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visarkh

This repository contains Python code for analyzing and predicting energy consumption using a RandomForestRegressor model. The dataset used (KwhConsumptionBlower78_1.csv) includes hourly energy consumption data.

Overview

The code performs the following steps:

  1. Loading and Preprocessing Data:

    • Loads the dataset (KwhConsumptionBlower78_1.csv).
    • Handles missing values by filling with mean values.
    • Converts date and time columns to datetime objects and sets datetime as index.
    • Extracts additional features like hour of day, day of week, and month.

  2. Exploratory Data Analysis:

    • Calculates average consumption per hour (avg_consumption_per_hour).
    • Identifies the hour with the lowest average consumption.

  3. Model Training and Evaluation:

    • Splits data into training and test sets.
    • Scales the features using StandardScaler.
    • Trains a RandomForestRegressor model.
    • Evaluates the model using Mean Squared Error (MSE) and R-squared metrics.
    • Performs hyperparameter tuning using GridSearchCV to optimize the model.

  4. Feature Importance:

    • Plots feature importances derived from the trained model.

  5. Prediction Visualization:

    • Visualizes actual vs predicted energy consumption using a scatter plot.

  6. Hourly Consumption Analysis:

    • Computes hourly consumption per day and plots it using a heatmap.

Dependencies

Ensure you have the following Python libraries installed:

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

Usage

  1. Clone the repository: 
    git clone https://github.com/yuvraajnarula/visarkh.git
cd visarkh
  2. Install dependencies 
    pip install -r requirements.txt
  3. Run the script 
    python server.py