Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.

This article introduces a new kind of histogram-based representation for univariate random variables, named the phistogram because of its perceptual qualities. The technique relies on shifted groupings of data, creating a color-gradient zone that evidences the uncertainty from smoothing and highlights sampling issues. In this way, the phistogram offers a deep and visually appealing perspective on the finite sample peculiarities, being capable of depicting the underlying distribution as well, thus, becoming an useful complement to histograms and other statistical summaries. Although not limited to it, the present construction is derived from the equal-area histogram, a variant that differs conceptually from the traditional one. As such a distinction is not greatly emphasized in the literature, the graphical fundamentals are described in detail, and an alternative terminology is proposed to separate some concepts. Additionally, a compact notation is adopted to integrate the representation’s metadata into the graphic itself.

NIH Chest X-rays Preprocessed Version

This dataset is a preprocessed version of the NIH Chest X-ray Dataset. The original images were systematically organized, explored, and enhanced to improve their quality for research and machine learning applications.

What was done:

• The full directory structure of the dataset was explored and documented, including image counts and sample filenames for each folder.
• All chest X-ray images were processed using CLAHE (Contrast Limited Adaptive Histogram Equalization) to improve local contrast and highlight important features in the images.
• The processed images were saved in a new directory structure that mirrors the original, ensuring easy traceability and organization.
• Sample images were visualized and compared before and after preprocessing, with histograms provided to illustrate the improvement in contrast.
• Batch processing was performed, with summary statistics and verification steps to confirm successful image enhancement and saving.

Outputs:

• A complete set of CLAHE-enhanced chest X-ray images, organized in the same way as the original dataset.
• Visualizations and statistics demonstrating the effectiveness of the preprocessing, including side-by-side comparisons and histogram overlays.
• Verified counts of processed images for each folder, ensuring data integrity.

This preprocessed dataset is ready for use in further analysis, model training, or clinical research, with improved image quality and consistent organization. No changes were made to the original labels or metadata.

code visualization

import plotly.express as pximport seaborn as snstips = sns.load_dataset("tips")fig = px.histogram(tips, x="total_bill", y="tip", color="sex", barmode="overlay", histfunc="sum", marginal="rug")fig.update_layout(xaxis_title="Total Bill", yaxis_title="Sum of Tip", legend_title="Sex")fig.show()

import plotly.express as pximport seaborn as snstips = sns.load_dataset("tips")fig = px.histogram(tips, x="total_bill", y="tip", color="sex", barmode="overlay"… See the full description on the dataset page: https://huggingface.co/datasets/justzhou/emma_testset.

The Analysis of large clusters with density histogram based approximate clustering.

The increasing use of ultrahigh-resolution mass spectrometry to investigate complex organic mixtures by nontargeted analysis using mainly direct infusion requires developing specialized software tools and algorithms to aid in and accelerate calibration, data processing, and analysis. To address this need, Punc’data, a JavaScript tool usable on a webpage for mass spectrometry (MS) data attribution, visualization, and comparison, was developed. Molecular formula attribution is performed using a network approach, where mass differences can be defined by the user or de novo determined by the software. Following the attribution process, the results obtained are visualized using charts commonly employed to study complex organic mixtures such as class histograms, van Krevelen diagrams, and Kendrick maps. Alternatively, data processed by other software programs can be imported for graphical representation. Emphasis has been placed on an interactive chart system designed to identify trends of chemical significance within, unique or common to different data sets. The comparison of different data sets is facilitated through principal component analysis.

This summary encapsulates the step-by-step process of conducting Hierarchical Cluster Analysis (HCA) using OPUS 8.5 software, covering data preparation, analysis execution, and result interpretation.

Software Launch: Open OPUS 8.5 software. Accessing Cluster Analysis: Navigate to the "Evaluate" dropdown menu. Selecting Cluster Analysis: Choose "Cluster Analysis" from the dropdown menu. Loading Method or Default: Load preferred method or proceed with default settings. Navigating to Spectra Reference Tab: Access the "Spectra Reference" tab within the Cluster Analysis interface. Adding Spectra: Import spectral data files into the software. Selecting Spectra: Choose specific spectra files for analysis. Preprocessing (If necessary): Apply preprocessing techniques such as Vector Normalization. Defining Analysis Region: Specify spectral region range for analysis. Initiating Cluster Analysis: Click "Analysis Cluster" to start the analysis process. Reviewing Analysis Report: Access and view the generated cluster analysis report. Exploring Analysis Reports: Explore different report formats (Dendritic, Histogram, Basic Data) for insights. Data Visualization: Utilize visualization tools for further documentation and reference. This summary encapsulates the step-by-step process of conducting Hierarchical Cluster Analysis (HCA) using OPUS 8.5 software, covering data preparation, analysis execution, and result interpretation.

