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Create Perfume recommendation system

This document outlines the process of analyzing perfume buyers and product data, encompassing steps like data preprocessing, exploratory data analysis (EDA), machine learning modeling, and result evaluation.

1. Dataset Overview

1.1 noon_perfumes_buyer_dataset.csv

1.2 noon_perfumes_dataset

2. Data Preprocessing

Removing Missing Values: Rows with missing values are removed using the .dropna() method. Deleting Empty String Data: Rows with empty strings in specific columns are removed. Feature Integration: brand and name are combined into a single feature. One-Hot Encoding: Base notes and middle notes are one-hot encoded.

2.1 Buyer Dataset

2.2 Perfume Dataset

3. Collaborative Filtering-Based Perfume Recommendation System

KNN Model: A perfume recommendation model is trained using NearestNeighbors. Recommendation Function: Provides perfume recommendations based on the input perfume name.

4. KNN Classification Model

Training and Evaluating KNN Classifier: The model is trained using KNeighborsClassifier, and evaluated with accuracy_score and classification_report. Elbow Graph: An elbow graph is plotted to find the optimal value of K.

Apply MinMaxScaler

5. Results and Analysis

Classification Report and Accuracy: The model's performance is summarized in a classification report, and its accuracy is calculated. This structured documentation provides a clear overview of the analysis process, making it easier to understand and follow the steps involved in the data analysis project.