Techniques to Explore the Data

This repository is on different types of data, types of missing values and how to handle missing value

A project investigating the relationship between wine quality and the chemical properties of the wine

This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.

This project demonstrates building a classification model for imbalanced data. Feature engineering, feature selection and extensive EDA. Comparing of logistic regression, random forest and ADA Boost models are done before finalizing the best model.

All the important elements of feature engineering are covered in this repository

In this notebook, i show a examples to implement imputation methods for handling missing values.

This repository contains pre-requisite notebooks of Data Cleaning work for my internship as a Machine Learning Application Developer at Technocolabs.

In the real world, a dataset with no missing values doesn't exist...So in this notebook, we explore different ways of dealing with it.

Final project program DBA mitra Ruangguru X Studi Independen Bersertifikat Kampus Merdeka batch 2

Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features

In this exercise, I'll apply Data cleaning using Handling missing values of San Francisco building permit.

Apply various methods to handling missing data - Practice

Simplilearn (EDA) - Masters in Data Science - Assignment

Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing

Data Science

The Titanic classification problem involves predicting whether a passenger on the Titanic survived or not, based on various features available about each passenger. The sinking of the Titanic in 1912 is one of the most infamous maritime disasters in history, and this dataset has been widely used as a benchmark for predictive modeling.

Exploratory Data Analysis - Using Python to find correlation between features

Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.

This repository contains data analysis programs in the Python programming language.