📊 Machine-Learning-Foundations

This repository contains my hands-on practice projects in Machine Learning, focusing on building a strong foundation in regression and classification algorithms using Python.

🚀 Projects Included

1. Simple Linear Regression

• Implemented linear regression on a height-weight dataset
• Understood the relationship between dependent and independent variables
• Visualized regression line and data distribution

2. Multiple Linear Regression

• Built a model with multiple input features
• Learned feature importance and multivariate relationships
• Saved trained model using .pkl file

3. Decision Tree Classifier (Diabetes Prediction)

• Applied classification using Decision Tree algorithm
• Worked on a real-world dataset for predicting diabetes
• Evaluated model performance using accuracy metrics

4. Regression Project (Algerian Forest Fires Dataset)

• Performed data cleaning and preprocessing
• Trained regression models on real-world environmental data
• Practiced feature selection and model evaluation

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🛠️ Skills Applied

• Python (NumPy, Pandas, Matplotlib, Scikit-learn)
• Data Preprocessing & Cleaning
• Exploratory Data Analysis (EDA)
• Regression & Classification Algorithms
• Model Training & Evaluation
• Model Serialization (Pickle)

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📂 Project Structure

• Jupyter Notebooks for implementation
• CSV datasets for training
• Pickle file for saved model
• Requirements file for dependencies

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🎯 Learning Outcome

Through these projects, I gained practical experience in:

• Understanding ML workflows (data → model → evaluation)
• Applying supervised learning techniques
• Improving model performance through experimentation

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📌 Future Improvements

• Add more advanced algorithms (Random Forest, XGBoost)
• Deploy models using Streamlit
• Work on real-world datasets and end-to-end projects

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