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