AI_DS-Projects

🤖 AI & Data Science Projects

A comprehensive collection of AI and Data Science projects covering data preprocessing, machine learning, deep learning, and natural language processing.
This repository serves as a portfolio of AI/ML notebooks and projects developed using Python, Jupyter Notebooks, and popular ML/DL libraries.

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📌 Overview

This repository includes various projects demonstrating:

• Exploratory Data Analysis (EDA)
• Data Preprocessing & Visualization
• Supervised Learning (Classification & Regression)
• Deep Learning & Neural Networks
• Natural Language Processing (NLP)
• End-to-End AI Project Workflows

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📂 Repository Structure

AI_DS-Projects/
│
├── Ai_Project.ipynb                     # General AI Project (ML/DL workflow)
├── Classification.ipynb                 # Supervised Learning - Classification
├── Preprocessing_and_Visualization_.ipynb # Data cleaning and visualization
├── Regression.ipynb                      # Supervised Learning - Regression
├── README.md                             # Project Documentation

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🧠 Included Projects

1️⃣ Data Preprocessing & Visualization

• Notebook: Preprocessing_and_Visualization_.ipynb
• Covers:
  • Handling missing values
  • Encoding categorical data
  • Feature scaling
  • Visualizing distributions and correlations

2️⃣ Classification Models

• Notebook: Classification.ipynb
• Algorithms used:
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machine (SVM)
• Metrics: Accuracy, Precision, Recall, F1-score

3️⃣ Regression Models

• Notebook: Regression.ipynb
• Algorithms used:
  • Linear Regression
  • Ridge & Lasso Regression
  • Decision Tree Regressor
• Metrics: MAE, MSE, R² Score

4️⃣ General AI Project

• Notebook: Ai_Project.ipynb
• Integrates preprocessing, model building, and evaluation into one workflow.

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🛠 Installation

# Clone the repository
git clone https://github.com/Montaser778/AI_DS-Projects.git
cd AI_DS-Projects

# Install dependencies
pip install -r requirements.txt

requirements.txt should include:

numpy
pandas
matplotlib
seaborn
scikit-learn
torch
tensorflow

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🚀 Usage

1. Open any notebook in Jupyter or Google Colab:

   jupyter notebook
   

2. Run the cells step by step to:
   • Explore datasets
   • Train models
   • Evaluate performance

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✅ Learning Outcome

By exploring this repository, you will learn:

• How to clean and preprocess data
• How to implement classification and regression models
• How to visualize data insights
• How to organize AI/ML projects in a portfolio-ready structure

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📜 License

This project is licensed under the MIT License.

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👤 Author

Montaser778 – AI & Data Science Enthusiast
Showcasing a complete AI/ML portfolio in Python