Definition: Time-Series Forecasting

Time-series forecasting, a technique for predicting future values based on historical data, is essential for demand forecasting, financial analysis, and operational planning. By analyzing data that we stored in the past, we can make informed decisions that can guide our business strategy and help us understand future trends.

ARIMA Models

AutoRegressive Integrated Moving Average, or ARIMA, is a forecasting method that combines both an autoregressive model and a moving average model. Autoregression uses observations from previous time steps to predict future values using a regression equation.

Data Science Roadmaps

https://roadmap.sh/ai-data-scientist

Top Notebooks for Data Science and Machine learning

1. Google Colab https://colab.research.google.com

Free cloud-based Jupyter notebook environment by Google.
Provides free access to GPUs & TPUs (with usage limits).
Supports Python and integrates well with TensorFlow and PyTorch.

2. Kaggle Notebooks https://www.kaggle.com/code

Free hosted Jupyter notebooks for machine learning and data science.
Comes with pre-installed libraries like TensorFlow, PyTorch, and Scikit-learn.
Provides access to free GPUs (limited usage).

3. Jupyter Notebook https://jupyter.org

Open-source interactive computing environment.
Can be installed locally or used via free online platforms like MyBinder.
Supports multiple programming languages through kernels.

4. Deepnote https://deepnote.com

Cloud-based Jupyter-compatible notebook with collaboration features.
Free plan includes limited resources but supports real-time teamwork.
Easy integration with SQL and cloud storage services.

5. Gradient Notebooks (Paperspace) https://gradient.paperspace.com

Free Jupyter notebooks with cloud GPUs.
Supports deep learning frameworks like TensorFlow and PyTorch.
Provides free GPU usage with restrictions.

6. Hex https://hex.tech

A collaborative data science platform with notebook-style coding.
Free tier available for personal projects with limited resources.
Designed for SQL, Python, and visualization tools.

7. Zeppelin Notebooks (Apache Zeppelin) https://zeppelin.apache.org

Open-source multi-purpose notebook for big data analytics.
Supports Apache Spark, Hive, and SQL.
Can be self-hosted for free.

This paper introduces Coupled Biased Random Walks (CBRW), a novel unsupervised outlier detection method for categorical data with varied frequency distributions and noisy features. Unlike existing techniques, CBRW models feature value couplings using biased random walks to identify outliers effectively. It provides outlier scores that can evaluate object outlierness or assist in feature weighting and selection. Experiments show that CBRW significantly outperforms current state-of-the-art methods and enhances performance on data with noise.
https://www.ijcai.org/Proceedings/16/Papers/272.pdf

Gradient Descent

Main ideas of gradient descent:
〽️ is optimizing a model's fit on the data.
🪛 is an iterative solution that incrementally steps toward an optimal solution and is used in various situations.

👉 start with an initial guess
👉 and improve the guess until it finds an optimal solution.

🔍 Understanding the Impact of Feature Selection vs. Feature Extraction in Dimensionality Reduction for Big Data 📊



In the era of big data, working with high-dimensional datasets presents major challenges in processing, visualization, and model performance. A recent study noscriptd "Comparison of Feature Selection and Feature Extraction Role in Dimensionality Reduction of Big Data" (Journal of Techniques, 2023) offers a comprehensive evaluation of Feature Selection (FS) and Feature Extraction (FE) using the ANSUR II dataset — a U.S. Army anthropometric dataset with 109 features and 6068 observations.



📌 Study Goals

To compare FS and FE techniques in terms of:

➡️ Dimensionality reduction

➡️ Predictive performance

➡️ Information retention

⚙️ Techniques Explored

🧹 Feature Selection:

🔸 Highly Correlated Filter – removes features with correlation > 0.88

🔸 Recursive Feature Elimination (RFE) – eliminates the least important features iteratively

🔄 Feature Extraction:

🔹 Principal Component Analysis (PCA) – transforms original features into orthogonal components

🧪 Methodology

🧼 Data preprocessing using Missing Value Ratio

🧠 Classification using ML models:

✅ K-Nearest Neighbors (KNN)

✅ Decision Tree

✅ Support Vector Machine (SVM)

✅ Neural Network

✅ Random Forest

🔍 Post-reduction classification using the same models

📈 Key Results

🏆 KNN consistently performed best, maintaining 83% accuracy pre- and post-reduction

🧠 RFE showed the highest accuracy among reduction techniques with 66% post-reduction accuracy

🧩 PCA effectively reduced features but slightly decreased accuracy and interpretability

💡 Takeaways

✅ Use Feature Selection when interpretability and maintaining original structure are important

✅ Use Feature Extraction for noisy or highly redundant datasets

🎯 The choice depends on your data and modeling objectives

📖 Read the full paper here: DOI: 10.51173/jt.v5i1.1027



This is an excellent reference for anyone navigating the complexities of dimensionality reduction in ML pipelines. Whether you're optimizing models or just curious about FS vs. FE, this study is gold! 🧠✨

#MachineLearning #DataScience #FeatureEngineering #DimensionalityReduction #BigData #AI #KNN #PCA #RFE #MLResearch #DataAnalytics

🚀 From One Junior Data Scientist to Another — Free Resources to Kickstart Your Journey!

