- Видео 203
- Просмотров 154 204
AnalytiCode
США
Добавлен 20 фев 2014
Welcome to the Ultimate Hub for Analytical Data Science & Measurement Science!
Howdy! I’m Chris Pulliam, a PhD Measurement Scientist with a passion for innovating at the intersection of measurement and data science. On this channel, I combine my 10+ years in the lab with expertise in data science and teaching to bring you:
- Signal processing tutorials
- Machine learning techniques
- Scientific data visualization
- Near IR and Mass Spectrometry data interpretation
- Analytical chemistry, and more!
Whether you're diving into data or just data curious, there’s a video or playlist here for you!
Disclaimer: Opinions are my own, not my employers.
Visit my Medium Blog to see many of these ideas written out: medium.com/@chrisjpulliam
If you want to chat find me on LinkedIn: www.linkedin.com/in/chrispulliam3/
Howdy! I’m Chris Pulliam, a PhD Measurement Scientist with a passion for innovating at the intersection of measurement and data science. On this channel, I combine my 10+ years in the lab with expertise in data science and teaching to bring you:
- Signal processing tutorials
- Machine learning techniques
- Scientific data visualization
- Near IR and Mass Spectrometry data interpretation
- Analytical chemistry, and more!
Whether you're diving into data or just data curious, there’s a video or playlist here for you!
Disclaimer: Opinions are my own, not my employers.
Visit my Medium Blog to see many of these ideas written out: medium.com/@chrisjpulliam
If you want to chat find me on LinkedIn: www.linkedin.com/in/chrispulliam3/
Want to DETECT Coffee Fraud? Watch This Now!
Curious about whether your coffee is authentic or adulterated? In this video, I’ll guide you through using Near Infrared (NIR) spectroscopy data with Python to uncover the truth! You’ll learn step-by-step how to:
1. Import and process your data using Pandas and Scikit-learn (sklearn).
2. Build a Support Vector Classifier (SVC) model to predict coffee adulteration.
Whether you’re a data enthusiast, chemist, or coffee lover, this tutorial simplifies complex concepts into practical, hands-on techniques you can try yourself. Let’s dive into the science of detecting adulterated coffee!
data links: github.com/chrisp33/Analytical_YT_Tutorials/tree/main/Data
1. Import and process your data using Pandas and Scikit-learn (sklearn).
2. Build a Support Vector Classifier (SVC) model to predict coffee adulteration.
Whether you’re a data enthusiast, chemist, or coffee lover, this tutorial simplifies complex concepts into practical, hands-on techniques you can try yourself. Let’s dive into the science of detecting adulterated coffee!
data links: github.com/chrisp33/Analytical_YT_Tutorials/tree/main/Data
Просмотров: 98
Видео
How to use Pandas Groupby Like a Pro!
Просмотров 25321 час назад
Pandas Groupby is a powerful tool for Python based data analysis. In this video I will demonstrate several techniques for using Pandas Groupby with various chemistry examples! 0:00 Introducing Groupby 0:42 Basic Groupby operations 1:49 Multilevel Groupby 2:50 Custom Groupby aggregations 3:40 Fill missing values with Groupby 4:57 Using Custom Functions with Groupby
Why You Should NEVER Use Pandas’ inplace Argument!
Просмотров 8814 дней назад
Is the Pandas inplace Argument Slowing You Down? In this video, I reveal the hidden dangers of using the Pandas inplace argument-and why it might be holding back your data analysis game. If you love method chaining for clean, efficient code, you’ll want to rethink inplace! I'll show you exactly how this argument can break your flow and lead to unpredictable behavior in your scripts. Curious to ...
How PCA and Signal Processing Can Save Your Near IR Data!
Просмотров 14221 день назад
Are you making this common mistake when analyzing Near IR data? In this video, we uncover a systematic error I discovered while running PCA on my Near IR coffee data. Before we can dive into building an accurate classification model, this error needs to be addressed. But how do you catch it, and more importantly, how do you fix it? I'll show you step-by-step how to use PCA to detect and correct...
Unlocking Insights: How to Perform EDA on 110 Coffee Samples
Просмотров 29328 дней назад
Exploratory Data Analysis on Coffee Adulteration: Unveiling Hidden Patterns! In this follow-up to my coffee adulteration video, I dive into Exploratory Data Analysis (EDA) on over 100 coffee samples, all analyzed with a Trinamix Near Infrared Spectrometer (NIRS). In this video, I demonstrate how heatmaps and advanced signal processing techniques can reveal novel features hidden within the data....
Can You Detect Fake Coffee? Building an Adulteration Dataset!
Просмотров 116Месяц назад
In this video, I demonstrate how to generate a dataset of authentic and intentionally adulterated coffee samples using a blend of fresh ground coffee and cornmeal. By carefully mixing these ingredients, I create synthetic samples that simulate real-world coffee adulteration-perfect for testing with our portable Near Infrared Spectrometer (NIRS) from Trinamix. Watch as I walk through the process...
How to Use Python & F-Test for NIR Data Feature Selection like a pro!
Просмотров 141Месяц назад
In this video, we dive into the exciting world of feature selection for Near-Infrared (NIR) spectroscopy data using Python's powerful F-test. Learn how to uncover the most significant features in your spectral data and boost the accuracy of your machine learning models. We'll guide you step-by-step on implementing the F-test in Python, helping you understand key concepts and apply them to real-...
How to Use Python Pandas to Merge, Join, and Concatenate Like A Pro!
