Dalam era kontemporer, penyampaian opini atau pandangan melalui teks menjadi kebutuhan di berbagai bisnis. Sebagai contoh, ulasan tentang suatu jasa atau produk memiliki potensi untuk menjadi sumber yang berharga dalam dunia bisnis. Ulasan ini memiliki fungsi ganda: pertama, mereka menjadi termometer yang menunjukkan sejauh mana pelanggan puas dengan produk atau jasa yang mereka gunakan. Kedua, ulasan tersebut menjadi sumber informasi berharga bagi perusahaan untuk mendengarkan kritik dan saran, serta menindaklanjuti isu-isu yang mungkin muncul terkait produk atau layanan yang ditawarkan. Namun, nilai dari informasi teks di media sosial tak terbatas hanya pada ulasan produk atau jasa. Teks-teks ini juga bisa menjadi sumber insight mengenai persepsi pengguna terhadap citra perusahaan, pemahaman mereka tentang dinamika pasar saat ini, tren yang sedang naik daun, dan bahkan mengungkap kebutuhan serta ekspektasi mereka yang mungkin belum terpenuhi. Memanfaatkan semua informasi ini bukanlah hal yang sederhana. Namun, dengan alat dan teknologi yang tepat, bisnis dapat dengan efisien mengumpulkan, menganalisis, dan memanfaatkan feedback ini untuk inovasi dan perbaikan. Di era digital saat ini, beragam platform telah menyediakan fitur-fitur khusus untuk mendukung interaksi pengguna, seperti kolom komentar dan fitur ulasan pada aplikasi atau website. Melalui platform ini, perusahaan memiliki akses langsung ke ocean of insights yang berasal dari pelanggan mereka, memberikan wawasan yang mendalam dan luas serta respons instan yang bisa membantu dalam pengambilan keputusan bisnis.

Our project aims to conduct an analysis of the Mall Customers data, which is sourced from Kaggle, using unsupervised machine learning techniques such as K-means clustering and principal component analysis. Specifically, we will assess how Gender, Age, and Income relate to a customer’s spending habits, with the Spending Score serving as an indicator of the amount a customer spends in the mall. This analysis will enable us to identify patterns and group customers based on similarities in their spending habits.

The sinking of the Titanic is one of the most infamous shipwrecks in history. On Sunday, April 14, 1912, during her maiden voyage, the widely considered unsinkable RMS Titanic sank after colliding with an iceberg. The Titanic’s distress signals were heard by a nearby ship. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in 1502 out of 2224 passengers and crew deaths. Federal law soon required that all large ocean-going vessels be equipped with wireless for safety reasons. David Sarnoff noted that the Titanic disaster brought radio to the front. Purpose of the project : - Know the relationship between Survived based on historical data. - Learn to use Logistic Regression & K-NN to predict Survived based on the data set.

The sinking of the Titanic is one of the most infamous shipwrecks in history. On Sunday, April 14, 1912, during her maiden voyage, the widely considered unsinkable RMS Titanic sank after colliding with an iceberg. The Titanic’s distress signals were heard by a nearby ship. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in 1502 out of 2224 passengers and crew deaths. Federal law soon required that all large ocean-going vessels be equipped with wireless for safety reasons. David Sarnoff noted that the Titanic disaster brought radio to the front. Purpose of the project: Know the relationship between `Survived` based on historical data. Learn to use Logistic Regression & K-NN to predict `Survived` based on the data set.

The sinking of the Titanic is one of the most infamous shipwrecks in history. On Sunday, April 14, 1912, during her maiden voyage, the widely considered unsinkable RMS Titanic sank after colliding with an iceberg. The Titanic’s distress signals were heard by a nearby ship. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. Federal law soon required that all large ocean-going vessels be equipped with wireless for safety reasons. David Sarnoff noted that the Titanic disaster brought radio to the front. Purpose of the project : - Learn to use Naive Bayes, Decision Tree & Random Forest to predict `Survived` based on the data set.

The sinking of the Titanic is one of the most infamous shipwrecks in history. On Sunday, April 14, 1912, during her maiden voyage, the widely considered unsinkable RMS Titanic sank after colliding with an iceberg. The Titanic’s distress signals were heard by a nearby ship. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. Federal law soon required that all large ocean-going vessels be equipped with wireless for safety reasons. David Sarnoff noted that the Titanic disaster brought radio to the front. Purpose of the project : - Know the relationship between [Survived] based on historical data. - Learn to use Generalized Linear Models & K-NN to predict [Survived] based on the data set.

The data-set related to life expectancy and health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. It was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang. Purpose of the project : - Know the relationship between "Life Expectancy" based on historical data. - Learn to use a linear regression model to predict "Life Expectancy" based on the dataset.

The data is retrieved from Kaggle. The goal of this project is to explore the train.csv (It will be useful for the analysis of what sorts of people were likely to survive in the next project).