#randomforest

[deba1407]

Running a Random Forest

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

Load the dataset

data = pd.read_csv("C:\Users\guy3404\OneDrive - MDLZ\Documents\Cross Functional Learning\AI COP\Coursera\machine_learning_data_analysis\Datasets\tree_addhealth.csv")

data.head()

Getting information aboubt the dataset

data.info()

Total size of data

len(data)

We observe some of the columns of the dataset contains null values . We need to drop them

Drop null values from dataset

data_clean = data.dropna()

data_clean.dtypes

data_clean.describe()

Length of dataset after dropping null values

len(data_clean)

Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

Build model on training data

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predict using random forest classifier on test data

predictions=classifier.predict(pred_test)

Print confusion matrix and accuracy score

sklearn.metrics.confusion_matrix(tar_test,predictions)

sklearn.metrics.accuracy_score(tar_test, predictions)

fit an Extra Trees model to the data

model = ExtraTreesClassifier() model.fit(pred_train,tar_train)

Get feature importances

feature_importances = model.feature_importances_

Create a Series with feature importances and corresponding feature names

feature_importance_series = pd.Series(feature_importances, index=pred_train.columns)

Sort features based on importance

sorted_feature_importance = feature_importance_series.sort_values(ascending=False)

Plot the feature importances

plt.figure(figsize=(10, 6)) sorted_feature_importance.plot(kind='barh') plt.title('Feature Importance') plt.xlabel('Importance Score') plt.show()

#Running different number of trees and see the effect of that on the accuracy of the prediction

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)): classifier=RandomForestClassifier(n_estimators=idx + 1) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

Random forest analysis was performed to evaluate the importance of series of variables in predicting whether a person is a regular smoker or not. We observed that out of all features, marijuana use has the highest feature importance, followed by deviance and GPA. The random model could predict with an accuracy score of 85%.

[d0nutzgg]

Autism Detection with Stacking Classifier

Introduction Navigating the intricate world of medical research, I've always been fascinated by the potential of artificial intelligence in health diagnostics. Today, I'm elated to unveil a project close to my heart, as I am diagnosed ASD, and my cousin who is 18 also has ASD. In my project, I employed machine learning to detect Adult Autism with a staggering accuracy of 95.7%. As followers of my blog know, my love for AI and medical research knows no bounds. This is a testament to the transformative power of AI in healthcare.

The Data My exploration commenced with a dataset (autism_screening.csv) which was full of scores and attributes related to Autism Spectrum Disorder (ASD). My initial step was to decipher the relationships between these scores, which I visualized using a heatmap. This correlation matrix was instrumental in highlighting the attributes most significantly associated with ASD.

The Process:

Feature Selection: Drawing insights from the correlation matrix, I pinpointed the following scores as the most correlated with ASD:

'A6_Score', 'A5_Score', 'A4_Score', 'A3_Score', 'A2_Score', 'A1_Score', 'A10_Score', 'A9_Score'

Data Preprocessing: I split the data into training and testing sets, ensuring a balanced representation. To guarantee the optimal performance of my model, I standardized the data using the StandardScaler.

Model Building: I opted for two powerhouse algorithms: RandomForest and XGBoost. With the aid of Optuna, a hyperparameter optimization framework, I fine-tuned these models.

Stacking for Enhanced Performance: To elevate the accuracy, I employed a stacking classifier. This technique combines the predictions of multiple models, leveraging the strengths of each to produce a final, more accurate prediction.

Evaluation: Testing my model, I was thrilled to achieve an accuracy of 95.7%. The Receiver Operating Characteristic (ROC) curve further validated the model's prowess, showcasing an area of 0.99.

Conclusion: This project's success is a beacon of hope and a testament to the transformative potential of AI in medical diagnostics. Achieving such a high accuracy in detecting Adult Autism is a stride towards early interventions and hope for many.

Note: For those intrigued by the technical details and eager to delve deeper, the complete code is available here. I would love to hear your feedback and questions!

Thank you for accompanying me on this journey. Together, let's keep pushing boundaries, learning, and making a tangible difference.

Stay curious, stay inspired.

[subb01]

R for Data Science: The Essential Guide to Start Your Data Science Journey

R is a powerhouse programming language for data analysis and visualization, making it a go-to tool for data scientists and statisticians worldwide. Whether you’re a beginner looking to break into data science or an experienced analyst wanting to sharpen your skills, understanding how to leverage R effectively can be a game-changer.

Table of Contents

Introduction to R for Data Science

Why Choose R for Data Science?

Setting Up R: Tools and Environment

Basic Data Manipulation in R

Data Visualization with ggplot2

Statistical Analysis in R

Machine Learning in R

Tips for Learning R Efficiently

Conclusion and Next Steps

1. Introduction to R for Data Science

R was initially developed for statistical computing and has evolved into a comprehensive language used for data analysis, machine learning, and data visualization. Its user-friendly syntax and the availability of numerous packages make it an ideal choice for data professionals.

2. Why Choose R for Data Science?

Rich Ecosystem of Packages: With packages like dplyr, tidyr, ggplot2, and caret, R makes it easy to handle complex data manipulation and visualization tasks.