Просмотров 175Месяц назад
Storing chemistry data often involves multiple data tables, whether it’s from different experiments, measurements, or metadata. Managing and combining this data can be a challenge, but with Pandas, it becomes simple using Merge, Join, and Concatenate functions. In this video, I’ll walk you through how to: ✅ Merge data on single and multiple keys ✅ Explore practical use cases for Merge, Join, an...
How A Chemist Tests For ADULTERATED Coffee at Home! (Proof of Concept)
Просмотров 601Месяц назад
In this video, we explore how a portable Near-Infrared (NIR) spectrometer can be used to detect coffee adulteration. Watch as we break down the process of analyzing the Near IR spectra using Python, uncovering differences in coffee composition that could indicate impurities or additives. Whether you're a coffee lover, a scientist, or just curious about practical spectroscopy, this tutorial show...
How to Use Pandas to Clean String Data!
Просмотров 233Месяц назад
text data can be quite messy but Pandas and python is a great quickly cleaning it. Recommended Video: ruclips.net/video/a39iVtDYjlE/видео.html 0:00 Intro 0:16 Getting into the notebook 0:24 Basic String Methods 1:00 Access dataframe columns for cleaining 2:34 Converting Datatypes String to Numerical 3:40 Extracting and Splitting Strings and Regex 6:25 Advance String Methods 7:24 Closing
How to use Kmeans and Ipywidgets to unlock new data insights!
Просмотров 118Месяц назад
In this video, we dive into how k-means clustering can be used to create labels for completely unlabeled data. The dataset includes authentic coffee samples and known adulterants, but none of it was labeled! Watch as we use Python to apply unsupervised machine learning on Near-Infrared (NIR) spectra, uncovering hidden patterns and distinguishing genuine coffee from its impure counterparts. This...
How to Handle Missing Values in Pandas Like a Pro!
Просмотров 168Месяц назад
In this video I will demonstrate how to handle missing values like a professional using Python Pandas!. By the end of this video you know how to find missing data, drop missing data, and fill missing data using Python pandas methods. If you have a completely empty dataframe check this video out: ruclips.net/video/WdxgSF-gvn0/видео.html 0:00 Intro 0:16 Setting up the notebook 0:49 Spotting missi...
Detecting Coffee Outliers with Near IR and Python
Просмотров 1652 месяца назад
In this video, I demonstrate how to use Principal Component Analysis (PCA) to detect potential chemical outliers in Near-Infrared Spectroscopy (NIRS) data from coffee samples. Watch as I build a PCA model using part of the dataset and then apply it to a second set of data to uncover hidden patterns and outliers. If you're interested in NIRS data analysis or PCA modeling, this video will provide...
Data Validation in Python: Using df.empty to Ensure Clean Data
Просмотров 1062 месяца назад
Are you a chemist looking to enhance your data analysis skills in Python? In this tutorial, we’ll demonstrate how to validate your Pandas DataFrame using the df.empty method. Ensuring data quality is crucial, especially when working with analytical datasets like Near IR spectroscopy. Learn how this simple yet powerful tool can prevent errors and improve your data validation process in your Pand...
Near IR + Python data analysis of 20 Coffees!
Просмотров 1822 месяца назад
Near IR Python data analysis of 20 Coffees!
The Truth About Standard Scaler: Can It Correct Skewed Data?
Просмотров 912 месяца назад
The Truth About Standard Scaler: Can It Correct Skewed Data?
Boost Performance with Python Feature Selection with NIRS data
Просмотров 2572 месяца назад
Boost Performance with Python Feature Selection with NIRS data
How to Boost Plastic Detection Accuracy with NIR Spectroscopy!
Просмотров 1223 месяца назад
How to Boost Plastic Detection Accuracy with NIR Spectroscopy!
Build Better Models with PCA | NIRS Plastic Prediction!
Просмотров 1413 месяца назад
Build Better Models with PCA | NIRS Plastic Prediction!
Let's explore total dissolved solids in the LA River!
Просмотров 513 месяца назад
Let's explore total dissolved solids in the LA River!
Evaluate Spectroscopy Signal with Percent RSD!
Просмотров 1544 месяца назад
Evaluate Spectroscopy Signal with Percent RSD!
Evaluate Sample Replicates with Pearson Correlation and Python!
Просмотров 1805 месяцев назад
Evaluate Sample Replicates with Pearson Correlation and Python!
Boosting Analytical Data with Derivative Signal Processing!
Просмотров 1925 месяцев назад
Boosting Analytical Data with Derivative Signal Processing!
Analyzing Vacuum Soil and Dryer Lint with Near IR Spectroscopy!
Просмотров 1935 месяцев назад
Analyzing Vacuum Soil and Dryer Lint with Near IR Spectroscopy!
Boosting Job Performance with Peloton: My Success Story
Просмотров 685 месяцев назад
Boosting Job Performance with Peloton: My Success Story
Catch Lemons BEFORE THEY ROT with Near IR!
Просмотров 1476 месяцев назад
Catch Lemons BEFORE THEY ROT with Near IR!
Chemical Analysis at Home: Analyzing Plastic Containers!
Просмотров 2076 месяцев назад
Chemical Analysis at Home: Analyzing Plastic Containers!
NIRS Analysis and Python Data Insights of Dryer Sheets!
Просмотров 1176 месяцев назад
NIRS Analysis and Python Data Insights of Dryer Sheets!