🏠 House Price Prediction with 13 ML Models + Streamlit Successfully built an end-to-end machine learning project where I trained, evaluated, and deployed 13 different regression models for house price prediction. Tech Stack: Python | Pandas |NumPy | Scikit-learn |XGBoost | LightGBM | Streamlit |Pickle Highlights: -Trained and compared 13 ML regression models -Evaluated models using MAE, MSE, and R² score -Logged model performance for easy comparison -Saved trained models as .pkl files -Built an interactive Streamlit web app -Predicts house prices based on user inputs ✔️✔️This project gave me strong hands-on experience in model comparison and ML deployment 🚀 #MachineLearning #Streamlit #Python #DataScience #MLProjects #AI git : https://lnkd.in/gqthUTEY

🚀 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

A powerful ML App is built on clean, well-prepared data, not just advanced algorithms. If preprocessing is weak, even the best model will fail. #DataPreprocessing #MachineLearning #DataScience #MLPipeline #AI #Analytics #FeatureEngineering #DataCleaning #Python #MLForBeginners #DataDriven #TechSkills

🚀 Day 4/15: Intermediate to Advanced Python for ML/DL/AI Projects 🐍 Forgotten cleanups causing leaks in your training runs? 😤 Today: Context Managers & contextlib — automatic resource handling that always works. Beginner-friendly breakdown + 5 interview questions with code → swipe the carousel! Save if you're writing robust pipelines! What's your go-to context manager trick? Tell me below 👇 #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps

Day 14 – Python & Machine Learning Learning Journey Today was all about revision + practice 📊🐍 🔹 Revised core Python & ML concepts 🔹 Worked on California Housing Dataset 🔹 Built & trained 5 Machine Learning models, including Linear Regression 🔹 Practiced House Price Prediction Concepts Revised & Applied: Training Data vs Testing Data Features & Labels ✔️ Train–Test Split ✔️ Prediction Workflow ✔️ Underfitting vs Overfitting ✔️ Exploratory Data Analysis (EDA) Also revised EDA concepts using the Titanic Dataset to better understand data patterns, distributions, and missing values before model training. 💡 Key Learning: A strong model doesn’t start with algorithms — it starts with understanding the data. Excited to move forward and apply these concepts to more real-world datasets Consistency is the key #Python #MachineLearning #DataScience #LearningJourney #EDA #LinearRegression #CaliforniaHousing #TitanicDataset #AI #100DaysOfCode #Day14

🚀 Day 10/15: Intermediate to Advanced Python for ML/DL/AI Projects 🐍 Ever waited 30+ minutes just for data preprocessing while your GPU sits idle? 😩 Single-core Python is painfully slow on big datasets. Today: Multiprocessing — unlock all your CPU cores to parallelize image resizing, feature extraction, tokenization, and more — often 5–10× faster! Swipe the document for: → Super simple “one chef vs many chefs” analogy → Step-by-step from basic Pool.map to real ML examples → How to use it safely in PyTorch/TensorFlow DataLoaders (num_workers!) → 10 interview Qs from beginner to pro with code snippets 💻 I added multiprocessing to my image pipeline — what used to take 45 minutes now finishes in ~6. Game-changer for iteration speed! Save this 📌 if you're tired of waiting and ready to speed up your workflows. Have you tried multiprocessing yet? What speedup did you get? Or any scary pickle errors? 😅 Share your story below 👇 I read every comment! Tomorrow: Pickle & Joblib — the right way to save/load models & big objects. Follow Vaishali Aggarwal for more such content #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps #PythonMultiprocessing #SpeedUpCode #DataPreprocessing #CodingTips #TechLearning

🚀 Day 38 – Machine Learning Journey Cross Validation (Python Implementation) Today I implemented Cross Validation in Python to evaluate models more reliably instead of trusting a single train–test split. 🔹 Why in code Measures consistency, not luck Reduces overfitting bias Gives a stable performance estimate 🐍 Minimal Python example (scikit-learn) from sklearn.model_selection import KFold, cross_val_score from sklearn.linear_model import LogisticRegression model = LogisticRegression() kf = KFold(n_splits=5, shuffle=True, random_state=42) scores = cross_val_score(model, X, y, cv=kf) print(scores.mean()) 🔹 What this does: Splits data into 5 folds Trains on 4, validates on 1 Repeats for all folds Reports the average score 🔑 Key takeaway: > Cross validation doesn’t change the model — it validates whether the model can be trusted. #Day38 #MachineLearning #CrossValidation #Python #ScikitLearn #LearningJourney #ModelEvaluation #DataScience #AI #LearningJourney

📊 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

🐍 Day 6 — Working with Numbers in Python Day 6 of #python365ai ➕➖ Python handles numbers very naturally. Example: x = 10 y = 3 print(x + y) print(x * y) print(x / y) Python supports: Addition + Subtraction - Multiplication * Division / Power ** 📌 Why this matters: From financial models to machine learning algorithms, Python’s numerical operations power everything. 📘 Practice task: Calculate the area of a rectangle using variables. Tomorrow: strings and text manipulation. #python365ai #PythonMath #NumbersInPython #CodingBasics #AI #LearnToCode

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