🧠 Artificial Intelligence & Machine Learning Lab

A Comprehensive Collection of AI/ML Implementation in Python

Explore the core concepts of Artificial Intelligence and Machine Learning through this series of lab experiments. Each directory contains hands-on implementations, from search algorithms to advanced clustering techniques.

Overview • Installation • Structure • Author

---

📚 Lab Experiments Overview

This repository covers a wide range of topics in the AI/ML curriculum, implemented with clarity and performance in mind.

#	Experiment Title	Key Algorithms & Libraries	Description
01	Data Analysis & Visualization	pandas, seaborn, matplotlib	Exploration of real-world datasets with statistical visualization.
02	Uninformed Search	BFS, Python	Solving the Tic-Tac-Toe game using Breadth-First Search.
03	Local Search	Genetic Algorithm, Hill Climbing	Solving the N-Queens problem efficiently using local search.
04	Constraint Satisfaction (CSP)	State Space Search, Water Jug	Solving classic logic puzzles through CSP models and BFS.
05	Data Preprocessing	Scikit-learn, LabelEncoder	Handling missing values, scaling, and encoding on real datasets.
06	Principal Component Analysis	PCA, Decomposition	Reducing dimensionality while preserving dataset variance.
07	Naive Bayes Classifier	GaussianNB, Classification	Predicting user behavior using probabilistic models.
08	Linear & Logistic Regression	LinearRegression, Prediction	Implementing prediction systems for continuous and categorical outputs.
09	K-Means Clustering	KMeans, Unsupervised	Segmenting data into distinct groups based on feature similarity.

---

🚀 Setup & Installation

Follow these steps to set up the lab environment on your local machine:

Prerequisites

• Python 3.8+
• Jupyter Notebook or VS Code

Installation

1. Clone the Repository

   git clone https://github.com/ha-re-ram/AIML-Lab.git
   cd AIML-Lab

2. Create a Virtual Environment (Optional but Recommended)

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

3. Install Dependencies

   pip install -r requirements.txt

---

🗂️ Project Structure

The project is organized into modular directories for each experiment:

AIML-Lab/
├── Experiment-01-DataAnalysis/      # Statistical exploration
├── Experiment-02-TicTacToe-BFS/     # Game AI implementing BFS
├── Experiment-03-8Queens-LocalSearch/ # Local search & heuristics
├── Experiment-04-CSP/               # Water Jug & Map Coloring
├── Experiment-05-Preprocessing/     # Data cleaning pipelines
├── Experiment-06-PCA/               # Dimensionality reduction
├── Experiment-07-NaiveBayes/        # Gaussian Naive Bayes model
├── Experiment-08-Linear-Logistic/   # Regression systems
├── Experiment-09-KMeans/            # Unsupervised clustering
├── assets/                          # Project media & banners
├── docs/                            # Additional documentation
├── requirements.txt                 # Project dependencies
└── LICENSE                          # MIT License

---

🛠️ Tools & Technologies

• Language: Python
• Data Manipulation: pandas, numpy
• Visualization: matplotlib, seaborn
• Machine Learning: scikit-learn
• Environment: Jupyter Notebook / Google Colab

---

🧑‍🎓 Author

Hareram Kushwaha CSE Student at KPRIET

"Passionate about bridging the gap between raw data and intelligent insights."

---

📄 License

Distributed under the MIT License. See LICENSE for more information.

---

Don't forget to star ⭐ this repository if it helped you!