Master Machine Learning 5 Projects: MLData Interview Showoff

Explore exploratory data analysis and preprocessing on a Kaggle playground dataset using Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn to clean, one-hot encode categorical features, and inspect distributions.

This lecture covers data transformation and visualization, showing preprocessing with a pipeline using iterative imputer and standard scalar for numerical features and one-hot encoding for categorical features, visualizing transformed distributions.

Discover how to train an XGBoost model with hyperparameter tuning, cross-validation, and early stopping to determine the optimal boosting rounds and prevent overfitting.

Explore telecom churn prediction by loading telco churn data, manipulating with pandas and numpy, visualizing with matplotlib and seaborn, and applying preprocessing, encoding, and baseline models to imbalanced churn data.

Utilize exploratory data analysis to pre-process telecom churn data, perform feature selection via correlation and feature importance, encode categorical features, train a logistic regression, and improve accuracy with grid search.

Explore ensemble methods, including bagging and boosting, using random forest and XGBoost; compare averaging and stacking approaches, and evaluate with cross-validation and metrics like accuracy, precision, recall, F1.

Explore a stacked ensemble of xgboost and logistic regression for churn prediction, interpret its feature importances and shap values, and prepare for deployment.

Learn how to download Kaggle data directly into Google Colab by mounting Drive and configuring a Google API token. Use Kaggle API commands to download and unzip datasets in Drive.

Apply transfer learning and fine tuning to cat and dog image classification, organize data into training, validation, and optional test sets, and visualize class balance with Plotly Express.

Use Keras to preprocess image data with the image data generator and rescale to 0–1. Create train, validation, and test generators via flow from directory; visualize batches with plot data.

Explore fraud detection with transactional data through exploratory data analysis, addressing class imbalance, missing values, outliers, feature distributions, correlations, and relationships to guide engineered features.

Build a fraud detection model using SMOTE oversampling and logistic regression, and evaluate with precision, recall, and F1 on a 30% validation set.

Explore advanced fraud detection techniques, combining ensemble models, anomaly detection, and deep learning to boost credit card fraud detection beyond baseline logistic regression.

Explore model evaluation using train-test split, an lstm network in keras, and feature importance from gradient boosting to interpret and improve fraud detection models.

Deploy a fraud detection model with pickle serialization and Flask to enable real-time predictions via an API, after training and evaluating with a train test split.

Explore house price regression with exploratory data analysis, visualize sale price distribution, examine feature correlations, and apply preprocessing steps, imputing missing values and removing outliers, before modeling.

Learn practical data pre-processing and feature engineering for house price prediction by handling missing values, encoding categorical features, and checking multicollinearity with VIF.

Train a linear regression model for housing data using Python and scikit-learn. Prepare features by dropping sale price and id, train on X_train and Y_train, and evaluate with r-squared 0.85.

Learn model validation techniques to gauge performance on unseen data and prevent overfitting. Use ridge regression, cross-validation, and grid search in sklearn to select the best model and regression metrics.