XGBoost_Codes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25973631-2576-4fdb-99f9-8fde272d9266",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import logging\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# import yfinance as yf\n",
    "# import plotly.graph_objs as go\n",
    "# import plotly.express as px\n",
    "\n",
    "# # Set up logging\n",
    "# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
    "\n",
    "# def download_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:\n",
    "#     \"\"\"Downloads stock data from Yahoo Finance.\"\"\"\n",
    "#     try:\n",
    "#         logging.info(f\"Downloading stock data for ticker: {ticker}\")\n",
    "#         data = yf.download(ticker, start=start_date, end=end_date)  # Corrected syntax\n",
    "#         data.reset_index(inplace=True)\n",
    "#         data['Date'] = pd.to_datetime(data['Date'])\n",
    "#         data.set_index('Date', inplace=True)\n",
    "#         return data\n",
    "#     except Exception as e:\n",
    "#         logging.error(f\"Failed to download stock data: {e}\")\n",
    "#         raise\n",
    "\n",
    "# def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:\n",
    "#     \"\"\"Preprocesses the stock data for exploratory data analysis and model preparation.\"\"\"\n",
    "#     # Handling missing values\n",
    "#     missing_summary = df.isnull().sum()\n",
    "#     logging.info(f\"Missing Values per Column:\\n{missing_summary}\")\n",
    "#     df.dropna(inplace=True)\n",
    "\n",
    "#     # Removing duplicates\n",
    "#     duplicates_count = df.duplicated().sum()\n",
    "#     logging.info(f\"Number of Duplicate Rows: {duplicates_count}\")\n",
    "#     df.drop_duplicates(inplace=True)\n",
    "\n",
    "#     # Statistical Summary\n",
    "#     summary_stats = df.describe()\n",
    "#     logging.info(f\"Statistical Summary:\\n{summary_stats}\")\n",
    "\n",
    "#     return df\n",
    "\n",
    "# def stock_data_eda(df: pd.DataFrame):\n",
    "#     \"\"\"Performs exploratory data analysis on stock data.\"\"\"\n",
    "#     # Line chart for Close Price\n",
    "#     fig = go.Figure()\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Close'], mode='lines', name='Close Price'))\n",
    "#     fig.update_layout(title='Close Price Over Time', xaxis_title='Date', yaxis_title='Close Price')\n",
    "#     fig.show()\n",
    "\n",
    "#     # Heatmap for correlation matrix\n",
    "#     corr = df.corr()\n",
    "#     fig = px.imshow(corr, text_auto=True, aspect='auto', color_continuous_scale='coolwarm')\n",
    "#     fig.update_layout(title='Feature Correlation Matrix')\n",
    "#     fig.show()\n",
    "\n",
    "# def main():\n",
    "#     ticker_symbol = 'TATAELXSI.NS'\n",
    "#     start_date = '2019-04-01'\n",
    "#     end_date = '2024-03-31'\n",
    "    \n",
    "#     stock_data = download_stock_data(ticker_symbol, start_date, end_date)\n",
    "#     preprocessed_data = preprocess_data(stock_data)\n",
    "    \n",
    "#     stock_data_eda(preprocessed_data)\n",
    "#     logging.info(\"Data preprocessing and EDA completed.\")\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6838aa76-19b5-4eb4-870f-194a25669a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import logging\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# import yfinance as yf\n",
    "# import plotly.graph_objs as go\n",
    "# import plotly.express as px\n",
    "\n",
    "# # Set up logging\n",
    "# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
    "\n",
    "# def download_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:\n",
    "#     \"\"\"Downloads stock data from Yahoo Finance.\"\"\"\n",
    "#     try:\n",
    "#         logging.info(f\"Downloading stock data for ticker: {ticker}\")\n",
    "#         data = yf.download(ticker, start=start_date, end=end_date)  # Corrected syntax\n",
