Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

Analyzed Netflix 2023 viewership data to optimize content strategy. Identified shows as key engagement drivers, highlighted strong non-English content, and pinpointed March/Spring as peak release times for max viewership. Provides actionable insights for content acquisition and scheduling.

Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

This repository is about cleaning and analyzing Netflix movies and tv shows data, and getting insights about the countries where they were filmed, their ratings, release years, and cast.

EDA, manipulating raw data, drawing conclusions from plots on Netflix data.

A Python project for visualizing and analyzing Netflix content data using pandas, matplotlib, and seaborn. The project explores trends, genres, content ratings, and country-wise statistics with insightful visualizations.

This project analyzes Netflix data to uncover viewer behaviors and content trends, aiming to optimize content strategies and enhance user experience.

This project focuses on analyzing Netflix’s content library to uncover meaningful insights about movies and TV shows across different years, countries, genres, and content types. Using data visualization techniques, the dashboard provides an interactive and intuitive way to understand how Netflix’s content has evolved globally.

Using SQL, and Tableau, I dove into Netflix's massive dataset of 82,000 rows containing shows and movies. I simplified and analyzed the data, revealing cool insights and interesting facts about what's available on the platform. These tools helped me find patterns, uncover trends, and share a clear picture of Netflix's diverse content world

This project forecasts Netflix subscriptions using ARIMA, analyzing data from 2013 to 2023 for insights.

The Netflix Data Analysis Project aims to explore, analyze, and visualize data related to Netflix shows and movies.

A dashboard for exploring and analyzing Netflix data, offering insights through various interactive charts and visualizations.

This project explores the Netflix dataset using Tableau, a powerful data visualization tool. It aims to analyze and visualize various aspects of Netflix's content catalog and provide insights into the streaming platform.

Analyzed my Netflix account data to uncover trends in genres, ratings, and user engagement. Visualized insights to inform content strategy and identify popular titles.

This project is a project designed to analyze and visualize the Netflix userbase, providing insights into user demographics, revenue trends, and subscription behaviors. The process involed in creating this includes; data preparation, data cleaning and visualization.

Introduction In this project, you will act as a data visualization developer at Yahoo Finance! You will be helping the "Netflix Stock Profile" team visualize the Netflix stock data. In finance, a stock profile is a series of studies, visualizations, and analyses that dive into different aspects a publicly traded company's data. For the purposes of the project, you will only visualize data for the year of 2017. Specifically, you will be in charge of creating the following visualizations: The distribution of the stock prices for the past year Netflix's earnings and revenue in the last four quarters The actual vs. estimated earnings per share for the four quarters in 2017 A comparison of the Netflix Stock price vs the Dow Jones Industrial Average price in 2017 Note: We are using the Dow Jones Industrial Average to compare the Netflix stock to the larter stock market. Learn more about why the Dow Jones Industrial Average is a general reflection of the larger stock market here. During this project, you will analyze, prepare, and plot data. Your visualizations will help the financial analysts asses the risk of the Netflix stock. After you complete your visualizations, you'll be creating a presentation to share the images with the rest of the Netflix Stock Profile team. Your slides should include: A title slide A list of your visualizations and your role in their creation for the "Stock Profile" team A visualization of the distribution of the stock prices for Netflix in 2017 A visualization and a summary of Netflix stock and revenue for the past four quarters and a summary A visualization and a brief summary of their earned versus actual earnings per share A visualization of Netflix stock against the Dow Jones stock (to get a sense of the market) in 2017 Financial Data Source: Yahoo Finance

In this project we will be analysing Netflix data and try to find interesting trends which the audience has for the most famous and most used online streaming platform.

Big-Data Analysis with Python

Analyze Your Netflix Data By Using this simple Program

Netflix’s personal viewing history data, analyze, and visualize them.

I used SQL Server to manipulate and analyze my Netflix viewing history data.

Analyzed Netflix's content and viewer data to recommend strategies for increasing viewer retention and engagement.

The Netflix EDA Project uncovers insights from streaming data to enhance content strategy. Analyzing genres, user behavior, and regional variations, it informs decision-making, improves user experiences, and ensures global relevance. An exciting exploration into Netflix's data-driven success.

This project, **Netflix Exploratory Data Analysis (EDA)**, focuses on analyzing Netflix’s dataset to uncover trends, patterns, and insights. Through EDA, it explores user behavior, popular genres, content trends, and more. The project helps understand key factors driving Netflix's content strategy and audience preferences.

Netflix Data Analysis :- This project analyzes a Netflix dataset to uncover trends and insights about content types, release patterns, and popular genres. Using Python and data visualization libraries, it provides a clear understanding of Netflix's content strategy and growth over time.

This Python-based data analysis project explores trends in Netflix movies using Pandas and Matplotlib. Analyzing release years, genres, and durations, the project visualizes changes in movie durations over the years.

This project analyzes Netflix movies data using Python. It includes cleaning the data, making charts, and finding insights like most common genres, release trends, ratings, and popular movies.

This repository contains a visually appealing and interactive dashboard built using Tableau to analyze and explore Netflix data. The dashboard provides key insights into various aspects of Netflix content, including trends, genres, ratings, and more.

Analyzed Netflix content and user data to uncover viewing trends, content popularity, and subscription behavior. Performed data cleaning, exploratory data analysis (EDA), and developed visualizations to identify patterns in genres, release years, and regional

Analyze Netflix data in Python, addressing duplicates, null values, and answering pivotal questions. Explore details about 'House of Cards,' discover release trends, and identify top directors. Clean visuals, code snippets, and insights await in this concise Netflix dataset exploration.