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

This is Kendrick vs Drake for nerds ngl

if we see this simple data

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 
     - 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.

📌 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 🚀