worklifesg / Machine-Learning-IBM

This repository is on Machine Learning using Python 3.8.3 using Visual Studio Code. Most of the programs are from IBM Machine Learning course and some algorithms (course out of scope) are presenterd only for learning purpose. These codes will be on topics like Regression, Classification, Clustering and Recommender Systems.

Machine-Learning-IBM

Table of contents

• General info
• Code setup Information
  • Simple and Multiple Linear Regression
  • Ordinal Logistic Regression
  • Non-Linear Regression
• Classification
• Clustering
• Final Project

General info

This repository is on Machine Learning using Python 3.8.3 using Visual Studio Code. Most of the programs are from IBM Machine Learning course and some algorithms (course out of scope) are presenterd only for learning purpose. These codes will be on topics like Regression, Classification, Clustering and Recommender Systems.

Code setup Information

• The code is writin in Visual Studio code using Python extension with Python 3.8.3 installed on the system.
• Output results are written in text file (.txt)
• The illustrations are saved as eps and using latex, each documentation is created as .pdf

Simple and Multiple Linear Regression

• Python files: Simple_Regression_example.py, Multiple_Regression.py
• Date file: FuelConsumptionCo2.csv
• A fuel consumption dataset, FuelConsumption.csv, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. Source: https://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64
• Output file: LinearReg.txt, MultipleReg.txt

Ordinal Logistic Regression

• Python files: Ordinal_Regression_example.py
• Date file: datasets_228_482_diabetes.csv
• Pima Indian Diabetes dataset. Source: https://www.kaggle.com/uciml/pima-indians-diabetes-database
• Output file: LogReg.txt

Non-Linear Regression

• Python files: Non_Linear.py, Non_Linear_Regression.py
• Date file: china_gdp.csv
• The dataset corresponding to China's GDP from 1960 to 2014. This dataset has two columns, the first, a year between 1960 and 2014, the second, China's corresponding annual gross domestic income in US dollars for that year.
• Output file: NonLinearReg.txt

Classification

• Separate folder 'Classification' for working on different (categorical) datasets and applying different Classification algorithms such as KNN, Decision Tree, Logisitc Regression and Support Vector Machine (SVM)

Clustering

• Separate folder 'Clustering' for working on different datasets and applying different Clustering algorithms such as K-means (Partitioning Clustering), Agglomerative (Hierarchical Clustering) and DBSCAN (Density Based Clustering)

Usage of basemap in DBSCAN Clustering Algorithm

Currently basemap is no longer in use in matplotlib toolkits since 2017. Another feature has been introduced 'Cartopy' but to finish this excerise in Visual Studio Code using basemap package, following steps are done.

• Install Python 2.7.18, where pip is already there as there are no pip files before 2.7.9 and needs to be bootstrapped if used.
• Install cp27-amd64 or cp27-amd32 .whl files for Pyproj and Basemap
• Using command prompt, use ' pip install pyproj‑1.9.6‑cp27‑cp27m‑win_amd64.whl' and ' pip install basemap‑1.2.1‑cp27‑cp27m‑win_amd64.whl'
• Either you can make python 2.7 as default version in user and system environment variables in system settings or can Select Interpreter in VS Code and choose Python 2.7
• Please remember when installing any version of Python, all other related packages need to be installed again such as numpy, pandas, scikit-learn, scipy etc.

Final Project

• Separate folder 'Final Project'- The final module of this course is a project to determine which basketball teams are most likely to make it to the semifinal round of the College Basketball Tournament known as the Final Four. It is performed on IBM Watson Studio AI in Notebook

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Note: Each program is compiled along with output log file and results in pdf using latex.