#linear regression analysis

[romanticize-until-you-drop]

So I just won a competition for my research project…

MOM DAD IM A REAL SCIENTIST!!

[boar410511]

Mars Crater Study-1

This article was written as a practice exercise with reference to the information provided in the COURSERA course, specifically the Mars Crater Study.

=========================================

My program,

import pandas as pd

import statsmodels.formula.api as smf

# Set display format

pd.set_option('display.float_format', lambda x: '%.2f' % x)

# Read dataset

data = pd.read_csv('marscrater_pds.csv')

# Convert necessary variables to numeric format

data['DIAM_CIRCLE_IMAGE'] = pd.to_numeric(data['DIAM_CIRCLE_IMAGE'], errors='coerce')

data['DEPTH_RIMFLOOR_TOPOG'] = pd.to_numeric(data['DEPTH_RIMFLOOR_TOPOG'], errors='coerce')

# Perform basic linear regression analysis

print("OLS regression model for the association between crater diameter and depth")

reg1 = smf.ols('DEPTH_RIMFLOOR_TOPOG ~ DIAM_CIRCLE_IMAGE', data=data).fit()

print(reg1.summary())

=========================================

Output results,

Dep. Variable:     DEPTH_RIMFLOOR_TOPOG

R-squared:0.344

Model: OLS

Adj. R-squared:0.344

Method:Least Squares  

F-statistic:2.018e+05

Date:Thu, 27 Mar 2025

Prob (F-statistic):0.00

Time:14:58:20

Log-Likelihood:1.1503e+05

No. Observations:384343

AIC:-2.301e+05

Df Residuals:384341

BIC:-2.300e+05

Df Model: 1                                        

Covariance Type:nonrobust                                        

                  coef    std err        t      P>|t|      [0.025      0.975]

Intercept   0.0220   0.000     70.370     0.000     0.021       0.023

DIAM_CIRCLE_IMAGE    

0.0151   3.37e-05   449.169    0.000    0.015    0.015

Omnibus:390327.615

Durbin-Watson:1.276

Prob(Omnibus):0.000  

Jarque-Bera (JB):4086668077.223

Skew: -3.506

Prob(JB):0.00

Kurtosis:508.113

Cond. No.10.1

=========================================

Results Summary:

Regression Model Results:

R-squared: 0.344, indicating that the model explains approximately 34.4% of the variability in crater depth.

Regression Coefficient (DIAMCIRCLEIMAGE): 0.0151, meaning that for each unit increase in crater diameter, the depth increases by an average of 0.0151 units.

p-value: 0.000, indicating that the effect of diameter on depth is statistically significant.

Intercept: 0.0220, which is the predicted crater depth when the diameter is zero.

Conclusion:

The analysis shows a significant positive association between crater diameter and depth. While the model provides some explanatory power, other factors likely influence crater depth, and further exploration is recommended.

[ineedfairypee]

Anything but collinearity

[politedemon]

FUCK data analysis. you should be able to look at my raw data and make your own conclusions fuck you

[darkmaga-returns]

Executive summary

If you wanted to show the American public that the COVID vaccines saved lives, the easiest proof is to plot each state (or county) based on its vaccination rate (x axis) and its change in mortality from pre-vaccination baseline on the y-axis.

I did this. The slope is positive.

This means that more vaccinations are associated with higher mortality.

Can this be challenged?

I don’t think so.

The data is the data. They can’t change it. And there is only one way to draw a line through the data. If the shots worked, the slope would be strongly negative and it’s not.

This is likely why not a single paper attempts to do the most obvious analysis. They probably did it, saw that it was not supporting the narrative, and then decided not to publish.

Results for state regression and county regression

The slope was positive and statistically significant for both the state and county analysis. This means that the COVID vaccines did the opposite of what they promised: it increased your risk of death.

The R2 value of .31 is quite spectacular and it means this isn’t an accident.

[covid-safer-hotties]

Long COVID symptom severity varies widely by age, gender, and socioeconomic status - Published Sept 2, 2024

By Dr. Sushama R. Chaphalkar, PhD.

