🚗 Car Price Prediction using Machine Learning Happy to share my ML project where I built a model to predict car prices based on various features. 🔧 Technologies Used: - Python - Scikit-learn - Pandas, NumPy 📌 Key Features: ✔ Data preprocessing ✔ Model training & evaluation ✔ Prediction system 🔗 GitHub Repository: https://lnkd.in/g_yrducF 🎥 Project Demo: [Paste your video link here] #MachineLearning #Python #DataScience #CodeAlpha #AI

I just published a new project on Kaggle exploring stock data using Python. I worked with Apple and AMD data using yfinance and Pandas to pull historical prices, visualize trends, and look at trading activity over time. Looking at AMD’s trading volume was especially interesting. The spikes over time start to reveal patterns in market activity and investor behavior in a way that’s much clearer when you actually see it plotted out. If you’ve worked with financial data or time-series analysis, I’d be curious to hear how you approach this kind of exploration. Kaggle: https://lnkd.in/er2FpTRq #DataAnalytics #Python #DataScience #Kaggle #Pandas #TimeSeries #AI #TechCareers

Built a Car Sales Prediction model using Machine Learning 🚗📊 • Analyzed dataset and visualized trends • Applied regression models for prediction • Evaluated performance using metrics This project improved my understanding of data analysis and business insights. 🔗 GitHub: https://lnkd.in/gBg6zAEp #DataScience #MachineLearning #Python #Analytics

Today, I focused on working with NumPy arrays. Building a solid foundation for data manipulation and analysis. Here’s what I practiced: 🔹 Created a 1D array with values from 1 to 15 🔹 Built a 2D array (3×4) filled with ones 🔹 Generated a 3×3 identity matrix 🔹 Explored key array properties like shape, type, and dimensions 🔹 Converted a regular Python list into a NumPy array This session helped me better understand how data is structured and handled in numerical computing. Getting comfortable with arrays is definitely a crucial step toward more advanced data analysis and machine learning tasks. Looking forward to building on this momentum 💡 #AI #MachineLearning #Python #NumPy #DataAnalysis #M4ACE

Here’s a new beginner-friendly tutorial I wrote on Geo AI for Industrial Engineering using Python. It walks through a simple hands-on mini-project: preparing location data, running light clustering, and visualizing the results on an interactive map. The goal is to make Geo AI feel practical and approachable, especially for students and early learners who want to see how spatial intelligence can support real decision-making. A good reminder that sometimes the best way to understand a new concept is not to start with heavy theory, but to build something small that makes the idea visible. https://lnkd.in/gTAs_5Bb #GeoAI #IndustrialEngineering #Python #DataVisualization

Day 2 of learning Machine Learning. Today I worked on a simple linear regression model using Python in Jupyter Notebook. The idea was straightforward: - Input (x): house size - Output (y): price Model used: f(x) = wx + b I understood how: - Training data is structured (x_train, y_train) - Parameters (w, b) define the relationship - The model uses this to make predictions on new inputs Also got hands-on with NumPy and basic plotting using Matplotlib. Still very early, but it's becoming clearer how data is converted into predictions. #MachineLearning #AI #Python #LearningInPublic

I built a machine learning web app that predicts whether a loan will be approved or rejected based on applicant financial data.In this project, I used Python, Scikit-learn, and Streamlit. I trained multiple models including Naive Bayes, KNN, and Logistic Regression, and selected the best-performing model for final deployment. Link:-https://lnkd.in/giKaMpyz

New video out today — this time on the PyCharm channel! We built a TensorFlow model from scratch, step by step, using a Jupyter notebook inside PyCharm. The goal was to make it genuinely beginner-friendly: no assumptions, no hand-waving, just actual code that runs. What we covered: — Loading and visualising a real dataset (fashion images — much more fun than MNIST) — Building and comparing two different model architectures — Evaluating accuracy and actually understanding what the number means — Digging into where the model gets confused and why (spoiler: shirts and pullovers are hard) — Using PyCharm's AI assistant to speed up the parts that don't need to be slow One thing I always try to do in these videos: show the thinking, not just the result. Why do we normalise the pixel values? Why ReLU? Why does the second model not actually justify its extra training time? If you're getting started with TensorFlow or just want to see how a clean ML workflow looks inside a proper IDE — this one's for you. 👉 https://lnkd.in/eAXj8K-F #TensorFlow #MachineLearning #Python #PyCharm #JetBrains JetBrains

This video is a collaboration with JetBrains — produced for the PyCharm channel as part of an ongoing DevRel partnership. It's a good example of what that kind of work looks like in practice: a technically honest, hands-on tutorial that serves the audience first and happens to showcase the tool naturally along the way. This is exactly the kind of content production I'm open to doing with more teams in 2026. If you work at a developer tools, AI, or robotics company and want to reach a technical audience through video content that actually gets watched - I'd love to talk. The Back to Engineering channel covers Physical AI, robotics, and embedded systems. But the production model of deep technical content, learn-in-public format, real code on screen translates across the developer tooling space. #DevRel #DeveloperEducation #TechnicalContent #BackToEngineering #JetBrains

🚀 Excited to share my latest project: AI Log Analyzer I built a web-based application using Python and Streamlit that can: ✔ Upload and analyze log files (.txt / .log) ✔ Classify logs into ERROR, WARNING, INFO, CRITICAL ✔ Visualize log distribution with graphs 📊 ✔ Search logs instantly 🔍 ✔ Generate downloadable reports 📄 ✔ Predict log type using Machine Learning 🤖 🌐 Live Demo: https://lnkd.in/gTNK_NQ5 This project helped me strengthen my skills in Python, data analysis, and basic machine learning using libraries like scikit-learn and matplotlib. Looking forward to exploring more real-world AI applications and improving this project further! #Python #MachineLearning #Streamlit #AI #DataScience #Projects #GitHub #Learning #Developer