Here You can find all the codes where you can see all the codes of feature engineering and ml algorithms.
- Working with csv files
- Working with Json files
- Api to Pandas DataFrame
- Web Scraping
- Understanding your descriptive data points
- Univariate Analysis
- Bivariate Analysis
- Pandas Profiling
- Standardization
- Normalization
- Ordinal Encoding
- One Hot Encoding
- Column Transformer
- SkLearn PipeLines
- Function Trasnsformer
- Power Trasnsformer
- Binning & Binarization
- Handling Mixed Variables
- Handling Date and time
- Complete Case Analysis
- Impute using Arbitrary Value and meand median mode
- Handling Missing Categorical Data
- Missing Indicator
- KNN Imputer
- Iterative Imputer
- Outlier Remover Using Z Score
- Outlier Remover Using IQR
- Outlier Remover Using Percentile Method
- Feature Construction & Featurer Splitting
- Feature Selection
- Principal Component Analysis(PCA)
- Simple Linear Regression
- Regression Metrics
- Multiple Linear Regression
- Gradient Descent
- Types Of Gradient Descent
- Polynomial Regression
- Ridge Regularization
- Lasso Regression
- ElasticNet Regression
- Logistic Regression
- Classification Metrics
- Logistic Regression Cont
- Decision Tree
- Voting Ensemble
- Bagging Ensemble
- Random Forest
- Adaboost
- K-Means
- Gradient Boosting
- Gradient Boosting Regression and Classification
- Stacking and Blending
- Agglomerative hierarchal clustering
- K Nearest Neighbour
- Support Vector Machine
- Data Accessing and clean
- Save Model using pickle and joblib
- Automated Machine Learning using MLBOX
- EVALML Automated ML and feature Engineering
- CatBoost Vs XGBoost Vs LightGBM