Assignment2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d640c0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#import functions and libraries\n",
    "\n",
    "import sklearn \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import datetime\n",
    "%matplotlib inline \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ec30349",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#importing github raw with csb file for TSLA\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/umaimehm/Intro_to_AI_2021/main/assignment2-machinelearning/TSLA.csv\"\n",
    "\n",
    "df = pd.read_csv(url, sep=',')\n",
    "df.set_index(pd.DatetimeIndex(df['Date']), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c725ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead 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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.508000</td>\n",
       "      <td>4.778000</td>\n",
       "      <td>4.778000</td>\n",
       "      <td>93831500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>5.158000</td>\n",
       "      <td>6.084000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>4.766000</td>\n",
       "      <td>4.766000</td>\n",
       "      <td>85935500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.184000</td>\n",
       "      <td>4.054000</td>\n",
       "      <td>4.392000</td>\n",
       "      <td>4.392000</td>\n",
       "      <td>41094000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.620000</td>\n",
       "      <td>3.742000</td>\n",
       "      <td>3.840000</td>\n",
       "      <td>3.840000</td>\n",
       "      <td>25699000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.166000</td>\n",
       "      <td>3.222000</td>\n",
       "      <td>3.222000</td>\n",
       "      <td>34334500</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>2021-10-08</th>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>796.210022</td>\n",
       "      <td>796.380005</td>\n",
       "      <td>780.909973</td>\n",
       "      <td>785.489990</td>\n",
       "      <td>785.489990</td>\n",
       "      <td>16711100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>787.650024</td>\n",
       "      <td>801.239990</td>\n",
       "      <td>785.500000</td>\n",
       "      <td>791.940002</td>\n",
       "      <td>791.940002</td>\n",
       "      <td>14200300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>800.929993</td>\n",
       "      <td>812.320007</td>\n",
       "      <td>796.570007</td>\n",
       "      <td>805.719971</td>\n",
       "      <td>805.719971</td>\n",
       "      <td>22020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>2021-10-13</td>\n",
       "      <td>810.469971</td>\n",
       "      <td>815.409973</td>\n",
       "      <td>805.780029</td>\n",
       "      <td>811.080017</td>\n",
       "      <td>811.080017</td>\n",
       "      <td>14120100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-14</th>\n",
       "      <td>2021-10-14</td>\n",
       "      <td>815.489990</td>\n",
       "      <td>820.250000</td>\n",
       "      <td>813.349976</td>\n",
       "      <td>818.320007</td>\n",
       "      <td>818.320007</td>\n",
       "      <td>12203200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2845 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date        Open        High         Low       Close  \\\n",
       "Date                                                                     \n",
       "2010-06-29  2010-06-29    3.800000    5.000000    3.508000    4.778000   \n",
       "2010-06-30  2010-06-30    5.158000    6.084000    4.660000    4.766000   \n",
       "2010-07-01  2010-07-01    5.000000    5.184000    4.054000    4.392000   \n",
       "2010-07-02  2010-07-02    4.600000    4.620000    3.742000    3.840000   \n",
       "2010-07-06  2010-07-06    4.000000    4.000000    3.166000    3.222000   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-10-08  2021-10-08  796.210022  796.380005  780.909973  785.489990   \n",
       "2021-10-11  2021-10-11  787.650024  801.239990  785.500000  791.940002   \n",
       "2021-10-12  2021-10-12  800.929993  812.320007  796.570007  805.719971   \n",
       "2021-10-13  2021-10-13  810.469971  815.409973  805.780029  811.080017   \n",
       "2021-10-14  2021-10-14  815.489990  820.250000  813.349976  818.320007   \n",
       "\n",
       "             Adj Close    Volume  \n",
       "Date                              \n",
       "2010-06-29    4.778000  93831500  \n",
       "2010-06-30    4.766000  85935500  \n",
       "2010-07-01    4.392000  41094000  \n",
       "2010-07-02    3.840000  25699000  \n",
       "2010-07-06    3.222000  34334500  \n",
       "...                ...       ...  \n",
       "2021-10-08  785.489990  16711100  \n",
       "2021-10-11  791.940002  14200300  \n",
       "2021-10-12  805.719971  22020000  \n",
       "2021-10-13  811.080017  14120100  \n",
       "2021-10-14  818.320007  12203200  \n",
       "\n",
       "[2845 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2dd05415",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['High','Low','Open','Volume']].values\n",
    "y = df['Close'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85b3a490",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#training model\n",
    "\n",
    "x_train,x_test,y_train,y_test =  train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f65c6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8a35a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using linear regression based on previous data\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22883cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9.67526692e-01  6.62921370e-01 -6.34627090e-01 -5.28288450e-09]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5cc23906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10122765785403942\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2b43e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca11a0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# making a dframe to check actual vs predicted results\n",