In a recent study published in the journal JRSM Open, researchers analyze self-reported symptoms of long coronavirus disease 2019 (LC) from individuals using a healthcare app to examine the potential impact of demographic factors on the severity of symptoms. The researchers found that LC symptom severity varied significantly by age, gender, race, education, and socioeconomic status.

Research highlights the urgent need for targeted interventions as age, gender, and social factors play a crucial role in the intensity of long COVID symptoms. What factors increase the risk of long COVID? Several months after recovering from coronavirus disease 2019 (COVID-19), patients with LC may continue to suffer from numerous symptoms, some of which include fatigue, brain fog, and chest pain. The prevalence of LC varies, with estimates ranging from 10-30% in non-hospitalized cases to 50-70% in hospitalized patients.

Although several digital health interventions (DHIs) and applications have been developed to monitor acute symptoms of COVID-19, few have been designed to track long-term symptoms of the disease. One DHI called "Living With COVID Recovery" (LWCR) was initiated to help individuals manage LC by self-reporting symptoms and tracking their intensity. However, there remains a lack of evidence on the risk factors, characteristics, and predictors of LC, thereby limiting the accurate identification of high-risk patients to target preventive strategies.

About the study In the present study, researchers investigate the prevalence and intensity of self-reported LC symptoms to analyze their potential relationship with demographic factors to inform targeted interventions and management strategies. To this end, LWCR was used to monitor and analyze self-reported LC symptoms from individuals in 31 LC clinics throughout England and Wales.

The study included 1,008 participants who reported 1,604 unique symptoms. All patients provided informed consent for the use of their anonymized data for research.

Multiple linear regression analysis was used to explore the relationship between symptom intensity and factors such as time since registration, age, ethnicity, education, gender, and socioeconomic status through indices of multiple deprivation (IMD) on a scale of one to 10.

Education was classified into four levels denoted as NVQ 1-2, NVQ 3, NVQ 4, and NVQ 5, which reflected those who were least educated at A level, degree level, and postgraduate level, respectively. The intensity of symptoms was measured on a scale from zero to 10, with zero being the lowest and 10 the highest intensity. Descriptive statistics identified variations in symptom intensity across different demographic groups.

Study findings Although 23% of patients experienced symptoms only once, 77% experienced symptoms multiple times. Corroborating with existing literature, the most prevalent symptoms included pain, neuropsychological issues, fatigue, and dyspnea, which affected 26.5%, 18.4%, 14.3%, and 7.4% of the cohort, respectively. Symptoms such as palpitations, light-headedness, insomnia, cough, diarrhea, and tinnitus were less prevalent.

Fifteen most prevalent LC symptoms. Multiple linear regression analysis revealed that symptom intensity was significantly associated with age, gender, ethnicity, education, and IMD decile. More specifically, individuals 68 years of age and older reported higher symptom intensity by 32.5% and 86%, respectively. These findings align with existing literature that highlights the increased risk of LC symptoms with age, which may be due to weakened immunity or the presence of comorbidities. Thus, they emphasize the need for targeted interventions for this population.

Females also reported higher symptom intensity than males, by 9.2%. Non-White individuals experienced higher symptom intensity by 23.5% as compared to White individuals.

Individuals with higher education levels reported up to 47% reduced symptom intensity as compared to those with lower education levels. Higher IMD deciles, which reflect less deprived areas, were associated with lower symptom intensity; however, no significant association was observed between the number of symptoms reported and the IMD decile.

Regression results with 95% confidence interval. Note: For age, the base group is people in the age category 18–27. For IMD, the base group is people from IMD decile 1. For education, the base group is people who left school before A-level (NVQ 1–2). A significant positive association was observed between symptom intensity and the duration between registration on the app and initial symptom reporting. This finding suggests individuals may become more aware of their symptoms or that worsening symptoms prompt reporting.

Some limitations of the current study include the lack of data on comorbidities, hospitalization, and vaccine status. There is also a potential for bias against individuals lacking technological proficiency or access, which may affect the sample's representativeness, particularly for older, socioeconomically disadvantaged, or non-English-speaking individuals. Excluding patients with severe symptoms or those who were ineligible for the app may also skew the findings.

