Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data. Data scientists perform data analysis and preparation, and their findings inform high-level decisions in many organizations.
This documentation is like a quick snapshot of my project in the data field, showing off my skills and know-how in this area.
A Heart Attack Risk Prediction Project
Random forest analysis of match statistics and team performances in five seasons of the English Premier League (EPL)
The welcome content for the Halerium platform
We solve a regression problem in which it consists of calculating the health insurance charge in the United States Where we will break down the project into 5 phases: Exploratory Analysis. Feature Engineering. Selection of the ideal model. Development of the final model. Creation of a web application in streamlit.
The project is to recognize fraudulent credit transactions. You only need to put the dataset and model will detect the fraudulent credit transactions.
Master's Final Degree Project on Artificial Intelligence and Big Data
Data science project consisting of 4 parts: 1-Web Scraping 2-Data Analysis 3-Data Visualization 4-Machine Learning
Scraping Data Science jobs, performing EDA, building a regression model and productionizing it.
A machine-learning model predicting crypto price from the time-series data
Using exploratory data analysis and k-means clustering to analyze competitive balance in soccer/football
Datasets I've collected which can be used for data science and database projects, study, learning, and practice.
Personal data science project in Python visualizing and predicting bicycle counts in Vancouver, BC
Customer segmentation is dividing the customers into segments based on RFM scores. In this project I've used RFM model in R to generate RFM score.
Alexa Sentiment Analysis interprets user emotions from their interactions with Amazon's virtual assistant. By analyzing speech patterns and language, it categorizes sentiments as positive, negative, or neutral. This enhances Alexa's responses, ensuring more personalized and effective interactions.