Energy Data and Predictions

# Energy Consumption Prediction & Data Analysis

Developed a complete Machine Learning project to analyze and predict electricity consumption using Python. The project included data preprocessing, exploratory data analysis (EDA), visualization, feature engineering, model training, and performance evaluation.

### Key Features:

* Cleaned and prepared the dataset by handling missing values and duplicates.

* Performed Exploratory Data Analysis (EDA) using Pandas, Matplotlib, and Seaborn.

* Visualized energy consumption and generation patterns across different cities and years.

* Applied feature scaling and data preprocessing techniques.

* Built and compared multiple regression models, including:

* Linear Regression

* Decision Tree Regressor

* Random Forest Regressor

* K-Nearest Neighbors (KNN) Regressor

* Evaluated model performance using the R² score and compared prediction accuracy across models.

* Created prediction visualizations to compare actual vs. predicted electricity consumption.

### Technologies Used:

Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook.