Author name: Kavya Kamasani

Pandas and Seaborn: Exploratory Data Analysis (EDA) in Python

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Using Pandas and Seaborn, this lesson explores exploratory data analysis (EDA) in Python, covering fundamental ideas, methods for exploring data, and visualization using real-world examples. It’s intended to improve your capacity for efficient data trend analysis and interpretation. EDA in Python Using Pandas and Seaborn The steps involved are illustrated in the code below. Follow …

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ARIMA time – series generator

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This tutorial explores the ARIMA model for time series forecasting in Python, covering fundamentals, model building, and tuning with practical examples. It’s designed to help you effectively forecast and analyze trends in time series data. ARIMA Model For Time Series The steps which involved are shown below: Step 1: Import Libraries import pandas as pd …

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Python-based Minimum Detectable Effect (MDE)

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In this tutorial, we explore how to calculate Minimum Detectable Effect (MDE) using Python. This guide introduces you to the concept of MDE in A/B testing, providing a step-by-step approach with code examples, making it easy to determine the smallest change that can be statistically detected in your experiments. Minimum-Detectable-Effect-(MDE)-Using-Python The Approach can be discussed …

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For identifying similarities between strings in two CSV files

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In this tutorial, we will learn how to match the strings in large datasets When working with huge datasets, especially in cases where data comes from different sources, it’s common to encounter slightly different versions of the same string. This post will guide you through performing fuzzy matching to identify and link similar strings between …

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