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6 days ago

Learn how to predict deal probability based on Ad created using machine learning. Perform data cleaning, visualization, and regression analysis to evaluate the model. Get the dataset from Kaggle and use libraries like pandas, seaborn, and plotly for analysis.

## Demand prediction based on the Ad created

#### Introduction

Description of the challenge taken from competetion page : When selling used goods online, a combination of little things can make a large difference.

And, even with an optimized product listing, demand for a product may simply not exist–frustrating sellers who may have over-invested in marketing. Avito, Russia’s largest classified advertisements website, is deeply familiar with this problem. Sellers on their platform sometimes feel frustrated with both too little demand (indicating something is wrong with the product or the product listing) or too much demand (indicating a hot item with a good description was underpriced).

Avito is one of the biggest classified advertisment website. In this project we are trying to predict the deal_probability using machine learning model before which we perform some operations related to data cleaning, visualization etc to understand the data. So that we can determine which are the essential factors for successfull deal_probability.

#### Evaluation Criteria

The regression model should be evaulated for Root Mean Squared Error 𝑅𝑀𝑆𝐸.

RMSE is defined as:

$\ {RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} 𝑦𝑖−𝑦̂ 𝑖^2}$

where y hat is the predicted value and y is the original value.