Hello There!,Thank you for visiting my profile! Hope you'll find some interesting stuff here! I've created Repositories containing portfolio of data science/ machine Learning projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks.

Note: Data used in the projects (accessed under data directory) is for demonstration purposes only.

1. 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.
2. Supervised Learning: Finding Donors for CharityML: Testing out several different supervised learning algorithms to build a model that accurately predicts whether an individual makes more than $50,000, to identify likely donors for a fictional non-profit organisation.
3. 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.

A. Python:

1. Titanic Dataset - Exploratory Analysis: Exploratory Analysis of the passengers onboard RMS Titanic using Pandas and Seaborn visualisations.
2. Stock Market Analysis for Tech Stocks: Analysis of technology stocks including change in price over time, daily returns, and stock behaviour prediction.
3. 2016 US General Election Poll Data Analysis: Very simple analysis of 2016 US General Election Poll data.
4. 911 Calls - Exploratory Analysis: Exploratory Data Analysis of the 911 calls dataset hosted on Kaggle. Demonstrates extraction of useful features from different variables. --Tools: Pandas, Folium, Seaborn and Matplotlib

A. Python:

1. ML with Logistic Regression: Using Logistic Regression to predict whether an internet user clicked an ad or not.
2. 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.
3. 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
4. Movie Recommendations using Recommender Systems: A micro project to build a recommendation system that makes movie recommendations based on user review similarities.

If you liked what you saw, want to have a chat with me about the portfolio, work opportunities, or collaboration, shoot an email at kunalsaga@outlook.com