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anomaly_detection

In this study we will proceed to the elaboration of an anomaly detection. For this, we retrieved a database on breast cancer. This dataset consists of 32 columns. Our analysis will be based mainly on the "diagnosis" column allowing us to know the condition of the cancer.

book

Book PDF

chat-haruhi-suzumiya

Chat凉宫春日, 由李鲁鲁, 冷子昂等同学开发的模仿二次元对话的聊天机器人。

chinese-alpaca-lora

骆驼:A Chinese finetuned instruction LLaMA. Developed by 陈启源 @ 华中师范大学 & 李鲁鲁 @ 商汤科技 & 冷子昂 @ 商汤科技

clustering_exercise

cnn_images

Using convolutional neural network to analyze a set of vegetable images via the Kaggle API.

confusion_matrix_exercise

detect money laundering using the features of a transaction

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

fraud_detection_on_bank_payments

We recovered a database of fraudulent transactions from a dataset. This database corresponds to payments from various customers made at different times and with different amounts.

gogocode

GoGoCode is a transformer for JavaScript/Typescript/HTML based on AST but providing a more intuitive API.

normal_chatbot

Creation of a simple chatbot for international student office center

pca_exercise

predict_machine_failure

predicton_of_subscription

Using logistic regression, decision tree and naive bayes on R Studio to predict whether a client will subscribe to the bank or not

recommendation_system_for_books

Our dataset comes from the world’s largest site for readers and book recommendations. The dataframe we decided to work on is a dataframe that include 11 features about the best 1500 books of the 21st century : index, book_name, author_name, book_genre, year_published, edition_language, avg_rating, no_of_rather, score, no_of_ppl_voted, book_url.

reinforcement_exercise

rshiny_visualization_housing_price

Using Rshiny to visualise OECD housing price during different time in different regions

streamlit_sentiment_analysis

Using streamlit library of python to create a mini web for tweet sentiment analysis

text_sentiment_analysis_python

This project uses the Python programming language for text sentiment analysis. The emotional content of a given text is analysed in order to obtain an overall emotional evaluation of the text.

time_series_for_airport_traffic_forecast

Forecast airport traffic time series: The database is presented as follows: a column giving the year, a second column corresponding to the month and a third gives us the number of flights per month.