Python_ML_Templates

Save time by using the templates for ML Models

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A-Z Machine Learning:

Credit to the Machine Learning A-Z™: Hands-On Python & R In Data Science Course by Udemy for the templates and intutition of the differnet ML models.

Visuals of the different models via Google Slide

Regression Models:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression (SVR) Intuition
• Decision Tree Regression intuition
• Random Forest intuition
• Regression Models Pros and Cons PDF

Classification Models:

• Logistic Regression
• K-Nearest-Neighbours
• Support Vector Machine (SVM)
• Support Vector Machine Kernel
• Naive Bayes
• Decision Tree
• Random Forest
• Classification Models Pros and Cons PDF

Clustering Models

• K Means Clustering

Refining the model:

• Applications of Grid_Search to customise and find the best Hyperparameters (user input parameters)
• Applications of K-fold Cross Validation to take different slices of the train and test data

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Pandas:

The library Panda is used to manipulate data, this includes grouping and finding statistical features etc.

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Matplotlib:

The library Matpltlib is used to plot interpretable graphs and visualisations, these include bar charts, scatter plots and histograms etc.

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Webscrapping:

The library BeautifulSoup is used to obtain hyperlinks and read html. This is a foundation to be able to scrape data from the web and put it in a database.