All Projects → TrainingByPackt → Data Science Projects With Python

TrainingByPackt / Data Science Projects With Python

Licence: mit
A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Data Science Projects With Python

Machinelearningcourse
A collection of notebooks of my Machine Learning class written in python 3
Stars: ✭ 35 (-82.32%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn, numpy
Machine Learning With Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Stars: ✭ 2,197 (+1009.6%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn, numpy
Pymc Example Project
Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.
Stars: ✭ 90 (-54.55%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn, numpy
Data Science Hacks
Data Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.
Stars: ✭ 273 (+37.88%)
Mutual labels:  jupyter-notebook, data-science, pandas, pandas-dataframe, numpy
Machine Learning Alpine
Alpine Container for Machine Learning
Stars: ✭ 30 (-84.85%)
Mutual labels:  jupyter-notebook, pandas, scikit-learn, numpy
Python for ml
brief introduction to Python for machine learning
Stars: ✭ 29 (-85.35%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn
Mlcourse.ai
Open Machine Learning Course
Stars: ✭ 7,963 (+3921.72%)
Mutual labels:  data-science, pandas, scikit-learn, numpy
Cheatsheets.pdf
📚 Various cheatsheets in PDF
Stars: ✭ 159 (-19.7%)
Mutual labels:  jupyter-notebook, pandas, scikit-learn, numpy
Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
Stars: ✭ 8,329 (+4106.57%)
Mutual labels:  jupyter-notebook, data-science, pandas, pandas-dataframe
Data Science Complete Tutorial
For extensive instructor led learning
Stars: ✭ 1,027 (+418.69%)
Mutual labels:  jupyter-notebook, pandas, scikit-learn, numpy
Py
Repository to store sample python programs for python learning
Stars: ✭ 4,154 (+1997.98%)
Mutual labels:  jupyter-notebook, pandas, pandas-dataframe, numpy
Seaborn Tutorial
This repository is my attempt to help Data Science aspirants gain necessary Data Visualization skills required to progress in their career. It includes all the types of plot offered by Seaborn, applied on random datasets.
Stars: ✭ 114 (-42.42%)
Mutual labels:  jupyter-notebook, data-science, pandas, numpy
Dat8
General Assembly's 2015 Data Science course in Washington, DC
Stars: ✭ 1,516 (+665.66%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn
Pythondatasciencehandbook
The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.
Stars: ✭ 31,995 (+16059.09%)
Mutual labels:  jupyter-notebook, pandas, scikit-learn, numpy
Crime Analysis
Association Rule Mining from Spatial Data for Crime Analysis
Stars: ✭ 20 (-89.9%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn
Ds and ml projects
Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.
Stars: ✭ 56 (-71.72%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn
Python Cheat Sheet
Python Cheat Sheet NumPy, Matplotlib
Stars: ✭ 1,739 (+778.28%)
Mutual labels:  data-science, pandas, scikit-learn, numpy
Data Science Portfolio
Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.
Stars: ✭ 559 (+182.32%)
Mutual labels:  jupyter-notebook, data-science, pandas, scikit-learn
Just Pandas Things
An ongoing list of pandas quirks
Stars: ✭ 660 (+233.33%)
Mutual labels:  jupyter-notebook, data-science, pandas, pandas-dataframe
Credit Risk Modelling
Credit Risk analysis by using Python and ML
Stars: ✭ 91 (-54.04%)
Mutual labels:  jupyter-notebook, pandas, scikit-learn, numpy

GitHub issues GitHub forks GitHub stars PRs Welcome

Data Science Projects with Python

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.

Data Science Projects with Python by Stephen Klosterman

What you will learn

  • Install the required packages to set up a data science coding environment
  • Load data into a Jupyter notebook running Python
  • Use Matplotlib to create data visualizations
  • Fit a model using scikit-learn
  • Use lasso and ridge regression to regularize the model
  • Fit and tune a random forest model and compare performance with logistic regression
  • Create visuals using the output of the Jupyter notebook
  • Use k-fold cross-validation to select the best combination of hyperparameters

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

  • Processor: Intel Core i5 or equivalent
  • Memory: 4 GB RAM or higher
  • Storage: 35 Gb or higher

Software requirements

  • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
  • Browser: Google Chrome/Mozilla Firefox Latest Version
  • Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter notecourse on your browser)
  • Python 3.4+ (latest is recommended) installed (from https://python.org)
  • Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so on)
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].