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UFC-Fighters (Clustering and Dimension Reduction)
This is a project looking at the attributes of the top 5 male and female fighters in each weight class in the UFC.
Dimension Reduction Project: Housing Dataset Using PCA
In this project, Principal Component Analysis (PCA) is applied to a housing dataset to explore the relationships between various housing features and prices. The analysis begins with a detailed data cleaning and exploratory data analysis (EDA) phase, followed by the application of PCA to reduce the dataset's dimensionality. Using statistical tests like Bartlett's test and the Kaiser-Meyer-Olkin (KMO) test, we evaluate the dataset's suitability for PCA. The results highlight the contributions of key variables and principal components, offering actionable insights while simplifying the dataset for further analysis. This project serves as a practical example of using PCA to handle high-dimensional data in the real estate sector, helping to streamline analysis and improve interpretability without sacrificing essential information.
Clustering Analysis of Club Goers: A Comparison of K-Means and DBSCAN
This analysis explores the clustering behavior of clubgoers based on various preferences, entry times, and demographic information. Using unsupervised learning techniques, we compare the performance of K-Means and DBSCAN clustering algorithms. The optimal number of clusters is determined using the elbow method, and the clustering results are visualized using PCA (Principal Component Analysis) to reveal distinct patterns within the data. DBSCAN's ability to detect irregular clusters and noise is compared with K-Means' performance on well-separated clusters. This study highlights the strengths and limitations of both algorithms in identifying meaningful segments among clubgoers.
Retail sales: Association Rules
This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.
Differential Expression Analysis - Filtering and Visualization
This analysis includes all genes in SCT logfc.threshold used = 0 min.pct=0 Then we used added column of mean expression and we used those columns for filtering. Here you have summary of genes before filtering and after filtering
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PPV1 isolates from wild boars were collected in 2022-2024 hunting seasons in Moscow, Tver and Belgorod regions. After phylogenetic analysis of VP1/2 sequences, 27-a-like strains were marked by red colour and Kresse-like strains were marked by yellow colour.
Differential Expression Analysis - Filtering and Visualization
Its with previous file All genes min.pct = 0 logfc.threshold = 0
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