Numpy

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DataScience
Python
Statistics

Scikit-learn LinearRegression vs Numpy Polyfit

What are differences between linear regression and polynomial regression? We must know these techniques well but it is still vague somewhat.

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DataScience
Python

DiCE -ML models with counterfactual explanations for the sunk Titanic data-

This article is an overview why counterfactual explanations are required in recent ML projects and how to apply Microsoft research DiCE library for Titatic data set. There have been some foreseen and ongoing trends in AI/ML for 2020 such as AutoML, MLOps, AI ethic those are to democratize AI/ML in the industries more than ever. […]

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DataScience
Python

How to draw support and resistence lines with DataFrame in Python

Support and resistence lines are referred to analyze chart patterns, and a direction of assets for traders. This is a guide to draw those lines in matplotlib.

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DataScience
Python

How to draw a trend line with DataFrame in Python

Talib library doesn’t support to draw trendlines. This is the article how to compute trendlines from DataFrame and draw it in matplotlib.

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DataScience
Python

How to draw 4 most common trend indicators in matplotlib in Python

Trend indicators are usable and handy for beginners. Here is to introduce how to draw chars and bars for Moving Averages, MACD, RSI and OBV in Python.

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Python

Mastering DataFrame – 3 tips for DataFrame.rolling function

Introduce 3 tips in DataFrame about the parameters center and min_periods, how to change the weight of window with win_type, how to use apply function.

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DataScience
Python

How to build Sentiment Analysis with NLTK and Sciki-learn in Python

Sentiment Analysis is one of major applicatioin in machine learning and natural language processing space. This is a guide how to code it in Python.

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DataScience
Python

How to compute price correlation for financial data in Python

We learn how to use Pandas rolling function to calculate rolling change, rolling standard deviation and rolling correlation for financial assets.

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DataScience
Python

Mastering DataFrame – how to aggregate OHLCV data in a different time period

OHLCV stands for Open, High, Low, Close and Volume (Volume is optional). It’s used for market data such as stock, forex, commodity and crypto and consists of a series of rows that represent 5 data points: the opening and closing price, the highest and lowest price during a certain period of time. Volume is the […]

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