This repository contains short code snippets or short projects of the Machine Learning & Data Science area Below are the items that could be found.

All the projects and code snippets are written in Python 3.6+ version and Jupyter Notebook Below are the installation steps

Windows

• installtion guide
• python version 3.6

Ubuntu

• Ubuntu 16.04 and 14.04 ships with both Python 3 pre-installed.

Mac

• Mac OS X comes preinstalled with python2.7
• python version 3.6
• please refere for more info

Anaconda Installation

• Anaconda Installation

Jupyter Notebook

• After installing the anaconda, open terminal or cmd and enter jupyter notebook

The main objecttive of the project is to predict the Survival Status of a patient based on some attributes.

The main objecttive of the project is to analysis the Uber's Supply-Demand-Gaps of cab

• One thing to note the seaborn version is 0.9.0

The main objective of the project is to apply KNN algorithm on amazon fine food review

• Convert the free text of food reviews into numerical vectors
• Apply TSNE algorithm to vidualise the data

The main objective of the project is to apply KNN algorithm on amazon fine food review

• Convert the free text of food reviews into numerical vectors
• Apply KNN model to get the prediction accuracy

The main objective of the project is to apply Naive Bayes algorithm on amazon fine food review

• Convert the free text of food reviews into numerical vectors
• Apply Naive Bayes model to get the prediction accuracy

The main objective of the project is to apply Logistic Regression algorithm on amazon fine food review

• Convert the free text of food reviews into numerical vectors
• Apply Logistic Regression model to get the prediction accuracy

The main objective of the project is to derive manual SGD and compare the result with sklearn's SGDRegressor

• Convert the free text of food reviews into numerical vectors
• Apply Support Vector Machine model to get the prediction accuracy

• Convert the free text of food reviews into numerical vectors
• Apply Decision Tree model to get the prediction accuracy

• Convert the free text of food reviews into numerical vectors
• Apply Random Forest and XGBoost model to get the prediction accuracy

• Convert the free text of food reviews into numerical vectors
• Apply KMeans, Aglomerative and DBSCAN model to get the prediction accuracy