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04 Oct

How to do Feature Encoding and Exploratory Data Analysis

geekycodesco0 CommentsData Science, Machine Learning, Pandas, PythonDataframe, DataScience, Datasets, Machine learning, Programming, Python

Categorical variables are those values that are selected from a group of categories or labels. For example, the variable Gender with the values of male or female is categorical, and so is the variable marital status with the values of never married, married, divorced, or widowed. In some categorical variables, the labels have an intrinsic order, for example, in the variable Student's grade, the values of A, B, C, or Fail are ordered, A being the highest grade and Fail the lowest. These are called ordinal categorical variables. Variables in which the categories do not have an intrinsic order are…

27 Sep

8 Essential Machine Learning Terms You must Know

geekycodesco0 CommentsData Science, Machine Learning, Unsupervised LearningDatasets, Machine learning, Programming

Data Wrangling Data Wrangling is the process of gathering, selecting, cleaning, structuring, and enriching raw data into the desired format for better decision-making in less time. If you want to create an efficient ETL pipeline(Extract, transform, and load) or create beautiful data visualizations, you should be prepared to do a lot of data wrangling-springboard. Data Imputation Data Imputation is the substitution of estimated values for missing or inconsistent data items(fields). The substituted values are intended to create a data record that does not fail edits. The most common technique is mean imputation, where you take the mean of the existing…

12 Mar

What is the difference between the append() and insert() list methods in Python?

geekycodesco0 CommentsPythonCoding, Dataframe, DataScience, Datasets, Machine learning, Programming, Python

Difference between append() and insert () Append(): This function is used to modify an already existing list. Adds a new specific element at the end of the list. Syntax: List_Name.append(item) Insert(): This function also modifies an already existing list. The only difference between append() and insert() is that the insert function allows us to add a specific element at a specified index of the list unlike append() where we can add the element only at end of the list. Syntax: List_Name.insert(index, item) Refer below example for better understanding Important Notice for college students If you’re a college student and have…

20 Feb

How to Predict Movie will be Flop or Hit and it’s Revenue?

geekycodesco0 CommentsData Science, Machine Learning, Pandas, Python, Random Forest, Real Life ExamplesClassification, Coding, Dataframe, DataScience, Datasets, Linear regression, Machine learning, Programming, Python, Regression, XG Boost, XGB

The Birth of the motion picture camera in the late 18th century gave birth to the most powerful form of entertainment available: Cinema. Movies have been able to entertain audiences from the emergence of a single second of horse racing in the 1890s to the introduction of sound in the 1920s to the birth of color in the 1930s to create 3D Movies in early 2010. Cinema had a humble background in terms of design, direction and acting (especially due to its very short time in its early days) but since then, the film industry around the world has been…

14 Feb

What are the good websites to learn data structures and algorithms?

geekycodesco0 CommentsAlgorithms, Blog, Interview, Real Life ExamplesC Programming, Coding, Dataframe, DataScience, Datasets, Machine learning, Programming, Python, SAP

Well! Programming is fun once you get it and the great part is a decent developer gets an enormous check from Top Tech Giants Like (Google, Amazon, Walmart, Microsoft, Facebook, and Apple). Data Structure and algorithms are needed for breaking interviews in these first-rate organizations. Regardless of whether you are a fledgling or middle in Algorithm abilities for the most part learning complete data structure, required 2-3 months. Likewise, getting ready code without help from anyone else is the fundamental model for the arrangement cycle. The following are a few decent assets for learning Data structure and Algorithms: 1. Geeksforgeeks: Geeksforgeeks has an expanse of issues.…

20 Jan

What is Dimensionality Reduction? Overview, Objectives, and Popular Techniques

geekycodesco0 CommentsData Science, Machine Learning, NLP, Pandas, Python, Random ForestCoding, CSV files, DataScience, Datasets, Machine learning, Programming, Python

