📊 Smart Grid Load Forecasting Machine Learning Competition

📌 Project Overview

This project focuses on building a machine learning model to analyze data and make predictions.
The project follows the complete machine learning pipeline including Data Understanding, Exploratory Data Analysis, Data Preprocessing, Feature Engineering, Model Training, Hyperparameter Tuning, Evaluation, and Prediction.

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🗂️ Project Workflow

1️⃣ Data Understanding

• Loaded the dataset and examined its structure
• Identified important features and target variable
• Checked data types and dataset shape
• Identified missing values

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2️⃣ Exploratory Data Analysis (EDA)

Performed exploratory analysis to understand patterns and relationships in the data.

Techniques used:

• Distribution plots
• Correlation analysis
• Feature relationships
• Outlier detection

Libraries used:

• Pandas
• Matplotlib
• Seaborn

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3️⃣ Data Preprocessing

Prepared the dataset for machine learning models.

Steps performed:

• Handling missing values
• Encoding categorical variables
• Feature scaling
• Removing unnecessary features
• Data cleaning

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4️⃣ Feature Engineering

Improved model performance by transforming and selecting useful features.

Methods used:

• Feature transformation
• Feature selection
• Encoding techniques

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5️⃣ Model Training

Trained machine learning models on the processed dataset.

Models used:

• XGBoost

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6️⃣ Hyperparameter Tuning

Optimized model performance using:

• GridSearchCV

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7️⃣ Model Evaluation

Evaluated models using train data .

Metrics used:

• MSE
• RMSE

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8️⃣ Prediction

Used the trained model to make predictions on unseen data.

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🛠️ Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost
• Jupyter Notebook / Google Colab

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🚀 Future Improvements

• Improve model performance with advanced algorithms
• Deploy the model using Streamlit
• Add more data for better generalization

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📌 Author

Prasanth M