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