  0.025932   \n",
       "1     Cardano    ADA -1.875534  0.027425  0.020690  0.025757  0.020816   \n",
       "2     Cardano    ADA -1.875436  0.022806  0.020864  0.020864  0.021931   \n",
       "3     Cardano    ADA -1.875338  0.022154  0.020859  0.021951  0.021489   \n",
       "4     Cardano    ADA -1.875240  0.021542  0.018360  0.021359  0.018539   \n",
       "...       ...    ...       ...       ...       ...       ...       ...   \n",
       "1240  Cardano    ADA  1.965221  1.133705  0.824145  1.098769  0.954709   \n",
       "1241  Cardano    ADA  1.965319  1.074832  0.902583  0.954958  1.051657   \n",
       "1242  Cardano    ADA  1.965417  1.172732  1.017986  1.051349  1.079116   \n",
       "1243  Cardano    ADA  1.965515  1.290047  0.995150  1.080828  1.241105   \n",
       "1244  Cardano    ADA  1.965612  1.476653  1.234138  1.242145  1.324860   \n",
       "\n",
       "         Volume  Marketcap  \n",
       "0     -0.290844  -0.619488  \n",
       "1     -0.319306  -0.636981  \n",
       "2     -0.324907  -0.631309  \n",
       "3     -0.327314  -0.633560  \n",
       "4     -0.325761  -0.648556  \n",
       "...         ...        ...  \n",
       "1240   7.513343   5.081729  \n",
       "1241   5.608165   5.673191  \n",
       "1242   5.444964   5.840718  \n",
       "1243  10.091972   6.828989  \n",
       "1244  13.073684   7.339966  \n",
       "\n",
       "[1245 rows x 9 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.0162\n",
      "Epoch 2/100\n",
      " - 0s - loss: 7.8456e-04\n",
      "Epoch 3/100\n",
      " - 0s - loss: 4.8932e-04\n",
      "Epoch 4/100\n",
      " - 0s - loss: 1.5084e-04\n",
      "Epoch 5/100\n",
      " - 0s - loss: 1.4124e-04\n",
      "Epoch 6/100\n",
      " - 0s - loss: 7.7194e-05\n",
      "Epoch 7/100\n",
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      "Epoch 8/100\n",
      " - 0s - loss: 5.8209e-05\n",
      "Epoch 9/100\n",
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      "Epoch 10/100\n",
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      "Epoch 11/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
      " - 0s - loss: 3.6503e-05\n",
      "Epoch 14/100\n",
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      "Epoch 15/100\n",
      " - 0s - loss: 4.0668e-05\n",
      "Epoch 16/100\n",
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      "Epoch 17/100\n",
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      "Epoch 18/100\n",
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      "Epoch 19/100\n",
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      "Epoch 20/100\n",
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      "Epoch 21/100\n",
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      "Epoch 22/100\n",
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      "Epoch 23/100\n",
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      "Epoch 24/100\n",
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      "Epoch 25/100\n",
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      "Epoch 26/100\n",
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      "Epoch 27/100\n",
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      "Epoch 28/100\n",
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      "Epoch 29/100\n",
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      "Epoch 30/100\n",
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      "Epoch 31/100\n",
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      "Epoch 32/100\n",
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      "Epoch 33/100\n",
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      "Epoch 37/100\n",
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      "Epoch 38/100\n",
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      "Epoch 39/100\n",
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      "Epoch 40/100\n",
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      "Epoch 41/100\n",
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      "Epoch 42/100\n",
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      "Epoch 43/100\n",
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      "Epoch 44/100\n",
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      "Epoch 45/100\n",
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      "Epoch 46/100\n",
      " - 0s - loss: 9.1104e-06\n",
      "Epoch 47/100\n",
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      "Epoch 48/100\n",
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      "Epoch 49/100\n",
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      "Epoch 50/100\n",
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      "Epoch 51/100\n",
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      "Epoch 52/100\n",
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      "Epoch 54/100\n",
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      "Epoch 55/100\n",
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      "Epoch 56/100\n",
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      "Epoch 57/100\n",
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      "Epoch 58/100\n",
