You will get Real Estate Price Prediction System: A Supervised ML Approach
Project details
The House Price Prediction dataset provides a rich collection of residential property features for advanced regression tasks. It includes continuous variables (lot size, living area, age), categorical features (neighborhoods, building styles), and ordinal data (quality ratings, condition levels), allowing diverse modeling approaches.
This dataset is inherently complex due to non-linear relationships, multicollinearity, missing values, and outliers, making it a strong benchmark for data preprocessing, feature engineering, and model optimization. It supports the application of linear and polynomial regression, tree-based models, ensembles (Random Forest, XGBoost, LightGBM), and deep learning architectures.
Researchers and practitioners can use it for EDA, correlation analysis, dimensionality reduction, and interpretability methods (SHAP, LIME) to uncover hidden patterns driving property valuation. With its high-dimensional and heterogeneous nature, the dataset is ideal for building robust supervised ML models that mimic real-world housing market dynamics.
This dataset is inherently complex due to non-linear relationships, multicollinearity, missing values, and outliers, making it a strong benchmark for data preprocessing, feature engineering, and model optimization. It supports the application of linear and polynomial regression, tree-based models, ensembles (Random Forest, XGBoost, LightGBM), and deep learning architectures.
Researchers and practitioners can use it for EDA, correlation analysis, dimensionality reduction, and interpretability methods (SHAP, LIME) to uncover hidden patterns driving property valuation. With its high-dimensional and heterogeneous nature, the dataset is ideal for building robust supervised ML models that mimic real-world housing market dynamics.
Machine Learning Tools
MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, SciPy, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$30
|
Advanced
$60
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 4 days |
Number of Revisions | 2 | 5 | 9 |
Number of Model Variations | 1 | 2 | 3 |
Number of Graphs/Charts | 10 | 20 | 30 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$5 - $8
Additional Model Variation
(+ 1 Day)
+$10
Additional Scenario
(+ 1 Day)
+$10
Model Documentation
+$10
Source Code
+$15About Sarim
BS AI /ML Engineer/ Expert,ML Model training and fine Tunning
Swabi, Pakistan - 11:40 pm local time
I have strong command over Google Colab and frequently use it for development, experiments, and sharing projects with clients. I am confident in handling different ML models and techniques, including:
Regression & Classification models (Linear/Logistic Regression, Ridge, Lasso, etc.)
Decision Trees, Random Forest, Naive Bayes, KNN, SVM
other like Bagging, Boosting , Voting and Ensemble methods & Optimizations
Model Training, Testing &Hyperparameter Tuning
Visualization & Performance Evaluation
I also have hands-on experience with essential Python libraries like NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn, which I use for data preprocessing, feature engineering, model building, and analysis.
I’m passionate about solving problems through data-driven approaches and always eager to learn and apply the latest techniques in AI/ML. My focus is on writing clean, understandable code and delivering results that add value.
I believe in accuracy, clarity, and efficiency. You can expect me to give my full dedication to your project, delivering reliable results on time with proper communication. If you need someone who treats your project as their own, I’m here to help.
Steps for completing your project
After purchasing the project, send requirements so Sarim can start the project.
Delivery time starts when Sarim receives requirements from you.
Sarim works on your project following the steps below.
Revisions may occur after the delivery date.
Collecting data and Ask what your desired output
Data Preprocessing and some visulaizations