University Project: building a random forest to predict loan defaults. This involves data processing, standardization, optimization, performance metrics, and model analysis.

Book price dataset analysis and modeling

Heart Disease Prediction using Decision Tree Classifier

This is a machine learning project that predicts credit card payment defaults using Support Vector Machine classification with comprehensive data preprocessing and model optimization.

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy

Data Mining course 1st/2 Assignment focused in extracting data and information for customer sentiment and metrics. Initially, data are pre-processed as described in the code comments. One-Hot-Encoding and Clustering implementations included.

Example of classification using the RandomForest algorithm, with visual exploratory analysis using seaborn and matplotlib plots, and data normalization using One-Hot Encoding and StandardScaler. Covered in the datascienceacademy course.

A lightweight R script for text mining and harmonizing medical phenotype data. Cleans, standardizes, and maps diagnoses to ICD-10 codes, with clinical annotations for enhanced data usability.

Understand the learning process of RNNs and discover the LSTM network architecture. Solve problems and perform Natural Language Processing using sequences of data

Категоризация данных товаров бисера: преобразование категориальных признаков, интервальное кодирование цен и создание бинарного представления

REST API built with FastAPI to predict house prices Features include a Random Forest regression model, Pydantic input validation, and one-hot encoding aligned with training data. Designed for clean, scalable, and testable code

📶In this repository, we will do feature engineering with Python.

Detecting ideal clusters from imdb's movie dataset to segment using unsupervised learning

Data visualization and one hot encoding of Kaggle dataset. Model trained with random forest classifier

Flight fare perdicting model