handling-missing-value
There are 32 repositories under handling-missing-value topic.
alifrmf/Data-Cleaning-Steps-to-Clean-Data
Data Science
Rushi21-kesh/Handling-Missing-Values
This repository is on different types of data, types of missing values and how to handle missing value
eiliaJafari/House-prices-in-Beijing
An analysis of house prices in Beijing
Muhammad-Sheraz-ds/100Days-of-Machine-Learning
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.
abhijha3011/Feature-Engineering-Techniques
Techniques to Explore the Data
arbaazkhaan/FIFA-Dataset-Refinement
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.
Bramitha-gowda-M/Movie-Recommendation-System
End-to-end movie recommendation system using ML, data analysis, NLTK, CountVectorizer, cosine similarity, and TMDB API. Deployed with Streamlit.
damaniayesh/Cognifyz_Internship_Tasks
The project provides Four Tasks which is given by Cognifyz Technology.
krashnagurme/linear-and-nonlinear-regression
This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.
MoinDalvs/Learn_Feature_Engineering
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
simrann20/Retail-Marketing-Exploratory-Data-Analysis-and-Data-Preprocessing
Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing
16danielvm/Different-Imputation-Methods-to-Handle-Missing-Data
In this notebook, i show a examples to implement imputation methods for handling missing values.
Anithanaidu33/Titanic-Classification
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.
damaniayesh/Covid_data_Prediction
This project provides the data based on classification to check if the patient is covid +ve or -ve.
dataninsight/EDA
Simplilearn (EDA) - Masters in Data Science - Assignment
Dspocean/Data-Cleaning
This repository contains pre-requisite notebooks of Data Cleaning work for my internship as a Machine Learning Application Developer at Technocolabs.
eiliaJafari/Comprehensive-market-data-analysis
An comprehensive data analysis of a particular market and its customers.
FadekemiAkinduyile/Correlation-Project
Exploratory Data Analysis - Using Python to find correlation between features
mansiaghera30/my_first-repository
In this exercise, I'll apply Data cleaning using Handling missing values of San Francisco building permit.
manya-gangoli/World-Population-2024-EDA-and-prediction
Implemented and compared various machine learning algorithms and visualizations on the World Population 2024 dataset to identify the most efficient predictive model. Additionally, evaluated model accuracy using different methods to ensure prediction reliability and precision.
mawada-sweis/Handling-Missing-Data
Apply various methods to handling missing data - Practice
rithvikrana/Feature-Engineering
All the important elements of feature engineering are covered in this repository
Samir-Zade/Feature-Engineering-and-Exploratory-Data-Analysis
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.
SmartNamDevoloper/Telecom_Customer_churn_Classification
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.
tezam84/Handling_missing_values_Boston_offenses_2021
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.
HasnaeTalibi/Predict-Wine-Quality
A project investigating the relationship between wine quality and the chemical properties of the wine
khushalvanani/Data-Cleaning-using-SQL-healthcare_dataset
This repository contains a project focused on data cleaning using SQL, applied to a healthcare dataset.
madhurimarawat/Intelligent-Data-Analysis
This repository contains data analysis programs in the Python programming language.
NabilahSharfina/Ruangguru-Bootcamp
Final project program DBA mitra Ruangguru X Studi Independen Bersertifikat Kampus Merdeka batch 2
PoojaP-atil/Titanic-case-study
* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering *Accuracy score *Confusion matrix *Classification report