**Dataset Overview ** The Titanic dataset is a widely used benchmark dataset for machine learning and data science tasks. It contains information about passengers who boarded the RMS Titanic in 1912, including their age, sex, social class, and whether they survived the sinking of the ship. The dataset is divided into two main parts:

Train.csv: This file contains information about 891 passengers who were used to train machine learning models. It includes the following features:

PassengerId: A unique identifier for each passenger Survived: Whether the passenger survived (1) or not (0) Pclass: The passenger's social class (1 = Upper, 2 = Middle, 3 = Lower) Name: The passenger's name Sex: The passenger's sex (Male or Female) Age: The passenger's age Sibsp: The number of siblings or spouses aboard the ship Parch: The number of parents or children aboard the ship Ticket: The passenger's ticket number Fare: The passenger's fare Cabin: The passenger's cabin number Embarked: The port where the passenger embarked (C = Cherbourg, Q = Queenstown, S = Southampton) Test.csv: This file contains information about 418 passengers who were not used to train machine learning models. It includes the same features as train.csv, but does not include the Survived label. The goal of machine learning models is to predict whether or not each passenger in the test.csv file survived.

**Data Preparation ** Before using the Titanic dataset for machine learning tasks, it is important to perform some data preparation steps. These steps may include:

Handling missing values: Some of the features in the dataset have missing values. These values can be imputed or removed, depending on the specific task. Encoding categorical variables: Some of the features in the dataset are categorical variables, such as Pclass, Sex, and Embarked. These variables need to be encoded numerically before they can be used by machine learning algorithms. Scaling numerical variables: Some of the features in the dataset are numerical variables, such as Age and Fare. These variables may need to be scaled to ensure that they are on the same scale. Data Visualization

Data visualization can be a useful tool for exploring the Titanic dataset and gaining insights into the data. Some common data visualization techniques that can be used with the Titanic dataset include:

Histograms: Histograms can be used to visualize the distribution of numerical variables, such as Age and Fare. Scatter plots: Scatter plots can be used to visualize the relationship between two numerical variables. Box plots: Box plots can be used to visualize the distribution of a numerical variable across different categories, such as Pclass and Sex. Machine Learning Tasks

The Titanic dataset can be used for a variety of machine learning tasks, including:

Classification: The most common task is to use the train.csv file to train a machine learning model to predict whether or not each passenger in the test.csv file survived. Regression: The dataset can also be used to train a machine learning model to predict the fare of a passenger based on their other features. Anomaly detection: The dataset can also be used to identify anomalies, such as passengers who are outliers in terms of their age, social class, or other features.

First results of a novel measurement technique that allows to extract quantitative data from tuft flow visualizations on real-world wind turbine blades are presented. The instantaneous flow structure is analyzed by tracking individual flow indicators in each of the snapshot images. The obtained per-tuft statistics are correlated with logged turbine data to provide an insight into the surface flow structure under the influence of wind speed. A histogram filter is used to identify two flow states: a separated flow state that occurs at higher wind speeds and a maximal attached flow state that mainly occurs in the lower wind speed range.

NFL Player Suspensions

Exploring Categories and Sources of Player Discipline

By FiveThirtyEight [source]

About this dataset

This dataset contains comprehensive information on NFL player suspensions. It includes detailed information such as the player's name, team they were playing for during the suspension, number of games suspended, and category of suspension. This data is ideal for anyone looking to analyze or research trends in NFL suspensions over time or compare different players' suspension records and can represent an invaluable source for new insights about professional football in America. So dive deep into this repository and see what meaningful stories you can tell—all under the Creative Commons Attribution 4.0 International License and MIT License. If you find this useful, let us know!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Key Columns/Variables

The following is a list of key columns present in this dataset: - Name: Name of the player who was suspended. (String) - Team: The team that the player was playing for when suspension was issued. (String) - Games: The number of games suspended for which includes postseason games if applicable. (Integer) - Category: A description/categorization of why player was suspended e.g ‘substance abuse’ or ‘personal conduct’.(String) * Desc.: A brief synopsis describingsuspension further - often indicates what action led suspension to take place (e.g drug use).(String) Year: The year suspension originally took place.(Integer) Source: Information source behind suspension data.(String).

