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missing-value-imputation

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This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline

  • Updated Mar 20, 2023
  • Jupyter Notebook

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

DMI Class implements the DMI imputation algorithm for imputing missing values in a dataset from Rahman, M. G., and Islam, M. Z. (2013): Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques

  • Updated Mar 24, 2023
  • Java

kDMI employs two levels of horizontal partitioning (based on a decision tree and k-NN algorithm) of a data set, in order to find the records that are very similar to the one with missing value/s. Additionally, it uses a novel approach to automatically find the value of k for each record.

  • Updated Mar 25, 2023
  • Java

This project analyzes road accident data using MS Excel to identify trends, patterns, and contributing factors to accidents. Through data visualization techniques and statistical analysis, it provides insights that can inform safety measures and policy decisions, aiming to enhance road safety and reduce accident rates.

  • Updated Sep 29, 2024

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