Statistical Strength: R was designed with statistics in mind, allowing for advanced statistical models and tests.

Community and Resources: R boasts a vibrant community with countless forums, tutorials, and resources available online, including the famous R for Data Science book by Hadley Wickham and Garrett Grolemund.

3. Setting Up R: Tools and Environment

To get started with R, follow these simple steps:

Install R: Download and install R from CRAN (The Comprehensive R Archive Network).

Install RStudio: RStudio is an integrated development environment (IDE) that provides a more intuitive interface for coding in R. Download it from RStudio’s official website.

4. Basic Data Manipulation in R

R’s power lies in its data manipulation capabilities. Here’s a brief overview of essential packages:

dplyr: Simplifies data manipulation tasks with functions like filter(), select(), mutate(), and summarize().

tidyr: Helps organize data into tidy formats using functions like pivot_longer() and pivot_wider().

Example: Using dplyr to filter and summarize data.

library(dplyr)

# Sample data frame data <- data.frame(Name = c(“A”, “B”, “C”, “D”), Score = c(85, 92, 78, 90))

# Filter and summarize filtered_data <- data %>% filter(Score > 80) %>% summarize(Mean_Score = mean(Score))

print(filtered_data)

5. Data Visualization with ggplot2

Visualization is a key part of data analysis. The ggplot2 package is one of R’s most powerful visualization tools.

Basic ggplot2 Example:

library(ggplot2)

# Create a simple bar chart ggplot(data, aes(x = Name, y = Score)) + geom_bar(stat = “identity”) + theme_minimal() + labs(title = “Student Scores”, x = “Name”, y = “Score”)

6. Statistical Analysis in R

R’s capabilities in statistical modeling make it the preferred tool for statisticians. From basic hypothesis testing (t.test(), anova()) to complex regression models (lm(), glm()), R covers it all.

Example: Simple Linear Regression

# Linear model model <- lm(Score ~ Name, data = data) summary(model)

7. Machine Learning in R

R provides robust support for machine learning through packages such as caret, randomForest, and xgboost.

Example: Training a Decision Tree Model

library(caret) library(rpart)

# Split data and train model model <- train(Score ~ ., data = data, method = “rpart”) print(model)

8. Tips for Learning R Efficiently

Hands-on Practice: The best way to learn is by doing. Work on datasets like mtcars, iris, or your data.

Explore Tutorials: Websites like R-bloggers and platforms like Coursera offer in-depth courses.

Join Forums: Engage in R communities on platforms like Stack Overflow and Reddit for peer support and advanced discussions.

9. Conclusion and Next Steps

Mastering R for data science opens doors to comprehensive data analysis, visualization, and predictive modeling. Start by practicing with real-world datasets and explore more advanced packages as you grow your expertise.

Ready to take your data science skills to the next level? Explore comprehensive courses and resources to deepen your knowledge and become proficient in R for data science.

Explore More on Data Science

For an in-depth look at R programming and live tutorials, be sure to check out this insightful YouTube session. This video provides practical guidance and additional tips to enhance your understanding of R and data science concepts. Don’t miss it!

[greatonlinetrainingsposts]

SAS vs. R vs. Python: Which Tool is Best for Data Analysis and Visualization?

What is SAS Programming?

SAS (Statistical Analysis System) is a software suite used for advanced analytics, business intelligence, data management, and predictive analytics. It’s widely used in industries like healthcare, finance, and government, particularly for data analysis that requires a high level of precision and regulatory compliance.

SAS programming is especially popular for:

Data manipulation and cleaning,

Statistical analysis and reporting,

Predictive modeling,

Business intelligence and visualization.

SAS programming has a long history, making it one of the most trusted tools for enterprises handling large datasets. Its ability to handle complex analyses and integration with databases makes it a solid choice for corporate environments.

If you're new to SAS, enrolling in a SAS online training course can be a great way to get started. These courses will help you understand the fundamental concepts and give you practical skills to effectively use SAS for your data science projects.

What is R?

R is an open-source programming language specifically designed for statistical computing and data visualization. It has a large and active community that continually contributes to its vast library of packages. R is widely used in academic research, statistics, and data science.

R is a strong choice for:

Statistical analysis (advanced statistical tests, hypothesis testing, etc.),

Data visualization (with libraries like ggplot2),

Machine learning (through packages like caret, randomForest, and xgboost).

One of R's biggest strengths is its ability to generate stunning data visualizations, making it ideal for projects where presenting data insights visually is important. Additionally, R is favored for its statistical analysis capabilities, especially when you need to perform complex statistical models or tests.

For those just starting out with R, following a R programming tutorial or taking a structured course can help you get comfortable with its syntax and functions.

What is Python?

Python is one of the most popular general-purpose programming languages used across multiple fields, including data science, web development, artificial intelligence, and more. Its versatility and simplicity have made it a favorite among data scientists and developers alike.

Python is widely used for:

Data analysis (with libraries like Pandas and NumPy),

Machine learning and deep learning (with frameworks like TensorFlow, scikit-learn, and PyTorch),

Data visualization (using libraries like Matplotlib and Seaborn).

