Machine Learning Project Guidelines

Translate business problems into analytics problems by clearly describing them in business and analytical terms, defining a specific, measurable problem aligned with business goals and choosing supervised or unsupervised approaches.

Experience hands-on business understanding in machine learning with Jupyter Notebooks, building binary classification models on the Pima Indians diabetes and mushroom datasets, plus PCA visualization and customer segmentation clustering.

Load datasets into a Jupyter notebook and perform exploratory data analysis with box plots, histograms, pie charts, bar charts, scatter plots, and correlation maps using CSV, NumPy, and pandas.

Explore how box and whisker plots visualize the five-number summary—minimum, Q1, median, Q3, maximum—calculate the IQR, identify outliers, and assess data skew.

Explore how a histogram uses bins and bar heights to show frequency, center, spread, skewness, variance, and outliers, with types including normal, right skewed, left skewed, bimodal, and uniform.

Discover how a pie chart shows parts of a whole and learn to use annotations and fewer slices, switching to a bar chart for more than five slices.

Understand data using bar charts, which display proportions with rectangular bars that can be plotted vertically or horizontally and are more readable than pie charts.

Explore correlation maps and correlation matrices for machine learning data understanding to summarize numerical data, read patterns clearly, and identify how price, x, y, and z relate through pairwise coefficients.

Master data understanding and exploratory data analysis through hands-on data loading with csv, read_csv, read_excel, and numpy, then perform text and visual EDA using seaborn and pandas.

Explore the types of machine learning algorithms—linear, non-linear, and ensemble for regression, classification, and clustering—along with evaluation metrics and supervised and unsupervised learning concepts.

Explore linear, nonlinear, and ensemble regression algorithms, including univariate and multivariate linear regression, residual error, and the best-fit line. See how curvilinear models and ensembles improve accuracy.

Learn how clustering algorithms identify natural groups in unlabeled data using k-means, and explore steps, convergence, and methods to choose k like elbow, silhouette, and Davies-Bouldin.

Explore how to select and interpret regression, classification, clustering, and probabilistic evaluation metrics, balance bias and variance, and use cross-validation to assess model performance.

Document research outcomes after data understanding by selecting suitable ML algorithms and evaluation metrics, present findings from your research, and note how these apply to classification, regression, and clustering projects.

Identify data leakage where training includes target information or test data. Protect model validity by splitting train and test data before transformation and using pipelines.

Identify and correct mistakes, incomplete or noisy data to prepare tidy data for analysis. Learn the three tidy data principles, outlier detection, and handling missing values and duplicates.

Learn to identify relevant input features for the target variable using feature selection techniques like Pearson correlation, ANOVA, chi-squared, mutual information, Spearman, and dimensionality reduction with PCA.

Apply dimensionality reduction, including PCA, to create compact data projections, and split preprocessed data into train and test sets using train_test_split or leave-one-out; manage outliers during cleaning or after splitting.

Apply data transforms to scale and normalize features, handle distributions, and engineer features with polynomial terms, while encoding categorical data and preventing data leakage by training and testing transforms separately.

Explore data preprocessing essentials: detecting leakage, cleaning data, and managing tidy versus untidy datasets. Master feature selection, engineering, PCA, dimensionality reduction, train-test splits, and balancing imbalanced classes.

Master data preprocessing in machine learning, covering data cleaning, transformations, missing values, duplicates, outliers, and addressing imbalanced classes with techniques like normalization, encoding, and SMOTE.

Train models on the training data, generate performance metrics and runtimes, and select 2–3 for refinement using regression and classification metrics and cutoff scores.

Explore differences between model parameters and hyperparameters, and learn hyperparameter tuning with grid search and random search to refine models based on training metrics.

Explore hyperparameter optimization with random search and grid search using scikit-learn's randomized search CV and grid search CV, with cross-validated evaluation to identify the best parameters.

Explore model evaluation in supervised learning by applying appropriate metrics for classification and regression, using the test data, learning curves, and error analysis to compare models and prevent overfitting.

Explore error analysis and troubleshooting prediction errors using train/validation/test splits, learning curves, and bias-variance trade-offs to decide when more data, features, or regularization help.

Compare underfitting, overfitting, and good fit in regression and classification, focusing on high bias and high variance. Learn to balance model complexity to improve generalization from training to unseen data.

Apply data cleaning, feature selection and engineering, and data transformation to limit overfitting; use regularization and k-fold cross-validation with a validation set to estimate unseen performance.