#### Exploring and Visualizing the Data

There are a variety of ways you can explore and analyze this data set including visualizations such as histograms, box plots, line graphs etc.. Additionally you can further explore correlations between various variables by performing linear regression or isolating individual instances by filtering out specific observations e.g all Substance Abuse offences committed against players in 2015 etc.. In order to identify meaningful relationships within data set we recommend performing univariate analysis i.e analyzing one variable at time and look for patterns which may be indicative wider trends within broader unit./population context which it represents! Here's example code snippet first step towards visualizing your own insights from NFL Suspension data set - generate histogram showing distribution type offense categories undertaken 2005 through 2015.

Research Ideas

• An analysis of suspension frequencies over time to determine overall trends in NFL player discipline.
• Comparing the types of suspensions for players on different teams to evaluate any differences in the consequences for violations of team rules and regulations.
• A cross-sectional analysis to assess correlations between types and length of suspensions issued given various violation categories, such as substance abuse or personal conduct violations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: nfl-suspensions-data.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | name | Name of the player who was suspended. (String) | | team | The team the player was suspended from. (String) | | games | The number of games the player was suspended for. (Integer) | | category | The category of the suspension. (String) | | desc. | A description of the suspension. (String) | | year | The year the suspension occurred. (Integer) | | source | The source of the suspension information. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit FiveThirtyEight.

The contains flight statistics for all airports in the United States from January 2011 to December 2020. Each observation is reported by month, year, airport, and airline. Flights can be categorized as on time, delayed, canceled, or diverted. Flight delays are attributed to five causes: carrier, weather, NAS, security, and late aircraft. The data was downloaded from the Bureau of Transportation Statistics website https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp.

The accompanying notebook explores commercial airplane flight delays in the United States using Python's visualization capabilities in Matplotlib and Seaborn, through the lenses of seasonality, airport traffic, and airline performance.

The clean data set (delays_clean.csv) is analyzed using the following visualizations:

Bar chart Bar chart subplots Lollipop chart Tree maps Line plot Histogram Histogram subplots Horizontal stacked bar chart Ranked horizontal bar chart Box plot Pareto chart - double axis Marginal histogram Pie charts Scatter plot Violin plot Map chart Linear regression

Exploratory Data Analysis (EDA) of Theatres Data in India

Steps
  1. Load Data
  2. Check Nulls and Update Data if required
  3. Perform Descriptive Statistics
  4. Data Visualization
     Univariate - Single Column Visualization
       categorical - countplot
       continuous - histogram
     Bivariate - 2 Columns Visualization
      continuous vs continuous  - scatterplot, regplot
      categorical vs continuous  - boxplot
      categorical vs categorical - crosstab, heatmap
     Multivariate - Multi Columns Visualization
      correlation plot
      pairplot

100 Students: Each student has a unique name, allowing for distinct identification. 5 Subjects: Marks are provided for five core subjects, offering insight into performance across different disciplines. Applications: Performance Analysis: Can be used to analyze individual student performance and overall class trends. Statistical Insights: Helps in generating insights such as average marks, distribution of scores, and identifying top and bottom performers. Data Visualization: Ideal for visualizations like bar charts, histograms, and box plots to study variations in marks. Structure: Student Name: Unique identifier for each student. Marks for 5 Subjects: Numeric values representing marks obtained in each subject.

Key Features

Brief Dive into the cinematic universe of 2023 with our meticulously curated dataset, 'CinePulse 2023 🎬💯.' Immerse yourself in the magic of the silver screen as we present the top 100 movies that defined the year. From heartwarming dramas to pulse-pounding blockbusters, each entry is a masterpiece in its own right. Explore the runtime, ratings, and the pulse of the audience, meticulously captured to bring you a comprehensive glimpse into the cinematic landscape of 2023. Whether you're a film enthusiast, critic, or casual viewer, CinePulse 2023 invites you to experience the essence of storytelling that captivated audiences worldwide.

