Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
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Updated
Sep 20, 2022 - Jupyter Notebook
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.
Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing
This project provides the data based on classification to check if the patient is covid +ve or -ve.
An comprehensive data analysis of a particular market and its customers.
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.
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 resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.
This repository contains data analysis programs in the Python programming language.
An analysis of house prices in Beijing
Final project program DBA mitra Ruangguru X Studi Independen Bersertifikat Kampus Merdeka batch 2
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.
The project provides Four Tasks which is given by Cognifyz Technology.
This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.
End-to-end movie recommendation system using ML, data analysis, NLTK, CountVectorizer, cosine similarity, and TMDB API. Deployed with Streamlit.
Techniques to Explore the Data
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.
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