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Diabetes mellitus, commonly known as diabetes is a metabolic disease that causes high blood sugar. The hormone insulin moves sugar from the blood into your cells to be stored or used for energy. With diabetes, your body either doesn’t make enough insulin or can’t effectively use its insulin.
Predicting the incidents raised by the customer
A parser for scikit-learn exported text models to execute in the Java runtime.
Machine Learning Recipes is a series of videos from Google Developers covering codes (python) and insights about ML.
Implementing a Generic Decision Tree Classifier in Swift and comparing it with a python version.
Data Science final project
SVM, Logistic Regression, K-Nearest Neighbors Classifier, GaussianNB, Random Forest, XGBoost, DecisionTree Classifier, Ensembled Classifier, ExtraTrees Classifier, Voting Classifier
This project used machine learning concept to predict disease on their symptoms
Predicting Tags for Stack Overflow
In this project I intend to predict customer churn on bank data.
This repo contains the Minor Project 1 named Fasal Fusion: An Algorithmic Approach to Transform Crop Recommendations
Different models to detect if a claim is fraudulent or not
Regression Analysis - Toyota Corolla price prediction
Code templates for data prep and different ML algorithms in Python.
A crop recommendation website which uses an ML model (decision tree classifier) to recommend the crop that can be grown in a particular area
Machine Learning lessons (Linear Regression, Logistic Regression, DecisionTreeClassifier, SVC, RandomForestClassifier, K Clustering, Naive Bayes) and data manipulation codes learned from this playlist: https://www.youtube.com/watch?v=gmvvaobm7eQ&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=1
Decision tree classifier to predict the right classes of the observations in the 'Iris' data set.
This is my own code. The data set is taken from Kaggle competitions. Here is the link: https://www.kaggle.com/competitions/titanic
IFT3395 Machine learning competition 1: Kaggle
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).
EC60091-Machine Intelligence & Expertise Systems Course,Autumn-2019
Create a model to predict if a customer will leave the bank.
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.
The AdaBoost algorithm is an ensemble learning method that combines multiple weak learners (base estimators) to create a stronger predictive model.
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.
A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.
The aim is to create a classifier that indicates whether a requested transaction is genuine or fraudulent.
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!
Predicting Music using given data with DecisionTreeClassifer
This is a simple python program to train a classification model using decision tree, random forest and Naive Bayes algorithms
Our task is to classify a Hotel Reservation as either booking canceled (class1) or no canceled(class0) and use more one model to arrive the best model.
Datamining concepts
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.
Machine Learning clasificación con SKLearn
Titanic ML Competition.
Loan Prediction using four ML Algorithms - Decision Tree, Logistic Regression, Random Forest, Neural Network