As a junior data scientist myself, I know how tough it can feel to break into this field from finding the right learning path to connecting with a supportive community. The good news? You don’t have to do it alone, and you don’t need to spend a fortune.

Here are two amazing (and FREE) resources that have been super valuable:

🎓 WorldQuant University

👉Offers 100% free online programs in Data Science, AI, and quantitative fields.
👉Project-based learning with an Applied Data Science Lab.

A great place to build strong foundations and hands-on experience.

🌍 Zindi Africa

👉A community and competition platform for data science & ML.
👉Work on real-world problems, build a portfolio, and grow with peers.
👉Amazing for networking and learning through collaboration.

✅ If you’re just starting out like me — don’t wait! These resources can help you learn, practice, and connect with others on the same path.

Let’s grow together in data 🚀📊

#DataScience #JuniorData #MachineLearning #FreeLearning #WorldQuantUniversity #ZindiAfrica #Community

🚀 Applications Now Open – Kifiya AI Mastery (KAIM) Training!

Are you ready to build a career in Artificial Intelligence and make an impact in Ethiopia’s FinTech sector?
The KAIM Program is a fully funded, 12-week online training designed to equip Ethiopia’s future AI leaders.

✨ What you’ll gain:

👉 Hands-on skills in Generative AI, Machine Learning & Data Engineering
👉 Mentorship from global experts
👉 Real-world projects for Ethiopia’s digital finance ecosystem

📌 Who can apply?

👉 Ethiopian youth aspiring to become AI Engineers
👉 Motivated learners ready for 12 weeks of intensive training

🗓 Deadline: October 24, 2025
📍 Format: 100% Online, Fully Funded

👉 Apply now: apply.10academy.org
📖 More info: Program Details

💡 About KAIM
KAIM is an initiative of Kifiya Financial Technology, supported by the Mastercard Foundation, and delivered by 10 Academy. It’s part of the SAFEE program, which helps Ethiopia move toward uncollateralized digital lending & data-driven banking, unlocking financial inclusion for MSMEs (only 30% currently have access to formal credit).

🔥 Don’t miss this chance to launch your AI career and contribute to Ethiopia’s digital transformation!

@data_to_pattern @data_to_pattern @data_to_pattern

🚀 Excited to share this opportunity!
The AI Bootcamp: From RAG to Agents, led by Alexey Grigorev, is a hands-on program to build real-world AI tools — from RAG assistants to production-ready AI agents.
Scholarships are available for motivated learners: https://forms.gle/ud21RVPUn3hLv3Xw5


@data_to_pattern @data_to_pattern @data_to_pattern

🌦 Zindi Challenge: Ghana’s Indigenous Intel Challenge [Beginners Only]

The future belongs to those who turn knowledge into action. Your ideas can predict the rain and empower communities.

Combine AI with traditional wisdom and make a real-world impact!

🗓 Timeline: 14 Aug 2025 – 13 Oct 2025
⏳ Days Remaining: 20
💰 Prize Pool: $2,500 USD

🥇 1st: $1,250
🥈 2nd: $750
🥉 3rd: $500

🌍 Who Can Join: Citizens of African countries, beginners only (no previous Zindi gold medals)

🧠 Challenge Goal:
Across Ghana’s Pra River Basin, farmers predict rainfall using the moon, stars, wind, birds, and plants. Your task: predict rainfall type (Heavy, Moderate, Small) in the next 12–24 hours using these indigenous ecological indicators.

💡 Why Participate:

* Validate and digitize centuries of indigenous weather knowledge
* Gain experience in AI & Responsible AI explainability (SHAP, LIME, Grad-CAM)
* Collaborate with RAIL for scientific publications and real-world implementations

📊 Dataset Includes:
* Farmers’ ecological indicators + actual rain measurements

Test dataset: indicators only (your predictions required)

🚀 Take the leap—turn centuries of wisdom into AI solutions that make a difference!
🔗 https://zindi.africa/competitions/ghana-indigenous-intel-challenge

@data_to_pattern @data_to_pattern @data_to_pattern

🚀 Join the Ethiopian Data Science & Machine Learning Community! 🇪🇹

Are you passionate about Data Science, Machine Learning, and AI?
Do you want to learn, share knowledge, and grow together with like-minded Ethiopians?

