Power_Consumptions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8f7e492-afc7-4839-9447-e1270ac9b37a",
   "metadata": {},
   "source": [
    "# Power_Consumption_Linear_Regression_Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287361f3-1742-432c-9225-740bd0d0b7f9",
   "metadata": {},
   "source": [
    "## ©M.P.Goutam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b031964-0d61-4a66-81e6-b0ce8318eb08",
   "metadata": {},
   "source": [
    "Potential Uses of the Model\n",
    "Billing Predictions:\n",
    "\n",
    "Objective: Estimate the total amount a user will need to pay based on their power consumption and other features.\n",
    "Use Case: Utilities companies can use this model to predict the total bill for users based on their consumption patterns and historical data.\n",
    "Customer Segmentation:\n",
    "\n",
    "Objective: Analyze how different factors influence the total amount to be paid.\n",
    "Use Case: Segment customers based on predicted billing amounts to tailor offers or payment plans.\n",
    "Financial Forecasting:\n",
    "\n",
    "Objective: Project future revenues based on changes in power consumption or pricing strategies.\n",
    "Use Case: Helps utilities plan and forecast future revenue streams based on varying consumption and unit charges.\n",
    "Anomaly Detection:\n",
    "\n",
    "Objective: Identify unusual billing amounts that deviate significantly from predictions.\n",
    "Use Case: Detect potential errors or fraud in billing by comparing actual amounts with predicted amounts.\n",
    "Cost Management:\n",
    "\n",
    "Objective: Understand how changes in features like unit charge or consumption impact billing.\n",
    "Use Case: Optimize pricing strategies or consumption management by analyzing the model’s predictions.\n",
    "Model Insights:\n",
    "Feature Impact: The model’s coefficients can indicate which features have the most significant impact on the total amount to be paid. For example, a higher unit charge or power consumption will likely increase the total amount.\n",
    "\n",
    "Residual Analysis: By examining the residuals (differences between actual and predicted values), you can identify areas where the model might need improvement or where certain factors are not being fully captured.\n",
    "\n",
    "Limitations and Improvements:\n",
    "Linear Assumptions: LinearRegression assumes a linear relationship between features and the target variable, which might not capture complex patterns. Consider other models like RandomForest, GradientBoosting, or neural networks if the relationships are non-linear.\n",
    "\n",
    "Feature Engineering: Further feature engineering and adding more relevant features could improve the model’s performance.\n",
    "\n",
    "Data Quality: Ensure the dataset is comprehensive and accurate. Missing values, outliers, or incorrect data can affect the model's predictions.\n",
    "\n",
    "By understanding these aspects, you can better leverage the model to address specific business needs and enhance decision-making related to billing and customer management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dd424d33-60d3-4ccd-8679-927f1c795b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of the dataset:\n",
      "             User ID  Power Consumption (kWh)  Unit Charge (per kWh)  \\\n",
      "0  G3hF7jK9LpR5sT2q1                      120                   0.15   \n",
      "1  F2eR6tY8hJ1kL4o7p                       90                   0.12   \n",
      "2  H4gT3nM1bV7cX9zQ6                      150                   0.18   \n",
      "3  J6uI8kO3pL9qA2wS5                       80                   0.11   \n",
      "4  K7lP2oM9nB5vC3xZ8                      100                   0.13   \n",
      "\n",
      "          City Street Address  Meter Number  Old Balance ($)  \\\n",
      "0     New York    123 Main St          1001             10.5   \n",
      "1  Los Angeles    456 Oak Ave          1002              8.5   \n",
      "2      Chicago     789 Elm St          1003             15.0   \n",
      "3      Houston   1010 Pine Ln          1004              6.5   \n",
      "4      Phoenix   111 Maple Rd          1005             12.0   \n",
      "\n",
      "   Total Amount to be Paid ($)  \n",
      "0                        20.25  \n",
      "1                        13.30  \n",
      "2                        26.10  \n",
      "3                        10.55  \n",
      "4                        16.25  \n",
      "\n",
      "Column names:\n",
      "Index(['User ID', 'Power Consumption (kWh)', 'Unit Charge (per kWh)', 'City',\n",
      "       'Street Address', 'Meter Number', 'Old Balance ($)',\n",
      "       'Total Amount to be Paid ($)'],\n",
      "      dtype='object')\n",
      "\n",
      "Missing values in the dataset:\n",
      "User ID                        0\n",
      "Power Consumption (kWh)        0\n",
      "Unit Charge (per kWh)          0\n",
      "City                           0\n",
      "Street Address                 0\n",
      "Meter Number                   0\n",
      "Old Balance ($)                0\n",
      "Total Amount to be Paid ($)    0\n",
      "dtype: int64\n",
      "\n",
      "Mean Squared Error: 0.29303003967072433\n",
      "\n",
      "Feature Importances:\n",
      "Feature: Power Consumption (kWh), Importance: 0.14330068780861166\n",
      "Feature: Unit Charge (per kWh), Importance: 0.7906374558301641\n",
      "Feature: City, Importance: 0.0006364635136611729\n",
      "Feature: Street Address, Importance: -0.009359309508843137\n",
      "Feature: Meter Number, Importance: -0.0006734095448008848\n",
      "Feature: Old Balance ($), Importance: 0.533792261986197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_22904\\3029560033.py:19: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  data.fillna(method='ffill', inplace=True)\n",
      "C:\\Users\\User\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Load data\n",
    "data = pd.read_csv('power_consumption_data.csv')\n",
    "\n",
    "# Display first few rows and column names\n",
    "print(\"First few rows of the dataset:\")\n",
    "print(data.head())\n",
    "print(\"\\nColumn names:\")\n",
    "print(data.columns)\n",
    "\n",
    "# Data Preprocessing\n",
    "# Fill missing values if any\n",
    "data.fillna(method='ffill', inplace=True)\n",
    "\n",
    "# Check for any remaining missing values\n",
    "print(\"\\nMissing values in the dataset:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Convert categorical columns to numerical values\n",
    "data['City'] = data['City'].astype('category').cat.codes\n",
    "data['Street Address'] = data['Street Address'].astype('category').cat.codes\n",
    "\n",
    "# Feature Engineering\n",
    "# Define features and target variable\n",
    "features = ['Power Consumption (kWh)', 'Unit Charge (per kWh)', 'City', 'Street Address', 'Meter Number', 'Old Balance ($)']\n",
    "target = 'Total Amount to be Paid ($)'\n",
    "\n",
    "X = data[features]\n",
    "y = data[target]\n",
    "\n",
    "# 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",
    "# Initialize and train the model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print(f\"\\nMean Squared Error: {mse}\")\n",
    "\n",
    "# Feature Importance\n",
    "feature_importance = model.coef_\n",
    "print(\"\\nFeature Importances:\")\n",
    "for feature, importance in zip(features, feature_importance):\n",
    "    print(f\"Feature: {feature}, Importance: {importance}\")\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Scatter plot of actual vs predicted values\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_test, y_pred, color='blue')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "\n",
    "# Residual plot\n",
    "residuals = y_test - y_pred\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(residuals, kde=True, color='red')\n",
    "plt.xlabel('Residuals')\n",
    "plt.title('Residuals Distribution')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00d0696a-2e50-4243-8450-654876c05010",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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