Improving a Machine Learning Model

Classification Model (End to End Classification of Heart Disease - UCI Data Set)

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

ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

Machine learning model Visualizer in web using streamlit

In this project I intend to predict customer churn on bank data.

Django Spam Classifier - Classifies given text is spam or not using Scikit Learn and Django Framework

This is a Simple Diabetes Prediction Project. It uses Random forest Classifier Algorithm to Predict whether the person is diabetic or not. It has 82% accuracy.

Repository for the ENSF 612 final project.

The following repository contains source code for a 100 Day personal machine learning coding challenge. It contains projects that I do as a part of my learning

Evaluation of the Models (Regression and Classification)

I would like to use ML to help high school students focus on the factors that really affect the college admission process and help them to get admitted.

Predict whether a stock price will increase based on headlines on a specific day. Data is Wrangled and Merged for modeling. The bag of words approach is used to vectorize textual data. A combination of NLP and ML models like RanfomForestClassifier is used to predict final results, plus the Naive Bayes approach with NLP to predict the results.

Modello Random Forest per la creazione di una mappa di suscettibilità da frane superficiali // // Tesi di Laurea Magistrale in Scienze della Terra (Geologia Applicata) - Università degli Studi di Milano

Notebook used to test Linear Regression model and RandomForest Classifier on to see which one can accurately predict the price range between cellphones.

Security Class Project classifying if a machine has a malware, using python machine learning algorithms (RandomForest & Logistic Regression fitted through Pipeline)

Finding High redshift quasars in large survey data using random forests. Code of Wenzl et al. 2021

Data Science Project