Python’s syntax is straightforward and easy to understand, making it a great option for beginners. It's also highly extensible, meaning you can integrate it with other languages and tools, making it perfect for a wide variety of tasks. Moreover, Python's rich ecosystem of libraries for machine learning, data manipulation, and visualization make it a top choice for data science projects that involve predictive modeling, automation, and AI.

SAS vs R vs Python: Key Differences

Now that we’ve briefly covered what each tool is good at, let’s dive deeper into how SAS programming, R, and Python compare when it comes to their suitability for data science projects:

1. Ease of Use

SAS: SAS has a steeper learning curve, especially if you're new to programming. However, its comprehensive documentation and user support make it a solid choice for those who need to quickly learn the basics. If you want to get up to speed fast, you can consider taking a SAS online training course, which provides structured guidance and real-world examples.

R: R has a more complicated syntax than Python but is tailored for statisticians, making it ideal for data analysis and complex mathematical tasks. Learning R can be a challenge at first, but many R programming tutorials provide helpful examples to guide beginners.

Python: Python is known for its simplicity and readability. It has a very straightforward syntax that is easy to learn, making it ideal for beginners. It also has a vast community, so you can easily find resources and tutorials to get started.

2. Data Handling and Performance

SAS: SAS excels in handling large datasets, especially when working in enterprise environments. It is optimized for performance in complex data management tasks and can process massive amounts of data without crashing. This makes it a go-to for industries like finance and healthcare, where data accuracy and performance are crucial.

R: R is very efficient for statistical analysis, but it can struggle with very large datasets due to its memory limitations. For smaller to medium-sized datasets, R is great. However, if you're dealing with very large data sets, R can become slow, unless you use specific packages like data.table.

Python: Python, with libraries like Pandas and NumPy, is excellent for handling datasets of varying sizes. It performs well with medium to large datasets and offers a variety of tools to scale up for big data through integration with distributed computing systems like Hadoop and Spark.

3. Statistical Analysis

SAS: SAS is known for its powerful statistical analysis capabilities, including regression analysis, ANOVA, time-series analysis, and more. It is trusted for high-quality, validated results, making it a great choice for sectors like healthcare, where accuracy is essential.

R: R is an excellent choice for performing complex statistical analysis. It has a wider array of built-in statistical tests and models than SAS or Python. With extensive libraries for statistical analysis, R is often the go-to language for statisticians and data scientists focused on research.

Python: Python offers good statistical analysis capabilities through libraries like SciPy and StatsModels, but it's not as extensive as R when it comes to statistical modeling. However, Python’s real strength lies in its machine learning capabilities, which have grown rapidly thanks to libraries like scikit-learn.

4. Machine Learning and AI

SAS: While SAS is excellent for traditional statistical analysis, it is not as flexible as Python when it comes to machine learning and AI. However, SAS does have tools for machine learning and is frequently used for predictive modeling and analytics in enterprise environments.

R: R is not as commonly used for machine learning as Python, but it still offers strong libraries for building machine learning models, like caret, randomForest, and xgboost. It’s more widely used in academic and research settings for statistical modeling and analysis.

Python: Python is by far the most popular language for machine learning and AI. With frameworks like TensorFlow, Keras, scikit-learn, and PyTorch, Python allows you to build complex models for deep learning, machine learning, and artificial intelligence. If your data science project involves these areas, Python is the clear winner.

5. Data Visualization

SAS: SAS offers built-in tools for data visualization like SAS Visual Analytics and PROC SGPLOT, which are powerful in a business setting. However, they are less flexible than the visualization tools offered by R and Python.

R: R is a data visualization powerhouse. With libraries like ggplot2 and plotly, R can generate stunning, customizable plots and charts. If your project requires intricate and detailed visualizations, R is a top choice.

Python: Python has excellent visualization libraries like Matplotlib, Seaborn, and Plotly. These libraries are easy to use, and Python’s flexible syntax allows you to create a wide range of visualizations, making it a great choice for data scientists who need to quickly explore and present data.

Conclusion: Which Tool Should You Choose?

When deciding between SAS programming, R, and Python, the best tool depends on your specific needs:

Choose SAS if you're working in an enterprise environment where data handling, performance, and regulatory compliance are crucial. SAS online training or a SAS programming full course can help you gain expertise in these areas.

Choose R if your project focuses heavily on statistical analysis and data visualization, especially if you’re in academic research or healthcare.

Choose Python if you want an all-around tool for data science, with strong support for machine learning, AI, and general data manipulation. Python is the go-to tool for data scientists looking to explore, analyze, and visualize data at scale.

For beginners, starting with a SAS programming tutorial or enrolling in a SAS online training course can help you quickly grasp the basics of SAS programming. Whether you choose SAS, R, or Python, mastering one of these tools will set you on the path to success in your data science career.

[himanshu123]

Top Programming Languages for AI Development in 2024 

As artificial intelligence continues to reshape industries and enhance everyday experiences, the demand for skilled developers who know how to develop an AI is surging. Choosing the right programming language is crucial for creating efficient, scalable, and powerful AI solutions. In 2024, several languages stand out as particularly well-suited for AI development. In this blog, we'll explore these languages, their features, and why they are favored in the AI community. 

