notebooks/2-data-preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Time Series - Stock Price Prediction\n",
    "## Part 2 - Data Preprocessing\n",
    "This notebook focuses on processing the filtered dataset containing the historical prices of Google stocks over the past five years.\n",
    "\n",
    "> **INPUT**: Filtered dataset containing Google's stock prices from the last five years, obtained from the previous phase. <br/>\n",
    "> **OUTPUT**: Preprocessed and transformed data divided into training, validation, and testing subsets, stored in an interim location for the training phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. INITIALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries and modules\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set output width\n",
    "pd.set_option(\"display.width\", 120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. LOADING DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data file location and name\n",
    "data_file_location = \"..//data//interim//\"\n",
    "data_file_name = \"google_stock_price_recent\"\n",
    "data_file_ext = \"csv\"\n",
    "\n",
    "# Load data file\n",
    "data = pd.read_csv(data_file_location + data_file_name + \".\" + data_file_ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1323, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check loaded data shape\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>50.828499</td>\n",
       "      <td>52.616001</td>\n",
       "      <td>50.785500</td>\n",
       "      <td>52.292500</td>\n",
       "      <td>52.292500</td>\n",
       "      <td>30652000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>52.049999</td>\n",
       "      <td>52.848999</td>\n",
       "      <td>50.703499</td>\n",
       "      <td>50.803001</td>\n",
       "      <td>50.803001</td>\n",
       "      <td>36822000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>51.629501</td>\n",
       "      <td>53.542000</td>\n",
       "      <td>51.370899</td>\n",
       "      <td>53.535500</td>\n",
       "      <td>53.535500</td>\n",
       "      <td>41878000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>53.575001</td>\n",
       "      <td>53.700001</td>\n",
       "      <td>52.737999</td>\n",
       "      <td>53.419498</td>\n",
       "      <td>53.419498</td>\n",
       "      <td>39638000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>53.805500</td>\n",
       "      <td>54.228001</td>\n",
       "      <td>53.026501</td>\n",
       "      <td>53.813999</td>\n",
       "      <td>53.813999</td>\n",
       "      <td>35298000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close    Volume\n",
       "0  2019-01-02  50.828499  52.616001  50.785500  52.292500  52.292500  30652000\n",
       "1  2019-01-03  52.049999  52.848999  50.703499  50.803001  50.803001  36822000\n",
       "2  2019-01-04  51.629501  53.542000  51.370899  53.535500  53.535500  41878000\n",
       "3  2019-01-07  53.575001  53.700001  52.737999  53.419498  53.419498  39638000\n",
       "4  2019-01-08  53.805500  54.228001  53.026501  53.813999  53.813999  35298000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check loaded data head\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Adj Close    float64\n",
       "Volume         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check columns types\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validate Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         datetime64[ns]\n",
       "Open                float64\n",
       "High                float64\n",
       "Low                 float64\n",
       "Close               float64\n",
       "Adj Close           float64\n",
       "Volume                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert date column to a valid Datetime format\n",
    "data[\"Date\"] = pd.to_datetime(data[\"Date\"])\n",
    "\n",
    "# Check column types\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select Independent Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this analysis is to implement a multi-variant prediction, taking into account possible impact of several independent features such as the Open price, Close price and Volume on future stock price performance.\n",
    "\n",
    "Therefore, in this analysis, we will incorporate all the available variables: \n",
    "- Opening price\n",
    "- Highest price\n",
    "- Lowest price\n",
    "- Closing price\n",
    "- Adjusted closing price\n",
    "- Trading volume\n",
    "\n",
    "These features will be utilized to forecast the future opening price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define selected features and target attribute\n",
    "features = [\"Open\", \"High\", \"Low\",\t\"Close\", \"Adj Close\", \"Volume\"]\n",
    "target = \"Open\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Train, Validation, and Test Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To monitor and assess the performance of our model, we will partition the recent stock price dataset into three segments: training, validation, and testing sets.\n",
    "\n",
    "The division will be structured as follows:\n",
    "- **Training dataset:** covering the period from the start of 2019 till June, 2023.\n",
