Welcome to the Regression section! This folder provides an introduction to regression techniques, which are fundamental for supervised learning tasks. Regression helps you model relationships between variables and make predictions, making it a crucial tool for analyzing trends and forecasting outcomes.

Note: The notebooks here are designed for beginners. They introduce foundational concepts but do not cover all regression methods or advanced techniques. For a more comprehensive understanding, please refer to the recommended resources provided below.

This folder currently includes:

• Linear Regression: A supervised Machine Learning technique to predict continous numerical values.
• Logistic Regression: A supervised Machine Learning technique to classify values into distinct binary classes.

Each section includes assignments to help reinforce your understanding, along with solutions for self-assessment.

Follow these steps to build a strong foundation in clustering techniques:

• Purpose: Demonstrate the relationship between independent and dependent variables.
• Topics to Cover:
  • Simple Linear Regression
  • Multiple Linear Regression
  • Gradient Descent
• Resources:
  • Linear Regression (Official Sklearn Documentation)
  • Linear Regression in Machine Learning (GeeksForGeeks)

• Purpose: Classify features into distinct binary classes.
• Topics to Cover:
  • Text preprocessing using NLTK (stopwords removal, tokenization, etc.).
  • Implement Logistic Regression using a SPAM v/s NOT SPAM classification problem
  • Feature extraction using TF-IDF Vectorization.
  • Model training using Scikit-Learn's Logistic Regression.
  • Performance evaluation using metrics like accuracy, precision, and recall.
• Resources:
  • Logistic Regression (Official Sklearn Documentation)

Each regression method comes with assignments designed to help you apply the concepts you've learned. Solutions are provided for self-evaluation. Try to complete the assignments independently before checking the solutions for the best learning experience.

1. Begin with Linear Regression: Start by understanding how Linear Regression works.
2. Go onto Gradient Descent: Learn how to optimize the regressor models.
3. Try Logistic Regression: Experiment with classification tasks using Logistic Regression using a hands - on Project.

Happy learning! Developing these skills will enable you to analyze data. For further learning, refer to the documentation and tutorials linked above.