Blog: Data prepocessing
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Data preprocessing in Python
Before training a model, you have to preprocess data. This is necessary to transform raw data into clean data suitable for analysis. In this guide, we will cover essential steps to preprocess data using Python. These include splitting the dataset into training and validation sets, handling missing values, managing categorical features, and normalizing the dataset.
- haskell
- machine learning
- haskell in production
- rust
- serokell
- elixir
- blockchain
- ghc
- introduction
- algorithms
- edsl
- neural networks
- computer science
- erlang
- web development
- data science
- elixir tutorial
- functional futures
- mathematics
- resource guide
- tezos
- elixir in production
- functional programming
- lorentz
- nix
- parsers
- rust in production
- smart contracts
- typescript
- dependent types
- elixir software
- haskell software
- history
- library
- metaprogramming
- remote work
- template haskell
- what's that typeclass
- agda
- computer vision
- deep learning
- formal verification
- ml resources
- trends
- ai tools
- big data
- conferences
- data analytics
- generative ai
- idris
- image generation
- learn haskell
- logic
- ml applications
- open source projects
- phoenix
- Python
- scala
- top projects
- type families
- ai
- ai ethics
- biotech
- chatgpt
- cybersecurity
- dependent haskell
- design
- ecto
- education
- events
- graph neural networks
- ml algorithms
- morley
- no code
- ocaml
- optimization
- outsourcing
- pattern recognition
- physics
- rust software
- rust tutorial
- supervised learning
- testing
- ton
- topology
- transformers
- unsupervised learning
- webassembly
- women in tech
- 2024
- agi
- AI agents
- ai app builders
- ai blockchain convergence
- ai business toola
- ai events
- AI in manufacturing
- ai in oil and gas
- ai tools 2023
- artificial general intelligence
- backpropagation
- bayesian optimization
- bert model
- blockchain app development
- blockchain scalability
- business
- cardano
- chain of thought prompting
- character ai
- chatgpt alternatives
- cloud native software
- clustering algorithms
- cnn
- collaboration tools
- compilers
- container orchestration
- coq
- cryptography
- data mining
- data prepocessing
- databases
- devops
- dlt
- drug repurposing
- effective accelerationism
- effective altruism
- egge ai
- ensemble learning
- enterprise data storage
- existential types
- feature engineering
- federated ml
- fintech
- fossa
- foundation models
- free monads
- game development
- generative ai security threats
- genetics
- github
- github copilot
- gitlab
- gleam
- gpt
- healthcare
- higher-rank types
- hobby
- hyperparameter tuning
- icfpc
- it conferences
- jei
- Kubernetes
- lambda calculus
- lean
- lisp
- LLaMA
- llms fine-tuning
- llms for business
- llms risks
- markdown
- medicine
- medtech conferences
- michelson
- microservices
- ml
- ml datasets
- ml ideas
- ml models
- ml projects
- mtl
- multi-runtime architecture
- nlp
- open source
- open source software
- OSS development
- programming languages
- project management
- purescript
- python development
- Python IDEs
- python libraries
- quantum computers
- random numbers
- reason
- reinforcement learning
- running llms
- rust business use cases
- rust libraries
- rust roadmap
- semi-supervised learning
- serokellchat
- servant
- signal processing
- software development
- software development trends 2024
- solana smart contract development
- sora
- support vector machine
- tagless final
- tech conferences 2024
- text analysis
- text-to-speech
- text-to-video
- textcontent
- time series analysis
- tinyML
- ton blockchain
- trends in AI
- typed lambda calculus
- web summit
- web3
- website deployment
- young innovative company
+ More