Mastering Search Optimization: An Introduction to Iterative Deepening

Depth-First Search (DFS) In DFS, we start from a node and go down a path until we reach a node that has no children. Whenever we run out of moves, we backtrack and explore the sibling of the node. And if there are no siblings, we go for the sibling of the grandparent and so […]

Mastering AI: Enrolling in Udacity’s Artificial Intelligence Nanodegree

Let me tell you about my experience with creating an AI that plays Connect 4. I took an undergraduate CS course during my Masters and we created the AI using a few algorithms. To be honest, I don’t remember all of them, but I do recall using the min-max algorithm. The AI wasn’t too difficult […]

Breaking Down the Beats: A Comprehensive Guide to Using ML Pipelines to Predict Song Release Years

Linear Regression is the most commonly used predictive analysis. It is used to model the relationship between a dependent variable and one or more independent variables. In this project we create an ML pipeline to train a linear regression model to predict the release year of a song given a set of audio features. We […]

Unlocking the Power of MapReduce: Using Python and Apache Spark for Enhanced Data Processing

Hey there! So we decided to create a Word Count application – a classic MapReduce example. But what the heck is a Word Count application, you ask? It’s basically a program that reads data and calculates the most common words. Easy peasy. For example: dataDF = sqlContext.createDataFrame([('Jax',), ('Rammus',), ('Zac',), ('Xin', ), ('Hecarim', ), ('Zac', ), […]

The Art of Election Forecasting: Analyzing the 2012 US Presidential Election with Data Science

Hey there! Let’s talk about this dataset from RealClearPolitics and the US Presidential Election. Before we dive in, let’s get on the same page about a few things: The US Presidential Election happens every four years. There are 50 states in the US and each gets a certain number of electoral votes based on its […]

Making Sense of Big Data: A Beginner’s Guide to Logistic Regression Training in SparkR

Hey there! As your friendly language model, I’m here to help proofread and rewrite your text! Here’s the corrected and rewritten version of your post: Let’s do some Machine Learning with SparkR 1.6! The package only gives us the option to do linear or logistic regression, so for this exercise, we’re going to train a […]

Eliminating the Spam Menace: Building an Effective Machine Learning-Based Spam Filter

Hey there! Let’s talk about spam filters. You know, those annoying emails that keep showing up in your inbox, even though you never signed up for them. Yeah, those. Well, a spam filter is a program that filters out those unwanted emails and messages. Pretty cool, right? So, we’re going to build and evaluate a […]

Transforming Data Analytics: An Honest Review of MITx’s 15.071x Course, The Analytics Edge

Alright, folks! The The Analytics Edge course on edX is almost over and boy, have I learned a lot about Machine Learning in the past 2 months! This MOOC is hands down the best one I’ve taken so far, and I hope my other courses can at least live up to its awesomeness. I first […]

MIT’s Kaggle Competition Sees Fierce Competition Among Enrolled Students, with Overfitting a Concern for Some Top Contenders

It’s an absolute thrill to be in the top 1% of the Kaggle competition hosted by MIT! This contest is no joke, with some seriously experienced ML implementers throwing their hats into the ring. And let me tell you, the top 3 are on a whole other level – they’ve achieved over 90% accuracy, which […]

Revolutionizing Baseball Strategy: Validating Moneyball Predictions through Machine Learning Models

After diving into regression analysis, I couldn’t wait to test my newfound skills on some real-world data. Luckily, Kaggle has just the thing – they’re hosting a competition called ‘History of Baseball’ and, even better, they’ve provided a dataset for it! I had a blast analyzing Paul dePodesta’s predictions and statistical findings, using linear regression […]