Ecosyste.ms: Awesome

https://github.com/ebadshabbir/naive_bayes_classification-gaussain-

A Python project that applies Naive Bayes classification to predict user purchases based on age and salary using the Social Network Ads dataset. The project includes data preprocessing, model training, prediction, and visualization of decision boundaries for both training and test datasets.

classification gaussian jupyter-notebook machine-learning matplotlib-pyplot naive-bayes-classifier numpy pandas python

Last synced: 31 Oct 2024

https://github.com/ebadshabbir/bluff-detection-model-polynomial_regression-

This project predicts employee salaries based on position levels using Linear and Polynomial Regression models. It trains models with degrees 2, 3, and 4 on a dataset of job titles, position levels, and salaries, and predicts the salary for a position level of 6.5. The results are visualized to compare model performance.

jupyter-notebook linear-regression machine-learning matplotlib-pyplot multiple-linear-regression numpy polynomial-regression python

Last synced: 31 Oct 2024

https://github.com/ebadshabbir/ebadshabbir

Config files for my GitHub profile.

config github-config

Last synced: 16 Nov 2024

https://github.com/ebadshabbir/decision_tree_algorithm

Decision Tree Classifier for Social Network Ads A Python implementation of a Decision Tree Classifier to predict user purchasing behavior based on age and estimated salary. Includes feature scaling, model evaluation (confusion matrix and accuracy), and visualizations of decision boundaries for both training and test sets.

decision-tree-classifier jupyter-notebook machine-learning matplotlib-pyplot numpy pandas python scikit-learn

Last synced: 16 Nov 2024

https://github.com/ebadshabbir/backward_elimination-50_startup-

Last synced: 16 Nov 2024

https://github.com/ebadshabbir/logistic_regression-binomial-

Logistic Regression on Social Network Ads Dataset This project applies Logistic Regression to predict whether a user will purchase a product based on their age and estimated salary, using the Social Network Ads dataset. The data is split into training and test sets, with feature scaling applied for normalization.

classification jupyter-notebook logistic-regression machine-learning matplotlib-pyplot numpy pandas python sklearn

Last synced: 31 Oct 2024

https://github.com/ebadshabbir/company_profit-onehotencoding-

This project uses multiple linear regression to predict startup profits based on spending and location data from the **50 Startups** dataset. It includes data preprocessing, model training, and performance evaluation using Scikit-Learn.

jupyter-notebook machine-learning matplotlib multiple-linear-regression onehot-encoding pandas pyhton regression sklearn

Last synced: 31 Oct 2024