Resource materials of the data science course using R and Python that I facilitated at Besant technologies
- Introduction to Python and R programming languages
- Data Science - Python.ipynb
- R_basic.Rmd
- Exploratory data analysis : Covers techniques such as Univariate and bivariate analysis, Missing value analysis, Outlier detection analysis, Percentile based outlier removal, correlation matrix etc.
- EDA - Python .ipynb
- Classication models: Covers K-nearest neighbors, SVM, RF, LR, and Xgboost techniques
- model-KNN.ipynb
- modle-SVM.ipynb
- model-random-forest.ipynb
- model-xgboost.ipynb
- model-logistic-regression.ipynb
- Regression techniques: Covers linear, lasso, ridge, polynomial and elasticnet regression techniques
- model-linear-regression.ipynb
- Regression_tech-Lasso_ridge_Elasticnet.ipynb
- Clustering techniques: Covers K-means, hierarchical clustering algorithms
- model-clustering.ipynb