To build a classification system to predict whether a customer will churn or not based on the IBM Telecom Data from Kaggle. Technically, it is a binary classifier that divides clients into two groups-those who leave and those who do not. The classifier will be built using bagging algorithms like Random Forest, boosting algorithms & Neural Networks

The aim of this project is to develop a machine learning model to predict the levels of CO in the air using historical datasets containing atmospheric variables. The project makes use of variables selection, decision trees, and cross-validation techniques to ensure robustness and model accuracy.

This is a collection of all the machine learning techniques required in any machine learning project. It contains detailed descriptions, videos, book recommendations, and additional material to properly grasp all the concepts. It also contains implementations in various frameworks.

Fast-API base StockSeer-API uses different machine learning alogs to forecast closing stock prices.

Forecasting Ethereum return quantiles using a handful of different statistical learning models and selecting the best based on out of sample error. Hopsworks feature store and model registry is used to automate the process. Ethereum quantile returns are predicted daily and displayed on a Streamlit dashboard.

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

All the Data Science projects I've done, during the course, or after.

Estimación de turbidez en el agua a la entrada de la planta de tratamiento de SAMEEP, utilizando los productos Sentinel-2 MSI L2A y aprendizaje automático.

This project leverages cutting-edge technologies like blockchain and machine learning to build trust and combat corruption in government systems. Secure Land Registry: Eliminates fraud with tamper-proof land ownership records. Automated Traffic & Challan System (using edge computing): Ensures transparency in traffic enforcement and reduces bribery

A library to train, evaluate and make inference using random forests.

Data Science Challenge from Coursera Project : Loan Default Prediction

This is my final year project "customer reviews classification and analysis system using data mining and nlp". It analyzes and then classifies the customer reviews on the basis of their fakeness, sentiments, contexts and topics discussed. The reviews are taken from various e-commerce platforms like daraz and amazon.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

Sales Forecasting

Detailed exploration of random forest classifiers, including data cleaning, model building, and performance evaluation on various datasets.

Machine Learning Notebooks for various Algorithms with data file

The main objective of this project is to develop a machine learning model to predict whether videos reported by users presented claims or opinions to improve triaging process of videos for further review by human moderators.

The project aims to develop a machine learning model that predicts whether an employee will leave the company. A successful model will help identify key variables that drive employee turnover.

This project aims to provide web application for house price prediction. EDA, model comparison through Pycaret, hyper-parameter tuning and making pipeline model are included. The result is shown as a web application.