How to build text preprocessing pipelines with Dask?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How to build text preprocessing pipelines with Dask?

How to build text preprocessing pipelines with Dask?

This recipe helps you build text preprocessing pipelines with Dask

0

Recipe Objective.

How to build text preprocessing pipelines with Dask?

`dask_ml.preprocessing` have same styled transformers of **scikit-learn** that we can use in Pipelines to perform different types of data transformations as the part of the model fitting process. These transformers works very nicely on dask collections (`dask.array, dask.dataframe`), NumPy arrays, or pandas dataframes.

Step 1- Importing Libraries.

!apt install dask_ml from dask_ml.preprocessing import Categorizer, OneHotEncoder from sklearn.linear_model import LogisticRegression from sklearn.pipeline import make_pipeline import pandas as pd import dask.dataframe as dd

Step 2- Creating a DataFrame.

We will create a dataframe and then divide it to x and y to fit them in the pipeline.

df = pd.DataFrame({"A": [1, 2, 3, 4, 5, 6], "B": ["a", "b", "c", "d", "e", "f"]}) x = dd.from_pandas(df, npartitions=2) y = dd.from_pandas(pd.Series([0, 1, 1, 0]), npartitions=2)

Step 3- Creating a pipeline.

We will create a pipeline in which we process the data through Categorizer, OneHotEncoder, LogisticRegression.

pipe = make_pipeline( Categorizer(), OneHotEncoder(), LogisticRegression(solver='lbfgs') ) pipe.fit(x, y) ``` Pipeline(steps=[('categorizer', Categorizer()), ('onehotencoder', OneHotEncoder()), ('logisticregression', LogisticRegression())]) ```

Relevant Projects

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.