Data Scientist

Ireoluwawolemi
Jeremiah Akindipe

ML Engineer·Python Developer·Analytics

Transforming raw, complex datasets into actionable business intelligence. Specializing in predictive modeling, time series forecasting, and statistical analysis across pharma, retail, and market domains.

View Projects LinkedIn ↗
5 repos
Projects
98 %
XGBoost R²
5 domains
ML Areas
● Open
to collaborate
Ireoluwawolemi Jeremiah Akindipe
Open to opportunities
analyst_dashboard.py running
Projects
6 repos
↑ active
Primary Stack
Py 3.x
+ Scikit-learn
ML Methods
8 +
classification, regression…
Status
● Open
to collaborate
Skill Proficiency
Python
92%
Pandas
88%
Scikit-learn
84%
Visualization
80%
Time Series
76%
01 //

About

Building Intelligence from Data

I'm a data scientist passionate about uncovering the stories hidden inside complex datasets. My work covers the full ML pipeline — from exploratory analysis and feature engineering, through model development and rigorous evaluation, to visualization and stakeholder-ready output.

I've applied these skills across pharmaceutical analytics, retail demand forecasting, customer segmentation, quality prediction, and real estate market analysis.

  • Predictive modeling — regression, classification, ensemble methods
  • Time series forecasting — ARIMA, Prophet, feature engineering
  • Unsupervised learning — K-Means clustering, PCA, dimensionality reduction
  • Exploratory data analysis and statistical modeling
  • Interactive dashboards and data storytelling
Core Language
Python Jupyter Numpy Pandas
Machine Learning
Scikit-learn XGBoost Random Forest K-Means PCA
Visualization
Matplotlib Seaborn Plotly Streamlit
Currently Learning
TensorFlow Docker MLOps
01.5 //

Certifications

TS Academy Certified: April 01, 2026
Data Science
Professional Certificate
Successfully completed a rigorous 3-month intensive training programme in Data Science, satisfying all programme requirements across machine learning, statistical modeling, time series analysis, and data visualization.
Duration: 3 Months Intensive Programme
Issued by: TS Academy — Adetola Durojaiye, Founder & CEO
Credential ID: TSAH9FR9SD6KDH
// credential id: TSAH9FR9SD6KDH
TS Academy Data Science Certificate
02 //

Projects

Dashboard · Prediction ↗
Pharma Sales & Price Prediction Dashboard
End-to-end data analysis and ML modeling project for a pharmaceutical company. Covers sales trend analysis, pricing dynamics, and predictive models for forecasting future revenue and price points. Includes interactive dashboard components designed for stakeholder consumption.
Regression
Model Type
Plotly
Visualization
E2E
Pipeline
Python Pandas Scikit-learn Plotly Seaborn Regression
// Project Visualizations
Max Sales by Product Class
Max Sales by Product Class
Total Sales by Sales Rep
Total Sales by Sales Rep
Total Sales of Each Product Class
Total Sales by Product Class
XGBoost Feature Importance
XGBoost Feature Importance
Time Series ↗
Walmart Weekly Sales Forecasting
Capstone project forecasting weekly department-level sales across Walmart stores. Advanced feature engineering with holiday effects, markdown data, and store metadata. Full model benchmarking across ARIMA and SARIMAX
ARIMA
Primary Model
SARIMAX
Benchmark
Time Series SARIMAX ARIMA Feature Eng.
// Project Visualizations
SARIMAX Forecast - Store 14 Dept 92
SARIMAX Forecast vs Actual
Trend in Weekly Sales
Weekly Sales Trend (12-month MA)
Actual vs Predicted Weekly Sales
Actual vs Predicted (Tuned XGBoost)
XGBoost Feature Importance
XGBoost Feature Importance
Classification ↗
Red Wine Quality Prediction
ML classification models predicting wine quality from physicochemical properties. Full EDA, feature importance analysis, and multi-class model comparison including Random Forest and XGBoost with performance visualization.
XGBoost
Top Model
Multi
Class
Classification XGBoost Random Forest EDA
// Project Visualizations
Decision Tree for Red Wine Quality
Decision Tree — Quality Classification
Feature Importance
Feature Importance — Top Predictors
Clustering ↗
Mall Customer Segmentation
Unsupervised K-Means clustering segmenting 200 mall customers into 5 distinct behavioral groups by income, spending score, age, and demographics — surfacing actionable marketing personas.
200
Customers
5
Segments
K-Means PCA Silhouette Seaborn
// Project Visualizations
Clusters Visualization
K-Means Clusters Visualization
Elbow Method
Elbow Method — Optimal k=5
Annual Income vs Spending Score
Income vs Spending Score by Gender
Average Features by Clusters
Average Features by Cluster
EDA · Market Analysis ↗
Albany Airbnb Market Analysis
Exploratory data analysis of Airbnb listings in Albany, NY — investigating pricing patterns, availability trends, neighborhood-level dynamics, and host behavior to surface strategic insights for hosts and travellers.
EDA
Method
Geo
Analysis
Price
Modeling
EDA Pandas Matplotlib Statistical Analysis Geospatial
// Project Visualizations
Price vs Availability
Price vs Availability (Days)
Price Category Distribution
Distribution of Price Categories
Avg Monthly Reviews by Neighbourhood
Avg Monthly Reviews by Neighbourhood
Feature Importance Random Forest
Feature Importance (Random Forest)
03 //

GitHub Stats

📁
6 repos
Public Repositories
↑ actively maintained
⚙️
100 %
Python / Jupyter
primary language stack
🧠
5 domains
ML Domains Covered
pharma · retail · market
Skill Proficiency self-assessed
Python92%
Pandas / NumPy88%
Scikit-learn84%
Data Visualization82%
Time Series78%
Unsupervised ML75%
Statistical Analysis80%
Project Domain Breakdown
Predictive Modeling 40%
Time Series 20%
Clustering / EDA 20%
Market Analysis 20%
Contribution Activity 2025–2026
—
Total Commits
6
Repos
2025
Active Since
04 //

Contact

Let's build something with data.

Open to collaborations, research projects, freelance data science work, and full-time opportunities. If you have an interesting problem that needs data-driven thinking, reach out.

GitHub
github.com/Dhela456
→
LinkedIn
ireoluwawolemi-akindipe
→
// Current Status
name Ireoluwawolemi J. Akindipe
role Data Scientist
focus ML · Time Series · Analytics
learning Deep Learning · MLOps · Docker
available Open to opportunities