Source for book "Feature Engineering A-Z"
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Updated
Oct 3, 2024 - HTML
Source for book "Feature Engineering A-Z"
Marker Selection by matching manifolds and elastic net
Marker gene selection from scRNA-seq data
Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.
Webapps using Flask for implementation of machine learning sentiment prediction with feature selection PSO and GA with SVM as classifier
A beginner level Machine Learning pipeline covering all basic steps.
Investigate the reasons behind bankruptcy and attempt to identify early warning signs. Perform exploratory data analytics using pandas profiling and apply missing value treatments and oversampling
Collection of end-to-end regression problems (in-depth: linear regression, logistic regression, poisson regression) 📈
Machine Learning Classification on Unbalanced Real World Dataset
Zindi competition on predicting the likelihood of credit default of ecommerce clients
Detecção de Fraudes no Tráfego de Cliques em Propagandas de Aplicações Mobile
Sample Review & Feature Selection for Audio Datasets
The Shakespeare-Method repository contains the code we used to develop a new method to identify attributed and unattributed potential adverse events using the unstructured notes portion of electronic health records.
Predicting the Likelihood of Diabetes Using Common Signs and Symptoms - About one-third of patients with diabetes do not know that they have diabetes according to the findings published by many diabetes institutes around the world. Detecting and treating diabetes patients at early stages is critical in order to keep them healthy and to ensure th…
Creating Customer Segments
Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervise…
End-to-end projects: customer churning prediction using the Random Forest Classifier Algorithm with 97% accuracy; performing pre-processing steps; EDA and Visulization fitting data into the algorithm; and hyper-parameter tuning to reduce TN and FN values to perform our model with new data. Finally, deploy the model using the Streamlit web app.
Statistical regularization
Exploratory Data Analysis (EDA), Data cleaning & Data preprocessing and Features Engineering,
Homework Solutions for Statistical Learning Course as Computer Science B.Sc. Student at Department of Mathematical Sciences, Sharif University of Technology
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