Conclusions There remains an urgent need to develop targeted interventions to address the severity of LC in relation to age, ethnicity, and socioeconomic factors. LC treatment should prioritize prevalent symptoms like pain, neuropsychological issues, fatigue, and dyspnea while also considering other possible symptoms. Furthermore, sustained support for LC clinics is essential to effectively manage the wide range of symptoms and complexities associated with LC and improve public health outcomes in the post-pandemic era.

Journal reference:

Sunkersing, D., Goodfellow, H., Mu, Y., et al. (2024). Long COVID symptoms and demographic associations: A retrospective case series study using healthcare application data. JRSM Open 15(7). doi:10.1177/20542704241274292.

journals.sagepub.com/doi/10.1177/20542704241274292

[duboisproposal]

Harvard Online Courses

After some eyebrow raising shit being thrown around from the (saddest most corrupt evil pos to hold the position) POTUS towards Harvard (something something Obama's daughter and Carney's child got in but Barron did not) Harvard has free online courses - (viral memes say these courses are new and a direct response but I'm not sure whether that is the case)

Course on Separation of Powers

Understanding the Constitution

Recognize a Dictatorship

How the Wealthy purchase politics

Pretty amazing right? Okay well I've been looking over the Harvard courses offered for free. The courses now available include

and

and

or

and while we are at it here is a short list of free Harvard courses that Look Really Cool To Me.

https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

https://pll.harvard.edu/course/contractsx-trust-promise-contract

https://pll.harvard.edu/course/food-fermentation-science-cooking-microbes

https://pll.harvard.edu/course/fundamentals-neuroscience-part-1-electrical-properties-neuron

https://pll.harvard.edu/course/data-science-linear-regression/2025-04

and more free courses at https://pll.harvard.edu/catalog?price%5B1%5D=1&max_price=&start_date=&keywords=&url=. Free micro courses online are amazing. Coursera and edx will often offer courses and then use Dark Patterns to hide the 'audit for free' button- but generally getting an overview of this stuff from some of the best professors on the planet is such a charming documentary overview into some good stuff.

[tapioca-puddingg]

Why GoWR Valhalla Is Important

Hey. It's me again. This time I'm not yelling about Kingdom Hearts or Drakengard, but I wanted to talk about God of War Ragnarök: Valhalla today and why I think it's important in trauma-centered narratives. This isn't a detailed analysis, just me spitballing.

SPOILER WARNING: There will be spoilers for God of War Ragnarök: Valhalla, so please proceed with caution!

EDITED: 2/26/24

As a brief summary, Kratos spent almost the entirety of GoW 2018 refusing to talk about his past. His guilt, shame, and trauma deeply affected his relationship with his son, to the point where he didn't want to be around Atreus bc he was terrified of being a bad influence on him. It was only when Atreus' life was in danger did it force him to finally admit just a sliver of the truth. Now I don't mean to say that Kratos revealing his godhood wasn't a big deal because it absolutely was, I'm just saying that it's just one piece of a MUCH bigger story. Anyway, he recognized his past mistakes, but the shame was too much for him to openly acknowledge it until damn near the end of the game.

Come Ragnarök, Kratos was pretty much an open book. He had grown SO much in those short years of fimbulwinter: He openly talked about his trauma to Mimir and Freya. He worked so hard to be a good father and a good support system to his friends. He went out of his way to make amends with Freya and restore their friendship. And he fought to restore peace to the Nine Realms.

But come Valhalla, Freya wants to recruit Kratos to be the new God of War of the nine realms, or at least to be a part of the new peacekeeping council that she's putting together. Kratos is extremely hesitant to take up the mantle. He doesn't feel worthy or deserving enough to hold this position given all that he's done. He and Mimir (and later on, Tyr) are constantly going back and forth about it. Both perspectives are completely valid. Valhalla is about Kratos facing his past in a more literal sense; parts of Greece have been manifested from Kratos' memories of it, so it's like he gets to be there in real time again. This is about helping him process what happened and to add some nuance to the conversation. It's like free therapy for Kratos.

It's funny too bc you have both opposing viewpoints being represented. On one hand, you have Mimir and Tyr being the supporting/validating voice, and Helios is the contrarian. Since he's a manifestation of Kratos' memories, he represents the doubts that Kratos has about himself. The harsh voice to show how hard he is on himself, and not without good reason.

