NumPy Revision: Handling Missing & Infinite Values

In this video, I’m working with a Diabetes Prediction in Jupyter Notebook using NumPy and basic Machine Learning concepts. The session covers data handling, operations on arrays, and understanding how healthcare data can be prepared for analysis and modeling. Recording my workflow helps me track progress and improve practical skills in Python and ML step by step. Learning by doing is the best way forward. 🚀 #MachineLearning #Python #NumPy #JupyterNotebook #Diabetes Prediction#HealthcareAnalytics #DataScienceJourney

🌸 Simple Iris Prediction – Streamlit Demo Built a Simple Iris Flower Prediction app to quickly learn and demonstrate the basics of machine learning and deployment. 🔍 What it does: Predicts Iris species using sepal and petal measurements. 🛠 Tech Stack: Python • Scikit-learn • Streamlit • NumPy • Pandas 🙏 Guided by my AI teacher Pukar Karki 🌐 Try the demo: https://lnkd.in/ge8ngeRH 💻 Source code: https://lnkd.in/g4ujJhT5 ✨ Try the app, leave a ⭐ on the repo, and let me know what feature you’d like to see next! #MachineLearning #Streamlit #Python #ScikitLearn #AIProjects #LearningByDoing

Linear regression confused me for weeks. Then I realized it is just drawing the best line through points. That is it. The school equation y = mx + b? That is literally linear regression. I built one from scratch. No fancy libraries. Just numpy and simple Python. If machine learning feels overwhelming, start here. Once this clicks, everything else gets easier. Wrote about my learning journey: Medium: https://lnkd.in/eBvAbdR7 Kaggle notebook: https://lnkd.in/edckUXz2 #MachineLearning #DataScience #Python #Learning

Here is my first Machine Learning model where I used a dummy dataset to predict salary using Linear Regression. The model achieved a prediction accuracy of 95.58% with exploratory data analysis (EDA) implemented in Python and Jupyter Notebook. #Python #JupyterNotebook #Pandas #Matplotlib #MachineLearning

Day 31 - NumPy Arrays Today I began working with NumPy, a foundational library for numerical computing in Python. NumPy arrays are more efficient and powerful than Python lists for data processing and mathematical operations, making them essential for data science and machine learning workflows. What I covered: -Creating NumPy arrays -Understanding key attributes (shape, size, dtype) -Working with multi-dimensional arrays -Performing basic array operations NumPy is the backbone of scientific computing in Python and underpins libraries like Pandas, SciPy, and TensorFlow. Day 31 repository: https://lnkd.in/gsxBQDpA #NumPy #Python #DataScience #MachineLearning #AI #LearningInPublic

𝐍𝐮𝐦𝐏𝐲 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 Today I came across some really useful NumPy cheat sheets — simple, clear, and packed with the essentials for anyone working with Python and data. -->Covers the basics: arrays, indexing, slicing -->Explains broadcasting & vectorized operations -->Handy functions for math, stats, and linear algebra -->Perfect quick reference for Data Science & ML projects Instead of searching through docs every time, a cheat sheet keeps everything at your fingertips. It’s like having a mini toolkit for Python’s most powerful library. #NumPy #Python #CheatSheet #DataScience #MachineLearning #Coding #Upskill

Learning NumPy – Array Slicing Today I practiced 1D & 2D array slicing in NumPy. Slicing helps us extract required rows and columns efficiently from large datasets. Example: array[row_index, column_slice] 🚀 Small concepts like slicing play a big role in Data Science & ML. #NumPy #Python #DataScience #LearningJourney #BCAStudent

🚀 K-Nearest Neighbors (KNN) 🤖📊 I’ve just published a new blog on K-Nearest Neighbors (KNN) — one of the most intuitive and beginner-friendly algorithms in Machine Learning. 🔍 In this blog, I’ve covered: - What KNN is and why it’s called a lazy learner - Step-by-step explanation of how KNN works - The math and intuition behind distance metrics - Why feature scaling is crucial (and often ignored!) - Real-world examples for better understanding - Classification vs Regression in KNN - Pros, cons, and practical use cases - Simple Python code to get started 📖 Read the full blog here: 👉 https://lnkd.in/dsn5P7-7 #MachineLearning #DataScience #KNN #ArtificialIntelligence #Python #LearningInPublic #Hashnode #TechBlog #MLAlgorithms #DataAnalytics

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗧𝗿𝗶𝗰𝗸 #2 When exploring a dataset, don’t start with modeling. Start by understanding the data shape and missing values. In Pandas, this one line gives a quick overview: df.isna().sum() It helps you instantly see which columns need cleaning before analysis or machine learning. Small steps like this save a lot of time later. #DataScience #MachineLearning #Python #Pandas #LearningInPublic #DataAnalytics

🏡 House Price Prediction – Machine Learning Project Built a house price prediction model using Linear Regression in Python. The project includes data preprocessing with Pandas, model training with Scikit-Learn, and performance evaluation to analyze how various factors influence housing prices. A great hands-on experience working with real-world data and applying regression techniques to solve a practical problem. Tools Used: Python, Pandas, Scikit-Learn #MLProjects #HousePricePrediction #PythonProjects #DataAnalytics #MachineLearning Mohd Talha Khan Dr. Gaurav Agarwal RK Shukla Divyank Chauhan Invertis University