an implementation of a Radial Basis Function Neural Network (RBFNN) for classification problem.

Diseases Prediction System though Machine Learning with code and documents

End to end implementation and deployment of Machine Learning Car Price Prediction using python, flask, gunicorn, scikit-Learn, etc. on Heroku web application platform.

Implementation & Learning of Compression of Image through use of K-means Clustering Algorithm

First exercise on simple linear regression and working with numpy and scikit-learn

Implements a genetic algorithm to select the most impactful features in a dataset to improve classifier performance. Written in Jupyter Notebook using pandas, numpy, scikit-learn. Results displayed with accuracy, precision, recall, F1 score comparison to using all features.

Classifier des mails avec des outils ML

Machine Learning model development for a transport company, the objective is to predict whether an order will arrive on time or not.

Trying to use power of machine learning to predict stock prices

Sentiment Analysis Using Bert Transformer

NLP repository with projects like Article Classifiers, Recommender Systems and Text Processing

A Website powered by Machine Learning to find the shortest and safest route dynamically, based on traffic changes for Delhi Metro Services.

PySpark Python ML Models

We solve a regression problem in which it consists of calculating the health insurance charge in the United States Where we will break down the project into 5 phases: Exploratory Analysis. Feature Engineering. Selection of the ideal model. Development of the final model. Creation of a web application in streamlit.

KNN and Decision Tree

Own Course for Udemy. In progress: Daily Projects for convert in Professional Python Developer.

The objective of this challenge is to accurately predict the soil moisture level multiple days in advance. This solution will help farmers prepare their irrigation schedules more efficiently

Image Processing Grades Auto filler with 2 models Graded Sheet Model & Bubble Sheet Model

PSO feature selection improves classifier performance. Implemented in Jupyter Notebook with pandas, numpy, scikit-learn. PSO done from scratch. Results compared using accuracy, precision, recall, F1 score. Improves results compared to using all features. Can be applied to various classification problems.