Table of Contents What is Dimensionality ReductionWhy Dimensionality Reduction is ImportantDimensionality Reduction Methods and ApproachesDimensionality Reduction TechniquesDimensionality Reduction Example Learning by machine is not an easy task. Okay, so that's a lesser statement. Artificial Intelligence and machine learning represent a major step in making computers think like humans, but both concepts are challenging to understand. Fortunately, the profit is worth the effort. Today we are dealing with the process of reducing size, analyzing a key component in machine learning. We will cover its meaning, why it is important, how to do it, and give you a related example to illustrate…

10 Jan

Interpreting ACF and PACF | Time Series

geekycodesco0 CommentsData Science, Machine Learning, Pandas, Python, Random ForestCoding, Dataframe, DataScience, Datasets, Machine learning, Programming, Python

Introduction Autocorrelation analysis is an important step in the Exploratory Data Analysis (EDA) of time series. The autocorrelation analysis helps in detecting hidden patterns and seasonality and in checking for randomness. It is especially important when you intend to use an ARIMA model for forecasting because the autocorrelation analysis helps to identify the AR and MA parameters for the ARIMA model. Overview FundamentalsAuto-Regressive and Moving Average ModelsStationarityAutocorrelation Function and Partial Autocorrelation FunctionOrder of AR, MA, and ARMA ModelExamplesAR(1) ProcessAR(2) ProcessMA(1) ProcessMA(2) ProcessPeriodicalTrendWhite NoiseRandom-WalkConstant🚀 Cheat SheetCase StudyBitcoinEthereumDiscussion on Random-Walk import numpy as np # linear algebra from numpy.random import seed import math import…

09 Dec

Predictive Analysis with different approaches

geekycodesco0 CommentsData Science, Decision Tree, Machine Learning, Pandas, Python, Random Forest, Real Life ExamplesDataframe, DataScience, Datasets, Machine learning, Programming, Python, Traffic Prediction, Web Prediction, Web Traffic, Web Traffic Prediction

The goal of this notebook is not to do the best model for each Time series. It is just a comparison of few models when you have one Time Series. The presentation present a different approaches to forecast a Time Series.In this notebook we will be using web traffic data from kaggle. The plan of the notebook is: I. Importation & Data CleaningII. Aggregation & VisualizationIII. Machine Learning ApproachIV Basic Model ApproachV. ARIMA approach (Autoregressive Integrated Moving Average)VI. (FB) Prophet ApproachVII. Keras StarterVIII. Comparaison & Conclusion I. Importation & Data Cleaning In this first part we will choose the Time…

29 Oct

All Cheat Sheets related to Machine Learning

geekycodesco0 CommentsData Science, Machine Learning, Real Life Examples, TutorialsCoding, CSV files, Dataframe, DataScience, Datasets, Machine learning, Programming, Python

Here we're going to provide Cheat sheets for machine learning are plentiful. Quality, concise technical cheat sheets, on the other hand... not so much. A good set of resources covering theoretical machine learning concepts would be invaluable. Shervine Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. The VIP cheat sheets, as Shervine and Afshine have dubbed them (Github repo with PDFs available here), are structured around covering key top-level topics in Stanford's CS 229 Machine Learning course, including: Notation and general conceptsLinear modelsClassificationClusteringNeural networks... and much more Links to individual cheat…

29 Sep

Free Popular APIs for your projects

geekycodesco0 CommentsBlog, C, JAVA, Python, TutorialsAPI, Coding, Dataframe, Datasets, JAVA, Programming, REST APIs

An application programming interface(APIs) is a connection between computers or between computer programs. It is a type of software interface, offering a service to other pieces of software. A document or standard that describes how to build such a connection or interface is called an API specification. There are a lot of opensource APIs available over internet. Here I'm going to tell you about some of them. Open Weather Map API Open weather map API provides the current weather information for any location including over 200K cities. This API is one of the very useful API for the programming beginners…