Fix Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "31d5fd21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "510136bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "close_dataset = pd.read_csv(\"Predictions/Close Price Prediction.csv\")\n",
    "open_dataset = pd.read_csv(\"Predictions/Open Price Prediction.csv\")\n",
    "low_dataset = pd.read_csv(\"Predictions/Low Price Prediction.csv\")\n",
    "high_dataset = pd.read_csv(\"Predictions/High Price Prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cdbd049c",
   "metadata": {},
   "outputs": [],
   "source": [
    "length = len(close_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c7576c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in range(length):\n",
    "    c, o, l, h = close_dataset.at[index, \"Close\"], open_dataset.at[index, \"Open\"], low_dataset.at[index, \"Low\"], high_dataset.at[index, \"High\"]\n",
    "    l = min(c, o, l, h)\n",
    "    h = max(c, o, l, h)\n",
    "    \n",
    "    low_dataset.loc[index, \"Low\"] = l\n",
    "    high_dataset.loc[index, \"High\"] = h\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3b86394e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>230.99556</td>\n",
       "      <td>231.45380</td>\n",
       "      <td>230.04018</td>\n",
       "      <td>230.04018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>231.00122</td>\n",
       "      <td>231.43726</td>\n",
       "      <td>230.02988</td>\n",
       "      <td>230.02988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>230.97928</td>\n",
       "      <td>231.40758</td>\n",
       "      <td>229.99214</td>\n",
       "      <td>229.99214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316</th>\n",
       "      <td>230.92430</td>\n",
       "      <td>231.36057</td>\n",
       "      <td>229.93149</td>\n",
       "      <td>229.93149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2317</th>\n",
       "      <td>230.95305</td>\n",
       "      <td>231.38630</td>\n",
       "      <td>229.95550</td>\n",
       "      <td>229.95550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>230.94061</td>\n",
       "      <td>231.37491</td>\n",
       "      <td>229.94392</td>\n",
       "      <td>229.94392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>231.00873</td>\n",
       "      <td>231.43938</td>\n",
       "      <td>230.01222</td>\n",
       "      <td>230.01222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2320</th>\n",
       "      <td>230.86769</td>\n",
       "      <td>231.32074</td>\n",
       "      <td>229.88654</td>\n",
       "      <td>229.88654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>230.87318</td>\n",
       "      <td>231.33075</td>\n",
       "      <td>229.89800</td>\n",
       "      <td>229.89800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>230.71930</td>\n",
       "      <td>231.18670</td>\n",
       "      <td>229.75221</td>\n",
       "      <td>229.75221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>230.73582</td>\n",
       "      <td>231.19736</td>\n",
       "      <td>229.75830</td>\n",
       "      <td>229.75830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>230.80956</td>\n",
       "      <td>231.24936</td>\n",
       "      <td>229.81334</td>\n",
       "      <td>229.81334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>231.00542</td>\n",
       "      <td>231.42519</td>\n",
       "      <td>229.99132</td>\n",
       "      <td>229.99132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>230.88542</td>\n",
       "      <td>231.34157</td>\n",
       "      <td>229.88867</td>\n",
       "      <td>229.88867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>230.92912</td>\n",
       "      <td>231.40187</td>\n",
       "      <td>229.95085</td>\n",
       "      <td>229.95085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>230.95435</td>\n",
       "      <td>231.41443</td>\n",
       "      <td>229.98439</td>\n",
       "      <td>229.98439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>230.78568</td>\n",
       "      <td>231.26785</td>\n",
       "      <td>229.82895</td>\n",
       "      <td>229.82895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>230.46996</td>\n",
       "      <td>230.99084</td>\n",
       "      <td>229.53477</td>\n",
       "      <td>229.53477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>230.33377</td>\n",
       "      <td>230.84792</td>\n",
       "      <td>229.38823</td>\n",
       "      <td>229.38823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>230.11935</td>\n",
       "      <td>230.63647</td>\n",
       "      <td>229.14973</td>\n",
       "      <td>229.14973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Open       High        Low      Close\n",
       "2313  230.99556  231.45380  230.04018  230.04018\n",
       "2314  231.00122  231.43726  230.02988  230.02988\n",
       "2315  230.97928  231.40758  229.99214  229.99214\n",
       "2316  230.92430  231.36057  229.93149  229.93149\n",
       "2317  230.95305  231.38630  229.95550  229.95550\n",
       "2318  230.94061  231.37491  229.94392  229.94392\n",
       "2319  231.00873  231.43938  230.01222  230.01222\n",
       "2320  230.86769  231.32074  229.88654  229.88654\n",
       "2321  230.87318  231.33075  229.89800  229.89800\n",
       "2322  230.71930  231.18670  229.75221  229.75221\n",
       "2323  230.73582  231.19736  229.75830  229.75830\n",
       "2324  230.80956  231.24936  229.81334  229.81334\n",
       "2325  231.00542  231.42519  229.99132  229.99132\n",
       "2326  230.88542  231.34157  229.88867  229.88867\n",
       "2327  230.92912  231.40187  229.95085  229.95085\n",
       "2328  230.95435  231.41443  229.98439  229.98439\n",
       "2329  230.78568  231.26785  229.82895  229.82895\n",
       "2330  230.46996  230.99084  229.53477  229.53477\n",
       "2331  230.33377  230.84792  229.38823  229.38823\n",
       "2332  230.11935  230.63647  229.14973  229.14973"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([open_dataset, high_dataset, low_dataset, close_dataset], axis=1)\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b6e24fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Predictions\\Overall_Predictions.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3fd23ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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