ML-Based Used Car Price Estimator
Budget / Salary$30–250
TypeFreelance project
LocationRemote
Posted1 hour ago
Used Car & Asset Price Prediction (Machine Learning Solution)
Do you want to estimate the accurate market price for a used car or asset based on its features and historical data?
I will analyze your pricing dataset and build a Machine Learning model to accurately predict asset prices. This process includes data cleaning and preparation, analyzing price-influencing factors, training an optimal regression model, and evaluating its performance.
What’s Included in This Service:
Data Cleaning & Preprocessing: Handling missing values, outliers, and invalid data points.
Exploratory Data Analysis (EDA): Analyzing key features and factors that directly impact the price.
Feature Engineering: Processing both numerical and categorical variables to make them model-ready.
Model Building & Training: Training a Machine Learning model tailored for accurate price estimation.
Performance Evaluation: Assessing model performance using standard regression metrics.
Data Visualization: Creating essential charts to easily interpret the data and model outcomes.
Price Estimation Capability: Delivering a working model capable of predicting prices based on input specs.
Service Scope & Limits:
Dataset Limit: One file containing up to 10,000 rows and 30 columns.
Model Scope: Training one single Machine Learning model dedicated to the price prediction task.
Requirements from You:
An Excel or CSV file containing historical asset data along with their past prices, clearly indicating the target column (price).
Key Features & Benefits:
Data Cleaning: Complete filtering of missing values and unsuitable data.
Data Analysis: Deep dive into key parameters driving price fluctuations.
Data Preparation: Converting and scaling features for optimal model accuracy.
Predictive Modeling: Building a dedicated ML model for accurate price estimation.
Model Evaluation: Measuring accuracy using standard evaluation metrics.
Visualizations: Clear, basic graphical representations of data trends and predictions.
Price Estimation: Applying the trained model to forecast prices for new asset specifications.
Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
Delivery Time: 2 Days.
Do you want to estimate the accurate market price for a used car or asset based on its features and historical data?
I will analyze your pricing dataset and build a Machine Learning model to accurately predict asset prices. This process includes data cleaning and preparation, analyzing price-influencing factors, training an optimal regression model, and evaluating its performance.
What’s Included in This Service:
Data Cleaning & Preprocessing: Handling missing values, outliers, and invalid data points.
Exploratory Data Analysis (EDA): Analyzing key features and factors that directly impact the price.
Feature Engineering: Processing both numerical and categorical variables to make them model-ready.
Model Building & Training: Training a Machine Learning model tailored for accurate price estimation.
Performance Evaluation: Assessing model performance using standard regression metrics.
Data Visualization: Creating essential charts to easily interpret the data and model outcomes.
Price Estimation Capability: Delivering a working model capable of predicting prices based on input specs.
Service Scope & Limits:
Dataset Limit: One file containing up to 10,000 rows and 30 columns.
Model Scope: Training one single Machine Learning model dedicated to the price prediction task.
Requirements from You:
An Excel or CSV file containing historical asset data along with their past prices, clearly indicating the target column (price).
Key Features & Benefits:
Data Cleaning: Complete filtering of missing values and unsuitable data.
Data Analysis: Deep dive into key parameters driving price fluctuations.
Data Preparation: Converting and scaling features for optimal model accuracy.
Predictive Modeling: Building a dedicated ML model for accurate price estimation.
Model Evaluation: Measuring accuracy using standard evaluation metrics.
Visualizations: Clear, basic graphical representations of data trends and predictions.
Price Estimation: Applying the trained model to forecast prices for new asset specifications.
Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
Delivery Time: 2 Days.
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