How to use this dataset

1. Exploratory Data Analysis (EDA):

   • Use descriptive statistics to understand the distribution of ratings, votes, and runtimes.
   • Explore the most common genres and identify patterns in movie genres over the top 100 list.

2. Visualization:

   • Create bar charts or histograms to visualize the distribution of ratings and votes.
   • Use a pie chart to display the distribution of genres across the dataset.
   • Plot a scatter plot to visualize the relationship between runtime and ratings.

3. Filtering and Sorting:

   • Filter the dataset to find movies with a specific rating threshold or a minimum number of votes.
   • Sort the dataset based on ratings or votes to identify the highest-rated or most popular movies.

4. Insights and Recommendations:

   • Identify trends in movie ratings over time or by genre.
   • Generate recommendations based on user preferences, e.g., recommending movies with high ratings or specific genres.

5. Integration with Other Data:

   • Combine this dataset with additional information, such as box office earnings or awards won, for a more comprehensive analysis.
   • Merge with a dataset containing information about actors, directors, or production companies.

6. Machine Learning (Optional):

   • If you have labeled data (e.g., user preferences or genres), you can build a machine learning model to predict ratings or user preferences.
   • Use clustering algorithms to group movies based on similarities in genres, ratings, or other features.

Netflix IMDB Scores

IMDB Scores for Netflix TV Shows and Movies

By Back 2 Viz Basics [source]

About this dataset

The Netflix TV Shows and Movies dataset provides comprehensive information about various titles available on the popular streaming platform. The dataset includes details such as the title's name, its type (whether it is a TV show or a movie), a brief description of the content, the year it was released, age certification rating, runtime (for TV shows: length of episodes; for movies: duration), IMDb score, and IMDb votes.

By analyzing this dataset, we can gain insights into the distribution of IMDb scores and ratings for both TV shows and movies available on Netflix. This information can help us understand the popularity and reception of titles based on user ratings.

The dataset has been carefully curated to ensure accuracy and relevance. It excludes any null values in IMDb scores to maintain data integrity. Each entry also contains an ID that corresponds to JustWatch (a platform for legal streaming) as well as the respective title ID on IMDb.

To visualize the distribution of IMDb scores effectively, we will be using histograms. Histograms categorize data into bins or intervals based on a chosen metric (in this case: IMDb score). The length of each bar within a bin represents the number of titles falling within that particular range of scores. With correct binning techniques, we can observe patterns and trends in how different shows and movies are rated by viewers.

When creating your visualization using Tableau or any other tool you prefer, feel free to experiment with color schemes to enhance your chart's visual impact without overshadowing its analytical purpose. However, remember not to sacrifice clarity and simplicity in pursuit of creativity.

We encourage you to take your time in crafting an insightful visualization over the next week. Once completed, share it on Tableau Public or through social media platforms like Twitter or LinkedIn using #B2VB hashtag while tagging ReadySetData and ItsElisaDavis for recognition. Don't forget to fill out our submission form on Back 2 Viz Basics website to officially participate in the challenge.

Best of luck, and we look forward to seeing your visualization!

How to use the dataset

• title: The name of the TV show or movie.

  This column contains the titles of various TV shows and movies available on Netflix. You can use this information to identify specific titles within the dataset.

• type: Indicates whether the entry is a TV show or a movie.

  The type column categorizes each entry as either a TV show or a movie. You can filter your analysis based on these categories to focus on either TV shows or movies specifically.

• description: A brief description of the TV show or movie.

  The description column provides a summary of each TV show or movie's plot or storyline. This information can help you get an overview of what each title is about before diving into further analysis.

• release_year: The year in which the TV show or movie was released.

  This column indicates the release year for each title in numeric format. You can utilize this data point to examine trends over time by grouping and aggregating titles based on their release years.

• age_certification: The age certification rating for the TV show or movie.

  The age_certification column specifies age ratings assigned to each title, indicating whether they are suitable for general audiences (e.g., all ages) or restricted due to mature content (e.g., rated R). Analyzing this attribute allows you to understand what type of content Netflix offers at different age levels.

6 .**runtime**: The length of episodes for TV shows OR duration for movies.

The runtime column provides the length of episodes for TV shows or the duration of movies in numeric format. This information can help you identify shorter or longer titles based on their runtime and compare them within your analysis.

• imdb_score: The score of the TV show or movie on IMDB.

