This project provides the data based on classification to check if the patient is covid +ve or -ve.

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

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

An comprehensive data analysis of a particular market and its customers.

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

Exploratory Data Analysis - Using Python to find correlation between features

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

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

The project provides Four Tasks which is given by Cognifyz Technology.

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

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.

* 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

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

Apply various methods to handling missing data - Practice

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.

Simplilearn (EDA) - Masters in Data Science - Assignment

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

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