Most of my practice machine learning codes that I have coded during my coursework.
These codes follow concepts taught in the class.
Please note the codes uploaded here are both in R and Python
Common Libraries used in Python code are:
- sklearn
- pandas
- numpy
- seaborn
Common Libraries used in the R code:
- DPLYR
- TIDYR
- FORCATS
- GGPLOT2
- GLMNET
The codes in this section touch upon different concepts: FDR(False Discovery Rate) Linear Regression Binary Logistic Regression & Multinomial Logistic Regression Regularization methods - Lasso and Ridge Regression Experimentation - Treating for continuous treatment variables Using Double Lass to remove confounding effect Principle Component Analysis(PCA) & PCR AIC, BIC, AICc RFECV, Random Forest Bagging, Boosting Trees, etc.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.