maa786/ml-and-feature-engineering

This repository contains all the feature engineering techniques and all ml algorithms solution.

Here You can find all the codes where you can see all the codes of feature engineering and ml algorithms.

1. Working with csv files
2. Working with Json files
3. Api to Pandas DataFrame
4. Web Scraping
5. Understanding your descriptive data points
6. Univariate Analysis
7. Bivariate Analysis
8. Pandas Profiling
9. Standardization
10. Normalization
11. Ordinal Encoding
12. One Hot Encoding
13. Column Transformer
14. SkLearn PipeLines
15. Function Trasnsformer
16. Power Trasnsformer
17. Binning & Binarization
18. Handling Mixed Variables
19. Handling Date and time
20. Complete Case Analysis
21. Impute using Arbitrary Value and meand median mode
22. Handling Missing Categorical Data
23. Missing Indicator
24. KNN Imputer
25. Iterative Imputer
26. Outlier Remover Using Z Score
27. Outlier Remover Using IQR
28. Outlier Remover Using Percentile Method
29. Feature Construction & Featurer Splitting
30. Feature Selection
31. Principal Component Analysis(PCA)
32. Simple Linear Regression
33. Regression Metrics
34. Multiple Linear Regression
35. Gradient Descent
36. Types Of Gradient Descent
37. Polynomial Regression
38. Ridge Regularization
39. Lasso Regression
40. ElasticNet Regression
41. Logistic Regression
42. Classification Metrics
43. Logistic Regression Cont
44. Decision Tree
45. Voting Ensemble
46. Bagging Ensemble
47. Random Forest
48. Adaboost
49. K-Means
50. Gradient Boosting
51. Gradient Boosting Regression and Classification
52. Stacking and Blending
53. Agglomerative hierarchal clustering
54. K Nearest Neighbour
55. Support Vector Machine
56. Data Accessing and clean
57. Save Model using pickle and joblib
58. Automated Machine Learning using MLBOX
59. EVALML Automated ML and feature Engineering
60. CatBoost Vs XGBoost Vs LightGBM