This project used various machine learning algorithms to predict rainfall.

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This project used Neural Networks to identify if the person would click or not click an ad.

Config files for my GitHub profile.

This project used Neural Networks to classify news based on their title into World, Sports, Business, and Science.

This project used Neural Networks and Deep Learning to classify pictures of checks whether they are blurry or not.

This project used Neural Networks and Deep Learning to Classify Tweets into Positive, Negative, Neutral, Extremely Positive, and Extremely Negative

In this project, I practiced using SQL magic on Jupyter notebook using the Chicago datasets including Chicago Crime Rates, Census Data, and Public Schools on https://data.cityofchicago.org/

In this project, I used a Support Vector Machine algorithm to determine if a cancer is benign or malignant. The model created is 94% accurate using the Jaccard score.

In this project, I calculated the probability of a customer leaving a telecommunication company's subscription using Logistic Regression.

This project used the yfinance, BeautifulSoup, and Pandas module to extract stocks data of Tesla and GameStop. YFinance was used to gather data using a module and BeautifulSoup was made to webscrape data. Finally, the plotly module was used to make an interactive dashboard on the stock prices.

This Project is made form the Application Programming Interface(API) tutorial on IBM Data Scientist Score. This used the API from Coin gecko to get the price of bitcoin. We also used pandas to create a new column converting ms units to date time. Lastly, we use Plotly to create a candle stick plot.

In this project, I created a machine learning model using ridge regression.

This is my project where I used the One Piece Arc Dataset to get some insights such as what are the filler episodes I can skip and finding the most badly paces arc.