The reason why I think Valhalla is so important is bc in media, survivor narratives are often linear. The character just "gets over" their trauma and then that trauma isn't addressed again. It's presented more as a hurdle than a lifelong battle. I guess this goes to show how misunderstood survivorhood is. But that isn't how healing works. We regress sometimes, and sometimes we still mull over the things that have happened to us. We might heal, but that trauma does leave emotional scars. So even after the many leaps and bounds Kratos has made, he's not "over" his past, far from it! It still haunts him every day and every night. Valhalla is Kratos still processing everything. From my own healing journey, I've learned that it takes a long, long time to fully process your trauma, if there even is a "fully", anyway. It takes a long time to learn and understand all the complexities and how it affects you in current day. And it takes even longer to process such a complicated history like Kratos'.

Generally speaking about the idea of processing trauma, I said earlier that survivorhood is extremely misunderstood by the masses. Imo, our society is very anti-victim/anti-survivor. So with that in mind, from the perspective of the audience, some might perceive the processing trauma bit as repetitive or "milking it". These are mediums of entertainment after all, so ofc I understand wanting to put out an engaging story where the audience doesn't lose interest. But screw those ppl lol. We have to understand why we do what we do if we want to do better, and it's amazing that a video game is willing to have these conversations. Being more open about all the nuances of processing trauma, grief, healing, etc will go such a long way.

Even the roguelite gameplay style perfectly reflects this theme. Processing this stuff is slow. It doesn't happen overnight. Unless you're in Valhalla, I suppose.

Okay I said this wasn't a detailed analysis but I lied. I'm a liar now

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[xaltius]

Unlocking the Power of Data: Essential Skills to Become a Data Scientist

In today's data-driven world, the demand for skilled data scientists is skyrocketing. These professionals are the key to transforming raw information into actionable insights, driving innovation and shaping business strategies. But what exactly does it take to become a data scientist? It's a multidisciplinary field, requiring a unique blend of technical prowess and analytical thinking. Let's break down the essential skills you'll need to embark on this exciting career path.

1. Strong Mathematical and Statistical Foundation:

At the heart of data science lies a deep understanding of mathematics and statistics. You'll need to grasp concepts like:

Linear Algebra and Calculus: Essential for understanding machine learning algorithms and optimizing models.

Probability and Statistics: Crucial for data analysis, hypothesis testing, and drawing meaningful conclusions from data.

2. Programming Proficiency (Python and/or R):

Data scientists are fluent in at least one, if not both, of the dominant programming languages in the field:

Python: Known for its readability and extensive libraries like Pandas, NumPy, Scikit-learn, and TensorFlow, making it ideal for data manipulation, analysis, and machine learning.

R: Specifically designed for statistical computing and graphics, R offers a rich ecosystem of packages for statistical modeling and visualization.

3. Data Wrangling and Preprocessing Skills:

Raw data is rarely clean and ready for analysis. A significant portion of a data scientist's time is spent on:

Data Cleaning: Handling missing values, outliers, and inconsistencies.

Data Transformation: Reshaping, merging, and aggregating data.

Feature Engineering: Creating new features from existing data to improve model performance.

4. Expertise in Databases and SQL:

Data often resides in databases. Proficiency in SQL (Structured Query Language) is essential for:

Extracting Data: Querying and retrieving data from various database systems.

Data Manipulation: Filtering, joining, and aggregating data within databases.

5. Machine Learning Mastery:

Machine learning is a core component of data science, enabling you to build models that learn from data and make predictions or classifications. Key areas include:

Supervised Learning: Regression, classification algorithms.

Unsupervised Learning: Clustering, dimensionality reduction.

Model Selection and Evaluation: Choosing the right algorithms and assessing their performance.

6. Data Visualization and Communication Skills:

Being able to effectively communicate your findings is just as important as the analysis itself. You'll need to:

Visualize Data: Create compelling charts and graphs to explore patterns and insights using libraries like Matplotlib, Seaborn (Python), or ggplot2 (R).

Tell Data Stories: Present your findings in a clear and concise manner that resonates with both technical and non-technical audiences.

7. Critical Thinking and Problem-Solving Abilities:

Data scientists are essentially problem solvers. You need to be able to:

Define Business Problems: Translate business challenges into data science questions.