  This column displays the IMDB score assigned to each title, representing its overall quality and popularity on IMDB. Utilize this metric to evaluate and rank different titles based on their ratings, potentially uncovering interesting patterns or insights.

• imdb_votes: The number of votes received by the TV show or

Research Ideas

• Analyzing the distribution of IMDB scores and ratings for TV shows and movies on Netflix can help identify trends and patterns in audience pr...

Credit Rating History Dataset

Credit Rating History

By Center for Municipal Finance [source]

About this dataset

The project that led to the creation of this dataset received funding from the Center for Corporate and Securities Law at the University of San Diego School of Law. The dataset itself can be accessed through a GitHub repository or on its dedicated website.

In terms of columns contained in this dataset, it encompasses a range of variables relevant to analyzing credit ratings. However, specific details about these columns are not provided in the given information. To acquire a more accurate understanding of the column labels and their corresponding attributes or measurements present in this dataset, further exploration or referencing additional resources may be required

How to use the dataset

• Understanding the Data

  The dataset consists of several columns that provide essential information about credit ratings and fixed income securities. Familiarize yourself with the column names and their meanings to better understand the data:

  • Column 1: [Credit Agency]
  • Column 2: [Issuer Name]
  • Column 3: [CUSIP/ISIN]
  • Column 4: [Rating Type]
  • Column 5: [Rating Source]
  • Column 6: [Rating Date]

• Exploratory Data Analysis (EDA)

  Before diving into detailed analysis, start by performing exploratory data analysis to get an overview of the dataset.

  • Identify Unique Values: Explore each column's unique values to understand rating agencies, issuers, rating types, sources, etc.

  • Frequency Distribution: Analyze the frequency distribution of various attributes like credit agencies or rating types to identify any imbalances or biases in the data.

• Data Visualization

  Visualizing your data can provide insights that are difficult to derive from tabular representation alone. Utilize various visualization techniques such as bar charts, pie charts, histograms, or line graphs based on your specific objectives.

  For example:

  • Plotting a histogram of each credit agency's ratings can help you understand their distribution across different categories.
  • A time-series line graph can show how ratings have evolved over time for specific issuers or industries.

• Analyzing Ratings Performance

  One of the main objectives of using credit rating datasets is to assess the performance and accuracy of different credit agencies. Conducting a thorough analysis can help you understand how ratings have changed over time and evaluate the consistency of each agency's ratings.

  • Rating Changes Over Time: Analyze how ratings for specific issuers or industries have changed over different periods.

  • Comparing Rating Agencies: Compare ratings from different agencies to identify any discrepancies or trends. Are there consistent differences in their assessments?

• Detecting Rating Trends

  The dataset allows you to detect trends and correlations between various factors related to

Research Ideas

• Credit Rating Analysis: This dataset can be used for analyzing credit ratings and trends of various fixed income securities. It provides historical credit rating data from different rating agencies, allowing researchers to study the performance, accuracy, and consistency of these ratings over time.
• Comparative Analysis: The dataset allows for comparative analysis between different agencies' credit ratings for a specific security or issuer. Researchers can compare the ratings assigned by different agencies and identify any discrepancies or differences in their assessments. This analysis can help in understanding variations in methodologies and improving the transparency of credit rating processes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all ...

Automated Flower Identification Using Convolutional Neural Networks

This project aims to develop a model for identifying five different flower species (rose, tulip, sunflower, dandelion, daisy) using Convolutional Neural Networks (CNNs).

Description

The dataset consists of 5,000 images (1,000 images per class) collected from various online sources. The model achieved an accuracy of 98.58% on the test set. Usage

This project requires Python 3.x and the following libraries:

TensorFlow: For making Neural Networks numpy: For numerical computing and array operations. pandas: For data manipulation and analysis. matplotlib: For creating visualizations such as line plots, bar plots, and histograms. seaborn: For advanced data visualization and creating statistically-informed graphics. scikit-learn: For machine learning algorithms and model training. To run the project:

Clone this repository.

Install the required libraries. Run the Jupyter Notebook: jupyter notebook flower_classification.ipynb Additional Information Link to code: https://github.com/Harshjaglan01/flower-classification-cnn License: MIT License

Flipkart Smart phones Dataset - Dataset contains price details, highlights, review details, reviews histogram breakup of various smart phones brands in india available from ecommerce website 'Flipkart' for the search url https://www.flipkart.com/search?q=smartphones&otracker=search&otracker1=\search&marketplace=FLIPKART&as-show=on&as=off&page=1 - Data scraped from above listing page 1 to 41. The maximum valid pagination at the time of scrape was 41.

