RandomSearch CV vs Grid Search

This project aims to develop a machine learning model to predict bike-sharing demand based on various factors such as weather conditions, time of day, and historical usage patterns. The dataset used for this project consists of 8760 records and 14 attributes.

Algorithms used to confirm whether a celestial body is a planet or not.

The business objective was to predict the present the price of the car Year,based on features such as selling price, present price, kilometers driven, fuel type, seller type, transmission, owner.

This is an End-to-End Data Science Project built in order to help an International E-commerce Company to predict whether their product will be delivered on the committed Delivery Time or not

GridSearchCV, RandomSearchCV For Model optimization and Saving/Loading the model

Implementation of Hyper-parameter tuning of ML models

Credit score prediction using classification models (Multi-class prediction)

Enhancing The Performance Of Classifiers In Detecting Abnormalities In Medical Data Using Nature Inspired Optimization Techniques

Analyzing a dataset of bank transactions and using gradient boosting classifier to capture as many fraudulent transactions as possible while minimizing false positives.

Exploratory data analysis exercises to understand the main characteristics of a given data set before performing more advanced analysis or further modeling

List of completed academic projects

Machine Learning with Sklearn

This repository has the implementation of hyperparameter tuning techniques (GridSearchCV and RandomSearchCV) on K-Nearest Neighbour (KNN) algorithm, from scratch.

End to End Machine Learning Projects Examples

Proyecto de Datacamp sobre predicción de dias en que un cliente alquilará un DVD basandose en algunas caracteristicas

machine learning regression

Building a plagiarism detector that examines a text file and performs binary classification labeling that file as either plagiarized or not, depending on how similar that text file is to a provided source text.

Use of Gradient Boosting Descision Trees for a Classification Problem

This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost