Python Tip: Pandas.value_counts(normalize=True) for Balanced Data

Linear Regression: Orange vs Python To perform Linear Regression on a dataset, the first step is to clearly identify the features (independent variables) and the target (dependent variable). The model learns how the target variable is influenced by one or more features. Once the data is prepared: The model is fitted on the dataset It is then trained and tested (typically using a train-test split) Predictions are generated, allowing comparison between actual values and predicted values To evaluate the model’s performance, key metrics such as: R² Score (explains how well the model captures variance) Mean Absolute Error (MAE) (more appropriate here than MSE if that’s what you intended) are calculated. I implemented Linear Regression using both Orange (a visual, no-code tool) and Python (code-based approach) on the same dataset. Interestingly, the results from both approaches were almost identical, with only negligible differences. This highlights an important insight: The underlying mathematics and algorithms remain the same, regardless of whether you use a visual tool like Orange or write code in Python. The difference lies mainly in ease of use, flexibility, and control, not in the core outcomes. #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #AI #GenerativeAI #Automation #FutureOfWork #Learning #Education #EdTech #LifelongLearning #SkillDevelopment

🚀 Day 50/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 2: Decision Tree Regression Today I explored Decision Tree Regression, a supervised machine learning algorithm used to predict continuous values by learning decision rules from the data. Unlike linear models, Decision Tree Regression works by splitting the dataset into smaller subsets based on feature values, forming a tree-like structure. Each split helps the model make more precise predictions by grouping similar data points together. One of the key advantages of Decision Tree Regression is its ability to capture non-linear relationships in the data and provide easy-to-understand decision rules. This algorithm is widely used in applications such as price prediction, demand forecasting, risk analysis, and customer behavior modeling. The learning journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

*Day 20* *The 30-Day AI & Analytics Sprint 🚀* In data processing with Python, a common question is: Why is `map()` sometimes faster than a `for` loop? The main reasons are related to how Python executes each approach: 🔹 1. Implemented in C The map() function is implemented in C internally in Python, which allows it to execute operations faster than a standard for loop that runs through the Python interpreter step by step. 🔹 2. Fewer operations during iteration A for loop performs multiple checks and operations in each iteration, while map() directly applies a function to every element in the iterable. 🔹 3. Cleaner and more functional style map() often leads to shorter and more functional-style code, which can improve readability in certain cases. Example: # Using a for loop numbers = [1, 2, 3, 4] squared = [] for n in numbers: squared.append(n * n) # Using map() numbers = [1, 2, 3, 4] squared = list(map(lambda x: x * x, numbers)) 📌 Note: In modern Python, list comprehension is often more readable and sometimes even faster than both approaches. squared = [x * x for x in numbers] 💡 The best choice usually depends on code readability, performance needs, and the specific use case. #Python #DataAnalytics #AI #MachineLearning #DataScience Instant Software Solutions Muhammed Al Reay Mariam Metawe'e

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! Machine learning models usually have low explainability, hence making it difficult to understand their predictions. This is a serious obstacle in many cases, including industries where black box models are unacceptable. Shapash is a Python library that lets you understand machine learning models by providing an interactive web dashboard. Furthermore, shapash can also be used to generate reports, hence being a significantly useful tool for data scientists and analysts! Visit the link below for more information, and make sure to follow me for regular data science content. 𝗦𝗵𝗮𝗽𝗮𝘀𝗵 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dDiid5Vj 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

I recently attended a Python Library session in our college where we explored two important libraries: Pandas and Matplotlib. During this session, we learned how to clean, transform, and analyze datasets using Pandas. It helped us understand how to handle missing values, organize data, and prepare it for analysis. We also learned how to visualize data using Matplotlib by creating different charts and graphs, which makes data easier to understand and interpret. Additionally, the session introduced us to AI tools that can assist in data analysis and improve productivity. Overall, it was a very informative and practical session that strengthened our understanding of data analytics using Python libraries. I would like to thank our instructor for conducting this valuable class. #Python #Pandas #Matplotlib #DataAnalytics #Learning #AI #PythonLibraries

