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Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
My Academic Major Project Movie Recommendation using Artificial Intelligence. We also developed a website named movie engine for the recommendation of movies.
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Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
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This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.
This document provides an overview of AlgoAnalytics, an analytics consultancy company that uses advanced machine learning techniques. The summary is as follows:
(1) AlgoAnalytics provides predictive analytics solutions for retail, healthcare, financial services, and other industries using techniques like deep learning, natural language processing, and computer vision on structured, text, image and sound data.
(2) The CEO and founder, Aniruddha Pant, has over 20 years of experience applying mathematical techniques to business problems. Some of AlgoAnalytics' work includes recommender systems, demand prediction, image analysis, and customer churn prevention for online retail.
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Tools and Methods for Big Data Analytics by Dahl WintersMelinda Thielbar
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Tools and Methods for Big Data Analytics by Dahl WintersMelinda Thielbar
Research Triangle Analysts October presentation on Big Data by Dahl Winters (formerly of Research Triangle Institute). Dahl takes her viewers on a whirlwind tour of big data tools such as Hadoop and big data algorithms such as MapReduce, clustering, and deep learning. These slides document the many resources available on the internet, as well as guidelines of when and where to use each.
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2. It describes extracting implicit feedback from users and engineering contextual features to create a large-scale dataset for learning recommendations.
3. An evaluation of the recommendation system shows that a learning to rank approach with contextual information outperforms other methods in accuracy while maintaining diversity and novelty, though recommending new programs requires more investigation.
Project Explanation: Book Recommendation System
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1. Data Collection: I gathered a comprehensive dataset of books, including information such as titles, authors, genres, and user ratings. This data was obtained from various reliable sources, such as online bookstores or publicly available book datasets.
2. Data Preprocessing: The collected data required cleaning and preprocessing to ensure its quality and consistency. I handled missing values, resolved inconsistencies in book titles or authors, and standardized the data format for further analysis.
3. Exploratory Data Analysis: I performed exploratory data analysis to gain insights into the dataset. This included analyzing book genres, distribution of user ratings, and identifying popular authors or books.
4. Feature Engineering: To capture the preferences and interests of users, I created relevant features from the available data. These features could include book genres, authors, user demographics, or historical reading behavior.
5. Recommendation Model Development: I developed a recommendation model using collaborative filtering techniques or content-based filtering methods. Collaborative filtering utilizes the preferences of similar users to make recommendations, while content-based filtering suggests books based on their attributes and user preferences. I employed popular machine learning algorithms, such as matrix factorization or k-nearest neighbors, to build the recommendation model.
6. Model Evaluation: I evaluated the performance of the recommendation system using metrics such as precision, recall, or mean average precision. I also conducted A/B testing or cross-validation to assess the system's effectiveness and optimize its performance.
7. User Interface Development: I created a user-friendly interface where users could input their preferences and receive personalized book recommendations. The interface provided an intuitive and interactive experience, allowing users to explore recommended books and provide feedback.
8. Deployment and Feedback Loop: The recommendation system was deployed in a production environment, where users could access it and provide feedback on the recommended books. This feedback was incorporated into the system to continually improve its accuracy and relevance over time.
By completing this project, I gained hands-on experience in data collection, preprocessing, exploratory data analysis, and recommendation system development. I demonstrated my ability to leverage machine learning algorithms and user data to build a personalized book recommendation system that enhances user engagement and satisfaction.
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Recommendation engines : Matching items to usersjobinwilson
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This document discusses several potential artificial intelligence projects from students at HKBK College of Engineering. It describes projects to develop a creative AI using deep learning to generate art, music and stories. Another project aims to use time series analysis and natural language processing to predict stock performance. A third project discusses using deep learning models to detect diseases from medical scans to improve healthcare.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
This document discusses building a web application using Dash to visualize and forecast stock prices through machine learning. It will fetch stock data using APIs, display historical prices and other metrics in plots, and use a support vector regression model to predict future prices based on user input of a stock ticker and number of days. The SVR model is trained on stock features like date, open, close, high, low, and volume to learn the relationship between these attributes and price changes over time. The final system allows users to analyze stock performance visually and obtain predicted price values.
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1. Data Collection: I gathered a comprehensive dataset of books, including information such as titles, authors, genres, and user ratings. This data was obtained from various reliable sources, such as online bookstores or publicly available book datasets.
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4. Feature Engineering: To capture the preferences and interests of users, I created relevant features from the available data. These features could include book genres, authors, user demographics, or historical reading behavior.
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7. User Interface Development: I created a user-friendly interface where users could input their preferences and receive personalized book recommendations. The interface provided an intuitive and interactive experience, allowing users to explore recommended books and provide feedback.
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ccording to the data, Olist E-commerce has about 99,440 orders. With about 89,940 orders being delivered, the company has a 90% delivery success rate.
