An ML project from idea to model: workflow and Kaggle

In short: A machine learning project consists of clarifying the problem, collecting and cleaning data, building a baseline, comparing models, evaluating cleanly and documenting; platforms such as Kaggle offer data and practice.

In more detail: Most time is not in the model but in question, data and evaluation. A clear procedure saves frustration and prevents false conclusions.

In Depth

The procedure

1. Problem and goal: what should be predicted, for what, how is success measured (metric), what does an error cost? Is ML needed at all (does a simple rule/statistics suffice)?
2. Obtain data: source, licence, privacy, size, quality.
3. Explore (EDA): distributions, missing values, outliers, correlations, plots (pandas, matplotlib).
4. Clean and prepare: missing values, duplicates, types, encode categories, scale, feature engineering.
5. Split data (train/val/test) and baseline (majority class, mean, simple regression).
6. Train and compare models (cross-validation, hyperparameters).
7. Evaluate and analyse errors (confusion matrix, examples, groups/fairness).
8. Document, deploy, monitor (MLOps, drift).

Tools

• Python, Jupyter (notebooks), pandas, NumPy, scikit-learn (classic models), PyTorch/TensorFlow (neural networks), matplotlib/seaborn.
• Versioning: Git + DVC/experiment tracking (MLflow, Weights & Biases); fixed seeds; requirements.txt/uv/conda.
• Compute: own PC, Colab/Kaggle notebooks (free GPU quotas), cloud.
• Data hubs: Kaggle Datasets, Hugging Face Datasets, UCI, data.gov/GovData, open data portals (read licences!).

Kaggle and competitions

Kaggle offers datasets, notebooks, courses (micro-courses) and competitions with a leaderboard. Beginner classics: Titanic (survival prediction), House Prices, Digit Recognizer (MNIST). Learning: read others’ notebooks, rebuild them yourself, read discussions. Public vs. private leaderboard (avoid overfitting to the public part). Competition tricks (ensembles, stacking) are not always production-ready: maintainability/latency/cost count in a job. Own projects (e.g. predicting retro game prices, spam detection, sensor data) also show more than competitions alone.

Typical mistakes and tips

• Too complex models too early; baseline first.
• Data leakage and faulty splitting (see cross-validation).
• Only accuracy with imbalanced classes.
• Unclean data/labels (see labelling): garbage in, garbage out.
• Ethics/bias: who is affected? Privacy (GDPR), fairness, transparency.
• Reproducibility: seeds, versions, data snapshot, README with results.
• Portfolio: 2–3 clean projects on GitHub with problem, data, method, result, limits (helps applications/training).
• Learning path: Python → pandas → scikit-learn → projects → neural networks → deployment.

See also: Conda and Jupyter, pandas, Cross-validation, MLOps