This project is based on programming with R. Use a huge dataset from Kaggle with purpose to predict the survival of the passengers of the Titanic with incomplete data.
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
Aaa ped-14-Ensemble Learning: About Ensemble LearningAminaRepo
In this section we will start talking effectively about ensemble learning. We will simply talk about the different methods that exist to combine different models. We will then implement those methods in Python.
[Notebook](https://colab.research.google.com/drive/1fNkOh7iQ_AnjNWxm3hWyR4DIGRUNwzsS)
Assessing Model Performance - Beginner's GuideMegan Verbakel
Introduction on how to assess the performance of a classifier model. Covers theories (bias-variance trade-off, over/under-fitting), data preparation (train/test split, cross-validation), common performance plots (e.g. ROC curve and confusion matrix), and common metrics (e.g. accuracy, precision, recall, f1-score).
24 Evaluation Metrics for Binary Classification.
For every metric information about:
- What is the definition and intuition behind it,
- The non-technical explanation that you can communicate to business stakeholders,
- How to calculate or plot it,
- When should you use it.
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
Detailed talk about Random Forest and its statistical techniques for classification and regression analysis with termonologies like Out of Bag (OOB) estimate of performance, Bias Variance Trade off, and model validation metrics.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakro
Classification using L1-Penalized Logistic RegressionSetia Pramana
L1-Penalized Logistic Regression, is commonly used for classification in high dimensional data such as microarray. This slide presents a brief overview of the algorithm.
Aaa ped-14-Ensemble Learning: About Ensemble LearningAminaRepo
In this section we will start talking effectively about ensemble learning. We will simply talk about the different methods that exist to combine different models. We will then implement those methods in Python.
[Notebook](https://colab.research.google.com/drive/1fNkOh7iQ_AnjNWxm3hWyR4DIGRUNwzsS)
Assessing Model Performance - Beginner's GuideMegan Verbakel
Introduction on how to assess the performance of a classifier model. Covers theories (bias-variance trade-off, over/under-fitting), data preparation (train/test split, cross-validation), common performance plots (e.g. ROC curve and confusion matrix), and common metrics (e.g. accuracy, precision, recall, f1-score).
24 Evaluation Metrics for Binary Classification.
For every metric information about:
- What is the definition and intuition behind it,
- The non-technical explanation that you can communicate to business stakeholders,
- How to calculate or plot it,
- When should you use it.
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
Detailed talk about Random Forest and its statistical techniques for classification and regression analysis with termonologies like Out of Bag (OOB) estimate of performance, Bias Variance Trade off, and model validation metrics.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakro
Classification using L1-Penalized Logistic RegressionSetia Pramana
L1-Penalized Logistic Regression, is commonly used for classification in high dimensional data such as microarray. This slide presents a brief overview of the algorithm.
Introduction to Data Analytics starting with
OLS.
This is the first of a series of essays. I will share essays on unsupervised learning, dimensionality reduction and anomaly/outlier detection.
In this project you will investigate the exponential distribution in R and compare it with the Central Limit Theorem. The exponential distribution can be simulated in R with rexp(n, lambda) where lambda is the rate parameter. The mean of exponential distribution is 1/lambda and the standard deviation is also 1/lambda. Set lambda = 0.2 for all of the simulations. You will investigate the distribution of averages of 40 exponentials. Note that you will need to do a thousand simulations.
Illustrate via simulation and associated explanatory text the properties of the distribution of the mean of 40 exponentials. You should
Show the sample mean and compare it to the theoretical mean of the distribution.
Show how variable the sample is (via variance) and compare it to the theoretical variance of the distribution.
Show that the distribution is approximately normal.
Convolutional Neural Network for Text ClassificationAnaïs Addad
Work under Pr. Nolan, with a team of 4 to implement a convolutional neural network for text classification in TensorFlow using a dataset of Amazon reviews
Explore how data science can be used to predict employee churn using this data science project presentation, allowing organizations to proactively address retention issues. This student presentation from Boston Institute of Analytics showcases the methodology, insights, and implications of predicting employee turnover. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
Predict Backorder on a supply chain data for an OrganizationPiyush Srivastava
Performed cleaning and founded the important variables and created a best model using different classification techniques (Random Forest, Naïve Bayes, Decision tree, KNN, Neural Network, Support Vector Machine) to predict the back-order for an organization using the best modelling and technique approach.