1. Python 

Python has long been the go-to language for AI development, and it remains a top choice in 2024. Its simplicity and readability make it an ideal option for both beginners and experienced developers. Python has a rich ecosystem of libraries and frameworks specifically designed for AI and machine learning, including TensorFlow, PyTorch, and Scikit-learn. These tools provide robust functionality, allowing developers to build and train complex models with ease. 

Additionally, Python's extensive community support means that developers can easily find resources, tutorials, and documentation. This is particularly beneficial for those who are just starting their journey in AI. 

2. R 

R is a programming language that excels in statistical analysis and data visualization, making it a valuable tool for data scientists and AI developers alike. Its rich set of packages, such as caret and randomForest, enables developers to implement machine learning algorithms and perform in-depth data analysis efficiently. 

In 2024, R continues to be popular among researchers and academics, especially in fields where data analytics plays a significant role. If your AI project requires heavy statistical computations and data exploration, R might be the perfect fit. 

3. Java 

Java is another solid choice for AI development, particularly for larger systems that require scalability and maintainability. Known for its portability, Java allows developers to write code once and run it anywhere, which is a significant advantage for enterprise applications. 

Java offers several libraries for AI, including Weka, Deeplearning4j, and Apache Mahout. These libraries provide functionalities for machine learning, data mining, and deep learning, making Java a versatile option for various AI applications. 

4. C++ 

C++ is often overlooked in favor of higher-level languages, but it has unique advantages that make it suitable for AI development. One of its biggest strengths is performance; C++ allows developers to create applications that run extremely fast, which is crucial for real-time AI applications like gaming or robotics. 

C++ is used in many AI projects that require performance optimization. It also offers libraries such as TensorFlow C++ API and Dlib, which help in building machine learning algorithms and models. For developers who prioritize speed and efficiency, C++ remains a viable choice. 

5. JavaScript 

With the rise of web-based AI applications, JavaScript has become increasingly relevant in the AI landscape. Frameworks like TensorFlow.js allow developers to run machine learning models directly in the browser, opening up new possibilities for interactive AI applications. 

JavaScript's ubiquity on the web makes it an excellent choice for integrating AI into online platforms. If your project involves client-side applications or requires real-time user interaction, JavaScript could be the language to choose. 

6. Julia 

Julia is gaining traction as a powerful language for numerical and scientific computing, making it a suitable option for AI development. Its ability to handle large datasets and perform high-level mathematical computations makes it particularly appealing for researchers and data scientists. 

In 2024, Julia's growing ecosystem, including libraries like Flux.jl for machine learning, positions it as a compelling choice for AI developers looking for a balance between performance and ease of use. 

7. Go 

Go, also known as Golang, is recognized for its simplicity and efficiency, making it a suitable language for AI projects that require speed and concurrency. Go's built-in support for concurrent programming allows developers to handle multiple tasks simultaneously, which is advantageous for AI applications processing large amounts of data. 

While Go may not have as extensive a library support as Python or R, its performance and ease of deployment make it an appealing option for certain AI applications. 

8. Swift 

As AI continues to penetrate the mobile app development space, Swift has emerged as a key language for building AI-driven mobile applications. With its clean syntax and strong performance, Swift allows developers to integrate machine learning models into iOS applications seamlessly. 

In 2024, the integration of frameworks like Core ML makes it easier than ever for Swift developers to implement AI features, enhancing user experiences in mobile applications. 

9. Rust 

Rust is a systems programming language known for its safety and performance. While it may not be the first language that comes to mind for AI development, Rust's features, such as memory safety and concurrency, make it an intriguing choice for building robust AI applications. 

As more developers recognize the benefits of Rust, it is slowly gaining a foothold in the AI community, particularly for projects that require low-level control and high performance. 

Using Tools Like a Mobile App Cost Calculator 

When embarking on an AI project, it’s essential to consider the financial aspects, especially if you're developing a mobile application that leverages AI. A mobile app cost calculator can help you estimate development expenses based on your specific requirements, such as complexity, features, and the technology stack. By utilizing this tool, you can plan your budget effectively and allocate resources accordingly. 

Conclusion 

Choosing the right programming language for AI development is critical to the success of your project. In 2024, Python, R, Java, C++, JavaScript, Julia, Go, Swift, and Rust each offer unique advantages that cater to different needs and preferences in the AI space. As you navigate your AI journey, consider your project requirements, your team's expertise, and the specific features you need. 

If you're ready to take the plunge into AI development, Book an Appointment with an expert to discuss how to leverage these programming languages effectively for your projects. Embracing the right tools and languages can significantly enhance your AI development experience, setting you on the path to creating innovative and impactful solutions. 

[aanandh]

How R Programming is Revolutionizing Data Science

How R Programming is Revolutionizing Data Science

In the rapidly evolving world of data science, R Programming stands out as a powerful tool that is transforming how we analyze and interpret data. Its extensive capabilities, user-friendly interface, and vibrant community make it a cornerstone of modern data science practices. Here’s how R Programming is revolutionizing the field

1. Advanced Statistical Analysis

R Programming was specifically designed for statistical analysis, making it a go-to tool for data scientists. Its comprehensive suite of packages and functions allows users to perform sophisticated statistical analyses with ease. From basic descriptive statistics to complex inferential models, R’s capabilities enable analysts to uncover deep insights from data, making it invaluable for academic research and industry applications alike.

