Repository containing various Machine learning projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks, and R markdown files (published at RPubs). For a more visually pleasant experience for browsing the portfolio, check out [email protected]
- Install dependencies using requirements.txt.
- Run notebooks as usual by using a jupyter notebook server, Vscode etc.
- ML with Logistic Regression : Using Logistic Regression to predict whether an internet user clicked an ad or not.
- ML with K Nearest Neighbours: Using KNN to classify instances from a fake dataset into two target classes, while choosing the best value for K using the elbow method.
- ML with Decision Trees and Random Forests: Using Decision Trees and Random Forests to predict whether a lender will pay their loan back. Uses publically available data from LendingClub.com
- Movie Recommendations using Recommender Systems: A micro project to build a recommendation system that makes movie recommendations based on user review similarities.
- Support Vector Machines: Using Support vector classifier on Breast Cancer dataset for classifying the type of cancer.
- Predicting Boston Housing Prices: A model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
- Unsupervised Learning: Creating Customer Segments: Analyzing a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for discovering internal structure, patterns and knowledge.
- Ensemble Learning technique: A model which will predict the loan status of a person based on various parameters. Using ensemble of various primitive machine learning model for predicting loan status.
- Model as a Service: Converting Logistic regression model as a service using Flask App.
Tools: scikit-learn, Pandas, Seaborn, Matplotlib.