model_research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data_pre_processing import DataPreProcessing as dpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>733971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>75</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>733972</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>574</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733973</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>262</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733974</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>575</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>733975</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2160</td>\n",
       "      <td>USD</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4415</th>\n",
       "      <td>738386</td>\n",
       "      <td>23201.6</td>\n",
       "      <td>23202.3</td>\n",
       "      <td>20807.8</td>\n",
       "      <td>20831.3</td>\n",
       "      <td>339472</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4416</th>\n",
       "      <td>738387</td>\n",
       "      <td>20830.7</td>\n",
       "      <td>21357.4</td>\n",
       "      <td>20784.8</td>\n",
       "      <td>21138.9</td>\n",
       "      <td>206943</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4417</th>\n",
       "      <td>738388</td>\n",
       "      <td>21138.9</td>\n",
       "      <td>21692.4</td>\n",
       "      <td>21077.4</td>\n",
       "      <td>21517.2</td>\n",
       "      <td>177522</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4418</th>\n",
       "      <td>738389</td>\n",
       "      <td>21516.8</td>\n",
       "      <td>21517.4</td>\n",
       "      <td>20912.1</td>\n",
       "      <td>21416.3</td>\n",
       "      <td>251833</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4419</th>\n",
       "      <td>738390</td>\n",
       "      <td>21416.5</td>\n",
       "      <td>21458.2</td>\n",
       "      <td>21271.2</td>\n",
       "      <td>21309.0</td>\n",
       "      <td>251695</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4420 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Open     High      Low    Close  Volume Currency\n",
       "0     733971      0.0      0.1      0.1      0.1      75      USD\n",
       "1     733972      0.1      0.1      0.1      0.1     574      USD\n",
       "2     733973      0.1      0.1      0.1      0.1     262      USD\n",
       "3     733974      0.1      0.1      0.1      0.1     575      USD\n",
       "4     733975      0.1      0.1      0.1      0.1    2160      USD\n",
       "...      ...      ...      ...      ...      ...     ...      ...\n",
       "4415  738386  23201.6  23202.3  20807.8  20831.3  339472      USD\n",
       "4416  738387  20830.7  21357.4  20784.8  21138.9  206943      USD\n",
       "4417  738388  21138.9  21692.4  21077.4  21517.2  177522      USD\n",
       "4418  738389  21516.8  21517.4  20912.1  21416.3  251833      USD\n",
       "4419  738390  21416.5  21458.2  21271.2  21309.0  251695      USD\n",
       "\n",
       "[4420 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpp = dpp()\n",
    "dpp.load_data(\"data\")\n",
    "df = dpp.get_data()\n",
    "df['bitcoin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test model on BTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3536, 5), (884, 5), (3536,), (884,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X = df['bitcoin'][['Date', 'Open', 'High', 'Low', 'Volume']]\n",
    "y = df['bitcoin']['Close']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mape_vectorized_v2(a, b): \n",
    "    mask = a != 0\n",
    "    return (np.fabs(a - b)/a)[mask].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 73537.13494032854\n",
      "Train R2: 0.9996433153264435\n",
      "Train MAPE: 1.2323318590846721\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "model = LinearRegression(copy_X=True, n_jobs=-1).fit(x_train, y_train)\n",
    "y_pred = model.predict(x_train)\n",
    "\n",
    "train_mse = mean_squared_error(y_train, y_pred)\n",
    "train_r2 = r2_score(y_train, y_pred)\n",
    "train_mape = mape_vectorized_v2(y_train, y_pred)\n",
    "\n",
    "print(f\"Train MSE: {train_mse}\")\n",
    "print(f\"Train R2: {train_r2}\")\n",
    "print(f\"Train MAPE: {train_mape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-3.880794</td>\n",
       "      <td>-1.745528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-3.929412</td>\n",
       "      <td>-1.744740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-3.925300</td>\n",
       "      <td>-1.743952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-3.921232</td>\n",
       "      <td>-1.743164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-3.917252</td>\n",
       "      <td>-1.742376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>23856.8</td>\n",
       "      <td>23842.208568</td>\n",
       "      <td>1.731723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>23338.0</td>\n",
       "      <td>23831.108139</td>\n",
       "      <td>1.732511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4414</th>\n",
       "      <td>23203.6</td>\n",
       "      <td>23298.668229</td>\n",
       "      <td>1.733299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4415</th>\n",
       "      <td>20831.3</td>\n",
       "      <td>21607.784068</td>\n",
       "      <td>1.734087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4416</th>\n",
       "      <td>21138.9</td>\n",
       "      <td>21165.743657</td>\n",
       "      <td>1.734875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3536 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual     Predicted      Date\n",
       "0         0.1     -3.880794 -1.745528\n",
       "1         0.1     -3.929412 -1.744740\n",
       "2         0.1     -3.925300 -1.743952\n",
       "3         0.1     -3.921232 -1.743164\n",
       "4         0.1     -3.917252 -1.742376\n",
       "...       ...           ...       ...\n",
       "4412  23856.8  23842.208568  1.731723\n",
       "4413  23338.0  23831.108139  1.732511\n",
       "4414  23203.6  23298.668229  1.733299\n",
       "4415  20831.3  21607.784068  1.734087\n",
       "4416  21138.9  21165.743657  1.734875\n",
       "\n",
       "[3536 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred = pd.DataFrame({'Actual': y_train, 'Predicted': y_pred, 'Date': x_train[:,0]})\n",
    "df_pred.sort_values(by='Date', inplace=True)\n",
    "df_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred[['Actual', 'Predicted']].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}