Approaches to online quantile estimationData Con LA
Data Con LA 2020
Description
This talk will explore and compare several compact data structures for estimation of quantiles on streams, including a discussion of how they balance accuracy against computational resource efficiency. A new approach providing more flexibility in specifying how computational resources should be expended across the distribution will also be explained. Quantiles (e.g., median, 99th percentile) are fundamental summary statistics of one-dimensional distributions. They are particularly important for SLA-type calculations and characterizing latency distributions, but unlike their simpler counterparts such as the mean and standard deviation, their computation is somewhat more expensive. The increasing importance of stream processing (in observability and other domains) and the impossibility of exact online quantile calculation together motivate the construction of compact data structures for estimation of quantiles on streams. In this talk we will explore and compare several such data structures (e.g., moment-based, KLL sketch, t-digest) with an eye towards how they balance accuracy against resource efficiency, theoretical guarantees, and desirable properties such as mergeability. We will also discuss a recent variation of the t-digest which provides more flexibility in specifying how computational resources should be expended across the distribution. No prior knowledge of the subject is assumed. Some familiarity with the general problem area would be helpful but is not required.
Speaker
Joe Ross, Splunk, Principal Data Scientist
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
This tutor shows the train and test set split with histogram and a probability density function in scikit-learn on synthetic datasets. The dataset is very simple as a reference of understanding.
MM - KBAC: Using mixed models to adjust for population structure in a rare-va...Golden Helix Inc
Confounding from population structure, extended families and inbreeding can be a significant issue for burden and kernel association tests on rare variants from next generation DNA sequencing. An obvious solution is to combine the power of a mixed model regression analysis with the ability to assess the rare variant burden using methods such as KBAC or CMC. Recent approaches have adjusted burden and kernel tests using linear regression models; this method adjusts for the relatedness of samples and includes that directly into a logistic regression model.
This webcast will focus on the details of bringing Mixed Model Regression and KBAC together, including: deriving an optimal logistic mixed model algorithm for calculating the reduced model score, how the kinship or random effects matrix should be specified, and how it all comes together into one algorithm. Results from applying the method to variants from the 1000 Genomes project will also be presented and compared to famSKAT.
Similar to Data Analysis project "TITANIC SURVIVAL" (20)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2.
Import CSV file and start exploring the dataset
Check the dimensions of the dataset
Call the first rows to understand the data
Check the variables and their types
Explore the dataset for Missing values and identify their location and their
number
Subset our data to obtain observations that contain no missing data by replacing
the lines with N/A with some specific values
Create new variables to help to train our data
Exploratory DATA Analysis
3.
My predictive model is based on some new variables, that I had to
create due to make my predictions more accurate.
CREATE
New variables
4. Out-of-bag (OOB) error :
is a method of measuring the
prediction error of random forests .So
the smaller the error, the more
accurate my model.
Random Forest:
Random forest builds multiple
decision trees and merges them
together to get a more accurate and
stable prediction.
IMPORTANCE OF VALUES
Random Forest has a feature of
presenting the
important variables.
83.95%
Accuracy
rf.label <- as.factor(train$Survived)
0:perish
1:survive
5. Using 10 fold Cross-validation divide my
train.data(=891 obs.) into 10 folds with almost the
same length each one
Fold1(89 obs.), Fold2(89 obs.),…..,Fold10(90obs.)
makeCluster(6,type=“SOCK”)
We call these groups 1 to 10 .The analysis is performed 10 times . The first
time the analysis is performed, groups 1 to 9 are used to train the
algorithm and group 10 is used to test the model.
I categorize the data (Clustering) into 6
sockets . Every socket commits CPU and it’s
working without waiting at the same time.
Performing 10 fold C-V find the cp-accuracy
of the model
6.
Visualization
Visualize the data is a powerful machine to understand well
your data and find the correlation between them.
This plot help me to find the survivng
rate based on Pclass and new title.
My final rpart model which give me the
most important variables of my
predictive model. As a result the best
accuracy for my model.
