Small ML projects to understand core concepts
- Predicting the housing price for the regions in USA using Linear regression in Scikit learn
- Decide whether to focus their efforts on their mobile app experience or their website using Linear Regression
- Working with the Titanic Data Set from Kaggle, predicting a classification of survival or deceased
- Working on advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We tried to create a model that will predict whether or not they will click on an ad based off the features of that user.
- KNN to create a model that directly predicts a class for a new data point based off of the features. classified data set from a company!
- Exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors. This model is created using Random Forest and decision tress in Scikit learn
- Clustering to cluster Universities into two groups, Private and Public using Kmeans Clustering
- Using SVN, trying to classify cancer whether it is malignant or benign
- Iris flower dataset, using SVN to classify into each of three species of Iris
- Basic problem using PCA for dimensionality reduction
- Creating a simple Recommender System using Python
- Created a model that can predict spam or ham using NLP which basically consists of combining machine learning techniques with text, and using math and statistics to get that text in a format that the machine learning algorithms can understand
- NLP project that is an attempt to classify Yelp Reviews from Yelp dataset in kaggle, into 1 star or 5 star categories based off the text content in the reviews.