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black_friday_sales's Introduction

Black Friday Sales Prediction Analysis - Regression

Complete Video Tutorial: https://youtu.be/ID8Lz5vR3qE

Dataset Information

This dataset comprises of sales transactions captured at a retail store. It’s a classic dataset to explore and expand your feature engineering skills and day to day understanding from multiple shopping experiences. This is a regression problem. The dataset has 550,069 rows and 12 columns.

Problem: Predict purchase amount

Attributes:

Column ID Column Name Data type Description Masked
0 User_ID int64 Unique Id of customer False
1 Product_ID object Unique Id of product False
2 Gender object Sex of customer False
3 Age object Age of customer False
4 Occupation int64 Occupation code of customer True
5 City_Category object City of customer True
6 Stay_In_Current_City_Years object Number of years of stay in city False
7 Marital_Status int64 Marital status of customer False
8 Product_Category_1 int64 Category of product True
9 Product_Category_2 float64 Category of product True
10 Product_Category_3 float64 Category of product True
11 Purchase int64 Purchase amount False

Download link: https://www.kaggle.com/kkartik93/black-friday-sales-prediction

Libraries

  • pandas
  • matplotlib
  • seaborn
  • scikit-learn

    Algorithms

  • Linear Regression
  • Decision Tree
  • Random Forest
  • Extra Tress

    Future Work

  • Hyperparameter Tuning
  • Different Models
  • Creation of new attributes
  • Normalization

    • black_friday_sales's People

    Contributors

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