MULTIPLE REGRESSION X1 X2 Y.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "448fd2c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a4b07733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Dependant</th>\n",
       "      <th>Wives</th>\n",
       "      <th>Projects</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kim</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sameer</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jones</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Dependant  Wives  Projects\n",
       "0     Kim          1      2         6\n",
       "1  Sameer          3      3         5\n",
       "2   Jones          2      4        10\n",
       "3    John          1      1        11"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_excel('Book1.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ea9129e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract independent variables (X) and dependent variable (Y)\n",
    "X = df[['Dependant', 'Wives']]\n",
    "Y = df['Projects']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "565bb3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit a linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8c708748",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "predictions = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ac3d5475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1. Regression coefficients:\n",
      "   Intercept (Bo): 10.0000\n",
      "   Coefficient for X1 | Dependant (B1): -2.0000\n",
      "   Coefficient for X2 | Wives (B2): 0.6000\n",
      "R-squared: 0.2615\n",
      "Adjusted R-squared: -1.2154\n",
      "Sum Squared Error (SSE): 19.2000\n"
     ]
    }
   ],
   "source": [
    "# Predictions on the same data\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "#Regression coefficients (Bo, B1, B2)\n",
    "intercept = model.intercept_ #Bo\n",
    "coefficients = model.coef_ #B1, B2\n",
    "\n",
    "# Calculate R-squared Coefficient of determination\n",
    "r2 = r2_score(Y, predictions)\n",
    "\n",
    "# Calculate Adjusted R-squared\n",
    "n = len(Y)\n",
    "p = X.shape[1]\n",
    "adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)\n",
    "\n",
    "# Calculate Sum Squared Error (SSE) and SSR\n",
    "sse = np.sum((Y - predictions)**2)\n",
    "ssr = np.sum((y_pred - np.mean(Y)) ** 2)\n",
    "\n",
    "print(f'\\n1. Regression coefficients:')\n",
    "print(f'   Intercept (Bo): {intercept:.4f}')\n",
    "print(f'   Coefficient for X1 | Dependant (B1): {coefficients[0]:.4f}')\n",
    "print(f'   Coefficient for X2 | Wives (B2): {coefficients[1]:.4f}')\n",
    "\n",
    "# Print R-squared, Adjusted R-squared, and SSE\n",
    "print(f'R-squared: {r2:.4f}')\n",
    "print(f'Adjusted R-squared: {adjusted_r2:.4f}')\n",
    "print(f'Sum Squared Error (SSE): {sse:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e37e7f84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table with Predicted Y:\n",
      "     Name  No of Dependant  No of Wives  Done Projects | Actual Y  \\\n",
      "0     Kim                1            2                         6   \n",
      "1  Sameer                3            3                         5   \n",
      "2   Jones                2            4                        10   \n",
      "3    John                1            1                        11   \n",
      "\n",
      "   Y_predicted   SSE  SSR  \n",
      "0          9.2  19.2  6.8  \n",
      "1          5.8  19.2  6.8  \n",
      "2          8.4  19.2  6.8  \n",
      "3          8.6  19.2  6.8  \n"
     ]
    }
   ],
   "source": [
    "# Print a table with predicted Y\n",
    "result_df = pd.DataFrame({'Name':df['Name'],'No of Dependant':df['Dependant'], 'No of Wives': df['Wives'], 'Done Projects | Actual Y': Y, 'Y_predicted': predictions})\n",
    "# Add SSE and SSR to the DataFrame\n",
    "result_df['SSE'] = sse\n",
    "result_df['SSR'] = ssr\n",
    "\n",
    "print('\\nTable with Predicted Y:')\n",
    "print(result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0387849a",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scatter plot with the best fit line\n",
    "plt.scatter(Y, predictions)\n",
    "plt.plot([min(Y), max(Y)], [min(Y), max(Y)], '--k')  # Best fit line\n",
    "plt.xlabel('Actual Y | number of Projects')\n",
    "plt.ylabel('Predicted Y')\n",
    "plt.title('Scatter Plot with Best Fit Line')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c1701b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-2.   0.6]\n",
      "Intercept: 10.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Print the coefficients and intercept\n",
    "print(\"Coefficients:\", model.coef_)\n",
    "print(\"Intercept:\", model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d6fb7a5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [5.  7.8]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shamim\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# New data for prediction for X1 and X2\n",
    "new_data = np.array([\n",
    "    [4, 5],\n",
    "    [2, 3],\n",
    "])\n",
    "# Make predictions\n",
    "predictions = model.predict(new_data)\n",
    "print(\"Predictions:\", predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7b2c3fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2615384615384617"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictions on the same data\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# Evaluate model accuracy\n",
    "r2 = r2_score(Y, y_pred)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2752c3e9",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Residuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>-3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted  Residuals\n",
       "0       6        9.2       -3.2\n",
       "1       5        5.8       -0.8\n",
       "2      10        8.4        1.6\n",
       "3      11        8.6        2.4"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate residuals\n",
    "predictions = model.predict(X)\n",
    "residuals = Y - predictions\n",
    "# Create a new DataFrame to store residuals\n",
    "residuals_df = pd.DataFrame({'Actual': Y, 'Predicted': predictions, 'Residuals': residuals})\n",
    " # Print the residuals DataFrame\n",
    "residuals_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "96e7ed49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shamim\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14f264da550>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(residuals,kind='kde')"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}