AI&ML_DataScience

📊 AI & Data Science Repository

Overview

This repository documents my learning journey and hands‑on projects in Data Science, Machine Learning, and Python programming. It covers foundational libraries (Pandas, NumPy, Matplotlib, Seaborn) and progresses into end‑to‑end machine learning workflows, classification/regression projects, and performance prediction analysis.

The goal is to demonstrate practical skills in data preprocessing, visualization, model building, and evaluation, aligned with industry practices and internship requirements.

📂 Repository Structure

• 01_Pandas – Data manipulation and cleaning with Pandas
• 02_Numpy – Numerical computing and array operations
• 03_Matplotlib – Data visualization basics
• 04_Seaborn – Advanced statistical visualizations
• End_to_End_Regression_Project – Complete ML pipeline for regression tasks
• classification_project – Supervised learning classification models
• data_preprocessing_ml_project – Feature engineering, scaling, and preprocessing workflows
• student_performance_prediction – Predictive modeling on student performance dataset
• CodeAlpha_DataScience_Task1.ipynb – Iris Flower Classification (ML basics)

🚀 Key Highlights

• Hands‑on implementation of data preprocessing, visualization, and ML models
• End‑to‑end projects showcasing regression and classification workflows
• Practical application of Python libraries (Pandas, NumPy, Matplotlib, Seaborn, scikit‑learn)
• GitHub commits documenting continuous learning and project updates

🔧 Technologies Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• scikit‑learn

🎯 Purpose

This repository serves as a portfolio of Data Science skills for internships and entry‑level roles. It demonstrates my ability to:

• Clean and preprocess real‑world datasets
• Visualize data trends and anomalies
• Build and evaluate machine learning models
• Document workflows in a professional, reproducible format

📎 How to Use

1.Clone the repository:

git clone https://github.com/Azizulhaq-professional/AI_DataScience.git

2. Navigate to any project folder.
3. Run notebooks in Jupyter or VS Code to explore datasets, visualizations, and models.

📌 Next Steps

• Future updates will include:
• Deep learning experiments (TensorFlow/PyTorch)
• Advanced feature engineering projects
• Deployment examples for ML models