    "#         data.reset_index(inplace=True)\n",
    "#         data['Date'] = pd.to_datetime(data['Date'])\n",
    "#         data.set_index('Date', inplace=True)\n",
    "#         return data\n",
    "#     except Exception as e:\n",
    "#         logging.error(f\"Failed to download stock data: {e}\")\n",
    "#         raise\n",
    "\n",
    "# def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:\n",
    "#     \"\"\"Preprocesses the stock data for exploratory data analysis and model preparation.\"\"\"\n",
    "#     # Handling missing values\n",
    "#     missing_summary = df.isnull().sum()\n",
    "#     logging.info(f\"Missing Values per Column:\\n{missing_summary}\")\n",
    "#     df.dropna(inplace=True)\n",
    "\n",
    "#     # Removing duplicates\n",
    "#     duplicates_count = df.duplicated().sum()\n",
    "#     logging.info(f\"Number of Duplicate Rows: {duplicates_count}\")\n",
    "#     df.drop_duplicates(inplace=True)\n",
    "\n",
    "#     # Statistical Summary\n",
    "#     summary_stats = df.describe()\n",
    "#     logging.info(f\"Statistical Summary:\\n{summary_stats}\")\n",
    "\n",
    "#     return df\n",
    "\n",
    "# # def stock_data_eda(df: pd.DataFrame):\n",
    "# #     \"\"\"Performs exploratory data analysis on stock data.\"\"\"\n",
    "# #     # Line chart for Close Price\n",
    "# #     fig = go.Figure()\n",
    "# #     fig.add_trace(go.Scatter(x=df.index, y=df['Close'], mode='lines', name='Close Price'))\n",
    "# #     fig.update_layout(title='Close Price Over Time', xaxis_title='Date', yaxis_title='Close Price')\n",
    "# #     fig.show()\n",
    "\n",
    "# #     # Heatmap for correlation matrix\n",
    "# #     corr = df.corr()\n",
    "# #     fig = px.imshow(corr, text_auto=True, aspect='auto', color_continuous_scale='coolwarm')\n",
    "# #     fig.update_layout(title='Feature Correlation Matrix')\n",
    "# #     fig.show()\n",
    "\n",
    "# def main():\n",
    "#     ticker_symbol = 'TATAELXSI.NS'\n",
    "#     start_date = '2019-04-01'\n",
    "#     end_date = '2024-03-31'\n",
    "    \n",
    "#     stock_data = download_stock_data(ticker_symbol, start_date, end_date)\n",
    "#     preprocessed_data = preprocess_data(stock_data)\n",
    "    \n",
    "#     stock_data_eda(preprocessed_data)\n",
    "#     logging.info(\"Data preprocessing and EDA completed.\")\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88b0739c-7750-4cb1-b59d-d006f9624da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import logging\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# import yfinance as yf\n",
    "# import plotly.graph_objs as go\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Set up logging\n",
    "# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message=s)')\n",
    "\n",
    "# def download_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:\n",
    "#     \"\"\"Downloads stock data from Yahoo Finance.\"\"\"\n",
    "#     try:\n",
    "#         logging.info(f\"Downloading stock data for ticker: {ticker}\")\n",
    "#         data = yf.download(ticker, start=start_date, end=end_date)\n",
    "#         data.reset_index(inplace=True)\n",
    "#         data['Date'] = pd.to_datetime(data['Date'])\n",
    "#         data.set_index('Date', inplace=True)\n",
    "#         return data\n",
    "#     except Exception as e:\n",
    "#         logging.error(f\"Failed to download stock data: {e}\")\n",
    "#         raise\n",
    "\n",
    "# def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:\n",
    "#     \"\"\"Preprocesses the stock data for exploratory data analysis and model preparation.\"\"\"\n",
    "#     # Handling missing values\n",
    "#     missing_summary = df.isnull().sum()\n",
    "#     logging.info(f\"Missing Values per Column:\\n{missing_summary}\")\n",
