Adhyan-1404/Exploratory-Data-Analysis-Projects

Contains the EDA Projects done using python.

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anupamsaha18/Fintech-Capstone-Project---Loan-Default-Analysis

The Loan Default Analysis project aims to identify key factors contributing to loan defaults by analyzing borrower profiles, financial data, and credit risk indicators. Using statistical methods, visualizations, and predictive modeling, the project provides insights to mitigate risks and improve lending strategies.

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khushalvanani/Data-Cleaning-using-SQL-healthcare_dataset

This repository contains a project focused on data cleaning using SQL, applied to a healthcare dataset.

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AmbreenMahhoor/What-Is-Complete-Case-Analysis-Or-CCA

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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.

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dataninsight/EDA

Simplilearn (EDA) - Masters in Data Science - Assignment

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eiliaJafari/House-prices-in-Beijing

An analysis of house prices in Beijing

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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.

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fitria-dwi/Data-Cleansing

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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.

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damaniayesh/Covid_data_Prediction

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

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damaniayesh/Cognifyz_Internship_Tasks

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

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NabilahSharfina/Rakamin-Academy

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madhurimarawat/Intelligent-Data-Analysis

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

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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.

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FadekemiAkinduyile/Correlation-Project

Exploratory Data Analysis - Using Python to find correlation between features

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abhijha3011/Feature-Engineering-Techniques

Techniques to Explore the Data

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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.

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alifrmf/Data-Cleaning-Steps-to-Clean-Data

Data Science

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simrann20/Retail-Marketing-Exploratory-Data-Analysis-and-Data-Preprocessing

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mawada-sweis/Handling-Missing-Data

Apply various methods to handling missing data - Practice

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Rushi21-kesh/Handling-Missing-Values

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

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MoinDalvs/Learn_Feature_Engineering

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

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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.

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NabilahSharfina/Ruangguru-Bootcamp

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

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16danielvm/Different-Imputation-Methods-to-Handle-Missing-Data

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

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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.

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