Stock_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed71e085-b859-4050-80b2-934f101b807b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data\n",
    "# Assuming the data is in a CSV file named 'nse_tataglobal.csv'\n",
    "df = pd.read_csv('NSE-TATAGLOBAL.csv')\n",
    "\n",
    "# Convert the 'Date' column to datetime\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "# Sort the dataframe by date\n",
    "df = df.sort_values('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "746d97dc-a5b5-4d52-834b-8709cdb56219",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create features\n",
    "df['PreviousClose'] = df['Close'].shift(1)\n",
    "df['PreviousVolume'] = df['Total Trade Quantity'].shift(1)\n",
    "df['PriceChange'] = df['Close'] - df['Open']\n",
    "df['HighLowDiff'] = df['High'] - df['Low']\n",
    "\n",
    "# Drop the first row which will have NaN values due to shifting\n",
    "df = df.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3471f3fa-5d99-478b-b9ce-e9608b444ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare features and target\n",
    "features = ['PreviousClose', 'PreviousVolume', 'PriceChange', 'HighLowDiff']\n",
    "X = df[features]\n",
    "y = df['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fdb65125-dd20-4787-8cf4-35f55a2caf4e",
   "metadata": {},
   "outputs": [
    {
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       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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       "  /* Definition of color scheme for fitted estimators */\n",
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       "\n",
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       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "#sk-container-id-2 div.sk-parallel {\n",
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       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
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       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
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       "\n",
       "/* Serial-specific style estimator block */\n",
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       "#sk-container-id-2 div.sk-serial {\n",
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       "#sk-container-id-2 div.sk-toggleable__content {\n",
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       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
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       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
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       "\n",
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       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
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       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
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       "#sk-container-id-2 div.sk-label label {\n",
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       "  color: var(--sklearn-color-text-on-default-background);\n",
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       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "#sk-container-id-2 div.sk-label label {\n",
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       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
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       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=100.0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=100.0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=100.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and train the Ridge regression model\n",
    "ridge_model = Ridge(alpha=5.0)  # Adjust alpha as needed to control regularization strength\n",
    "ridge_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b69c8c83-29e0-4498-a680-a5e7457fee81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 1.79\n",
      "R-squared score: 1.00\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred_ridge = ridge_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred_ridge)\n",
    "r2 = r2_score(y_test, y_pred_ridge)\n",
    "\n",
    "print(f\"Mean squared error: {mse:.2f}\")\n",
    "print(f\"R-squared score: {r2:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "022a26e3-6f5c-4e54-822b-008b2df60d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_test, color='blue', alpha=0.5, label='Actual Prices')\n",
    "plt.scatter(y_test, y_pred_ridge, color='red', alpha=0.5, label='Predicted Prices')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'g--', lw=2, label='Perfect Prediction')\n",
    "plt.xlabel('Actual Close Price')\n",
    "plt.ylabel('Price')\n",
    "plt.title('NSE-TATAGLOBAL: Actual vs Predicted Close Prices (Ridge Regression)')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bfc2c89-ff3a-4981-b5a1-1ffcba248042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Importances:\n",
      "          feature    importance\n",
      "0   PreviousClose  1.001956e+00\n",
      "2     PriceChange  9.262981e-01\n",
      "1  PreviousVolume  4.990664e-08\n",
      "3     HighLowDiff -9.713573e-02\n"
     ]
    }
   ],
   "source": [
    "# Print feature importances (coefficients in Ridge regression)\n",
    "importance = pd.DataFrame({'feature': features, 'importance': ridge_model.coef_})\n",
    "print(\"\\nFeature Importances:\")\n",
    "print(importance.sort_values('importance', ascending=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01e4f874-40d2-4652-a0dd-327de1d17297",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the next day's closing price\n",
    "last_day = df.iloc[-1]\n",
    "next_day_features = np.array([[\n",
    "    last_day['Close'],\n",
    "    last_day['Total Trade Quantity'],\n",
    "    last_day['Close'] - last_day['Open'],\n",
    "    last_day['High'] - last_day['Low']\n",
    "]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6eab2db1-55f7-4817-bb08-c388af215f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted closing price for next trading day: 233.87\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\miniforge3\\lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but Ridge was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "next_day_prediction = ridge_model.predict(next_day_features)\n",
    "print(f\"\\nPredicted closing price for next trading day: {next_day_prediction[0]:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c909e08e-15d5-4589-bd3b-8c6bff6fdc20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}