Many ML model comparisons in papers may be statistically underpowered — and a quick power analysis shows why. I built a small Python library, MLci, to run more rigorous comparisons between models. While testing it on a standard benchmark, I ran a simple question: how many random seeds do we actually need to detect small improvements? Some results from that experiment: - Detecting a ~1% accuracy difference required around 15 seeds. - Detecting a 0.5% difference with ~80% power required about 50+ seeds. But many ML papers report results based on ≤20 seeds, sometimes far fewer. When improvements are this small, that means comparisons can easily become noisy: real improvements may be missed, while occasional “significant” results can appear just by chance. In one test (Random Forest vs Gradient Boosting), a classical test gave p = 0.048 with 20 seeds — seemingly significant. But a Bayesian hierarchical model placed ~99.8% of the posterior mass inside a region of practical equivalence, suggesting the models were effectively indistinguishable. Small effect sizes + stochastic training make careful statistical comparison more important than we often acknowledge. Curious to hear how others handle model comparison and uncertainty in practice. Repo: https://lnkd.in/e8dBPXyv

🚀 #30DaysOfLearning – Day 2 Today, I explored one of the most important foundations in Machine Learning — Data Types and Variables in Python 🐍 At first, they may seem basic, but they are the building blocks of everything in programming and AI. Here’s what I learned: 🔹 Variables are used to store data Example: name = "Nasiff" age = 26 🔹 Common Data Types in Python: String (str) → Text (e.g., "Hello World") Integer (int) → Whole numbers (e.g., 10) Float (float) → Decimal numbers (e.g., 3.14) Boolean (bool) → True or False 🔹 Python automatically detects the data type — no need to declare it manually (which makes it beginner-friendly!) 💡 One key takeaway: Understanding data types helps prevent errors and makes your code more efficient and readable. 📌 Small progress is still progress. Consistency is the goal! #M4aceLearningChallenge #MachineLearning #Python #AI #DataScience #LearningJourney #TechSkills #BeginnersInTech

🚨 Most people got this Python question WRONG! Let’s fix it 👇 Yesterday, I posted a poll on LinkedIn asking: 👉 What is the output of these two codes? x = [10, 20, 30] x.append([40, 50]) print(len(x)) x = [10, 20, 30] x.extend([40, 50]) print(len(x)) 📊 The majority answered: 5 for both ❌ ✅ Correct Answers: 👉 append() → 4 👉 extend() → 5 💡 Why? 🔹 append() adds the entire list as ONE element Result: [10, 20, 30, [40, 50]] → length = 4 🔹 extend() adds elements individually Result:[10, 20, 30, 40, 50] → length = 5 🎯 Key Insight: append = “add as one” extend = “spread and add” 🔥 Why this matters: This small difference can create hidden bugs in: Data preprocessing Feature engineering ML pipelines 💬 Did you get it right? Comment your answer! #Python #DataAnalytics #DataScience #Learning #Coding #InterviewPrep

Most people learn Python for data and immediately jump into complex machine learning models and fancy algorithms. But the real magic? It happens in the basics. The analysts and engineers who move the fastest are not the ones who know the most libraries. They are the ones who deeply understand a few simple tools and use them really, really well. Here's what actually matters when using Python for data work. Readability beats cleverness. Code you wrote 6 months ago should make sense to you today. If it doesn't, it's too clever. Simple, clean logic wins every time. Automate the boring stuff first. The biggest wins I've seen aren't from fancy models they're from automating repetitive data cleaning and reporting tasks that were eating up hours every week. Pandas is not just a library, it's a mindset. Once you truly understand how to think in dataframes, the way you approach every data problem completely changes. Your biggest skill is not syntax, it's knowing WHAT to ask. Python just executes your thinking. The better your questions, the better your analysis. Consistency beats intensity. 30 minutes of Python every day beats a weekend marathon once a month. Always. #Python #DataAnalytics #DataEngineering #PythonForData #DataScience #LearningEveryDay #GrowthMindset #DataCommunity #Pandas #Numpy #MachineLearning #DataAnalytics

Leveling up my Python Data Modeling with Bayesian Inference. 📊 I just wrapped up a deep dive into Bayesian Statistics and how to bring these powerful concepts into a Python workflow. Traditional models give you a point estimate. Bayesian models give you a probability distribution. This is a game-changer when you're dealing with risk, finance, or any field where being "mostly sure" isn't enough. My current Python toolkit for this: PyMC / NumPyro: For defining probabilistic models and running MCMC simulations. ArviZ: For visualizing posterior distributions and checking model health (trace plots are a life-saver!). Bambi: For high-level Bayesian regression that feels like scikit-learn. Biggest takeaway: Bayesian models don't just tell you what the answer is; they tell you how much they trust that answer. Looking forward to applying this to Healthcare/Finance projects! #Python #DataEngineering #BayesianStats #AI #DataModeling #PyMC