This study compares Binary Logistic Regression and Multinomial Logistic Regression as two approaches for analyzing categorical data. Binary Logistic Regression is applied when the dependent variable has two categories (e.g., “yes” or “no”), while Multinomial Logistic Regression is suitable for variables with more than two categories (e.g., “low”, “medium”, “high”). The comparison aims to explore the differences in model structure, assumptions, and predictive performance between the two methods across various data contexts. The results are expected to provide a clearer understanding of when each model is most appropriately applied.

This project aims to analyze the factors influencing the success of marketing strategies using the Logistic Regression method. The dataset includes variables such as Advertising Budget, Salespeople, Customer Satisfaction, and Competition Level, with the target variable Success (1 = success, 0 = failure). Through this analysis, a predictive model is developed to estimate the probability of marketing success and evaluate its performance using statistical metrics such as accuracy and AUC.

Laporan ini menyajikan hasil analisis eksploratif terhadap dataset kesehatan yang mencakup informasi pasien, seperti jenis kelamin, usia, kondisi medis, asuransi, hasil pemeriksaan, serta tanggal masuk rumah sakit. Tujuan dari analisis ini adalah untuk memahami karakteristik demografis pasien, distribusi kondisi medis

Analisi penjualan yang melewati beberapa proses seperti Wrangling Data

This task applies the Fishbone Diagram from the QC Seven Tools to identify the root causes of low customer satisfaction in Shopee. Causes are grouped into six categories (Manpower, Method, Machine, Material, Mother Nature, Measurement) for a structured root cause analysis.

Data wrangling is the process of cleaning, transforming, and organizing raw data into a structured format suitable for analysis. This step involves handling missing values, converting data types (such as dates), filling in incomplete information, and ensuring consistency across the dataset. The main goal is to improve data quality and readability, enabling more accurate and meaningful analysis in the next stages.

This document contains an interactive dashboard displaying real-time weather data, complete with charts and trend analysis.

Descriptive visualization uses charts, graphs, and tables to summarize and present data clearly, helping identify patterns, trends, and distributions. It is commonly used in exploratory data analysis (EDA) to gain insights and communicate findings effectively without making predictions. Common examples include: Bar and pie charts for category comparisons Line charts for trends Histograms and box plots for distributions Scatter plots for relationships between variables

An exploratory data analysis of Amazon Prime movies and TV shows using R. Includes data cleaning, interactive visualizations (plotly), and insights on content trends, types, and top genres. Created with R Markdown.

This business plan outlines the strategy for launching short-term rental apartments in Jakarta. It covers market analysis, target audience, competitive landscape, marketing approach, operational plan, and financial projections to ensure the successful establishment and growth of the rental property venture in the city.

Beberapa teknik visualisasi data agar kita mudah untuk membaca data sehingga menghasilkan analisis yang tepat

Dokumen ini menjelaskan langkah-langkah proses transformasi dan analisis data kesehatan pasien. Data yang digunakan mencakup informasi tanggal pemeriksaan, lokasi, dan kondisi kesehatan. Proses yang dilakukan meliputi konversi format tanggal, penentuan hari dalam seminggu, identifikasi akhir pekan, serta pembuatan variabel dummy dari faktor lokasi dan kondisi kesehatan. Tujuan dari dokumen ini adalah untuk memudahkan analisis statistik dan pengolahan data secara efisien.

Proses analisis dari penjualan e-commerce dari waktu ke waktu yang di dalamnya terdapat beberapa proses sebelum di analisis seperti pembersiahan data, transformasi data, serta analisis data

UAS Statistika Dasar Prodi Sains Data INSTITUT TEKNOLOGI SAINS BANDUNG

masih perllu revisi