AlmaBetter Capstone Project 3 - Regression : TED Talks Views Prediction
This project is a part of the Almabetter Pro Program Curriculum(Full Stack Data Science)
-- Project Status: [Completed]
-- Score: [ 93 / 100 ]
Project Summary :
Problem Statement :
The main objective is to build a predictive model, which could help in predicting the views of the videos uploaded on the TEDx website.
About the Data :
We have the data of previous TED talk events , which contains data points such as the length (duration ) of the talk, topics , speaker occupation and textual features such as Transcript , Title , and Description And most importantly , the target variable : the view of the video The Data is available for 4005 TED talks .
Dataset info
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1.Number of records: 4,005
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2.Number of features: 19
Features information:
The dataset contains features like:
- talk_id: Talk identification number provided by TED
- title: Title of the talk
- speaker_1: First speaker in TED's speaker list
- all_speakers: Speakers in the talk
- occupations: Occupations of the speakers
- about_speakers: Blurb about each speaker
- recorded_date: Date the talk was recorded
- published_date: Date the talk was published to TED.com
- event: Event or medium in which the talk was given
- native_lang: Language the talk was given in
- available_lang: All available languages (lang_code) for a talk
- comments: Count of comments
- duration: Duration in seconds
- topics: Related tags or topics for the talk
- related_talks: Related talks (key='talk_id',value='title')
- url: URL of the talk
- description: Description of the talk
- transcript: Full transcript of the talk
Target Variable :
- views: Contains Count of views of every talk
Project Work flow
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Importing Libraries
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Loading the dataset
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Data Summary
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Data Cleaning & Data Analysis
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Feature selection
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Implementing Various Regression Algorithms
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HyperParameter Tuning
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Final selection of the model
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Conclusion
Technical Details for ML :
We used many Algorithms ( Random Forest , XGBoost and GBRegressor , Lasso , Ridge ) We used GridSearchCV for HyperParameter Tuning Comparing both R2 Score , we can see that GBRegressor and XGBoost model performs the best
Miscellaneous :
- Google colab tools