Sponsored LinkedIn post
A dedicated post to introduce your brand to my audience.
Olamide Alonge
Data Entry & ICT Officer @ Buven Communications Nigeria
Open to collaborations
Companies pay Olamide to write a post about their product on LinkedIn.
This page is where they book it. Below: Olamide's audience, recent posts and price. Naano, the platform behind this page, handles the brief, your approval of the post and the payment.
€13per post, fixed price
You approve the post before it goes live
Looking for Olamide? View LinkedIn profileA creator yourself? Create your own page
About Olamide Alonge
Machine Learning & AI Engineer | Turning Data Into Predictive Systems, Intelligent Automation, and Real-World AI Solutions
I’m a Machine Learning and AI Engineer who helps organizations turn raw data into predictive systems, intelligent automation, and decision-support tools that solve real business problems. Many teams collect large amounts of data but struggle to turn it into systems that actively detect risks, automate processes, or guide smarter decisions. That’s where I come in. I design and deploy end-to-end machine learning solutions, from data preprocessing and model training to APIs and production deployment, ensuring AI moves beyond experiments and becomes a working, scalable asset. Here are examples of problems I’ve solved with AI: • Built a real-time ransomware detection system with automated hardware alerts for faster threat response • Developed multilingual sentiment analysis models (English, Yoruba, Nigerian Pidgin) for understanding customer feedback at scale • Created a hate speech detection platform with moderation dashboards and explainable AI • Designed a student subject recommender system to support better academic decision-making • Implemented network intrusion detection using anomaly detection models • Built an SMS spam filtering system to automatically block unwanted messages • Developed text-based emotion detection models for understanding user sentiment and behavior My work combines strong technical depth with practical execution, focusing on building AI systems that are not only accurate, but usable, deployable, and impactful. 📩 If you're exploring how machine learning, predictive modeling, or AI automation can improve security, decision-making, or operations in your organization, feel free to connect or send a message.
AISaaSSoftware
My LinkedIn audience
- Followers
- 735
- Est. median reach
- 697
- Engagement
- 1.4%
Public LinkedIn data · Sep 26, 2026
Recent posts
All posts on LinkedInOlamide Alonge
🎵 Spotify Music Mood Classification – Week 3, Data Challenge Poshem Technologies Institute Built a machine learning model that predicts song moods from Spotify's audio features. Analyzed 277,938 songs to classify them as Sad, Happy, Energetic, or Calm using energy, valence, tempo, and loudness. The challenge: Music streaming services can't manually categorize millions of songs by mood. What I built: Trained 5 models - Random Forest won with 97.6% accuracy. Key findings: Sad songs: Low energy (0.2-0.4) + low valence Happy songs: High valence (0.7-0.9) + high danceability Energetic: Energy 0.8-1.0 + fast tempo (130-180 BPM) Calm: High acousticness + slow tempo Musical mood is measurable at 97.6% accuracy. Problems I fixed: 1. Class imbalance - Some moods had 3x more songs. Used oversampling. Recall jumped from 72% to 97.6%. 2. Feature scaling - Tempo (0-250), valence (0-1), loudness (-60 to 5). Applied StandardScaler. SVM accuracy +15%. 3. Model uncertainty - Trained 5 models systematically. Random Forest won. Overfitting - Used 3-fold cross-validation. Confirmed it generalizes (97.6% ± 0.06%). Real-world use: Powers "Play something energetic" commands, workout playlists, mental health apps, mood-based recommendations. Built a Streamlit app for instant mood predictions: https://lnkd.in/eQaVVhMK Lessons: 1. Train multiple models instead of guessing. 2. Class imbalance kills models silently - check early. 3. Cross-validation isn't optional. This reminded me why I'm in data science - making subjective experiences quantifiable to build systems that help people. Thanks Poshem Technologies Institute! Tools: Python | pandas | scikit-learn | LightGBM | Streamlit #DataScience #MachineLearning #Python #MusicClassification #PoshemTech #SpotifyAPI #RandomForest
10 reactions · 0 commentsViewOlamide Alonge
🎯Customer Financial Risk Prediction System Just completed an end-to-end unsupervised ML + NLP solution for customer segmentation in African financial markets! THE PROJECT Analyzed 5,200 customers across 6 African countries (Nigeria, Ghana, Kenya, South Africa, Uganda, Tanzania) to identify distinct behavioral segments without labeled data. TECHNICAL APPROACH • Hybrid encoding strategy (ordinal + label + one-hot) for 27 features • Spacy for text preprocessing with lemmatization • VADER sentiment analysis on customer feedback • LDA topic modeling to extract themes • TF-IDF embeddings with PCA dimensionality reduction • Compared 4 algorithms: K-Means (winner), Hierarchical, GMM, DBSCAN Key Finding: MinMaxScaler outperformed StandardScaler by 54% in clustering quality! RESULTS - 4 Customer Segments Identified: 1. Mobile App Users (24.8%) - Target for premium features 2. Traditional USSD Users (30.3%) - Digital migration