Develop Analytical Frameworks: Structure your approach to solve complex problems.

Interpret Results: Draw meaningful conclusions and translate them into actionable recommendations.

8. Domain Knowledge (Optional but Highly Beneficial):

Having expertise in the specific industry or domain you're working in can give you a significant advantage. It helps you understand the context of the data and formulate more relevant questions.

9. Curiosity and a Growth Mindset:

The field of data science is constantly evolving. A genuine curiosity and a willingness to learn new technologies and techniques are crucial for long-term success.

10. Strong Communication and Collaboration Skills:

Data scientists often work in teams and need to collaborate effectively with engineers, business stakeholders, and other experts.

Kickstart Your Data Science Journey with Xaltius Academy's Data Science and AI Program:

Acquiring these skills can seem like a daunting task, but structured learning programs can provide a clear and effective path. Xaltius Academy's Data Science and AI Program is designed to equip you with the essential knowledge and practical experience to become a successful data scientist.

Key benefits of the program:

Comprehensive Curriculum: Covers all the core skills mentioned above, from foundational mathematics to advanced machine learning techniques.

Hands-on Projects: Provides practical experience working with real-world datasets and building a strong portfolio.

Expert Instructors: Learn from industry professionals with years of experience in data science and AI.

Career Support: Offers guidance and resources to help you launch your data science career.

Becoming a data scientist is a rewarding journey that blends technical expertise with analytical thinking. By focusing on developing these key skills and leveraging resources like Xaltius Academy's program, you can position yourself for a successful and impactful career in this in-demand field. The power of data is waiting to be unlocked – are you ready to take the challenge?

[digitaldetoxworld]

Machine Learning: A Comprehensive Overview

 Machine Learning (ML) is a subfield of synthetic intelligence (AI) that offers structures with the capacity to robotically examine and enhance from revel in without being explicitly programmed. Instead of using a fixed set of guidelines or commands, device studying algorithms perceive styles in facts and use the ones styles to make predictions or decisions. Over the beyond decade, ML has transformed how we have interaction with generation, touching nearly each aspect of our every day lives — from personalised recommendations on streaming services to actual-time fraud detection in banking.

Machine learning algorithms

What is Machine Learning?

At its center, gadget learning entails feeding facts right into a pc algorithm that allows the gadget to adjust its parameters and improve its overall performance on a project through the years. The more statistics the machine sees, the better it usually turns into. This is corresponding to how humans study — through trial, error, and revel in.

Arthur Samuel, a pioneer within the discipline, defined gadget gaining knowledge of in 1959 as “a discipline of take a look at that offers computers the capability to study without being explicitly programmed.” Today, ML is a critical technology powering a huge array of packages in enterprise, healthcare, science, and enjoyment.

Types of Machine Learning

Machine studying can be broadly categorised into 4 major categories:

1. Supervised Learning

 For example, in a spam electronic mail detection device, emails are classified as "spam" or "no longer unsolicited mail," and the algorithm learns to classify new emails for this reason.

Common algorithms include:

Linear Regression

Logistic Regression

Support Vector Machines (SVM)

Decision Trees

Random Forests

Neural Networks

2. Unsupervised Learning

Unsupervised mastering offers with unlabeled information. Clustering and association are commonplace obligations on this class.

Key strategies encompass:

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Autoencoders

three. Semi-Supervised Learning

It is specifically beneficial when acquiring categorised data is highly-priced or time-consuming, as in scientific diagnosis.

Four. Reinforcement Learning

Reinforcement mastering includes an agent that interacts with an surroundings and learns to make choices with the aid of receiving rewards or consequences. It is broadly utilized in areas like robotics, recreation gambling (e.G., AlphaGo), and independent vehicles.

Popular algorithms encompass:

Q-Learning

Deep Q-Networks (DQN)

Policy Gradient Methods

Key Components of Machine Learning Systems

1. Data

Data is the muse of any machine learning version. The pleasant and quantity of the facts directly effect the performance of the version. Preprocessing — consisting of cleansing, normalization, and transformation — is vital to make sure beneficial insights can be extracted.

