🤖 Machine Learning Portfolio

👋 Introduction

This repository is a student portfolio showcasing various Machine Learning projects that demonstrate practical skills in solving real-world problems.
The projects include tasks such as:

• Data preprocessing and feature engineering: Cleaning datasets, handling missing values, encoding categorical features, scaling numerical features.
• Supervised learning: Implementing regression and classification algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines.
• Unsupervised learning: Applying clustering (K-Means, Hierarchical) and dimensionality reduction techniques (PCA) to discover patterns in data.
• Model evaluation: Using metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices to assess model performance.
• Visualization and insights: Generating plots, charts, and dashboards to communicate results effectively.

This portfolio highlights my ability to take a dataset from raw data to actionable insights, prepare it for machine learning, implement models, evaluate their performance, and visualize the outcomes in a clear and professional manner.

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🎯 Objectives

• Explore and preprocess datasets for machine learning tasks
• Implement supervised learning algorithms (e.g., regression, classification)
• Implement unsupervised learning algorithms (e.g., clustering, dimensionality reduction)
• Evaluate models using metrics like accuracy, precision, recall, F1-score
• Visualize results and derive actionable insights

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🧠 Skills & Concepts Covered

• Data Cleaning & Preprocessing
• Feature Engineering
• Supervised Learning (Linear Regression, Decision Trees, Random Forest, etc.)
• Unsupervised Learning (K-Means, PCA, Clustering)
• Model Evaluation & Validation
• Hyperparameter Tuning

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🛠️ Tools & Technologies

• Programming Language: Python
• Libraries:
  • NumPy, Pandas, Matplotlib, Seaborn, Plotly / Plotly Express
  • Scikit-learn (for preprocessing, model building, evaluation)
  • SciPy, Statsmodels
  • Joblib / Pickle (for saving models)
  • Jupyter Notebook

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📂 Repository Structure

📁 data # Raw and processed datasets
📁 notebooks # Jupyter notebooks for ML projects (EDA, supervised & unsupervised models)
📁 models # Saved trained ML models (.pkl / .joblib)
📁 results # Plots, charts, confusion matrices, evaluation reports
📄 README.md # Project documentation and instructions
📄 requirements.txt # Python libraries required to run all projects

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▶️ How to Run the Projects

1. Clone the repository: git clone https://github.com/nawabkhanyarmal/Machine-Learning-.git

2. Navigate to the project directory: cd Machine-Learning-

3. Install required dependencies: pip install -r requirements.txt

4. Launch Jupyter Notebook: jupyter notebook

5. Open and run the notebooks from the notebooks folder.

📊 Sample Outputs Accuracy / Loss curves

Confusion matrices

ROC curves and other evaluation metrics

Feature importance plots

Cluster visualizations

(Outputs are available in the results folder.)

📈 Learning Outcomes Through these projects, I gained hands-on experience in:

Building and evaluating machine learning models

Preprocessing and feature engineering for real-world datasets

Selecting appropriate algorithms and evaluation metrics

Communicating model results effectively using visualizations

🚀 Future Improvements Integrate deep learning models for complex datasets

Add automated hyperparameter tuning pipelines

Deploy models as web applications

Explore more advanced ML techniques (ensemble methods, boosting, etc.)

👤 Author Nawab Khan Computer Science Student | Machine Learning & AI Enthusiast

📄 License This project is licensed under the MIT License.