Internship Program: AICTE–Edunet Foundation | 4-Week Virtual Internship
Project Title: Carbon Emission Prediction Using Machine Learning
Internship Phase: ✅ Completed Weeks 1 to 4 Domain Focus: Artificial Intelligence × Environmental Sustainability
Dataset Source: Kaggle (Global Emission Data)
This project explores the use of Artificial Intelligence and Machine Learning to address one of the most critical issues of our time — carbon emissions. Over the course of this internship, we focused on building a system that could predict carbon dioxide emissions based on historical and environmental data.
The project is structured into 4 weeks, each building on the last, combining data science techniques with sustainability goals.
- 🔍 Explored the Kaggle dataset on global carbon emissions
- 🧹 Understood raw data characteristics (missing values, data types, inconsistencies)
- 🗂️ Learned how to preprocess environmental data for machine learning
- 📈 Used
PandasandMatplotlibfor basic data visualization
- 🧼 Cleaned and transformed the dataset →
data_cleaned.csv - 🚫 Handled missing/null values and removed unnecessary fields
- 📊 Performed Exploratory Data Analysis (EDA) to uncover hidden patterns
- 🔗 Created a correlation matrix to identify features impacting carbon emissions
- ✅ Implemented Linear Regression on the cleaned dataset
- 📉 Evaluated model performance (R², MAE, MSE)
- 📊 Visualized feature impact using bar plots & scatter plots
- 🧠 Developed a basic understanding of predictive modeling for real-world environmental data
- 🌲 Implement Random Forest ⚡ models
- 🛠️ Perform hyperparameter tuning for optimal model performance
- 📝 Finalize 📚 documentation and 🎤 presentation for submission
| Area | Key Learning |
|---|---|
| 🌍 Sustainability | Real-world impact of emissions & how AI can be applied to green tech |
| 🧹 Data Cleaning | Removing noise and preparing real-world data for ML |
| 📊 Data Visualization | Representing data using plots, heatmaps, and correlation diagrams |
| 🧠 Machine Learning | Training and evaluating regression models |
| 🛠️ Tool Proficiency | Working hands-on with Python, Pandas, Seaborn, Matplotlib, Scikit-learn |
- ✅ Data Preprocessing & Cleaning
- ✅ EDA (Exploratory Data Analysis)
- ✅ Correlation & Feature Selection
- ✅ Linear Regression Modeling
- ✅ Data Visualization
- ✅ Model Evaluation Metrics
- ✅ Real-world dataset handling
This internship is perfect for aspiring data scientists or AI beginners looking to apply their skills to real-world environmental problems.
- Understanding climate data structure
- Applying basic Python for data analysis
- Building a simple prediction model
- Creating insightful graphs and charts
- Learning the end-to-end ML pipeline (from data to prediction)
- Programming Language:
Python - Data Analysis:
Pandas,NumPy - Visualization:
Matplotlib,Seaborn - Machine Learning:
Scikit-learn - Development Tools:
Jupyter Notebook,Google Colab
This internship also includes a PowerPoint presentation to summarize progress across weeks.
Predicting carbon emissions isn’t just a technical task — it’s a step toward climate action. This project teaches you how AI and sustainability can come together to make a real difference.
This project is part of the AICTE–Edunet Foundation Virtual Internship on AI & Green Technology. Grateful for the mentors, sessions, and opportunities provided during this incredible learning experience.
"Code for a cause. Predict for the planet." 🌍💡