    "- **Validation dataset:** representing the stock prices from July, 2023 till the end of 2023.\n",
    "- **Testing dataset:** representing the stock prices for the first two months of 2024."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define start and end time for each period\n",
    "train_end_date = pd.to_datetime(\"2023-06-30\")\n",
    "validate_start_date = pd.to_datetime(\"2023-07-01\")\n",
    "validate_end_date = pd.to_datetime(\"2023-12-31\")\n",
    "test_start_date = pd.to_datetime(\"2024-01-01\")\n",
    "test_end_date = pd.to_datetime(\"2024-02-29\")\n",
    "\n",
    "# Split dataset into training, validation, and testing\n",
    "data_train = data[data[\"Date\"] <= train_end_date][features]\n",
    "data_train_dates = data[data[\"Date\"] <= train_end_date][\"Date\"]\n",
    "data_validate = data[(data[\"Date\"] >= validate_start_date) & (data[\"Date\"] <= validate_end_date)][features]\n",
    "data_validate_dates = data[(data[\"Date\"] >= validate_start_date) & (data[\"Date\"] <= validate_end_date)][\"Date\"]\n",
    "data_test = data[(data[\"Date\"] >= test_start_date) & (data[\"Date\"] <= test_end_date)][features]\n",
    "data_test_dates = data[(data[\"Date\"] >= test_start_date) & (data[\"Date\"] <= test_end_date)][\"Date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set: (1132, 6)\n",
      "Validation Set: (126, 6)\n",
      "Testing Set: (41, 6)\n"
     ]
    }
   ],
   "source": [
    "# Display the shape of each dataset\n",
    "print(f\"Training Set: {data_train.shape}\")\n",
    "print(f\"Validation Set: {data_validate.shape}\")\n",
    "print(f\"Testing Set: {data_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset:\n",
      "        Open       High        Low      Close  Adj Close    Volume\n",
      "0  50.828499  52.616001  50.785500  52.292500  52.292500  30652000\n",
      "1  52.049999  52.848999  50.703499  50.803001  50.803001  36822000\n",
      "2  51.629501  53.542000  51.370899  53.535500  53.535500  41878000\n",
      "3  53.575001  53.700001  52.737999  53.419498  53.419498  39638000\n",
      "4  53.805500  54.228001  53.026501  53.813999  53.813999  35298000\n",
      "Validation Dataset:\n",
      "            Open        High         Low       Close   Adj Close    Volume\n",
      "1132  120.320000  121.019997  119.705002  120.559998  120.559998  13888300\n",
      "1133  120.059998  123.370003  120.059998  122.629997  122.629997  17830300\n",
      "1134  120.639999  121.150002  119.250000  120.930000  120.930000  17732500\n",
      "1135  120.889999  121.750000  120.089996  120.139999  120.139999  20982400\n",
      "1136  119.070000  119.070000  116.639999  116.870003  116.870003  32960100\n",
      "Testing Dataset:\n",
      "            Open        High         Low       Close   Adj Close    Volume\n",
      "1258  139.600006  140.615005  137.740005  139.559998  139.559998  20071900\n",
      "1259  138.600006  141.089996  138.429993  140.360001  140.360001  18974300\n",
      "1260  139.850006  140.634995  138.009995  138.039993  138.039993  18253300\n",
      "1261  138.352005  138.809998  136.850006  137.389999  137.389999  15433200\n",
      "1262  138.000000  140.639999  137.880005  140.529999  140.529999  17645300\n"
     ]
    }
   ],
   "source": [
    "# Display a summary of each dataset\n",
    "print(\"Training Dataset:\")\n",
    "print(data_train.head())\n",
    "print(\"Validation Dataset:\")\n",
    "print(data_validate.head())\n",
    "print(\"Testing Dataset:\")\n",
    "print(data_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot stock prices for each data split\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.plot(data_train_dates, data_train[\"Open\"], color=\"cornflowerblue\")\n",
    "plt.plot(data_validate_dates, data_validate[\"Open\"], color=\"orange\")\n",
    "plt.plot(data_test_dates, data_test[\"Open\"], color=\"green\")\n",
    "plt.legend([\"Train Data\", \"Validation Data\", \"Test Data\"])\n",
    "plt.title(\"Data Split for Google Stock Price\")\n",
    "plt.xlabel(\"Samples Over Time\")\n",
    "plt.ylabel(\"Price (USD)\")\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=2))\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1.323000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.882880</td>\n",
       "      <td>102.042442</td>\n",
       "      <td>99.853834</td>\n",
       "      <td>100.974635</td>\n",
       "      <td>100.974635</td>\n",
       "      <td>2.882756e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.078299</td>\n",
       "      <td>31.360869</td>\n",
       "      <td>30.780336</td>\n",
       "      <td>31.059813</td>\n",
       "      <td>31.059813</td>\n",
       "      <td>1.268332e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.828499</td>\n",
       "      <td>52.374500</td>\n",
       "      <td>50.676800</td>\n",
       "      <td>50.803001</td>\n",
       "      <td>50.803001</td>\n",
       "      <td>6.936000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>71.211247</td>\n",
       "      <td>71.905250</td>\n",
       "      <td>70.356251</td>\n",
       "      <td>70.992001</td>\n",
       "      <td>70.992001</td>\n",
       "      <td>2.076335e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>102.825996</td>\n",
       "      <td>103.927498</td>\n",
