├── Decision Trees ├── Decision Trees with python │ ├── data │ │ └── kyphosis.csv │ └── project-1.ipynb └── Note │ └── New Text Document.txt ├── K Means Clustering ├── K Means Clustering Project │ ├── K Means Project.ipynb │ ├── data │ │ └── College.csv │ └── output.png └── K Means Clustering with python │ ├── K_Means_Clustering_with_python.ipynb │ └── output.png ├── K Nearest Neighbors (KNN) ├── K Nearest Neighbors (KNN) Project │ ├── Data │ │ └── KNN_Project_Data │ └── K Nearest Neighbors (KNN) Project.ipynb └── KNN with Python │ ├── Data │ └── Classified Data │ ├── KNN_with_Python.ipynb │ ├── KNN_with_Python2.ipynb │ └── output.png ├── Linear Regression ├── Linear Regression Project On Ecommerce Customer │ ├── Linear_Regression_Project_On Ecommerce_Customer.ipynb │ └── data │ │ └── Ecommerce Customers.csv └── Linear Regression with Python │ ├── Linear_Regression.ipynb │ └── data │ └── USA_Housing.csv ├── Logistic Regression ├── Advertising Project With Logistic Regression │ ├── Advertising Project With Logistic Regression.ipynb │ ├── Data │ │ └── Advertising.csv │ ├── output.png │ └── output2.png └── Logistic Regression with Python │ ├── Logistic_Regression_For_titanic_File.ipynb │ └── data │ └── titanic_train.csv ├── Natural Language Processing (NLP) ├── NLP Project │ ├── NLP Project.ipynb │ ├── data │ │ └── yelp.csv │ └── output.png └── NLP with Python │ ├── NLP with Python.ipynb │ └── data │ ├── SMSSpamCollection │ └── readme ├── Principal Component Analysis(PCA) ├── Note │ └── New Text Document.txt └── Principal Component Analysis(PCA) Project with python │ ├── PCA Project.ipynb │ └── output.png ├── README.md ├── Random Forest ├── Note │ └── New Text Document.txt └── Random Forest With Python │ ├── data │ └── kyphosis.csv │ └── project-1.ipynb └── Support Vector Machines(SVM) ├── SVM Project ├── SVM Project.ipynb └── output.png └── Support Vector Machines with Python └── Support Vector Machines with Python.ipynb /Decision Trees/Decision Trees with 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