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Predicting Multivitamin Purchase/readme.md

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+### Predicting Multivitamin Purchase
+
+## Overview
+The Predicting Multivitamin Purchase project leverages machine learning techniques to forecast the likelihood of customers purchasing multivitamins. The goal is to analyze customer behavior, identify key factors influencing their decisions, and build a predictive model that helps businesses optimize marketing strategies and increase sales.
+
+## Introduction
+Predicting customer purchases is a critical task for businesses aiming to target specific audiences and increase sales. This project focuses on multivitamin products, using historical data to identify buying patterns and predict future purchases. The insights derived can help in targeted marketing and better customer engagement.
+
+## Dataset
+The dataset used in this project includes customer demographics, previous purchase history, and behavioral data that may influence the decision to buy multivitamins.
+
+File: multivitamins_data.csv
+Size: 200+ rows, multiple features (e.g., rating , reviews , price , drug name, etc.)
+
+# Project Workflow
+Data Collection: Gathering relevant data on customer behavior 
+Data Preprocessing: Cleaning and preparing data for model training.
+Feature Engineering: Creating new features to improve model accuracy.
+Model Training: Building and training predictive models (e.g., Logistic Regression, Random Forest, XGBoost).
+Model Evaluation: Analyzing model performance using metrics like accuracy, precision.
+Prediction: Predicting the likelihood of multivitamin purchases for new data.
+Visualization: Presenting insights through visualizations.
+
+## Model Evaluation
+The project involves evaluating different machine learning models to identify the most accurate predictor for multivitamin purchases. Evaluation metrics include:
+
+Accuracy: Overall correctness of the model.
+Precision: Proportion of true positive predictions.
+