https://github.com/florence-nyokabi/house-power-consumption

Machine Learning: Exploring Regression Analysis
https://github.com/florence-nyokabi/house-power-consumption

data-analysis data-cleaning data-science data-visualization feature-engineering jupyter-notebook jupyterlab machine-learning pandas-python regression-analysis regression-models

Last synced: 16 days ago
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Machine Learning: Exploring Regression Analysis

• Host: GitHub
• URL: https://github.com/florence-nyokabi/house-power-consumption
• Owner: Florence-nyokabi
• Created: 2024-08-28T13:15:58.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-28T13:39:34.000Z (5 months ago)
• Last Synced: 2024-11-14T06:17:43.162Z (3 months ago)
• Topics: data-analysis, data-cleaning, data-science, data-visualization, feature-engineering, jupyter-notebook, jupyterlab, machine-learning, pandas-python, regression-analysis, regression-models
• Language: Jupyter Notebook
• Homepage:
• Size: 1.77 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# House-Power-Consumption

Machine Learning:  Exploring Regression Analysis

- **Chosen Dataset**: **Individual Household Electric Power Consumption Dataset**

- **Source**: UCI Machine Learning Repository

- **Link**: [Individual Household Electric Power Consumption Dataset](https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+ci)

 - This dataset is publicly available and has been sourced from the UCI Machine Learning Repository. It consists of measurements of electric power consumption in a single household over an extended period (from December 2006 to November 2010). The dataset captures various attributes related to power usage, including Global_active_power, Global_reactive_power, Voltage, Global_intensity, and sub-metering values.

**Dataset Overview**

- **Source**: UCI Machine Learning Repository

- **Number of Instances**: 2,075,259 (rows)

- **Number of Attributes**: 9 (columns)

- **Target Variable**: Global_active_power (continuous)

- **Feature Variables**: Date, Time, Global_reactive_power, Voltage, Global_intensity, Sub_metering_1, Sub_metering_2, Sub_metering_3.

**Suitability for Regression Analysis**

- This dataset is ideal for regression analysis due to its continuous target variable and the presence of multiple explanatory variables. By using regression models, I aim to predict household power consumption based on the available features, exploring the influence of each feature on the target variable. Additionally, the dataset’s time-series nature allows for the consideration of temporal patterns in power usage, which can further enhance the predictive models.ing power consumption.

 ## Steps:

- Data Acquisition

- Data Cleaning

- Feature Engineering

- Data Visualization

- Regression Modeling