This project was developed as part of an AI/ML Internship to understand and apply fundamental machine learning concepts using real-world data.
The project focuses on data preprocessing, exploratory data analysis, model implementation, and result evaluation using Python-based machine learning libraries. It is intended for learning and hands-on practice, not for production deployment.
- AI & Machine Learning Internship Project
- Data Analysis & Model Building Project
- Academic / Training-Based Project
- Understand the end-to-end machine learning workflow
- Perform data cleaning and preprocessing
- Analyze datasets using exploratory data analysis (EDA)
- Train and evaluate machine learning models
- Interpret results and understand model behavior
- Uses a real-world dataset suitable for machine learning tasks
- Dataset includes multiple features and a target variable
- Data is processed for missing values, scaling, and formatting
- Dataset is used strictly for educational purposes
(Dataset is loaded and handled inside the Jupyter Notebook)
- Data loading and inspection
- Data preprocessing and feature selection
- Exploratory Data Analysis (EDA)
- Model training
- Model evaluation and result interpretation
- Python
- Jupyter Notebook (Google Colab)
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
This project was completed as part of an AI & Machine Learning Internship to gain practical exposure to machine learning concepts, tools, and workflows.
The focus of the internship project was learning, experimentation, and understanding, rather than building a production-grade system.
This project is created only for educational and internship learning purposes. The models and results are not intended for real-world or production use.
- Understanding of machine learning pipelines
- Hands-on experience with real-world datasets
- Practical knowledge of data preprocessing and EDA
- Model training and evaluation skills
- Confidence in using Python ML libraries
- Apply advanced machine learning algorithms
- Improve model performance through tuning
- Work with larger and more complex datasets
- Deploy models using basic ML deployment tools