fuyou84's Stars
AustinDoolittle/Pytorch-Gain
An implementation of GAIN heatmap network in pytorch. Original paper: https://arxiv.org/abs/1802.10171
ngxbac/GAIN
Pytorch implementation of paper: [Tell Me Where to Look: Guided Attention Inference Network](http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Tell_Me_Where_CVPR_2018_paper.pdf)
youseefmoemen/Generative-Adversarial-Imputation-Networks
Implementation for Generative Adversarial Imputation Networks for handling missing data
wangy8989/Bankruptcy-Prediction-using-Machine-Learning
Using various machine learning models to predict whether a company will go bankrupt
juanesgutiz19/ia-project-bankruptcy-prediction
techietrader/XGBoost-on-Bankruptcy-Dataset-Imbalance-Data
keywords - XGboost, Smote, Hyperparameter tuning, Python
jsyoon0823/GAIN
Codebase for Generative Adversarial Imputation Networks (GAIN) - ICML 2018
BankanalRamya/KNNImputer
ashinde8/Data-Preprocessing-and-Machine-Learning
- The dataset consists of 1042 rows and 20 columns. This is a regression problem where we can the target variable is 'price' which I have predicted using Machine Learning Modeling. - Dropped the columns 'id', 'time_created','time_updated','external_id','url','latitude' and 'longitude' from the dataset, as these variables do not provide information significant in modeling. - Here I have observed that the variable 'status' has only one value throughout the dataset i.e. 'active', hence I have can drop this variable as it is not providing us significant information. - I observed that the variables 'bedrooms' ,'bathrooms', 'garages' ,'parkings' ,'offering' ,'erf_size' ,' floor_size' have missing values and the target variable 'price' also has missing values. Hence I took care of this by filling the missing values of the independent features and the target variable. - After making the above observation I filled the two rows which have value '[None]' in the property_type column with 'house' as the value for the'agency' variable for these rows is 'rawson' and the mode for the variable 'property_type' for the agency 'rawson' is 'house' and also mode for the 'property_type' variable for the area 'Constantia' is also 'house' - Predicted the missing Values Using Imputers From sklearn.preprocessing - Here I used the KNNImputer to fill the missing values in the variables 'price', "garages","parkings","erf_size","floor_size" by predicting the values using the KNNImputer library. - We go through a range of values from 1 to 20, for the parameter 'n_neighbors' in the KNNImputer, as we want to find which value of 'n_neighbors' gives the maximum value of correlation between the target variable 'price' and the feature 'floor_size'. The reason I have selected the variable 'floor_size' to calculate the correlation with the target variable 'price' is that, before imputing the missing values the target variable 'price' had the highest corrleation with the independent variable 'floor_size' which was 0.5319914806523912. Now I am finding the maximum correaltion value between the target variable 'price' and the variable 'floor_size' after the missing values are imputed using the KNNImputer, for different values of the parameter 'n_neighbors' and then compare it with 0.5319914806523912, whcih is the correlation for the original dataset whcih consists of missing values. - Here we observe that the maximum correlation between the target variable 'price' and the independent variable 'floor_size' is 0.4233518730063556, when the value for 'n_neighbors' is 6. This value is less than the value of correlation for the orignal dataset, hence we move on to another Imputer to fill the missing values as after the missing values were filled using the KNNImputer the correlation decreased whcih is not desirable. - Here we observe that the correlation between the target variable 'price' and the independent variable 'floor_size' is 0.6703992976511615 after the imputation of missing values using IterativeImpueter. This value is more than the correlation value for the original dataset. Hence we allow the imputation of the missing values using IterativeImputer into the orignal dataset. - Now while filling the variable 'bathrooms' and 'bedrooms'; there are 4 and 14 NaN values respectively. Hence I have decided to fill the values on a case by case basis. I have decided to fill the 'NaN' values based on their 'property_type'. So for filling the 'bathrooms' variable which has 'property_type' as 'house', I have filled these values with the mode for the 'bathrooms' and 'bedrooms' variable. Similarly I have done the same for the other 'property_type' 'apartment'. - Performed Data Visualizations for the features to draw more insights. - Here, you can see outliers in the target variable 'price' from the above figure. While price outliers would not be a concern because it is the target feature,the presence of outliers in predictors, in this case there aren't any, would affect the model’s performance. Detecting outliers and choosing the appropriate scaling method to minimize their effect would ultimately improve performance. - From the correlation matrix, we can see that there is varying extent to which the independent variables are correlated with the target. Lower correlation means weak linear relationship but there may be a strong non-linear relationship so, we can’t pass any judgement at this level, let the algorithm work for us. - Build the regression models Linear Regression, XGBoost, AdaBoost, Decision Tree, Random Forest, KNN and SVM. - Performed Hyperparameter tuning for all the above algorithms. - Predicted the prices using the above models and used the metrics RMSE, R -square and Adjusted R-square. - As expected, the Adjusted R² score is slightly lower than the R² score for each model and if we evaluate based on this metric, the best fit model would be XGBoost with the highest Adjusted R² score and the worst would be SVM Regressor with the least R² score. - However, this metric is only a relative measure of fitness so, we must look at the RMSE values. - In this case, XGBoost and SVM have the lowest and highest RMSE values respectively and the rest models are in the exact same order as their Adjusted R² scores.
dhanajitb/GAIN-Pytorch
Pytorch implementation of GAIN for missing data imputation
ppjjhh0515/KNNImputer_NaN_vals_prac
996icu/996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
shripadaRao/CtganExample
BackyardofAbela/EnsembleLearning
包括决策树和随机森林进行离职人员预测，Xgboost和lightGBM的应用
ArdalanM/pyLightGBM
Python binding for Microsoft LightGBM
ljk423/ctgan-tf
📊 TensorFlow 2.X implementation of Conditional Tabular Generative Adversarial Network.
Alxe1/FeatureSelectionsAndExtractions
feature selections and extractions
CzRzChao/SimpleGA
用python实现简单的遗传算法
rogeroyer/feature_selection_GAAlgorithm
基于遗传算法的特征选择
cainsmile/GA_for_Feature_Selection
使用遗传算法结合决策树做特征选择/Using genetic algorithm for feature selection with decision tree
Fangzhenxuan/AI_Projects
Artificial Intelligence
kaushalshetty/FeatureSelectionGA
Feature Selection using Genetic Algorithm (DEAP Framework)
ashishpatel26/Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
WillKoehrsen/automated-feature-engineering
Automated feature engineering in Python with Featuretools
willyiamyu/house_prices
Submission for Kaggle Competition "House Prices: Advanced Regression Techniques" (Top 30%) Using RFECV and XGBoost
Yimeng-Zhang/feature-engineering-and-feature-selection
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
solegalli/feature-selection-for-machine-learning
Code repository for the online course Feature Selection for Machine Learning
nbswords/Ranking-Titanic-with-Recursive-Feature-Elimination
A practice of Recursive Feature Elimination (RFE) using Titanic dataset
anujdutt9/Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
sdv-dev/CTGAN
Conditional GAN for generating synthetic tabular data.