📢 Channel (Updates & Opportunities):
👉 https://news.1rj.ru/str/Ethiopian_ds_ml

💬 Group (Discussions & Networking):
👉 https://news.1rj.ru/str/Ethiopian_ds_ml_community

What you’ll find:
✅ Events, workshops
✅ Challenges & hackathons 🏆
✅ Networking with fellow enthusiasts 🌐

Let’s build Ethiopia’s future in AI & Data Science together! 💡


@data_to_pattern @data_to_pattern @data_to_pattern
#DataScience #MachineLearning #AI #Ethiopia #Hackathon #Community

#Opportunity_Alerts📣

🌟 Call for Applications: AI/ML Internship Fellowship at SRHIN! 🌟

✨Are you passionate about Artificial Intelligence (AI) and Machine Learning (ML)? The Slum and Rural Health Initiative (SRHIN) is excited to announce its AI Internship Fellowship — a unique program designed for aspiring and professional AI/ML enthusiasts who want to build impactful technology.

What You’ll Gain:
🔹Hands-on experience building AI-driven solutions for health and development
🔹Personalized mentorship from leading innovators
🔹A stipend to support your learning and participation

Who Can Apply?
🔸AI/ML students, enthusiasts, or professionals eager to apply their skills.
🔸Individuals passionate about public health, development, and technology.
🔸Motivated learners ready to commit time, creativity, and energy to impactful projects.

🔗 Apply: https://tinyurl.com/AI-SRHIN-Fellowship

Follow us👇for more opportunities
@data_to_pattern @data_to_pattern @data_to_pattern

DUPLICATED DATA

Duplicate data in machine learning refers to the presence of identical or highly similar records within a dataset. This can occur in various data modalities, including text, images, audio, video, and tabular data.

Types of Duplicate Data:
* Exact Duplicates: Records that are identical in every field or characteristic.
* Near Duplicates: Records that are almost identical but may have minor variations due to typographical errors, formatting differences, or inconsistencies in data entry.
* Similar or Paraphrased Versions: Particularly relevant in textual data, where the same meaning is expressed using different wording.

Causes of Duplicate Data:
- Human error: Accidental re-entry of information.
- System glitches: Errors during data processing or storage.
- Merging datasets: Combining data from multiple sources can lead to overlapping records.
- Data collection processes: Web scraping identical content, social media reposts, or copied articles.

Impact of Duplicate Data on Machine Learning:
👉 Skewed Analysis: Duplicates can lead to inaccurate statistical analyses and misleading conclusions.
👉 Overfitting: Machine learning models can overfit to the duplicated data, reducing their ability to generalize to new, unseen data.
👉 Inaccurate Model Performance Estimates: Duplicates can inflate performance metrics during evaluation, as the model may be tested on the same data points multiple times.
👉 Increased Computational Costs: Processing duplicate data consumes unnecessary computational resources and storage space.
👉 Data Redundancy and Complexity: Duplicates make data management and maintenance more challenging.

Addressing Duplicate Data:
✅ Identification: Techniques like using duplicated() in Pandas for tabular data, or content-based hashing for images and text, can help identify duplicates.
✅ Removal/Deduplication: Once identified, duplicates are typically removed from the dataset to ensure each unique observation is represented only once.
✅ Fuzzy Matching and Machine Learning: For near duplicates or similar content, fuzzy matching algorithms and machine learning models can be employed to identify and consolidate records based on similarity scores.

MORE
https://dagshub.com/blog/mastering-duplicate-data-management-in-machine-learning-for-optimal-model-performance/

