Python ML Regression Model for Housing Prices

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

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

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

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

Day 2 of my ML journey 🚀 ✅ Watched Andrew Ng ML course ✅ Built Titanic Survival Prediction model ✅ Compared Logistic Regression (82%) vs Random Forest (84%) ✅ Submitted to Kaggle competition — scored 78.4% GitHub: https://lnkd.in/dFuZhjp7 #MachineLearning #Kaggle #Python #DataScience”

📊 Multiple Linear Regression — Visualized in 3D I implemented Multiple Linear Regression completely from scratch using NumPy and visualized how a regression plane fits the data when we have two input features. 🔄 The GIF shows the regression plane rotating in 3D to build intuition about: • how multiple features influence predictions • why linear models form planes (not lines) • why visualization breaks beyond 2 features After visualization, I evaluated the same implementation on higher-dimensional datasets using R² score, since plots are no longer possible. 🔗 Full implementation & clean repo: https://lnkd.in/gDzhWKAW Learning ML by building from scratch 🚀 #MachineLearning #MultipleLinearRegression #MLFromScratch #Python #NumPy #DataScience

Computational Models in Finance: Stock Market Clustering 📈 I just completed a project using Unsupervised Machine Learning to optimize portfolio selection. By applying K-Means Clustering across multiple time horizons (2-week to 12-month), I was able to segment stocks by their risk-return profiles rather than just their industry. The Result? A data-driven recommendation engine that identifies high-potential assets—specifically optimized for a $1,500 budget. 🔹 Tools used: Python, Scikit-Learn, Pandas, Matplotlib. 🔹 Key Logic: Elbow Method for optimal 'k', volatility filtering, and budget-constrained scoring. Check out the full code and analysis on my GitHub: [https://lnkd.in/gY74QrJC] #FinancialEngineering #DataScience #Python #MachineLearning #StockMarket #InvestmentAnalysis

Small Actions, Big Momentum Some nights I feel like I haven’t learned enough. Then I remind myself: Small, consistent actions beat random intensity. Today, I: • Practiced a tiny Python script • Fixed one small bug • Noted one lesson learned It doesn’t look like much. But done every day, this adds up — fast. If you’re learning anything in tech, remember: You don’t need to do everything. Just do something daily. Follow me if you want to see a daily journey in Python, AI & Data Science — building in public, one step at a time. #PythonLearning #AIJourney #DataScienceStudent #LearningInPublic #Consistency #BuildInPublic #TechGrowth

Features vs Labels In Machine Learning, everything starts with data. But data has two important parts: 1) Features 2) Labels What are Features? Features = Input variables These are the characteristics or properties the model uses to learn. Imagine we are training a model to recognize a cat. The model might look at: - Ears - Eyes - Nose - Whiskers - Fur pattern All of these are features. Features are the inputs the model observes. What is a Label? Label = Output or Target This is what we want the model to predict. In this case: “Cat” is the label. The label is the correct answer we want the model to learn. #Python #MachineLearning #DataScience #ArtificialIntelligence #MLBasics #DeepLearning #LearningJourney #DataEngineering

Lets move to the final Advance Level with Codveda Technologies Task 1: Predictive Modeling (Classification) Description: Build and evaluate a classification model to predict categorical outcomes. Objectives: Preprocess the data (handle categorical variables, feature scaling). Train and test multiple classification models (e.g., Decision Trees, Logistic Regression, Random Forest). Evaluate models using accuracy, precision, recall, and F1-score. Perform hyperparameter tuning using grid search. Tools: Python, scikit-learn, pandas, matplotlib. #CodvedaJourney #CodvedaExperience #FutureWithCodveda