Scikit-learn

This repository contains structured notes on Scikit-learn's machine learning workflow, from preprocessing raw data to applying meta estimators for enhanced predictions. It is intended as a concise, reference-friendly guide for anyone learning or refining their skills in Scikit-learn and a project Heart Disease Predictor

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Table of Contents

• Basic Workflow
• Preprocessing
• Modeling and Evaluation
• GridSearchCV and Cross Validation
• Sample Weights vs Class Weights
• Outlier Detection
• Precision vs Recall
• Meta Estimators
• Heart Disease Predictor
• Summary
• Author
• License

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Basic Workflow

Data → Model → Fit → Predict → Evaluate
The standard process involves defining input features (X), target labels (y), fitting a model, making predictions, and evaluating results using metrics.

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Preprocessing

Preprocessing ensures data consistency and better model performance.
Key tools:

• StandardScaler, MinMaxScaler for normalization
• PolynomialFeatures for non-linear terms
• QuantileTransformer for reshaping distributions
• OneHotEncoder for categorical encoding

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Modeling and Evaluation

Steps:

1. Import libraries and load data
2. Separate into X and y
3. Preprocess and build pipelines
4. Train and tune models
5. Evaluate with metrics and visualization

Note: Avoid deprecated datasets such as Boston due to bias concerns.

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GridSearchCV and Cross Validation

GridSearchCV automates hyperparameter tuning and improves generalization by testing multiple configurations through cross-validation.

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Sample Weights vs Class Weights

Term	Purpose
class_weight	Balances imbalanced target classes
sample_weight	Assigns custom importance to specific samples

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Outlier Detection

Example: Isolation Forest
Unsupervised anomaly detection by isolating observations. Outliers are identified as points requiring fewer splits.

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Precision vs Recall

Metric	Meaning
Precision	Of predicted positives, how many were correct
Recall	Of actual positives, how many were identified

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Meta Estimators

Meta estimators enhance or combine models:

• VotingClassifier for model aggregation
• Threshold adjusters for classification control
• FeatureUnion for combining feature transformations
• Group-based predictors for segmented training

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Summary

Scikit-learn workflows follow a modular approach: preprocess data, define pipelines, tune with GridSearchCV, and optionally apply meta estimators for improved performance. The process encourages reproducibility, scalability, and clarity in machine learning projects.

• Unrealistic/extreme inputs cause unstable predictions; realistic values give consistent results. Inconsistent outputs were due to random splits without a fixed seed.

• Dataset imbalance needs stratified splitting or resampling.

• Saving models with pickle ensures correct loading and reuse.

• Preprocessing (e.g., scaling) generally improves stability and performance.

• Achieved accuracy 98% without overfitting.

• Balanced data, realistic inputs, reproducibility, and preprocessing are key for reliable ML pipelines.

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Thank You

Knowledge should not be gated behind paywalls or exclusivity. This repository exists so that anyone can access structured, practical Scikit-learn notes without restriction.
The journey doesn’t end here. After mastering meta estimators, take the next step with the full-fledged Scikit-learn project

am_i_cooked A more advanced heart disease predictor built on a larger dataset, featuring a Flask-based web UI for deployment.

Author

Created and maintained by Aaditya Yadav

License

This project is licensed under the .