    "dframe = pd.DataFrame({'Actual':y_test.flatten(),'Predicted':predicted.flatten()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4fcf079a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.436001</td>\n",
       "      <td>38.610541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>673.580017</td>\n",
       "      <td>664.424655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.582000</td>\n",
       "      <td>5.628072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.094000</td>\n",
       "      <td>7.098933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156.376007</td>\n",
       "      <td>160.346724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48.243999</td>\n",
       "      <td>47.964242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45.001999</td>\n",
       "      <td>45.239484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48.556000</td>\n",
       "      <td>48.913865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46.464001</td>\n",
       "      <td>46.800102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.592000</td>\n",
       "      <td>4.611518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>62.084000</td>\n",
       "      <td>60.344226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>791.359985</td>\n",
       "      <td>792.355906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.756000</td>\n",
       "      <td>3.805511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40.619999</td>\n",
       "      <td>41.027111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>576.830017</td>\n",
       "      <td>577.281476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.468000</td>\n",
       "      <td>5.487355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47.444000</td>\n",
       "      <td>47.269002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.364000</td>\n",
       "      <td>5.446747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>68.800003</td>\n",
       "      <td>68.339173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.170000</td>\n",
       "      <td>5.403811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual   Predicted\n",
       "0    38.436001   38.610541\n",
       "1   673.580017  664.424655\n",
       "2     5.582000    5.628072\n",
       "3     7.094000    7.098933\n",
       "4   156.376007  160.346724\n",
       "5    48.243999   47.964242\n",
       "6    45.001999   45.239484\n",
       "7    48.556000   48.913865\n",
       "8    46.464001   46.800102\n",
       "9     4.592000    4.611518\n",
       "10   62.084000   60.344226\n",
       "11  791.359985  792.355906\n",
       "12    3.756000    3.805511\n",
       "13   40.619999   41.027111\n",
       "14  576.830017  577.281476\n",
       "15    5.468000    5.487355\n",
       "16   47.444000   47.269002\n",
       "17    5.364000    5.446747\n",
       "18   68.800003   68.339173\n",
       "19    5.170000    5.403811"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6aeec36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Difference: 0.9653622592148507\n",
      "Mean Squared Difference: 6.616145787760759\n",
      "Root Mean Squared Difference: 2.572186965941776\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# checking for difference/error in code\n",
    "\n",
    "print('Mean Absolute Difference:', metrics.mean_absolute_error(y_test,predicted))\n",
    "print('Mean Squared Difference:', metrics.mean_squared_error(y_test,predicted))\n",
    "print('Root Mean Squared Difference:', math.sqrt(metrics.mean_squared_error(y_test,predicted)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea8ced39",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = dframe.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b044e936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "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": [
    "#plotting for actual v predicted\n",
    "\n",
    "graph.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cf77925c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up for forecast, actual future predictions\n",
    "forecast_col = 'Adj Close'\n",
    "df.fillna(value=-99999, inplace=True)\n",
    "forecast_out = int(math.ceil(0.01 * len(df)))\n",
    "df['label'] = df[forecast_col].shift(-forecast_out)\n",
    "\n",
    "x = np.array(df.drop(['label'], 1))\n",
    "x_lately = x[-forecast_out:]\n",
    "x = x[:-forecast_out]\n",
    "\n",
    "x = x.flatten('C')\n",
    "\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "y = np.array(df['label'])\n",
    "\n",
    "\n",
    "clf = LinearRegression(n_jobs=-1)\n",
    "clf.fit(x_train, y_train)\n",
    "confidence = clf.score(x_test, y_test)\n",
    "\n",
    "forecast_set = clf.predict(x_test) # dummy x value, because other x values fail\n",
    "df['Forecast'] = np.nan\n",
    "\n",
    "last_date = df.iloc[-1].name\n",
    "last_unix = last_date.timestamp()\n",
    "one_day = 86400\n",
    "next_unix = last_unix + one_day\n",
    "\n",
    "\n",
    "\n",
    "for i in forecast_set:\n",
    "    next_date = datetime.datetime.fromtimestamp(next_unix)\n",
    "    next_unix += 86400\n",
    "    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4881c936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Failing to show forecast because of incorrect x-values \n",
    "#Also missing input values for x prediction so that you can choose a certain date\n",
    "\n",
    "df['Close'].plot()\n",
    "df['Forecast'].plot()\n",
    "plt.legend(loc=4)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c597747",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}