You will get a complete jupyter notebook with trained models and clear insights
Project details
I built a machine learning solution that predicts house prices with 76% accuracy to help real estate investors make smarter buying decisions. Using Python, I analyzed 21,000+ property records, identified key price drivers through data analysis, and developed a Random Forest model that outperformed baseline methods by 19%. This project demonstrates my ability to turn raw data into actionable business insights, helping investors avoid overpaying and maximize ROI through data-driven property valuation.
Machine Learning Tools
pandas, Python, Python Scikit-Learn, scikit-learnWhat's included
| Service Tiers |
Starter
$60
|
Standard
$120
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 4 days | 6 days | 8 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 4 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 4 | 7 | 12 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
About Priscilla
Data Scientist |Machine Learning| Turning data into business insights.
Nairobi, Kenya - 12:11 am local time
Skills:
🔹 Machine Learning & AI: Predictive modeling (XGBoost, Random Forest, Gradient Boosting, Decision Trees, K-NN, SVM), classification (Logistic Regression, Naive Bayes), regression analysis, clustering, recommendation systems (collaborative & content-based filtering), sentiment analysis (SpaCy, NLP).
🔹 Programming & Development: Python, Google Colab, Jupyter Notebook, Git & GitHub.
🔹 Data Management: SQL, MySQL, database design and optimization.
🔹 Visualization & Reporting: Python (Matplotlib, Seaborn), Power BI, interactive dashboards.
🔹 Deployment & Production: Streamlit, Flask for web applications.
What I Deliver:
1. Data Cleaning & Preparation - Transform messy datasets into analysis-ready formats through systematic cleaning and preprocessing.
2. Deep Exploratory Analysis - Uncover hidden patterns and insights in your data through statistical analysis and compelling visualizations.
3. Predictive Models - From real estate price forecasting to customer behavior prediction, I create accurate models that give you competitive insights.
4. Recommendation Systems - Boost engagement and sales with personalized recommendation engines using both collaborative and content-based approaches.
5. Customer Sentiment Analysis - Extract actionable insights from reviews, feedback, and social media using advanced NLP techniques.
6. Database Optimization - Design efficient database structures and write optimized SQL queries for better performance and scalability.
7. Production-Ready Solutions - Turn your models into interactive web applications using Streamlit, making complex analytics accessible to your team.
Ready to transform your data into strategic business insights? Let's connect and discuss your project.
Steps for completing your project
After purchasing the project, send requirements so Priscilla can start the project.
Delivery time starts when Priscilla receives requirements from you.
Priscilla works on your project following the steps below.
Revisions may occur after the delivery date.
1. Data Assessment & Cleaning (1 day)
Review property data quality, handle missing values, identify outliers, and understand market distribution. Ensure data is ready for analysis.
2. Exploratory Analysis (1 day)
Analyze relationships between features and prices. Identify which factors most strongly influence property values in your market.