📢 Hello Dears,
Congratulations! 🎉
The registration page for the Huawei ICT Competition – Northern Africa Innovation Track is now officially published.
As you all know, the Huawei ICT Competition is divided into three tracks to engage both students and instructors:
❶. Practice Competition – Focused on testing ICT knowledge and hands-on skills (Networking, Cloud, Computing, Security, etc.).
❷.  Innovation Competition – Team-based projects solving real-world problems using Huawei technologies like AI and Cloud.
❸. Teacher Competition – Designed for ICT instructors to strengthen teaching capacity and showcase expertise.
👉 Each competition has its own registration link, rules, and deadlines:
•  The Practice Competition registration link has already been shared: 👉 https://e.huawei.com/en/talent/#/ict-academy/ict-competition/regional-competition?zoneCode=026902&zoneId=98269659&compId=85132004&divisionName=Northern%20Africa&type=C001&isCollectGender=N&enrollmentDeadline=2025-12-31%2023%3A59%3A59&compTotalApplicantCount=797
• The Innovation Competition registration link is now available (see below).
• The Teacher Competition registration link will be released very soon — please stay tuned.
We encourage all eligible students and instructors to register and actively participate. This is a great opportunity to enhance your skills, collaborate, and showcase your talent on an international stage. 🌍✨
🔗 Innovation Competition Registration Page:
https://e.huawei.com/en/talent/#/ict/innovation-details?zoneCode=026902&zoneId=98269677&compId=85132008&divisionName=Northern%20Africa&type=C002&isCollectGender=N&enrollmentDeadline=2025-12-24%2023%3A59%3A59&compTotalApplicantCount=0%20%20%EF%BC%88
Steps to Participate:
⓵. Register for the Innovation Competition using the above link.
⓶. Complete the online learning space.
⓷. Upload your project before the deadline.
📌 Competition Instructions:
👉Team Formation & Requirements:
👉Each team must consist of three students AND one instructor (mandatory).
👉Participants must be current undergraduates, master’s, or PhD students.
👉Teams are encouraged to come from the same university, ideally with members from different grades to maximize complementary skills.

👉Instructor role: Guides the team, supports project planning, and ensures the proper use of Huawei technologies..
#️⃣ Eligibility:
✍️Each student can only participate in one track (either Innovation OR Practice, not both). Once registered, team members cannot be changed.

#️⃣ Project Requirements:
✍️Submissions must use Huawei AI-related technologies (MindSpore, CANN, ModelArts).
✍️ Projects must solve real-life or industry-specific challenges (software or software + hardware systems).
✍️Entries must be original,practical, functional, and innovative.
✍️ Huawei technologies must be clearly highlighted in diagrams, process flows, or codes.
✍️ Final submissions should include design scheme, functions, value, and problem solved.
Disqualification Rules:
👉Teams unable to demonstrate functionality will be disqualified.
👉 Failure to use Huawei’s specified technologies will make entries ineligible.
👉 Reusing previous projects without improvements is prohibited.
👉 Entries must not violate laws, contain discriminatory content, or infringe on privacy.

Dimension reduction

Dimension reduction is the process of reducing the number of variables (dimensions) in a dataset while keeping its most important information. It is a powerful technique for simplifying complex data, which offers benefits such as improved computational efficiency, better model performance, and easier data visualization.

Why reduce dimensions?

💡 Curse of dimensionality: When a dataset has too many dimensions relative to the number of data points, it can become sparse, making it difficult for machine learning models to find meaningful patterns.
🔑 Eliminate redundancy and noise: Datasets often contain variables that are highly correlated or irrelevant, adding noise and complexity that can confuse models.
📊 Improve visualization: The human brain is limited to visualizing data in two or three dimensions. Dimensionality reduction allows you to represent high-dimensional data in a way that is easier for people to understand.
🎯 Increase efficiency: Fewer dimensions mean less computational time and resources are needed to process the data, which is especially important for large datasets.
⚡️ Prevent overfitting: By simplifying the dataset and removing noise, a model is less likely to learn the random fluctuations in the data and more likely to generalize well to new data.

Common techniques
There are two primary approaches to dimensionality reduction:

1. Feature extraction
This method transforms the original variables into a new, smaller set of variables (components) that are combinations of the original ones.
👉 Principal Component Analysis (PCA): A popular unsupervised method that creates new, uncorrelated components, ordered by the amount of variance they explain.
👉 Factor Analysis (EFA): An unsupervised method used to identify underlying, unobserved (latent) factors that cause the correlations among the observed variables.
👉 t-SNE (t-Distributed Stochastic Neighbor Embedding): A nonlinear method especially useful for visualizing high-dimensional data by placing similar data points closer together in a lower-dimensional space.

2. Feature selection
This method selects a subset of the most relevant original variables, discarding the rest. It does not transform the variables.

Filter methods: Use statistical measures to score features and keep the best ones, for example, by filtering out low-variance or highly correlated variables.
Wrapper methods: Evaluate different subsets of features by training and testing a model with each subset to see which performs best.

https://medium.com/@souravbanerjee423/demystify-the-power-of-dimensionality-reduction-in-machine-learning-26b70b882571

@data_to_pattern @data_to_pattern @data_to_pattern