✔Their average product rating is 4.09 stars, with product categories going as high as 4.67 stars and as low as 2.5 stars. 1 Star reviews are on third place in the review score distribution ranking which likely indicates that there could be problems with product quality in some product categories
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For More Info.Reach our Big Data Technical Team@ +91 96677211551/56
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2. INDEX
• Introduction
• Proposed System/Solution
• System Development Approach
(Technology Used)
• Algorithm & Deployment
• Result
• Conclusion
• Future Scope
• References
• Contact
• Resources
3. Exploring Anime
Trends: A Data-Driven
Analysis
Introduction to
the Project:
• The world of anime is vast and ever-evolving,
reflecting diverse viewer preferences and
trends. Our project delves into the heart of
this dynamic industry, seeking to understand
and analyze the intricate patterns that
define viewer choices.
4. Objective:
• Unveil the underlying trends and
preferences of anime enthusiasts through
comprehensive data analysis.
Key Focus:
• Understand what captivates audiences in
terms of genres, series lengths, and viewer
ratings.
• Identify the highest-rated anime within
various genres to provide valuable
recommendations.
5. Problem
Statement:
• Analyze and understand user preferences and
trends in the anime industry based on a dataset.
• Determine the highest-rated anime genres, audience
preferences for different mediums, and explore
relationships between the number of episodes and
ratings.
7. Back to Agenda Page
Objectives: Investigate Viewer Preferences:
Examine the factors that influence audience choices, providing valuable
insights for content creators and distributors.
Explore Series Lengths:
Analyze the distribution of series lengths, identifying patterns that
resonate with viewers
Genre Popularity:
Dive into the popularity of different genres, uncovering the genres that
captivate audiences the most.
Identify Top-Rated Anime by Genre:
Pinpoint the highest-rated anime within various genres, facilitating
personalized recommendations for anime enthusiasts.
9. Data Loading and
Exploration:
Pandas Library:
• Employs Pandas for efficient
data manipulation and
exploration.
System
Development
Approach
Empowering Analysis with
Cutting-Edge Technologies
Seaborn and Matplotlib:
• Utilizes Seaborn and Matplotlib
for visually engaging data
representation.
10. Data Cleaning:
System
Development
Approach
Empowering Analysis with
Cutting-Edge Technologies
• Pandas Data Cleaning:
• Applies Pandas for the
replacement of missing values
and conversion of data types.
Data Binning:
• Equal Width Binning and Square
Root Rule:
• Implements these techniques for
binning the 'episodes' data, providing
structured insights.
11. Genre Analysis:
System
Development
Approach
Empowering Analysis with
Cutting-Edge Technologies
Pandas 'explode':
• Exploits the 'explode'
function for genre data
manipulation.
• Seaborn and Matplotlib:
• Utilizes these tools for
visualizing average ratings by
genre.
12. Algorithm &
Deployment
• Our project adopts a primarily
exploratory data analysis (EDA)
approach, focusing on statistical
measures and visualization
algorithms.
Algorithmic Approach:
• Unlike traditional machine learning projects,
our emphasis is on understanding patterns
and relationships within the dataset rather
than predictive modeling.
No Specific ML Algorithms:
13. Deployment:
The analytical system is designed
for deployment within a Jupyter
Notebook environment.
Iterative data exploration using
EDA algorithms.
Visualization techniques for
depicting trends and
relationships.
Key Features:
Content Based Recommender
14. Result
Detailed Exploration:
A comprehensive exploration and analysis
of the anime dataset have yielded
intriguing insights into viewer preferences
and industry trends.
Unveiling Insights: Visualizations and
Key Findings
19. Conclusion
Drawing Meaningful Conclusions
Summary of Insights:
The exploration of the anime
dataset has provided valuable
insights into viewer preferences,
series lengths, and genre
popularity.
example of result
20. Future Scope
Integration of Machine Learning Models:
• Future iterations could involve the
integration of machine learning
models for predictive analytics and
personalized recommendations.
Beyond Exploration: Future Directions
Real-time Data Analysis:
• Exploring the potential for real-
time data analysis to capture
ongoing trends in the dynamic
anime industry.
21. Opportunities for collaborations with
industry experts, content creators,
and data scientists to enhance the
depth and scope of the analysis.
Future Scope
Collaborations:
Real-time data analysis and
updating for ongoing trends in the
anime industry.
22. References
Acknowledgments and References
External
Datasets
Anime Recommendations Database source
kaggle used for data set such as file name
Anime.csv and Rating .csv
Libraries and Tools:
• Pandas:
⚬ Used for data manipulation, including data loading,
cleaning, and exploration.
• NumPy:
⚬ Likely used in conjunction with Pandas for numerical
operations and array manipulations.
• Seaborn:
⚬ Utilized for creating statistical data visualizations,
including bar plots and scatter plots.
• Matplotlib:
⚬ Used for creating static, interactive, and animated
visualizations in Python.
• Jupyter Notebook:
⚬ Chosen as the development environment for interactive
and iterative data analysis.
Credits:
dataset creator kaggle user
ppt created by Rahul Meshram