This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

Atlantic: Automated Data Preprocessing Framework for Machine Learning

Implementation of popular data preprocessing algorithms for Machine learning

A machine learning project predicting Titanic passenger survival using data preprocessing, feature engineering, and model optimization with Logistic Regression, Random Forest, and XGBoost.

A python script to deploy One-Hot encoding in Pandas Dataframes

Extensions and extras for tidy processing.

Automatic Response Generation to Conversational Stimuli

python package to encode protein using different methods for machine learning

Small tools for csv file processing (onehot encoding, format checking and converting to libsvm).

No description provided.

A comprehensive end-to-end machine learning system that predicts loan default risk using advanced algorithms and provides a professional web interface for risk assessment.

No description provided.

This my entry for the Titanic competition on Kaggle. May 2019: public score is 0.80382, which is a top 10% ranking on the leader board of around 11.249 participants.

A host of data science + machine learning projects with Python, pandas, scikit-learn and more!

This is in regard to algorithmic trading bot with the use of machine learning to predict potential returns and actual returns.

Challenge 1: Agriculture Commodities, Prices & Seasons Aim: Your team is working on building a variety of insight packs to measure key trends in the Agriculture sector in India. You are presented with a data set around Agriculture and your aim is to understand trends in APMC (Agricultural produce market committee)/mandi price & quantity arrival data for different commodities in Maharashtra. Objective: Test and filter outliers. Understand price fluctuations accounting the seasonal effect Detect seasonality type (multiplicative or additive) for each cluster of APMC and commodities De-seasonalise prices for each commodity and APMC according to the detected seasonality type Compare prices in APMC/Mandi with MSP(Minimum Support Price)- raw and deseasonalised Flag set of APMC/mandis and commodities with highest price fluctuation across different commodities in each relevant season, and year.

OneHotVector and K means

Value to Business :: Using this Regression model, the decision-makers will able to understand the properties of various products and stores which play an important and key role in optimizing the Marketing efforts and results in increased sales.

Electronic Music Classification ML

This repository contains jupyter notebooks explaining the basics of TF and deep learning classification model using TF

No description provided.

Employed hyper-parameter tuning (Gridsearch CV) and ensemble methods (Voting Classifier) to combine the results of the best models. Data Cleaning and Exploration using Pandas. Stratified Cross Validation to model and validate the training data

T20 World Cup Prediction System -- This GitHub repository contains the code for a T20 World Cup prediction system implemented in Python. The project utilizes popular libraries such as pandas, NumPy, and XGBoost for data manipulation, cleaning, and building predictive models.

Website Kualitas Jeruk dengan Jupyter Notebook dan Streamlit, menggunakan algoritma LogisticRegression sampai StandardScaler dan OneHotEncoder

Data Preprocessing for Machine Learning

No description provided.

Machine Learning Project

Website Kualitas Kopi dengan Jupyter Notebook dan Streamlit, menggunakan algoritma LogisticRegression sampai StandardScaler dan OneHotEncoder

Machine Learning Drug Classification

Ulangan Praktik Mapel Pilihan (AI) - Machine Learning, StandardScaler, OneHotEncoder, OrdinalEncoder, Logistic Regression, dan lain sebagainya.