OIBSIP_DataAnalytics_Task2 Wine Quality dataset

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In this project, we will work with a dataset containing information about hosts, geographical availability, and Airbnb. The goal is to perform data cleaning in order to identify and correct data quality issues, such as missing values, duplicates, inconsistent formats, and outliers, in order to improve the reliability and usability of the dataset.

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This project focuses on exploratory data analysis (EDA) of retail sales data to understand sales trends, customer behavior, and product performance. Various data cleaning and visualization techniques were used to identify patterns, seasonal trends, and key insights that can help improve business decision making.

Customer Segmentation Analysis using Mall Customers Dataset using Python for OIBSIP task 2 project

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Customer Segmentation Analysis using K-Means Clustering

level 2

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Cleaning Data

Customer Segmentation and Clustering Analysis on iFood Dataset using Python, Pandas, and Machine Learning techniques (K-Means, DBSCAN, PCA).

Customer Segmentation Analysis

Customer Segmentation Analysis using K-Means Clustering

Internship Task 2 Customer Segmentation Analysis

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Customer Segmentation Analysis

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Customer Segmentation Analysis using Clustering Algorithms (K-Means)

This project predicts wine quality based on chemical attributes such as acidity and density. Machine learning models including Random Forest, Stochastic Gradient Descent, and Support Vector Classifier are used to analyze the dataset and classify wine quality while visualizing patterns using data analysis libraries.

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This project focuses on detecting fraudulent financial transactions using machine learning techniques. By applying anomaly detection and pattern recognition, the system distinguishes between legitimate and suspicious activities. The model analyzes transaction patterns and supports real-time monitoring to improve fraud prevention accuracy.

Customer segmentation analysis on e-commerce data to group customers based on purchasing behavior. The project includes data cleaning, descriptive statistics, K-Means clustering, and visualizations to identify distinct customer segments that help businesses create targeted marketing strategies.

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