Logistic Regression with Python and scikit-learn

Python Basics for Machine Learning I’ve uploaded a video covering the core Python data structures used in machine learning: • Lists • Tuples • Sets • Dictionaries These concepts are essential for handling data and writing efficient ML code. This video is part of my Advanced Machine Learning with LLM series, focused on building strong foundations before moving into complex topics. https://lnkd.in/gSg6rBKM #Python #MachineLearning #DataStructures #LLM #AI #Learning

#PrincipalComponentAnalysis (PCA) is more than just a technique for dimensionality reduction - it’s one of the most powerful applications of eigenanalysis in data science. By identifying the directions of maximum variance, PCA simplifies complex datasets while preserving their essential structure. What’s inside this guide: * The math: Covariance matrices and Eigen-decomposition.  * The logic: From data centering to explained variance.  * The code: Python realizations using NumPy and scikit-learn. Swipe through the carousel below to explore the mechanics of PCA! The link to the full #Medium article with complete code is in the first comment. #DataScience #MachineLearning #Python #LinearAlgebra #AI #STEM

Hyperparameter Optimization Machine Learning using MLBox #machinelearning #datascience #hyperparameteroptimization #mlbox MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…). Prediction with models interpretation. https://lnkd.in/gN4BuVTM

📊 Day 4 | Linear Regression 📈📉 Today, I learned about Linear Regression, one of the simplest and most widely used Machine Learning algorithms. It is used to predict a continuous value based on input data. The idea is to find a straight line (best fit line) that represents the relationship between variables. 📌 Example: Predicting product price based on cost or features. To understand this, I implemented a simple Linear Regression model using Python 💻 This helped me see how machines can learn patterns and make predictions. Linear Regression is often the first step into Machine Learning models 📊 #MachineLearning #LinearRegression #DataScience #LearningInPublic #Python

📊 Student Performance Predictor Built a regression model to estimate student GPA using different ML techniques. The project involved proper data cleaning, exploratory data analysis, and selecting the most impactful features. Compared Linear Regression and Random Forest, where Random Forest performed better in terms of accuracy. Some key factors influencing performance: Studytimeweekly, Absences, .... etc. 🛠 Tools: Python, Pandas, Scikit-learn, Plotly #MachineLearning #DataScience #Python #StudentProject #MLProjects

We built a Spam Email Classifier as a group using Machine Learning in Python. What it does: Detects whether an email is spam or not. Dataset: 10,000 emails 🤖 Model: Random Forest Classifier Accuracy: 88.7% | F1-Score: 86% Using a dataset from kaggle https://lnkd.in/dNZfH4Fr Tools used: Python · Scikit-learn · Pandas · Matplotlib It is now on my github https://lnkd.in/drKeE_se #MachineLearning #Python #AI #DataScience #StudentProject

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐦𝐞 While exploring datasets in Python recently, I spent some time understanding how correlation works between variables. Using pandas, it’s surprisingly easy to calculate a correlation matrix and see how different columns relate to each other. Sometimes two variables move together strongly, and sometimes there’s almost no relationship at all. What I found interesting is that correlations can quickly highlight patterns that might not be obvious just by looking at raw numbers. Still learning how to interpret these relationships properly, but it’s definitely making the analysis process more insightful. #Python #Pandas #DataAnalytics

🚀 Just delved into a fascinating exploration of random number generation and various distributions in Python, using numpy and matplotlib. From understanding uniform distributions and normal distributions to simulating coin flips and drawing from discrete sets, it's incredible how powerful these tools are for statistical analysis and modeling. Learning to seed the RNG for reproducible results, visualizing CDFs, and even creating random DNA sequences! This foundational knowledge is crucial for everything from A/B testing to machine learning. What are your favorite random number generation tricks or applications? DataScience #Python #Numpy #Matplotlib #Statistics #RandomNumbers #MachineLearning #DataAnalysis #Coding

Claude just diagnosed me with a classic developer bug 😂 After hours of learning Python — functions, loops, dictionaries, if/else, and AI agent architecture — I started asking the same questions twice. Claude's response? ``` while awake == True:   ask_questions()       if questions == repeat:     print("Go to sleep Anil! 😄")     break ``` Turns out even humans need a break statement. 😄 The grind is real. But so is the progress. 💪 #Python #AI #MachineLearning #CareerChange #AIAgent #LearningToCode #Claude #100DaysOfCode

Day 2 of learning Machine Learning. Today I worked on a simple linear regression model using Python in Jupyter Notebook. The idea was straightforward: - Input (x): house size - Output (y): price Model used: f(x) = wx + b I understood how: - Training data is structured (x_train, y_train) - Parameters (w, b) define the relationship - The model uses this to make predictions on new inputs Also got hands-on with NumPy and basic plotting using Matplotlib. Still very early, but it's becoming clearer how data is converted into predictions. #MachineLearning #AI #Python #LearningInPublic