Day 06 of my NumPy Revision ✅ Today I revised how to handle missing (NaN) and infinite values using NumPy. These concepts are very important for data preprocessing and machine learning. ✔ np.isnan() – detect missing values ✔ np.nan_to_num() – replace NaN and infinite values ✔ np.isinf() – detect infinite values ✔ np.isfinite() – validate clean numeric data I am documenting my complete learning journey step-by-step on GitHub. More revisions coming soon on Pandas #NumPy #DataScience #Python #MachineLearning #LearningJourney #GitHubPortfolio

🌸 Simple Iris Prediction – Streamlit Demo Built a Simple Iris Flower Prediction app to quickly learn and demonstrate the basics of machine learning and deployment. 🔍 What it does: Predicts Iris species using sepal and petal measurements. 🛠 Tech Stack: Python • Scikit-learn • Streamlit • NumPy • Pandas 🙏 Guided by my AI teacher Pukar Karki 🌐 Try the demo: https://lnkd.in/ge8ngeRH 💻 Source code: https://lnkd.in/g4ujJhT5 ✨ Try the app, leave a ⭐ on the repo, and let me know what feature you’d like to see next! #MachineLearning #Streamlit #Python #ScikitLearn #AIProjects #LearningByDoing

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

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 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

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! 🖥️ Machine learning models lack explainability, hence making their predictions difficult to interpret. This can be a significant challenge in regulated industries, where black box implementations are unacceptable. explainerdashboard is a Python library that helps you understand machine learning models by providing an interactive dashboard. The library supports various approaches, including SHAP values, permutation importances and dependence plots. Check the link below for more information, and make sure to follow me for regular data science content! 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗿𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘀𝗶𝘁𝗲: https://lnkd.in/dfFkMGjH 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

📊 Seaborn makes data easy to understand, not just easy to plot. In Python, Seaborn stands out because it focuses on clarity over complexity. ✔ Clean visuals by default ✔ Built for statistical insights ✔ Works seamlessly with Pandas ✔ Perfect for analytics, ML, and data engineering Good visuals don’t just look nice — they drive better decisions. If you work with data, Seaborn is a skill worth mastering. #Python #Seaborn #DataVisualization #DataAnalytics #DataScience

Completed an exploratory project on building a Machine Learning web app using Streamlit and Python. This project was part of the UCS654 Predictive Analytics coursework as a guided exercise and served as an introduction to deploying classical machine learning models through a simple interactive interface. I experimented with models such as SVM, Logistic Regression, and Random Forest, and explored basic performance evaluation using Confusion Matrix, ROC Curve, and Precision Recall Curve. Overall, it was a useful hands on exercise to better understand how ML models can be packaged and exposed through a web application. GitHub Repository: https://lnkd.in/gVttjdZH Live Web App: https://lnkd.in/gpuUY48D #TIET #ThaparUniversity #ThaparOutcomeBasedLearning #ThaparCoursera #Coursera #UCS654_Predictive_Analytics

🚀 Task 1 Completed: House Price Prediction using Machine Learning As part of my Machine Learning learning journey, I worked on a House Price Prediction model using Python. 🏠 Project Overview: The goal was to predict house prices based on key features such as: Square Feet Area Number of Bedrooms 🛠 Tech Stack Used: Python Pandas Scikit-learn Linear Regression 📌 What I learned: Working with real-world CSV datasets Feature selection for regression problems Training and using a Linear Regression model Taking user input and making predictions This task helped me understand the end-to-end machine learning workflow, from data loading to prediction. 📂 Project available on GitHub (README included) https://lnkd.in/gf2kq6rk #MachineLearning #Python #LinearRegression #AI #DataScience #LearningByDoing #StudentDeveloper