i was playing with some data and trying ML models, yup it feels awesome 🙂
📊 Intro to Scikit-Learn (sklearn) 🚀
Scikit-Learn is a powerful Python library for machine learning, making it easy to build and evaluate models. Here's a quick workflow to get started:
1️⃣ End-to-End Workflow: Follow a structured approach for ML success.
2️⃣ Getting the Data Ready: Preprocess data by handling missing values, scaling, and encoding.
3️⃣ Choosing the Right Estimator: Select the best algorithm for your problem (e.g., regression, classification).
4️⃣ Fit the Model: Train your model and make predictions on new data.
5️⃣ Evaluate the Model: Use metrics (e.g., accuracy, RMSE) to assess performance.
6️⃣ Improve the Model: Tune hyperparameters or try advanced techniques like ensemble methods.
7️⃣ Save & Load Models: Use
8️⃣ Putting It All Together; Combine everything into a robust ML pipeline!
👉 Dive into Scikit-Learn to turn your data into insights!
#ScikitLearn #MachineLearning #Python
Scikit-Learn is a powerful Python library for machine learning, making it easy to build and evaluate models. Here's a quick workflow to get started:
1️⃣ End-to-End Workflow: Follow a structured approach for ML success.
2️⃣ Getting the Data Ready: Preprocess data by handling missing values, scaling, and encoding.
3️⃣ Choosing the Right Estimator: Select the best algorithm for your problem (e.g., regression, classification).
4️⃣ Fit the Model: Train your model and make predictions on new data.
5️⃣ Evaluate the Model: Use metrics (e.g., accuracy, RMSE) to assess performance.
6️⃣ Improve the Model: Tune hyperparameters or try advanced techniques like ensemble methods.
7️⃣ Save & Load Models: Use
pickle or joblib to persist trained models. 8️⃣ Putting It All Together; Combine everything into a robust ML pipeline!
👉 Dive into Scikit-Learn to turn your data into insights!
#ScikitLearn #MachineLearning #Python
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Dealing with Missing Data in Python
Missing data? No problem
I explore 2 powerful methods to handle it:
1️⃣ Filling Missing Data with NumPy/Pandas
✔️ Use .fillna() to replace missing values.
Replace categorical values with "missing".
Replace numerical values with a constant or the column's mean.
2️⃣ Filling Missing Data with Scikit-Learn
✔️ Use SimpleImputer for flexible, scalable imputation.
Define strategies like constant (e.g., "missing", 4) or mean.
Handle categorical, numerical, and mixed datasets easily.
🔗 Combine Scikit-learn with ColumnTransformer to handle multi-type columns in one step.
📊 Master these methods and make your data analysis more robust!
#Python #DataScience #ScikitLearn #NumPy
Missing data? No problem
I explore 2 powerful methods to handle it:
1️⃣ Filling Missing Data with NumPy/Pandas
✔️ Use .fillna() to replace missing values.
Replace categorical values with "missing".
Replace numerical values with a constant or the column's mean.
2️⃣ Filling Missing Data with Scikit-Learn
✔️ Use SimpleImputer for flexible, scalable imputation.
Define strategies like constant (e.g., "missing", 4) or mean.
Handle categorical, numerical, and mixed datasets easily.
🔗 Combine Scikit-learn with ColumnTransformer to handle multi-type columns in one step.
📊 Master these methods and make your data analysis more robust!
#Python #DataScience #ScikitLearn #NumPy
⚡3
Mike's ML Forge
you can check it on sklearn website
I can't just see this and keep my mouth shut, so the thing is
How to Pick the Right Machine Learning Algorithm
One of the hardest parts of machine learning is choosing the right algorithm for the job. Different algorithms are suited for different types of problems. Here’s a simple way to break it down:
Step 1: What kind of problem are you solving?
