rsvp movies sql assignment github

SQL_RSVP_Movies

Problem statement:.

RSVP Movies is an Indian film production company that wants to release a movie for the global audience in 2022. They have approached you, a data analyst and SQL expert, to help them analyze data from the past three years of movies to draw meaningful insights that can help them with their new project.

The problem statement is divided into four segments:

Segment 1: Analyze the number of movies released each year and month to identify the most popular time of year for movie releases.

Segment 2: Analyze the genres of movies released to identify the most popular genres.

Segment 3: Analyze the production houses that have released the most successful movies to identify the most successful production houses.

Segment 4: Analyze the actors and actresses who have starred in the most successful movies to identify the most popular actors and actresses.

• Executive Summary PDF.
• README File.

SQL-RSVP-Movies-Case-Study

Sql rsvp movies case study.

SQL queries performed on the IMDb database to provide recommendations to RSVP Movies based on insights.

Problem Introduction

RSVP Movies is an Indian film production company that has produced many super-hit movies. They have usually released movies for the Indian audience but for their next project, they are planning to release a movie for the global audience in 2022.

The production company wants to plan its every move analytically based on data and has approached you for help with this new project. You have been provided with the data on the movies that have been released in the past three years. You must analyze the data set and draw meaningful insights that can help them start their new project.

You are a data analyst and an SQL expert. You must use SQL to analyze the given data and give recommendations to RSVP Movies based on the insights. For your convenience, the entire analytics process has been divided into four segments, where each segment leads to significant insights from different combinations of tables. The questions in each segment with business objectives are written in the script.

Data Set and Database Creation

• Download the IMDb dataset.
• The first tab contains the ERD and the table details. Study that carefully and understand the relationships between the table.
• Inspect each table given in the subsequent tabs and understand the features associated with each of them.
• Open your MySQL Workbench and start writing the DDL and DML commands to create the database.

If you don’t wish to perform the data loading part, you can directly download the SQL script file containing all the commands and data required for the database creation and start directly with the querying.

Instantly share code, notes, and snippets.

JonathanCass / movies.sql

• Download ZIP
• Star ( 0 ) 0 You must be signed in to star a gist
• Fork ( 0 ) 0 You must be signed in to fork a gist
• Embed Embed this gist in your website.
• Share Copy sharable link for this gist.
• Clone via HTTPS Clone using the web URL.
• Learn more about clone URLs
• Save JonathanCass/7c2f530455e806167147fe7be04c9709 to your computer and use it in GitHub Desktop.

Overview - datagrad/IMDB-Data-Analysis-in-SQL GitHub Wiki

Imdb-analysis-in-sql.

This analysis is carried out to support RSVP Movies with a well-analyzed list of global stars to plan a movie for the global audience in 2022.

With this, we will be able to answer a set of analytical questions to suggest RSVP Production House on which set of actors, directors, and production houses would be the best fit for a super hit commercial movie.

RSVP Movies is an Indian film production company that has produced many super-hit movies. They have usually released movies for the Indian audience but for their next project, they are planning to release a movie for the global audience in 2022.

Why this Analysis?

The production company wants to plan its every move analytically based on data and has approached for help with this new project.

We have been provided with the data of the movies that have been released in the past three years. Let's analyze the data set and draw meaningful insights that can help them start their new project.

We will use SQL to analyze the given data and give recommendations to RSVP Movies based on the insights.

We will be carrying out the entire analytics process into four segments, where each segment leads to significant insights from different combinations of tables.

Vasundhara Kadekodi Portfolio

Marketing and retail-analytics.

💰🛍️📊 Reduce costs and increase revenue with OList E-commerce Inventory Management! 📈🔍 Using Pareto analysis and market basket analysis, we identify top-performing products and customer purchase behavior to optimize inventory and prioritize profitable categories. 📉💡 Our recommendations include focusing on top-performing products, creating product bundles, and removing slow-moving items from categories. 💪

• View Project

Resume Screening

🔍📄 Traditional manual resume screening is time-consuming and prone to biases. But, 🤖 machine learning models can automate the process, leading to a more efficient and objective screening process. In this project, we preprocess the data to extract relevant features, analyze it using various ML techniques, and build models to predict the category of each resume based on job requirements. The models are evaluated using performance metrics, and the insights are used to improve the screening process. 📈💡👨‍💻

Bike Sharing A Machine Learning Model

Multiple linear regression 📈 was performed on the Boombikes bike rental dataset to find the number of rentals issued 🚴 based on various independent values such as temperature 🌡️, weather 🌤️, and holidays 🏖️. The R-squared value suggests a good model, with temperature 🌡️, light rain ☔, and year 📅 being the top predictor variables. Recommendations include increasing bike availability 🚲 and promotions 🎉 during the summer months.

Lead Score A Machine Learning Model

🔍📈 The Lead Score Analysis for X Education 🎓 helps to increase enrollment by identifying the most influential factors in converting potential customers into registered students. By analyzing data on site visits, time spent on the site, lead origin, notable activity, and more, the analysis provides actionable insights that can be used to optimize the website and increase conversions. Recommendations include improving the UI/UX, offering discounts 💰, and prioritizing the key variables identified in the analysis.

Instagram Analytics in SQL

📈💻 In this project, I worked with the Instagram product team to provide insights on user behavior and engagement through SQL fundamentals. 🔍👥 I analyzed user data to help the marketing team with campaigns, such as identifying loyal and inactive users and suggesting commonly used hashtags. 💡📊 Additionally, I provided investor metrics on user engagement and identified fake accounts on the platform.

RSVP Movie Case-Study in SQL

🎥🌍 In this project, I analyzed data from RSVP Movies, an Indian film production company, to provide meaningful insights for their upcoming global movie release in 2022. By analyzing data on past movie releases, I was able to make recommendations and help the company plan their project analytically. This project gave me valuable experience in data analysis and the opportunity to contribute to the success of a major film production company.

IPL Match Visualization

📊🏏 It is an interactive Tableau dashboard to showcase IPL statistics from 2008 to 2017. used two datasets, 'matches.csv' and 'deliveries.csv', to create visualizations for match, player, and team statistics. The dashboard included subcategories such as toss outcome vs match outcome, biggest wins, highest totals, orange and purple cap contenders, and team performance. The end goal is to provide important insights and statistics for an infographic that will be used in a newsletter. 📈👨‍💻

Road Accident Dashboard

🚗📊 The UK road accident dashboard, built with Tableau, provides users with a detailed overview of road accidents in the country. The interactive visualizations enable users to explore the data based on accident severity, region, and road type, as well as track patterns by year. 💻🔍

Covid-19 Data Visualization

🌎📊 An interactive Tableau dashboard for COVID-19 cases worldwide with visualizations and insights on death and infection rates. It also displays data based on geographical areas.

Stroke Prediction

🧠📈 This project builds a machine learning model to predict stroke likelihood based on risk factors like age, hypertension, and smoking status. We used Python 🐍, Scikit-learn, and several algorithms to build the model and evaluated its accuracy using performance metrics. Our model can help healthcare professionals identify high-risk patients and prevent strokes. 💪🏥

EDA on Bank Loan Dataset

💼📈 A consumer finance company wants to identify loan default patterns through EDA to make informed decisions and reduce financial risks. 🔍📊 We will analyze data and identify strong indicators of default to understand driving factors and utilize this knowledge for risk assessment. 💻🤔 Research on risk analytics will help us make data-driven decisions to ensure applicants capable of repaying the loan are not rejected.

(+91) 9380581879

[email protected]