2. Features

 Feature engineering, the technique of selecting and reworking variables to enhance model accuracy, is one of the most important steps within the ML workflow.

Three. Algorithms

Algorithms define the rules and mathematical fashions that help machines study from information. Choosing the proper set of rules relies upon at the trouble, the records, and the desired accuracy and interpretability.

4. Model Evaluation

Models are evaluated the use of numerous metrics along with accuracy, precision, consider, F1-score (for class), or RMSE and R² (for regression). Cross-validation enables check how nicely a model generalizes to unseen statistics.

Applications of Machine Learning

Machine getting to know is now deeply incorporated into severa domain names, together with:

1. Healthcare

ML is used for disorder prognosis, drug discovery, customized medicinal drug, and clinical imaging. Algorithms assist locate situations like cancer and diabetes from clinical facts and scans.

2. Finance

Fraud detection, algorithmic buying and selling, credit score scoring, and client segmentation are pushed with the aid of machine gaining knowledge of within the financial area.

3. Retail and E-commerce

Recommendation engines, stock management, dynamic pricing, and sentiment evaluation assist businesses boom sales and improve patron revel in.

Four. Transportation

Self-riding motors, traffic prediction, and route optimization all rely upon real-time gadget getting to know models.

6. Cybersecurity

Anomaly detection algorithms help in identifying suspicious activities and capacity cyber threats.

Challenges in Machine Learning

Despite its rapid development, machine mastering still faces numerous demanding situations:

1. Data Quality and Quantity

Accessing fantastic, categorised statistics is often a bottleneck. Incomplete, imbalanced, or biased datasets can cause misguided fashions.

2. Overfitting and Underfitting

Overfitting occurs when the model learns the education statistics too nicely and fails to generalize. 

Three. Interpretability

Many modern fashions, specifically deep neural networks, act as "black boxes," making it tough to recognize how predictions are made — a concern in excessive-stakes regions like healthcare and law.

4. Ethical and Fairness Issues

Algorithms can inadvertently study and enlarge biases gift inside the training facts. Ensuring equity, transparency, and duty in ML structures is a growing area of studies.

5. Security

Adversarial assaults — in which small changes to enter information can fool ML models — present critical dangers, especially in applications like facial reputation and autonomous riding.

Future of Machine Learning

The destiny of system studying is each interesting and complicated. Some promising instructions consist of:

1. Explainable AI (XAI)

Efforts are underway to make ML models greater obvious and understandable, allowing customers to believe and interpret decisions made through algorithms.

2. Automated Machine Learning (AutoML)

AutoML aims to automate the stop-to-cease manner of applying ML to real-world issues, making it extra reachable to non-professionals.

3. Federated Learning

This approach permits fashions to gain knowledge of across a couple of gadgets or servers with out sharing uncooked records, enhancing privateness and efficiency.

4. Edge ML

Deploying device mastering models on side devices like smartphones and IoT devices permits real-time processing with reduced latency and value.

Five. Integration with Other Technologies

ML will maintain to converge with fields like blockchain, quantum computing, and augmented fact, growing new opportunities and challenges.

[religion-is-a-mental-illness]

Abstract

Objective: To assess the association between transgender or gender-questioning identity and screen use (recreational screen time and problematic screen use) in a demographically diverse national sample of early adolescents in the U.S.

Methods: We analyzed cross-sectional data from Year 3 of the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®, N = 9859, 2019-2021, mostly 12-13-years-old). Multiple linear regression analyses estimated the associations between transgender or questioning gender identity and screen time, as well as problematic use of video games, social media, and mobile phones, adjusting for confounders.

Results: In a sample of 9859 adolescents (48.8% female, 47.6% racial/ethnic minority, 1.0% transgender, 1.1% gender-questioning), transgender adolescents reported 4.51 (95% CI 1.17-7.85) more hours of total daily recreational screen time including more time on television/movies, video games, texting, social media, and the internet, compared to cisgender adolescents. Gender-questioning adolescents reported 3.41 (95% CI 1.16-5.67) more hours of total daily recreational screen time compared to cisgender adolescents. Transgender identification and questioning one's gender identity was associated with higher problematic social media, video game, and mobile phone use, compared to cisgender identification.

