Launched my first ML project: La Liga Predictor 0.1 for BTTS

A mini project about Supervised Learning, applied it by predicting house prices using the California Housing Dataset from Kaggle. Tools: Python, Pandas, Scikit-learn, Matplotlib Steps: Cleaned and visualized the dataset Trained a Linear Regression model Evaluated using mean squared error and r2 score Achieved an RMSE of 69,297.72 and visualized predictions vs actual prices. GitHub: https://lnkd.in/d8CkpV_b #MachineLearning #DataScience #Python #LearningJourney #AI

Iris Flower Classification using Machine Learning Excited to share my latest hands-on project where I trained and tested a Random Forest Classifier on the Iris dataset using Python and scikit-learn! 🔹 The first notebook focuses on quick model training and testing 🔹 The second notebook calculates and verifies accuracy This project highlights the end-to-end ML workflow — from data preprocessing to model evaluation. 💻 View the complete code and notebooks on my GitHub Repository here: https://lnkd.in/gtyUV7-Z #MachineLearning #Python #DataScience #ArtificialIntelligence #MLProjects #IrisDataset #ScikitLearn #RandomForest #OpenSource #GitHubProjects

Today, I explored one of the most fundamental algorithms in Machine Learning — Linear Regression. I created a Jupyter Notebook where I implemented Linear Regression from scratch and also using Scikit-learn. Here’s what I covered: ✅Understanding the concept of Line of Best Fit ✅Exploring the relationship between independent and dependent variables ✅Visualizing data using Matplotlib ✅Training and testing the model using Scikit-learn This hands-on project really helped me understand how regression models make predictions based on data. Github :- https://lnkd.in/dTRMczDs 📘 Tools used: Python, NumPy, Pandas, Matplotlib, Scikit-learn #MachineLearning #LinearRegression #DataScience #Python #JupyterNotebook #LearningJourney

✨ Excited to share my latest Python practical on Logistic Regression! In this practical, I explored how Logistic Regression helps in predicting categorical outcomes and understanding relationships between variables. It was interesting to see how data patterns can be classified efficiently using this model. This exercise enhanced my understanding of supervised learning and how it can be applied to real-world problems like binary classification. 📁 Here's the Google drive : linkhttps://lnkd.in/gxfhQ8cB 🔗GitHub account : https://lnkd.in/gcCiRDfS #Python #MachineLearning #LogisticRegression #DataScience #LearningJourney

Practical 8 – Classifier: Logistic Regression Built a Logistic Regression classifier to predict heart disease outcomes from patient data. Concepts: Data cleaning, confusion matrix visualization, accuracy evaluation, and model comparison. Tools: Python, Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib 🔗 GitHub Repository:https://lnkd.in/dvcKYQqe) #LogisticRegression #MachineLearning #Python #DataScience #GitHub #Learning

🧠 Day 78 — Scikit-learn Base Jupyter Notebook Today I learned how to build a simple Machine Learning model using Scikit-learn in Jupyter Notebook. From loading data to saving the trained model — this covered the full ML workflow. I used the “tips” dataset, prepared the data, trained a Linear Regression model, made predictions, and evaluated it using MAE and R² Score. Finally, I saved the model using pickle for future use. This practice helped me understand the complete process of creating, testing, and saving an ML model in Python. ✨ #Day78 #MachineLearning #ScikitLearn #Python #DataScienceJourney #LearningEveryday

🚀 Today, I explored some more about NumPy! NumPy is the backbone of numerical computing in Python, and it’s incredible how much we can achieve with just a few lines of code. 💻✨ Efficient array and matrix manipulations Powerful mathematical and statistical functions Essential for data science, ML, and AI projects Some more about what I tried: Calculated matrix determinants and inverses Practiced matrix multiplication and element-wise operations Explored reshaping and stacking arrays for better data handling Excited to keep building my Python and data skills with practical hands-on examples! #Python #NumPy #DataScience #MachineLearning #LearningJourney

🍁 Experiment 7: Simple Linear Regression using Python 🤖 In this lab, I explored the fundamentals of Simple Linear Regression, one of the most widely used techniques in predictive modeling. 🔍 Key learning outcomes: • Understanding the relationship between independent and dependent variables • Implementing linear regression using scikit-learn • Evaluating model performance using metrics like MSE and R² This experiment enhanced my understanding of how regression helps in predicting continuous outcomes and serves as a foundation for advanced machine learning algorithms. 📁 Explore the repository here : https://lnkd.in/epWys7e7 #DataScience #MachineLearning #Python #ScikitLearn #Statistics #DataAnalysis #PredictiveModeling #LinearRegression #LearningJourney #JupyterNotebook Ashish Sawant

🚀Excited to share my latest Python practical on Simple Linear Regression! 📊 In this exercise, I explored how to model the relationship between two variables using linear regression. I learned how to train the model, make predictions, and visualize the best-fit line — an essential concept in data science and machine learning. This practical enhanced my understanding of how regression helps in analyzing trends and making data-driven predictions. 📁 Here's the Google drive : linkhttps://lnkd.in/gxfhQ8cB 🔗GitHub account : https://lnkd.in/gcCiRDfS #DataVisualization #Python #Matplotlib #Seaborn #DataScience #LearningJourney #PracticalLearning #LinearRegression

Behind every powerful data analysis, there’s a NumPy array silently doing the heavy lifting. NumPy isn’t just a library — it’s the foundation of modern data science. From arrays to matrices, it makes complex computations faster and cleaner. 💡 If you’re learning Python, mastering NumPy should be your first step. 🚀 #️⃣ Hashtags: #DataScience #NumPy #Python #MachineLearning #Analytics #AI #CodingJourney #Learning