    "#     df.dropna(inplace=True)\n",
    "\n",
    "#     # Removing duplicates\n",
    "#     duplicates_count = df.duplicated().sum()\n",
    "#     logging.info(f\"Number of Duplicate Rows: {duplicates_count}\")\n",
    "#     df.drop_duplicates(inplace=True)\n",
    "\n",
    "#     # Statistical Summary\n",
    "#     summary_stats = df.describe()\n",
    "#     logging.info(f\"Statistical Summary:\\n{summary_stats}\")\n",
    "\n",
    "#     # Convert the summary statistics to an HTML table\n",
    "#     summary_html = summary_stats.to_html()\n",
    "#     with open('statistical_summary.html', 'w') as f:\n",
    "#         f.write(summary_html)\n",
    "\n",
    "#     return df\n",
    "\n",
    "# def stock_data_eda(df: pd.DataFrame):\n",
    "#     \"\"\"Performs exploratory data analysis on stock data.\"\"\"\n",
    "#     # Line chart for Closing, Opening, and Adjusted Closing Price\n",
    "#     fig = go.Figure()\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Close'], mode='lines', name='Close Price'))\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Open'], mode='lines', name='Open Price'))\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Adj Close'], mode='lines', name='Adjusted Close Price'))\n",
    "\n",
    "#     # Calculate and add Average Stock Price in a Day\n",
    "#     df['Average'] = (df['High'] + df['Low']) / 2\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Average'], mode='lines', name='Average Price'))\n",
    "\n",
    "#     fig.update_layout(title='Stock Price Over Time',\n",
    "#                       xaxis_title='Date', yaxis_title='Price')\n",
    "#     fig.show()\n",
    "\n",
    "#     # Heatmap for correlation matrix using Seaborn\n",
    "#     plt.figure(figsize=(10, 6))\n",
    "#     sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
    "#     plt.title('Feature Correlation Matrix')\n",
    "#     plt.show()\n",
    "\n",
    "# def main():\n",
    "#     ticker_symbol = 'TATAELXSI.NS'\n",
    "#     start_date = '2019-04-01'\n",
    "#     end_date = '2024-03-31'\n",
    "    \n",
    "#     stock_data = download_stock_data(ticker_symbol, start_date, end_date)\n",
    "#     preprocessed_data = preprocess_data(stock_data)\n",
    "    \n",
    "#     stock_data_eda(preprocessed_data)\n",
    "#     logging.info(\"Data preprocessing and EDA completed.\")\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "443cada2-a05d-4809-92fc-fd8b333feac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def feature_engineering(df: pd.DataFrame) -> pd.DataFrame:\n",
    "#     \"\"\"Adds engineered features to the stock data for analysis.\n",
    "    \n",
    "#     Args:\n",
    "#         df (pd.DataFrame): The preprocessed stock data.\n",
    "\n",
    "#     Returns:\n",
    "#         pd.DataFrame: Stock data with engineered features.\n",
    "#     \"\"\"\n",
    "#     # Lag features\n",
    "#     df['Lag1'] = df['Close'].shift(1)\n",
    "#     df['Lag5'] = df['Close'].shift(5)\n",
    "#     df['Lag10'] = df['Close'].shift(10)\n",
    "\n",
    "#     # Moving averages\n",
    "#     df['MA5'] = df['Close'].rolling(window=5).mean()\n",
    "#     df['MA10'] = df['Close'].rolling(window=10).mean()\n",
    "#     df['MA20'] = df['Close'].rolling(window=20).mean()\n",
    "\n",
    "#     # Volatility\n",
    "#     df['Volatility'] = df['Close'].rolling(window=5).std()\n",
    "\n",
    "#     # Remove NaN values resulting from rolling windows\n",
    "#     df.dropna(inplace=True)\n",
    "    \n",
    "#     return df\n",
    "\n",
    "# def main():\n",
    "#     ticker_symbol = 'TATAELXSI.NS'\n",
    "#     start_date = '2019-04-01'\n",
    "#     end_date = '2024-03-31'\n",
    "    \n",
    "#     stock_data = download_stock_data(ticker_symbol, start_date, end_date)\n",
