Machine Learning Model for Pricing
Budget: $10 – $30 USD
Developing a Machine Learning Model for Product Price Prediction
Description:
I would like to implement a simple machine learning model that can predict product prices (such as cars, real estate, or electronic devices) based on a set of input features that will be provided (e.g., age, condition, brand, specifications, etc.).
The main goal is to build a model capable of predicting prices with reasonable accuracy in order to provide approximate estimations.
Deliverables:
A Jupyter Notebook file containing the complete code with comments and explanations.
A README file with clear instructions on how to run and reuse the model.
A short report summarizing the model’s performance using metrics such as RMSE or MAE.
A reusable version of the trained model (Pickle or Joblib).
Requirements:
Programming Language: Python
Libraries: Pandas, Numpy, Scikit-learn, Matplotlib/Seaborn
Steps to be implemented:
Data cleaning and handling missing values.
Encoding categorical features.
Splitting the dataset into Train/Test sets.
Training the model using algorithms such as Linear Regression or Random Forest.
Evaluating the model and presenting the results.
Description:
I would like to implement a simple machine learning model that can predict product prices (such as cars, real estate, or electronic devices) based on a set of input features that will be provided (e.g., age, condition, brand, specifications, etc.).
The main goal is to build a model capable of predicting prices with reasonable accuracy in order to provide approximate estimations.
Deliverables:
A Jupyter Notebook file containing the complete code with comments and explanations.
A README file with clear instructions on how to run and reuse the model.
A short report summarizing the model’s performance using metrics such as RMSE or MAE.
A reusable version of the trained model (Pickle or Joblib).
Requirements:
Programming Language: Python
Libraries: Pandas, Numpy, Scikit-learn, Matplotlib/Seaborn
Steps to be implemented:
Data cleaning and handling missing values.
Encoding categorical features.
Splitting the dataset into Train/Test sets.
Training the model using algorithms such as Linear Regression or Random Forest.
Evaluating the model and presenting the results.