Basic practice projects for Data Science and Machine Learning

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

Prerequisite:

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

Haberman's Breast Cancer Survival

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

Uber Supply Demand Gap Analysis

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

Application of TSNE on Amazon's Fine Food Review

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

Application of KNN on Amazon's Fine Food Review

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

Application of NAIVE BAYES on Amazon's Fine Food Review

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

Application of LOGISTIC REGRESSION on Amazon's Fine Food Review

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

Application of SGD Implementation on BOSTON Data

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

Application of SVM Implementation on Amazon's Fine Food Review

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

Application of Decision Tree Implementation on Amazon's Fine Food Review

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

Application of Ensemble Model Implementation on Amazon's Fine Food Review

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

Application of Unsupervised Model Implementation on Amazon's Fine Food Review

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