House Price Prediction with Machine Learning

🚀 Day-60 of #100DaysOfCode 📊 NumPy Practice – Correlation Between Two Arrays Today I implemented correlation analysis using NumPy. 🔹 Concepts Practiced: ✔ np.corrcoef() ✔ Correlation matrix interpretation ✔ Relationship analysis between variables ✔ Basic statistical computation 🔹 Key Learning: Correlation helps understand how strongly two variables are related — a fundamental concept in Data Analysis and Machine Learning. From array manipulation → to statistical insights 💡🔥 #Python #NumPy #DataAnalysis #Statistics #MachineLearning #100DaysOfCode

🎉 Setting the ball rolling in Python & Machine Learning! Kicking off my journey by building a Student Perfomance Prediction App using the UCI dataset 📚 - built with Python & Streamlit for an interactive experience. One thing I learned? You can't rely on just one model. So I trained and compared multiple models: 🔹 Linear Regression 🔹 SVM 🔹 Random Forest 🔹 Gradient Boosting 🔹 XGBoost 🔹 LightGBM Now the big question — how did I evaluate them? 🤔 Here comes 📊 R² Score and 📉 Mean Squared Error (MSE). And the best performer was… Gradient Boosting 🏆 But wait… should users just accept predictions without knowing why? 👀 They do deserve transparency. That's where the explainability heroes step in : 🦸♂️ SHAP – to understand overall feature impact 🦸♀️ LIME – to explain individual predictions 🔗 GitHub Repository: https://lnkd.in/gQSRS9iG Even though it’s a simple application, it helped me understand model training, evaluation, ensemble learning, and most importantly — making ML explainable. Long way to go. Just getting started 🔥 #MachineLearning #Python #ExplainableAI #SHAP #LIME #DataScience #LearningJourney

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

Algorithms don’t fix bad data. Transformation is the quiet skill that separates models that work from models that just look impressive. We created a simple PDF breaking down: When to log When to scale When to normalize If you're serious about building models that generalize — this is foundational. Interested in a workshop? Let us know. — Team QuantLyft #DataTransformation #DataPreprocessing #FeatureEngineering #DataScience #Statistics #RProgramming #Python

Day 12 – Saving & Reusing Machine Learning Models Today I learned how to save and reload a trained machine learning model using joblib. After building and tuning my churn prediction model, I exported it as a .pkl file so it can be reused without retraining. Key Learning: Training a model is one step — deploying and reusing it is what makes it practical in real-world applications. This step gave me insight into how ML models move from development to production. #MachineLearning #DataScience #ModelDeployment #Python #LearningJourney

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

I'm committing to building popular ML algorithms from scratch daily without using anything but Python built-ins and NumPy. No sklearn. No shortcuts. Just pure code and first principles. Day 5: Support Vector Machine (SVM) ✅ SVM intuition is simple: imagine you have two groups of points on a 2D plane and you want to draw a line that separates them. But not just any line, the one with the biggest gap between the two groups. That gap is called the margin. And the data points sitting right on the edge of that margin are the Support Vectors, the only points that actually define where the line goes. Remove any other point, and the line stays the same. SVM finds the maximum margin boundary using the Hinge Loss and gradient descent, penalizing points that end up on the wrong side. This is fully open if you want to collaborate, add an algorithm, or drop a suggestion in the comments or issues tab. Feel free to do so. 🤝 👉 GitHub: https://lnkd.in/duTd7jie #MachineLearning #Python #NumPy #DataScience #OpenSource #LearnML #100DaysOfCode #SVM #SupportVectorMachine #Classification

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

I'm committing to building popular ML algorithms from scratch daily without using anything but Python built-ins and NumPy. No sklearn. No shortcuts. Just pure code and first principles. Day 2: Linear Regression ✅ Linear Regression intuition is simple: imagine you're trying to draw the best possible straight line through a scatter of points on a graph. That line represents the relationship between your input and output. But how do we find the "best" line? That's where Gradient Descent comes in. We start with a random line, measure how wrong it is using the Mean Squared Error, then slowly nudge the line in the direction that reduces the error, repeating this thousands of times until we converge. This is fully open if you want to collaborate, add an algorithm, or drop a suggestion in the comments or issues tab. Feel free to do so. 🤝 👉 GitHub: https://lnkd.in/duTd7jie #MachineLearning #Python #NumPy #DataScience #OpenSource #LearnML #100DaysOfCode #LinearRegression #GradientDescent

Gradient Descent explained — with live, runnable Python code. 🐍 I built this interactive notebook that walks through all 3 variants: 📌 Batch Gradient Descent 📌 Stochastic Gradient Descent (SGD) 📌 Mini-Batch Gradient Descent Each one is implemented from scratch using NumPy, with cost function plots so you can literally see the model learning. 🔗 Open the notebook here (no sign-up needed): https://lnkd.in/dKwuP6FU --- This notebook was built on sciFI — an AI-powered Python notebook workspace. The AI copilot wrote the code, fixed the errors, and helped structure the whole thing. I just described what I wanted. If you work with data and Python, it's worth a look 👇 🌐 https://scifi.ink — free beta, no credit card. #DataScience #MachineLearning #Python #GradientDescent #AI #sciFI