Machine Learning House Price Prediction Model Comparison

🐍 Day 76 — Standard Deviation Day 76 of #python365ai 📏 Standard deviation shows the typical distance from the mean. Example: np.std(data) 📌 Why this matters: Standard deviation is widely used in statistics and machine learning. 📘 Practice task: Compare standard deviation for two datasets. #python365ai #StandardDeviation #Statistics #Python

🚀 Day 4 – Model Improvement & Evaluation | Student Performance Prediction Today I focused on improving and evaluating my Linear Regression model. 🔹 Compared training & testing R² scores 🔹 Detected overfitting and underfitting scenarios 🔹 Analyzed feature importance using model.coef_ 🔹 Identified that previous grades (G1, G2) strongly influence final performance 🔹 Performed feature selection and retrained the model 🔹 Compared results with Decision Tree Regressor Improving the model step by step is helping me understand the real-world ML workflow deeply. 🚀 #MachineLearning #ModelEvaluation #LinearRegression #FeatureSelection #StudentPerformancePrediction #Python #ScikitLearn #MLJourney #LearningInPublic

Exploring Model Evaluation and Optimization in Machine Learning In this presentation, I explored two important concepts used in building reliable machine learning models: Cross Validation and Hyperparameter Tuning. Cross Validation helps evaluate a model’s performance by splitting the dataset into multiple folds and testing the model across different training and testing sets. This provides a more reliable estimate of how the model will perform on unseen data. Hyperparameter Tuning focuses on selecting the best parameter values that control how a model learns. Techniques such as Grid Search and Random Search are commonly used to identify the optimal configuration and improve model performance. Understanding these techniques is essential for building models that generalize well and deliver accurate predictions. #MachineLearning #DataScience #ModelEvaluation #CrossValidation #HyperparameterTuning #Python #ScikitLearn

🐍 Day 62 — Scatter Plots Day 62 of #python365ai 🎯 Scatter plots reveal relationships between variables. Example: plt.scatter([1,2,3], [2,4,5]) plt.show() 📌 Why this matters: Scatter plots are foundational in regression analysis and ML. 📘 Practice task: Plot two related numeric lists. #python365ai #ScatterPlot #MachineLearning #Python

🚀 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

Back to basics: The Iris dataset is the 'Hello World' of Machine Learning. I used it to demonstrate how clear-cut decision boundaries can be when features are perfectly separated. What was the first dataset that made you fall in love with Machine Learning? Tech Stack: Python | Scikit-Learn | Pandas | Matplotlib | Plotly | Machine Learning #DataScience #Python #MachineLearning #ArtificialIntelligence #Portfolio

A Simple Probability Question That Tricks Many People This problem looks so simple that most people answer it almost instantly. And that’s exactly why it’s interesting. The intuition many people rely on leads them to the wrong conclusion. The correct answer turns out to be quite surprising. Let’s see what answer you get. #python #statistics #probability #two_children_problem #paradox

Temporal.io is emerging as a top choice for building reliable agentic workflows in production. I've been working through their AI cookbook examples and putting together a reference repo as I go: https://lnkd.in/gnwP-a6k #Temporal #TemporalIO #AgenticAI #AIAgents #Python #LLM #WorkflowAutomation

Day 48/60 — Gradient Boosting Today I trained a Gradient Boosting Regression model, a technique where each new model learns from the mistakes of the previous one. The model kept improving step by step until the prediction error dropped to about 2.27 RMSE, showing that the predictions are getting closer to the real values. What I find interesting about Gradient Boosting is how it learns from errors and gradually becomes smarter with every step. Small improvements… big learning. #DataScience #MachineLearning #GradientBoosting #Python #DataAnalytics #AI #LearningInPublic #BuildInPublic #60DaysOfLearning #TechJourney #Programming

SkillCourse Day 5/30: Mastering User Interaction in Python I just wrapped up Day 5 of the "30 Days of Python with AI" challenge by Satish Dhawale sir! Today was all about making programs interactive. Key takeaways: The input() function: Learning how to capture user data. Type Casting: Why converting strings to int() or float() is crucial for calculations (no more 1 + 1 = 11 errors!). Data Integrity: Understanding how Python handles different data types during input. #Python #CodingChallenge #AI #LearningInPublic #SatishDhawale #DataAnalyst