       "      <td>101.500000</td>\n",
       "      <td>102.751503</td>\n",
       "      <td>102.751503</td>\n",
       "      <td>2.575600e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>130.778503</td>\n",
       "      <td>132.021675</td>\n",
       "      <td>129.389999</td>\n",
       "      <td>130.440002</td>\n",
       "      <td>130.440002</td>\n",
       "      <td>3.287305e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>155.080002</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>154.132004</td>\n",
       "      <td>156.500000</td>\n",
       "      <td>156.500000</td>\n",
       "      <td>1.241400e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close        Volume\n",
       "count  1323.000000  1323.000000  1323.000000  1323.000000  1323.000000  1.323000e+03\n",
       "mean    100.882880   102.042442    99.853834   100.974635   100.974635  2.882756e+07\n",
       "std      31.078299    31.360869    30.780336    31.059813    31.059813  1.268332e+07\n",
       "min      50.828499    52.374500    50.676800    50.803001    50.803001  6.936000e+06\n",
       "25%      71.211247    71.905250    70.356251    70.992001    70.992001  2.076335e+07\n",
       "50%     102.825996   103.927498   101.500000   102.751503   102.751503  2.575600e+07\n",
       "75%     130.778503   132.021675   129.389999   130.440002   130.440002  3.287305e+07\n",
       "max     155.080002   157.000000   154.132004   156.500000   156.500000  1.241400e+08"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the distribution of input features\n",
    "data[features].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these details, it's noticeable and anticipated that all price values exhibit similar distributions given they log the price fluctuation on daily basis.\n",
    "\n",
    "Conversely, the trading volume presents a distinct distribution that differs significantly.\n",
    "\n",
    "Accordingly, input features need to be transformed into a unified scale and since the distribution doesn't indicate any outliers, we will use the [0,1] range to normalize all features.\n",
    "\n",
    "To prevent data leakage, we will fit the scaler solely to the training data. Subsequently, we will use this fitted scaler to transform the training, validation, and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize scaler with range [0,1]\n",
    "sc = MinMaxScaler(feature_range=(0,1))\n",
    "\n",
    "# Fit and transform scaler to training set\n",
    "data_train_scaled = sc.fit_transform(data_train)\n",
    "\n",
    "# Transform validating and testing datasets\n",
    "data_validate_scaled = sc.transform(data_validate)\n",
    "data_test_scaled = sc.transform(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scaler employed here will also be utilized in subsequent phases to revert the scaled data back to its original distribution. Therefore, it is essential to save this scaler to a local folder for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['..//models//google_stock_price_scaler.gz']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare scaler model name and location\n",
    "scaler_model_location = \"..//models//\"\n",
    "scaler_model_name = \"google_stock_price_scaler\"\n",
    "scaler_model_ext = \"gz\"\n",
    "\n",
    "# Store scaler model\n",
    "joblib.dump(sc, scaler_model_location + scaler_model_name + \".\" + scaler_model_ext)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. STORING PROCESSED DATASETS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training, validation, and testing datasets have been processed and are prepared for training the LSTM model in the next phase.\n",
    "\n",
    "Prior to saving these datasets, it is necessary to reassemble the dates corresponding to each dataset. This will facilitate later evaluation of the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine dates with each corresponding dataset\n",
    "data_train_scaled_final = pd.DataFrame(data_train_scaled, columns=features, index=None)\n",
    "data_train_scaled_final[\"Date\"] = data_train_dates.values\n",
    "\n",
    "data_validate_scaled_final = pd.DataFrame(data_validate_scaled, columns=features, index=None)\n",
    "data_validate_scaled_final[\"Date\"] = data_validate_dates.values\n",
    "\n",
    "data_test_scaled_final = pd.DataFrame(data_test_scaled, columns=features, index=None)\n",
    "data_test_scaled_final[\"Date\"] = data_test_dates.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare datasets files and location\n",
    "data_file_location = \"..//data//processed//\"\n",
    "data_file_name_train = \"google_stock_price_processed_train\"\n",
    "data_file_name_validate = \"google_stock_price_processed_validate\"\n",
    "data_file_name_test = \"google_stock_price_processed_test\"\n",
    "data_file_ext = \"csv\"\n",
    "\n",
    "# Store datasets\n",
    "data_train_scaled_final.to_csv(data_file_location + data_file_name_train + \".\" + data_file_ext, index=None)\n",
    "data_validate_scaled_final.to_csv(data_file_location + data_file_name_validate + \".\" + data_file_ext, index=None)\n",
    "data_test_scaled_final.to_csv(data_file_location + data_file_name_test + \".\" + data_file_ext, index=None)"
   ]
  }
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