https://github.com/dbriane208/python-for-data-science

Machine Learning and Data Science repository. Love crafting Machine Learning models.
https://github.com/dbriane208/python-for-data-science

data-analysis data-science data-visualization machine-learning numpy pandas python seaborn

Last synced: 12 months ago
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Machine Learning and Data Science repository. Love crafting Machine Learning models.

• Host: GitHub
• URL: https://github.com/dbriane208/python-for-data-science
• Owner: Dbriane208
• Created: 2023-05-29T14:49:07.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-29T12:03:00.000Z (over 1 year ago)
• Last Synced: 2024-06-29T13:25:36.490Z (over 1 year ago)
• Topics: data-analysis, data-science, data-visualization, machine-learning, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 21.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Python-for-Data-Science

Machine Learning and Data Science repository. This repository contains machine learning projects from begginer to advance. By the end one will understand how to build an end-to-end machine learning project using python and jupyter notebook. 

# Steps followed in creating the project

1. Form a hypothesis

2. Find and explore the data

3. Do data pre-processing

4. Do data visualization

5. Train the model

# Installation

To follow through the projects you need to have the following tools in your device :

1. Python 3+ locally

2. Python packages

   - Pandas

   - Numpy

   - Seaborn

   - Matplotlib

  

# The Data Science and Machine Learning workflow

The Data science workflow consists of :

   1. Scope of the project : This clearly tells the aim of the project and directions the project will take.

   2. Gathering Data : A project is as strong as the underlying data. It is the foundation of analysis. Data sources included files,Databases, Websites,APIs.

   3. Cleaning Data : It is important in producing accurate and relieable results. Data cleaning tasks include :

          - Correcting data types

          - Imputing missing data

          - Dealing with inconsistent data

          - Reformating the data

   4. Exploring Data : Explatory data analysis helps in understanding and visualizing the data your're working with. EDA tasks may include :

          - Slicing and dicing the data

          - Summarizing the data

          - Visualizing the data

   5. Modeling Data : Involves structuring and preparing data for specific modelling techniques and applying models to make predictions. Data modelling tasks include:

          - Restructuring the data

          - Feature engineering (adding new fields)

          - Applying Machine Learning Algorithms

   6. Sharing Insights : Involves summarizing your key findings and sharing insights with end users and stakeholders.

          - Reiterate the problem

          - Interprate the results of your analysis

          - Share recommendations and insights

          - Focus on potential impact and not technical deatails

          - Deploying the model or put it into production