Everything starts with understanding what you want to predict or classify. Your problem will fall into one of these categories:
1. Classification – When you need to categorize things (e.g., "Is this email spam or not?").
2. Regression – When you need to predict a number (e.g., "How much will a house cost?").
3. Clustering – When you want the computer to group things automatically without labels (e.g., "Group customers by similar behavior").
4. Dimensionality Reduction – When you have too much data and need to simplify it while keeping the important parts
How to Pick the Right Machine Learning Algorithm
One of the hardest parts of machine learning is choosing the right algorithm for the job. Different algorithms are suited for different types of problems. Here’s a simple way to break it down:
Step 1: What kind of problem are you solving?
Everything starts with understanding what you want to predict or classify. Your problem will fall into one of these categories:
1. Classification – When you need to categorize things (e.g., "Is this email spam or not?").
2. Regression – When you need to predict a number (e.g., "How much will a house cost?").
3. Clustering – When you want the computer to group things automatically without labels (e.g., "Group customers by similar behavior").
4. Dimensionality Reduction – When you have too much data and need to simplify it while keeping the important parts
if we see this simple data
1. Data Preparation
-
-
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2. Model Training and Evaluation
- Linear Support Vector Classifier (LinearSVC)
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- Model accuracy on the test set: 76.95% (
- Random Forest Classifier
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- Model accuracy on the test set: 83.54% (
### Observations:
- Random Forest performs better than LinearSVC on this dataset.
1. Data Preparation
-
disease.drop("target", axis=1): Extracts feature variables (X). -
disease["target"]: Extracts the target variable (y). -
train_test_split(x, y, train_size=0.2): Splits the data into training and test sets, with 20% allocated for training.2. Model Training and Evaluation
- Linear Support Vector Classifier (LinearSVC)
-
LinearSVC() is initialized and trained using fit(x_train, y_train). - Model accuracy on the test set: 76.95% (
0.7695).- Random Forest Classifier
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RandomForestClassifier(n_estimators=100): A Random Forest model with 100 decision trees. - Model accuracy on the test set: 83.54% (
0.8354), which is better than LinearSVC.### Observations:
- Random Forest performs better than LinearSVC on this dataset.
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📌 Model Comparison & Evaluation in Machine Learning
When building classification models, evaluating them properly ensures the best performance. Here’s how to do it effectively:
🔹 Key Evaluation Metrics
✅ Accuracy – Measures overall correctness but isn’t ideal for imbalanced datasets.
✅ AUC-ROC – Higher AUC means better class separation.
✅ Confusion Matrix – Shows the breakdown of correct & incorrect predictions.
✅ Classification Report – Includes Precision, Recall, and F1-score for deeper insights.
🔹 Comparing Multiple Models
1️⃣ Train different models (Logistic Regression, SVM, Random Forest, etc.).
2️⃣ Use Cross-Validation to get reliable performance scores.
3️⃣ Optimize with Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV).
4️⃣ Compare models using AUC-ROC, F1-score, or accuracy for better decision-making.
#MachineLearning #AI #ModelEvaluation #DataScience #AUCROC #ConfusionMatrix 🚀
When building classification models, evaluating them properly ensures the best performance. Here’s how to do it effectively:
🔹 Key Evaluation Metrics
✅ Accuracy – Measures overall correctness but isn’t ideal for imbalanced datasets.
✅ AUC-ROC – Higher AUC means better class separation.
✅ Confusion Matrix – Shows the breakdown of correct & incorrect predictions.
✅ Classification Report – Includes Precision, Recall, and F1-score for deeper insights.
🔹 Comparing Multiple Models
1️⃣ Train different models (Logistic Regression, SVM, Random Forest, etc.).
2️⃣ Use Cross-Validation to get reliable performance scores.
3️⃣ Optimize with Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV).
4️⃣ Compare models using AUC-ROC, F1-score, or accuracy for better decision-making.
#MachineLearning #AI #ModelEvaluation #DataScience #AUCROC #ConfusionMatrix 🚀