2. Rich Ecosystem of Packages

One of R’s standout features is its rich ecosystem of packages. CRAN, the Comprehensive R Archive Network, hosts thousands of packages that extend R’s functionality. These packages cover a wide range of applications, including data manipulation, visualization, and machine learning. Packages like ggplot2 for visualization, dplyr for data manipulation, and caret for machine learning provide data scientists with powerful tools to streamline their workflows and tackle complex problems efficiently.

3. Enhanced Data Visualization

Data visualization is crucial for interpreting and communicating data insights effectively. R excels in this area with its advanced visualization packages. ggplot2, one of the most popular visualization libraries, enables users to create highly customizable and aesthetically pleasing graphics. Its layered approach to plotting allows for the creation of intricate visualizations that can convey complex data patterns and relationships clearly.

4. Reproducible Research

Reproducibility is a cornerstone of scientific research, and R Programming supports this through tools like R Markdown. R Markdown allows users to create dynamic documents that integrate code and narrative. Researchers can generate reports that include code, results, and commentary in a single document, ensuring that analyses are reproducible and transparent. This feature is particularly valuable for collaborative projects and academic publications.

5. Integration with Other Tools

R’s ability to integrate with other tools and programming languages enhances its versatility. It can interface with databases, perform data manipulation, and even call Python code when needed. This integration capability makes R a flexible choice for data scientists who work within diverse technical ecosystems. Additionally, R can interact with tools like Excel, SQL databases, and web APIs, facilitating seamless data import and export processes.

6. Support for Machine Learning

Machine learning is a rapidly growing field, and R is well-equipped to support its demands. With packages like caret, xgboost, and randomForest, R provides robust frameworks for building and evaluating machine learning models. These packages offer a range of algorithms and techniques, from classification and regression to ensemble methods, empowering data scientists to develop predictive models and derive actionable insights from data.

7. Community and Support

R boasts a vibrant and active community that contributes to its growth and evolution. The community provides extensive resources, including tutorials, forums, and documentation, which can help users overcome challenges and stay updated with the latest developments. This support network ensures that R programmers have access to a wealth of knowledge and expertise, enhancing their ability to leverage R effectively in their data science projects.

8. Cost-Effective and Open Source

R is an open-source programming language, which means it is freely available to anyone. This cost-effectiveness makes R an attractive option for individuals and organizations looking to minimize expenses while still accessing powerful data analysis tools. Its open-source nature also fosters a collaborative environment where users can contribute to the language’s development and improvement.

Conclusion

R Programming is revolutionizing data science by providing a robust, versatile, and user-friendly platform for data analysis and visualization. Its advanced statistical capabilities, rich ecosystem of packages, and support for reproducible research and machine learning make it a vital tool for modern data scientists. As the field of data science continues to evolve, R will remain at the forefront, driving innovation and shaping the future of data analysis.

[intelarti]

RandomForest Project

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics

# Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

""" Modeling and Prediction

Split into training and testing sets """

predictors = data_clean[[ 'CODPRIPER', 'CODULTPER', 'SEXOC']]

targets = data_clean.SITALUC

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

""" Running a different number of trees and see the effect of that on the accuracy of the prediction """

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)): classifier=RandomForestClassifier(n_estimators=idx + 1) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

The accuracy of a RandomForestClassifier varies with the number of trees (n_estimators). A RandomForestClassifier is trained and evaluated with an increasing number of trees, from 1 up to the size of the trees list. The resulting accuracy for each number of trees is plotted, allowing visualization of the impact of the number of trees on the model's accuracy, that is growing.

This type of analysis is useful for determining the optimal number of trees in a random forest, balancing the improvement in accuracy with the additional computational cost of adding more trees.

[cybister015]

If anyone's curious about my analyses thus far:

Even with the small sample size, I wanted to explore what patterns I could find. Thus far, the data is biased towards subs. We have almost no dom data. I think this is just a function of who is active on tumblr (it is mostly subs). I would love more vanilla data points for controls.

[analyticsquareforyou]

Data Science with R Programming: Unveiling Insights through Analytics

In the realm of data science, the utilization of R programming has emerged as a powerful tool for uncovering actionable insights from complex datasets. This comprehensive Data Science course delves into the intricacies of R programming and its application in the analytics domain, equipping participants with the skills to navigate through data complexities and extract valuable insights.

Keywords in Data Science with R Programming:

R Programming: At the heart of this course lies R programming, a versatile language specifically designed for statistical computing and graphics. Participants will gain proficiency in R programming syntax, data structures, and functions, empowering them to manipulate, analyze, and visualize data effectively.

Data Manipulation: An essential aspect of data science is the ability to wrangle and preprocess data efficiently. Participants will learn various techniques for data manipulation in R, including data cleaning, transformation, aggregation, and reshaping, to prepare datasets for analysis.

