saadarazzaq / logistic-regression

Code without built in ML libraries => ML ASSIGNMENT 1 Q2

Logistic Regression Model 📊

Problem Statement 🎯

Introduction 🚀

Logistic regression is a fundamental technique in machine learning used for binary classification tasks. It models the probability that a given input belongs to a particular class.

Data Preparation and Preprocessing 🛠️

• Read the data files 'DataX.dat' and 'ClassY.dat'.
• Standardize features for better convergence.
• Ensure proper preprocessing to handle missing values and outliers.

Training Logistic Regression Model 🧠

• Implemented the sigmoid function to model the probability.
• Utilized gradient descent optimization to minimize the cost function.
• Monitored the cost to ensure convergence.
• Tuned hyperparameters such as learning rate and number of iterations.

Evaluation Metrics 📏

• Assess model performance using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score.
• Utilize techniques like cross-validation to estimate the generalization error.

Results and Analysis 📈

• Interpret the model coefficients to understand feature importance.
• Visualize decision boundaries and predictions to gain insights into model behavior.
• Compare performance with other classification algorithms if applicable.

Conclusion 📝

• Logistic regression is a powerful tool for binary classification tasks.
• Proper data preprocessing and hyperparameter tuning are crucial for model performance.
• Continuous evaluation and refinement are essential for maintaining model effectiveness.

Further Improvements 🌟

• Experiment with different feature engineering techniques to enhance model performance.
• Explore advanced optimization algorithms for faster convergence.
• Consider ensemble methods or deep learning approaches for more complex datasets.