onehotencoder

Predictive Analytics

Imbalanced Classes, Resampling techniques, filling null value, Date,Time, Alphanumeric data

Feature encoding (OneHot, Ordinal, Label) and scaling (Standardization, Normalization) techniques on the Breast Cancer dataset using scikit-learn with proper train/test data leakage prevention.

A collection of essential machine learning algorithms implemented from scratch and with libraries. Ideal for students and beginners to understand core ML concepts through hands-on examples.

Trying to predict which species are most threatened with extinction in the near future.

Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.

Apply unsupervised learning techniques to identify customers segments.

scikit_learn

Embedding

Predict employee attrition using LogisticRegression and RandomForestClassifier.

A Machine Learning web application that predicts student placement using Gradient Boosting, built with Scikit-learn and Streamlit.

OneHotEncoder : Kategorik verileri "binary" (0 ve 1) sütunlarına ayırır. Her benzersiz kategori için ayrı bir sütun oluşturur. Özellikle sıralı olmayan kategorik değişkenler için uygundur.

Data cleaning tools, handling missing data, categorical data, feature scaling

This project predicts whether a person survived the Titanic disaster based on various features using machine learning. It utilizes pipelines, ColumnTransformer, and model serialization for efficient processing and prediction.

Loan approval prediction system using ML pipelines and Gradio, deployed on Hugging Face Spaces.

Car Price Prediction

This is the Traffic Analysis and Prediction based project from RandomForest Classifier.

To predict whether a user will click on ad or not.

Machine learning and neural networks used to create a binary classifier capable of predicting whether applicants will be successful if funded by Alphabet Soup.