    "#     preprocessed_data = preprocess_data(stock_data)\n",
    "    \n",
    "#     # Feature Engineering Step\n",
    "#     engineered_data = feature_engineering(preprocessed_data)\n",
    "\n",
    "#     stock_data_eda(engineered_data)\n",
    "#     logging.info(\"Data preprocessing, feature engineering, and EDA completed.\")\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62808f5a-2227-43e7-ad55-dcb85dd844c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import logging\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# import yfinance as yf\n",
    "# import matplotlib.pyplot as plt\n",
    "# import seaborn as sns\n",
    "\n",
    "# # Set up logging\n",
    "# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
    "\n",
    "# def download_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:\n",
    "#     \"\"\"Downloads stock data from Yahoo Finance.\"\"\"\n",
    "#     try:\n",
    "#         logging.info(f\"Downloading stock data for ticker: {ticker}\")\n",
    "#         data = yf.download(ticker, start=start_date, end=end_date)\n",
    "#         data.reset_index(inplace=True)\n",
    "#         data['Date'] = pd.to_datetime(data['Date'])\n",
    "#         data.set_index('Date', inplace=True)\n",
    "#         return data\n",
    "#     except Exception as e:\n",
    "#         logging.error(f\"Failed to download stock data: {e}\")\n",
    "#         raise\n",
    "\n",
    "# def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:\n",
    "#     \"\"\"Preprocesses the stock data for exploratory data analysis and model preparation.\"\"\"\n",
    "#     # Handling missing values\n",
    "#     missing_summary = df.isnull().sum()\n",
    "#     logging.info(f\"Missing Values per Column:\\n{missing_summary}\")\n",
    "\n",
    "#     df.dropna(inplace=True)  # Drop rows with missing values\n",
    "\n",
    "#     # Removing duplicates\n",
    "#     duplicates_count = df.duplicated().sum()\n",
    "#     logging.info(f\"Number of Duplicate Rows: {duplicates_count}\")\n",
    "\n",
    "#     df.drop_duplicates(inplace=True)\n",
    "\n",
    "#     # Statistical Summary\n",
    "#     summary_stats = df.describe()\n",
    "#     logging.info(f\"Statistical Summary:\\n{summary_stats}\")\n",
    "\n",
    "#     return df\n",
    "\n",
    "# def stock_data_eda(df: pd.DataFrame):\n",
    "#     \"\"\"Performs exploratory data analysis on stock data.\"\"\"\n",
    "#     plt.figure(figsize=(12, 6))\n",
    "#     plt.plot(df['Close'], label='Close Price')\n",
    "#     plt.xlabel('Date')\n",
    "#     plt.ylabel('Close Price')\n",
    "#     plt.title('Close Price Over Time')\n",
    "#     plt.legend()\n",
    "#     plt.show()\n",
    "\n",
    "#     plt.figure(figsize=(12, 6))\n",
    "#     sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
    "#     plt.title('Feature Correlation Matrix')\n",
    "#     plt.show()\n",
    "\n",
    "# def main():\n",
    "#     ticker_symbol = 'TATAELXSI.NS'\n",
    "#     start_date = '2019-04-01'\n",
    "#     end_date = '2024-03-31'\n",
    "    \n",
    "#     stock_data = download_stock_data(ticker_symbol, start_date, end_date)\n",
    "#     preprocessed_data = preprocess_data(stock_data)\n",
    "    \n",
    "#     stock_data_eda(preprocessed_data)\n",
    "#     logging.info(\"Data preprocessing and EDA completed.\")\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ec81996-fa17-47a0-8b40-6a75a2860325",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import logging\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "# import yfinance as yf\n",
    "# import plotly.graph_objs as go\n",
    "# import plotly.express as px\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Set up logging\n",
    "# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message=s)')\n",
    "\n",
    "# def download_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:\n",
