Price-Prediction-Model

Price Prediction model using Linear Regression.

This repository contains a Linear Regression model for predicting the sale price of product listings based on various features such as product name, condition, category, brand, shipping information, and item description.

Dataset

The dataset used for training and testing the model is provided by Mercari.
It consists of the following files:

• train.tsv: Contains the training data with the following columns:

• train_id: Unique identifier for each training instance

• name: Title of the product listing

• item_condition_id: Condition of the item provided by the seller

• category_name: Category of the listing

• brand_name: Brand of the product

• price: Target variable, the price at which the item was sold (in USD)

• shipping: 1 if the shipping fee is paid by the seller, 0 if paid by the buyer

• item_description: Full description of the item

• test.tsv: Contains the test data with the same columns as train.tsv, except for the price column, which needs to be predicted.

• sample_submission.csv: A sample submission file in the correct format.

Model

The Linear Regression model is implemented using [library/framework] and is trained on the train.tsv dataset. The model takes the following features as input:

• name
• item_condition_id
• category_name
• brand_name
• shipping
• item_description

The target variable is the price column.

Usage

1. Clone the repository
2. Install the required dependencies
3. Run the train.py script to train the model
4. Run the predict.py script to make predictions on the test data

The predicted prices will be saved in a submission file following the format of sample_submission.csv.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for any improvements or bug fixes.