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numerical-data

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EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

  • Updated May 28, 2021
  • Jupyter Notebook

This repository contains code for a comprehensive hybrid machine learning and deep learning frameworks for accurately predicting hybrid nanofluid density using stacking ensembles, advanced data augmentation, and metaheuristic optimization techniques.

  • Updated Mar 3, 2025

Dataset for the IEEE Access research paper: "A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms"

  • Updated Mar 3, 2025

Problem to solve: find the patterns for increased suicide rates (1985 to 2016) among different cohorts globally, across the socioeconomic spectrum, using exploratory data analysis. Using bivariate analysis, I try to determine if there is any relationship between two variables.

  • Updated Sep 21, 2022
  • Jupyter Notebook

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