opportunity 3. Engaged Mobile Users (24.7%) - Brand ambassadors with highest satisfaction 4. Standard Mobile Users (20.2%) - Cross-sell savings products Business Insight: 30% still use USSD channels - major opportunity for digital transformation! PRODUCTION DEPLOYMENT Built two complete solutions: 📱 Interactive Streamlit Dashboard - Real-time filters and KPIs - 10+ interactive visualizations - Cluster profiling and data export 🌐 FastAPI REST API - Single and batch prediction endpoints - Real-time sentiment analysis - Topic assignment - Business recommendations - Full Swagger documentation TECH STACK Python | Scikit-learn | Spacy | VADER | Streamlit | FastAPI | Pandas | Plotly KEY TAKEAWAYS • Unsupervised learning reveals hidden patterns without labels • Combining structured data with NLP enriches insights • Feature scaling choice dramatically impacts results • Production deployment makes models actionable This showcases how unsupervised ML helps emerging markets where traditional credit scoring may be limited. Thanks to Poshem Technologies Institute Technology Institute for this comprehensive challenge! 🙏 #DataScience #MachineLearning #NLP #Clustering #Python #AI #FinTech #CustomerSegmentation #UnsupervisedLearning #AfricanTech #PoshemTechnologiesInstitute What's your experience with unsupervised learning in financial services? I'd love to hear your thoughts! 💬
9 reactions · 5 commentsViewOlamide Alonge
I am excited to share the successful completion of a comprehensive data challenge project with Poshem Technologies Institute. Over the past week, I analyzed 9,800 sales transactions spanning four years (2015-2018) to uncover actionable business insights for Poshem Business School. Key Insights Gained: Market Opportunities: - West and East regions dominate with over 60% of orders, highlighting expansion potential in Central and South regions. - Corporate customers show 3.5% higher order values despite lower volume, revealing untapped account management opportunities. - Office Supplies drives transaction volume (60%), while Technology leads revenue (37%), indicating that different strategies are needed for each. Data Quality Lessons: - Discovered 11 missing postal codes (all Burlington, Vermont), reinforcing the importance of data validation. - Identified critical missing columns (Profit and Order Status), learning to communicate data gaps to stakeholders effectively. Business Impact: - Recommended a regional expansion strategy targeting underserved markets. - Developed segment-specific approaches for Consumer versus Corporate customers. - Proposed shipping optimization to reduce delivery time by 30%. What I Learned: This project taught me that data analysis isn't just about running code; it's about: - Identifying what's missing (profit data gap). - Communicating findings to non-technical stakeholders. - Turning insights into actionable business recommendations. - Understanding that sometimes the biggest insight is knowing what you don't know. I am grateful to Poshem Technologies Institute for this hands-on learning opportunity, guidance, and mentorship, as well as to the data analytics community for continuous learning support. This project reinforced my passion for transforming raw data into strategic business value, and I am excited to apply these skills to future challenges. #DataAnalytics #Python #ProjectCompletion #PoshemTechnologies #DataScience #BusinessIntelligence
8 reactions · 3 commentsView
Work with Olamide Alonge
Your collaboration, managed on Naano
- Share your brief with the creator
- Review the content before publication
- Manage your collaboration and payment
How does this price compare? B2B creator rates 2026
Who engages with my posts
Based on 34 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 34 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 34 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 34 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Storefront by Naano · Create yours
Booking Olamide Alonge: frequently asked questions
Olamide Alonge is a B2B LinkedIn creator based in Nigeria with 735 followers, posting about AI, SaaS, and Software. On Naano, one sponsored LinkedIn post by Olamide Alonge costs €13, a fixed price per post.
- How much does Olamide Alonge charge for a sponsored LinkedIn post?
- One sponsored LinkedIn post with Olamide Alonge costs €13 on Naano. It is a fixed price per post, the amount your brand pays. Compare with the B2B creator rates 2026 report
- How do I book Olamide Alonge?
- Create a brand account on Naano, choose an offer on this page and send your brief. The price is held when Olamide Alonge accepts and paid when the post URL is submitted. You can turn on draft approval to review the post before it goes live.
- Can Naano run the campaign for me?
- Yes. With Managed Campaigns, the Naano team sets the strategy, casts the creators, writes the briefs, reviews the drafts, pays the creators and reports results per creator and campaign. It is a three-month programme: a management fee from €3,000 per month, plus a creator budget from €6,000 per month. See Managed Campaigns
Want Naano to run the whole campaign?
The Naano team casts the creators, writes the briefs, reviews every draft with you and reports results per creator. You approve the shortlist and every draft.