Conclusions: Transgender and gender-questioning adolescents spend a disproportionate amount of time engaging in screen-based activities and have more problematic use across social media, video game, and mobile phone platforms.

Introduction

Screen-based digital media is integral to the daily lives of adolescents in multifaceted ways [1] but problematic screen use (characterized by inability to control usage and detrimental consequences from excessive use including preoccupation, tolerance, relapse, withdrawal, and conflict) [2], [3], has been linked with harmful mental and physical health outcomes, such as depression, poor sleep, and cardiometabolic disease [4], [5]. Transgender and gender-questioning adolescents (i.e., adolescents who are questioning their gender identity) experience a higher prevalence of bullying (adjusted prevalence ratio [aPR] 1.88 and 1.62), suicide attempts (aPR 2.65 and 2.26), and binge drinking (aPR 1.80 and 1.50), respectively, compared to their cisgender peers [6], [7], [8], [9], [10]. Transgender and gender-questioning adolescents may engage in screen-based activities that are problematic and associated with negative health outcomes but also in a way that is different from their cisgender peers in order to form communities, explore health education about their gender identity, and seek refuge from isolating or unsafe environments [11].

One study found that sexual and gender minority (SGM) adolescents (e.g., lesbian, gay, bisexual, and transgender), aged 13–18 years old, spent an average of 5 h per day online, approximately 45 min more than non-SGM adolescents in 2010–2011 [12]. However, this study grouped SGM together as a single group, conflating the experiences of gender minorities (e.g., transgender, gender-questioning) with those of sexual minorites (e.g., lesbian, gay, bisexual), and the data are now over a decade old. In a nationally representative sample of adolescents aged 13–18 years old in the U.S., transgender adolescents had higher probabilities of problematic internet use than cisgender adolescents. However, this analysis did not measure modality-specific problematic screen use such as problematic social media, video game, or mobile phone use, which may further inform the function that media use plays in the lives of gender minority adolescents [13]. While this prior research provides important groundwork to understand screen time and problematic use in gender minority adolescents, gaps remain in understanding differences in screen time and specific modalities of problematic screen use in gender minority early adolescents.

Our study aims to address the gaps in the current literature by studying associations between transgender and gender-questioning identity and screen time across several modalities including recreational and problematic social media, video game, and mobile phone use in a large, national sample of early adolescents. We hypothesized that among early adolescents, transgender identification and questioning one’s gender identity would be positively associated with greater recreational screen time and problematic screen use compared to cisgender identification.

==

tl;dr: Gender-mania is an online social contagion.

No shit. That's why these "authentic selves" and "innate identities" tend to evaporate when kids are detoxed from the internet.

[darkmaga-returns]

Did R0 go up?

R0, which is the reproductive number, is a measure of how fast a virus replicates. Reducing the risk of infection and transmission both lower R0 leading to lower peaks and a longer infection time which doesn’t overwhelm medical care. That was the goal of these shots.

Here’s your answer, courtesy of Israel, one of the most vaccinated places on earth.

A spike like the one above to 60,000 cases per million people is a clear signal that means R0 was increased relative to the 75% vaccinated US.

The slope and the height are dead giveaways of a higher R0.

There is no hiding from this data. It’s been in plain sight for years if anyone looks.

How R0 affects the shape of a virus outbreak

As the Israel data clearly shows, R0 was increased by heavy vaccination.

Because the infection fatality rate went up too as I showed earlier by looking at case fatality rates (CFR) in the nursing home before vs. after vaccine rollout (a “longitudinal analysis”), all these countries KILLED people.This is how a virus spreads. The higher R0, the higher the peak, the more people who are infected and the sharper the shape. This is why the Omicron spike in Israel was a crystal clear signal that the vaccine increased R0. Israel should have had the flattest curve in the world if the vaccine worked.

Other studies

The Cleveland Clinic (CC) study and the second CC study showed the vaccines increased your risk of contracting COVID. Other studies found the same effect: here, here, here, here, here, here, here.

A new Japan study confirmed the CC results that more vaccines→more cases: “The odds of contracting COVID-19 increased with the number of vaccine doses: one to two doses (OR: 1.63, 95% CI: 1.08-2.46, p = 0.020), three to four doses (OR: 2.04, 95% CI: 1.35-3.08, p = 0.001), and five to seven doses (OR: 2.21, 95% CI: 1.07-4.56, p = 0.033).” This is consistent with Table 2 in the CC study.

