Machine-Learning-Projects

Regression Certificate

Classification Certificate

Techniques used: Python 2.x, Python 3.x, pandas, numpy, scikit, matplotlib

I have used pandas for feature engineering and scikit to build out modeling and matplotlib to visualize data and also implemented various ML algo from scratch.

Projects

• Linear Regression

  Project Overview: How to predict a house's price? How to evaluate model?

  Project 1: Estimating House Price based on Crime Rate (nbviewer)

  Project 2: Implementing-closed-form-solution-for-simple-regression (nbviewer)

  Project 3: Predicting-House-Price-using-Multiple-regression (nbviewer)

  Project 4: Polynomial Regression (nbviewer)

  Project 5: Ridge regression and using 10-fold CV method to tune L2-penalty (nbviewer)

  Project 6: Feature selection using LASSO and Coordinate descent (nbviewer)

  Project 7: Implementation of k-nearest neighbors regression from scratch to predict house price (nbviewer)

• Classification

  Project 1 Overview: Build classification modeling to predict if an Amazon review is positive.

  Project 2 Overview: Is this loan safe or risky?

  Project 1: Sentiment analysis of Amazon product's reviews (nbviewer)

  Project 2: Implementing logistic regression from scratch (nbviewer)

  Project 3: Logistic Regression with L2 regularization (nbviewer)

  Project 4: Identifying safe loans with decision trees (nbviewer)

  Project 5: Decision Trees in Practice (nbviewer)

  Project 6: Gradient boosting classifier to classify between risky loans and safe loans (nbviewer)

  Project 7: Implementing Adaboost on decision tree from scratch (nbviewer)

  Project 8: Exploring precision and recall (nbviewer)