Skip to content
View CyprianFusi's full-sized avatar
  • England, UK
  • 10:32 (UTC +01:00)
Block or Report

Block or report CyprianFusi

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
CyprianFusi/README.md
  • 👋 Hi, I’m @CyprianFusi
  • 👀 I’m interested in Data Science and Machine Learning
  • 🌱 I’m currently learning Feature Engineering and AI
  • 💞️ I’m looking to collaborate on Machine Learning projects
  • 📫 How to reach me [email protected]

Popular repositories Loading

  1. Predicting-Heart-Disease-using-K-Nearest-Neighbours Predicting-Heart-Disease-using-K-Nearest-Neighbours Public

    Up to 90% accuracy with just 5 features using KNN algorithm and PCA for feature engineering. The dataset contained less than 1000 observations. The model's accuracy could be improved using more obs…

    Jupyter Notebook 2

  2. Predicting-Heart-Disease-using-Logistic-Regression-Classification-Algorithm Predicting-Heart-Disease-using-Logistic-Regression-Classification-Algorithm Public

    With a precision of 86% and model's CAP curve showing an accuracy of 100%! This means it is capable of correctly predicting 100% of patients with a heart disease after processing 50% of the data. T…

    Jupyter Notebook 2

  3. image_classification_using_GTSRB image_classification_using_GTSRB Public

    Image Classification using the German Traffic Sign Recognition Benchmark (GTSRB) using tensorflow2.0

    Jupyter Notebook 1

  4. Using-our-SQL-skills-to-answer-business-questions Using-our-SQL-skills-to-answer-business-questions Public

    Using complex SQL queries to answer specific business questions. Notably using multiple named subqueries, views to extract data from a database to address specific problems.

    Jupyter Notebook 1

  5. Implementing-a-Spam-Filter-using-Naive-Bayes-Algorithm-from-scratch Implementing-a-Spam-Filter-using-Naive-Bayes-Algorithm-from-scratch Public

    An 87% efficient Spam Filter implemented from scratch using Naive Bayes Algorithm.

    Jupyter Notebook 1

  6. Hello-World Hello-World Public

    Getting started!