Giannis Tolios’ Post

Machine Learning Project | House Price Prediction I built an end-to-end Machine Learning project to predict house prices using regression techniques. What I did: • Explored and cleaned the dataset • Engineered new features to capture non-linear effects • Encoded categorical variables • Trained and evaluated a regression model using RMSE and R² • Interpreted model coefficients for insights Result: The model achieved a strong R² score, showing good predictive performance. Tools: Python | Pandas | Scikit-learn | Google Colab GitHub Repository: [https://lnkd.in/d-3yTf5P] #MachineLearning #DataScience #Python #Scikit-learn #NumPy #Pandas

Day 22 & 23 | AI/ML Learning Journey | Python —Pandas Topic: Pandas (Practice) Over the last two days, I focused on Pandas fundamentals by working with real datasets. What I covered: •DataFrame methods — head(),tail(),info(), describe() etc. •Loading datasets from Kaggle •Data selection — iloc(position) , loc(label) •Filtering & Query filter •Data cleaning techniques  • Handling missing values  • Removing duplicates  • Converting data types Consistency Challenges. #AIML #DataScience #Pandas #Python #MachineLearning #LearningJourney #Kaggle #DataCleaning

Today's Python session: Revised & solved problems on data structures + more! Key highlights: Tuples are immutable → great for fixed data (unlike lists) Sets for unique elements & fast lookups (O(1) membership) Lambda + map/filter = clean one-liners (e.g. squared evens: list(filter(lambda x: x%2==0, map(lambda x: x**2, nums)))) File handling: with open() as f → auto close, no leaks Try/except blocks saved me from crashes Classes & objects: finally comfortable with init and self Solid foundation strengthening for upcoming ML projects. What’s one Python concept you wish you mastered earlier? #Python #Coding #BuildInPublic #AI

Today I built an end-to-end machine learning regression model in Python to predict housing prices from multiple features (square footage, beds, baths, age). The project covers the full ML workflow: • data loading and preprocessing • train/test split • model training with scikit-learn • evaluation using MSE and R² • visualization of actual vs. predicted values Seeing predictions line up closely with real values is always a good reminder of how powerful even simple models can be when the fundamentals are done right. Tools: Python, pandas, scikit-learn, matplotlib #ComputerScience #MachineLearning #DataScience #Python #LearningByDoing

A lot of people think learning Python for data means memorizing every library. That’s understandable. The ecosystem looks overwhelming at first. But good data work isn’t about knowing everything. It’s about knowing which tool to use, and when. Each library exists for a reason — NumPy for math, Pandas for tables, Polars for speed, Scikit-learn for models, Plotly for interaction, TensorFlow/PyTorch for deep learning. Once you stop treating Python libraries as a checklist and start treating them as purpose-built tools, things get simpler. That’s when data projects move faster and cleaner. [python, datascience, libraries, tools, analytics, machinelearning, learning, clarity] #python #datascience #datatools #machinelearning #analytics

Not all preprocessing is the same. Sometimes, the difference is mathematical. In this project, I focused on feature transformation specifically understanding when to scale and when to normalize. Using Python, I worked with real-world data to: • Apply Min-Max Scaling for distance-based algorithms • Use Box-Cox transformation to correct skewed distributions • Compare distribution behavior before and after transformation • Analyze how statistical assumptions influence model choice The objective wasn’t just transformation. It was understanding why certain models require specific data behavior. Scaling adjusts magnitude. Normalization adjusts distribution. Small preprocessing decisions can significantly influence model stability and interpretability. #DataScience #MachineLearning #RegressionAnalysis #Statistics #FeatureEngineering #Python

🤖 Just built an AI Text Classifier in Python! 🐍 I’ve been diving deeper into machine learning and just finished a project building a text classifier to automatically identify and filter messages. Using Python and scikit-learn, I implemented a Multinomial Naive Bayes model. It’s a fast and efficient way to categorize text—perfect for building moderation systems or sentiment analysis tools. Key takeaways from the project: Data Vectorization: Used TfidfVectorizer to convert raw text into numerical data that the AI can understand [03:57]. Model Training: Trained the model on labeled positive and negative datasets [04:24]. Real-time Prediction: The model can now accurately flag "bad" or "good" messages based on context [06:40]. Check out the full walkthrough here: https://lnkd.in/eUXymV_S #Python #AI #MachineLearning #DataScience #ScikitLearn #Programming #WebDevelopment

I used a simple Python chart today and it reminded me why accuracy can be misleading in machine learning. When a dataset is imbalanced (one class appears way more than the other), a model can look “good” just by predicting the majority class most of the time. Here’s what I did : 1. Plotted the class distribution 2. Checked what a “dumb baseline” accuracy would be if I always predicted the majority class 3. Decided to focus more on Precision, Recall, F1, and ROC-AUC instead of accuracy alone If 90% of the data is one class, a model can get ~90% accuracy while being useless for the minority class (which is often the important one). So, what I've learned is Before training any model, I now always do: Class distribution plot Baseline check Choose metrics that match the real goal ❓ Quick question In a high-stakes problem (fraud, health, risk), would you prioritise precision or recall — and why? #DataScience #MachineLearning #Python #DataVisualization #BuildInPublic

🚀 Day-37 of #100DaysOfCode 🐍 Python Sorting Algorithm Challenge Today I implemented Quick Sort, a powerful and efficient sorting algorithm based on the divide and conquer technique. 🔹 What is Quick Sort? Quick Sort works by: Selecting a pivot element Partitioning the array so that elements smaller than the pivot are on the left and larger ones on the right Recursively applying the same logic to subarrays 🔹 Concepts Practiced: ✔ Recursion ✔ Partitioning logic ✔ In-place swapping ✔ Divide and Conquer strategy 🔹 Approach: Choose the last element as the pivot Rearrange elements around the pivot Recursively sort the left and right partitions 🔹 Key Insight: Quick Sort has an average time complexity of O(n log n) and is widely used due to its speed and in-place sorting nature. Implementing such algorithms helps deepen understanding of efficient data processing and algorithmic thinking 💡 #Python #QuickSort #SortingAlgorithms #DivideAndConquer #CorePython #DSA #100DaysOfCode #Day37 #LearnPython #CodingPractice #PythonDeveloper

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗛𝗮𝗯𝗶𝘁 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝘀 𝗠𝗮𝗻𝘆 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 Before training any model, always look at a few rows of your data. df.head() You immediately notice: wrong formats unexpected values columns that don’t make sense Many problems are visible in seconds if you simply look at the data first. Two minutes of checking can save hours of confusion later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic