Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Introduction Quintile analysis is a statistical method used to divide a data set into five equal parts, each representing 20% of the total observations. This method is often used in finance, economics, and sociology to analyze distributions, rankings, or performance. Quintile Analysis: with random data Quintile analysis is a common framework for evaluating the efficacy … Read more
Installation or Setup Detailed instructions on getting pandas set up or installed can be found here in the official documentation. Installing pandas with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not only pandas, but Python and the … Read more
Appending a new row to DataFrame import pandas as pd df = pd.DataFrame(columns = [‘A’, ‘B’, ‘C’]) Appending a row by a single column value: df.loc[0, ‘A’] = 1 df Appending a row, given list of values: df.loc[1] = [2, 3, 4] df Appending a row given a dictionary: df.loc[2] = {‘A’: 3, ‘C’: 9, … Read more
Introduction I decided to write this kernel because Titanic: Machine Learning from Disaster is one of my favorite competitions on Kaggle. This is a beginner level kernel which focuses on Exploratory Data Analysis and Feature Engineering. A lot of people start Kaggle with this competition and they get lost in extremely long tutorial kernels. This is a short kernel compared … Read more
The text discusses the curse of dimensionality in machine learning, highlighting challenges in high-dimensional spaces. It suggests reducing features to improve training efficiency and visualization, while addressing potential information loss and risks of overfitting with increased dimensions. Dimensionality reduction techniques will be explored further.
In many cases, the data you need to work with won’t appear within a library, such as the toy datasets in the Scikit-learn library. Real-world data usually appears in a file of some type, and a flat file presents the easiest kind of file to work with. In a flat file, the data appears as … Read more
Introduction: Linear regression is one of the fundamental techniques in machine learning and statistics used for modeling the relationship between a dependent variable and one or more independent variables. In this tutorial, we’ll delve into the implementation of simple linear regression from scratch using Python. By understanding the mathematical intuition behind linear regression and its … Read more
Introduction Working with date data often involves extracting relevant information, such as the financial year. In this blog post, we’ll explore how to extract the financial year from a date column in both Pandas and PySpark DataFrames. Extracting Financial Year in Pandas DataFrame Sample Data Let’s start by creating a sample Pandas DataFrame with a … Read more