I’ve done simple linear regression on the US data as well and the slopes are all positive (meaning it increases your risk of getting infected).

The answer to the poll was B

Choice B was the data from all the sites in the least 5 highly vaccinated states in the US. The peaks are lower.

You can click each image to see the states included in the image.

Summary

The Phase 3 clinical trials were a fraud.

Your government lied to you.

The wastewater data is dispositive: the vaccine did not lower R0. If anything, it increased it as you can clearly see from the Israel data. If the vaccine worked, they wouldn’t have the highest peak in the world; they’d have the lowest.

Isn’t it great what you can learn if you can read the wastewater tea leaves?

Now you know why all the health authorities avoid this topic.

[saltcherry]

Glassdoor has a **For You** tab that uses "AI" to "help" you find jobs, well. It just suggested me a whole page of Special Ed Teacher jobs and I realized it was because it "read" my resume as listing the skill "regression analysis" as in the phrase "multiple linear regression and other types of analysis" and "regression" is a term used in sped a lot for children's behavior.

great tool. my ability to do a fully crossed ANOVA definitely makes me a behavior specialist for disabled kids.

[sonadukane]

How to Become a Data Scientist in 2025 (Roadmap for Absolute Beginners)

Want to become a data scientist in 2025 but don’t know where to start? You’re not alone. With job roles, tech stacks, and buzzwords changing rapidly, it’s easy to feel lost.

But here’s the good news: you don’t need a PhD or years of coding experience to get started. You just need the right roadmap.

Let’s break down the beginner-friendly path to becoming a data scientist in 2025.

✈️ Step 1: Get Comfortable with Python

Python is the most beginner-friendly programming language in data science.

What to learn:

Variables, loops, functions

Libraries like NumPy, Pandas, and Matplotlib

Why: It’s the backbone of everything you’ll do in data analysis and machine learning.

🔢 Step 2: Learn Basic Math & Stats

You don’t need to be a math genius. But you do need to understand:

Descriptive statistics

Probability

Linear algebra basics

Hypothesis testing

These concepts help you interpret data and build reliable models.

📊 Step 3: Master Data Handling

You’ll spend 70% of your time cleaning and preparing data.

Skills to focus on:

Working with CSV/Excel files

Cleaning missing data

Data transformation with Pandas

Visualizing data with Seaborn/Matplotlib

This is the “real work” most data scientists do daily.

🧬 Step 4: Learn Machine Learning (ML)

Once you’re solid with data handling, dive into ML.

Start with:

Supervised learning (Linear Regression, Decision Trees, KNN)

Unsupervised learning (Clustering)

Model evaluation metrics (accuracy, recall, precision)

Toolkits: Scikit-learn, XGBoost

🚀 Step 5: Work on Real Projects

Projects are what make your resume pop.

Try solving:

Customer churn

Sales forecasting

Sentiment analysis

Fraud detection

Pro tip: Document everything on GitHub and write blogs about your process.

✏️ Step 6: Learn SQL and Databases

Data lives in databases. Knowing how to query it with SQL is a must-have skill.

Focus on:

SELECT, JOIN, GROUP BY

Creating and updating tables

Writing nested queries

🌍 Step 7: Understand the Business Side

Data science isn’t just tech. You need to translate insights into decisions.

Learn to:

Tell stories with data (data storytelling)

Build dashboards with tools like Power BI or Tableau

Align your analysis with business goals

🎥 Want a Structured Way to Learn All This?

Instead of guessing what to learn next, check out Intellipaat’s full Data Science course on YouTube. It covers Python, ML, real projects, and everything you need to build job-ready skills.

https://www.youtube.com/watch?v=rxNDw68XcE4

🔄 Final Thoughts

Becoming a data scientist in 2025 is 100% possible — even for beginners. All you need is consistency, a good learning path, and a little curiosity.

Start simple. Build as you go. And let your projects speak louder than your resume.

Drop a comment if you’re starting your journey. And don’t forget to check out the free Intellipaat course to speed up your progress!