This repository contains my lab experiment codes and practical assignments for the Master of Engineering — Artificial Intelligence (ME-AI) second semester at Chandigarh University. It includes hands-on implementations across four core lab courses.

• DSR_Lab/ — Data Analysis using R (Statistical analysis, data visualization, and R programming)
• CV_Lab/ — Computer Vision (Image processing, object detection, and visual recognition)
• ADBMS_Lab/ — Advanced Database Management System (Database design, queries, and optimization)
• ML_Lab/ — Machine Learning (ML algorithms, model training, and evaluation)

Each folder contains practical codes, datasets (where applicable), and documentation for the respective lab experiments.

• Language: R
• Libraries: ggplot2, dplyr, tidyr, caret, readr, data.table
• Tools: RStudio, R Markdown

• Language: Python
• Libraries: OpenCV, PIL/Pillow, NumPy, Matplotlib, scikit-image
• Frameworks: TensorFlow/Keras (for deep learning-based CV)

• Languages: SQL, PL/SQL, Python
• Databases: MySQL, PostgreSQL, MongoDB
• Tools: MySQL Workbench, pgAdmin, DBeaver

• Language: Python
• Libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
• Frameworks: TensorFlow, Keras (for neural networks)
• Tools: Jupyter Notebook, Google Colab

git clone https://github.com/s-satyajit/mtech_second_sem_exp.git
cd mtech_second_sem_exp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install required R packages
install.packages(c("ggplot2", "dplyr", "tidyr", "caret", "readr"))

# For Python labs
jupyter notebook

# For R labs
# Open .R files in RStudio and run

# For SQL labs (ADBMS)
# Import .sql files into your database management system

• Ensure R and RStudio are installed.
• Some experiments use large datasets — download links are provided inside respective folders.

• Image datasets are not included due to size constraints.
• Dataset download instructions are available in CV_Lab/README.md.

• Set up local database instances (MySQL / PostgreSQL) before running queries.
• Database schemas and sample data are provided.

• Some experiments benefit from GPU acceleration.
• Recommended: Use Google Colab for resource‑intensive tasks.

• Each lab folder contains its own README.md with setup instructions and experiment objectives.

• Statistical data analysis and hypothesis testing
• Data visualization and exploratory data analysis (EDA)
• Data manipulation using dplyr and tidyr
• Building predictive models in R

• Image preprocessing and transformation techniques
• Feature extraction and edge detection
• Object detection and recognition algorithms
• Deep learning for image classification

• Advanced SQL queries and stored procedures
• Database normalization and optimization
• Transaction management and concurrency control
• NoSQL databases and distributed systems

• Supervised learning (regression, classification)
• Unsupervised learning (clustering, dimensionality reduction)
• Model evaluation and performance metrics
• Hyperparameter tuning and cross‑validation
• Ensemble methods and neural networks

Satyajit Samal ME (Artificial Intelligence), Chandigarh University

• GitHub: https://github.com/s-satyajit/
• Portfolio: https://satyajit-samal.vercel.app/
• LinkedIn: https://www.linkedin.com/in/satyajitsamal/
• Twitter / X: https://x.com/satyajitstwt

This repository is for educational purposes only. Feel free to use the code for learning, but please provide appropriate attribution.

• First Semester Experiments — ADSA, APP, AI Basics
• Check my GitHub profile for more AI / ML projects

Building expertise in Data Science, Computer Vision, Databases, and Machine Learning 🚀

machine-learning
computer-vision
data-analysis
r-programming
advanced-database
opencv
scikit-learn
sql
mysql
postgresql
python
jupyter-notebook
chandigarh-university
me-ai
lab-experiments
data-visualization
image-processing
ggplot2

# Core libraries
numpy>=1.24.0
pandas>=2.0.0
matplotlib>=3.7.0
seaborn>=0.12.0

# Machine Learning
scikit-learn>=1.2.0
scipy>=1.10.0

# Computer Vision
opencv-python>=4.7.0
Pillow>=9.5.0
scikit-image>=0.20.0

# Deep Learning
tensorflow>=2.12.0
keras>=2.12.0

# Database connectivity
mysql-connector-python>=8.0.33
psycopg2-binary>=2.9.6
pymongo>=4.3.3
SQLAlchemy>=2.0.0

# Utilities
jupyter>=1.0.0
notebook>=6.5.0
ipykernel>=6.22.0
tqdm>=4.65.0

# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
env/
venv/
*.egg-info/

# R
.Rhistory
.RData
.Rproj.user/
*.Rproj

# Jupyter
.ipynb_checkpoints/

# Datasets
datasets/
data/
*.csv
*.json
*.db
*.sqlite

# Database
*.sql.backup
dump/

# Images
*.jpg
*.png
*.jpeg

# Models
*.h5
*.pkl
*.joblib

# IDE
.vscode/
.idea/
*.swp

# OS
.DS_Store
Thumbs.db