Statistical Analysis: In the realm of data science, statistical analysis serves as a cornerstone for deriving meaningful insights from data. Participants will explore statistical techniques such as hypothesis testing, regression analysis, and multivariate analysis, leveraging R's extensive library of statistical functions and packages.

Data Visualization: Visualizing data is crucial for gaining insights and communicating findings effectively. Participants will learn how to create insightful visualizations using R's visualization libraries, including ggplot2 and plotly, to explore patterns, trends, and relationships within datasets.

Machine Learning: Machine learning algorithms empower data scientists to build predictive models and make data-driven decisions. Participants will delve into machine learning techniques such as classification, regression, clustering, and dimensionality reduction, implementing algorithms using R's machine learning packages like caret, randomForest, and xgboost.

Analytic Square: Empowering Data-driven Decision Making

As participants embark on their journey through this Data Science course, they will step into the realm of Analytic Square, a metaphorical space where data-driven decisions are forged. Within Analytic Square, participants will harness the power of R programming to navigate through the four corners of data science:

Data Collection: Participants will gather diverse datasets from various sources, including structured databases, unstructured text, and streaming data sources, to fuel their analytical endeavors.

Data Preparation: Armed with R programming skills, participants will cleanse, transform, and preprocess raw data, ensuring its quality and usability for analysis.

Data Analysis: In the heart of Analytic Square, participants will conduct in-depth analysis using statistical techniques and machine learning algorithms, unveiling insights and patterns hidden within the data.

Insight Generation: Finally, participants will leverage their analytical findings to generate actionable insights and recommendations, empowering stakeholders to make informed decisions and drive organizational success.

By immersing themselves in the world of Data Science with R Programming and embracing the principles of Analytic Square, participants will emerge as proficient data scientists equipped to tackle real-world challenges and unlock the transformative power of data-driven decision-making.

[futuretechwords]

The Rise Of R Programming Language: Where And Why To Use?

The Rise of R Programming Language: Top 6 uses

In the ever-expanding landscape of programming languages, R has emerged as a powerhouse for data analysis, statistical computing, and machine learning. Its versatility and robust capabilities have propelled its rise to prominence across various industries and domains.

Unleashing the Potential of R:

1. Data Analysis and Visualization: R's extensive library of packages, including ggplot2 and dplyr, empowers analysts to manipulate data and create stunning visualizations with ease.

2. Statistical Computing: With built-in functions for statistical modeling and hypothesis testing, R is the preferred choice for statisticians and researchers worldwide.

3. Machine Learning: R's machine learning packages, such as caret and randomForest, enable developers to build predictive models and uncover patterns in data.

4. Bioinformatics: R is widely used in bioinformatics for analyzing genomic data, DNA sequencing, and protein structure prediction.

5. Finance: In finance, R is employed for risk modeling, portfolio optimization, and algorithmic trading strategies.

6. Social Sciences: Researchers leverage R for survey analysis, experimental design, and sentiment analysis in social sciences.

7. Healthcare: From clinical trials to epidemiological studies, R plays a pivotal role in analyzing healthcare data and improving patient outcomes.

8. Marketing and Advertising: Marketers utilize R for customer segmentation, campaign optimization, and sentiment analysis on social media data.

Why Choose R?

1. Open Source: R is open-source and free to use, making it accessible to a wide range of users, from students to seasoned professionals.

2. Rich Ecosystem: R boasts a vibrant community and extensive package ecosystem, providing users with a wealth of resources and tools for their projects.

3. Interactivity and Reproducibility: R's interactive environment allows for iterative exploration and analysis, while its scripting capabilities facilitate reproducible research and collaboration.

4. Integration with Other Languages: R seamlessly integrates with other programming languages like Python and SQL, enabling users to leverage the strengths of different tools within their workflows.

As industries increasingly rely on data-driven insights to make informed decisions, the demand for skilled R programmers continues to soar. Whether you're a data scientist, researcher, or industry professional, mastering R opens doors to a world of opportunities in data analytics and beyond.

For an in-depth exploration of the rise of R programming language, visit FutureTech Words. Unlock the potential of R today!

[kareamemad]

