Implementation of Data Preprocessing on Titanic DatasetWith step by step ImplementationAfroz ChakureBlockedUnblockFollowFollowingJun 29What is Required ?Python, Numpy, PandasKaggle titanic dataset : https://www. kaggle.…
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Reshaping Pandas DataFrames
To do this, I'll take our DataFrame and make the following adjustments:Remove the extra columns. Drop rows where language value…
Continue ReadingApplying the Universal Machine Learning Workflow to the UCI Mushroom Dataset
Lepiota Mushrooms – Image Credit : East Tennessee WildflowersApplying the Universal Machine Learning Workflow to the UCI Mushroom DatasetThis post is intended…
Continue ReadingTransforming Xs and Ys (Mostly Ys) into Football Formations
If the Ys describes how low or high up the pitch they are, the Xs become irrelevant. Again, for simplicity’s…
Continue ReadingInspecting a 15 year CDC Chronic Disease Dataset
Inspecting a 15 year CDC Chronic Disease DatasetAn exploratory data analysis of population health indicators using Python and data science techniquesDaniel…
Continue ReadingThe Pandas Library for Python
In fact, it's often helpful for beginners experienced with . csv or excel files to think about how they would solve…
Continue ReadingRelationships validated between population health chronic indicators
This is where visualization of the data will paint a picture to understand the overall relationships. Originally, I started out…
Continue ReadingA kind of “Hello, World!” in ML (using a basic workflow)
Photo by Martim Braz on UnsplashA kind of “Hello, World!” in ML (using a basic workflow)Antonello Calamea, CTO and certified ML…
Continue ReadingSimple Soybean Price Regression with Fast.ai Random Forests
Simple Soybean Price Regression with Fast. ai Random ForestsMatthew ArthurBlockedUnblockFollowFollowingJan 22image credit: Pexels. comApplying cutting-edge machine learning to commodity prices. As…
Continue ReadingPredicting Stars, Galaxies & Quasars with Random Forest Classifiers in Python
The output of :df.info()is shown below:<class 'pandas.core.frame.DataFrame'>RangeIndex: 10000 entries, 0 to 9999Data columns (total 18 columns):objid 10000 non-null float64ra 10000 non-null…
Continue ReadingA Simple Guide to creating Predictive Models in Python, Part-2b
Therefore the below method is easier and scalable# first just take a look at all the columnslist(deep_feat.columns)Output:['CreditScore', 'Geography', 'Gender', 'Age',…
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