    "#     \"\"\"Downloads stock data from Yahoo Finance.\"\"\"\n",
    "#     try:\n",
    "#         logging.info(f\"Downloading stock data for ticker: {ticker}\")\n",
    "#         data = yf.download(ticker, start=start_date, end=end_date)\n",
    "#         data.reset_index(inplace=True)\n",
    "#         data['Date'] = pd.to_datetime(data['Date'])\n",
    "#         data.set_index('Date', inplace=True)\n",
    "#         return data\n",
    "#     except Exception as e:\n",
    "#         logging.error(f\"Failed to download stock data: {e}\")\n",
    "#         raise\n",
    "\n",
    "# def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:\n",
    "#     \"\"\"Preprocesses the stock data for exploratory data analysis and model preparation.\"\"\"\n",
    "#     # Handling missing values\n",
    "#     missing_summary = df.isnull().sum()\n",
    "#     logging.info(f\"Missing Values per Column:\\n{missing_summary}\")\n",
    "#     df.dropna(inplace=True)\n",
    "\n",
    "#     # Removing duplicates\n",
    "#     duplicates_count = df.duplicated().sum()\n",
    "#     logging.info(f\"Number of Duplicate Rows: {duplicates_count}\")\n",
    "#     df.drop_duplicates(inplace=True)\n",
    "\n",
    "#     # Statistical Summary\n",
    "#     summary_stats = df.describe()\n",
    "#     logging.info(f\"Statistical Summary:\\n{summary_stats}\")\n",
    "\n",
    "#     # Convert the summary statistics to an HTML table\n",
    "#     summary_html = summary_stats.to_html()\n",
    "#     with open('statistical_summary.html', 'w') as f:\n",
    "#         f.write(summary_html)\n",
    "\n",
    "#     return df\n",
    "\n",
    "# def stock_data_eda(df: pd.DataFrame):\n",
    "#     \"\"\"Performs exploratory data analysis on stock data.\"\"\"\n",
    "#     # Line chart for Closing, Opening, and Adjusted Closing Price\n",
    "#     fig = go.Figure()\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Close'], mode='lines', name='Close Price'))\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Open'], mode='lines', name='Open Price'))\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Adj Close'], mode='lines', name='Adjusted Close Price'))\n",
    "\n",
    "#     # Calculate and add Average Stock Price in a Day\n",
    "#     df['Average'] = (df['High'] + df['Low']) / 2\n",
    "#     fig.add_trace(go.Scatter(x=df.index, y=df['Average'], mode='lines', name='Average Price'))\n",
    "\n",
    "#     fig.update_layout(title='Stock Price Over Time',\n",
    "#                       xaxis_title='Date', yaxis_title='Price')\n",
    "#     fig.show()\n",
    "\n",
    "#     # Histogram for data richness\n",
    "#     fig_hist = px.histogram(df, x='Close', nbins=30, title='Distribution of Closing Prices')\n",
    "#     fig_hist.show()\n",
    "\n",
    "#     # Heatmap for correlation matrix using Seaborn\n",
    "#     plt.figure(figsize=(10, 6))\n",
    "#     sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
    "#     plt.title('Feature Correlation Matrix')\n",
    "#     plt.show()\n",
    "\n",
    "# def main():\n",
    "#     ticker_symbol = 'TATAELXSI.NS'\n",
    "#     start_date = '2019-04-01'\n",
    "#     end_date = '2024-03-31'\n",
    "    \n",
    "#     stock_data = download_stock_data(ticker_symbol, start_date, end_date)\n",
    "#     preprocessed_data = preprocess_data(stock_data)\n",
    "    \n",
    "#     stock_data_eda(preprocessed_data)\n",
    "#     logging.info(\"Data preprocessing and EDA completed.\")\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07413ff2-265b-4d3d-ab41-d3b95be82687",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d04f14e-a449-4237-8d13-bf83ba8ade25",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64531bd4-6e30-43a0-b89b-8f4b4c4f71d0",
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