Encoding: converting categorical data into a numerical data
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Mar 8, 2023 - Jupyter Notebook
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ordinal-encoding
Here are 23 public repositories matching this topic...
Feature Engineering
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Mar 18, 2023 - Jupyter Notebook
Feature Engineering with Python
pipeline data-normalization imbalanced-data outlier-removal zscore iqr label-encoding simple-imputer dummy-variables onehot-encoding ordinal-encoding column-transformer knn-imputer data-standardization modified-zscore
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Nov 2, 2024 - Jupyter Notebook
This repository is totally focused on Feature Engineering Concepts in detail, I hope you'll find it helpful.
standardization feature-engineering principal-component-analysis binarization z-score normalisation onehot-encoding simpleimputer ordinal-encoding labelencoder knn-imputer winsorization iterative-imputer percentile-method discritisation mean-median-imputation frequent-value-imputation
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Apr 7, 2023 - Jupyter Notebook
Machine Learning Project
linear-regression scikit-learn matplotlib kmeans-clustering smote r2 random-forest-classifier onehot-encoder standardscaler modelselection ordinal-encoding
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Jul 23, 2024 - Jupyter Notebook
[Modeling] Project in 2022 - Simple Model of important factors in the incidence of heart disease and prediction model
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Dec 27, 2022 - Jupyter Notebook
Focus on selecting datasets suitable for a machine learning experiment, with an emphasis on data cleaning, encoding, and transformation steps necessary to prepare the data.
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Nov 23, 2023 - Python
Data Cleaning and Data Visualization with python libraries like numpy , pandas, sklean,seaborn, matplotlib-pyplot
python encoding numpy pandas-dataframe sklearn pandas seaborn boxplot dataengineering datavisualization pandas-library datacleaning matplotlib-pyplot onehot-encoding imputer ordinal-encoding labelencoding
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Apr 17, 2024 - Jupyter Notebook
Прогнозирование рыночной стоимости автомобилей
python random-forest regression pandas lightgbm data-analysis feature-importance target-encoding randomized-search ordinal-encoding column-transformer make-pipeline ohe-encoding
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Nov 7, 2022 - Jupyter Notebook
Why do employees leave? This project first compares the predictive performance of three different models, then uses the best model to help reveal the top contributing factors.
machine-learning random-forest confusion-matrix logisitic-regression xgboost-model shapley-values one-hot-encoding permutation-importances partial-dependence-plots ordinal-encoding gini-importance
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May 24, 2022 - Jupyter Notebook
House Price Prediction (Kaggle)
neural-network random-forest sgd data-preprocessing decision-trees lasso-regression gradient-boosting-regressor xgboost-regression ordinal-encoding
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Jan 31, 2024 - Jupyter Notebook
Ordinal Encoding - Label Encoding
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Apr 21, 2022 - Jupyter Notebook
Book price dataset analysis and modeling
nlp eda feature-extraction outlier-detection scaling feature-engineering random-forest-regressor bivariate-analysis feature-importance univariate-analysis one-hot-encoding ordinal-encoding
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Dec 5, 2023 - Jupyter Notebook
Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier
exploratory-data-analysis cross-validation data-visualization feature-selection naive-bayes-classifier xgboost hyperparameter-optimization feature-engineering knn model-evaluation model-building feature-importance label-encoding smotetomek numerical-encoding ordinal-encoding correlation-map imbalanced-dataset
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May 9, 2020 - Jupyter Notebook
Students Placement based on some characteristics.
data-science machine-learning random-forest linear-regression exploratory-data-analysis pipelines classification logistic-regression data-preprocessing knn data-cleaning regression-analysis onehot-encoding ordinal-encoding
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Aug 23, 2021 - Jupyter Notebook
Job-A-thon ML challenge
machine-learning cross-validation regression eda boosting-algorithms hyperparameter-tuning gridsearchcv onehot-encoding r2score ordinal-encoding gradientboostingregressor columntransformation
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Jun 27, 2023 - Jupyter Notebook
Feature engineering or feature extraction or feature discovery is the process of extracting features from raw data.
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Sep 3, 2023 - Jupyter Notebook
A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python)
visualization python machine-learning random-forest sklearn cross-validation xgboost house-price-prediction xgboost-model target-encoding sklearn-library onehot-encoding anova-test ordinal-encoding random-forest-regressors
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Jul 26, 2024 - Jupyter Notebook
Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.
sklearn feature-selection outliers outlier-detection standardization features feature-scaling feature-transformation normalization outlier-removal power-transformers datapreprocessing onehot-encoding ordinal-encoding labelencoder function-transformer
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Aug 14, 2024 - Jupyter Notebook
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May 19, 2023 - Jupyter Notebook
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