Sure! Here’s a template for a README file for your project titled "POWER-CONSUMPTION-MODELS":

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POWER-CONSUMPTION-MODELS

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Data
6. Model Description
7. Results
8. Contributing
9. License

Introduction

The POWER-CONSUMPTION-MODELS project aims to develop predictive models to analyze and forecast power consumption. The goal is to provide accurate predictions that can help in efficient energy management, reducing costs, and planning future energy needs.

Features

• Data preprocessing and cleaning
• Exploratory data analysis (EDA)
• Various machine learning models for prediction
• Model evaluation and comparison
• Visualization of results

Installation

To get started with this project, clone the repository and install the required dependencies.

git clone https://github.com/yourusername/POWER-CONSUMPTION-MODELS.git
cd POWER-CONSUMPTION-MODELS
pip install -r requirements.txt

Usage

After installation, you can start using the models and scripts provided. Here's a basic example of how to run the preprocessing script and train a model.

python preprocess_data.py --input data/raw_data.csv --output data/clean_data.csv
python train_model.py --input data/clean_data.csv --model_output models/power_model.pkl

Data

The data used in this project should be placed in the data directory. It includes historical power consumption data with features such as:

• Timestamp
• Power consumption values
• Weather data (temperature, humidity, etc.)

Sure, here is the updated section including the SVM model description:

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Model Description

This project includes several machine learning models for predicting power consumption:

1. Linear Regression: A basic model for establishing a baseline. It models the relationship between the dependent variable (power consumption) and one or more independent variables by fitting a linear equation to the observed data.
2. Random Forest: An ensemble learning method that improves prediction accuracy by constructing a multitude of decision trees during training and outputting the mean prediction (regression) of the individual trees.
3. Support Vector Machine (SVM): A supervised learning model used for both classification and regression tasks. In this project, SVM is used for regression (SVR). It works by finding the hyperplane that best fits the data in a high-dimensional space and is particularly effective in high-dimensional spaces and when the number of dimensions exceeds the number of samples.

Results

The results of the models, including their performance metrics (RMSE, MAE, R²), are documented in the results directory. Visualization of predictions versus actual values can also be found in this directory.

Contributing

We welcome contributions from the community. If you wish to contribute, please fork the repository and create a pull request with your changes. Ensure that your code follows the project's style guidelines and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for more details.