deepsparkchaker Goto Github PK

followers: 8.0 following: 3.0 repos: 63.0 gists: 1.0

Name: DeepSparkChaker

Type: User

Company: Aviation School Borj el Amri-latice lab

Bio: I am a data scientist. I am very passionate about this field; ,it gives me the chance to mix all my skills (managerial, Math,engineer..)

Location: Tunis-Tunisia

Blog: https://www.linkedin.com/in/bannour-c-66887616b/

Data Science Guide:

Cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics mode.

CRISP-DM breaks the process of data mining into six major phases:

Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment

The sequence of the phases is not strict and moving back and forth between different phases as it is always required. ==>The objective is to share a real guide to master the science of data from 0 to hero:

Master Python for Data science
EDA
Modeling Machine learning Algorithms Using (Regression , Classification..)
NLP
Big Data Analytics tools(spark)
......and many more

1- Data visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. Common general types of data visualization:

Charts
Tables
Graphs
Maps
Infographics
Dashboards

More specific examples of methods to visualize data:

Area Chart
Bar Chart
Box-and-whisker Plots
Bubble Cloud
Bullet Graph
Cartogram
Circle View
Dot Distribution Map
Gantt Chart
Heat Map
Highlight Table
Histogram
Matrix
Network
Polar Area
Radial Tree
Scatter Plot (2D or 3D)
Streamgraph
Text Tables
Timeline
Treemap
Wedge Stack Graph
Word Cloud
And any mix-and-match combination in a dashboard!

DeepSparkChaker's Projects

genre-streamlit

github-commands-lines

A list of commonly used Git commands

givemecredit_top5_solution_kaggle

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.

income_prediction

jenkins

kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

keras-attention-mechanism

Attention mechanism Implementation for Keras.

kmeans_scartch

machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data

machinelearning

Machine learning for beginner(Data Science enthusiast)

michigan-university--introduction-to-data-science-in-python

michigan-university-applied-plotting-charting-data-representation-in-python

mlops

Learn how to responsible deliver value using MLOps.

named-entity-recognition

Name Entity Recognition using Python and Keras

natural-language-processing-specialization

This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera

ner_spacy_app

Flask app of named entity recognition with spacy

nlp-usescases

nlp_andrew_specialization

pythoncheatsheetfordatascience

sequence_model_andrew

spam_detection_fastapi

This api will let us detect spam messages

spam_detection_fastapi_streamlit_docker_compose

This repo contains code for a small webapp to predict if we have a spam messages . The application consists of a frontend and a backend part and is used to demonstrate how to serve ML models as microservices through an API. The frontend is built with Streamlit and used to acquire messages.

deepsparkchaker Goto Github PK

Data Science Guide:

1- Data visualization

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