RT @SecurityTrybe: Top 9 HTTP Requests Methods

RT @milan_milanovic: 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗢𝗿𝗱𝗲𝗿. We utilize SQL queries to access a collection of records stored in our database tables. C….

RT @NikkiSiapno: How to use Big O to ace your technical interviews:. Firstly, what is Big O Notation?. Big O describes an algorithm's runti….

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RT @shedntcare_: ChatGPT was just the starting point . More than 2000 new AI tools were released in the last 30 days . Here's 30 cutting-ed….

RT @madzadev: 9 AI tools you don't want to miss in 2024 🔥🔥. Code: @tabnine. Images: @midjourney. Video: @runwayml. Design: @DesignsdotAI. W….

Hey @Apple, not thrilled with my iPhone 14 Pro Max – battery drains like it's on a race and heating up faster than a microwave. Seriously, not expected this from apple, this will be the first and last apple product for me. 🤔🔥 #iPhoneProblems #NotHappy #Apple.

📊 Hyperparameter Tuning 📊.Algorithms have settings called hyperparameters that affect performance. Tuning them optimizes the model. Think of it as adjusting knobs for better sound on a stereo. 🎛️🎶 #HyperparameterTuning.

📊 Cross-Validation 📊.To ensure our model’s reliability, we use techniques like cross-validation. This divides data into multiple sets for training and testing, reducing the risk of overfitting. It’s like having multiple quizzes! 📚🧪 #CrossValidation.

🚀 Beyond Logistic Regression 🚀.While Logistic Regression is great, more complex algorithms like Random Forest or Neural Networks can capture intricate patterns. Experiment with various models to find the best fit for your data! 🌲🧠 #ExploreModels.

📉 Model Improvement 📉.By adding the “TotalPurchaseAmount” feature, our model might better understand customers’ spending behaviors. It’s incredible how a single tweak can enhance accuracy and predictions! 📊💡 #ModelImprovement.

# Feature engineering.data['TotalPurchaseAmount'] = data['PastPurchases'] * data['AveragePurchaseAmount']. # Update X with the new feature.X = data[['Age', 'TotalPurchaseAmount']]. # Split data.X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,.

📚 Creating New Features 📚.In our example, we could create a “TotalPurchaseAmount” feature by combining “PastPurchases” and “AveragePurchaseAmount”. This might help the model capture spending patterns better. 💰📈 #NewFeatures.

🔗 Feature Engineering 🔗.Ever wondered how to improve predictions? That’s where feature engineering comes in! By creating new features from existing data, you provide your model with richer information to learn from. Let’s dive in! 🛠️🔍 #FeatureEngineering.

🌐 Real-World Impact 🌐.Imagine applying this model to customer data in a retail store. It could help target promotions, leading to increased sales. That’s the magic of ML – it transforms data into actionable insights! 📊✨ #MLMagic.

🔎 Interpretation & Improvement 🔎.After running the code, you’ll see the model accuracy. This is a good starting point, but it’s essential to dig deeper. Are there other features that could enhance predictions? Experiment and iterate! 🕵️‍♂️🔄 #IterateImprovement.

📊 Model Training & Evaluation 📊.By using the .fit() method, we train our model on the training data. Then, we use the .score() method to evaluate its accuracy on the testing data. The output tells us how well our model is performing. 📚📏 #ModelTraining.

🔀 Data Splitting 🔀.Notice how we split our data into training and testing sets using train_test_split. This ensures that our model learns from one set and gets tested on another, preventing it from memorizing the answers! 🧩📊 #DataSplit.

📚 Logistic Regression 📚.In our code, we imported the LogisticRegression class from the Scikit-Learn library. This algorithm is great for binary classification tasks, like predicting whether a customer will buy or not. 🔄🔍 #LogisticRegression.

🛠️ Hands-on ML Example 🛠️.Let’s take a closer look at the code snippet from before. In this example, we’re using a simple Logistic Regression model to predict customer purchases based on age and past purchases. 📈💻 #HandsOnML.