James Pardue’s Post

Day 60 - 100 Day Data Science Today, I explored more plots with the help of the seaborn library in python and its different use-cases. The following were the major topics I covered today: 1) Lineplots - spining and despining, temp axes 2) Different methods of adding multiple plots 3) Color pallets 4) Relational plots 5) Regression plots 6) Matrix plots Today was very insightful and interesting, introducing me to new aspects of exploratory data analysis. You can refer to an article I wrote on the same explaining each of these with relevant code and plots. hashtag #datascience #dataanalytics #dataviusalisation #learningjourney #python

I have just submitted my reproduction of Cara Thompson's work, #BarBeRight, to the plotnine contest. Here is the link to the tutorial: https://lnkd.in/gBtktGsR Credit goes to Cara Thompson for her insightful tutorials and superb work on Data Visualization. #plotnine #datascience #rstats #python

This article takes a closer look at the world of crime, looking at how weapons and gender affect things. Using visualizations, it reveals facts regarding our communities. Check it out and share your thoughts! #DataInsights #CrimeAnalysis #datavisualization #python #datamining

Hello everyone! In week 7 with Digital Skola, I will be sharing about statistical theory, hypotheses, p-values, data visualization with Python, and many other exciting topics. Stay tuned! #DigitalSkola #LearningProgressReview #DataScience

Day 59 - 100 Day Data Science Today, I went on to explore another Python library that helps one to make sense out of numbers, in the form of graphs. Seaborn is a Python library that is usually used for statistical plotting. I first analysed different types of plots that exist. Distribution plots Relational plots Regression plots Categorical plots Multi-plot grids Matrix plots Post this, I analysed different types of plots that exist and the use cases for each of them. You can refer to an article I wrote on the same explaining each of these with relevant code and plots. #datascience #dataanalytics #dataviusalisation #learningjourney #python

In my learning path of data analysis, I use #R more frequently, but it is very nice when data visualization can be done in #Python using grammar of graphics (ggplot2) through #Plotnine.

Just completed an engaging project on credit data! I focused on data preprocessing and harnessed the power of logistic regression for predictions. 📈🔍 Take a look at the comparison between normal and preprocessed data, along with the Python implementation. I’d love to hear your thoughts! 🤝 #DataScience #MachineLearning #Python #Prediction #EDA github Link : https://lnkd.in/g5eCt4ii The Datasets and Python Notebook provided here!

Day 3 of #100daysoflearningchallenge 📊 So today, I learnt about 'Data Manipulation with Pandas 🐼' So basically this is how it works, it is like a toolbox that lets you effortlessly clean, organize, and analyze data in Python. That's Pandas! Whether you're a beginner or a pro, it's a game-changer for anyone working with data. #DataScience #PandasPower #DataAnalysis Ingressive For Good DataCamp

Excited to Share My Latest Data Cleaning Portfolio Project In this project, my focus was on: 1. Importing the dataset into Python for cleaning and transformation. 2. Modifying the schema of the existing dataset using pandas. 3. Handling missing/null values. 4. Changing the data types of columns. 5. Creating derived columns. I plan to share a few EDA projects in the future. Suggestions and feedback are welcome! #DataScience #DataCleaning #Pandas #Python #DataAnalysis #MachineLearning #DataTransformation #DataEngineering