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Data Science Internship Prep
·
Module 6
Supervised Learning: Regression and Tree-Based Models
1
Linear Regression Basics
Train and interpret a linear regression model.
Logistic Regression for Binary Classification
Train a logistic regression model for binary classification and interpret its output probabilities.
Decision Tree Induction and Visualization
Train decision tree models and visualize their structure to understand their logic.
4
Random Forests: Reducing Variance with Bagging
Train a random forest model and explain how it reduces variance through bagging.
XGBoost and Gradient Boosting Explained
Train a gradient boosting model (e.g., XGBoost) and explain how it improves performance through boosting.
6
Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
Perform hyperparameter tuning for any model using GridSearchCV and RandomizedSearchCV.
7
Algorithm Performance Comparison
Compare the performance of different algorithms on a given dataset to select the best one.
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Feature Engineering and Preprocessing Pipelines
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Model Explainability and Deployment