عالم الاوفيس| لغات البرمجة

عالم الاوفيس| لغات البرمجة   ماهى لغات البرمجة؟ لغات البرمجة هي وسيلة للتواصل مع الحاسوب وإعطائه تعليمات لتنفيذ مهام محددة. تعد لغات البرمجة متنوعة ومتعددة، وتستخدم في مجالات مثل تطوير الويب، وتطبيقات الجوال، والذكاء الاصطناعي، والروبوتات، وغيرها. في هذا المقال، سنلقي نظرة عامة على بعض أبرز لغات البرمجة المستخدمة حاليًا. 1. لغة Python:    - تُعتبر Python واحدة من أكثر لغات البرمجة شيوعًا وقوة في الوقت الحالي.    - تتميز بقوتها في مجالات الذكاء الاصطناعي وتحليل البيانات وتطوير تطبيقات الويب.    - تتميز بقواعد بسيطة وسهلة التعلم وقدرتها على القراءة والكتابة بشكل مشابه للإنسان. 2. لغة JavaScript:    - تُستخدم على نطاق واسع في تطوير مواقع الويب وتطبيقات الويب الديناميكية.    - تدعم تفاعل المستخدم وإضافة مؤثرات بصرية وتحقيق التواصل مع خوادم الويب.    - تعتبر أساسية لتكنولوجيا الويب الحديثة مثل React وAngular وVue.js. 3. لغة Java:    - تُستخدم في تطوير تطبيقات سطح المكتب وتطبيقات الجوال وتطبيقات الويب.    - تتميز بقوتها في مجالات الأتمتة والأمان والأداء.    - تعتبر لغة قوية ومستقرة وقابلة للتوسع. 4. لغة C++:    - تُستخدم في تطوير برامج النظام وتطبيقات الألعاب والروبوتات والتحكم الصناعي.    - تتميز بأداء عالٍ وقدرة على التحكم المباشر في الموارد والذاكرة. 5. لغة Ruby:    - تُستخدم في تطوير تطبيقات الويب والتطبيقات الديناميكية.    - ت��تهر ببساطتها وقابلية قراءة الكود وسهولة التعلم. 6. لغة Swift:    - تستخدم في تطوير تطبيقات iOS وmacOS وwatchOS وtvOS.    - تتميز ببنية اللغة الحديثة وقوة في تعامل مع البرمجة المتعددة الخيوط. 7. لغة PHP:    - تستخدم في تطوير تطبيقات الويب الديناميكية وتفاعل المستخدم.    - تشتهر بسهولة التعلم وتوافر الموارد والأدوات. هذه مجرد نبذة عن بعض لغات البرمجة المهمة واستخداماتها المتنوعة. هناك العديد من اللغات الأخرى مثل C#, Go, Kotlin, Rust وغيرها التي يمكن استك��افها. ما هي بعض اللغات البرمجية المستخدمة في تطوير الذكاء الاصطناعي؟ هنا بعض اللغات البرمجية المستخدمة في تطوير الذكاء الاصطناعي: 1. Python:    - Python هي واحدة من أكثر اللغات استخدامًا في مجال الذكاء الاصطناعي.    - توفر مجموعة قوية من المكتبات والإطارات مثل TensorFlow وPyTorch وKeras وScikit-learn.    - تتميز بسهولة التعلم وقدرتها على التعامل مع البيانات وتنفيذ النماذج الذكية. 2. R:    - R هي لغة برمجة وبيئة تحليل البيانات شائعة الاستخدام في مجال الذكاء الاصطناعي والإحصاء.    - تقدم مجموعة واسعة من الحزم والمكتبات المخصصة لتحليل البيانات والتعلم الآلي مثل "caret" و "randomForest" و "ggplot2". 3. Java:    - Java لديها مكتبات وإطارات مثل Deeplearning4j وWeka التي تدعم الذكاء الاصطناعي.    - تستخدم Java في تطوير تطبيقات ذكاء اصطناعي مثل تحليل النصوص والتحليل الصوتي والتعرف على الصور. 4. C++:    - C++ يستخدم في تطوير البرامج التي تتطلب أداءً عاليًا وموارد محسّنة.    - يتم استخدامه في تنفيذ الخوارزميات المعقدة للذكاء الاصطناعي والتعلم العميق. 5. Julia:    - Julia هي لغة برمجة مصممة خصيصًا للتحليل العلمي والحوسبة الفائقة.    - تتميز بأداء سريع وقدرة على التعامل مع البيانات الكبيرة وتنفيذ الخوارزميات المعقدة للذكاء الاصطناعي. 6. Lisp:    - Lisp هي لغة برمجة تاريخية مستخدمة في مجال الذكاء الاصطناعي.    - تتميز بقدرتها على تمثيل المعرفة والتعامل مع البيانات الهيكلية. 7. Prolog:    - Prolog هي لغة برمجة منطقية تستخدم في الذكاء الاصطناعي والترجمة الآلية.    - تتميز بقدرتها على التعبير عن المنطق وتنفيذ القواعد والاستعلامات. تذكر أن هذه مجرد بعض اللغات البرمجية المستخدمة في مجال الذكاء الاصطناعي، وهناك المزيد من اللغات المتاحة والتي يمكن استخدامها وفقًا لاحتياجات المشروع والمجال المحدد. مهارات الاكسل via عالم الاوفيس https://ift.tt/obc6gHx October 06, 2023 at 03:20AM

[mainakmondal]

Run a Random Forest

This assignment is intended for Coursera course "Machine Learning for Data Analysis by Wesleyan University”.

It is for "Week 2: Peer-graded Assignment: Running a Random Forest".

Code

Plots

For visualization purposes, the number of dimensions was reduced to two by applying MDS method with cosine distance. The plot illustrates that our classes are not clearly divided into parts.

Moreover, our classes are highly unbalanced, so in our classifier we should add parameter class_weight='balanced'.

RandomForest classifier

Results

Random forest and ExtraTrees classifier were deployed to evaluate the importance of a series of explanatory variables in predicting a categorical response variable - red wine quality (score between 0 and 10). The following explanatory variables were included: fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates and alcohol.

