Priyanka Sanjay Lad's repositories
Handwritten-Digit-Recognition
Problem Statement: Create a basic handwriting recognition system using a pre-trained machine learning model (e.g., MNIST dataset). Users can write characters or digits, and the model should predict what they've written.
IRIS-FLOWER-CLASSIFICATION-
The Iris dataset is analyzed, visualized, and used to train machine learning models, including Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, and Decision Trees. The Naive Bayes model yields the highest accuracy at 94.7%, making it the selected model for predicting Iris species based on sepal and petal features.
Titanic-Survival-Prediction
The Titanic Survival Prediction project analyzes passenger data, explores survival factors, and employs machine learning models. After cleaning and visualizing data, models like Logistic Regression and Naive Bayes predict survival. Naive Bayes achieves 76% accuracy, offering insights into survival patterns.
priyankalad123-Comcast-Telecom-Consumer-Complaints
Problem Statement: Analyze Comcast Telecom consumer complaints data to identify trends, categorize issues, and assess resolution rates. The objective is to derive actionable insights for improving customer satisfaction and service quality, ultimately enhancing Comcast's complaint resolution processes.