#Experiment Data Analysis Techniques

Runnnn🥲 . . #microhelpdesk #westernblot #sdspage

👇 Just drop any comment and I’ll send you the link This Chemistry Lab app is actually wild ⚗️ You try chemical reactions as if you're right in the lab — super visual, and just makes sense. #chemistryapp #scienceforkids #virtualexperiments

It’s changed so much in so little time @verdent__ai - - #Verdent #VerdentAI #Vibecoding #AIcoding - https://www.verdent.ai/?id=700041

1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn 2. Spotify Audio Feature Analyzer Analyze songs by tempo, energy and danceability using Spotify API. Tech Stack: Python, Spotipy, Matplotlib, Plotly 3. YouTube Trending Video Analyzer Discover what makes a video go viral. Tech Stack: Python, Pandas, BeautifulSoup, Seaborn 4. Resume Scanner using NLP Parse and rank resumes based on job description matching. Tech Stack: Python, SpaCy, NLTK, Streamlit 5. Crypto Price Predictor Predict BTC/ETH prices using historical data. Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib 6. Instagram Hashtag Recommender Suggest hashtags based on image captions or niche. Tech Stack: Python, NLP, TF-IDF, Cosine Similarity 7. Reddit Sentiment Tracker Analyze community sentiment on hot topics using Reddit API. Tech Stack: Python, PRAW, VADER, Plotly 8. AI Job Postings Dashboard Scrape and visualize job trends by tech stack and location. Tech Stack: Python, Selenium/BeautifulSoup, Streamlit 9. Airbnb Price Estimator Predict listing prices based on location and amenities. Tech Stack: Python, Scikit-learn, Pandas, XGBoost 10. Food Calorie Image Classifier Estimate calories from food images using CNNs. Tech Stack: Python, TensorFlow/Keras, OpenCV Each project can be completed in 1-2 weekends. #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects

how to learn ml with no experience - been getting asked a ton about this #techcareer #ai #machinelearning #careergrowthtips #careerdevelopment #datascience

third one is my favorite 🤭🚗

The least squares method is a technique used to find the best-fitting line for a set of data points. Suppose we are given pairs of values (x1, y1), (x2, y2), and so on, and we want to describe the relationship between x and y using a linear equation of the form y = ax + b. In most real-world situations, the points will not lie perfectly on a single line, so we measure the error at each point as the vertical difference between the actual value yi and the predicted value axi + b. The least squares method chooses the numbers a and b that make the total squared error as small as possible. We square the errors so that negative and positive differences do not cancel each other out and so that larger errors are penalized more heavily. To find the best values of a and b, we form the sum of all squared errors and treat it as a function of these unknowns. We then minimize this function using calculus, which leads to a system of equations known as the normal equations. Solving this system gives formulas for the slope and intercept in terms of averages and sums computed from the data. Geometrically, the least squares solution can be understood as projecting the observed data onto the space of possible linear models. The method extends naturally to more complicated models, including polynomial regression and multiple regression, and it forms the foundation of many techniques in statistics, economics, and data science. Like and follow @mathswithmuza for more! #math #statistics #square #foryou #stocks

K-Means is a popular clustering algorithm used in data analysis and machine learning to group data points into a specified number of clusters, k, based on their similarity. It works by assigning each data point to the cluster whose center (called a centroid) is closest to it, then recalculating the centroids until the assignments stop changing or the improvement becomes minimal. The main goal of K-Means is to minimize the Within-Cluster Sum of Squares (WCSS)—a measure of how tightly the points in each cluster are grouped around their centroid. Lower WCSS values indicate more compact clusters, meaning the data points within each cluster are close together and well-separated from other clusters. However, WCSS alone doesn’t always give a full picture of how good the clustering is, which is where the average Silhouette Score (avg SIL) becomes useful. The silhouette score compares how similar each point is to its own cluster compared to other clusters, producing values between –1 and 1. A higher avg SIL means that clusters are both compact and well-separated, suggesting an appropriate choice of k. Analysts often use both WCSS and avg SIL together: WCSS helps identify the “elbow point” where adding more clusters stops significantly improving the fit, and avg SIL confirms whether those clusters are meaningful. This combination makes K-Means a simple yet powerful tool for uncovering hidden structure in data. Like this video and follow @mathswithmuza for more! #math #maths #mathematics #learn #learning #foryou #coding #ai #chatgpt #animation #physics #manim #fyp #reels #study #education #stem #ai #chatgpt #algebra #school #highschool #exam #college #university #cool #trigonometry #statistics #experiment #methods

Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai

Drop of whiskey vs drop of blood🤮😱🔬. . . . . . . . . . . . . #fyp #microscope #undermicroscope #reels #viral

Let’s build a Machine Learning Model for Sentiment Analysis! 🤖💬 Using this dataset that I found online, I was able to experiment with building ML Models using Tensorflow and Python. 💻 This is the first time I’ve made a video about building an ML Model, so let me know if you’d like to see more! 🎥 After testing this, I was pretty impressed with the results. Would you like to see that video? 👀

The salt I used in this video is monoammonium phosphate. As you can see at the start of the video, when monoammonium phosphate comes into contact with water, its crystal structure breaks apart almost instantly, dissolving into ammonium and dihydrogen phosphate ions that become surrounded by water molecules. . As the water begins to evaporate on the glass slide, the ions become more concentrated and start to reorganize. Nucleation points form where the first tiny crystals appear, and from those points, larger crystals grow outwards. . Monoammonium phosphate is birefringent (it splits light into different paths depending on crystal orientation) so when I use polarized light, these recrystallized structures look like colorful microscopic geometrical jewels. Those pyramidal crystals under the microscope and with polarized light are just amazing! . For this video, I used an Olympus BX41 microscope at up to 200x magnification. #microscopy #microscope #crystals #polarizedlight