Major Attributes-

1. title: title of the product
2. ram: ram of the mdoel
3. brand: name of the mobile manufacturer
4. url: product url
5. product_id: product id or sku in flipkart.com
6. listing_id: listing id of product
7. highlights: highlight section available in the product page
8. availability: availability of product
9. selling_price: selling price or discounted price of the product
10. original_price: actual price of the model or the pre-discounted price
11. currency: currency of selling and original price
12. avg_rating: average rating of the product
13. ratings_count: total number of ratings
14. reviews_count: total number of reviews
15. attributes one stars count, two stars count, three stars count, four stars count and five stars count represent the histogram breakup of Ratings & Reviews section in the product page.

📌 Overview This dataset provides detailed insights into e-commerce grocery delivery services, focusing on Blinkit, Swiggy Instamart, and JioMart. It includes customer feedback, delivery times, service ratings, and various factors affecting delivery performance. This dataset is useful for analyzing customer satisfaction, identifying service trends, and optimizing delivery logistics.

📊 Key Features 📍 Order ID – Unique identifier for each order. 📍 Platform – The e-commerce platform (Blinkit, Swiggy Instamart, JioMart). 📍 Order Date & Time – Timestamp of when the order was placed. 📍 Delivery Time (Minutes) – Time taken for order delivery. 📍 Customer Feedback – Text-based feedback provided by the customer. 📍 Service Rating (1-5) – Customer rating for delivery service. 📍 Delivery Distance (km) – Distance covered by the delivery agent. 📍 Payment Method – Mode of payment (Cash, UPI, Card, Wallet). 📍 Order Value (INR) – Total value of the order in Indian Rupees. 📍 Discount Applied (INR) – Discount provided on the order. 📍 Delivery Charges (INR) – Charges applied for delivery. 📍 Order Status – (Delivered, Cancelled, Delayed, etc.).

📈 Potential Use Cases ✅ Customer Sentiment Analysis – Understand feedback trends and satisfaction levels. ✅ Delivery Time Optimization – Identify patterns affecting delivery speed. ✅ Platform Comparison – Compare performance across Blinkit, Swiggy Instamart, and JioMart. ✅ Sales and Revenue Insights – Analyze trends in order value, discounts, and payment methods. ✅ Predictive Analysis – Forecast delivery delays and customer preferences.

🛠️ Suggested Visualizations 🎯 Histogram of delivery times to analyze service efficiency. 🎯 Bar Chart showing average delivery time per platform. 🎯 Heatmap to identify peak ordering times. 🎯Word Cloud for customer feedback insights.

Did We Solve the Problem? The objective of this analysis was to predict high streaming counts on Spotify and perform a detailed cluster analysis to understand user behavior. Here’s a summary of how we addressed each part of the objective:

Prediction of High Streaming Counts:

Implemented Multiple Models: We utilized several machine learning models including Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). Comparison and Evaluation: These models were evaluated based on classification metrics like accuracy, precision, recall, and F1-score. The Gradient Boosting and Random Forest models were found to be the most effective in predicting high streaming counts. Cluster Analysis:

K-means Clustering: We applied K-means clustering to segment users into three clusters based on their listening behavior. Detailed Characterization: Each cluster was analyzed to understand the distinct characteristics, such as average playtime, skip rate, offline usage, and shuffle usage. Visualizations: Histograms and scatter plots were used to visualize the distributions and relationships within each cluster. Results and Insights Effective Models: The Gradient Boosting and Random Forest models provided the highest accuracy and balanced performance for predicting high streaming counts. User Segmentation: The cluster analysis revealed three distinct user segments: Cluster 1: Users with longer playtimes and lower skip rates. Cluster 2: Users with moderate playtimes and skip rates. Cluster 3: Users with shorter playtimes and higher skip rates. These insights can be leveraged for targeted marketing, personalized recommendations, and improving user engagement on Spotify.

Conclusion Yes, we solved the problem. We successfully predicted high streaming counts using effective machine learning models and provided a detailed cluster analysis to understand user behavior. The analysis offers valuable insights for enhancing Spotify’s recommendation system and user experience.