Data science project consisting of 4 parts: 1-Web Scraping 2-Data Preprocessing 3-Data Visualization 4-Machine Learning
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Updated
Jul 23, 2024 - Jupyter Notebook
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.
Data science project consisting of 4 parts: 1-Web Scraping 2-Data Preprocessing 3-Data Visualization 4-Machine Learning
So, market size analysis is a crucial aspect of market research that determines the potential sales volume within a given market
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.
A Heart Attack Risk Prediction Project
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 machine-learning model predicting crypto price from the time-series data
Scraping Data Science jobs, performing EDA, building a regression model and productionizing it.
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.
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.
Master's Final Degree Project on Artificial Intelligence and Big Data
Datasets I've collected which can be used for data science and database projects, study, learning, and practice.
The project is to recognize fraudulent credit transactions. You only need to put the dataset and model will detect the fraudulent credit transactions.
Random forest analysis of match statistics and team performances in five seasons of the English Premier League (EPL)
Personal data science project in Python visualizing and predicting bicycle counts in Vancouver, BC
Using exploratory data analysis and k-means clustering to analyze competitive balance in soccer/football