Predicting house prices with Python and Scikit-learn

Day 10 – PYTHON VARIABLES 🧠🐍 (MY TechRise cohort 2.0 journal). Today in my TechRise Cohort 2 journey, I learned about Python Variables — the building blocks of every program! Variables are like containers that hold data, and I explored different data types such as integers, floats, strings, booleans, and even complex numbers. I also practiced data type conversion in Python using simple code examples. Here’s a quick snippet from my learning: a = 10 k = float(a) p = complex(a) print(k) print(p) Every new lesson makes Python more exciting and practical for real-world AI and Machine Learning applications. 🚀 #TechRiseCohort2 #Python #AI #MachineLearning #CodingJourney #DigitalSkills

⭐ Excited to share my Random Forest practical 🧠, I implemented this powerful ensemble algorithm using Python 🐍 (Scikit-learn). It was amazing to see how multiple Decision Trees work together through majority voting to improve accuracy, reduce overfitting, and balance bias-variance 🌿. Hands-on experiments like this make learning truly insightful, showing how ensemble methods turn raw data into reliable predictions 💡. Guided by Ashish Sawant Sir. 🔗 GitHub: https://lnkd.in/ez_NstrZ 📁 Google Drive: https://lnkd.in/ezXFx_py #RandomForest #MachineLearning #DataScience #AI #Python #EnsembleLearning #DataDriven #MLPracticals #LearningByDoing

🍁 Experiment 7: Simple Linear Regression using Python 🤖 In this lab, I explored the fundamentals of Simple Linear Regression, one of the most widely used techniques in predictive modeling. 🔍 Key learning outcomes: • Understanding the relationship between independent and dependent variables • Implementing linear regression using scikit-learn • Evaluating model performance using metrics like MSE and R² This experiment enhanced my understanding of how regression helps in predicting continuous outcomes and serves as a foundation for advanced machine learning algorithms. 📁 Explore the repository here : https://lnkd.in/epWys7e7 #DataScience #MachineLearning #Python #ScikitLearn #Statistics #DataAnalysis #PredictiveModeling #LinearRegression #LearningJourney #JupyterNotebook Ashish Sawant

Im exited to share "La liga Predictor 0.1" my first machine learning project, i was trying to make a predictor of BTTS (Both teams to score) using real data! It analyzes 1140+ Matches with temporal validation Used Python, Pandas, XGBoost The model is not fully complete, right now it has bias and the predictions are not good but still a good to learn and as my first ML project!! You can find the code here: https://lnkd.in/gk4JcXuV #MachineLearning #DataScience #Python #DataEngineering #MLOps #TechCareer #PortfolioProject #XGBoost #DataEngineering #SportsAnalitycs #FootballAnalytics

🚀 Today, I explored some more about NumPy! NumPy is the backbone of numerical computing in Python, and it’s incredible how much we can achieve with just a few lines of code. 💻✨ Efficient array and matrix manipulations Powerful mathematical and statistical functions Essential for data science, ML, and AI projects Some more about what I tried: Calculated matrix determinants and inverses Practiced matrix multiplication and element-wise operations Explored reshaping and stacking arrays for better data handling Excited to keep building my Python and data skills with practical hands-on examples! #Python #NumPy #DataScience #MachineLearning #LearningJourney

Day 11 – PYTHON VARIABLES 🧠🐍 (My Techrise cohort 2 journal) Today in my TechRise Cohort 2 journey, I learned about Python Variables — the building blocks of every program! Variables are like containers that hold data, and I explored different data types such as integers, floats, strings, booleans, and even complex numbers. I also practiced data type conversion in Python using simple code examples. Here’s a quick snippet from my learning: a = 10 k = float(a) p = complex(a) print(k) print(p) Every new lesson makes Python more exciting and practical for real-world AI and Machine Learning applications. 🚀 #TechRiseCohort2 #Python #AI #MachineLearning #CodingJourney #DigitalSkills

👉 🚀 Hands-on Machine Learning Project: Linear Regression 🧠 Excited to share my latest project — Linear Regression Model built in Python (Jupyter Notebook)! 🎯 In this project, I explored how to predict house prices based on house size using one of the most fundamental algorithms in Machine Learning — Linear Regression. This project helped me understand: ✅ How the model finds the best-fit line ✅ The relationship between features and target variables ✅ How to visualize and interpret predictions 🔗 Check out my full project on GitHub: 👉 https://lnkd.in/dM6f7ik8 #MachineLearning #DataScience #Python #LinearRegression #GitHub #DataAnalytics #AI #LearningByDoing #WomenInTech #CareerGrowth

Today, I explored one of the most fundamental algorithms in Machine Learning — Linear Regression. I created a Jupyter Notebook where I implemented Linear Regression from scratch and also using Scikit-learn. Here’s what I covered: ✅Understanding the concept of Line of Best Fit ✅Exploring the relationship between independent and dependent variables ✅Visualizing data using Matplotlib ✅Training and testing the model using Scikit-learn This hands-on project really helped me understand how regression models make predictions based on data. Github :- https://lnkd.in/dTRMczDs 📘 Tools used: Python, NumPy, Pandas, Matplotlib, Scikit-learn #MachineLearning #LinearRegression #DataScience #Python #JupyterNotebook #LearningJourney

Behind every powerful data analysis, there’s a NumPy array silently doing the heavy lifting. NumPy isn’t just a library — it’s the foundation of modern data science. From arrays to matrices, it makes complex computations faster and cleaner. 💡 If you’re learning Python, mastering NumPy should be your first step. 🚀 #️⃣ Hashtags: #DataScience #NumPy #Python #MachineLearning #Analytics #AI #CodingJourney #Learning

🧠 Day 78 — Scikit-learn Base Jupyter Notebook Today I learned how to build a simple Machine Learning model using Scikit-learn in Jupyter Notebook. From loading data to saving the trained model — this covered the full ML workflow. I used the “tips” dataset, prepared the data, trained a Linear Regression model, made predictions, and evaluated it using MAE and R² Score. Finally, I saved the model using pickle for future use. This practice helped me understand the complete process of creating, testing, and saving an ML model in Python. ✨ #Day78 #MachineLearning #ScikitLearn #Python #DataScienceJourney #LearningEveryday