Machine-Learning-Based-Product-Prediction-System-for-Drop-shippers-

This project focuses on developing a Machine Learning-Based Product Prediction System for dropshippers. The system uses a Random Forest Classifier to predict whether a product is suitable for dropshipping based on various parameters.

Overview

• Problem Statement: Dropshippers face challenges in selecting the right products to offer due to the vast product choices available, leading to suboptimal business engagement and limited sales growth.

• Objective: Develop a machine learning-based system to provide intelligent product recommendations, enhancing dropshippers' decision-making process and business engagement.

• Solution:

  • Create an interface for dropshippers to input product details.
  • Use a Random Forest Classifier to predict product suitability with an accuracy of 92.47%.
  • Provide accurate recommendations to help dropshippers make informed decisions.

FILTERED Notebook This file contains the filtering process according to the areas you need or acc the requirments

Preprocessing This file contains all the preprocessing part

KNN,SVM,Random Forest This file includes the various algo used each file has its own gui too

Here is a list of all the main libraries used in the project, including the GUI library:

1. pandas: Used for data manipulation and analysis
2. scikit-learn (sklearn): Used for machine learning algorithms and tools, including the RandomForestClassifier
3. numpy: Provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays
4. tkinter: The standard GUI toolkit for Python. It is used to create the user interface for the project

I have uplaoded the code by using other alforithms too if you feel that there are some changes needed please contact me

Contributors

1. Akbar khan

2. Saman Solapure

3. Kruna Nikam

4. Aarya Deshpande