You will get Build a House Price Prediction Model Using XGBoost and EDA
Project details
ou will get a high-quality, explainable, and data-driven House Price Prediction model using XGBoost — one of the most powerful machine learning algorithms in use today.
With over 80 housing features analyzed through expert exploratory data analysis (EDA), your dataset will be cleaned, visualized, and modeled to uncover the most important drivers of property prices.
I specialize in delivering production-ready ML solutions that are not only accurate but also easy to interpret — ensuring your business or research project benefits from both performance and clarity.
✅ Clean Code (Jupyter Notebook)
✅ Visualizations for Insight
✅ Feature Importance Summary
✅ Model Files & Optional Prediction App
Whether you're a real estate analyst, business owner, or data enthusiast, I’ll tailor the output to suit your exact need — from price predictions to actionable dashboards.
With over 80 housing features analyzed through expert exploratory data analysis (EDA), your dataset will be cleaned, visualized, and modeled to uncover the most important drivers of property prices.
I specialize in delivering production-ready ML solutions that are not only accurate but also easy to interpret — ensuring your business or research project benefits from both performance and clarity.
✅ Clean Code (Jupyter Notebook)
✅ Visualizations for Insight
✅ Feature Importance Summary
✅ Model Files & Optional Prediction App
Whether you're a real estate analyst, business owner, or data enthusiast, I’ll tailor the output to suit your exact need — from price predictions to actionable dashboards.
Machine Learning Tools
ChatGPT, Keras, Microsoft Excel, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, SQL, Tableau, TensorFlowWhat's included
| Service Tiers |
Starter
$40
|
Standard
$100
|
Advanced
$180
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 7 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
About Niher
ML Engineer | Python | Computer Vision |Deep Learning | Kaggle Project
Dhaka, Bangladesh - 7:33 pm local time
I help clients solve business and technical problems using:
📊 Machine Learning (regression, classification, XGBoost, evaluation)
🧠 Deep Learning & Computer Vision (CNN, facial keypoints detection)
📈 Data Analysis & Visualization (Pandas, Seaborn, Matplotlib)
🛠️ Python, Jupyter, TensorFlow/Keras, OpenCV
✅ I’ve completed practical ML projects including:
🧠 Facial Keypoints Detection using CNN (RMSE = 0.0230)
🏠 House Price Prediction with XGBoost (explaining features & impact)
💼 A simulated client business prediction project from scratch.
Steps for completing your project
After purchasing the project, send requirements so Niher can start the project.
Delivery time starts when Niher receives requirements from you.
Niher works on your project following the steps below.
Revisions may occur after the delivery date.
Review Dataset & Requirements
I will review the uploaded dataset, confirm column relevance, and clarify goals to align with your business needs.
Perform Exploratory Data Analysis (EDA)
I’ll clean the data, visualize key variables, and identify missing values or outliers that could affect the model.