This project uses data mining to analyze student performance and predict pass/fail outcomes.

A classification task to predict the likelihood of a stroke using various machine learning models based

This repo contains the Minor Project 1 named Fasal Fusion: An Algorithmic Approach to Transform Crop Recommendations

A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.

A parser for scikit-learn exported text models to execute in the Java runtime.

Machine Learning clasificación con SKLearn

Predicting Colorado forest cover types using diverse ML models for classification. Baseline creation, feature selection, comparison, and tuning optimize accuracy in this University of Ottawa Master's Machine Learning course final project (2023).

Using Supervised Machine Learning to predict the success of a Falcon 9 landing.- DataScience

IFT3395 Machine learning competition 1: Kaggle

Creating a sophisticated web application for transaction analysis, incorporating ML, Bootstrap, Dash, and Plotly. Users can seamlessly upload credit card CSV files, exploring transactions interactively in both tabular and dashboard report formats.

Some of the Classifiers and Projects related to ML. The dataset used here is Iris Dataset you can get a copy as .csv from Kaggle.com

Titanic ML Competition.

Different models to detect if a claim is fraudulent or not

Beta Bank is losing customers monthly. Employees want to focus on client retention. As a Data Scientist, I created a model to predict the chance of a customer leaving, based on past behavior and contract terminations.

As a Data Scientist at Megaline, a leading mobile operator, I developed a model to analyze consumer behavior. I aimed to recommend either the Smart or Ultra package from Megaline's latest offerings, with a minimum accuracy of 0.75.

This is my own code. The data set is taken from Kaggle competitions. Here is the link: https://www.kaggle.com/competitions/titanic

Welcome to my Lets Grow More internship project repository! Explore a collection of data science and business analytics projects showcasing my skills in predictive modeling, classification, and forecasting. Each project features a detailed Jupyter Notebook with code and visualizations. Join me on this data-driven journey!

The AdaBoost algorithm is an ensemble learning method that combines multiple weak learners (base estimators) to create a stronger predictive model.

Scala Library for extracting useful information from trained Spark Model (DecisionTreeClassificationModel)

Predicting Music using given data with DecisionTreeClassifer