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      "Epoch 59/100\n",
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      "Epoch 60/100\n",
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      "Epoch 61/100\n",
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      "Epoch 62/100\n",
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      "Epoch 63/100\n",
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      "Epoch 64/100\n",
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      "Epoch 65/100\n",
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      "Epoch 66/100\n",
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      "Epoch 71/100\n",
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      "Epoch 72/100\n",
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      "Epoch 73/100\n",
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      "Epoch 74/100\n",
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      "Epoch 75/100\n",
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      "Epoch 76/100\n",
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      "Epoch 77/100\n",
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      "Epoch 78/100\n",
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      "Epoch 79/100\n",
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      "Epoch 80/100\n",
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      "Epoch 81/100\n",
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      "Epoch 82/100\n",
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      "Epoch 83/100\n",
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      "Epoch 84/100\n",
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      "Epoch 85/100\n",
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      "Epoch 86/100\n",
      " - 0s - loss: 3.6212e-05\n",
      "Epoch 87/100\n",
      " - 0s - loss: 1.4024e-05\n",
      "Epoch 88/100\n",
      " - 0s - loss: 1.2674e-05\n",
      "Epoch 89/100\n",
      " - 0s - loss: 8.4405e-06\n",
      "Epoch 90/100\n",
      " - 0s - loss: 9.9593e-06\n",
      "Epoch 91/100\n",
      " - 0s - loss: 8.8652e-06\n",
      "Epoch 92/100\n",
      " - 0s - loss: 8.3353e-06\n",
      "Epoch 93/100\n",
      " - 0s - loss: 7.9560e-06\n",
      "Epoch 94/100\n",
      " - 0s - loss: 5.9691e-06\n",
      "Epoch 95/100\n",
      " - 0s - loss: 5.9671e-06\n",
      "Epoch 96/100\n",
      " - 0s - loss: 7.8067e-06\n",
      "Epoch 97/100\n",
      " - 0s - loss: 5.4415e-06\n",
      "Epoch 98/100\n",
      " - 0s - loss: 4.2833e-06\n",
      "Epoch 99/100\n",
      " - 0s - loss: 4.6070e-06\n",
      "Epoch 100/100\n",
      " - 0s - loss: 1.6729e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f41dc20afd0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from keras.layers import Dropout\n",
    "\n",
    "\n",
    "df.drop('Name',1,inplace=True)\n",
    "df.drop('Symbol',1,inplace=True)\n",
    "#encode_numeric_zscore(df, 'Volume')\n",
    "#encode_numeric_zscore(df, 'Marketcap')\n",
    "x,y = to_xy(df,'Close')\n",
    "model = Sequential()\n",
    "model.add(Dense(100, input_dim=x.shape[1], activation='relu')) # Hidden 1\n",
    "model.add(Dense(10, activation='relu')) # Hidden 2\n",
    "model.add(Dense(1)) # Output\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(x,y,verbose=2,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (1245, 1)\n",
      "[[0.01866377]\n",
      " [0.0141762 ]\n",
      " [0.01490614]\n",
      " ...\n",
      " [1.0875826 ]\n",
      " [1.223474  ]\n",
      " [1.3485312 ]]\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(x)\n",
    "print(\"Shape: {}\".format(pred.shape))\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1245, 6)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pred'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart_regression(pred.flatten(),y, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score (RMSE): 0.007158439140766859\n"
     ]
    }
   ],
   "source": [
    "# Measure RMSE error.  RMSE is common for regression.\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,y))\n",
    "print(\"Final score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Close: [0.0259318], predicted Close: [0.01866377]\n",
      "2. Close: [0.0208158], predicted Close: [0.0141762]\n",
      "3. Close: [0.0219315], predicted Close: [0.01490614]\n",
      "4. Close: [0.0214887], predicted Close: [0.01450209]\n",
      "5. Close: [0.0185391], predicted Close: [0.01237602]\n",
      "6. Close: [0.0209409], predicted Close: [0.01442138]\n",
      "7. Close: [0.0204772], predicted Close: [0.01357895]\n",
      "8. Close: [0.0221137], predicted Close: [0.01495693]\n",
      "9. Close: [0.0215313], predicted Close: [0.01435667]\n",
      "10. Close: [0.0225195], predicted Close: [0.01528808]\n"
     ]
    }
   ],
   "source": [
    "# Sample predictions\n",
    "for i in range(10):\n",
    "    print(\"{}. Close: {}, predicted Close: {}\".format(i+1,y[i],pred[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='pred'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Date', y='Close', kind='scatter', alpha=0.5)\n",
    "df.plot(x='Date', y='pred', kind='scatter', alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}