https://github.com/saadarazzaq/logistic-regression

Code without built in ML libraries => ML ASSIGNMENT 1 Q2
https://github.com/saadarazzaq/logistic-regression

algorithm-implementation logistic-regression python

Last synced: about 1 month ago
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Code without built in ML libraries => ML ASSIGNMENT 1 Q2

• Host: GitHub
• URL: https://github.com/saadarazzaq/logistic-regression
• Owner: SaadARazzaq
• Created: 2023-09-19T23:13:15.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-01T14:26:14.000Z (10 months ago)
• Last Synced: 2024-03-01T15:33:36.932Z (10 months ago)
• Topics: algorithm-implementation, logistic-regression, python
• Language: Jupyter Notebook
• Homepage:
• Size: 478 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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.