[himanshu123]

Top Programming Languages for AI Development in 2024 

As artificial intelligence continues to reshape industries and enhance everyday experiences, the demand for skilled developers who know how to develop an AI is surging. Choosing the right programming language is crucial for creating efficient, scalable, and powerful AI solutions. In 2024, several languages stand out as particularly well-suited for AI development. In this blog, we'll explore these languages, their features, and why they are favored in the AI community. 

1. Python 

Python has long been the go-to language for AI development, and it remains a top choice in 2024. Its simplicity and readability make it an ideal option for both beginners and experienced developers. Python has a rich ecosystem of libraries and frameworks specifically designed for AI and machine learning, including TensorFlow, PyTorch, and Scikit-learn. These tools provide robust functionality, allowing developers to build and train complex models with ease. 

Additionally, Python's extensive community support means that developers can easily find resources, tutorials, and documentation. This is particularly beneficial for those who are just starting their journey in AI. 

2. R 

R is a programming language that excels in statistical analysis and data visualization, making it a valuable tool for data scientists and AI developers alike. Its rich set of packages, such as caret and randomForest, enables developers to implement machine learning algorithms and perform in-depth data analysis efficiently. 

In 2024, R continues to be popular among researchers and academics, especially in fields where data analytics plays a significant role. If your AI project requires heavy statistical computations and data exploration, R might be the perfect fit. 

3. Java 

Java is another solid choice for AI development, particularly for larger systems that require scalability and maintainability. Known for its portability, Java allows developers to write code once and run it anywhere, which is a significant advantage for enterprise applications. 

Java offers several libraries for AI, including Weka, Deeplearning4j, and Apache Mahout. These libraries provide functionalities for machine learning, data mining, and deep learning, making Java a versatile option for various AI applications. 

4. C++ 

C++ is often overlooked in favor of higher-level languages, but it has unique advantages that make it suitable for AI development. One of its biggest strengths is performance; C++ allows developers to create applications that run extremely fast, which is crucial for real-time AI applications like gaming or robotics. 

C++ is used in many AI projects that require performance optimization. It also offers libraries such as TensorFlow C++ API and Dlib, which help in building machine learning algorithms and models. For developers who prioritize speed and efficiency, C++ remains a viable choice. 

5. JavaScript 

With the rise of web-based AI applications, JavaScript has become increasingly relevant in the AI landscape. Frameworks like TensorFlow.js allow developers to run machine learning models directly in the browser, opening up new possibilities for interactive AI applications. 

JavaScript's ubiquity on the web makes it an excellent choice for integrating AI into online platforms. If your project involves client-side applications or requires real-time user interaction, JavaScript could be the language to choose. 

6. Julia 

Julia is gaining traction as a powerful language for numerical and scientific computing, making it a suitable option for AI development. Its ability to handle large datasets and perform high-level mathematical computations makes it particularly appealing for researchers and data scientists. 

In 2024, Julia's growing ecosystem, including libraries like Flux.jl for machine learning, positions it as a compelling choice for AI developers looking for a balance between performance and ease of use. 

7. Go 

Go, also known as Golang, is recognized for its simplicity and efficiency, making it a suitable language for AI projects that require speed and concurrency. Go's built-in support for concurrent programming allows developers to handle multiple tasks simultaneously, which is advantageous for AI applications processing large amounts of data. 

While Go may not have as extensive a library support as Python or R, its performance and ease of deployment make it an appealing option for certain AI applications. 

8. Swift 

As AI continues to penetrate the mobile app development space, Swift has emerged as a key language for building AI-driven mobile applications. With its clean syntax and strong performance, Swift allows developers to integrate machine learning models into iOS applications seamlessly. 

In 2024, the integration of frameworks like Core ML makes it easier than ever for Swift developers to implement AI features, enhancing user experiences in mobile applications. 

9. Rust 

Rust is a systems programming language known for its safety and performance. While it may not be the first language that comes to mind for AI development, Rust's features, such as memory safety and concurrency, make it an intriguing choice for building robust AI applications. 

As more developers recognize the benefits of Rust, it is slowly gaining a foothold in the AI community, particularly for projects that require low-level control and high performance. 

Using Tools Like a Mobile App Cost Calculator 

When embarking on an AI project, it’s essential to consider the financial aspects, especially if you're developing a mobile application that leverages AI. A mobile app cost calculator can help you estimate development expenses based on your specific requirements, such as complexity, features, and the technology stack. By utilizing this tool, you can plan your budget effectively and allocate resources accordingly. 

Conclusion 

Choosing the right programming language for AI development is critical to the success of your project. In 2024, Python, R, Java, C++, JavaScript, Julia, Go, Swift, and Rust each offer unique advantages that cater to different needs and preferences in the AI space. As you navigate your AI journey, consider your project requirements, your team's expertise, and the specific features you need. 

If you're ready to take the plunge into AI development, Book an Appointment with an expert to discuss how to leverage these programming languages effectively for your projects. Embracing the right tools and languages can significantly enhance your AI development experience, setting you on the path to creating innovative and impactful solutions.