There are 1 repository under handling-missing-value topic.
Data Science
This repository is on different types of data, types of missing values and how to handle missing value
Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.
Techniques to Explore the Data
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.
End-to-end movie recommendation system using ML, data analysis, NLTK, CountVectorizer, cosine similarity, and TMDB API. Deployed with Streamlit.
An analysis of house prices in Beijing
This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.
Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing
In this notebook, i show a examples to implement imputation methods for handling missing values.
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.
This repository contains pre-requisite notebooks of Data Cleaning work for my internship as a Machine Learning Application Developer at Technocolabs.
An comprehensive data analysis of a particular market and its customers.
Exploratory Data Analysis - Using Python to find correlation between features
Apply various methods to handling missing data - Practice
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.
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.
The project provides Four Tasks which is given by Cognifyz Technology.
This project provides the data based on classification to check if the patient is covid +ve or -ve.
A project investigating the relationship between wine quality and the chemical properties of the wine
This repository contains data analysis programs in the Python programming language.
In this exercise, I'll apply Data cleaning using Handling missing values of San Francisco building permit.
Final project program DBA mitra Ruangguru X Studi Independen Bersertifikat Kampus Merdeka batch 2
All the important elements of feature engineering are covered in this repository