Performance Metrics for Classification and Regression Algorithms

We will examine many performance indicators used frequently in machine learning in this post. The performance and efficiency of our machine learning model are measured using performance metrics. In machine learning, the performance of a model is often evaluated using performance metrics. These metrics help assess the accuracy and effectiveness of classification and regression algorithms. […]

How to do Ensembling in machine learning?

Ensembling is a powerful technique for improving the performance of machine learning models. This article will provide an overview of ensembling and explore popular techniques such as bagging, boosting, and stacking. By using ensembling methods, you can improve model accuracy and generalization. There are several types of ensembling techniques, including: To select the best ensemble […]

Random Forest Algorithm

Random Forest is a robust machine-learning algorithm that is used for both classification and regression tasks. It is a type of ensemble learning method, which means that it combines multiple decision trees to create a more accurate and stable model. The mathematical intuition behind Random Forest is rooted in the concept of decision trees and […]

Which one to use – RandomForest vs SVM vs KNN?

The basic steps to deciding which algorithm to use will depend on a number of factors. A few factors which one can look for are listed below: The number of examples in the training set. Dimensions of featured space. Do we have correlated features? Is overfitting a problem? These are just a few factors on […]

How to create Movie Recommendation System

Movie Recommendation System

In this notebook, I will try to use a few recommendation algorithms (content-based, popular-based and shared filters) and try to build a collection of these models to come up with our final movie recommendation system. For us, we have two MovieLens data sets. Full Data Set: Contains 26,000,000 ratings and 750,000 tag requests applied to […]

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

Movie Recommendation System

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 […]

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

Table of Contents What is Dimensionality Reduction Why Dimensionality Reduction is Important Dimensionality Reduction Methods and Approaches Dimensionality Reduction Techniques Dimensionality 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 […]

Interpreting ACF and PACF | Time Series


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 […]

Predictive Analysis with different approaches

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 […]

Analysis on campus recruitment data

Campus recruitment is a strategy for sourcing, engaging and hiring young talent for internship and entry-level positions. College recruiting is typically a tactic for medium- to large-sized companies with high-volume recruiting needs, but can range from small efforts (like working with university career centers to source potential candidates) to large-scale operations (like visiting a wide array […]

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